Reviewerly/exHarmony huggingface dataset:
Trends in Super-High-Definition Imaging Techniques Based on Deep Neural Networks

Images captured by cameras in closed-circuit televisions and black boxes in cities have low or poor quality owing to lens distortion and optical blur. Moreover, actual images acquired through imaging sensors of cameras such as charge-coupled devices and complementary metal-oxide-semiconductors generally include noise with spatial-variant characteristics that follow Poisson distributions. If compression is directly applied to an image with such spatial-variant sensor noises at the transmitting end, complex and difficult noises called compressed Poisson noises occur at the receiving end. The super-high-definition imaging technology based on deep neural networks improves the image resolution as well as effectively removes the undesired compressed Poisson noises that may occur during real image acquisition and compression as well as in transmission and reception systems. This solution of using deep neural networks at the receiving end to solve the image degradation problem can be used in the intelligent image analysis platform that performs accurate image processing and analysis using high-definition images obtained from various camera sources such as closed-circuit televisions and black boxes. In this review article, we investigate the current state-of-the-art super-high-definition imaging techniques in terms of image denoising for removing the compressed Poisson noises as well as super-resolution based on the deep neural networks.
Extending Fuzzy Cognitive Maps with Tensor-Based Distance Metrics

Cognitive maps are high level representations of the key topological attributes of real or abstract spatial environments progressively built by a sequence of noisy observations. Currently such maps play a crucial role in cognitive sciences as it is believed this is how clusters of dedicated neurons at hippocampus construct internal representations. The latter include physical space and, perhaps more interestingly, abstract fields comprising of interconnected notions such as natural languages. In deep learning cognitive graphs are effective tools for simultaneous dimensionality reduction and visualization with applications among others to edge prediction, ontology alignment, and transfer learning. Fuzzy cognitive graphs have been proposed for representing maps with incomplete knowledge or errors caused by noisy or insufficient observations. The primary contribution of this article is the construction of cognitive map for the sixteen Myers-Briggs personality types with a tensor distance metric. The latter combines two categories of natural language attributes extracted from the namesake Kaggle dataset. To the best of our knowledge linguistic attributes are separated in categories. Moreover, a fuzzy variant of this map is also proposed where a certain personality may be assigned to up to two types with equal probability. The two maps were evaluated based on their topological properties, on their clustering quality, and on how well they fared against the dataset ground truth. The results indicate a superior performance of both maps with the fuzzy variant being better. Based on the findings recommendations are given for engineers and practitioners.
COMPARING TOOLS PROVIDED BY PYTHON AND R FOR EXPLORATORY DATA ANALYSIS

To uncover the insight behind the data, a comprehensive analysis is needed. Exploratory Data Analysis (EDA) is one of practical data analysis that will guide how to reveal any hidden information in the data. By doing EDA, any pattern and issue in the data will be seen and eventually will lead the hypothesis. To do EDA, besides any basic statistic is needed, a good tool to simplify the analysis is also a consideration. Python and R as a famous programming language in the data science world provide methods to implement that analysis. This paper will show how to perform EDA by utilizing Python and R programming.
Ethical considerations of the Andrra Qielli “sting” by Albana Berisha Qehaja

This letter examines the ethics of a “sting” paper submitted by Dr. Albana Berisha Qehaja, a faculty member of the Department of Management and Informatics, Faculty of Economy, University of Prishtina, Republic of Kosovo. In that paper, which stung this journal, IJRBS, Dr. Qehaja created a fake paper, with a fake name and fake institutional affiliation, as well as specially crafted email and ORCID accounts to masquerade the identity of the true author of the fake paper, namely Dr. Qehaja. Such behavior is not only an act of extreme scholarly malpractice, it is one of the most unethical behaviors in academic publishing because it violates all of the journal’s and international ethics’ practices for submission of a paper to a journal. Other academics thinking of employing such unethical tactics for any publishing-related objective should reconsider their options and carefully calculate the ethical, legal, and possibly criminal consequences of such actions
Performance Optimization of Lattice Post-Quantum Cryptographic Algorithms on Many-Core Processors

Current public-key cryptography systems are vulnerable to quantum computing based attacks. Post-quantum cryptographic (PQC) schemes, based on mathematical paradigms such as lattice-based hard problems, are under consideration by NIST as quantum-safe alternatives. Profiling of several latticebased cryptography algorithms reveals that polynomial multiplication and random number generation are the most time consuming components. The nature of these computations and challenges in vectorizing them are discussed in this paper. Vectorization of the identified time-consuming primitives results in 52% and 83% improvement in performance for the CRYSTALS-Kyber KEM SHA3 variant and AES variant, respectively.
Training Deep Neural Networks Using Conjugate Gradient-like Methods

The goal of this article is to train deep neural networks that accelerate useful adaptive learning rate optimization algorithms such as AdaGrad, RMSProp, Adam, and AMSGrad. To reach this goal, we devise an iterative algorithm combining the existing adaptive learning rate optimization algorithms with conjugate gradient-like methods, which are useful for constrained optimization. Convergence analyses show that the proposed algorithm with a small constant learning rate approximates a stationary point of a nonconvex optimization problem in deep learning. Furthermore, it is shown that the proposed algorithm with diminishing learning rates converges to a stationary point of the nonconvex optimization problem. The convergence and performance of the algorithm are demonstrated through numerical comparisons with the existing adaptive learning rate optimization algorithms for image and text classification. The numerical results show that the proposed algorithm with a constant learning rate is superior for training neural networks.
Analysis of the Paradigm on Tor Attack Studies

The massive popularity of Tor as an anonymity platform is undeniable as researchers have studied multiple attacks on the platform. With various attack studies that had been published over 15 years, this paper intends to survey, investigate and classify these studies' paradigm in the perspective of study type, attack mode, target type and mitigation suggestion. Knowledge of these studies' paradigm will allow for an in-depth understanding of the practice, direction and trend of Tor attack studies over the years. In addition, this will present a better comprehension on the diverse techniques used in Tor attack studies. At the end of this study, specific trends of Tor attack studies could be highlighted. Out of 85 reviewed Tor attack studies, only five is non-experimental studies. The most focus attack mode is an active attack and followed by a passive attack. For the target type, the majority of the studies is concentrating on the user as their attack target. Also, out of 85 reviewed Tor attack studies, only 32 studies had discussed a mitigation technique to their proposed attack.
Bloch–Siegert oscillations in the Rabi model with an amplitude-modulated driving field

We study the coherent dynamics of a qubit excited by an amplitude-modulated electromagnetic field under the Rabi resonance when the frequency of the low-frequency modulation field matches the Rabi frequency in the high-frequency field. Due to the destructive interference of multiple photon processes within the ultrastrong coupling between the qubit and the low-frequency driving field, Rabi oscillations result exclusively from the Bloch–Siegert effect. It is directly observed in the time-resolved coherent dynamics as the Bloch–Siegert oscillation. In this case, triplets in the Fourier spectra of the coherent response are transformed into doublets with the splitting between the lines equal to twice the Bloch–Siegert shift. These unusual properties are demonstrated in the conditions of experiments with a nitrogen vacancy center in the diamond.
Slice-Aware Neural Ranking

Understanding when and why neural ranking models fail for an IR task via error analysis is an important part of the research cycle.Here we focus on the challenges of (i) identifying categories of difficult instances (a pair of question and response candidates) for which a neural ranker is ineffective and (ii) improving neural ranking for such instances.To address both challenges we resort to slice-based learning (Chen et al., 2019) for which the goal is to improve effectiveness of neural models for slices (subsets) of data.We address challenge (i) by proposing different slicing functions (SFs) that select slices of the datasetbased on prior work we heuristically capture different failures of neural rankers.Then, for challenge (ii) we adapt a neural ranking model to learn slice-aware representations, i.e. the adapted model learns to represent the question and responses differently based on the model's prediction of which slices they belong to.Our experimental results 1 across three different ranking tasks and four corpora show that slice-based learning improves the effectiveness by an average of 2% over a neural ranker that is not slice-aware.
Attending to Long-Distance Document Context for Sequence Labeling

We present in this work a method for incorporating global context in long documents when making local decisions in sequence labeling problems like NER.Inspired by work in featurized log-linear models (Chieu and Ng, 2002;Sutton and McCallum, 2004), our model learns to attend to multiple mentions of the same word type in generating a representation for each token in context, extending that work to learning representations that can be incorporated into modern neural models.Attending to broader context at test time provides complementary information to pretraining (Gururangan et al., 2020), yields strong gains over equivalently parameterized models lacking such context, and performs best at recognizing entities with high TF-IDF scores (i.e., those that are important within a document).
Intelligent Video Surveillance Technology in Intelligent Transportation

Along with the strength of the country’s overall strength, the people’s pockets have become more and more popular, and there have been significant improvements in all aspects of life, especially in terms of travel methods. This reflects the increase in residents’ income, but it also brings huge traffic pressure. In the long run, traffic congestion is not only detrimental to urban development, but frequent traffic accidents threaten residents’ travel safety. Effective monitoring methods are essential to solving these problems, so it is necessary to carry out research on intelligent video monitoring technology in intelligent transportation. The purpose of this article is to solve the current situation of excessive traffic pressure in the city. Through the study of intelligent video surveillance technology in intelligent traffic, the use of constrained least squares algorithm to remove motion blur and apply Kalan filtering to the sharpening process is used to eliminate noise ambiguity and make a brief introduction to various classic moving target detection methods to realize real-time monitoring of intelligent traffic conditions and continuously adjust and verify the monitoring situation, and then establish intelligent video in intelligent traffic monitoring technology research system. The research results show that this kind of intelligent video surveillance technology research in intelligent transportation can effectively increase the awareness of intelligent video surveillance technology and improve the level of intelligent video surveillance technology. The data measurement time has been shortened by one hour, the aggregation time has been changed from three hours to two hours, and the analysis time has been shortened by half. Eased urban traffic road pressure and greatly reduced the incidence of traffic accidents, which is conducive to socialist harmony social construction.
Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech

Natural language contains information at multiple timescales. To understand how the human brain represents this information, one approach is to build encoding mod- els that predict fMRI responses to natural language using representations extracted from neural network language models (LMs). However, these LM-derived repre- sentations do not explicitly separate information at different timescales, making it difficult to interpret the encoding models. In this work we construct interpretable multi-timescale representations by forcing individual units in an LSTM LM to integrate information over specific temporal scales. This allows us to explicitly and directly map the timescale of information encoded by each individual fMRI voxel. Further, the standard fMRI encoding procedure does not account for varying temporal properties in the encoding features. We modify the procedure so that it can capture both short- and long-timescale information. This approach outper- forms other encoding models, particularly for voxels that represent long-timescale information. It also provides a finer-grained map of timescale information in the human language pathway. This serves as a framework for future work investigating temporal hierarchies across artificial and biological language systems.
Fourier-Based and Rational Graph Filters for Spectral Processing

Data are represented as graphs in a wide range of applications, such as Computer Vision (e.g., images) and Graphics (e.g., 3D meshes), network analysis (e.g., social networks), and bio-informatics (e.g., molecules). In this context, our overall goal is the definition of novel Fourier-based and graph filters induced by rational polynomials for graph processing, which generalise polynomial filters and the Fourier transform to non-euclidean domains. For the efficient evaluation of discrete spectral Fourier-based and wavelet operators, we introduce a spectrum-free approach, which requires the solution of a small set of sparse, symmetric, well-conditioned linear systems and is oblivious of the evaluation of the Laplacian or kernel spectrum. Approximating arbitrary graph filters with rational polynomials provides a more accurate and numerically stable alternative with respect to polynomials. To achieve these goals, we also study the link between spectral operators, wavelets, and filtered convolution with integral operators induced by spectral kernels. According to our tests, main advantages of the proposed approach are (i) its generality with respect to the input data (e.g., graphs, 3D shapes), applications (e.g., signal reconstruction and smoothing, shape correspondence), and filters (e.g., polynomial, rational polynomial), and (ii) a spectrum-free computation with a generally low computational cost and storage overhead.
Reading Between the Lines: Exploring Infilling in Visual Narratives

Generating long form narratives such as stories and procedures from multiple modalities has been a long standing dream for artificial intelligence.In this regard, there is often crucial subtext that is derived from the surrounding contexts.The general seq2seq training methods render the models shorthanded while attempting to bridge the gap between these neighbouring contexts.In this paper, we tackle this problem by using infilling techniques involving prediction of missing steps in a narrative while generating textual descriptions from a sequence of images.We also present a new large scale visual procedure telling (ViPT) dataset with a total of 46,200 procedures and around 340k pairwise images and textual descriptions that is rich in such contextual dependencies.Generating steps using infilling technique demonstrates the effectiveness in visual procedures with more coherent texts.We conclusively show a ME-TEOR score of 27.51 on procedures which is higher than the state-of-the-art on visual storytelling.We also demonstrate the effects of interposing new text with missing images during inference.
Fast and Efficient Entropy Compression of ALICE Data using ANS Coding

In LHC Run 3, the upgraded ALICE detector will record 50 kHz Pb-Pb collisions using continuous readout. The resulting stream of raw data to be inspected increases to ~ 1 TB/s a hundredfold increase over Run 2 must be processed with a set of lossy and lossless compression and data reduction techniques to decrease the data rate to storage to 90 GB/s without affecting the physics. This contribution focuses on lossless entropy coding for ALICE Run 3 data which is the final component in the compression stage. We analyze data from the ALICE TPC and point out the challenges imposed by the non-standard data with a patchy distribution and symbol sizes of up to 25 Bit. We then explain why rANS, a variant of Asymmetric Numeral System coders is suitable for compressing this data effectively. Finally we present first compression performance numbers and bandwidth measurements obtained from a prototype implementation and give an outlook for future developments.
Chinese Short Text Entity Disambiguation Based on the Dual-Channel Hybrid Network

Entity disambiguation refers to the accurate inference of the real mention of an entity with the same name according to the context. Most existing studies focused on long texts, for short texts, the performance has been unsatisfactory due to sparsity. In this paper, we treat the entity disambiguation task as a classification problem. we propose a novel neural network-based capsule network and convolutional neural network for entity disambiguation, leveraging full semantic information of short text data. In particular, a self-attention mechanism is utilized to further filter the semantic information extracted from the capsule network. On the other hand, a convolutional neural network with combined pooling is established to capture semantics from another channel. In the end, the semantic features obtained by the above models are combined through a fully connected layer to complete the task of entity disambiguation. The experimental results on the CCKS 2019 entity linking dataset showed that the dual-channel hybrid network proposed in this paper achieved an F1-score of 88.04%, which is superior to that of the existing mainstream deep learning model, thereby verifying the effectiveness of the model.
Polarity Detection on Real-Time News Data Using Opinion Mining

Sentimental Analysis or Opinion Mining plays a vital role in the experimentation field that determines the user’s opinions, emotions and sentiments concealing a text. News on the Internet is becoming vast, and it is drawing attention and has reached the point of adequately affecting political and social realities. The popular way of checking online content, i.e. manual knowledge-based on the facts, is practically impossible because of the enormous amount of data that has now generated online. The issue can address by using Machine Learning Algorithms and Artificial Intelligence. One of the Machine Learning techniques used in this is Naive Bayes classifier. In this paper, the polarity of the news article determined whether the given news article is a positive, negative or neutral Naive Bayes Classifier, which works well with NLP (Natural Language problems) used for many purposes. It is a family of probabilistic algorithms that used to identify a word from a given text. In this, we calculate the probability of each word in a given text. Using Bayes theorem, they are getting the probabilities based on the given conditions. Topic Modeling is analytical modelling for finding the abstract of topics from a cluster of documents. Latent Dirichlet Allocation (LDA) is a topic model is used to classify the text in a given document to a specified topic. The news article is classified as positive or negative or neutral using Naive Bayes classifier by calculating the probabilities of each word from a given news article. By using topic modelling (LDA), topics of articles are detected and record data separately. The calculation of the overall sentiment of a chosen topic from different newspapers from previously recorded data done.
Don’t Read Too Much Into It: Adaptive Computation for Open-Domain Question Answering

Most approaches to Open-Domain Question Answering consist of a light-weight retriever that selects a set of candidate passages, and a computationally expensive reader that examines the passages to identify the correct answer.Previous works have shown that as the number of retrieved passages increases, so does the performance of the reader.However, they assume all retrieved passages are of equal importance and allocate the same amount of computation to them, leading to a substantial increase in computational cost.To reduce this cost, we propose the use of adaptive computation to control the computational budget allocated for the passages to be read.We first introduce a technique operating on individual passages in isolation which relies on anytime prediction and a per-layer estimation of an early exit probability.We then introduce SKY-LINEBUILDER, an approach for dynamically deciding on which passage to allocate computation at each step, based on a resource allocation policy trained via reinforcement learning.Our results on SQuAD-Open show that adaptive computation with global prioritisation improves over several strong static and adaptive methods, leading to a 4.3x reduction in computation while retaining 95% performance of the full model.
A Novel Hybrid Firefly Algorithm Based on the Vector Angle Learning Mechanism

The firefly algorithm (FA) is one of the swarm intelligence algorithms which can solve global optimization problems accurately. In the traditional FA, the position of each firefly can only be updated by the brightness of other fireflies around it. As a result, it is simple to update the firefly position but easy to fall into local optimum. In this paper, a novel hybrid firefly algorithm based on the vector angle learning mechanism (HFA-VAL) is proposed, which can combine the advantages of both the firefly algorithm (FA) and differential evolution (DE) by the vector angle learning mechanism. HFA-VAL employs vector angle parameters to adaptively adjust the moving step length of firefly in order to avoid falling into local optimum. In the evolutionary process, the difference method is used to update the dominant leader, so as to improve the moving direction of other fireflies and expand the search ability. In order to understand the strengths and weaknesses of HFA-VAL, several experiments are carried out on 25 benchmark functions in CEC2005. Experimental results show that the performance of HFA-VAL algorithm is better than other the-state-of-art algorithms.
Imaging Noise Suppression: Fourth-Order Partial Differential Equations and Travelling Wave Solutions

In this paper, we discuss travelling wave solutions for image smoothing based on a fourth-order partial differential equation. One of the recurring issues of digital imaging is the amount of noise. One solution to this is to minimise the total variation norm of the image, thus giving rise to non-linear equations. We investigate the variational properties of the Lagrange functionals associated with these minimisation problems.