Reviewerly/exHarmony huggingface dataset:
A Survey on News Classification Techniques

With the advancement and decentralization over the past two decades, the internet has become the most democratic of all mass media. Online news organizations have emerged during this period making news accessible to people which has become efficient, faster and convenient for the readers. The development of information technology and these organizations have led to an increase in the amount of disarrayed data, i.e. news, available over the internet. Over the past years researchers have come up with many techniques to achieve news classification, but the pursuit for higher precision of these techniques is incessant. The modern approaches by researchers to classify news items make use Support Vector Machines (SVMs), Naïve Bayes algorithm, Hierarchical Multi-label Classification, Bayesian Network, Clustering algorithms, etc. In this paper we have studied various research works and techniques and examined their robustness and accuracy to classify news items.
MotionFlow

Robots have been gradually weaving into the fabric of many areas, including children's education and new media arts. Such combination promotes children's creativity and provides new artistic expression paradigms for artists. However, for robot systems that contain many degrees of freedom, the motion programming can be very complicated.
APL since 1978

The Evolution of APL , the HOPL I paper by Falkoff and Iverson on APL, recounted the fundamental design principles which shaped the implementation of the APL language in 1966, and the early uses and other influences which shaped its first decade of enhancements. In the 40 years that have elapsed since HOPL I, several dozen APL implementations have come and gone. In the first decade or two, interpreters were typically born and buried along with the hardware or operating system that they were created for. More recently, the use of C as an implementation language provided APL interpreters with greater longevity and portability. APL started its life on IBM mainframes which were time-shared by multiple users. As the demand for computing resources grew and costs dropped, APL first moved in-house to mainframes, then to mini - and micro -computers. Today, APL runs on PCs and tablets, Apples and Raspberry Pis, smartphones and watches. The operating systems, and the software application platforms that APL runs on, have evolved beyond recognition. Tools like database systems have taken over many of the tasks that were initially implemented in APL or provided by the APL system, and new capabilities like parallel hardware have also changed the focus of design and implementation efforts through the years. The first wave of significant language enhancements occurred shortly after HOPL I, resulting in so-called second-generation APL systems. The most important feature of the second generation is the addition of general arrays—in which any item of an array can be another array—and a number of new functions and operators aligned with, if not always motivated by, the new data structures. The majority of implementations followed IBM’s path with APL2 “floating” arrays; others aligned themselves with SHARP APL and “grounded” arrays. While the APL2 style of APL interpreters came to dominate the mainstream of the APL community, two new cousins of APL descended from the SHARP APL family tree: J (created by Iverson and Hui) and k (created by Arthur Whitney). We attempt to follow a reasonable number of threads through the last 40 years, to identify the most important factors that have shaped the evolution of APL. We will discuss the details of what we believe are the most significant language features that made it through the occasionally unnatural selection imposed by the loss of habitats that disappeared with hardware, software platforms, and business models. The history of APL now spans six decades. It is still the case, as Falkoff and Iverson remarked at the end of the HOPL I paper, that: Although this is not the place to discuss the future, it should be remarked that the evolution of APL is far from finished.
Sleep Monitoring With Hidden Markov Model for Physical Conditions Tracking

Sleep monitoring is an important health monitoring approach. Generally, one's physical conditions fall into several qualities such as normal, weak, etc. And the clues of physical conditions are buried in the mass monitoring data. In this paper, a physical condition tracking method is proposed based on sleep monitoring with Hidden Markov Model (HMM). Firstly, physical features are acquired during sleep over a fixed time period using a micro movement sensitive mattress sleep monitoring system (MSMSMS). A feature matrix is created and used to derive an HMM that represents the condition of the subject. The connection between the variation of physical conditions and the Markov process is established. Bayesian information criterion is utilized to determine the number of hidden states in an HMM. Once a reference HMM is established, long-term physical conditions variation tracking can be realized by evaluating the fitnesses between upcoming monitoring data and the reference HMM. The proposed method is verified on a sleep monitoring experiment using the MSMSMS.
Emerging Trends in Artificial Intelligence

This study outlines and examines the meaning, purpose, utility and threat of artificial intelligence (AI). John McCarthy, along with Herbert A. Simon, Allen Newell, Alan Turing and Marvin Minsky are the 'founding fathers' of AI. Artificial intelligence (AI) is the capacity and potential of a machine or a computer program to think, learn, grasp and comprehend information. Apart from this, it is a particular branch or sphere of activity which endeavours to create 'smart' computers. In India, the government is concentrating in developing AI applications that have benefits to society such as agriculture, health care and education. The author also recommends certain suggestions to improve the quality and pace of AI research in India. The dangers from and advantages of AI have to be balanced and evened out. The writer suggests that more stringent policy and implementation has to be brought about to bridge the gap between the industry and academia, among other methods.
An Ontology of Changes in Normative Systems from an Agentive Viewpoint

Defeasible deontic logic has shown to be expressive enough to represent a normative system, and therefore compliance to such a system can be automatically checked by means of classical model checking techniques of logical systems. However, normative systems are not static, as they can be actively changed by the legislator over time, directly, by changing one norm. Moreover norms can change passively, either by effect of the change of another piece of the normative system, or by means of the change of meaning that affects terms employed in the norm. Although some efforts have been carried out by scholars in the field of legal reasoning about norm change, there is a lack of uniformity in the representation of these changes, and this is an issue when we aim at deploying the law as an automated platform: we need to introduce changes as effects in the semantics of derivation in a logical system, when the unified viewpoint admits a unified representation as well. We adopt the logical paradigm of agency and provide a classification of changes from an agentive viewpoint that allows a unified representation within the logical language for agents LegalRuleML.
Enabling Time-Centric Computation for Efficient Temporal Graph Traversals From Multiple Sources

Temporal graph traversal is an approach for analyzing how information spreads throughout a network over time.A system has been recently proposed as an initial effort for efficient analyses against higher time complexity and infinitely evolving data unlike static graph.However, with the system, the response time for traversals from multiple sources is proportional to the number of sources; thus, application domains of the system can be limited.To resolve this problem, the state-of-the-art vertex-centric paradigm can be considered; however, we have found that the paradigm is not fitted into this computation.The paper proposes a novel time-centric computation approach for efficient all-pairs temporal graph traversals.One benefit of this approach is that users only need to focus on designing a repetitive task for graph elements that are valid at each sliding time, which simplifies the program logic and alleviates the burden of writing codes.Another benefit is that the approach is expected to enhance the performance by facilitating the reuse of intermediate results of multiple sources.The proposed approach is evaluated with a prototyped system, the recipes for existing algorithms, and the experiments with open temporal datasets.In addition, we also discuss how to handle ever-evolving real-world temporal networks.
Automatic Identification of Temples in Digital Images through Scale Invariant Feature Transform

Identification of Temples via computer technology is very tough in end user images. Since, in the CV fewer data available to presume the discrepancy in the testing digital images. Entity recognition is exactly tough and sensibly helpful problem in the field of computer based tactic. In our projected paper, we present a computerized scheme for knowing temples in the captured digital icons. As we are very aware an enormous amount of effort has been applied on automobiles, Architectures, Creatures and many more. Therefore, here we planned to exertion designed for temple identification to facilitate integrated on authors assembled photos, Till time no tests had been intended for temple identification within pictures. From the CV sentiment, temple recognition is not easy because depiction or contours of a variety of Architectures are identical. Therefore, we directed a fresh slant to touch contour on temple identification in the digital photos using Scale Invariant Feature Transform. Here we also include a few scenes which have similar shape in digital images of temples and similar in building classification. In the planned effort employed sculpt in which contour attributes are in use for discover temple in lots of Architectures however identical figures are found within buildings.
AN EXPRESSIVIST ANALYSIS OF THE INDICATIVE CONDITIONAL WITH A RESTRICTOR SEMANTICS

Abstract A globally expressivist analysis of the indicative conditional based on the Ramsey Test is presented. The analysis is a form of ‘global’ expressivism in that it supplies acceptance and rejection conditions for all the sentence forming connectives of propositional logic (negation, disjunction, etc.) and so allows the conditional to embed in arbitrarily complex sentences (thus avoiding the Frege–Geach problem). The expressivist framework is semantically characterized in a restrictor semantics due to Vann McGee, and is completely axiomatized in a logic dubbed ICL (‘Indicative Conditional Logic’). The expressivist framework extends the AGM (after Alchourron, Gärdenfors, Makinson) framework for belief revision and so provides a categorical (‘yes’–‘no’) epistemology for conditionals that complements McGee’s probabilistic framework while drawing on the same semantics. The result is an account of the semantics and acceptability conditions of the indicative conditional that fits well with the linguistic data (as pooled by linguists and from psychological experiments) while integrating both expressivist and semanticist perspectives.
An Immersive Teaching Approach: Singapore Method through Virtual Reality

Education has not evolved as it should, teaching-learning processes are orthodox and do not allow the total development of the student. This panorama is presented in all areas of learning, however, when speaking of exact sciences such as mathematics or physics these problems are more serious. For these reasons, this article proposes the development of a virtual reality system focused on the teaching of mathematics. This educational system is based on the Singapore method, whose benefits allow students to develop their potential and creativity, guiding them to the solution of real-life mathematical problems through reasoning and deduction processes. It also uses a HMD (head mounted display) and visual feedback in order to improve user immersion and generate a correct learning process. The present study has been applied in an educational unit of Ambato-Ecuador. Results showed a significant improvement in grades, for example, there was a 29.93% reduction in the number of students with grades in the range between 4.00 and 6.00, there was an increase of 27,02 % of students with grades in the range between 7.01 and 8.99 and an increase of 2.71 % of students with grades between 9.00 and 10.00.
The Use of Immersive Technologies for Design Reviews

Analysing and manipulating 3D objects are key competences for product designers. Although there is a substantial amount of research on review tools based on immersive technologies related to industrial practices, a comparison with traditional design reviews on an educational level is less prominent in scientific literature. This paper consists of a comparison between reviewing a CAD model on a 2D laptop screen and a more immersive method, using an augmented reality (AR) headset. The participants are twelve industrial design students. They were asked to review two products and look for ergonomic and technical design errors. Based on the evaluation of different parameters, the presented AR method scored high on spatial insight and contextual readability. Also, it was uncovered that the AR method was more demanding both physically and mentally. The findings state that immersive technologies can positively influence the industrial design process, anticipating that future AR technology becomes mature enough.
Grammatical Error Correction for Vietnamese Using Machine Translation

Correction of Vietnamese grammatical errors plays an important role in Natural Language Processing. In this paper, we propose a new method using Machine Translation. We consider the grammatical error correction problem like machine translation problem with source language as grammatical wrong text and target language as grammatical right texts, respectively. Additionally, we carry out pre-processing step with grammatical wrong text using spelling checker such as MS Word spelling tool before using Machine translation model. Our experiments based on the state-of-the-art Machine Translation systems combining with pre-processing step. Experimental results achieved 84.32 BLEU score with Vietnamese grammatical error correct based on SMT architecture and 88.71 BLEU score system based on NMT architecture, which indicates that our method achieves promising results.
Towards Practical Detection of Unproductive Struggle

Extensive literature in artificial intelligence in education focuses on developing automated methods for detecting cases in which students struggle to master content while working with educational software. Such cases have often been called “wheel-spinning,” “unproductive persistence,” or “unproductive struggle.” We argue that most existing efforts rely on operationalizations and prediction targets that are misaligned to the approaches of real-world instructional systems. We illustrate facets of misalignment using Carnegie Learning’s MATHia as a case study, raising important questions being addressed by on-going efforts and for future work.
Evolutionary Recurrent Neural Architecture Search

Deep learning has promoted remarkable progress in various tasks while the effort devoted to these hand-crafting neural networks has motivated so-called neural architecture search (NAS) to discover them automatically. Recent aging evolution (AE) automatic search algorithm turns to discard the oldest model in population and finds image classifiers beyond manual design. However, it achieves a low speed of convergence. A nonaging evolution (NAE) algorithm tends to neglect the worst architecture in population to accelerate the search process whereas it obtains a lower performance compared with AE. To address this issue, in this letter, we propose to use an optimized evolution algorithm for recurrent NAS (EvoRNAS) by setting a probability ϵ to remove the worst or oldest model in population alternatively, which can balance the performance and time length. Besides, parameter sharing mechanism is introduced in our approach due to the heavy cost of evaluating the candidate models in both AE and NAE. Furthermore, we train the sharing parameters only once instead of many epochs like ENAS, which makes the evaluation of candidate models faster. On Penn Treebank, we first explore different ϵ in EvoRNAS and find the best value suited for the learning task, which is also better than AE and NAE. Second, the optimal cell found by EvoRNAS can achieve state-of-the-art performance within only 0.6 GPU hours, which is 20 × and 40 × faster than ENAS and DARTS. Moreover, the transferability of the learned architecture to WikiText-2 also shows strong performance compared with ENAS or DARTS.
Evaluation methodology for Speech To Text Services similarity and speed characteristics focused on small size computers

Abstract The currently small size computers use and the high tendency to use human – voice interfaces to interact with any systems lead to merge these technologies. But implements strong Speech To Text algorithms on a small micro controlled system will reduce its computational capacity getting lower the system performance to execute other tasks. This work presents some selection criteria and a methodology to evaluate the Speech To Text services, using similarity and speed as metrics and the results obtained from an experiment deployed on a Raspberry Pi 3 using the proposed methodology with Spanish speakers. The application of the proposed methodology helped to detect the Google’s STT system as a best option over IBM and Microsoft STT systems because shows a faster conversion and better similarity measures.
Visual Features for Multivariate Time Series

Visual analytics combines the capabilities of computers and humans to explore the insight of data. It provides coupling interactive visual representations with underlying analytical processes (e.g., visual feature extraction) so that users can utilize their cognitive and reasoning capabilities to perform complex tasks effectively or to make decisions. This paper applies successfulness of visual analytics to multivariate temporal data by proposing an interactive web prototype and an approach that enables users to explore data and detect visual features of interest. A list of nonparametric quantities is proposed to extract visual patterns of time series as well as to compute the similarity between them. The prototype integrates visualization and dimensional reduction techniques to support the exploration processes. Many different temporal datasets are used to justify the effectiveness of this approach, and some remarkable results are presented to show its value.
An infrastructure for embedded systems using task scheduling

Task scheduling in heterogeneous environments such as cloud data centers is considered to be an NP-complete problem. Efficient task scheduling will lead to balance the load on the virtual machines (VMs) thereby achieving effective resource utilization. Hence there is a need for a new scheduling framework to perform load balancing amid considering multiple quality of service (QoS) metrics such as makespan, response time, execution time, and task priority. Multi-core Web server is difficult to achieve dynamic balance in the process of remote dynamic request scheduling, so it is necessary to improve it based on the traditional scheduling algorithm to enhance the actual effect of the algorithm. This article do research on the multi-core Web server, Focusing on multi-core Web server queuing model. On this basis, the author draws the drawbacks of the multi-core Web server in the remote dynamic request scheduling algorithm, and improves the traditional algorithm with the demand analysis. Not only it overcomes the drawbacks of traditional algorithms, but also promotes the system threads carrying the same amount of tasks, and promotes the server being always in a dynamic balance. On the basis of this, it achieves an effective solution to customer requests.
Optimizing Inspection Process Severity by Machine Learning Under Label Uncertainty

The increasing competition forces manufacturing companies striving for Zero Defect Manufacturing to constantly improve their products and processes. This vision cannot be realized completely however, so cost-efficient inspection of quality is of high importance: While no defects should remain undetected, this always comes at the expense of pseudo defects. As this effect is common knowledge, the automatically generated inspection results have to be verified by human process experts. As this manual verification leads to tremendous inspection costs, reducing pseudo defects is a major business case nowadays. This paper presents an approach to reduce pseudo defects by applying Machine Learning (ML). A decision support system based on recorded inspection data and ML techniques has been developed to reduce manual verification efforts.
Implementation of the Neural Network (NN) Algorithm in Analysis of Student Class Increment Data Based on Report Card Value

Abstract Formal education is education in schools that takes place regularly and gradually follows clear and strict conditions, the purpose of which is to add insight or knowledge in a person and enrich character, and prepare someone to be able and skilled in a particular field. 12 years formal education that must be obtained by everyone starting from elementary, middle and middle school, in general in the process of class improvement, several stages must be passed by students to be able to proceed to higher grade levels, including good behavior, level attendance of at least 70% and the value must be above the KKM (Minimum Settlement Criteria). KKM is the level of achievement of basic competencies that must be achieved by students. So that in the analysis of determining student achievement classes, data mining techniques are used by applying the Neural Network (NN) algorithm to facilitate the parties involved in analyzing the level of increase in student class and can find out the results of how high the accuracy of the algorithm applied. In this experiment, testing was carried out 3 times with different K-Fold Validations on the cross-validation operator. K-Fold Validation functions to divide the amount of training data and test the data on the tested data, the result of the accuracy that has been tested is 99.26%.
Complex heterogeneity learning: A theoretical and empirical study

Data heterogeneity such as task heterogeneity, view heterogeneity, and instance heterogeneity often co-exist in many real-world applications including insider threat detection, traffic prediction, brain image analysis, quality control in manufacturing processes, etc. However, most of the existing techniques might not take fully advantage of the rich heterogeneity. To address this problem, we propose a novel graph-based approach named M3 to simultaneously model triple heterogeneity in a principled framework. The main idea is to employ the hybrid graphs to jointly model the task relatedness, view consistency, and bag-instance correlation by enhancing the labeling consistency between nearby nodes on the graphs. Furthermore, we analyze the generalization performance of the proposed method based on Rademacher complexity, which sheds light on the benefits of jointly modeling multiple types of heterogeneity. The resulting optimization problem is challenging since the objective function is non-smooth and non-convex. We propose an iterative algorithm based on block coordinate descent and bundle method to solve the problem. Experimental results on various datasets demonstrate the effectiveness of the proposed method.