Reviewerly/exHarmony huggingface dataset:
Social Media Data Integration: From Data Lake to NoSQL Data Warehouse

As more social media platforms expand through our lives, the amount of data exchanged across them has sharply upsurged. Data coming from social network sites can be immensely useful for all companies for determining customer trends and increase operational efficiency to get a competitive edge. At the same time, traditional decision support systems are unable to meet the growing needs of the modern enterprise to integrate and analyze a wide variety of data generated by social networks platforms. This emergence of large amounts of data requires new techniques of data management and data storage architectures able to find information quickly in a large volume of data. In this context, a data storage concept known under the name of data lake appeared, which refers to one of the latest technologies that were introduced to address this challenge in the last period. A data lake is a large raw data repository that stores and manages all company data in raw form before integrating them into the data warehouse. In this paper, we provide a new approach to design a NoSQL data warehouse from a data lake. More precisely, we start by introducing some of the recent literature reviews on NoSQL data warehouse design approaches. Then, we describe the main concepts of a NoSQL data lake that allows storing the big data collected from social networks such as Facebook, Twitter, and Youtube. Finally, we define a set of mapping rules to integrate social media data from the data lake into the NoSQL data warehouse based on two NoSQL logical models: column-oriented and document-oriented.
Agent-Based Autonomic Semantic Context-Aware Platform for Smart Health Monitoring and Disease Detection

Abstract Nowadays, mobile applications have been widely used in various health and social domains. While there are large number of smart devices that connect and transmit their context data in free manner, these mobile applications have limitations regarding the increasing number of heterogeneous context data sent by devices, conducting the problem of identifying user-centric situations and providing services in real-time. Many smart mobile health systems were proposed to ensure context-aware personal health monitoring and diseases detection, while most of them are failed to ensure a right level of dynamicity and enough flexibility for assisting users everywhere, anytime, through widespread sensors and mobile devices. In fact, it becomes necessary to rethink a new way to minimize the response time combining competitive agents with semantic-based situation reasoning strategy. In this paper, we introduce a novel agent-based platform with three-layered ontology for the semantic description and parallel management of services selection approach dedicated to context-aware health mobile applications. In addition, we propose an innovative parallel services discovery and optimal selection process, which involved a set of filtered and classified semantic health multipath according to user’s context and preferences with consistent mobility of users and limited resources (low battery, processing capabilities, memory and others). Experimental results show the effectiveness of the proposed approach as it includes the semantic service information on cooperative agents. In addition, the proposed approach ensures fast response time and prolongs the continuity of the mobile application.
Global Convergence of Newton Method for Empirical Risk Minimization in Reproducing Kernel Hilbert Space

In supervised learning using kernel methods, we encounter a large-scale finite-sum minimization over a reproducing kernel Hilbert space (RKHS). Often times large-scale finite-sum problems can be solved using efficient variants of Newton’s method, where the Hessian is approximated via subsamples. In RKHS, however, the dependence of the penalty function to the kernel makes standard sub-sampling approaches inapplicable, since the gram matrix is not readily available in a low-rank form. In this paper, we observe that for this class of problems, one can naturally use kernel approximation to speed up the Newton’s method. Focusing on randomized features for kernel approximation, we provide a novel second-order algorithm that enjoys global convergence in the high probability sense. The key to our analysis is showing that the approximated Hessian via random features preserves the spectrum of the original Hessian. We provide numerical experiments verifying the efficiency of our approach, compared to variants of subsampling methods.
Multi-core Median Redescending M-Estimator for Impulsive Denoising in Color Images

In this paper, to reduce impulsive noise in color images we propose an extension of the Median Redescending M-Estimator. For that purpose, a multitasking approach was developed such as a multi-core processing in order to reduce in parallel the noise on R, G and B color channels. With this paradigm, an acceleration up to three times can be guaranteed compared to the sequential paradigm, while having the ability to reduce corrupted data up to densities of $$80\%$$ of fixed-value and $$40\%$$ of random-value impulsive noises, guaranteeing the preservation of edges. The effectiveness of our proposal is verified by quantitative and qualitative results.
Shifting Interactions and Countershifting Opacity: A Note on Opacity in Harmonic Serialism

This squib proposes to extend the traditional taxonomy of pairwise process interactions (which contains “feeding,” “bleeding,” “counterfeeding,” and “counterbleeding”) to include the classes “shifting” and “countershifting.” A process “shifts” another if it does not feed or bleed it but rather causes it to apply in a different way. “Countershifting” is the opaque counterfactual inverse of shifting, and it fills a terminological gap identified by Kiparsky (2015). The class of countershifting interactions is claimed to be theoretically significant: Harmonic Serialism is able to apply the opaque process in countershifting interactions but generally not in counterfeeding or counterbleeding.
Average concurrence and entanglement swapping

We study the role of average concurrence in entanglement swapping in quantum networks. We begin with qubit pure states, and there is a very simple rule governing the propagation of average concurrence in multiple swaps. We look at examples of mixed qubit states, and find the relation for pure states gives an upper bound on what is possible with mixed states. We then move on to qudits, where we make use of the I-concurrence. Here the situation is not as simple as for qubits, but in some cases relatively straightforward results can be obtained.
Visualization of Topic Transitions in SNSs Using Document Embedding and Dimensionality Reduction

Social networking services (SNSs) have become the main avenue, where people speak their thoughts. Accordingly, we can explore people's thoughts by analyzing topics in SNS. When do topics change? Do they ever come back? What do people mainly talk about? In this study, we design and propose a novel visual analytics system to answer these interesting questions. We abstract the topic per unit time as a point in a two-dimensional space through document embedding and dimensionality reduction techniques and provide supplemented charts that represent words appearing at a certain time and the time-series change of word occurrence over the entire period. We employ a novel text visualization technique, called semantic preserving word bubbles, to visualize words at a certain time. In addition, we demonstrate the effectiveness of our system using Twitter data about early COVID-19 trends in Japan. We propose our system to help users to explore and understand transitions in posted contents on SNS.
Feature Extraction Techniques in Facial Expression Recognition

Facial expression recognition became a very important and very challenging aera within past few years in computer science vision field. It (FER) a very hot and active topic for research. FER play interesting role in aera of computer vision processing. This paper reviews the different algorithms which is used in facial expression recognition. usually, Facial expression recognition has 3 Majour stages first one is preprocessing I.e., face detection second is Feature extraction or selection (the aim of this paper) and last one is classification. In this paper study we review some current work done for Facial expression recognition technique. we know that feature extraction method is very important step for image recognition so in this paper we see that after the preprocessing the images how feature extraction done, we see some technique like local binary pattern (LBP), Gabor filter etc. And also, this paper gives an evaluation of Majour characteristic feature extraction and based on that their performance in terms of accuracy and them limitations. arrangement of this FER paper is following in order: section I contain the introduction section II contains a sample process of facial expression recognition section III contains the different technique of feature extraction and section IV contains the review discussion and last section V contain the conclusion.to making these algorithms efficient and more effective feature extraction technique played an important role.
Mobile Game Education About using Capitals Hiragana and Katakana Fisher-Yates Shuffle Algorithm and Fuzzy Tsukamoto

Japanese is a different language because it uses the letters Katakana and Hiragana. Japanese learning at the Kansai Vocational School Pekanbaru encountered several obstacles including the lack of learning facilities and a learning atmosphere that tends to be boring so that many students have difficulty learning and choosing to play games. Educational games on mobile devices are a new learning method that is considered to be more attractive to someone to learn. Fisher-Yates is a randomization technique on questions so questions that come out will be different and can be generated without repetition and duplication. Fuzzy Tsukamoto is a method used in decision-making to determine the score at the end of the quiz. From this research, it can be found that Fisher-Yates can determine the randomization solution that is not multiple and varied object randomization. Fuzzy Tsukamoto has a fairly good accuracy between calculations based on the system and calculations manually, although it does not show results that are 100% the same in each calculation. Educational game recognizing the letters Katakana and Hiragana is expected to help students overcome difficulties in understanding and learning Japanese related to the mastery of the letters Katakana and Hiragana.
Multi-Camera Joint Self-Calibration from Observations of Pedestrians

Calculating camera parameters by vanishing points is a widely used self-calibration method, but this method is usually accompanied by large geometric errors. In this paper, we propose a novel multi-camera joint self-calibration method based on vanishing points estimation and evolutionary algorithm, which only uses a few human walking videos as input. The vanishing point and vanishing line are clustered and regressed respectively according to the torso detection results instead of head and foot points, so as to reduce the influence of human posture changes. Then the initial camera parameters of each camera calculated by vanishing points are simultaneously optimized through the joint geometric error. Experiments on the open data set show the accuracy and flexibility of the proposed method.
Challenges and Mitigation for Application Deployment over SaaS Platform in Banking and Financial Services Industry

Banking and Financial Services Corporations operating on cloud infrastructure is constantly upgrading the application and cloud technologies based on business requirements and new features available. With IaaS and PaaS platforms being managed and upgraded by cloud services providers, the SaaS platform is operated, monitored and governed by bank's technology department. SaaS continues to be the platform under constant upgrade and development considering the need and change in business scenarios. SaaS platform in cloud infrastructure also acts as a channel to enable banking business with new tools and techniques, keeping existing infrastructure capability & scalability within compliance framework. SaaS platform hosts critical applications required for every operations of Bank which includes core banking application, internet banking platform, Card processing applications, ATM & EFTPOS networks and security monitoring tools. Considering the business criticality and application value, SaaS platform is configured to avoid business disruptions. In this paper we have discussed the challenges in deploying the changes required in applications over SaaS platform and methods to mitigate those challenges with minimum or no disruption to cloud architecture infrastructure.
Underwater Image Enhancement Based on Multi-Scale Spatial and Channel Attention Fusion

Degraded underwater image enhancement is a challenging task. Due to the light scattering and absorption of suspended particles in water, the original underwater image has a low definition, blurred details, and color distortion, thus affecting advanced underwater visual tasks such as underwater target detection. However, the existing methods are difficult to enhance the image details effectively. In this paper, we proposed an effective deep convolutional neural network model of multi-scale spatial and channel attention fusion (MS-SCANet) for underwater image enhancement. First, a new training data set (UIRDs) is constructed from the existing data. Then, a multi-loss function is constructed to enhance the detail of the image. Finally, the performance of the model in image visibility and color correction is discussed. Through experiments and comparative analysis on two test sets, our method is superior to the existing traditional methods and deep learning models in terms of image visibility, detail enhancement, and color correction.
A New Action-Based Reasoning Approach for Playing Chess

Many previous research studies have demonstrated game strategies enabling virtual players to play and take actions mimicking humans.The Case-Based Reasoning (CBR) strategy tries to simulate human thinking regarding solving problems based on constructed knowledge.This paper suggests a new Action-Based Reasoning (ABR) strategy for a chess engine.This strategy mimics human experts' approaches when playing chess, with the help of the CBR phases.This proposed engine consists of the following processes.Firstly, an action library compiled by parsing many grandmasters' cases with their actions from different games is built.Secondly, this library reduces the search space by using two filtration steps based on the defined action-based and encoding-based similarity schemes.Thirdly, the minimax search tree is fed with a list extracted from the filtering stage using the alpha-beta algorithm to prune the search.The proposed evaluation function estimates the retrievably reactive moves.Finally, the best move will be selected, played on the board, and stored in the action library for future use.Many experiments were conducted to evaluate the performance of the proposed engine.Moreover, the engine played 200 games against Rybka 2.3.2ascoring 2500, 2300, 2100, and 1900 rating points.Moreover, they used the Bayeselo tool to estimate these rating points of the engine.The results illustrated that the proposed approach achieved high rating points, reaching as high as 2483 points.
On Average-Case Hardness in $$\mathsf {TFNP}$$ from One-Way Functions

The complexity class $$\mathsf {TFNP}$$ consists of all $$\mathsf {NP}$$ search problems that are total in the sense that a solution is guaranteed to exist for all instances. Over the years, this class has proved to illuminate surprising connections among several diverse subfields of mathematics like combinatorics, computational topology, and algorithmic game theory. More recently, we are starting to better understand its interplay with cryptography. We know that certain cryptographic primitives (e.g. one-way permutations, collision-resistant hash functions, or indistinguishability obfuscation) imply average-case hardness in $$\mathsf {TFNP}$$ and its important subclasses. However, its relationship with the most basic cryptographic primitive – i.e., one-way functions (OWFs) – still remains unresolved. Under an additional complexity theoretic assumption, OWFs imply hardness in $$\mathsf {TFNP}$$ (Hubáček, Naor, and Yogev, ITCS 2017). It is also known that average-case hardness in most structured subclasses of $$\mathsf {TFNP}$$ does not imply any form of cryptographic hardness in a black-box way (Rosen, Segev, and Shahaf, TCC 2017) and, thus, one-way functions might be sufficient. Specifically, no negative result which would rule out basing average-case hardness in $$\mathsf {TFNP}$$ solely on OWFs is currently known. In this work, we further explore the interplay between $$\mathsf {TFNP}$$ and OWFs and give the first negative results. As our main result, we show that there cannot exist constructions of average-case (and, in fact, even worst-case) hard $$\mathsf {TFNP}$$ problem from OWFs with a certain type of simple black-box security reductions. The class of reductions we rule out is, however, rich enough to capture many of the currently known cryptographic hardness results for $$\mathsf {TFNP}$$ . Our results are established using the framework of black-box separations (Impagliazzo and Rudich, STOC 1989) and involve a novel application of the reconstruction paradigm (Gennaro and Trevisan, FOCS 2000).
Efficient FPGA Design of Exception-Free Generic Elliptic Curve Cryptosystems

Elliptic curve cryptography (ECC) is one of promising cryptosystems in embedded systems as it provides high security levels with short keys. Scalar multiplication is a dominating and time-consuming process that ensures security in ECC. We implement hardware modules for generic ECC over 256-bit prime fields on field-programmable gate array (FPGA). The key points in our design are (1) secure and exception-free for any scalar with less memory usage, (2) long-bit modular arithmetic modules utilizing today's advanced and high-performance programmable logic and considering balance between the modules in terms of propagation delay, (3) parallelism extraction inside each elliptic curve point computation as well as between the point computations, and (4) efficient hardware–software co-processing facilitated by application interfaces between a processing core and hardware modules. The evaluation results demonstrate that our design achieves the best performance to existing FPGA designs without using a table for generic ECC.
Fusion-based Spammer Detection Method by Embedding Review Texts and Weak Social Relations

Detecting spam reviews and spammers is a longstanding challenge, where review texts and social network information are two vital aspects used in many previous works. However, most of previous works treat multiple reviews as a whole for feature extraction, while ignoring the internal similarities and differences among them, which may cause the semantics of reviews disorder and cannot capture the most discriminative information. Furthermore, the social network information, which we call strong social relationship, is easy to be manipulated by spammers. Thus, using this strong relationship may discount the performance. In this work, we design a co-attention module and orthogonal decomposition module to automatically learn the similarities and differences of multiple reviews. To overcome the problems that the strong social relationship faced, we define the weak social relation graph based on the dynamic interactions. Then a general graph representation framework is presented to learn the interaction relationship information. Finally, we fuse the two aspects information to detect spammers. The experimental results significantly outperform the state-of-the-art approaches on two real-world benchmark datasets, which confirms the superiority of our model.
Strong homotopy in finite topological adjacency category

The present paper investigates a strong homotopy (i.e., SA-homotopy) in a finite topological adjacency category. We prove that two minimal finite spaces are SA-homotopy equivalent if and only if they are A-isomorphic in the category, and that two finite T0-spaces are SA-homotopy equivalent if and only if they have A-isomorphic cores. As an application, we prove that all simple closed KA-curves are not SA-contractible. The SA-homotopy is a generalization of the classical topological homotopy. We also reveal similar properties between SA-homotopy and the classical topological homotopy. Moreover, in the sense of SA-homotopy we answer two questions having close relationships with that posed by Boxer (2005) [4].
Research on the Application of Computer Virtual Technology in Basketball Sports Training

Abstract Virtual this word presumably everybody is not strange. Alibaba has created another miracle on Singles’ Day, with its Taobao and other virtual stores benefiting people all over the country. The virtual world in the game makes countless young people bow down unexpectedly. Recently the World Cup is in full swing, so that countless fans are excited about the Internet to bet on their favorite team, in fact, all these behind the virtual technology can be said to be of no use. In today’s society, virtual technology can be seen in almost all fields. In the process of teaching, the application of virtual technology in physical training is more intuitive. Based on this, this paper discusses the application of computer virtual technology in basketball sports training.
Automated real-time solids content and salinity analysis of well construction fluids using in-line XRF measurements

Measurements and monitoring of solids content and salinity in fluids to maintain these at ideal levels is essential for optimum well construction operations during drilling, completion, workover and abandonment phases. Currently, API measurements are used for solids and salinity determination, which have certain drawbacks and are difficult to automate. As an alternative, a novel in-line X-ray fluorescence (XRF) measurement approach was investigated to make determination of solids content and salinity of well construction fluids much simpler and more efficient through automation. Laboratory experiments were conducted to generate a comprehensive dataset of X-ray photon counts from an in-line XRF setup coupled to a flow loop to evaluate common solids and solutes in well construction fluids. Using this dataset, calibration curves were generated to determine the high-gravity solids (HGS) and low-gravity solids (LGS) content as well as the salinity of water/brine, oil, and synthetic-based fluids at a high level of accuracy in real-time with full automation. It was observed that with the XRF measurements, certain solids can be measured and monitored by analyzing the concentration of certain elements in the compounds (i.e., barium (Ba) for barite concentration, potassium (K) for potassium chloride concentration, iron (Fe) for clay concentration). The mean average errors in estimating the HGS, LGS and salinity values were found to be less than 2%. Using this method together with automated rheology, density and water-cut measurements decreases these errors to significantly lower levels. The system presented here offers the possibility for real-time solids content management and optimization, opening the door to fully automated fluid measurements, maintenance, and solids control in well construction operations. This will benefit well construction efficiency (e.g. lowering drilling costs), quality, productivity (e.g. reducing reservoir impairment from unwanted solids) and safety (e.g. lowering incidence of well control and lost circulation events due to better equivalent circulating density (ECD) management in narrow margin drilling environments).