Reviewerly/exHarmony huggingface dataset:
ABNORMAL EVENT DETECTION BY MACHINE VISION USING DEEP LEARNING

This paper proposes an abnormal event detection system through surveillance camera using machine vision, in corporation with deep learning, which analyzes footages of crowded scenes and detects abnormal events.In the proposed solution, the concept of basic deep neural network model has been widely adopted.DNN is a mathematical model that categorizes video input frames and separates the anomaly present in the video events.If any one of the anomaly namely arrest, assault and abuse is occurred in a video, that anomaly is detected from specified frames of video.The proposed model mainly depicts how deep learning elevates the quality of machine vision in abnormal event.
Dispersed EM-VAEs for Interpretable Text Generation

Deep generative models are commonly used for generating images and text. Interpretability of these models is one important pursuit, other than the generation quality. Variational auto-encoder (VAE) with Gaussian distribution as prior has been successfully applied in text generation, but it is hard to interpret the meaning of the latent variable. To enhance the controllability and interpretability, one can replace the Gaussian prior with a mixture of Gaussian distributions (GM-VAE), whose mixture components could be related to hidden semantic aspects of data. In this paper, we generalize the practice and introduce DEM-VAE, a class of models for text generation using VAEs with a mixture distribution of exponential family. Unfortunately, a standard variational training algorithm fails due to the mode-collapse problem. We theoretically identify the root cause of the problem and propose an effective algorithm to train DEM-VAE. Our method penalizes the training with an extra dispersion term to induce a well-structured latent space. Experimental results show that our approach does obtain a meaningful space, and it outperforms strong baselines in text generation benchmarks. The code is available at this https URL.
Improving Segmentation for Technical Support Problems

Technical support problems are often long and complex.They typically contain user descriptions of the problem, the setup, and steps for attempted resolution.Often they also contain various non-natural language text elements like outputs of commands, snippets of code, error messages or stack traces.These elements contain potentially crucial information for problem resolution.However, they cannot be correctly parsed by tools designed for natural language.In this paper, we address the problem of segmentation for technical support questions.We formulate the problem as a sequence labelling task, and study the performance of state of the art approaches.We compare this against an intuitive contextual sentence-level classification baseline, and a state of the art supervised text-segmentation approach.We also introduce a novel component of combining contextual embeddings from multiple language models pre-trained on different data sources, which achieves a marked improvement over using embeddings from a single pre-trained language model.Finally, we also demonstrate the usefulness of such segmentation with improvements on the downstream task of answer retrieval.
A Girl Has A Name: Detecting Authorship Obfuscation

Authorship attribution aims to identify the author of a text based on the stylometric analysis.Authorship obfuscation, on the other hand, aims to protect against authorship attribution by modifying a text's style.In this paper, we evaluate the stealthiness of state-of-the-art authorship obfuscation methods under an adversarial threat model.An obfuscator is stealthy to the extent an adversary finds it challenging to detect whether or not a text modified by the obfuscator is obfuscated -a decision that is key to the adversary interested in authorship attribution.We show that the existing authorship obfuscation methods are not stealthy as their obfuscated texts can be identified with an average F1 score of 0.87.The reason for the lack of stealthiness is that these obfuscators degrade text smoothness, as ascertained by neural language models, in a detectable manner.Our results highlight the need to develop stealthy authorship obfuscation methods that can better protect the identity of an author seeking anonymity.
Audit Tata Kelola Teknologi Informasi Menggunakan Framework Cobit 5 Pada Pt. Bisma Tunas Jaya Sentral

Abstract PT. Bisma Tunas Jaya Sentral is a company engaged in the sale of finished goods (especially motorcycles), spare parts, and motorcycle service specifically for the Yamaha brand. To support business services, IT services in the form of software and hardware are used in business processes.To ensure optimal IT management, measurements of the level of IT governance that have been carried out and compare it with the level expected by management. This measurement refers to the COBIT domain 5. The steps of this study start from research planning, design of data collection instruments, data collection and data processing to conduct a final evaluation that results in recommendations for improved management in accordance with the maturity of the COBIT model.The results of data processing that have been carried out indicate that the IT infrastructure management method that is already running at PT Bisma Tunas Jaya is not good enough. It is proven by the high gap between the current management level and the level desired by management.
Multiobjective Parallel Chaos Optimization Algorithm with Crossover and Merging Operation

Chaos optimization algorithm (COA) usually utilizes chaotic maps to generate the pseudorandom numbers mapped as the decision variables for global optimization problems. Recently, COA has been applied to many single objective optimization problems and simulations results have demonstrated its effectiveness. In this paper, a novel parallel chaos optimization algorithm (PCOA) will be proposed for multiobjective optimization problems (MOOPs). As an improvement to COA, the PCOA is a kind of population-based optimization algorithm which not only detracts the sensitivity of initial values but also adjusts itself suitable for MOOPs. In the proposed PCOA, crossover and merging operation will be applied to exchange information between parallel solutions and produce new potential solutions, which can enhance the global and fast search ability of the proposed algorithm. To test the performance of the PCOA, it is simulated with several benchmark functions for MOOPs and mixed <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mo>/</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> controller design. The simulation results show that PCOA is an alternative approach for MOOPs.
Structure, composition and quality of the scientific and educational space of higher education

This article is devoted to the assessment of the scientific and educational space of higher education on the basis of statistics of the database of the United Fund of electronic resources "Science and education" (Ofernio) for the period from 2009 to the present.The object of research is universities in Russia.The subject of research is electronic and information resources developed by universities.The research methods are contentanalytical, statistical, expert, and logical methods.
Intelligent business cloud service platform based on SpringBoot framework

This article is based on the SpringBoot platform, which displays real-time news and information on various economies and policies in countries and regions along the "Belt and Road", and solves the problem of difficult data processing of the "Belt and Road" complex data. In order to improve the readability of the data, this platform analyzes the cluttered data to generate statistical charts, and obtains key information in real time and intuitively. This system uses Spring, SpringBoot, Mybatis as the background framework scheme, and the front end uses the more popular Vue with strong performance and good performance. Finally, the intelligent cloud service platform system based on B / S architecture is designed, which has practical application value.
SelectScale: Mining More Patterns from Images via Selective and Soft Dropout

Convolutional neural networks (CNNs) have achieved remarkable success in image recognition. Although the internal patterns of the input images are effectively learned by the CNNs, these patterns only constitute a small proportion of useful patterns contained in the input images. This can be attributed to the fact that the CNNs will stop learning if the learned patterns are enough to make a correct classification. Network regularization methods like dropout and SpatialDropout can ease this problem. During training, they randomly drop the features. These dropout methods, in essence, change the patterns learned by the networks, and in turn, forces the networks to learn other patterns to make the correct classification. However, the above methods have an important drawback. Randomly dropping features is generally inefficient and can introduce unnecessary noise. To tackle this problem, we propose SelectScale. Instead of randomly dropping units, SelectScale selects the important features in networks and adjusts them during training. Using SelectScale, we improve the performance of CNNs on CIFAR and ImageNet.
Is Access Enough? Interrogating the Influence of Money and Power in Shaping Information

Abstract Access to information encourages innovation and leads to participation in society of individuals. The emergence of Open Access supports the inclusion of all, including the voices of the traditionally marginalized, yet access alone is insufficient to enable consumers to effectively use information. Power structures can influence the information available and silence opposing viewpoints. Industry disinformation can influence viewpoints and shape policy in ways that can be detrimental to individuals and the community. Information consumers may not possess the competence required to navigate the complex information ecosystem to find the accurate, high-quality, resources required to meet their need. Libraries have a role in assisting consumers develop the critical evaluation capabilities essential to the exercise of informed skepticism when evaluating truth claims. Access is essential; however, without the knowledge to determine the quality and validity of information, a consumer can be misled in ways that can cause harm to themselves and society.
SOLAR ENERGY COLLECTION, STORAGE AND UTILIZATION FOR DOMESTIC PURPOSES

Two solar radiation models (Duffie & Beckman and ASHRAE) were computed and evaluated on the middle area of Coastal Delta, Egypt (latitude and longitude angles are 30.49°N and 30.59°E, respectively). A comparison study of the two models was conducted using measured incident solar radiation on a horizontal plane to assess the proper model can dependable be used for different solar applications. To compute the total incident solar radiation on inclined surface, two different tilt angles (30.5° and 45.3°) were functioned all over the year. The comparison study between the two estimation models and the measured values of incident solar radiation showed that, the highest value of R2 at horizontal and tilted surface using Duffie and Beckman model. Useful heat gain for the measured solar radiation was ranged between 6.54 and 8.82 kWh and calculated solar radiation by Duffie and Beckman model, was 5.99 and 7.34 kWh and calculated solar radiation by ASHRAE model, was 6.79 and 9.0 kWh with tilt angle 30.5°. While, at tilt angle 45.3° useful heat gain for measured solar radiation was ranged between 7.89 and 9.06 kWh. The  electrical  energy  for heating domestic water ranged from 7.61 to 10.12 kWh for winter months .This study gave the best way by calculated solar radiation replacement of expensive solar radiation instruments and always it fixed in weather stations and also, this study gave an idea to use the solar water heaters for domestic purposes.
Frugal Paradigm Completion

Lexica distinguishing all morphologically related forms of each lexeme are crucial to many language technologies, yet building them is expensive. We propose a frugal paradigm completion approach that predicts all related forms in a morphological paradigm from as few manually provided forms as possible. It induces typological information during training which it uses to determine the best sources at test time. We evaluate our language-agnostic approach on 7 diverse languages. Compared to popular alternative approaches, ours reduces manual labor by 16-63% and is the most robust to typological variation.
Alternatively Assessing Conceptual Learning in an Emergency Clinical Environment—A Mixed Methods Design

Modeling cognitive processes in clinical learning environments is a necessary first step towards improving learning assessment and medical practice by using an alternative assessment model. Verbal protocol and cognitive content analyses are effective methods of exploring such cognitive processes. For the purpose of simplifying the discussion, we have labeled these processes as Identification of Information, Advanced Cognition, and Medical Cognitive Action. Exploring problem solving processes with Bayesian network techniques can characterize students' dynamic learning processes quantitatively, identify differences in cognitive components at different stages of learning and better represent clinical problem solving features.We develop a hierarchical cognitive model as a cognitive assessment tool to describe the complex cognitive network relations, which can be applied to various clinical cognitive situations. The study concludes that the cognitive model was useful in identifying students' learning trajectories by representing the different cognitive features.
The effect of marketing ambidexterity on improvement of marketing performance in telecommunication companies in Jordan

This study aimed to reveal the effect of marketing ambidexterity (exploring opportunities, exploiting opportunities, and marketing flexibility) on improvement of marketing performance in telecom companies in Jordan.A purposeful sample of (45) marketing managers who represent marketing departments in telecom companies was selected, a questionnaire was distributed among them, and 42 valid questionnaires were retrieved and analyzed.The field results provide a strong statistically significant effect (α ≤ 0.05) of the marketing ambidexterity dimensions (exploring opportunities, exploiting opportunities, marketing flexibility) on improvement of marketing performance in telecom companies in Jordan.It also provides a high level of interest of both marketing ambidexterity dimensions practice, and marketing performance improvement in telecom companies in Jordan.
A benchmark dataset for real-time detection of icons in mobile apps and a small-scale feature module

Icons are widely used as a UI component in mobile applications. However, it is considerably difficult to detect icons because of their small size. Existing rapid object detection frameworks, such as YOLO and SSD, cannot perform well on small objects, such as icons, because of the multiple downsampling layers of convolutional networks and prediction grids. This paper summarizes our recent efforts toward the goal of small-scale icon detection. First, we describe the tiny icon collection (named MobileIcon), which contains 32,670 images spanning 20 different icon categories. This database allows us to systematically study icon detection in mobile apps and to establish a benchmark for icon recognition. We also explore a way to detect icons in real-time called IconYOLO, which detects icons at the native resolution of App snapshots. We introduce a small-scale feature module based on a modified passthrough operation to fuse deep semantic information with shallow detail features. Different from the original passthrough operation, our structure employs more upsamples and DBL units for feature maps fusing. The vanishing gradient issue can be addressed by residual units. Finally, icons can be detected on a single-scale grid rather than a multiscale grid in YOLOv3. Compared with YOLOv3, IconYOLO performs 9.63% better on the MobileIcon dataset. To further verify the generalization of our network, an experiment on the traditional PASCAL VOC dataset is performed.
The Application and Development of Humanities in Physical Education

The responsibility of physical education teachers is to make students become moral, intellectual and physical all-round development and have a strong body, but in the physical education of various teaching methods of dissent, as well as how to face the experience and lessons of the problem has been long debated, independent.Sometimes in discussing the problem of physical education is like a lump in the throat, the tasteless food, abandon the pity.All kinds of schools at all levels and physical education teaching accident exposure rate most of the "news", the conflict between students and PE teachers, PE teachers and students' parents dispute all the year round, the reason is the requirement for physical education, sports teaching outline with the understanding of physical education of the students and the tests, the entrance examination for students of the physical examination system, the relations between and among points.Therefore, more and more people are calling for humanities to be involved in physical education courses and teaching methods.In order to accelerate the transformation from the teaching mode centered on schools and teachers to the mode centered on people and students, it is necessary to accelerate the development and active application of humanities in physical education.
The Uniform Variants of the Glivenko-Cantelli and Donsker Type Theorems for a Sequential Integral Process of Independence

In the analysis of statistical data in biomedical treatments, engineering, insurance, demography, and also in other areas of practical researches, the random variables of interest take their possible values depending on the implementation of certain events.So in tests of physical systems (or individuals) on duration of uptime values of operating systems depend on subsystems failures, in insurance business insurance company payments to its customers depend on insurance claims.In such experimental situations, naturally become problems of studying the dependence of random variables on the corresponding events.The main task of statistics of such incomplete observations is estimating the distribution function or what is the same, the survival function of the tested objects.To date, there are numerous estimates of these characteristics or their functionals in various models of incomplete observations.In this paper investigated the asymptotic properties of sequential processes of independence of the integral structure and uniform versions of the strong law of large numbers and the central limit theorem for integral processes of independence by indexed classes are established.The obtained results can be used to construct statistics of criteria for testing a hypothesis of independence of random variables on the corresponding events.
Nonlinear dynamics and synchronization of computational cognitive model in educational science

A computational cognitive model is derived from nonlinear interactions of (Neo) Piagetian-Vygostkian constructs to explain, and predict cognitive processes during collaborative learning.Learning is re-conceptualized as continuous perturbations of cognitive state which unfolds stable cognitive trajectories near Piagetian equilibrium.The model explicates topologically equivalent cognitive patterns, attributed to multi-modal representation of sensory information presented to the learners.Synchronization of the cognitive model is obtained via active control functions which predicts convergence of cognitive states.The synchronized cognitive model is stabilized using Lyapunov matrix equation.These qualitative behaviors emerged due to learner-to-learner and instructor-to-learner scaffolding driven by cognitive executive functions.The dynamical behaviors of the cognitive model are simulated using control parameters with estimated datasets showing viable cognitive trajectories.
Data-driven learning in the academic writing classroom: Citation and stance

Acquiring academic writing skills in English may be a true challenge, especially for undergraduate students for whom English is not their first language, but still have to accomplish writing tasks in a more or less successful way as a requirement to pass certain subjects and eventually qualify in higher education. Citation is perhaps the most distinctive property of academic writing and, at the same time, a feature which requires learners to understand how citation structures and reporting verbs can express the writer’s stance towards the imported information and the source authors themselves. This paper seeks to explore the use of a corpus of research papers written by native speakers of English in an English for Academic Purposes classroom with non-native speakers of the language. The proposal aims at boosting the students’ academic writing skills by providing them with a set of data-driven learning activities which promote both reflection and practice on a range of citation strategies. Teaching them how to use citation structures and reporting verbs effectively will ultimately allow them to take an adequate stance in their academic papers by shaping their claims in an appropriate way within a given disciplinary discourse community.
Melanoma Detection in Histopathological Images using Deep Learning

Histopathological images are widely used to diagnose diseases such as skin cancer.As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of abnormal cell nuclei would be very helpful for doctors to perform fast diagnosis.In this paper, we propose a technique, using deep learning algorithms, to first segment the cell nuclei in Hematoxylin and Eosin (H&E) stained images and detect the abnormal melanocytes on the histopathological images.The cell segmentation is done by using a novel Convolutional Neural Network (CNN) architecture.The segmented cells are then classified into melanoma and other nuclei using a Support Vector Machine classifier.Experimental results show that the CNN can segment the nuclei with more than 90% accuracy.The proposed technique has a low computational complexity.
Multidirectional Associative Optimization of Function-Specific Word Representations

We present a neural framework for learning associations between interrelated groups of words such as the ones found in Subject-Verb-Object (SVO) structures.Our model induces a joint function-specific word vector space, where vectors of e.g.plausible SVO compositions lie close together.The model retains information about word group membership even in the joint space, and can thereby effectively be applied to a number of tasks reasoning over the SVO structure.We show the robustness and versatility of the proposed framework by reporting state-of-the-art results on the tasks of estimating selectional preference and event similarity.The results indicate that the combinations of representations learned with our task-independent model outperform task-specific architectures from prior work, while reducing the number of parameters by up to 95%.