Reviewerly/exHarmony huggingface dataset:
Model variational consumer preferences based on online reviews using sentiment analysis and PSO-based DENFIS approaches

Previous studies developed consumer preference models mainly through customer surveys, ignoring the variability of consumer preferences over time. In addition, it is difficult to obtain time series data based on the customer surveys. In recent years, some previous studies tried to analyse consumer preferences based on online reviews. However, they have not solved the problems of modelling variational consumer preference based on time series data with the consideration of the ambiguity of emotions expressed by customers in online reviews. To solve the above problems, this article proposes the particle swarm optimization (PSO) based dynamic evolving neural-fuzzy inference system (DENFIS) method to model variational consumer preferences based on online customer reviews. Using the time series data mined by the sentiment analysis method and the product attribute settings of the review products, the PSO-based DENFIS method is offered to dynamically model consumer preferences, in which PSO is used to adjust DENFIS parameters adaptively.
A Novel Approach for Robust S-Box Construction Using a 5-D Chaotic Map and Its Application to Image Cryptosystem

Data privacy and security play a vital task in our daily life, which chaotic systems are commonly utilized for designing modern cryptosystems. This chapter presents a new chaos-based substitution box (S-box) and its application for securing images. The construction of the presented S-box approach is based on a 5-D chaotic system, which the experimental results prove that the suggested S-box approach has high nonlinearity and good cryptographic characteristics. In addition, we utilize the effectiveness of the presented S-box approach in designing a novel image cryptosystem, which the experimental outcomes prove the effectiveness of the presented image cryptosystem.
Research on Knowledge Graph Completion Model Combining Temporal Convolutional Network and Monte Carlo Tree Search

In knowledge graph completion (KGC) and other applications, learning how to move from a source node to a target node with a given query is an important problem. It can be formulated as a reinforcement learning (RL) problem transition model under a given state. In order to overcome the challenges of sparse rewards and historical state encoding, we develop a deep agent network (graph-agent, GA), which combines temporal convolutional network (TCN) and Monte Carlo Tree Search (MCTS). Firstly, we combine MCTS with neural network to generate more positive reward trajectories, which can effectively solve the problem of sparse rewards. TCN is used to encode the history state, which is used for policy and Q-value respectively. Secondly, according to these trajectories, we use Q-Learning to improve the network and parameter sharing to enhance TCN strategy. We apply these steps repeatedly to learn the model. Thirdly, in the prediction stage of the model, Monte Carlo Tree Search combined with Q-value method is used to predict the target nodes. The experimental results on several graph-walking benchmarks show that GA is better than other RL methods based on-policy gradient. The performance of GA is also better than the traditional KGC baselines.
Finite-Exemplar-Based Super-Resolution for Specific Gaming Videos

At present, the super-resolution of gaming video (GVSR) has not received special attention. In fact, gaming videos are quite different from natural videos, such as different contents and textures, limited scenes, and high similarity between videos. Therefore, this paper proposes a special GVSR method based on the characteristics of gaming videos, namely finite-exemplar guided network (FEGN). For specific game, the FEGN can reproduce high visual quality results by using finite representative exemplars. We propose a high-frequency matching and fusion module to reduce the computational cost of patch-by-patch matching process. In addition, a dual-branch reconstruction module is designed to promote the exchange of information between low-resolution and reference features. Experimental results on gaming video datasets demonstrate the effectiveness and generalization of the proposed method.
Class Balancing Methods for Fraud Detection using Deep Learning

The handling of imbalanced datasets in real-world applications like fraud detection systems is a challenging task. In most cases, the number of legitimate transactions exceeds the fraudulent transactions. The class imbalance problem can be solved through various means, such as data-level methods and algorithmic-level approaches. This paper reviews the various techniques used to handle the imbalanced datasets. Performance evaluation of the algorithmic-level and data-level class balancing methods has been performed. It has been found that algorithmic methods can be more efficient at handling the data in their imbalanced form.
Design of Convolutional Neural Network Based on FPGA

Recently with the rapid development of artificial intelligence AI, various deep learning algorithms represented by Convolutional Neural Networks (CNN) have been widely utilized in various fields, showing their unique advantages; especially in Skin Cancer (SC) imaging Neural networks (NN) are methods for performing machine learning (ML) and reside in what's called deep learning (DL). DL refers to the utilization of multiple layers during a neural network to perform the training and classification of data. The Convolutional Neural Networks (CNNs), a kind of neural network and a prominent machine learning algorithm go through multiple phases before they get implemented in hardware to perform particular tasks for a specific application. State-of-the-art CNNs are computationally intensive, yet their parallel and modular nature make platforms like Field Programmable Gate Arrays (FPGAs) compatible with the acceleration process. The objective of this paper is to implement a hardware architecture capable of running on an FPGA platform of a convolutional neural network CNN, for that, a study was made by describing the operation of the concerned modules, we detail them then we propose a hardware architecture with RTL scheme for each of these modules using the software ISE (Xilinx). The main objective is to show the efficiency of such a realization compared to a GPU based execution. An experimental study is accomplished for the PH2 database set of benchmark images. The proposed FPGA-based CNN design gives competitive results and shows well its efficiency.
Coherent photonic neuromorphic computing for high-speed deep learning applications

The emergence of demanding machine learning and AI workloads in modern computational systems and Data Centers (DC) has fueled a drive towards custom hardware, designed to accelerate Multiply-Accumulate (MAC) operations. In this context, neuromorphic photonics have recently attracted attention as a promising technological candidate, that can transfer photonics low-power, high bandwidth credentials in neuromorphic hardware implementations. However, the deployment of such systems necessitates progress in both the underlying constituent building blocks as well as the development of deep learning training models that can take into account the physical properties of the employed photonic components and compensate for their non-ideal performance. Herein, we present an overview of our progress in photonic neuromorphic computing based on coherent layouts, that exploits the phase of the light traversing the photonic circuitry both for sign representation and matrix manipulation. Our approach breaks-through the direct trade-off of insertion loss and modulation bandwidth of State-Of-The-Art coherent architectures and allows high-speed operation in reasonable energy envelopes. We present a silicon-integrated coherent linear neuron (COLN) that relies on electro-absorption modulators (EAM) both for its on-chip data generation and weighting, demonstrating a record-high 32 GMAC/sec/axon compute linerate and an experimentally obtained accuracy of 95.91% in the MNIST classification task. Moreover, we present our progress on component specific neuromorphic circuitry training, considering both the photonic link thermal noise and its channel response. Finally, we present our roadmap on scaling our architecture using a novel optical crossbar design towards a 32×32 layout that can offer >;32 GMAC/sec/axon computational power in ~0.09 pJ/MAC.
HyperUNet for Medical Hyperspectral Image Segmentation on a Choledochal Database

Microscopy medical hyperspectral images, which are characterized in multiple observation bands under different spectral frequencies, contain profuse spectral information for disease diagnosis. Consequently, an increasing number of deep learning methods have recently been proposed to solve the medical hyperspectral image segmentation task. In this study, we propose a new segmentation network (HyperUNet) as a better version of UNet for medical hyperspectral image segmentation on a choledochal database. Considering the useless spectral in-formation that exists in the hyperspectral image that is irrelevant to our task, HyperUNet first uses the linear transformation block to extract the useful spectral information from the hyperspectral image, and then applies the UNet model to it to capture the tumor area. Finally, when reconstructing the mask, HyperUNet applies the multi -scale loss function in cases of underuse and overuse of low-level detailed features and high-level semantic features. We compare our HyperUNet to other competing methods, and the results show that our HyperUNet is superior.
A User Study on Clarifying Comparative Questions

Vague or ambiguous queries can make it difficult for a search engine to correctly interpret a user’s underlying information need. A relatively “simple” solution then is result diversification to cover different interpretations, while in more “conversational” search interfaces, the user can be prompted to clarify their original request. We study clarification in the scenario of comparative questions that ask to compare several options. In our experiment that reflects a conversational search interface with a clarification component, 70% of the study participants find clarifications useful to retrieve relevant results for questions with unclear comparison aspects (e.g., “Which is better, Bali or Phuket?”) or without explicit comparison objects and aspects (e.g., “What is the best antibiotic?”).
Argumentation in Suboptimal Settings

Abstract When parties attempt to persuade their opponents of the tenability of a certain standpoint using reasons, they will often find that the circumstances of the dialogue hinder their chances of resolution. Power imbalances, cognitive biases, lack of time or hidden interests are some of the circumstances they need to face. I will label these circumstances as suboptimal settings for argumentation . According to the pragma-dialectical tradition, higher-order conditions for critical discussion are unfulfilled in these cases (van Eemeren, Grootendorst, Jacobs, &amp; Jackson, 1993). The main question of this paper is the following: what is the normative standard that parties in a discussion need to follow to arrive at a resolution within such circumstances? I will defend a middle-ground solution between two extreme ones. The first extreme position, the anything-goes policy , claims that, given that the conditions for a reasonable exchange of reasons are not satisfied, the dialogue stands outside the domain of reason, so anything goes for the parties. The second extreme position, the business as usual policy , claims that, since critical discussion is a normative model, the same rules should apply in suboptimal settings. Finally, the supernormal policy that I defend claims that we need a more general and comprehensive norm that I refer to as a supernorm to evaluate these cases. The supernormal policy divides argumentation into two stages: preparation and resolution. In the preparation stage, the parties attempt to restore or compensate for the suboptimality of the setting, while in the resolution stage, they attempt to resolve their disagreement. I contend that the moves of the preparation stage should be evaluated by using the supernorm instead of by the rules for critical discussion (van Eemeren and Grootendorst 2004). At this point, the paper considers theoretical insights from Gilbert (1995, 1997, 2002) and Jacobs (2000, 2006) to understand what this entails.
Control of the Solar Radiation Reception Rate (SRRR) Using a Novel Poly-Tilted Segmented Panel (PTSP) in the Region of Makkah, Saudi Arabia

This work deals with controlling the solar radiation reception rate (SRRR) (ratio of the incident solar radiation on tilted panel to the global incident solar radiation). Controlling the SRRR will permit the amount of the received solar energy on solar panels to be adjusted. This SRRR control is very useful for several technological applications such as solar thermal and photovoltaic technologies in extremely sunny regions around the world, such as the case of Makkah, Saudi Arabia. Thus, the sustainability of the cities and villages, located in such regions, is promoted. A novel design proposing a poly-tilted segmented panel (PTSP) is proposed as an original techno-logical solution enabling the control of the SRRR. Design technical details are clearly explained. The proposed design presents a cheap, simple and effective alternative to conventional sun tracking systems. The SRRR on the proposed PTSP is mathematically modeled. The influence of the combinations “number of segment/tilt angles” on the SRRR is assessed for the most significant days in the year: equinox, summer solstice and winter solstice. A specific “document-aided design”, showing the SRRR level reached by each specific combination “num-ber of segment/tilt angles”, is provided. Based on these documents, the adequate combination “number of segment/tilt angles” is easily determined by knowing the desired SRRR level. The SRRR level is determined based on the global incident solar radiation and the desired level of the incident so-lar radiation on the tilted panel. Results are properly presented, discussed and interpreted for each segment/tilt angles combination.
Sarcasm Detection for Sentiment Analysis: A RNN-Based Approach Using Machine Learning

With the advent of social media, publicly expressing opinions about businesses are hassle-free. Terms like ‘opinion mining’ and ‘microblogging’ have carved a niche. Organizations’ concern for brand reputation has accentuated the need for understanding customer’s choices and interests. Sentiment analysis scrutinizes users’ opinions through machine learning (ML) and natural language processing (NLP), a subset of which is sarcasm detection. The sarcasm detection database was fetched from Kaggle, containing nearly 30,000 tweet headlines curated for sarcasm detection, having high-quality labels with less noise. On implementing four ML algorithms, it is observed that training and testing data accuracies of random forest are 98.83% and 77.22%, linear support vector is 90.69% and 83.23%, logistic regression is 87.78% and 82.52%, Naive Bayes is 79.77% and 71.69%. This paper aims to build an RNN model based on LSTM architecture to accurately classify any given input as sarcastic or not.
Research on Audit Simulation of Accounting Computerization Based on Internet Complex Discrete Dynamic Modeling Technology

With the rapid development of science and technology, modeling technology involves more and more fields and the data objects studied are more and more abstract. Based on the Internet plus complex discrete dynamic modeling technology, this paper studies the auditing and control of computerized accounting computerization. In order to maintain the steady state of the discrete dynamic system, the lifting method boosting, least square method, gradient boost, and LS-Ensem algorithm are added in the modeling process. Then, in order to improve the data recognition ability of the system, the recognition algorithm is added. The results show that the complex discrete dynamic system composed of the above algorithms is different from the ordinary system and can be better suitable for small-scale data. It also improves the stability of the whole system and then improves the accuracy of the model. Compared with ordinary discrete systems, the complex discrete dynamic systems studied in this paper can better overcome external factors and avoid errors. The system can quickly analyze and process the data that need statistics and has good application value.
An Efficient Conformable Fractional Chaotic Map-Based Online/Offline IBSS Scheme for Provable Security in ROM

Chaos distributes with a covert method to condense the dynamic of complexity and satisfies the security requirements of a cryptographic system. This study gives an ability online/offline (O/O) ID-based short signature (IBSS) scheme using conformable fractional chaotic maps. Furthermore, we establish its security under IBSS existential unforgeability of identity-based short signature (IBSS) under chosen message attack (EUF-IBSS-CMA) in the random oracle model (ROM). Some of the stimulating preparations of obtainable processes are that they give a multiperiod application of the offline storage, which licenses the agent to recycle the offline pre-registered data in time series (especially the polynomial time), rather than one-period usage in all past IBSS processes.
Selection of investment portfolios with social responsibility: a multiobjective model and a Tabu search method

Abstract In this study, a model for the selection of investment portfolios is proposed with three objectives. In addition to the traditional objectives of maximizing profitability and minimizing risk, maximization of social responsibility is also considered. Moreover, with the purpose of controlling transaction costs, a limit is placed on the number of assets for selection. To the best of our knowledge, this specific model has not been considered in the literature to date. This model is difficult (NP-Hard), and therefore, only very small instances may be solved in an exact way. This paper proposes a method based on tabu search and multiobjective adaptive memory programming (MOAMP) strategies. With this method it is possible to obtain sets of nondominated solutions in short computational times. To check the performance of our method it is compared with adaptations of the nondominated sorting genetic algorithm (NSGA-II), strength Pareto evolutionary algorithm (SPEA-II) and multiobjective particle swarm optimization (MOPSO). The results of different computational experiments show that our tabu search-MOAMP method performed best. The quality of the sets of solutions that were obtained and the speed of execution mean that our tabu search-MOAMP can be used as a tool for financial assessment and analysis (including online services). This tool, as we can see in this work with some examples, can take into account the social concerns of many clients and their overall risk profile (very conservative, conservative, moderate, or fearless). This approach is also in line with current legal regulations that oblige financial advisors to take the client profile into account to provide greater protection and propose good financial advice.
Decentralized Public Distribution System on the Ethereum Blockchain Interact with MyEtherWallet

Blockchain technology is an innovative platform that provides distributed ledgers. With decentralized and transparent transmission mechanism across many businesses and businesses. In this work we proposed the future innovation of the public distribution system using the blockchain platform. We propose a decentralized public distribution system on the Ethereum network using solidity language, and interact with MyEtherWallet, which has the most features and functionality. Here, we create a blockchain-based application that is used to record all transactions between parties. This paper is required for all food transactions involving the central government, state governments, district level offices, retail ration stores, and consumers.
Contour-guided saliency detection with long-range interactions

The guided search theory suggests that global sources of scenes are important for robust visual processing. In this study, we use an eye-tracking experiment to verify that contours play a crucial role in guiding visual attention. Moreover, the experimental data show that subjects pay more attention to the closed regions of line drawings, which are usually related to the dominant objects in the corresponding scenes. In addition, human attention is also guided by scene structure. Inspired by these findings, we designed two long-range selective pooling (LRSP) modules of convolutional neural networks to improve saliency detection by integrating long-range features. The proposed LRSP modules (including regional pooling and layout pooling modules) are inspired by the long-range interactions in the biological visual cortexes and are beneficial for capturing long-range information guided by contours. Extensive experiments show that the proposed method achieves comparable results to state-of-the-art methods, but with a more lightweight network structure. Moreover, benefiting from the contour guidance, the proposed model with long-range interactions demonstrates higher robustness and generalization in the saliency detection task compared with other methods.
A New Test for Hamming-Weight Dependencies

We describe a new statistical test for pseudorandom number generators (PRNGs). Our test can find bias induced by dependencies among the Hamming weights of the outputs of a PRNG, even for PRNGs that pass state-of-the-art tests of the same kind from the literature, and particularly for generators based on F 2 -linear transformations such as the dSFMT [ 22 ], xoroshiro1024+ [ 1 ], and WELL512 [ 19 ].
Fundamental Concepts on Wavelet Transforms

This chapter presents some fundamental concepts on wavelet transforms, both discrete and continuous, each are useful to understand the ability of the wavelet transform to capture sudden changes in material properties or parameters along the span of structural components being assessed.The chapter also discusses some families of wavelet transforms, some wavelet properties and regularization aspects.
The Tag Genome Dataset for Books

Attaching tags to items, such as books or movies, is found in many online systems. While a majority of these systems use binary tags, continuous item-tag relevance scores, such as those in tag genome, offer richer descriptions of item content. For example, tag genome for movies assigns the tag “gangster” to the movie “The Godfather (1972)” with a score of 0.93 on a scale of 0 to 1. Tag genome has received considerable attention in recommender systems research and has been used in a wide variety of studies, from investigating the effects of recommender systems on users to generating ideas for movies that appeal to certain user groups.