Reviewerly/exHarmony huggingface dataset:
AN ANALYSIS OF STUDENTS’ ABILITY IN WRITING NARRATIVE TEXT ( A STUDY AT THE EIGHTH GRADE OF SMP NEGERI 2 TAMBANGAN IN 2018/2019 ACADEMIC YEAR)

This study aims to know the student’s ability in writing narrative text in terms of content, organization, vocabulary, language use, and mechanics mastery at the eightth grade of SMP Negeri 2 Tambangan and to know why the students’ ability in writing narrative text in terms of content, organization, vocabulary, language use, and mechanics mastery are low at the eighth grade of SMP Negeri 2 Tambangan. The research was conducted by using mix method(quantitative and qualitative research) with 9 students as the informants. The documentation and interview were used in collecting the data. Based on data analysis, it could be found (a)the result of the from their ability in writing narrative text in terms of content, organization, vocabulary, language use, and mechanics still below the criteria minimum (KKM). The average all of the participants just on average 65 while the KKM value demanded that they must except to get the score on the average of criteria minimum at 75. and b) The result of the data finding showed that most of the students are unable in writing narrative text includes in content, organization, vocabulary, language use, and mechanics. It is proved from their score n writing narrative text are still below the KKM. and c)Based on the writer’s interview to the 9 informants, most of students think writing is difficult skill for them because they have limited understanding in vocabulary, grammar and tenses mastery.
4. Computer-aided speech therapy for dysarthric speakers: Statistical acoustic modeling for automated verification of pronunciation accuracy

The objective of this study is to develop statistical modeling techniques for characterizing phonetic variation in automatic speech recognition (ASR). One issue addressed in this domain is to reliably detect the phoneme-level mispronunciations in speech utterances that arise from speech therapy applications. Another issue addressed in this work is to study the ability of ASR systems to model the phonetic variation that often exists in speaker-independent recognition tasks. In order to address these issues, first, a phoneme-level pronunciation verification (PV) scenario is investigated for detecting the mispronunciation occurrences in speech utterances recorded from a population of impaired children with neuromuscular disorders. The well-known continuous density hidden Markov model (CDHMM) is used as a phoneme decoder which generates a finite state network of phoneme string hypotheses for input speech utterances. The phoneme-level confidence measures can be constructed from this network, and PV decision can be made by comparing the confidence measures with a pre-selected threshold. Second, the subspace Gaussian mixture model (SGMM) formalism is incorporated into a new PV scenario. A new kind of pronunciation confidence measure used for making mispronunciation verification decisions is extracted directly from the state-level model parameters. Both session-level and utterance-level PV scenarios based on the SGMM-based confidence measures are proposed. In the session-level PV task, the equal error rate can be reduced by 15.35% when combining the SGMM-based confidence measures with the above phoneme decoder-based confidence measures. In the utterance-level PV task, the equal error rate can be reduced by 12.94%. This equal error rate reduction is believed to result from an efficient characterization of pronunciation variation for each phoneme by the SGMM.
Continuous Enhancement of the Educational Capability toward Cultivation of Practical Mechanical Engineers

The comprehensive course on Mechanical Engineering at Setsunan University, provided by the Department of Mechanical Engineering, has received evaluation and accreditation from the Japan Accreditation Board for Engineering Education (JABEE) in 2019 with the six-year of validity after achieving the first certificate in 2013 followed by re-examination in 2016. We have always pursued continuous enhancement of our education capability, aiming at cultivating "future" mechanical engineers. This paper introduces our efforts in mechanical engineering education by applying a smaller-class system, project-based learning, and by fostering a logical thinking ability. We here look at whether our ongoing education improvement activities can lead to raising faculties’ and undergraduates’ awareness of learning.
Challenge for Consolidation of Individual Email Services into a Cloud Service

Email is a traditional but still important global communication tool. An email address is a kind of personal identifier, and email addresses printed on publications require persistent reachability. Kyushu University provides a university-wide email service, Primary Mail Service, and assigns a Primary Mail Address for each member. Divisions of the university additionally operate individual email services for their internet subdomains and administer member email addresses. Since email is a major means of cyberattacks nowadays, the secure operation of an email server demands considerable effort and high skill. This article describes a challenge at Kyushu University for consolidating individual email services. Since 2018, the Primary Mail Service has been operated using Microsoft’s cloud service, Exchange Online, which supports multiple internet domains on a tenant. The approach employed is registering divisional subdomains to the tenant and configuring forwarding addresses from addresses of the subdomains to the Primary Mail Addresses or external addresses. A desirable scheme is for each domain administrator to manage forwarding addresses of the domain, but Exchange Online is unable to delegate administration to the domains. To overcome this, a system was designed and developed for domain administrators to create, read, update, and delete forwarding addresses. Beginning in July 2020, a new service to import divisional domains was offered. We are now planning measures for promoting the consolidation of individual email services.
Accounting Information Systems Evaluation of Medicines Management

Good management, supervision and control of drug supplies is very important in order to avoid loss and damage to drug supplies. The truth of accounting data and avoiding fraud can be detrimental to the hospital. This research is a type of research that is a case study at PT. Indofarma, Tbk for Drug Management at PT. Indofarma, Tbk. The purpose of this study was to determine the description and problem solving regarding information systems at PT. Indofarma, Tbk, especially the accounting information system for drug procurement and management. This study uses qualitative research with a case study method. Data obtained by conducting interviews, observations, documentation, and distributing questionnaires. The focus of this study is a drug inventory accounting system consisting of planning and determining procedures, procurement procedures, storage procedures, distribution procedures, elimination procedures and physical counting procedures. The results showed that the accounting information system for drug inventory at PT. Indofarma, Tbk was in accordance with the theoretical study. But there are still weaknesses, such as the trapping of the function of the warehouse which in addition to receiving and storing drugs. Suggested improvement is good control by related parties so that fraud does not occur which can harm at PT. Indofarma, Tbk.
Displaying Data on the Web

In this chapter, I will cover how to display a list of people with their addresses on our ASP.NET Core Razor Pages application using Entity Framework Core 5. I will show how to use Visual Studio scaffolding to create the initial Razor Pages and then how to customize it to display addresses on the details page. Lastly, we will add a menu link named “Contacts” that points to the generated Index page.
APPROACHES TO THE REALIZATION OF TECHNOLOGIES OF COORDINATED FORMATION OF STUDENTS’ PROFESSIONAL AND FLEXIBLE COMPETENCIES IN THE PROCESS OF PROFESSIONAL TRAINING

The article examines a range of issues related to approaches to the formation of students’ professional competencies and soft skills in the process of implementation of technologies for their coordination. The practical use of an automated training complex developed based on modern information technology learning tools as a didactic means for the formation of professional competencies and soft skills of future specialists is demonstrated. Particular methods of coordinated formation of professional competencies and soft skills in practice are identified on the example of several specific competencies. The materials presented in the article may be used by the authors and developers of new approaches and conceptions in the modernization of professional training content and technologies.
RL: a Language for Formal Engineering

Reflection is a notion that naturally emerges from philosophy, mathematics, and sciences. In short, reflection is the ability of an entity to alter its own behaviour. This paper suggests that reflection is crucial to the development of complex and trustworthy software systems. We present RL, a reflective computational model that aims to support the development of a large-scale framework for modelling and manipulating structured data. We give the formal semantics for this computational model. We also share preliminary work on a proof-of-concept implementation of RL and discuss future work.
Facial expression recognition based on multi-radius local gradient binary pattern

The Facial Expression Recognition system recognizes the emotional state of a person by capturing the face, which is then filtered and matched with a pre-trained data-set. The automatic facial expression system impacts critical applications in many areas, such as consumer system for better human communication fields, customer satisfaction for product reviews in business sectors, dynamic texture recognition and aerial image analysis as well. This paper improves the traditional Local binary pattern and proposes a new feature vector named “Multi-radius Local Gradient Binary Pattern” (MLGBP). Initially, we extract the localinformation of facial region by calculating two Local Gradient Binary Pattern (LGBP) with adjacent radii and different kernels named LGBP_8;1 and LGBP_8;2 respectively. The proposed feature vector LGBP is more precise about the shadow and light effect of the face parts, which mainly decides the emotional states of a face. After that, we derive MLGBP by employing XOR operation on the LGBPs to find the correlate between them. This MLGBP feature vector can identify local gradient changes caused by light and noise by capturing information of various radii without an increment in feature dimension. We also studied the recognition rate of the proposed MLGBP feature vector with respect of several classifier and found that KNN provides the best result. Results of the CK+ database demonstrate the efficiency and effectiveness of the proposed system.
Lattice-based Signcryption with Equality Test in Standard Model

A signcryption, which is an integration of a public key encryption and a digital signature, can provide confidentiality and authenticity simultaneously. Additionally, a signcryption associated with equality test allows a third party (e.g., a cloud server) to check whether or not two ciphertexts are encrypted from the same message without knowing the message. This application plays an important role especially in computing on encrypted data. In this paper, we propose the first lattice-based signcryption scheme equipped with a solution to testing the message equality in the standard model. The proposed signcryption scheme is proven to be secure against insider attacks under the learning with errors assumption and the intractability of the short integer solution problem. As a by-product, we also show that some existing lattice-based signcryptions either is insecure or does not work correctly.
Repetitive Activity Counting by Sight and Sound

This paper strives for repetitive activity counting in videos. Different from existing works, which all analyze the visual video content only, we incorporate for the first time the corresponding sound into the repetition counting process. This benefits accuracy in challenging vision conditions such as occlusion, dramatic camera view changes, low resolution, etc. We propose a model that starts with analyzing the sight and sound streams separately. Then an audiovisual temporal stride decision module and a reliability estimation module are introduced to exploit cross-modal temporal interaction. For learning and evaluation, an existing dataset is repurposed and reorganized to allow for repetition counting with sight and sound. We also introduce a variant of this dataset for repetition counting under challenging vision conditions. Experiments demonstrate the benefit of sound, as well as the other introduced modules, for repetition counting. Our sight-only model already outperforms the state-of-the-art by itself, when we add sound, results improve notably, especially under harsh vision conditions.
Aggregative Self-Supervised Feature Learning from a Limited Sample

Self-supervised learning (SSL) is an efficient approach that addresses the issue of limited training data and annotation shortage. The key part in SSL is its proxy task that defines the supervisory signals and drives the learning toward effective feature representations. However, most SSL approaches usually focus on a single proxy task, which greatly limits the expressive power of the learned features and therefore deteriorates the network generalization capacity. In this regard, we hereby propose two strategies of aggregation in terms of complementarity of various forms to boost the robustness of self-supervised learned features. We firstly propose a principled framework of multi-task aggregative self-supervised learning from a limited sample to form a unified representation, with an intent of exploiting feature complementarity among different tasks. Then, in self-aggregative SSL, we propose to self-complement an existing proxy task with an auxiliary loss function based on a linear centered kernel alignment metric, which explicitly promotes the exploring of where are uncovered by the features learned from a proxy task at hand to further boost the modeling capability. Our extensive experiments on 2D natural image and 3D medical image classification tasks under limited data and annotation scenarios confirm that the proposed aggregation strategies successfully boost the classification accuracy.
THE THREE DIMENSIONS OF INTENTIONALITY

The issue of intentionality was posed anew in philosophy by Franz Brentano. However, it was Brentano himself who indicated that the source of intentionality-related problems dates back to Classical Antiquity and the Middle Ages. The search for the original traces of this issue in the history of philosophy has led me to conclude that intentionality as an inalienable characteristic of consciousness is characterized by three-dimensionality, which is expressed in theoria, praxis and poiesis. Contemporary research focuses primarily on cognitive intentionality, examining in particular either the very subject-object relation or the immanent (intentional) object, in-existing in psychical experience (in the acts of consciousness). And yet, intentionality is a basic feature of the whole consciousness-anchored (mental) life of a human being. It determines the whole consciousness-based activity of the subject in abstract theorizing, practice and production. Therefore, it manifests itself as a mode of being of a conscious (mental) entity, i.e. an entity partially constituted by intentional content, relationality, reference, directionality, openness and conscious awareness , as well as determining the meaning and the creation of purely intentional beings. Intentionality is revealed as a primary factor in the awakening of consciousness, through the building (constituting) of conscious experiences that are poietic, practical and theoretical. Each of these three ways of categorizing the nature of experience, however, indicates only the predominant aspect of a given experience, for strictly speaking experiences are determined by all three aspects. Intentionality and – consequently – all conscious experience, are thus characterized by three-dimensions: cognitive, activistic and productive. Any act of consciousness is always a form of activity that is informed by its cognitive aspect and produces something transcendent with regard to itself. The recognition of the three-dimensional nature of intentionality allows us to understand the human being and the dilemmas concerning his actions, knowledge and creativity.
The entropy of quantum causal networks

Quantum networks play a key role in many scenarios of quantum information theory. Here we consider the quantum causal networks in the manner of entropy. First we present a revised smooth max-relative entropy of quantum combs, then we present a lower and upper bound of a type II error of the hypothesis testing. Next we present a lower bound of the smooth max-relative entropy for the quantum combs with asymptotic equipartition. At last, we consider the score to quantify the performance of an operator. We present a quantity equaling to the smooth asymptotic version of the performance of a quantum positive operator.
Light polarization imaging recovery technique in marine fish observations

Recently, the research and exploration of the ocean has attracted the attention of a large number of scholars. Due to the light scattering and absorption in underwater imaging, the fish observation images cause imaging blurred and details lost. To handle the above limitation, an underwater polarization imaging recovery technique is proposed to recover visibility restoration and color correction of images. First, in the CIE-Lab model, morphological filtering is used to repair the missing details of the L channel, and color correction is performed on A channel. Secondly, in the RGB model, adaptive contrast stretching is performed to enhance the contrast of the image. To further improve the image clarity, dark channel dehazing is implemented to reduce the atomization caused by scattering. Extensive experiments demonstrate that the method can significantly enhance the visual effect, clarity, and detail information of the fish image. The restoration and division of the contour information of fish in the image are improved.
Designing of Loss Function for 3D Pedestrian Detection using CenterNet

research-article Share on Designing of Loss Function for 3D Pedestrian Detection using CenterNet Authors: Chan Yong Kim Chung-Ang University, South Korea Chung-Ang University, South KoreaView Profile , Doo Hee Lee Chung-Ang University, South Korea Chung-Ang University, South KoreaView Profile , Hyo Jin Kim Chung-Ang University, South Korea Chung-Ang University, South KoreaView Profile , Asif Aziz Memon Chung-Ang University, South Korea Chung-Ang University, South KoreaView Profile , Ehtesham Iqbal Chung-Ang University, South Korea Chung-Ang University, South KoreaView Profile , Kwang Nam Choi Chung-Ang University, South Korea Chung-Ang University, South KoreaView Profile Authors Info & Claims AICCC 2020: 2020 3rd Artificial Intelligence and Cloud Computing ConferenceDecember 2020 Pages 5–10https://doi.org/10.1145/3442536.3442538Published:15 March 2021Publication History 0citation72DownloadsMetricsTotal Citations0Total Downloads72Last 12 Months24Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Significant Role of Economic Growth in Alleviating Poverty

The problem of poverty is related to various regional conditions, not only in rural areas but also in urban areas.Poverty in urban areas has its own characteristics with various factors that influence it.This study aims to determine the role of economic growth, inflation, and population growth on poverty level.This was a quantitative study with path analysis and multiple regression analysis.The results of the study evidenced that economic growth had a significant effect on poverty level with a value (tcountof 2.896).Inflation (tcountof 0.096) and population growth (tcountof 0.726) had bo significant effect on poverty levels.Open unemployment rate variable (tcountof 1.721) and human development index (tcountof 1.255) also had no significant effect on poverty level.Based on these findings, efforts to allevite poverty level need to be pursued continuously by optimizing macroeconomic variables such as the rate of economic growth and controlling the inflation rate.
Disentanglement-based Cross-Domain Feature Augmentation for Effective Unsupervised Domain Adaptive Person Re-identification

Unsupervised domain adaptive (UDA) person re-identification (ReID) aims to transfer the knowledge from the labeled source domain to the unlabeled target domain for person matching. One challenge is how to generate target domain samples with reliable labels for training. To address this problem, we propose a Disentanglement-based Cross-Domain Feature Augmentation (DCDFA) strategy, where the augmented features characterize well the target and source domain data distributions while inheriting reliable identity labels. Particularly, we disentangle each sample feature into a robust domain-invariant/shared feature and a domain-specific feature, and perform cross-domain feature recomposition to enhance the diversity of samples used in the training, with the constraints of cross-domain ReID loss and domain classification loss. Each recomposed feature, obtained based on the domain-invariant feature (which enables a reliable inheritance of identity) and an enhancement from a domain specific feature (which enables the approximation of real distributions), is thus an "ideal" augmentation. Extensive experimental results demonstrate the effectiveness of our method, which achieves the state-of-the-art performance.
Underwater image restoration based on modified color-line model

Underwater images suffer from haze, color distortion, and low contrast due to the absorption and scattering of transmitted light. A systematic approach including accurate background light (BL) estimation and transmission (TM) estimation is proposed based on color-line model for underwater images restoration. The estimation of BL candidate region is improved by combining quadtree subdivision and minimum unary gray entropy evaluation, to realize a high-quality restoration and improve accuracy of TM estimation with color-line model. Given incomplete intersection of color lines with BL, a convex optimization function is proposed to improve accuracy of TM estimation. On the basis of restorations with BL and TM estimations, pixel distribution stretching and Unsharp Masking are employed to correct color and enhance edge information for better performance of vision. Comprehensive evaluations illustrate that the proposed method could efficiently restore underwater images with visibility improvement, natural color correction, and contrast enhancement.
What happened? Vote by mail worked in 2020. But this was no way to run an election.

The COVID-19 pandemic meant that voting by mail was more important than ever during the 2020 US General Election. Though it generally worked well, Priscilla Southwell argues that issues such as the rejection of so-called “naked” and “cured” ballots by Republicans show that there are still important areas for improvement.