Reviewerly/exHarmony huggingface dataset:
Exploiting Ratings, Reviews and Relationships for Item Recommendations in Topic Based Social Networks

Many e-commerce platforms today allow users to give their rating scores and reviews on items as well as to establish social relationships with other users. As a result, such platforms accumulate heterogeneous data including numeric scores, short textual reviews, and social relationships. However, many recommender systems only consider historical user feedbacks in modeling user preferences. More specifically, most existing recommendation approaches only use rating scores but ignore reviews and social relationships in the user-generated data. In this paper, we propose TSNPF-a latent factor model to effectively capture user preferences and item features. Employing Poisson factorization, TSNPF fully exploits the wealth of information in rating scores, review text and social relationships altogether. It extracts topics of items and users from the review text and makes use of similarities between user pairs with social relationships, which results in a comprehensive understanding of user preferences. Experimental results on real-world datasets demonstrate that our TSNPF approach is highly effective at recommending items to users.
Reconstructing the shape of large synformal folds based on the solution of the 2D non-linear magnetic inverse problem

This paper deals with a new approach to reconstructing the shape of a poorly-exposed synform that folds strongly magnetic layers. The approach is based on the solution of the 2D non-linear magnetic inverse problem in areas best suited for 2D modeling. The interpretation model consists of two parts: (i) a geometric model imitating a synform cross-section and (ii) a set of elementary layers of a fold characterized by anisotropic magnetic susceptibility. The main task is to determine the deepest point of magnetic layers and the direction and amount of their dip. It can be solved by a selection of optimal model parameters from a set of random numbers with a priory model constraints. The solution is an iterative process which defines intervals of geometrical model parameters that correspond to minimal values of the misfit between observed and modeled magnetic fields. The proposed approach is characterized by a stability of the determination of reference fold points' coordinates, which is sufficient to confidently answer a number of questions important for the geologist. These include: whether a synformal fold is upright, inclined or overturned, determining the depth of its trough line, which combined with the fold outline on the ground surface allows an evaluation of the hinge line curvature. As an example, the large Paleoproterozoic Serpovidny isoclinal synformal anticline in the Keivy terrane of the northeastern Fennoscandian Shield has been chosen. According to field observations, this synform is a giant sheath fold consisting of the poorly exposed strongly magnetic core and weakly magnetic limbs. The 3D morphology of the Serpovidny fold reconstructed using the new approach is shown to be very close to the geometry determined from field observations. Thereby, this study favors the conclusion based the sheath geometry of the Serpovidny isoclinal synformal anticline that an Alpine-type tectonics operated in the Paleoproterozoic.
Towards Key Principles of Fact Based Thinking

In this paper, we present ten principles that, in our view, underlie and define the practice and science of ‘Fact Based Thinking’. In itself, Fact Based Thinking underpins Fact Based Modelling (FBM) in all its forms. FBM has been around for decades, and has brought forth a number of meta-models and formalizations. The principles as discussed in this paper focus on Fact Based Thinking rather than on matters of representation and precise semantics, which have been elaborately discussed elsewhere. The principles presented are deliberately worded for broad use and inspirational purposes, rather than worked out in detail. As such, this paper suggests the initialization of further work rather than presenting a final result. The sketch of the principles presented aims to express the basics of Fact Based Thinking in a way that most members of the FBM community can feel at home with.
Analytic robustness bound for self-testing of the singlet with two binary measurements

Self-testing refers to a way of uniquely identifying an uncharacterized quantum device based solely on certain extremal quantum correlations. It is known that in the ideal case of a two-qubit singlet all the extremal points that can be obtained by measuring the singlet form the whole criteria for self-testing the singlet and the associated measurements. To make these self-testing criteria more suitable for practical applications, relevant robustness bounds are necessary to discuss. However, the previous robustness analyses for these criteria were obtained by the swap method, which may not be guaranteed to be tight. In this paper, we construct a general extraction map applying to all self-testing criteria of the singlet state with two binary measurements for each party and derive analytic robustness bounds for a self-testing singlet in different regions of the boundary of the extremal quantum correlation set. The comparison shows that our robustness bounds are better than the known results and close to optimal for these self-testing criteria of a singlet state with two binary measurements.
Numerical Solution of Some Class of Nonlinear Partial Differential Equations Using Wavelet-Based Full Approximation Scheme

In the last decades, wavelets have become a dominant tool for having applications in almost all the areas of engineering and science such as numerical simulation of partial differential equations (PDEs). The performance of the conventional numerical methods has been found to involve some difficulty to observe fast convergence in low computational time. To overcome this difficulty, we presented wavelet-based full approximation scheme (WFAS) for the numerical solution of some class of nonlinear PDEs using Daubechies wavelet intergrid operators. The numerical results obtained by this scheme are compared with the exact solution to reveal the accuracy and also speed up convergence in lesser computational time as compared with the existing schemes. Some test problems are presented to show the applicability and attractiveness of WFAS.
Real‐time removal of impulse noise from MR images for radiosurgery applications

Summary In the recent years, image processing techniques are used as a tool to improve detection and diagnostic capabilities in the medical applications. Among these techniques, medical image enhancement algorithms play an essential role in the removal of the noise, which can be produced by medical instruments and during image transfer. Impulse noise is a major type of noise, which is produced by medical imaging systems, such as MRI, computed tomography (CT), and angiography instruments. An embeddable hardware module, which can denoise medical images before and during surgical operations, could be very helpful. In this paper, an accurate algorithm is proposed for real‐time removal of impulse noise in medical images. Our algorithm categorizes all image blocks into three types of edge, smooth, and disordered areas. A different reconstruction method is applied to each category of blocks for noise removal. The proposed method is tested on MR images. Simulation results show acceptable denoising accuracy for various levels of noise. Also, an field programmable gate array (FPGA) implementation of our denoising algorithm shows acceptable hardware resource utilization. Hence, the algorithm is suitable for embedding in medical hardware instruments such as radiosurgery devices.
A case study on the use of data mining for detecting and classifying abnormal power system modal behaviors

This article presents a case study involving data mining (DM) and the knowledge discovery (KD) process for the Western US power grid. With the installation of phasor measurement units (PMUs) in the US power systems, there is potential to improve wide-area situational awareness using data analytics. The KD process is challenging due to the complex nature of the power grid, data propriety issues, the file size of the time-series data, and questions of how to extract meaningful features. This case study demonstrates how the KD process can be implemented in light of these challenges to detect the anomalies of interest.
Password Policies Adopted by South African Organizations: Influential Factors and Weaknesses

Organizations worldwide are revisiting the design of their password policies. This is partly motivated by the security and usability limitations of user-generated passwords. While research on password policies has been ongoing, this has taken place in the Global North. Accordingly, little is known about the strengths and weaknesses of password policies deployed in the Global South, especially Africa. As such, this study researched password policies deployed on South African websites. Password policies of thirty frequently visited websites belonging to South African organizations were analyzed. Our observations show diverse password requirements. Even though the desire for strong passwords is the dominant motivator of complex password policies, South African organizations often adopt obsolete measures for attaining password security. The ten most common passwords in the literature were considered acceptable on most sites. In addition, some sites did not explicitly display password requirements and only a few sites adopted measures for providing real-time feedback and effective guidance during password generation.
A Web Service for Evaluating the Level of Speech in Korean

Speaking is a way for humans to communicate with others using language. The ability to speak according to the speaker is very diverse. In general, language skills improve as intelligence improves. Therefore, it is known that the analysis of a speaker’s utterances is a good tool to evaluate the intellectual maturity of the speaker. Until recently, these evaluations have been done manually based on the experience of a handful of experts, but this approach is not only time consuming and costly, but also highly subjective. In this paper, we propose a Korean automatic speech analysis system based on Natural Language Processing (NLP) and web service to solve this problem. For this study, we constructed a web service based on Django to respond to the requests of various users. When a user delivered a transcription file of utterances to the server via the web, the server analyzed the speech ability of the speaker based on various indicators. The server compared the transcription file with the language ability indicators of persons of the same age as the speaker and displayed the result immediately to the user. In this study, we used KoNLPy, a Korean language-processing tool. The automatic speech analysis service analyzed not only the overall language ability of the speaker but also individual domains such as sentence completion ability and vocabulary ability. In addition, a faster and immediate service was made possible without sacrificing accuracy as compared to human analysis.
LCS Based Diversity Maintenance in Adaptive Genetic Algorithms

A genetic algorithm (GA) experiences premature convergence when the diversity is lost in the population. Adaptive GAs aim to maintain diversity in the population by trading off a balance between exploring the problem space and exploiting known solutions. Existing metrics for population diversity measures only examine the similarity between individuals on a genetic level. However, similarities in the order of genes in individuals in ordered problems, such as the travelling salesman problem (TSP) can play an important role in effective diversity measures. By examining the similarities of individuals by the order of their genes, this paper proposes longest common subsequence (LCS) based metrics for measuring population diversity and its application in adaptive GAs for solving TSP. Extensive experimental results demonstrate the superiority of our proposal to existing approaches.
Formal Security Analysis of Cloud-Connected Industrial Control Systems

Industrial control systems are changing from isolated to remotely accessible cloud-connected architectures. Despite their advantages, these architectures introduce extra complexity, which makes it more difficult to ensure the security of these systems prior to deployment. One way to address this is by using formal methods to reason about the security properties of these systems during the early stages of development. Specifically, by analyzing security attacks and verifying that the corresponding mitigation strategies work as intended. In this paper, we present a formal framework for security analysis of cloud-connected industrial control systems. We consider several well-known attack scenarios and formally verify mitigation strategies for each of them. Our framework is mechanized using TLA+ in order to enable formal verification of security properties. Finally we demonstrate the applicability of our work using an industrial case study.
A Human-in-the-loop Attribute Design Framework for Classification

In this paper, we present a semi-automated, “human-in-the-loop” framework for attribute design that assists human analysts to transform raw attributes into effective derived attributes for classification problems. Our proposed framework is optimization guided and fully agnostic to the underlying classification model. We present an algebra with various operators (arithmetic, relational, and logical) to transform raw attributes into derived attributes and solve two technical problems: (a) the top-k buckets design problem aims at presenting human analysts with k buckets, each bucket containing promising choices of raw attributes that she can focus on only without having to look at all raw attributes; and (b) the top-l snippets generation problem, which iteratively aids human analysts with top-l derived attributes involving an attribute. For the former problem, we present an effective exact bottom-up algorithm that is empowered by pruning capability, as well as random walk based heuristic algorithms that are intuitive and work well in practice. For the latter, we present a greedy heuristic algorithm that is scalable and effective. Rigorous evaluations are conducted involving 6 different real world datasets to showcase that our framework generates effective derived attributes compared to fully manual or fully automated methods.
Multi-agent Coordination Subject to Counting Constraints: A Hierarchical Approach

This paper considers the problem of generating multi-agent trajectories to satisfy properties given in counting temporal logic. A hierarchical solution approach is proposed where a coarse plan that satisfies the logic constraints is computed first at the higher-level, followed by a lower-level task of solving a sequence of multi-agent reachability problems. Collision avoidance and potential asynchronous executions are also dealt with at the lower-level. When lower-level planning problems are found to be infeasible, these infeasibility certificates are incorporated into the higher-level problem to re-generate plans. The results are demonstrated with several examples that show how the proposed approach scales with respect to different parameters.
Higher Education Challenge Characterization to Implement Automated Essay Scoring Model for Universities with a Current Traditional Learning Evaluation System

Higher education is currently challenged to respond to a massive interest in learning with a current model that shows increasing evidence of too much cost, effort and decreasing efficacy of the operational learning process. Artificial intelligence has gained presence as a solution, but the integration process is already reporting problems and will not be implemented easily, in particular for universities with low degree of automation integrated to their systems. Universities need to quickly adapt and develop organizational and individual competencies, and clarity about the elements for new learning evaluation systems. This work contributes to propose a model to help universities to define these new systems making the most of artificial intelligence tools for academic essays scoring.
Improved Epoch Extraction From Speech Signals Using Wavelet Synchrosqueezed Transform

Synchrosqueezed wavelet transform (WSST) is an effective tool in tracking instantaneous frequency of a given signal. The objective of the present work is to propose a WSST based method for accurate epoch estimation from speech. Epochs in speech represent the instants where the excitation to the vocaltract is maximum and instantaneous F0 contour is derived from epoch locations. The proposed hypothesis in this paper is that the signal reconstructed by discarding higher frequency modes (above the mean F0) in the WSST transformed time frequency domain observed to predominantly represent source characteristics. The presence of the source characteristics in the modified WSST reconstructed signal is validated by the improved identification accuracy obtained for the epochs estimated from clean speech utterances of CMU-Arctic database. To further demonstrate the effectiveness of the WSST in improving the overall epoch estimation performance, a WSST modified zero frequency filtering (ZFF) of speech, which is one of the simple and effective tools for epoch extraction, is proposed. The sharp instantaneous frequency representation by WSST also found to be effective in estimating epochs emotion utterances where rapid pitch variations are present. The improved epoch estimation performance from emotive utterances are confirmed by validating on the German emotion speech corpus(EmoDb).
N-Gram Representation for Web Service Description Classification

Despite increasing availability of Web Services (WS), their automatic processing (classification, grouping or composition) slows down because of the difficulty to read the WSDL service descriptions without related technical knowledge. Categorizing services for automatic service discovery and composition has become a challenging problem. The paper argues that n-gram representation of the data extracted from the different sections of the WSDL description (types, messages and operations) along with the weighing scheme can benefit the classification of services. Experiments are carried out with three different classifiers over available collections of WS descriptions. It is shown that such representations as word bigrams or letter trigrams extracted from WSDL Operations and Types service description features with TF-IDF as n-gram weighting scheme, can improve automatic WS classification.
Parallel pairwise learning to rank for collaborative filtering

Summary Pairwise learning to rank is known to be suitable for a wide range of collaborative filtering applications. In this work, we show that its efficiency can be greatly improved with parallel stochastic gradient descent schemes. Accordingly, we first propose to extrapolate two such state‐of‐the‐art schemes to the pairwise learning to rank problem setting. We then show the versatility of these proposals by showing the applicability of several important extensions commonly desired in practice. Theoretical as well as extensive empirical analyses of our proposals show remarkable efficiency results for pairwise learning to rank in offline and stream learning settings.
A Novel Discrete Grey Wolf Optimizer for Solving the Bounded Knapsack Problem

Grey Wolf Optimizer (GWO) is a recently proposed metaheuristic optimizer inspired by the leadership hierarchy and hunting mechanism of grey wolves. In order to solve the bounded knapsack problem by the GWO, a novel Discrete Grey Wolf Optimizer (DGWO) is proposed in this paper. On the basis of DGWO, the crossover strategy of the genetic algorithm is introduced to enhance its local search ability, and infeasible solutions are processed by a Repair and Optimization method based on the greedy strategy, which could not only ensure the effectiveness but also speed up the convergence. Experiment using three kinds of large-scale instances of the bounded knapsack problem is carried out to verify the validity and stability of the DGWO. By comparing and analyzing the results with other well-established algorithms, computational results show that the convergence speed of the DGWO is faster than that of other algorithms, solutions of these instances of the bounded knapsack problem are all well obtained with approximation ratio bound close to 1.
Information Retrieval Using Image Attribute Possessions

Extracting defined information from the huge data set really challenging task for many researchers, especially this data set like image data’s process is too complex. As image data consist of motion, time, text, audio, pixel difference and more. From this complex data set, extracting the domain knowledge takes more time. This process differs from traditional text mining, because the nature of the data sets. Extracting information from image data, user needs additional knowledge; i.e., users required domain knowledge. This attracts many users concentrate on this field. Currently, many research works carried on this particular domain. Advancement of technology more and more image data is created and uses, for this urgent attention required in the field of image mining. This paper focuses on image mining help of clustering technique. First video data are grouped into frames, from the cleaned frameset process are done client- and server-side operations. The proposed technique works well, and experimental results also verified this.
An Improved Firefly Algorithm Hybrid with Fireworks

Firefly algorithm (FA) is a global optimization algorithm with simple, less parameter and faster convergence speed. However, the FA is easy to fall into local optimum, and the solution accuracy of the FA is lower. In order to overcome these problems. An improved Firefly algorithm hybrid with Fireworks (FWFA) is proposed in this paper. Because the local search ability of the fireworks algorithm’s search strategy is strong, we introduce the fireworks algorithm neighborhood search operator of the fireworks algorithm into the firefly algorithm to improve the local search ability of the Firefly algorithm. Through the simulation and analysis of 28 benchmark functions, verify the effectiveness and reliability of the new algorithm. The experimental results show that the new algorithm has excellent search ability in solving unimodal functions and multimodal functions.
A Fast Traffic Video Stabilization Method Based on Trajectory Derivatives

This paper aims to solve the stabilization problem of traffic videos, which are mostly captured by the cameras mounted on the vehicles. Compared with normal videos captured with handheld cameras, traffic videos often face more challenges, such as dynamic scenes, dominant foreground objects, and significant parallax. Conventional methods often regard videos stabilization as an optimization problem with complex constraints and thus cost much computation time. To address the above issues, we propose a fast method by making use of trajectory derivatives at adjacent frames. When the parallax is not serious or the inter-frame time is short enough, the non-rigid characteristic of camera jitter can be ignored. Then, we simplify the perspective transformation to affine transformation between adjacent frames, i.e., the trajectory derivative holds linear with respect to the position of the trajectory. We combine our method with a feedback-based foreground trajectory judgment strategy and significantly speed up the processing speed. The new algorithm can satisfy the real-time requirement, which is critical for real applications, at the cost of tolerable stabilization performance degradation.