Reviewerly/exHarmony huggingface dataset:
A Novel Anti-Jamming Technique for INS/GNSS Integration Based on Black Box Variational Inference

In this paper, a novel anti-jamming technique based on black box variational inference for INS/GNSS integration with time-varying measurement noise covariance matrices is presented. We proved that the time-varying measurement noise is more similar to the Gaussian distribution with time-varying mean value than to the Inv-Gamma or Inv-Wishart distribution found by Kullback–Leibler divergence. Therefore, we assumed the prior distribution of measurement noise covariance matrices as Gaussian, and calculated the Gaussian parameters by the black box variational inference method. Finally, we obtained the measurement noise covariance matrices by using the Gaussian parameters. The experimental results illustrate that the proposed algorithm performs better in resisting time-varying measurement noise than the existing Variational Bayesian adaptive filter.
Matrix Fisher–Gaussian Distribution on $\mathrm{SO}(3)\times \mathbb {R}^{ n }$ and Bayesian Attitude Estimation

In this article, a new probability distribution, referred to as the matrix Fisher–Gaussian distribution, is proposed on the product manifold of three-dimensional special orthogonal group and Euclidean space. It is constructed by conditioning a multivariate Gaussian distribution from the ambient Euclidean space into the manifold, while imposing a certain geometric constraint on the correlation term to avoid over parameterization. The unique feature is that it may represent large uncertainties in attitudes, linear variables of an arbitrary dimension, and angular–linear correlations between them in a global fashion without singularities. Various stochastic properties and an approximate maximum likelihood estimator are developed. Furthermore, two methods are developed to propagate uncertainties through a stochastic differential equation representing attitude kinematics. Based on these, a Bayesian estimator is proposed to estimate the attitude and time-varying gyro bias concurrently. Numerical studies indicate that the proposed estimator provides more accurate estimates against the multiplicative extended Kalman filter and unscented Kalman filter for challenging cases.
The Asymmetric Effect of Panic Index on Cryptocurrencies

The Covid 19 pandemic is the first major crisis facing cryptocurrencies. Therefore, the reaction of the cryptocurrency markets is important. News about epidemics affects investors' decisions. Panic index (PIndex) is an index created from news about the Covid 19 outbreak. In the study, it is used to measure the impact of decisions on the crypto money market. As cryptocurrencies, Bitcoin (BTC), Etherium (ETH), and Ripple (XRP), which have the highest transaction volume in the crypto money market, are included in the analysis. The relationship between Panic Index and the three major cryptocurrencies with the largest share in the cryptocurrency market was investigated by Ardl and Hatemi-J asymmetric causality test. Traditional causality tests acknowledge that the effects of positive and negative changes are the same. However, there may be asymmetric information and different investor behaviors in financial markets. In the study, Hatemi-J [ 1 ] Asymmetric Causality Test was conducted to examine the asymmetric relationship and symmetric relationship between Pindex and cryptocurrencies by separating them into positive and negative shocks. According to the results of the Hatemi-J causality analysis, positive shocks in the panic index are the cause of negative shocks for all cryptocurrencies. In other words, increases in the panic index are caused to fall the value of Bitcoin, Ethereum, and Ripple cryptocurrencies decrease. The results show that cryptocurrencies were not a safe haven for the investor during the Covid 19 period, as they acted similarly to other financial assets.
Language Models for Lexical Inference in Context

Lexical inference in context (LIiC) is the task of recognizing textual entailment between two very similar sentences, i.e., sentences that only differ in one expression.It can therefore be seen as a variant of the natural language inference task that is focused on lexical semantics.We formulate and evaluate the first approaches based on pretrained language models (LMs) for this task: (i) a few-shot NLI classifier, (ii) a relation induction approach based on handcrafted patterns expressing the semantics of lexical inference, and (iii) a variant of (ii) with patterns that were automatically extracted from a corpus.All our approaches outperform the previous state of the art, showing the potential of pretrained LMs for LIiC.In an extensive analysis, we investigate factors of success and failure of our three approaches. 1
CLU

Convolutional/Deep Neural Networks (CNNs/DNNs) are rapidly growing workloads for the emerging AI-based systems. The gap between the processing speed and the memory-access latency in multi-core systems affects the performance and energy efficiency of the CNN/DNN tasks. This article aims to alleviate this gap by providing a simple and yet efficient near-memory accelerator-based system that expedites the CNN inference. Towards this goal, we first design an efficient parallel algorithm to accelerate CNN/DNN tasks. The data is partitioned across the multiple memory channels (vaults) to assist in the execution of the parallel algorithm. Second, we design a hardware unit, namely the convolutional logic unit (CLU), which implements the parallel algorithm. To optimize the inference, the CLU is designed, and it works in three phases for layer-wise processing of data. Last, to harness the benefits of near-memory processing (NMP), we integrate homogeneous CLUs on the logic layer of the 3D memory, specifically the Hybrid Memory Cube (HMC). The combined effect of these results in a high-performing and energy-efficient system for CNNs/DNNs. The proposed system achieves a substantial gain in the performance and energy reduction compared to multi-core CPU- and GPU-based systems with a minimal area overhead of 2.37%.
Sentence boundary detection of various forms of Tunisian Arabic

Sentence boundary detection (SBD) is an essential step for a very large number of natural language processing applications such as parsing, information retrieval, automatic summarization, machine translation, etc. In this paper, we tackle the problem of SBD of dialectal Arabic, especially for the Tunisian dialect. We compare the efficiency of three learning algorithms: Deep Neuronal Networks (DNN), Support Vector Machines (SVM) and Conditional Random Fields (CRF) to detect the boundaries of sentences written in different types of dialect. The best model achieved an F-measure of 84.37% using CRF which is a popular formalism for structured prediction in NLP and it has been widely applied in text segmentation.
A Traceable Smart Grid Trading System under Blockchain

In recently years, the traditional bi-directional smart grid system helps users to save energy such as electricity and also could perform the energy trading with other users in the same grid. However, it also requires a trusted third party to help gird users to maintain the transaction to be fair. If there are some users to deny a transaction in the grid, how the trusted third party is able to trace the real identity is the serious problem to be handle currently. As a result, we proposed a traceable smart grid trading system with smart contract in the peer-to peer(p2p) network. We also combine the smart contract into our scheme and adopt the token-based way to make transaction to be fair in case of unexpected problems happened such as the transaction failure.
Application of Wiener Filter to Suppress White Noise in Images: Wavelet vs Fourier Basis

A method for suppressing additive white noise in images based on Wiener filtering in the domain of discrete wavelet transform (DWT) is proposed. The method outperforms such traditional methods as "hard" and "soft" DWT thresholding. Having less computational complexity, the proposed method shows the results of denoising similar to Wiener filtering in the frequency domain.
Reconstruction of frescoes by sequential layers of feature extraction

This work presents the development of a new method for solving puzzles, based on sequence of layers, called Retention Layers Method, using feature extraction algorithms with a new method to select the corresponding keypoints between puzzle pieces and reference image, called the Middle Triangle Method. Promising results, both in the correct positioning of the fragments and in the disposal of spurious fragments, can be seen by the high ratio of correctly positioned fragments, reaching up to 100%, surpassing the analyzed puzzle solvers.
Blockchain Technology for Improving Transparency and Citizen’s Trust

Public service organizations are always driven by goals to uplift the living standard of citizens. Transparency of information and procedures plays vital role for gaining the citizens' trust. This article is a study about Blockchain Technology's roles in managing the transparency, trust, citizen's satisfaction and minimizing corruption so as to improve the efficiency of public service delivery. Transparency and trust have close relation to corruption and citizen's satisfaction with public services. Various case studies and analysis clarifies the necessity of additional layer in the e-services delivered by public organization. Based on some case studies of ongoing Blockchain-based public services, the potential benefits as well as the existing challenges when introducing blockchain technology in the public sector is presented. Findings show that blockchain technology has the capacity to enable transparency and build citizen's trust in public service delivery while maintaining a sufficient level of privacy. Therefore, keeping the transparency and trust as an important driver, blockchain technology could be the promising technology.
Mean Shift Fusion Color Histogram Algorithm for Nonrigid Complex Target Tracking in Sports Video

We analyze and study the tracking of nonrigid complex targets of sports video based on mean shift fusion color histogram algorithm. A simple and controllable 3D template generation method based on monocular video sequences is constructed, which is used as a preprocessing stage of dynamic target 3D reconstruction algorithm to achieve the construction of templates for a variety of complex objects, such as human faces and human hands, broadening the use of the reconstruction method. This stage requires video sequences of rigid moving target objects or sets of target images taken from different angles as input. First, the standard rigid body method of Visuals is used to obtain the external camera parameters of the sequence frames as well as the sparse feature point reconstruction data, and the algorithm has high accuracy and robustness. Then, a dense depth map is computed for each input image frame by the Multi-View Stereo algorithm. The depth reconstruction with a too high resolution not only increases the processing time significantly but also generates more noise, so the resolution of the depth map is controlled by parameters. The multiple hypothesis target tracking algorithms are used to track multiple targets, while the chunking feature is used to solve the problem of mutual occlusion and adhesion between targets. After finishing the matching, the target and background models are updated online separately to ensure the validity of the target and background models. Our results of nonrigid complex target tracking by mean shift fusion color histogram algorithm for sports video improve the accuracy by about 8% compared to other studies. The proposed tracking method based on the mean shift algorithm and color histogram algorithm can not only estimate the position of the target effectively but also depict the shape of the target well, which solves the problem that the nonrigid targets in sports video have complicated shapes and are not easy to track. An example is given to demonstrate the effectiveness and adaptiveness of the applied method.
IIoT-ARAS

As IT/OT convergence continues to evolve, the traditionally isolated ICS/OT systems are increasingly exposed to a myriad of online and offline threats. Although IIoT enhances the reachability in ICS, improved data analytics, ensuring ease of access and decision making, it unwittingly opens the ICS environment to attackers. The design of IIoT introduces multiple entry points to an isolated system, which is used to protect itself via air-gapping and risk avoidance strategies. This study explores a comprehensive mapping of threats and risks for IT/OT convergence. Additionally, we propose IIoT-ARAS - an automated risk assessment system based on OCTAVE Allegro and ISO/IEC 27030 methodologies. The design of IIoT-ARAS is aimed to be agentless, with minimum interruptions to the OT environment. Furthermore, the system performs automated regular asset inventory checks, threshold optimization, probability computation, risk evaluations, and contingency plan configuration.
mVote: A Mobile Voting System to Conduct Election during COVID-19 Pandemic

Over years, the voting system has been a democratic way to make a collective decision to express an opinion or selecting a leader. In organizations, voting can occur to elect members of the committee or to choose the roles of candidates. At present, in this era of technological advancement, the voting process became a major issue in order to avoid vulnerabilities like missing votes, multiple voting, and miscount in an election. The manual or semi-automated systems of voting are quite a time consuming, inefficient and unfortunately lack security. Moreover, for COVID-19, physical manual vote taking has become colossal. In this paper, a secure android based mobile voting system named 'mVote' is developed for conducting vote at different institutes and organizations during and post-pandemic situation. The usability and usefulness of the 'mVote' mobile application were evaluated through the System Usability Scale (SUS) which was replicated with 75 participants. The findings of the evaluation study showed that the developed 'mVote' application is usable and will help different organizations and institutes to improve the voting experience during and post-pandemic situation.
Neural architecture search as program transformation exploration

Improving the performance of deep neural networks (DNNs) is important to both the compiler and neural architecture search (NAS) communities. Compilers apply program transformations in order to exploit hardware parallelism and memory hierarchy. However, legality concerns mean they fail to exploit the natural robustness of neural networks. In contrast, NAS techniques mutate networks by operations such as the grouping or bottlenecking of convolutions, exploiting the resilience of DNNs. In this work, we express such neural architecture operations as program transformations whose legality depends on a notion of representational capacity. This allows them to be combined with existing transformations into a unified optimization framework. This unification allows us to express existing NAS operations as combinations of simpler transformations. Crucially, it allows us to generate and explore new tensor convolutions. We prototyped the combined framework in TVM and were able to find optimizations across different DNNs, that significantly reduce inference time - over 3× in the majority of cases. Furthermore, our scheme dramatically reduces NAS search time.
Hybrid CNN-LSTM Model with GloVe Word Vector for Sentiment Analysis on Football Specific Tweets

The football fans feelings get unfold during the different phases of the football match, they express their opinions, views, thoughts, judgments of the match, attitude towards player, and emotions, stance on social media like Twitter. The change in fans opinions are reflected in a series of tweets written by fans. This research work focuses on identifying and analyzing sentiment in tweets expressed by football fans on Twitter. To perform sentiment analysis, we analyzed different word embedding techniques to represent word vectors that capture semantic and syntactic information. In this paper, we use a global vector(GloVe) word embedding technique which produces word vector with substructure and leverages statistics. In addition to GloVe, we also construct a sentiment lexicon as additional information. The word vector produced by GloVe and sentiment lexicon is the inputs for the proposed hybrid CNN-LSTM deep learning model. The proposed CNN-LSTM model blend benefits of CNN and LSTM, CNN used to excerpt features from word embedding that reflect short-term sentiment dependency while LSTM used to build long-term sentiment relationships among words. This the paper also used machine learning algorithms such as Random Forest, Support Vector Machine, Multinomial Nave Bayes, KNearest Neighbours(KNN) and XG Boost for sentiment analysis and sentiment classification. We evaluated the performance of proposed hybrid CNN-LSTM with GloVe word embedding approach with 2018 FIFA world cup tweets dataset, our experiment results show 85.46% and 92.56% validation and testing accuracy respectively. Further, our experiment results also demonstrate that the Random Forest algorithm perform consistent and robust performance compared to other machine learning classifiers, it perceive fan's sentiment during football events.
A fuzzy Dempster–Shafer classifier for detecting Web spams

The Web spam identification problem can be modeled as an instance of the conventional classification problem. Web spams aim at deceiving web crawlers by advertising certain Web pages through elevation of their page rankings superficially than their actual weights. Web spams are intended to produce fraudulent results of web search queries and degenerate the client’s experience by directing users to fake Web pages. We present a fuzzy evidence-based methodology for identifying Web spams by which the spamicity of web hosts is formulated as a reasoning problem in the presence of uncertainty. However, any classification task intrinsically suffers from incomplete or vague evidence and ambiguity in the class assignment based on evidence. In this work, we combine fuzzy reasoning as the decision maker for selecting the most suitable evidence in a multi-source Dempster–Shafer (DS) based classification algorithm. The introduced approach has the benefit of providing more reliable solution to detect spams without any prior information. The evidence theory offers flexible support that takes into account the multi-dimensional nature of implementation decisions. The experimental results show that the fuzzy reasoning in combination with DS theory, reduces the conflicts among evidence leading to enhanced classification results. The aim of this paper is to describe the potential of fuzzy reasoning and the Dempster–Shafer Theory (DST) as a decision model for the web spams classification problem.
Info-flow Enhanced GANs for Recommender

Recommendation systems can help users process large amounts of information, and generative adversarial networks (GANs) show great potential in recommendation systems. In this paper, we propose a new GAN model to enhance the information flow within the generator based on the information flow between the original generator and discriminator. Our experimental results indicate that our model reduces the discrepancy between the generator and the discriminator. Both the generator and discriminator yield considerable performance improvements compared to other strong baselines. The improvements by [email protected] and MRR are significant, which can reach 30.98% and 30.17%, respectively.
High-dimensional Sparse Embeddings for Collaborative Filtering

A widely adopted paradigm in the design of recommender systems is to represent users and items as vectors, often referred to as latent factors or embeddings. Embeddings can be obtained using a variety of recommendation models and served in production using a variety of data engineering solutions. Embeddings also facilitate transfer learning, where trained embeddings from one model are reused in another. In contrast, some of the best-performing collaborative filtering models today are high-dimensional linear models that do not rely on factorization, and so they do not produce embeddings [27, 28]. They also require pruning, amounting to a trade-off between the model size and the density of the predicted affinities. This paper argues for the use of high-dimensional, sparse latent factor models, instead. We propose a new recommendation model based on a full-rank factorization of the inverse Gram matrix. The resulting high-dimensional embeddings can be made sparse while still factorizing a dense affinity matrix. We show how the embeddings combine the advantages of latent representations with the performance of high-dimensional linear models.
Highly Liquid Temporal Interaction Graph Embeddings

Capturing the topological and temporal information of interactions and predicting future interactions are crucial for many domains, such as social networks, financial transactions, and e-commerce. With the advent of co-evolutional models, the mutual influence between the interacted users and items are captured. However, existing models only update the interaction information of nodes along the timeline. It causes the problem of information asymmetry, where early updated nodes often have much less information than the most recently updated nodes. The information asymmetry is essentially a blockage of information flow. We propose HILI (Highly Liquid Temporal Interaction Graph Embeddings) to predict highly liquid embeddings on temporal interaction graphs. Our embedding model makes interaction information highly liquid without information asymmetry. A specific least recently used-based and frequency-based windows are used to determine the priority of the nodes that receive the latest interaction information. HILI updates node embeddings by attention layers. The attention layers learn the correlation between nodes and update node embedding simply and quickly. In addition, HILI elaborately designs, a self-linear layer, a linear layer initialized in a novel method. A self-linear layer reduces the expected space of predicted embedding of the next interacting node and makes predicted embedding focus more on relevant nodes. We illustrate the geometric meaning of a self-linear layer in the paper. Furthermore, the results of the experiments show that our model outperforms other state-of-the-art temporal interaction prediction models.
Prominent Object Detection in Underwater Environment using a Dual-feature Framework

Tracking of a fish or some specific fishes in a school of fish is quite a challenging task. This could help in understanding the behavior of a fish or a small group of fish in a crowd of different varieties of fishes. In this paper we propose a technique to detect prominent objects among a large group of fishes. The problem is formulated with a stationary camera setup. The moving objects are initially detected by a spatio-contextual Gaussian mixture model based background subtraction method. Further, all the detected objects are analyzed to determine a predefined number of the most prominent objects in the scene of view. To characterize the objects we have employed a dual-feature framework, which includes color and texture features. The overall feature strength is computed by combining the two feature-strengths in an adaptive way so that, the color gets more weight if color degradation is less otherwise texture gets more weight. This weight is adaptively computed with the prior information of color degradation phenomena in underwater environment. The proposed technique is tested with a large number of underwater videos and found to perform satisfactorily.