Reviewerly/exHarmony huggingface dataset:
Competing for Resources: Estimating Adversary Strategy for Effective Plan Generation

Effective decision making while competing for limited resources in adversarial environments is important for many real-world applications (e.g. two Taxi companies competing for customers). Decision-making techniques such as Automated planning have to take into account possible actions of adversary (or competing) agents. That said, the agent should know what the competitor will likely do and then generate its plan accordingly. In this paper we propose a novel approach for estimating strategies of the adversary (or the competitor), sampling its actions that might hinder agent's goals by interfering with the agent's actions. The estimated competitor strategies are used in plan generation such that agent's actions have to be applied prior to the ones of the competitor, whose estimated times dictate the deadlines. We empirically evaluate our approach leveraging sampling of competitor's actions by comparing it to the naive approach optimising the make-span (not taking the competing agent into account at all) and to Nash Equilibrium (mixed) strategies.
Transductive polyhedral conic classifiers for machine learning applications

In this paper, we introduce novel methods called Transductive Polyhedral Conic Classifiers that use both labeled and unlabeled data for classification. The proposed methodology utilizes the concave-convex procedure to solve the resulting optimization problems as in the Robust Transductive Support Vector Machines (RTSVMs). However, unlike RTSVM that uses SVM formulation, our proposed methods use the polyhedral conic classifier formulation that returns tight and closed decision boundaries compared to SVM. We tested the proposed methods on various datasets and experimental results show that our proposed methods yield better results than the existing transductive learning classifiers.
Remediatisation, media interdiscursivity and ideological ambivalence in online news reports on sexual assault

Nowadays, the integration of informal, evaluative and entertaining elements in news production is not an uncommon practice in certain types of news genres. Our focus is on such elements expressed innovatively through image-based digital resources (IDRs) borrowed from participatory internet media culture, which include internet memes, emojis, screenshots of TV dramas/movies and digital archives, in social media news reporting. Our case study deals with news reports about sexual assaults by a Chinese official news outlet People.cn on Sina Weibo, one of the largest social media platforms in mainland China. Using the concepts of ‘remediatisation’ and ‘media interdiscursivity’, we unpack the ideological implications of the representation of sexual assaults and the social actors (perpetrators and victims) involved in the cases. Our study shows that the unexpected and innovative use of IDRs by the news media generates particular kinds of affect that result in an ideological ambivalence about sexual assaults and sexual crime reporting.
Implementation of Learning Continuity Plan: A Basis for a Sustainable Development Program

The Basic Education Learning Continuity Plan (BE-LCP) ensures that students' learning progresses even amidst a pandemic. It is also focused on improving the quality of basic education with enhanced teacher performance and improved mastery of learning competencies on the part of the learners. This research aimed to determine the extent of the implementation of the BE-LCP and the challenges encountered in aligning the four strategic thrusts in the new normal education. Furthermore, the respondents of the study are the sixty (60) teachers of Banlic Elementary School, Division of Calamba City. The researchers used the descriptive method and the researcher-made questionnaire as the main data gathering tool. Weighted mean, Frequency Count, Percentage, Ranking, and one-way ANOVA are the statistical tools used in analyzing the data gathered. The results revealed that despite the challenges encountered, most of the respondents represent a high extent of LCP implementation along with the four strategic thrusts. Thus, age, educational background, length of service, and relevant training have a significant relationship in the implementation of advancing hand holding initiatives and amplifying defined technical assistance to target delivery units; consequently, age and length of service have a significant correlation on appraising stakeholders on the progress of interventions. Based on the conclusion drawn from the study, a proposed development program was crafted to enhance the implementation of the BE-LCP.
COVID-19 and Higher Education in Uzbekistan: Lessons from Two Universities

COVID-19 led to a global movement as higher education policy-makers and leaders are disseminating their experiences during the 2020-2022 pandemic era under the heading of "Lessons Learned from COVID-19".The available research, policy-briefs, interviews, books, conferences, and webinars might include innovative titles and themes; however, the mainstream of all works specially by late 2021 and during 2022 is on the lessons learned from COVID-19.The present research with its qualitative research design aims to portray the experience of academic staff and students from two universities in Uzbekistan: Urgench State University (UrSU) and Tashkent State University of Economics (TSUE).A total number of 414 responses (academic staff 64 and students 350) were received through Google Forms.Data analysis was administered through Strauss and Corbin's (1998) Constant Comparative method of data analysis with peer reviewing to ensure the credibility of the emerged themes.Educational Weaknesses and Strengths, as well as Psychological Problems: Study at Home and Depression were the two main emerged themes.Leadership advice is also presented by members of both universities to help Higher Education (HE) policy-makers and leaders in Uzbekistan improve the quality of education at their universities and colleges.
Learning to predict diverse trajectory from human motion patterns

Diverse and reasonable multi-modal trajectory prediction of pedestrian is vital for the safety of intelligent autonomous systems. Currently, most of existing methods primarily focus on modeling the extrinsic interactions of pedestrians, but neglect to take advantage of the intrinsic properties of pedestrian motion, which may generate unrealistic pedestrian movement. We investigate the natural motion of pedestrians and explore future motion patterns of pedestrians with similar observed trajectories, which provides a new perspective for multi-modal prediction of pedestrian trajectories. On this basis, we propose a novel model to learn diverse trajectories from human motion patterns, which is mainly composed of a motion pattern selector and a multi-modal trajectory generator. With a constructed pattern gallery, the motion pattern selector mines the possible future motion patterns based on the similar observed trajectories. Subsequently, the diverse future motion patterns are fed into the multi-modal trajectory generator, in which these motion patterns are selected and refined through a scoring network and a regression network, and then directly generate multiple diverse trajectories. We use two public pedestrian trajectory ETH/UCY datasets to compare our method with several baseline methods. Experimental results show that our model outperforms most of the state-of-the-art approaches both in accuracy and diversity by fully leveraging the intrinsic information of trajectories.
Qore-DL: A QoS-aware joint optimization framework for distributed deep learning training

Resource management of deep learning training (DLT) jobs is critical for cluster resource efficiency and client QoS assurance. Most existing scheduling frameworks require clients to specify job resource configuration, which can lead to over-provision or under-provision issues. Additionally, the performance of some static scheduling frameworks degrades in highly dynamic clusters. In this paper, we propose a Qo S-aware joint re source optimization framework called Qore-DL for distributed DLT jobs. We divide the lifecycle of a DLT job into submission, queuing and running stages. Qore-DL automatically configures reasonable resources for submitted jobs and greedily assigns scheduled jobs to hosts. For running jobs, Qore-DL employs a heuristic scheme to adjust their resources. Qore-DL jointly considers the optimization of QoS satisfaction and resource efficiency at the three stages of DLT jobs. We implemented the prototype of Qore-DL in TensorFlow based on Kubernetes and conducted extensive experiments in CPU and GPU clusters to evaluate its performance. The experiment results show that, compared with its counterparts, Qore-DL can improve the job completion rate by up to 42.4% and the cluster resource efficiency by up to 21.8%.
Simulasi Numerik Kapal Katamaran Pendeteksi Kedalaman Banjir Menggunakan Software Maxsurf Untuk Mencari Resistance

Indonesia is a country prone to natural disasters, one of the most frequent natural disasters is flooding. when there is a flood in an area, it is necessary to measure the depth of the flood, this serves to reduce the occurrence of accidents when a flood occurs. To reduce this risk, a vessel that measures flood depths is designed using a catamaran hull, then the design results will be simulated using the software. Design and numerical simulation in this study using Maxsurf software. The trial and error method is used to determine the size of the ship. By using Maxsurf software, several parameters can be known including resistance, power, and displacement. In the calculation of the resistance in this study using the Holtrop method, the ship's speed has variations with speed (0.5, 1, 1.5, 2, and 2.5). The design of the ship with a displacement of 1,665 kg, obtained the main size of the ship Length: 60 cm; Breadth: 30 cm; Depth:15 cm; Draft: 6 cm. Based on the results of the numerical simulation, the resistance with a speed of 0.5 m/s gets 0.46 N, 1 m/s gets 1.6 N, 1.5 m/s gets 3.18 N, 2 m/s gets 4.92 N, and 2.5 m/s get 6.89 N From the results obtained the graph will form a linear trend. Thus the speed of the ship will affect the resulting resistance. The higher the speed of the ship, the greater the resulting resistance.
SRKCD: A stabilized Runge–Kutta method for stochastic optimization

We introduce a family of stochastic optimization methods based on the Runge–Kutta–Chebyshev (RKC) schemes. The RKC methods are explicit methods originally designed for solving stiff ordinary differential equations by ensuring that their stability regions are of maximal size. In the optimization context, this allows for larger step sizes (learning rates) and better robustness compared to e.g. the popular stochastic gradient descent method. Our main contribution is a convergence proof for essentially all stochastic Runge–Kutta optimization methods. This shows convergence in expectation with an optimal sublinear rate under standard assumptions of strong convexity and Lipschitz-continuous gradients. For non-convex objectives, we get convergence to zero in expectation of the gradients. The proof requires certain natural conditions on the Runge–Kutta coefficients, and we further demonstrate that the RKC schemes satisfy these. Finally, we illustrate the improved stability properties of the methods in practice by performing numerical experiments on both a small-scale test example and on a problem arising from an image classification application in machine learning.
A g-Normal fuzzy relational hybrid model based control design for hybrid systems with continuous-valued inputs

In this paper, the indirect adaptive control of a special class of hybrid systems is proposed. The proposed control scheme is based on g-normal fuzzy relational hybrid models (g-Normal FRHMs) in which the model of both plant and controller are proposed to be modeled by FRHM structure. There is an assumption on control action to be of continuous-valued type. In order to design a control scheme for both regulation and reference tracking, an additional simplifying assumption is considered. Indeed, when the tuning mechanism is extracted, it is assumed that the defuzzifier of the controller’s model and the fuzzifier of the plant’s model are approximately inverse of each other and can be simplified. The proposed control approach is then applied to a DC-DC buck converter as our case study. The simulation results show that the proposed adaptive control based on g-Normal FRHM can regulate and also track the reference input in the presence of both load and supply voltage disturbance.
A social and news media benchmark dataset for topic modeling

Topic modeling is an active research area with several unanswered questions. The focus of recent research in this area is on the use of a vector embedding representation of the input text with both generative and evolutionary topic modeling techniques. Unfortunately, it is hard to compare different techniques when the underlying data and preprocessing steps that were used to develop the models are not available. This paper presents two secondary datasets that can help address this gap. These datasets are derived from two primary datasets. The first consists of 8145 posts from the r/Cancer health forum and the second consists of 18,294 messages submitted to 20 different news groups. The same preprocessing procedure is applied to both datasets by removing punctuation, stop words and high frequency words. Each dataset is then clustered using three different topic modeling techniques: pPSO, ETM and NVDM and three topic numbers: 10, 20, 30. In addition, for pPSO two text embeddings representation are considered: sBERT and Skipgram. The secondary datasets were originally developed in support of a comparative analysis of the aforementioned topic modeling techniques in a study titled "Comparing PSO-based Clustering over Contextual Vector Embeddings to Modern Topic Modeling" submitted to the Journal of Information Processing and Management. The present paper provides a detailed description of the two secondary datasets including the unique identifier that can be used to retrieve the original documents, the pre-processing scripts, the topic keywords generated by the three topic modeling techniques with varying topic numbers and embedding representations. As such, the datasets allow direct comparison with other topic modeling techniques. To further facilitate this process, the algorithm underlying the evolutionary topic modeling technique, pPSO, proposed by the authors is also provided.
An Aero-Engine Damage Detection Method with Low-Energy Consumption Based on Multi-Layer Contrastive Learning

The health of aero-engines is pivotal to the safe operation of aircraft. With increasing service time, the internal components of the engine will be damaged by threats from different sources, so it is necessary to regularly detect the damage inside the engine. At present, most of the detection methods of major airlines rely on the internal images of the engine obtained by manual use of a borescope to detect damage or traditional machine learning methods, which consume high levels of human and computational resources but have low efficiency. Artificial intelligence in various fields can achieve better performance than traditional methods, but to achieve the industrialization standard of Green AI, we need further research. Accordingly, we introduce a multi-layer contrastive learning method to a lightweight target detection model design, which is applied to real aero-engine borescope images of complex components to accomplish real-time damage detection. We intensively conduct comparative experiments to evaluate the effectiveness of our method. The verification results demonstrate that the method can help our model perform excellently compared with other available baseline models.
FastCredit: Expediting credit-based congestion control in datacenters

Receiver-driven transports that leverage hop-by-hop credits have the merits of fast convergence, short queueing, and strong congestion avoidability, making them high-efficiency in managing congestion in current high-speed data centers. However, these transport protocols treat long and latency-sensitive short flows equally, increasing flow completion times and affecting user experiences. Although flow scheduling mechanisms have been studied extensively, they are hard to be deployed into hop-by-hop credit-based transports. The root cause is that most traditional flow scheduling mechanisms mainly work in the long queue containing flows of various sizes, while credit-based proactive transports maintain the extremely short bounded queue. Based on this observation, this paper presents FastCredit, making the first attempt to expedite short-flow scheduling in hop-by-hop credit-based congestion control. In FastCredit, we schedule credit transmission at both receivers and switches to indirectly perform data flow scheduling, and we develop an ECN-based credit rate control mechanism to mitigate the problems caused by credit loss. Compared to the state-of-the-art credit-based transport protocol, FastCredit reduces average flow completion time to 0.71x and greatly improves the short flow transmission latency to 0.45x in realistic workloads. Especially, FastCredit reduces average flow completion time to 0.72x under incast circumstances and 0.56x in many-to-one traffic mode. Moreover, FastCredit still maintains the advantages of the short queue and high throughput and essentially reduces credit loss by employing credit rate control.
Letter on the paper “On the two-parameter Bell–Touchard discrete distribution”

"Letter on the paper “On the two-parameter Bell–Touchard discrete distribution”." Communications in Statistics - Theory and Methods, ahead-of-print(ahead-of-print), pp. 1–2
A Design Contribution to Ergonomic PC Mice Development

Developing new manual computer pointing devices abiding to the requirements set out in ergonomic product design literature necessitates joining contributions from several areas, including the ergonomic guidelines applicable to hand tool design, human-system interaction, and certain user characteristics such as anthropometric data. Computer mice are hand tools enabling the interaction with the computer, for use by people from both sexes and practically all ages. Moreover, the PC mouse's intensive usage is able to cause musculoskeletal disorders. This paper reports on a study aimed at developing new computer mouse shapes, reducing forearm pronation, and providing hand-palm holding, supported by a literature review and an adequate design methodology, starting from known shapes of commercial products, the traditional (horizontal) computer mouse, and the vertical computer mouse. In this regard, potential concepts were generated as solutions to the previously specified problem through a set of creative tasks based on the specifications. Four new shapes were proposed to be evaluated through an assessment matrix; as a result, two new PC mice geometries were designed and fully prototyped. This study also reports on selected results of usability and an electromyographic evaluation of the prototypes against three commercial PC mice (horizontal, slanted, and vertical) by a sample of 20 participants, supporting validation of the development process and the newly developed geometries, with emphasis on the slanted conical innovative shape.
Tablet inscription recognition based on the convolutional neural network

Text image recognition has opened up a new path for intelligent text sorting and research. The number of epitaph inscriptions is very large and of great value. Due to the limitation of objective conditions, there are many deficiencies in the study of its text arrangement and it is urgent to adopt new methods and new technologies to overcome them. This paper attaches great importance to the design of an automatic recognition algorithm for tablets based on the convolutional neural network. It also describes the application prospects of using the algorithm for the reading and distinguishing of inscriptions, the sorting of inscriptions, and the building of intelligent databases.
Introduction

This special issue of SiSAL Journal features papers from the last Research Network on Learner Autonomy (ReNLA) Symposium. The most recent Association Internationale de Linguistique Appliquée (or International Association of Applied Linguistics/ AILA) Congress took place in August 2021.
APEX-Net: Automatic Plot Extraction Network

Automatic plot extraction involves understanding and inferring the data distribution and therefore, extracting individual line plots from an image containing multiple 2D line plots. It is an important problem having many real-world applications. The existing methods for addressing this problem involve a significant amount of human intervention. To minimize this intervention, we propose APEX-Net, a deep learning based framework with novel loss functions for solving the plot extraction problem. Further, we introduce APEX-1M - a new large scale dataset that contains both the plot images and the raw data. We demonstrate the performance of APEX-Net on the APEX-1M test set and show that it obtains impressive accuracy. We also show visual results of our network on unseen plot images and demonstrate that it extracts the shape of the plots to a great extent. Finally, we develop a GUI for plot extraction that can benefit the community at large. The dataset and code will be made publicly available.
The effects of Self-regulation and Attention concentration on Computational Thinking according to Coding Play experience of children

Objectives The purpose of this study is to examine whether there is a difference in the effect of self-regulation, attention concentration on computational thinking according to the presence or absence of coding play experience in children.&#x0D; Methods The subjects of the study were 135 5-year-old children enrolled in kindergartens and daycare centers in Gyeonggi-do, and data were collected through one-to-one interviews for children and questionnaires for teachers and parents. For the analysis of the collected data, descriptive statistics, t-test, correlation analysis, and multiple regression analysis were performed using the SPSS WIN 22.0 program.&#x0D; Results First, it was found that computational thinking, self-regulation, attention concentration were all significantly higher than that of children without coding play experience. Second, it was found that there was a statistically significant positive (+) correlation between computational thinking and attention concentration of children with coding play experience, but there was no significant correlation between computational thinking and self-regulation. However, among the sub-factors of computational thinking, logical thinking ability showed a statistically significant positive correlation with self-regulation ability. Third, it was found that attention concentration had a significant effect on the computing thinking ability of infants with coding play experience, and self-regulation did not have a significant effect. Attention concentration was found to have 21.4% explanatory power, and neither variable had a significant effect on the computational thinking of children without coding play experience.&#x0D; Conclusions This study revealed the influence of self-regulation and attention concentration as predictive variables that can explain computational thinking in early childhood, and confirmed that coding play experience is an important factor in enhancing computing thinking ability. Therefore, important implications for the applicability and necessity of coding play (or coding education) in the field of early childhood education were presented to improve children's computational thinking.&#x0D; &#x0D;