Reviewerly/exHarmony huggingface dataset:
ARC-SL: Association rule-based classification with soft labels

As one of the most promising classification schemes, association classification (AC) effectively integrates classification with association rule discovery, but it requires precisely labelled data which is usually expensive or hard to obtain in practice. This paper extends the AC to solve the classification of data with soft labels represented as belief functions defined over the set of classes. To characterize the imprecision of labels, a more general rule, called imprecise classification association rule (ICAR), is first introduced so that the consequent is more flexible, being any subset of the class set. Meanwhile, measures of support and confidence are proposed for ICAR by taking into account the belief functions encoded in soft labels. With the new rule structure, an association rule-based soft labelled classification method, called ARC-SL, is then developed to build a more accurate classification model containing three phases: entropy-based adaptive partition for deriving fuzzy regions of continuous attributes, Apriori-based rule mining for generating a set of ICARs with variable support and confidence thresholds, and rule pruning for discarding redundant or poor rules. Finally, a belief reasoning procedure is presented to classify each input instance through combining the activated rules in the framework of belief functions. Experiments on benchmark datasets and an application to facial expression recognition demonstrate the effectiveness of the proposed method.
Privacy-preserving smart grid traceability using blockchain over IoT connectivity

Smart grid (SG) technology comes with various advantages, among others, of efficient power distribution, reduction in cost consumption, maintain energy loss, and peak quality level in its supply chain. SG aims to provide all required and advanced features to overcome the issues of the existing power grid and to fulfill the rapidly growing demands of power generation, transmission, distribution, and monitoring energy consumption. However, SG has been facing various challenges to ensure the nature of every transaction, transaction verification, and recording in the power supply chain, especially in the context of a large scale IoT(Internet of things) network. Another issue is maintaining the privacy of every transaction, as well as the privacy of participating entities in the power supply chain. In this paper, we propose a blockchain-based proof-of-concept solution to manage every transaction that occurs in an IoT-aided smart grid system. Our solution, IoT-aided smart grid system with blockchain, provides an immutable transaction record, which is always shared and transparent to each participant of the system. Transactions are carried through IoT objects connected in a smart grid, and are recorded, verified, and validated on a blockchain immutable ledger. Furthermore, to verify and maintain the privacy of participants, cryptographic pseudonyms are used by each participant to interact with the SG supply chain, without revealing personal identities and important private information of the participants to malicious entities in the system.
Application of Fuzzy Logic for Collision Avoidance of Mobile Robots in Dynamic-Indoor Environments

Application of Mobile Robots have increased many folds in recent years. A heuristic effort is dedicated in making Mobile Robots adept to indoor environments to perform different type of activities. Autonomous Mobile Robots, for a long time, have remained in the focal point of many researches due to its potentials in multifunctional application and its suitability in well-defined, structured environments. As indoor environments can easily be represented as a structured environment, mobile robots become a potential candidate to make indoor activities human free. Nevertheless, the prime features like path planning and motion control need to have further development s before these autonomous mobile robots can be used successfully in highly dynamic environments. One of the important behaviors to achieve successful motion planning is the collision avoidance behavior of mobile robots. In this paper, we address the design of a collision avoidance controller with fuzzy logic. The first part of the paper addresses the improvement of the existing fuzzy logic controller for a more complex environment involving more than two known-dynamic obstacles. And in the second part, we have improved the design of the controller for unknown-dynamic obstacles in indoor environment based on information of onboard sensor. The results of the MATLAB simulations, in structured 2D environments, illustrates the successful navigation of mobile robots in both scenarios and thus validating the design of the controllers.
E-Commerce Product Reviews Using Aspect Based Hindi Sentiment Analysis

In recent times, online shopping is increasing rapidly due to its convenience to buy products from anywhere, anytime and also comparing features of products easily from other purchasers and their reviews. Sentiment analysis deals with the analyzing the reviews of explicit by its consumers for any product. If this research is carried out at a deeper level, we can define the consumer's sense of each product function. The field of Aspect based sentiment analysis (ABSA) analyzing and categorizing the opinions expressed on numerous aspects present in these opinions to make determinative recommendations. In the product reviews on the Indian e-commerce portals, we have tried to find out different indischen text. This paper explores the study and development of ABSA model on Hindi reviews in order to measure the overall interpretation of the Hindi text entered, so that the each sentence is graded as positive, negative, neutral and conflictual.
Security analysis of reversible logic cryptography design with LFSR key on 32-bit microcontroller

Abstract This paper presents a detailed security analysis of the research article on the digital image encryption scheme entitled Logic Cryptography Design (RLCD) with Linear Feedback Shift Register (LFSR) (Karunamurthi S, and Natarajan VK, Microprocessors and Microsystems, 2019). Although the inadequate length of its 4-bit LFSR key makes the scheme extremely vulnerable to quick brute force attack, analyzing the various error metrics concerning the security of the encrypted images, this scheme provides statistically pleasing results. The major shortcoming identified on this RLCD-LFSR scheme is the traceable patterns that appear on its encrypted images due to the absence of confusion to break the pixels' correlation. In addition to the chosen plaintext attack, edge detection based cryptanalysis proposed in this paper to be sufficient to crack the RLCD-LFSR scheme. The enhancement made by the insertion of a confusion module in RLCD-LFSR scheme wipes out the perceptible patterns and edges from the encrypted images to resist the attacks. The failure of enhanced RLCD-LFSR under NIST tests confirms the flaws in the design of the Reversible Logic Gate (RLG) based diffusion process and its ineffectiveness for image encryption. Besides the security analysis, the performance of RLCD-LFSR scheme and the proposed improved version of the same is implemented on a 32-bit microcontroller to evaluate their suitability for real-time embedded applications.
Interaction Based Redirected Walking

With a significant improvement in virtual reality (VR) devices, the number of interaction-based applications for consumers and industrial products is naturally increasing. As a result, many people can use VR in their homes or offices where they are limited by the physical tracking space. One way to overcome this limitation of natural walking is to use perception-inspired locomotion techniques such as redirected walking (RDW). RDW utilizes imperfections of human perception to introduce small changes like rotations or translations to steer the user away from the tracking boundaries. In this work we evaluate the detection threshold for discrete manipulation rotations to reorient the users in the scene during interactions. We show the thresholds of five most common interactions (Looking, Picking Up, Throwing, Shooting and Sword Fighting) that can be used as a distraction for RDW, which was confirmed in a user study. Based on the findings we propose a novel steer-to-action technique that helps game developers to improve VR games experiences.
An Efficient SQL Injection Detection System Using Deep Learning

SQL Injection makes most of the applications that are based on different types of databases be it used in any devices vulnerable to cyber threat. SQL Injection is said to be one of the top most threat that database-based applications on the web. SQL Injection makes all the user`s information present in the database vulnerable and the user`s data may be either sold in black market or may be misused. The disadvantages of previously implemented SQLI model is that they will not know how will they be able to categorize new patterns, they will only be able to detect the patterns which they have experienced before or trained on, But our model will be able to identify whether the data entered is SQL injected or not identifying patterns in the input. The advantages to our system will be that it will be able to detect all and every type of Injection techniques. All the feature extraction and selection will be done by the model itself. Just the user should need to enter the text. It is also scalable and can extend it to a wide variety of applications. With the help of MLP model, we have achieved a cross-validated accuracy of 98% with a precision of 98% and recall of 97%.
Deep Contextualized Utterance Representations for Response Selection and Dialogue Analysis

The NOESIS II challenge, as the Track 2 in the Eighth Dialogue System Technology Challenge (DSTC 8), is the extension of Track 1 in DSTC 7. Three new elements are incorporated into the extended track, i.e., dialogue with multiple participants, dialogue success, and dialogue disentanglement. These are vital for the creation of a deployed task-oriented dialogue system. This track is divided into four subtasks, the first two of which are evaluated in the form of response selection and the last two focus on dialogue analysis. This paper describes our methods developed for these four subtasks, which all employ deep contextualized utterance representations to make models aware of contextual information and to keep the intrinsic property of multi-turn dialogue systems. In the released evaluation results of Track 2 in DSTC 8, our proposed methods ranked fourth in subtask 1, third in subtask 2, and first in subtask 3 and subtask 4 respectively. In addition to the challenge tasks, we also compare our proposed methods with previous ones on public benchmark datasets. Experimental results show that our proposed methods outperform existing ones by large margins and achieve new state-of-the-art performances on multi-turn response selection and dialogue disentanglement.
W-MMP2Vec: Topic-driven network embedding model for link prediction in content-based heterogeneous information network

Link prediction on heterogeneous information network (HIN) is considered as a challenge problem due to the complexity and diversity in types of nodes and links. Currently, there are remained challenges of meta-path-based link prediction in HIN. Previous works of link prediction in HIN via network embedding approach are mainly focused on exploiting features of node rather than existing relations in forms of meta-paths between nodes. In fact, predicting the existence of new links between non-linked nodes is absolutely inconvincible. Moreover, recent HIN-based embedding models also lack of thorough evaluations on the topic similarity between text-based nodes along given meta-paths. To tackle these challenges, in this paper, we proposed a novel approach of topic-driven multiple meta-path-based HIN representation learning framework, namely W-MMP2Vec. Our model leverages the quality of node representations by combining multiple meta-paths as well as calculating the topic similarity weight for each meta-path during the processes of network embedding learning in content-based HINs. To validate our approach, we apply W-TMP2Vec model in solving several link prediction tasks in both content-based and non-content-based HINs (DBLP, IMDB and BlogCatalog). The experimental outputs demonstrate the effectiveness of proposed model which outperforms recent state-of-the-art HIN representation learning models.
A Joint 2D-3D Complementary Network for Stereo Matching

Stereo matching is an important research field of computer vision. Due to the dimension of cost aggregation, current neural network-based stereo methods are difficult to trade-off speed and accuracy. To this end, we integrate fast 2D stereo methods with accurate 3D networks to improve performance and reduce running time. We leverage a 2D encoder-decoder network to generate a rough disparity map and construct a disparity range to guide the 3D aggregation network, which can significantly improve the accuracy and reduce the computational cost. We use a stacked hourglass structure to refine the disparity from coarse to fine. We evaluated our method on three public datasets. According to the KITTI official website results, Our network can generate an accurate result in 80 ms on a modern GPU. Compared to other 2D stereo networks (AANet, DeepPruner, FADNet, etc.), our network has a big improvement in accuracy. Meanwhile, it is significantly faster than other 3D stereo networks (5× than PSMNet, 7.5× than CSN and 22.5× than GANet, etc.), demonstrating the effectiveness of our method.
Automatic Identification of Metastasis in Histopathological Images Using Deep Learning

Metastatic tumor is one that spreads from its place of origin to other parts of the body. A tumor formed by metastatic cancer cells is called a metastatic tumor or metastasis. The early identification of these tumors is essential to increase the chances of success in treating the disease. However, for this identification it is necessary to analyze extensive tissues of the affected organs, which is a tiring and error-prone task. In this paper, it is present three deep learning strategies for automatic identification of metastasis in histopathological images. For the development and evaluation of these strategies it was used the PCam database, which is composed of 327,680 color images extracted from histopathological exams of sections of lymph nodes. The obtained results using the fine tuning technique are promising, showing that deep learning models can be used for metastasis identification.
Investigating the Performance of Fine-tuned Text Classification Models Based-on Bert

Recently, deep learning has achieved impressive success in text mining and Natural Language Processing tasks. Bert is one of the remarkably rewarding deep learning models that is employed in a variety of NLP classification tasks such as intent and topic detection, question answering, sentiment analysis, hate speech detection, and so on. Plenty of studies have implemented different models of classification using pre-trained Bert models. Fine-tuning is done by adding either a simple fully connected layer, BiLSTM, convolutional layers, or a combination of them. Each of those models has fine-tuned Bert for a specific task. The results do not always approve neither the efficiency of using complex fine-tuned models of Bert nor the generalization of them. In this study, we extensively inspected various Bert-based fine-tuning models for different text classification tasks. Several types of fine-tuning Bert models varying in their classification layer are implemented and the performance of them is meticulously investigated. The implemented fine-tuning models are using alternatively deep learning networks such as convolutional networks and BiLSTM. The output layer of each model is studied to receive entirely varying inputs coming from the distinct layers of Bert. We conducted considerable experiments to find the most general outperforming model. We discover that adding a simple dense layer to the pre-trained Bert model, as a classifier, surpasses other types of deep neural network layers in the investigated tasks. We examine different values of hyperparameters to find the optimized combination providing the highest performance.
Vulnerability Assessment for ICS system Based on Zero-day Attack Graph

The numerous attacks on ICS systems have made severe threats to critical infrastructure. Extensive studies have focussed on the risk assessment of discovering vulnerabilities. However, to identify Zero-day vulnerabilities is challenging because they are unknown to defenders. Here we sought to measure ICS system zero-day risk by building an enhanced attack graph for expected attack path exploiting zero-day vulnerability. In this study, we define the security metrics of Zero-day vulnerability for an ICS. Then we created a Zero-day attack graph to guide how to harden the system by measuring attack paths that exploiting zero-day vulnerabilities. Our studies identify the vulnerability assessment method on ICS systems considering Zero-day Vulnerability by zero-day attack graph. Together, our work is essential to ICS systems security. By assessing unknown vulnerability risk to close the imbalance between attackers and defenders.
Real-time limb tracking in single depth images based on circle matching and line fitting

Abstract Modern lower limb prostheses neither measure nor incorporate healthy residual leg information for intent recognition or device control. In order to increase robustness and reduce misclassification of devices like these, we propose a vision-based solution for real-time 3D human contralateral limb tracking (CoLiTrack). An inertial measurement unit and a depth camera are placed on the side of the prosthesis. The system is capable of estimating the shank axis of the healthy leg. Initially, the 3D input is transformed into a stabilized coordinate system. By splitting the subsequent shank estimation problem into two less computationally intensive steps, the computation time is significantly reduced: First, an iterative closest point algorithm is applied to fit circular models against 2D projections. Second, the random sample consensus method is used to determine the final shank axis. In our study, three experiments were conducted to validate the static, the dynamic and the real-world performance of our CoLiTrack approach. The shank angle can be tracked at 20 Hz for one sixth of the entire human gait cycle with an angle estimation error below $$2.8\pm 2.1^{\circ }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>2.8</mml:mn> <mml:mo>±</mml:mo> <mml:mn>2</mml:mn> <mml:mo>.</mml:mo> <mml:msup> <mml:mn>1</mml:mn> <mml:mo>∘</mml:mo> </mml:msup> </mml:mrow> </mml:math> . Our promising results demonstrate the robustness of the novel CoLiTrack approach to make “next-generation prostheses” more user-friendly, functional and safe.
Application of Artificial Intelligence in the Process of Ecological Water Environment Governance and Its Impact on Economic Growth

With the increasing pollution of the ecological water environment, the treatment of the ecological water environment has become the focus of everyone’s attention. At present, there are many research results on water environment governance, but the effect is not ideal. In order to effectively control the ecological water environment and promote sustainable economic growth, this research combines artificial intelligence algorithms and applies them to the governance process to explore its application effects and its impact on economic growth. First, the environmental sensor of the corresponding module is designed according to the water environment factor, and the data of dissolved oxygen content, water temperature, turbidity, temperature and humidity, and smoke concentration in the water environment are collected. Then the dynamic time-varying exponential smoothing prediction method is used to predict water quality, and a water quality prediction model is established. Then use support vector machine (SVM) to train the collected data samples, use the decision tree-based SVM classification method to classify the data samples, establish a water quality evaluation model, and use particle swarm optimization algorithm to optimize the evaluation model. Put the sensors and predictive evaluation models established in this research design into the governance of a certain river reach, and collect relevant data from 7 : 00 to 18 : 00 on October 11, 2019. And predict and evaluate its water quality. The experimental results show that the average absolute error of predicting dissolved oxygen content is 0.97%, and the average absolute error of predicting phosphorus content is 2.27%. This shows that the application of artificial intelligence algorithms in the process of ecological water environmental governance can effectively help collect effective information and make more accurate predictions and evaluations of water quality, thereby improving governance efficiency and promoting sustainable economic growth.
Characterizing the Communication Requirements of GNN Accelerators: A Model-Based Approach

Relational data present in real world graph representations demands for tools capable to study it accurately. In this regard Graph Neural Network (GNN) is a powerful tool, wherein various models for it have also been developed over the past decade. Recently, there has been a significant push towards creating accelerators that speed up the inference and training process of GNNs. These accelerators, however, do not delve into the impact of their dataflows on the overall data movement and, hence, on the communication requirements. In this paper, we formulate analytical models that capture the amount of data movement in the most recent GNN accelerator frameworks. Specifically, the proposed models capture the dataflows and hardware setup of these accelerator designs and expose their scalability characteristics for a set of hardware, GNN model and input graph parameters. Additionally, the proposed approach provides means for the comparative analysis of the vastly different GNN accelerators.
Traffic thermal infrared texture generation based on siamese semantic CycleGAN

Thermal infrared texture generation is a promising infrared imaging framework for various applications. A novel thermal infrared texture generation algorithm, based on siamese semantic CycleGAN (SS-CycleGAN), is proposed for thermal infrared systems. Different from traditional infrared simulation frameworks, SS-CycleGAN depends on no extra environmental information, such as air temperature, humidity and radiation properties of objects. In other words, visible images could be directly transformed into thermal infrared images like using style transfer algorithms, after traffic scene has been fully understood through training CNN. In this paper, style transfer is firstly introduced for generating thermal textures from color visible images. Siamese semantic loss for visible-infrared transformation is designed and introduced to generate object-oriented thermal infrared textures, while maintaining high definition. Compared to other style transfer algorithms, SS-CycleGAN could generate reasonable thermal infrared textures with clear edge details, in traffic scenes.