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Grewe, Dennis, Wagner, Marco, Ambalavanan, Uthra, Liu, Liming, Nayak, Naresh, Schildt, Sebastian.  2021.  On the Design of an Information-Centric Networking Extension for IoT APIs. 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall). :1–6.
Both the Internet of Things (IoT) and Information Centric Networking (ICN) have gathered a lot of attention from both research and industry in recent years. While ICN has proved to be beneficial in many situations, it is not widely deployed outside research projects, also not addressing needs of IoT application programming interfaces (APIs). On the other hand, today's IoT solutions are built on top of the host-centric communication model associated with the usage of the Internet Protocol (IP). This paper contributes a discussion on the need of an integration of a specific form of IoT APIs, namely WebSocket based streaming APIs, into an ICN. Furthermore, different access models are discussed and requirements are derived from real world APIs. Finally, the design of an ICN-style extension is presented using one of the examined APIs.
Safitri, Cutifa, Nguyen, Quang Ngoc, Deo Lumoindong, Christoforus Williem, Ayu, Media Anugerah, Mantoro, Teddy.  2021.  Advanced Forwarding Strategy Towards Delay Tolerant Information-Centric Networking. 2021 IEEE 7th International Conference on Computing, Engineering and Design (ICCED). :1–5.
Information-Centric Networking (ICN) is among the promising architecture that can drive the need and versatility towards the future generation (xG) needs. In the future, support for network communication relies on the area of telemedicine, autonomous vehicles, and disaster recovery. In the disaster recovery case, there is a high possibility where the communication path is severed. Multicast communication and DTN-friendly route algorithm are becoming suitable options to send a packet message to get a faster response and to see any of the nodes available for service, this approach could give burden to the core network. Also, during disaster cases, many people would like to communicate, receive help, and find family members. Flooding the already disturbed/severed network will further reduce communication performance efficiency even further. Thus, this study takes into consideration prioritization factors to allow networks to process and delivering priority content. For this purpose, the proposed technique introduces the Routable Prefix Identifier (RP-ID) that takes into account the prioritization factor to enable optimization in Delay Tolerant ICN communication.
Huang, Yudong, Wang, Shuo, Feng, Tao, Wang, Jiasen, Huang, Tao, Huo, Ru, Liu, Yunjie.  2021.  Towards Network-Wide Scheduling for Cyclic Traffic in IP-based Deterministic Networks. 2021 4th International Conference on Hot Information-Centric Networking (HotICN). :117–122.
The emerging time-sensitive applications, such as industrial automation, smart grids, and telesurgery, pose strong demands for enabling large-scale IP-based deterministic networks. The IETF DetNet working group recently proposes a Cycle Specified Queuing and Forwarding (CSQF) solution. However, CSQF only specifies an underlying device-level primitive while how to achieve network-wide flow scheduling remains undefined. Previous scheduling mechanisms are mostly oriented to the context of local area networks, making them inapplicable to the cyclic traffic in wide area networks. In this paper, we design the Cycle Tags Planning (CTP) mechanism, a first mathematical model to enable network-wide scheduling for cyclic traffic in large-scale deterministic networks. Then, a novel scheduling algorithm named flow offset and cycle shift (FO-CS) is designed to compute the flows' cycle tags. The FO-CS algorithm is evaluated under long-distance network topologies in remote industrial control scenarios. Compared with the Naive algorithm without using FO-CS, simulation results demonstrate that FO-CS improves the scheduling flow number by 31.2% in few seconds.
Nakamura, Ryo, Kamiyama, Noriaki.  2021.  Proposal of Keyword-Based Information-Centric Delay-Tolerant Network. 2021 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR 2021). :1–7.
In this paper, we focus on Information-Centric Delay-Tolerant Network (ICDTN), which incorporates the communication paradigm of Information-Centric Networking (ICN) into Delay-Tolerant Networking (DTN). Conventional ICNs adopt a naming scheme that names the content with the content identifier. However, a past study proposed an alternative naming scheme that describes the name of content with the content descriptor. We believe that, in ICDTN, it is more suitable to utilize the approach using the content descriptor. In this paper, we therefore propose keyword-based ICDTN that resolves content requests and deliveries contents based on keywords, i.e., content descriptor, in the request and response messages.
Lei, Kai, Ye, Hao, Liang, Yuzhi, Xiao, Jing, Chen, Peiwu.  2021.  Towards a Translation-Based Method for Dynamic Heterogeneous Network Embedding. ICC 2021 - IEEE International Conference on Communications. :1–6.
Network embedding, which aims to map the discrete network topology to a continuous low-dimensional representation space with the major topological properties preserved, has emerged as an essential technique to support various network inference tasks. However, incorporating both the evolutionary nature and the network's heterogeneity remains a challenge for existing network embedding methods. In this study, we propose a novel Translation-Based Dynamic Heterogeneous Network Embedding (TransDHE) approach to consider both the aspects simultaneously. For a dynamic heterogeneous network with a sequence of snapshots and multiple types of nodes and edges, we introduce a translation-based embedding module to capture the heterogeneous characteristics (e.g., type information) of each single snapshot. An orthogonal alignment module and RNN-based aggregation module are then applied to explore the evolutionary patterns among multiple successive snapshots for the final representation learning. Extensive experiments on a set of real-world networks demonstrate that TransDHE can derive the more informative embedding result for the network dynamic and heterogeneity over state-of-the-art network embedding baselines.
Fazea, Yousef, Mohammed, Fathey, Madi, Mohammed, Alkahtani, Ammar Ahmed.  2021.  Review on Network Function Virtualization in Information-Centric Networking. 2021 International Conference of Technology, Science and Administration (ICTSA). :1–6.
Network function virtualization (NFV / VNF) and information-centric networking (ICN) are two trending technologies that have attracted expert's attention. NFV is a technique in which network functions (NF) are decoupling from commodity hardware to run on to create virtual communication services. The virtualized class nodes can bring several advantages such as reduce Operating Expenses (OPEX) and Capital Expenses (CAPEX). On the other hand, ICN is a technique that breaks the host-centric paradigm and shifts the focus to “named information” or content-centric. ICN provides highly efficient content retrieval network architecture where popular contents are cached to minimize duplicate transmissions and allow mobile users to access popular contents from caches of network gateways. This paper investigates the implementation of NFV in ICN. Besides, reviewing and discussing the weaknesses and strengths of each architecture in a critical analysis manner of both network architectures. Eventually, highlighted the current issues and future challenges of both architectures.
Pellenz, Marcelo E., Lachowski, Rosana, Jamhour, Edgard, Brante, Glauber, Moritz, Guilherme Luiz, Souza, Richard Demo.  2021.  In-Network Data Aggregation for Information-Centric WSNs using Unsupervised Machine Learning Techniques. 2021 IEEE Symposium on Computers and Communications (ISCC). :1–7.
IoT applications are changing our daily lives. These innovative applications are supported by new communication technologies and protocols. Particularly, the information-centric network (ICN) paradigm is well suited for many IoT application scenarios that involve large-scale wireless sensor networks (WSNs). Even though the ICN approach can significantly reduce the network traffic by optimizing the process of information recovery from network nodes, it is also possible to apply data aggregation strategies. This paper proposes an unsupervised machine learning-based data aggregation strategy for multi-hop information-centric WSNs. The results show that the proposed algorithm can significantly reduce the ICN data traffic while having reduced information degradation.
Raza, Khuhawar Arif, Asheralieva, Alia, Karim, Md Monjurul, Sharif, Kashif, Gheisari, Mehdi, Khan, Salabat.  2021.  A Novel Forwarding and Caching Scheme for Information-Centric Software-Defined Networks. 2021 International Symposium on Networks, Computers and Communications (ISNCC). :1–8.
This paper integrates Software-Defined Networking (SDN) and Information -Centric Networking (ICN) framework to enable low latency-based stateful routing and caching management by leveraging a novel forwarding and caching strategy. The framework is implemented in a clean- slate environment that does not rely on the TCP/IP principle. It utilizes Pending Interest Tables (PIT) instead of Forwarding Information Base (FIB) to perform data dissemination among peers in the proposed IC-SDN framework. As a result, all data exchanged and cached in the system are organized in chunks with the same interest resulting in reduced packet overhead costs. Additionally, we propose an efficient caching strategy that leverages in- network caching and naming of contents through an IC-SDN controller to support off- path caching. The testbed evaluation shows that the proposed IC-SDN implementation achieves an increased throughput and reduced latency compared to the traditional information-centric environment, especially in the high load scenarios.
Sukjaimuk, Rungrot, Nguyen, Quang N., Sato, Takuro.  2021.  An Efficient Congestion Control Model utilizing IoT wireless sensors in Information-Centric Networks. 2021 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering. :210–213.
Congestion control is one of the essential keys to enhance network efficiency so that the network can perform well even in the case of packet drop. This problem is even more challenging in Information-Centric Networking (ICN), a typical Future Internet design, which employs the packet flooding policy for forwarding the information. To diminish the high traffic load due to the huge number of packets in the era of the Internet of Things (IoT), this paper proposes an effective caching and forwarding algorithm to diminish the congestion rate of the IoT wireless sensor in ICN. The proposed network system utilizes accumulative popularity-based delay transmission time for forwarding strategy and includes the consecutive chunks-based segment caching scheme. The evaluation results using ndnSIM, a widely-used ns-3 based ICN simulator, demonstrated that the proposed system can achieve less interest packet drop rate, more cache hit rate, and higher network throughput, compared to the relevant ICN-based benchmarks. These results prove that the proposed ICN design can achieve higher network efficiency with a lower congestion rate than that of the other related ICN systems using IoT sensors.
Fazea, Yousef, Mohammed, Fathey.  2021.  Software Defined Networking based Information Centric Networking: An Overview of Approaches and Challenges. 2021 International Congress of Advanced Technology and Engineering (ICOTEN). :1–8.
ICN (Information-Centric Networking) is a traditional networking approach which focuses on Internet design, while SDN (Software Defined Networking) is known as a speedy and flexible networking approach. Integrating these two approaches can solve different kinds of traditional networking problems. On the other hand, it may expose new challenges. In this paper, we study how these two networking approaches are been combined to form SDN-based ICN architecture to improve network administration. Recent research is explored to identify the SDN-based ICN challenges, provide a critical analysis of the current integration approaches, and determine open issues for further research.
Sabeena, M, Abraham, Lizy, Sreelekshmi, P R.  2021.  Copy-move Image Forgery Localization Using Deep Feature Pyramidal Network. 2021 International Conference on Advances in Computing and Communications (ICACC). :1–6.
Fake news, frequently making use of tampered photos, has currently emerged as a global epidemic, mainly due to the widespread use of social media as a present alternative to traditional news outlets. This development is often due to the swiftly declining price of advanced cameras and phones, which prompts the simple making of computerized pictures. The accessibility and usability of picture-altering softwares make picture-altering or controlling processes significantly simple, regardless of whether it is for the blameless or malicious plan. Various investigations have been utilized around to distinguish this sort of controlled media to deal with this issue. This paper proposes an efficient technique of copy-move forgery detection using the deep learning method. Two deep learning models such as Buster Net and VGG with FPN are used here to detect copy move forgery in digital images. The two models' performance is evaluated using the CoMoFoD dataset. The experimental result shows that VGG with FPN outperforms the Buster Net model for detecting forgery in images with an accuracy of 99.8% whereas the accuracy for the Buster Net model is 96.9%.
Weixian, Wang, Ping, Chen, Mingyu, Pan, Xianglong, Li, Zhuoqun, Li, Ruixin, He.  2021.  Design of Collaborative Control Scheme between On-chain and Off-chain Power Data. 2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE). :1–6.
The transmission and storage process for the power data in an intelligent grid has problems such as a single point of failure in the central node, low data credibility, and malicious manipulation or data theft. The characteristics of decentralization and tamper-proofing of blockchain and its distributed storage architecture can effectively solve malicious manipulation and the single point of failure. However, there are few safe and reliable data transmission methods for the significant number and various identities of users and the complex node types in the power blockchain. Thus, this paper proposes a collaborative control scheme between on-chain and off-chain power data based on the distributed oracle technology. By building a trusted on-chain transmission mechanism based on distributed oracles, the scheme solves the credibility problem of massive data transmission and interactive power data between smart contracts and off-chain physical devices safely and effectively. Analysis and discussion show that the proposed scheme can realize the collaborative control between on-chain and off-chain data efficiently, safely, and reliably.
Takemoto, Shu, Ikezaki, Yoshiya, Nozaki, Yusuke, Yoshikawa, Masaya.  2021.  Hardware Trojan for Lightweight Cryptoraphy Elephant. 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE). :944–945.
While a huge number of IoT devices are connecting to the cyber physical systems, the demand for security of these devices are increasing. Due to the demand, world-wide competition for lightweight cryptography oriented towards small devices have been held. Although tamper resistance against illegal attacks were evaluated in the competition, there is no evaluation for embedded malicious circuits such as hardware Trojan.To achieve security evaluation for embedded malicious circuits, this study proposes an implementation method of hardware Trojan for Elephant which is one of the finalists in the competition. And also, the implementation overhead of hardware Trojans and the security risk of hardware Trojan are evaluated.
Wu, Peiyan, Chen, Wenbin, Wu, Hualin, Qi, Ke, Liu, Miao.  2021.  Enhanced Game Theoretical Spectrum Sharing Method Based on Blockchain Consensus. 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall). :1–7.
The limited spectrum resources need to provide safe and efficient spectrum service for the intensive users. Malicious spectrum work nodes will affect the normal operation of the entire system. Using the blockchain model, consensus algorithm Praft based on optimized Raft is to solve the consensus problem in Byzantine environment. Message digital signatures give the spectrum node some fault tolerance and tamper resistance. Spectrum sharing among spectrum nodes is carried out in combination with game theory. The existing game theoretical algorithm does not consider the influence of spectrum occupancy of primary users and cognitive users on primary users' utility and enthusiasm at the same time. We elicits a reinforcement factor and analyzes the effect of the reinforcement factor on strategy performance. This scheme optimizes the previous strategy so that the profits of spectrum nodes are improved and a good Nash equilibrium is shown, while Praft solves the Byzantine problem left by Raft.
Kösemen, Cem, Dalkiliç, Gökhan.  2021.  Tamper Resistance Functions on Internet of Things Devices. 2021 Innovations in Intelligent Systems and Applications Conference (ASYU). :1–5.
As the number of Internet of things devices increases, there is a growing importance of securely managing and storing the secret and private keys in these devices. Public-key cryptosystems or symmetric encryption algorithms both use special keys that need to be kept secret from other peers in the network. Additionally, ensuring the integrity of the installed application firmware of these devices is another security problem. In this study, private key storage methods are explained in general. Also, ESP32-S2 device is used for experimental case study for its robust built-in trusted platform module. Secure boot and flash encryption functionalities of ESP32-S2 device, which offers a solution to these security problems, are explained and tested in detail.
Arab, Farnaz, Zamani, Mazdak.  2021.  Video Watermarking Schemes Resistance Against Tampering Attacks. 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). :1–4.
This paper reviews the video watermarking schemes resistance against tampering attacks. There are several transform methods which are used for Video Watermarking including Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), Discrete wavelet transform (DWT) and are discussed and compared in this paper. The results are presented in a table with a summary of their advantages.
Shiomi, Jun, Kotsugi, Shuya, Dong, Boyu, Onodera, Hidetoshi, Shinya, Akihiko, Notomi, Masaya.  2021.  Tamper-Resistant Optical Logic Circuits Based on Integrated Nanophotonics. 2021 58th ACM/IEEE Design Automation Conference (DAC). :139–144.
A tamper-resistant logical operation method based on integrated nanophotonics is proposed focusing on electromagnetic side-channel attacks. In the proposed method, only the phase of each optical signal is modulated depending on its logical state, which keeps the power of optical signals in optical logic circuits constant. This provides logic-gate-level tamper resistance which is difficult to achieve with CMOS circuits. An optical implementation method based on electronically-controlled phase shifters is then proposed. The electrical part of proposed circuits achieves 300 times less instantaneous current change, which is proportional to intensity of the leaked electromagnetic wave, than a CMOS logic gate.
Li, Haofeng, Meng, Haining, Zheng, Hengjie, Cao, Liqing, Lu, Jie, Li, Lian, Gao, Lin.  2021.  Scaling Up the IFDS Algorithm with Efficient Disk-Assisted Computing. 2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO). :236–247.
The IFDS algorithm can be memory-intensive, requiring a memory budget of more than 100 GB of RAM for some applications. The large memory requirements significantly restrict the deployment of IFDS-based tools in practise. To improve this, we propose a disk-assisted solution that drastically reduces the memory requirements of traditional IFDS solvers. Our solution saves memory by 1) recomputing instead of memorizing intermediate analysis data, and 2) swapping in-memory data to disk when memory usages reach a threshold. We implement sophisticated scheduling schemes to swap data between memory and disks efficiently. We have developed a new taint analysis tool, DiskDroid, based on our disk-assisted IFDS solver. Compared to FlowDroid, a state-of-the-art IFDS-based taint analysis tool, for a set of 19 apps which take from 10 to 128 GB of RAM by FlowDroid, DiskDroid can analyze them with less than 10GB of RAM at a slight performance improvement of 8.6%. In addition, for 21 apps requiring more than 128GB of RAM by FlowDroid, DiskDroid can analyze each app in 3 hours, under the same memory budget of 10GB. This makes the tool deployable to normal desktop environments. We make the tool publicly available at
Kong, Xiangdong, Tang, Yong, Wang, Pengfei, Wei, Shuning, Yue, Tai.  2021.  HashMTI: Scalable Mutation-based Taint Inference with Hash Records. 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). :84–95.
Mutation-based taint inference (MTI) is a novel technique for taint analysis. Compared with traditional techniques that track propagations of taint tags, MTI infers a variable is tainted if its values change due to input mutations, which is lightweight and conceptually sound. However, there are 3 challenges to its efficiency and scalability: (1) it cannot efficiently record variable values to monitor their changes; (2) it consumes a large amount of memory monitoring variable values, especially on complex programs; and (3) its excessive memory overhead leads to a low hit ratio of CPU cache, which slows down the speed of taint inference. This paper presents an efficient and scalable solution named HashMTI. We first explain the above challenges based on 4 observations. Motivated by these challenges, we propose a hash record scheme to efficiently monitor changes in variable values and significantly reduce the memory overhead. The scheme is based on our specially selected and optimized hash functions that possess 3 crucial properties. Moreover, we propose the DoubleMutation strategy, which applies additional mutations to mitigate the limitation of the hash record and detect more taint information. We implemented a prototype of HashMTI and evaluated it on 18 real-world programs and 4 LAVA-M programs. Compared with the baseline OrigMTI, HashMTI significantly reduces the overhead while having similar accuracy. It achieves a speedup of 2.5X to 23.5X and consumes little memory which is on average 70.4 times less than that of OrigMTI.
Anusha, M, Leelavathi, R.  2021.  Analysis on Sentiment Analytics Using Deep Learning Techniques. 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). :542–547.
Sentiment analytics is the process of applying natural language processing and methods for text-based information to define and extract subjective knowledge of the text. Natural language processing and text classifications can deal with limited corpus data and more attention has been gained by semantic texts and word embedding methods. Deep learning is a powerful method that learns different layers of representations or qualities of information and produces state-of-the-art prediction results. In different applications of sentiment analytics, deep learning methods are used at the sentence, document, and aspect levels. This review paper is based on the main difficulties in the sentiment assessment stage that significantly affect sentiment score, pooling, and polarity detection. The most popular deep learning methods are a Convolution Neural Network and Recurrent Neural Network. Finally, a comparative study is made with a vast literature survey using deep learning models.
Zhang, Xiaoyu, Fujiwara, Takanori, Chandrasegaran, Senthil, Brundage, Michael P., Sexton, Thurston, Dima, Alden, Ma, Kwan-Liu.  2021.  A Visual Analytics Approach for the Diagnosis of Heterogeneous and Multidimensional Machine Maintenance Data. 2021 IEEE 14th Pacific Visualization Symposium (PacificVis). :196–205.
Analysis of large, high-dimensional, and heterogeneous datasets is challenging as no one technique is suitable for visualizing and clustering such data in order to make sense of the underlying information. For instance, heterogeneous logs detailing machine repair and maintenance in an organization often need to be analyzed to diagnose errors and identify abnormal patterns, formalize root-cause analyses, and plan preventive maintenance. Such real-world datasets are also beset by issues such as inconsistent and/or missing entries. To conduct an effective diagnosis, it is important to extract and understand patterns from the data with support from analytic algorithms (e.g., finding that certain kinds of machine complaints occur more in the summer) while involving the human-in-the-loop. To address these challenges, we adopt existing techniques for dimensionality reduction (DR) and clustering of numerical, categorical, and text data dimensions, and introduce a visual analytics approach that uses multiple coordinated views to connect DR + clustering results across each kind of the data dimension stated. To help analysts label the clusters, each clustering view is supplemented with techniques and visualizations that contrast a cluster of interest with the rest of the dataset. Our approach assists analysts to make sense of machine maintenance logs and their errors. Then the gained insights help them carry out preventive maintenance. We illustrate and evaluate our approach through use cases and expert studies respectively, and discuss generalization of the approach to other heterogeneous data.
Qing-chao, Ni, Cong-jue, Yin, Dong-hua, Zhao.  2021.  Research on Small Sample Text Classification Based on Attribute Extraction and Data Augmentation. 2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA). :53–57.
With the development of deep learning and the progress of natural language processing technology, as well as the continuous disclosure of judicial data such as judicial documents, legal intelligence has gradually become a research hot spot. The crime classification task is an important branch of text classification, which can help people related to the law to improve their work efficiency. However, in the actual research, the sample data is small and the distribution of crime categories is not balanced. To solve these two problems, BERT was used as the encoder to solve the problem of small data volume, and attribute extraction network was added to solve the problem of unbalanced distribution. Finally, the accuracy of 90.35% on small sample data set could be achieved, and F1 value was 67.62, which was close to the best model performance under sufficient data. Finally, a text enhancement method based on back-translation technology is proposed. Different models are used to conduct experiments. Finally, it is found that LSTM model is improved to some extent, but BERT is not improved to some extent.
Zhang, Xiangyu, Yang, Jianfeng, Li, Xiumei, Liu, Minghao, Kang, Ruichun, Wang, Runmin.  2021.  Deeply Multi-channel guided Fusion Mechanism for Natural Scene Text Detection. 2021 7th International Conference on Big Data and Information Analytics (BigDIA). :149–156.
Scene text detection methods have developed greatly in the past few years. However, due to the limitation of the diversity of the text background of natural scene, the previous methods often failed when detecting more complicated text instances (e.g., super-long text and arbitrarily shaped text). In this paper, a text detection method based on multi -channel bounding box fusion is designed to address the problem. Firstly, the convolutional neural network is used as the basic network for feature extraction, including shallow text feature map and deep semantic text feature map. Secondly, the whole convolutional network is used for upsampling of feature map and fusion of feature map at each layer, so as to obtain pixel-level text and non-text classification results. Then, two independent text detection boxes channels are designed: the boundary box regression channel and get the bounding box directly on the score map channel. Finally, the result is obtained by combining multi-channel boundary box fusion mechanism with the detection box of the two channels. Experiments on ICDAR2013 and ICDAR2015 demonstrate that the proposed method achieves competitive results in scene text detection.
Zhang, Feng, Pan, Zaifeng, Zhou, Yanliang, Zhai, Jidong, Shen, Xipeng, Mutlu, Onur, Du, Xiaoyong.  2021.  G-TADOC: Enabling Efficient GPU-Based Text Analytics without Decompression. 2021 IEEE 37th International Conference on Data Engineering (ICDE). :1679–1690.
Text analytics directly on compression (TADOC) has proven to be a promising technology for big data analytics. GPUs are extremely popular accelerators for data analytics systems. Unfortunately, no work so far shows how to utilize GPUs to accelerate TADOC. We describe G-TADOC, the first framework that provides GPU-based text analytics directly on compression, effectively enabling efficient text analytics on GPUs without decompressing the input data. G-TADOC solves three major challenges. First, TADOC involves a large amount of dependencies, which makes it difficult to exploit massive parallelism on a GPU. We develop a novel fine-grained thread-level workload scheduling strategy for GPU threads, which partitions heavily-dependent loads adaptively in a fine-grained manner. Second, in developing G-TADOC, thousands of GPU threads writing to the same result buffer leads to inconsistency while directly using locks and atomic operations lead to large synchronization overheads. We develop a memory pool with thread-safe data structures on GPUs to handle such difficulties. Third, maintaining the sequence information among words is essential for lossless compression. We design a sequence-support strategy, which maintains high GPU parallelism while ensuring sequence information. Our experimental evaluations show that G-TADOC provides 31.1× average speedup compared to state-of-the-art TADOC.
Rabbani, Mustafa Raza, Bashar, Abu, Atif, Mohd, Jreisat, Ammar, Zulfikar, Zehra, Naseem, Yusra.  2021.  Text mining and visual analytics in research: Exploring the innovative tools. 2021 International Conference on Decision Aid Sciences and Application (DASA). :1087–1091.
The aim of the study is to present an advanced overview and potential application of the innovative tools/software's/methods used for data visualization, text mining, scientific mapping, and bibliometric analysis. Text mining and data visualization has been a topic of research for several years for academic researchers and practitioners. With the advancement in technology and innovation in the data analysis techniques, there are many online and offline software tools available for text mining and visualisation. The purpose of this study is to present an advanced overview of latest, sophisticated, and innovative tools available for this purpose. The unique characteristic about this study is that it provides an overview with examples of the five most adopted software tools such as VOSviewer, Biblioshiny, Gephi, HistCite and CiteSpace in social science research. This study will contribute to the academic literature and will help the researchers and practitioners to apply these tools in future research to present their findings in a more scientific manner.