Reviewerly/exHarmony huggingface dataset:
Distribution and gradient constrained embedding model for zero-shot learning with fewer seen samples

Zero-Shot Learning (ZSL), which aims to recognize unseen classes with no training data, has made great progress in recent years. However, established ZSL methods implicitly assumed that there exist sufficient labeled samples for each seen class, which is quite idealistic in general as collecting sufficient labeled samples is a labor-intensive task and may even be naturally impractical for some low-probability events. Accordingly, we investigate how to perform ZSL with fewer seen samples. Specifically, we propose a Distribution and Gradient constrained Embedding Model (DGEM), which aims to predict the visual prototypes (means) for the given semantic vectors of seen classes. Specifically, we summarize the main challenges brought by limited seen samples as the representation bias problem and the over-fitting problem. Correspondingly, two regularizers are proposed to solve them: (1) a prototype refinement loss which uses the relative distribution of class semantics to constrain that of the predicted visual prototypes; (2) a projection smoothing constraint that prevents the model from forming sharp decision boundaries. We validate the effectiveness of DGEM on five ZSL datasets and compare it with several representative ZSL methods. Experimental results show that DGEM outperforms the other established methods when each seen class has only 1/5 sample(s).
Effectiveness and acceptability of textbook instruction paired with locally-developed quality-assured MELC-based learning activity sheets for modular instruction

This study assessed effectiveness and acceptability of Textbook instruction paired with locally developed quality assured MELC-based Learning Activity Sheets for modular instruction in DepEd Schools Division of Calbayog City during the curriculum year 2020. Utilizing the descriptive-assessment research method, this involved all school heads &amp; teachers in DepEd Schools Division of Calbayog City thereby incorporating total or complete enumeration as its sampling technique and the one thousand learner respondents that can understand the content of the questionnaires chosen via random sampling. Results revealed that self-learning modules create an effective learning environment for the learners to learn. These contain the answers to all possible queries, confusions, and questions that may come into the mind of the learner at the time of learning. These also provide immediate feedback on the performance of the students. These also help to maintain a high-interest level and sufficient motivation for the learners enriching its features such as Self-explanatory, Self-contained, Self- directed, Self-motivating and Self-evaluating which help to cater the needs of all types of learners.
Artificial Neural Network Application to Analyze 3D Image Printing Using Artificial Intelligence in COVID-19

Three-dimensional image recognition is one of the activities used to check the original image. The method is focused on how to extract data from the available samples. The data extraction step was dividing the sample into several research areas. Then from each region the active pixel value is taken so that numerical data can be obtained as much as the available area. The numerical result data is then normalized by fixed compare, where each numeric data comes from the same data. To prove the method, the writer made a supporting application, namely char-cognition with a basic visual programming language. The final result of the analysis shows that in the pattern produced by this method the authenticity of the three-dimensional image can be well recognized. Manual examination is considered inefficient because it faces problems with the eye’s foresight and accuracy. Three-dimensional images are generally identical, but not the same in terms of shape and slope. Automatic three-dimensional image matching via a computer system is essential for better and more accurate identification of three-dimensional images. The artificial neural network method is used for extraction with various data in three-dimensional images. From the five of the simulation it can be concluded that the best Learn Rates by the lowest Error Level is 0.1 and the best Learn Rates based on the highest Match Level is 0.3. Otherwise, due to successful rates from this experiment not being measured by the low rate of Error Level, but from the high of Match Level rate, Simulation 2 with Learn Rates = 0.3 will be used in the next experiment.
Tasrif: processing wearable data in Python

This paper introduces Tasrif, an open source Python framework that facilitates processing of wearable data. Based on the pipes and filter design pattern, Tasrif provides multiple functionalities from data readers of large wearable datasets to integration with popular ML frameworks. The vision of Tasrif is to obviate or at least significantly accelerate the wearable data wrangling step. The code is publicly available at https://github.com/qcri/tasrif/.
An Android Application for Calculating Trip Expenses Using Firebase

Tracking daily expenses is not so innovative. Many conventional and technical methods for tracking our costs and budgeting have been developed. To compute profit or savings, we used to write down our expenses in the register, which we still do today. Furthermore, numerous desktop and mobile programmes have been created specifically for this purpose. Budget revenue, on the other hand, limits spending. As a result, we must keep track of our spending to ensure that we do not go over budget. People used to manually track their expenses, which was extremely time consuming and tedious. We developed an application in which the user can enter her expenses in the category of her choice using a category button. The user can also delete and amend the categories as needed. It is designed to track daily paid and unpaid expenses using a computerised system, eliminating the need for laborious paper work. It helps in easy access of data stored by the user.
Cognitive Inferences, Mental Representations

This chapter elaborates what you learned in Chapter 7. It points out that despite the fact that many problems have an obvious visual representation, we need to be able to incorporate more abstract cognitive representations in our theory of problem solving. Traditionally, multidimensional scaling (MDS) has been used to infer Euclidean representations of concepts based on judged similarities. Here, after providing an example of how MDS has been used in vision, some cautionary comments are made about what MDS can and cannot provide. Because MDS is usually used to represent clusters of concepts, a formal discussion of clustering is included in this chapter. The chapter continues with two examples: one that is related to clustering in long-term memory and the other related to clustering in short-term memory. In both cases, clustering is used to interpret the mental navigation of memory representations as being analogous to navigation in our physical environment, just as it was when we discussed the TSP. The last section of this chapter illustrates how MDS can be used to explain how TSP tours are produced in the presence of obstacles where obstacles change the pairwise distances and make the distances not Euclidean. MDS can use the pairwise distances around obstacles to produce a Euclidean approximation. Preliminary experimental evidence suggests that this is what the human mind does.
Model Checking Algorithm for Repairing Inference between Conjunctive Forms

Let K be a propositional formula and let ϕ be a query, the propositional inference problem K |= ϕ is a Co-NP-complete problem for propositional formulas without restrictions. We show the existence of polynomial-time cases for some fragments of propositional formulas K and ϕ that are different from the already known case of Horn formulas. For example, in the case that K is a formula in the fragment Krom or Horn or Monotone, then K |= ϕ can be decided in polynomial-time for any CNF ϕ. Given two conjunctive normal forms (CNF’s) K and ϕ, when K ̸|= ϕ, our proposal builds a minimal set of independent clauses S. The falsifying assignments of S are exactly the subset of models of K, which are not models for ϕ. In this way, the CNF S could extend the initial formula K in such a way that repair the inference, it is (S ∪ K) |= ϕ is held.
MANAGEMENT IMPACT ON THE SOCIAL SYSTEM OF TAJIK STUDENTS STUDYING IN THE RUSSIAN FEDERATION: FORMALIZING THE MANAGEMENT PROCESS

The article discusses the features of forming the control impact on the social system of Tajik students studying at the Russian universities. The focus is on formalizing the processes of facilitating the students’ enrolment to study at a university; the processes of taking higher education programmes; the processes of involving the students in the active social life; the processes of possible expulsion; the processes of Tajik students’ graduation upon successful completion of the university. The methodological feature of the study is the use of context diagrams that provide the process of managing the social system of Tajik students studying in the Russian Federation in IDEF0 notation, as well as the subsequent formalization of all input and output parameters of the model. In addition, decomposition is presented that makes it possible to understand the features of admission management of the social system representatives of Tajik students to Russian universities using the tools of the BPMN specification. It is shown how to provide assistance to the successful implementation of the educational process of the social system representatives of Tajik students studying at the Russian universities. Also, the process of assistance in forming the social and living environment for learning and living for the social system representatives of Tajik students studying in the Russian Federation is formalized; educational work with the social system representatives of Tajik students during their studies at the Russian universities is carried out; assistance in conducting vocational training and in holding a job fair are fulfilled.
Permutation Based Algorithm Improved by Classes for Similarity Searching

Similarity searching is the most important task in multimedia databases, It consists in retrieving the most similar elements to a given query from a database, knowing that an element identical to the query would not be found. Dissimilarity between objects is measured with a distance function (usually expensive to compute), this allows approaching this problem with a metric space. Many algorithms have been designed to address this problem, in particular, the Permutation Based index has shown an unbeatable performance. This technique uses reference objects to determine a string for each element in the database that is a permutation of the same string. However, Huge databases and the memory required for these indexes make this problem a real challenge. In this paper, we present an improvement to the first approach where classes of reference objects were used instead of single references. In this paper, a new way to choose these classes is proposed and a new way to evaluate similarity between permutations. Our experiments show that we can avoid distance evaluations up to 90% with respect to the original technique, and up to 80% to the first approach.
Breast, Lung and Liver Cancer Classification from Structured and Unstructured Data

Currently, cancer is a worldwide public health problem. Machine and deep learning techniques hold great promise in healthcare by analyzing Electronic Health Records (EHR) that contain a large collection of structured and unstructured data. However, most research has been done with structured data, and valuable data is also found in doctor’s plain-text notes. Thus, this paper proposes an approach to classify breast, liver, and lung cancer based on structured and unstructured data obtained from the MIMIC-II clinical database by using machine and deep learning techniques. In particular, the Paragraph Vector algorithm is used as a deep learning approach to text representation. The goal of this work is to help physicians in early diagnosis of cancer. The proposed approach was tested on a balanced dataset of breast, liver, and lung cancer patient records. Pre-processing is done with structured and unstructured data, and the result is used as input variables to three machine learning models: Support Vector Machines, Multi Layer Perceptron, and Adaboost-SAMME. Then, the scoring metrics for these models are calculated in different training data configurations to choose the best performing model for classification. Results show that the best performing model was obtained with MLP, achieving 89% precision using unstructured data.
Reproducibility Companion Paper: Human Object Interaction Detection via Multi-level Conditioned Network

To support the replication of ?Human Object Interaction Detection via Multi-level Conditioned Network", which was presented at ICMR'20, this companion paper provides the details of the artifacts. Human Object Interaction Detection (HOID) aims to recognize fine-grained object-specific human actions, which demands the capabilities of both visual perception and reasoning. In this paper, we explain the file structure of the source code and publish the details of our experiments settings. We also provide a program for component analysis to assist other researchers with experiments on alternative models that are not included in our experiments. Moreover, we provide a demo program for facilitating the use of our model.
TWO-WAY COMMUNICATION IN PAY SYSTEM AND JOB SATISFACTION: MEDIATING EFFECT OF FAIRNESS PERCEPTIONS

This study examines the mediating effect of fairness perceptions in the relationship between two-way communication in pay system and job satisfaction. Although the relationship between such variables has been generally investigated, the mediating effect of two specific types of fairness perceptions, namely procedural fairness and distributive fairness, has not been thoroughly discussed in organizational pay system research literature. Thus, this study was conducted to fulfil this gap. Self-report questionnaires were utilized to gather data from employees in Malaysian public research universities. The study is also novel in that it utilized SmartPLS to assess the psychometric of measurement scale and then test the research hypotheses. Findings from this study prove that the implementation of two-way pay communication between administrators and employees about pay system has strongly invoked employees’ perceptions of procedural fairness and distributive fairness. Consequently, this fairness perception may lead to a higher job satisfaction. Thus, this finding may be used as important guidelines by senior administrators to understand different paradigms of fairness perception concept and formulate fair pay systems for different job levels and structures to maintain and support their strategies and goals in times of global competition and unpredictable economic situation.
A Behavior Analysis of the Impact of Semantic Relationships on Topic Discovery

Information Technologies have generated large amounts of documents available for analysis and use. Information systems can provide the user with the necessary data for a specific purpose without human intervention, saving time in providing the response expected by the user. Some traditional models of topic discovery provide essential information in the literature, but it is still necessary to incorporate the knowledge that a person can use when reading a document. In this work, an analysis of the behavior of the techniques of Latent Dirichlet Analysis, Latent Semantic Analysis, and Probabilistic Latent Semantic Analysis is carried out, incorporating the semantic relationships of the type hypernym, hyponym, synonymy, holonymy, and meronymy extracted from the external source of knowledge WordNet, in order to improve the results obtained by applying the three mentioned techniques in a set of documents without adding external knowledge. The experimental results improved when incorporating semantic relationships such as hypernyms and synonyms compared to the initial results, but the best result was when using a disambiguation algorithm Lesk and subsequently applying Latent Dirichlet Analysis.
A Systematic Literature Review on the Hybrid Approaches for Recommender Systems

Recommender systems represent a high economic, social, and technological impact at international level due to the most relevant technological companies have been used them as their main services considering that user experience and companies sales have been improved. For this reason, these systems are a principal research area, and the companies optimize their algorithms with hybrid approaches that combine two or more recommendation strategies. A systematic literature review on the hybrid approaches for recommender systems is generated by this work, the objectives are to analyze research line progress and to identify opportunity areas for future investigations. Further, the recent trends about challenges, methodologies, datasets, application domains and evaluation metrics on hybrid approach are identified. An art state from 2016 to 2020 is developed with information systems guide than unlike others works that use less recent guide and software engineering guide. This research will benefit recommender systems community.
Theory of Mind

By now we all know how important mental representations are for any goal-directed actions. But, if I have my mental representations and you have yours, can I improve my actions by having a representation of your representation? If I have such a representation, I am able to acknowledge that you are like me, in the sense that you have your own mental representations and goal-directed actions. This dialogue is referred to as the Theory of Mind because I have a “theory” (a model or representation) of your mind. In fact, I may even have a representation of your representation of my representations. These kinds of first-order and second-order representations are important in evaluating false beliefs and deception. The simpler types of reasoning are already present in preschool children, as illustrated in the first study described in this chapter. More complex types of reasoning show up later in development, so adults can use them. Forming representations of others’ representations happens in visual perception, as well, where it is called “visual perspective taking.” Children are capable of reasoning about what other people can or cannot see and they can make these visual inferences not only by looking at a 3D scene, but also by looking at pictures of 3D scenes. The second half of this chapter describes in some detail a systematic, quantitative study of a matrix game that has been used to examine levels of recursive resoning.
Proposal for a Forensic Action Guide for Internet of Things (IoT) Environments

Internet of Things (IoT) is considered the communication environment of the future, in which technological components of any kind are connected to share data, making possible the process and control of functionalities through an integrated framework available for the user. This context, with many advantages for improving the quality of life of people, with a wide impact on the economy, health and industry, is also available to those who search new ways of chime using the Internet. And it is from this perspective that the IoT environment is approached, to study and propose an action guide that allows an adequate and sufficient forensic analysis when it must search digital evidence in this context. The work includes the review of a set of methodologies for forensic analysis in general –and IoT in particular- from which a Forensic Action Guide for IoT Environments (GAFIoT) derives.
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Three-way optimization of privacy and utility of location data

With the recent bloom of data and the drive towards an information-based society, the urge of and the advancements in data analytics is surging like never before. And with this, the risks of privacy violation of various kinds are also increasing manifold. Most of the methods to mitigate the privacy risks for location data resort to adding some noise to the location, like the planar Laplace used to achieve geo-indistinguishability. However, the noise should be calibrated carefully, taking into account the implications for utility, because it is far from ideal for the service providers to completely lose the utility of the collected data succumbing to the privacy requirements of the users. Similarly, the quality of service for the users should be optimized with their personalized needs of privacy protection used to shield their sensitive information. In this paper, we address this age-old battle between privacy and utility from three ends: privacy of the users' data, the quality of service (QoS) received by them in exchange for sharing their privatized data, and the statistical utility of the privatized data for the service providers who wish to perform various kinds of analysis and research on the data collected from the users. We propose a method to produce a geo-indistinguishable location-privacy mechanism that advances to optimize simultaneously between the level of privacy attained, the QoS, and the statistical utility achieved by the obfuscated data. We illustrate the soundness of this three-way privacy-utility optimization mechanism both analytically and with experiments. Apart from the novelty of the proposed method, this work is aimed to engender an analytical perspective to bridge between geo-indistinguishable location-privacy, QoS, and statistical utilities used in standard data analytics, from an information theoretical, probabilistic, and statistical perspective.
Untangling the Overlap Between Blockchain and DLTs

The proven ability of Bitcoin and other cryptocurrencies to operate autonomously on the trustless Internet has sparked a big interest in the underlying technology - Blockchain. However, the portage of Blockchain technology outside its initial use case led to the inception of new types of Blockchains adapted to different specifications and with different designs. This unplanned evolution resulted in multiple definitions of what a Blockchain is. The technology has diverged from its baseline (Bitcoin) to the point where some systems marketed as “blockchain” share only a few design concepts with the original Blockchain design. This conceptual divergence alongside the lack of comprehensive models and standards made it difficult for both system designers and decision-makers to clearly understand what is a blockchain or to choose a suitable blockchain solution. To tackle this issue, we propose in this paper “DCEA" a holistic reference model for conceptualizing and analysing blockchains and distributed ledger technologies (DLT) using a layer-wise framework that envisions all these systems as constructed of four layers: the data, consensus, execution and application layers.