Reviewerly/exHarmony huggingface dataset:
The Stereotyping Problem in Collaboratively Filtered Recommender Systems

Recommender systems play a crucial role in mediating our access to online information. We show that such algorithms induce a particular kind of stereotyping: if preferences for a set of items are anti-correlated in the general user population, then those items may not be recommended together to a user, regardless of that user’s preferences and rating history. First, we introduce a notion of joint accessibility, which measures the extent to which a set of items can jointly be accessed by users. We then study joint accessibility under the standard factorization-based collaborative filtering framework, and provide theoretical necessary and sufficient conditions when joint accessibility is violated. Moreover, we show that these conditions can easily be violated when the users are represented by a single feature vector. To improve joint accessibility, we further propose an alternative modelling fix, which is designed to capture the diverse multiple interests of each user using a multi-vector representation. We conduct extensive experiments on real and simulated datasets, demonstrating the stereotyping problem with standard single-vector matrix factorization models.
Development of an Optimal Entropy Classifier and Prudent Learning Model

This article gives the representation of both probabilistic uncertainty and possibilistic certainty including the Bayesian learning in the framework of information set theory which is an offshoot of the Hanman–Anirban entropy function. Being information theoretic and parametric, this function deals with both probability and possibility. If a set of information source (attribute) values is fitted with this entropy function it gives rise to information values and the sum of these values is certainty. An adaptive form of this function yields the Hanman transform (HT) that gives the higher order certainty. An optimal entropy classifier is developed by learning the weight (support) vectors of all classes by minimizing this entropy of all the error vectors between the training feature vectors and the weight vector. To this end, we have proposed prudent learning model that favors competition with both the worst performer and the best performer based on the HT. The conversion of Renyi entropy function into the possibilistic domain helps us generate Renyi sigmoid and Renyi energy features. These new features and classifier are implemented on two datasets: Finger-knuckle-print for the authentication of persons and defect classification in the fabrics. The experimental results vindicate the effectiveness of the proposed features, classifier, and the learning model.
Intelligent Analysis of Abnormal Vehicle Behavior Based on a Digital Twin

Analyzing a vehicle's abnormal behavior in surveillance videos is a challenging field, mainly due to the wide variety of anomaly cases and the complexity of surveillance videos. In this study, a novel intelligent vehicle behavior analysis framework based on a digital twin is proposed. First, detecting vehicles based on deep learning is implemented, and Kalman filtering and feature matching are used to track vehicles. Subsequently, the tracked vehicle is mapped to a digital-twin virtual scene developed in the Unity game engine, and each vehicle's behavior is tested according to the customized detection conditions set up in the scene. The stored behavior data can be used to reconstruct the scene again in Unity for a secondary analysis. The experimental results using real videos from traffic cameras illustrate that the detection rate of the proposed framework is close to that of the state-of-the-art abnormal event detection systems. In addition, the implementation and analysis process show the usability, generalization, and effectiveness of the proposed framework.
Trisk

Due to the long-run and unpredictable nature of stream processing, any statically configuredexecution of stream jobs fails to process data in a timely and efficient manner. To achieve performance requirements, stream jobs need to be reconfigured dynamically. In this paper, we present Trisk, a control plane that support versatile reconfigurations while keeping high efficiency with easy-to-use programming APIs. Trisk enables versatile reconfigurations with usability based on a task-centric abstraction, and encapsulates primitive operations such that reconfigurations can be described by compositing the primitive operations on the abstraction. Trisk adopts a partial pause-and-resume design for efficiency, through which synchronization mechanisms in the native stream systems can further be leveraged. We implement Trisk on Apache Flink and demonstrate its usage and performance under realistic application scenarios. We show that Trisk executes reconfigurations with shorter completion time and comparable latency compared to a state-of-the-art fluid mechanism for state management.
Named Entity Recognition in Electric Power Metering Domain Based on Attention Mechanism

Named Entity Recognition(NER) is one key step for constructing power domain knowledge graph which is increasingly urgent in building smart grid. This paper proposes a new NER model called Att-CNN-BiGRU-CRF which consists of the following five layers. A joint feature embedding layer combines the character embedding and word embedding based on BERT to obtain more semantic information. A convolutional attention layer combines the local attention mechanism and CNN to capture the local context relationship. A BiGRU layer extracts higher-level features of power metering text. A global multi-head attention layer optimizes the processing of sentence level information. A CRF layer obtains the output tag sequences. This paper also constructs a corresponding power metering corpus data set with a new entity classification method. Experimental results show that the proposed model has high recall rate 88.16% and precision rate 89.33% which is better than the state-of-the-art models.
Predicate Transformer Semantics for Hybrid Systems

Abstract We present a semantic framework for the deductive verification of hybrid systems with Isabelle/HOL. It supports reasoning about the temporal evolutions of hybrid programs in the style of differential dynamic logic modelled by flows or invariant sets for vector fields. We introduce the semantic foundations of this framework and summarise their Isabelle formalisation as well as the resulting verification components. A series of simple examples shows our approach at work.
A Conceptual Enhancement of LSTM Using Knowledge Distillation for Hate Speech Detection

Hate speech is by no means always on the rise due to the high rate of remote service usage such as communication, online studies, meeting, dating, etc. With the recent outbreak of COVID-19, there has been an increase in the number of users on different social media platforms. This increase in number has brought about an increase in issues such as hate speech, among others. This paper aims to provide a detailed process of improving LSTM used for hate speech detection using knowledge distillation. The knowledge transfer is done from the more extensive network (teacher) to the smaller student network. The teacher has trained for five entire epochs to output accuracy of 76.8%, the student network trained from the teacher network for three whole epochs attained an accuracy of 82.6%. Another student model cloned and trained from scratch for three entire epochs instead of the teacher network achieves an accuracy of 75.4%.
Translating The Benefits Of Wide-band Display Environments Into An XR Space

Large, high-resolution displays (LHRD) are known to be well suited to support visual analysis in ways that surpass desktop/laptop displays. This paper describes our work on SageXR, an initial effort to determine the extent to which physical displays can be replaced by head mounted displays to support the visual analysis process from start to finish. Findings from studies of veteran LHRD users showed that: HMD workspaces has future potential towards providing the benefits of a LHRD; all participants used at least two times more virtual space than physical space - given the opportunity users desire and are able to make meaningful use of more working space; the virtual viewing area used by a single participant was equivalent to physical LHRDs; and while participants tended to surround themselves with data, most actually wanted a virtualized LHRD.
Enhancement of degraded document images via augmented fourth order partial differential equation and Total Variation-based illumination estimation

This paper presents variational and partial differential equation(PDE)-based enhancement of degraded document images. The stain and uneven illumination degradation is mitigated via augmented Homomorphic L1 Total Variation estimation. Furthermore, a fourth order forward-backward Diffusion-Laplacian contrast enhancement PDE is used to restore faint text intensity and edges. Additionally, pre-smoothing is used for document images with extensive bleed-through effects with strong edges. Comparisons with existing approaches show improved/superior binarization results based on subjective and objective analysis
Digital Creative Projects in the Formation of Digital Competence of Teachers of English as a Foreign Language

The article discusses how digital creative projects can be used in the practice of professional training of English as a Foreign Language (EFL) teachers at a higher education institution. As the nature of creativity and creative activity is being examined in the theoretical part of the article, it acquires a definition that any activity, both theoretical and practical, in any professional environment can be creative. Creativity is determined not by the type of activity, but by the presence and degree of manifestation of such qualities as conscious goal-setting, novelty, the significance of the methods of its implementation or result. The need for creative tasks in professional training is determined. Since the object of our study is professional training of future EFL teachers, the structure of professional competence of an EFL teacher is described with a proposal of expanding it with a new digital competence. In the practical part of the article the experimental training conducted at the Graduate school of applied linguistics, interpreting and translation is described. The experimental training consists of studying international regulatory documents on digital competence and completing a digital creative project of an online course on digital literacy development.
Identifying Malignancy of Lung Cancer Using Deep Learning Concepts

This paper deals with identifying if a tumor present in the lung is benign, unsure, or malignant using Deep Learning concepts on a dataset with CT, PET scans of the lungs of patients. The graveness of a tumor is highly dependent on the intrinsic ordinal relationship of the nodules in the lung at various stages- by stages, those are benign, unsure, or malignant. Our focus through this study is to work around detecting the type of tumor and improve the efficiency in identifying unsure or pre-malignant tumors using VGG-16 neural frameworks on the LIDC-IDRI dataset.
Should I Follow this Model? The Effect of Uncertainty Visualization on the Acceptance of Time Series Forecasts

Time series forecasts are ubiquitous, ranging from daily weather forecasts to projections of pandemics such as COVID-19. Communicating the uncertainty associated with such forecasts is important, because it may affect users’ trust in a forecasting model and, in turn, the decisions made based on the model. Although there exists a growing body of research on visualizing uncertainty in general, the important case of visualizing prediction uncertainty in time series forecasting is under-researched. Against this background, we investigated how different visualizations of predictive uncertainty affect the extent to which people follow predictions of a time series forecasting model. More specifically, we conducted an online experiment on forecasting occupied hospital beds due to the COVID-19 pandemic, measuring the influence of uncertainty visualization of algorithmic predictions on participants’ own predictions. In contrast to prior studies, our empirical results suggest that more salient visualizations of uncertainty lead to decreased willingness to follow algorithmic forecasts.
Image Inpainting using Orthogonal Viewpoints and Structure Consistency in Manhattan World

This paper proposes a fast and straightforward method for restoring a damaged hole in the Manhattan world, which has three orthogonality with one unknown camera to produce a perspective view that can foreshorten planes of similar structure. There are two essential guidance for image inpainting algorithms: Orthogonal Viewpoints and Structure Consistency. First, due to perspective view, we build orthogonal viewpoints from an image by determining the vanishing points of different planes. Second, we present structure consistency for reconstructing unknown regions of possibly foreshortened planes due to the displacement vector of repeated structures in perspective planes. For image inpainting, PatchMatch [1] algorithm handles matching structures based on two prior-primary guided methods. Our experiment results prove that our approach completes image inpainting in challenging scenes, such as perspective views, repetitive structures, and facades.
Collaborative image compression and classification with multi-task learning for visual Internet of Things

Widespread deployment of the Internet of Things (IoT) has changed the way that network services are developed, deployed, and operated. Most onboard advanced IoT devices are equipped with visual sensors that form the so-called visual IoT. Typically, the sender would compress images, and then through the communication network, the receiver would decode images, and then analyze the images for applications. However, image compression and semantic inference are generally conducted separately, and thus, current compression algorithms cannot be transplanted for the use of semantic inference directly. A collaborative image compression and classification framework for visual IoT applications is proposed, which combines image compression with semantic inference by using multi-task learning. In particular, the multi-task Generative Adversarial Networks (GANs) are described, which include encoder, quantizer, generator, discriminator, and classifier to conduct simultaneously image compression and classification. The key to the proposed framework is the quantized latent representation used for compression and classification. GANs with perceptual quality can achieve low bitrate compression and reduce the amount of data transmitted. In addition, the design in which two tasks share the same feature can greatly reduce computing resources, which is especially applicable for environments with extremely limited resources. Using extensive experiments, the collaborative compression and classification framework is effective and useful for visual IoT applications.
Study of occurrence states and precipitation mechanism of tellurium in Chengmenshan porphyry-skarn deposit from the Middle-Lower Yangtze River Valley Metallogenic Belt

碲是一种重要的战略性关键金属,主要以共伴生形式产出多个类型矿床中,斑岩-矽卡岩型矿床是碲资源重要的来源之一。城门山铜金矿床是长江中下游成矿带中一典型斑岩-矽卡岩型矿床,也是该区目前已报道的最大的共伴生碲矿床,然而碲在该矿床的赋存状态、碲的沉淀机制等尚不清楚。本文在详细划分成矿阶段基础上,对该矿床不同类型矿石中的矿物开展电子探针测试、LA-ICP-MS微量元素分析以及面扫描研究工作。测试结果表明碲元素主要以碲银矿、碲铋银矿等碲化物和辉碲铋矿、硫楚碲铋矿、碲黝铜矿等碲的硫化物形式存在,少量呈类质同象赋存在黄铁矿和黄铜矿中。黄铜矿中Te含量在矽卡岩型矿石中变化范围(0.15×10<sup>-6</sup>~20.50×10<sup>-6</sup>)大于碳酸盐岩交代型矿石(0~3.44×10<sup>-6</sup>)。黄铁矿中Te含量在碳酸盐岩交代型矿石中最高,可达173×10<sup>-6</sup>;黄铁矿mapping显示在同一颗矿物中Te含量分布也不均匀。通过300℃ logf<sub>Te<sub>2</sub></sub>-logf<sub>S<sub>2</sub></sub>相图分析,硫逸度介于-6.8~-11.4,碲逸度介于-7.8~-14.8;自花岗闪长斑岩向碳酸盐岩地层围岩,从成矿早阶段到晚阶段,硫逸度和温度逐渐降低,碲逸度升高,这是城门山矿床碲矿物沉淀的主要机制。城门山矿床中无论是单颗粒黄铁矿硫同位素还是原位硫同位素均显示岩浆来源特征,结合岩相学碲矿物与黄铁矿等硫化物密切共生,具有成因联系,表明碲的成矿物质来源也应为岩浆来源,与花岗闪长斑岩岩浆热液作用有关。;Tellurium is an important critical metal, which mainly occurs in kinds of deposits in the form of byproduct. Porphyry and skarn deposit is one of the important sources of tellurium resources. Chengmenshan copper-gold deposit is a typical porphyry-skarn deposit in the Middle-Lower Yangtze River Valley Metallogenic Belt (MLYB), and it is also the largest reported byproduct tellurium deposit in MLYB. However, the occurrence state and precipitation mechanism of tellurium are not clear. In this study, we describe the detailed ore formation stages, and carry on the electron microprobe analyses, LA-ICP-MS trace element analysis and LA-ICP-MS mapping for minerals from different types of ores in the Chengmenshan deposit. The results show that the tellurium minerals are telluride and tellurium sulfide, such as hessite, volynskite, tetradymite, bismuth sulphurate, goldfieldite etc., and a small amount tellurium element exists in pyrite and chalcopyrite in isomorphism. The chalcopyrite variation value of Te element in skarn is greater than that in carbonate metasomatic ores. The value of Te in pyrite is the highest in carbonate metasomatic ores, and up to 173×10<sup>-6</sup>; The distribution of Te content in the pyrite mineral is also inhomogeneous. Through 300℃ logf<sub>Te<sub>2</sub></sub>-logf<sub>S<sub>2</sub></sub> phase diagram, the sulfur fugacity in the main metallogenic stage is between -6.8~-11.4 and the tellurium fugacity is between -7.8~-14.8. From the granite to the carbonate formation, the sulfur fugacity and temperature gradually decrease and the tellurium fugacity increases, which is the main mechanism of Telluride precipitation in Chengmenshan deposit. Both single grain pyrite sulfur isotope and in-situ sulfur isotope in Chengmenshan deposit show the characteristics of magmatic source. Combined with the tellurium minerals and pyrite and other sulfides, indicating that the metallogenic material source of tellurium should also be magmatic source, which is related to granodiorite porphyry magmatism.
A Robust Anonymous Authentication Scheme using Biometrics for Digital Rights Management System

As digital content transmission through the internet is convenient and quick, so the outspread of digital content is very high. However, along with this incredible speed and ease, current communication technologies and computers have also brought with them plenty of digital rights management complications. Digital Rights Management Systems are designed to limit the access to the utilization, alternation, and distribution of persevered digital content. This article scrutinized two recent schemes of Lee et al. and Yu et al. and it is found that these schemes are suspected to an insider attack, stolen smart-card attack, Daniel of services (Dos) attack, and impersonation attack. Furthermore, their proposal also suffers from incorrect issues. To fix these flaws, a robust anonymous authentication scheme using biometrics for Digital Rights Management System is proposed in this article. The proposed scheme is checked for correctness and its security is proved through BAN logic. The performance of the scheme is also analyzed using computation time and communication time. The results show that the designed scheme is highly secure with the same computation and communication cost as the existing protocols.
On the Detection of Alzheimer’s Disease using Support Vector Machine Based Majority Voter Classifier

Early detection of Alzheimer’s disease (AD) has drawn enough attention of researchers throughout the globe because of the lack of well-defined diagnosis of the disease. This has become one of the major threats for the elderly people in particular. This work makes a novel attempt to classify the brain MRI images into two classes viz. AD and non-AD using the volumetric information of white matter (WM), grey matter (GM) and cerebro spinal fluid (CSF). This has been accomplished with the help of three parallel support vector classifiers followed by a majority voter classifier. Performance of this proposition has been measured with the help of accuracy, sensitivity & specificity and subsequently is compared with some of the existing methods.
Relevance Classification of Trending Topic and Twitter Content Using Support Vector Machine

Twitter is one of the most influential social media recently and has become an important information portal. Users can use the trending topics feature or hashtags popular to see current popular news. However, trending topics are often irrelevant to the content being discussed. Many users take advantage of trending topics so that their tweet content gets more public attention. Therefore, a text classification system is needed to determine the relevance of trending topics to Twitter content. Relevance Classification using the Support Vector Machine method and related to politics topic that use the Indonesian language. The research steps were data collection, data labeling, text preprocessing, TF-IDF weighting, SVM classification, and evaluation and analysis. The results of classification results show an average accuracy value of 86&#x0025;. The average of precision, recall, and F1-measure values are 75&#x0025;, 72&#x0025;, and 70&#x0025;. In this paper, the Support Vector Machine method has good performance for relevance classification on Twitter content.
Crafting Strategies of Security Breaches

In recent years, the number of financial technology players and users have increased at a significant rate due to the rapid technological advancement in financial technology. While smart devices are providing more useful features to users, they have also made it possible for cyber threats to migrate from desktops to smart devices. Thus, it is important for smart device users to be aware that their device could be exposed to cyber threats and that users could protect their devices by employing data-centric cyber security measures. This study reveals how financial technology business model responded to the breach phenomenon by employing data-centric protection approaches. The result is very interesting. Data-centric security is very needed as it is capable of protecting data as a whole. It provides a gapless protection, meaning to say, the data are encrypted and classified wherever it moves. With persistent protection and cross-platform operability, data-centric security will eliminate gaps and keep data protected.
A social media strategy for academic libraries

This study was carried out to analyse strategies regarding the use of social media for academic library services in Ghana. The research design was quantitative and adopted the survey approach. Questionnaires served as the main data collection instrument. The study population comprised of one hundred and ten library staff from public and private universities in Ghana. As indicated by the study results, the library staff agreed that there was a need for a well-formulated social media strategy to help academic libraries identify the most appropriate social media tools, establish contact with targeted audience, and increase engagement rates to enable an effective and efficient distribution of its resources and services. The results also indicated that the academic libraries, however, seem to neglect these strategies which could drive patrons' use of such platforms. Recommendations therefore suggest the need to implement social media plans and policies, user engagement, social media update, and dedicated staff with the requisite skills in the use of social media platforms for library services delivery.