Reviewerly/exHarmony huggingface dataset:
Reinforcement Learning for Production‐Based Cognitive Models

Production-based cognitive models, such as Adaptive Control of Thought-Rational (ACT-R) or Soar agents, have been a popular tool in cognitive science to model sequential decision processes. While the models have been useful in articulating assumptions and predictions of various theories, they unfortunately require a significant amount of hand coding, both with respect to what building blocks cognitive processes should consist of and with respect to how these building blocks are selected and ordered in a sequential decision process. Hand coding of large, realistic models poses a challenge for modelers, and also makes it unclear whether the models can be learned and are thus cognitively plausible. The learnability issue is probably most starkly present in cognitive models of linguistic skills, since linguistic skills involve richly structured representations and highly complex rules. We investigate how reinforcement learning (RL) methods can be used to solve the production selection and production ordering problem in ACT-R. We focus on four algorithms from the Q learning family, tabular Q and three versions of deep Q networks (DQNs), as well as the ACT-R utility learning algorithm, which provides a baseline for the Q algorithms. We compare the performance of these five algorithms in a range of lexical decision (LD) tasks framed as sequential decision problems. We observe that, unlike the ACT-R baseline, the Q agents learn even the more complex LD tasks fairly well. However, tabular Q and DQNs show a trade-off between speed of learning, applicability to more complex tasks, and how noisy the learned rules are. This indicates that the ACT-R subsymbolic system for procedural memory could be improved by incorporating more insights from RL approaches, particularly the function-approximation-based ones, which learn and generalize effectively in complex, more realistic tasks.
Re-discovering Archaeological Discoveries. Experiments with reproducing archaeological survey analysis

This article describes an attempt to reproduce the published analysis from three archaeological field-walking surveys by using datasets collected between 1990 and 2005 which are publicly available in digital format. The exact methodologies used to produce the analyses (diagrams, statistical analysis, maps, etc.) are often incomplete, leaving a gap between the dataset and the published report. By using the published descriptions to reconstruct how the outputs were manipulated, I expected to reproduce and corroborate the results. While these experiments highlight some successes, they also point to significant problems in reproducing an analysis at various stages, from reading the data to plotting the results. Consequently, this article proposes some guidance on how to increase the reproducibility of data in order to assist aspirations of refining results or methodology. Without a stronger emphasis on reproducibility, the published datasets may not be sufficient to confirm published results and the scientific process of self-correction is at risk.
Pengaruh Kompetensi Pedagogik, Kompetensi Profesional Terhadap Kinerja Pengenalan Lapangan Persekolahan Mahasiswa Pendidikan Akuntansi

The study was conducted with the aim of knowing whether there was an effect of pedagogic and professional competence on the performance of accounting students partially or simultaneously. This research is included in causal associative research, which means that this study looks for a causal relationship between variables. The research was conducted on accounting education students at the State University of Surabaya who had implemented PLP with a total population of 245 students. The sample used is simple random sampling, so it is found a sample of 152 students. Data collection techniques using secondary data obtained from student scores. The results of the t test: (1) pedagogical competence affects PLP performance with a significant result of 0.000 (2) professional competence affects PLP performance with a significant result of 0.001. The result of the F test is 22,309 with a significant result of 0,000, this indicates that there is a simultaneous effect of pedagogic and professional competence on the performance of accounting education students at the State University of Surabaya. In addition to the t test and F test, there is an R Square value of 23% which indicates that PLP performance is affected by 23% by the pedagogical and professional competency variables, while 77% is influenced by other factors or variables. From the results of the research conducted, it shows that there is an influence of Pedagogic Competence and Professional Competence on the performance of accounting education students' school field introduction either partially or simultaneously.
A Neural Network Based Algorithm Selector for Radar Task Scheduling

A neural network based algorithm selector, to choose the most appropriate scheduling algorithm, is proposed in this paper. The approach uses the recurrent neural network (RNN) to learn and to select. The earliest start time algorithm and the random shifted start time algorithm, are considered in the RNN. The network is trained with 400,000 samples, and validated with 40,000 samples, resulting in a correct selection rate of 92%. The evaluation is done by numerical simulations, and the result shows an improved overall performance in terms of the schedule cost. The selection approach takes about 11 ms, thus it is practical for real world applications.
PEDAGOGICAL COMPONENTS AND STAGES OF HEALTH OF DEVELOP CHILDREN THROUGH MUSIC EDUCATION

The article discusses the pedagogical and psychological features of the rehabilitation of children with disabilities, the role of the education system in ensuring their place in society as active individuals, the need to effectively use the opportunities of music education in this process, its pedagogical the system and components are studied.
A Survey on Security Challenges of Virtualization Technology in Cloud Computing

Virtualization has become a widely and attractive employed technology in cloud computing environments. Sharing of a single physical machine between multiple isolated virtual machines leading to a more optimized hardware usage, as well as make the migration and management of a virtual system more efficiently than its physical counterpart. Virtualization is a fundamental technology in a cloud environment. However, the presence of an additional abstraction layer among software and hardware causes new security issues. Security issues related to virtualization technology have become a significant concern for organizations due to arising some new security challenges. This paper aims to identify the main challenges and risks of virtualization in cloud computing environments. Furthermore, it focuses on some common virtual-related threats and attacks affect the security of cloud computing. The survey was conducted to obtain the views of the cloud stakeholders on virtualization vulnerabilities, threats, and approaches that can be used to overcome them. Finally, we propose recommendations for improving security, and mitigating risks encounter virtualization that necessary to adopt secure cloud computing.
Determining overfitting and underfitting in generative adversarial networks using Fréchet distance

Generative adversarial networks (GANs) can be used in a wide range of applications where drawing samples from a data probability distribution without explicitly representing it is essential. Unlike the deep convolutional neural networks (CNNs) trained for mapping an input to one of the multiple outputs, monitoring the overfitting and underfitting in GANs is not trivial since they are not classifying but generating a data. While training set and validation set accuracy give a direct sense of success in terms of overfitting and underfitting for CNNs during the training process, evaluating the GANs mainly depends on the visual inspection of the generated samples and generator/discriminator costs of the GANs. Unfortunately, visual inspection is far away of being objective and generator/discriminator costs are very nonintuitive. In this paper, a method was proposed for quantitatively determining the overfitting and underfitting in the GANs during the training process by calculating the approximate derivative of the Fréchet distance between generated data distribution and real data distribution unconditionally or conditioned on a specific class. Both of the distributions can be obtained from the distribution of the embedding in the discriminator network of the GAN. The method is independent of the design architecture and the cost function of the GAN and empirical results on MNIST and CIFAR-10 support the effectiveness of the proposed method.
A novel anti‐Doppler SEI algorithm based on the vector diagram decomposition

In practical applications, communication emitters are often carried by low orbit satellites aircraft, ships, and other moving carriers. Doppler phenomenon usually exists due to the relative motion between the emitter and the receiver, which seriously affects the identification performance. Given this situation, a method based on the signal vector decomposition and the multi-feature fusion is proposed. By normalizing and demodulating the target signal, the signal vector diagram is decomposed into four parts. We present a multi-feature fusion model with varying features extracted from each diagram by a deep learning network. Experimental results show that the proposed method is effective in the presence of Doppler effect.
Queue-priority optimized algorithm: a novel task scheduling for runtime systems of application integration platforms

The need for integration of applications and services in business processes from enterprises has increased with the advancement of cloud and mobile applications. Enterprises started dealing with high volumes of data from the cloud and from mobile applications, besides their own. This is the reason why integration tools must adapt themselves to handle with high volumes of data, and to exploit the scalability of cloud computational resources without increasing enterprise operations costs. Integration platforms are tools that integrate enterprises’ applications through integration processes, which are nothing but workflows composed of a set of atomic tasks connected through communication channels. Many integration platforms schedule tasks to be executed by computational resources through the First-in-first-out heuristic. This article proposes a Queue-priority algorithm that uses a novel heuristic and tackles high volumes of data in the task scheduling of integration processes. This heuristic is optimized by the Particle Swarm Optimization computational method. The results of our experiments were confirmed by statistical tests, and validated the proposal as a feasible alternative to improve integration platforms in the execution of integration processes under a high volume of data.
LAPREL: A Label-Aware Parallel Network for Relation Extraction

Relation extraction is a crucial task in natural language processing (NLP) that aims to extract all relational triples from a given sentence. Extracting overlapping relational triples from complex texts is challenging and has received extensive research attention. Most existing methods are based on cascade models and employ language models to transform the given sentence into vectorized representations. The cascaded structure can cause exposure bias issue; however, the vectorized representation of each sentence needs to be closely related to the relation extraction with pre-defined relation types. In this paper, we propose a label-aware parallel network (LAPREL) for relation extraction. To solve the exposure bias issue, we apply a parallel network, instead of the cascade framework, based on the table-filling method with a symmetric relation pair tagger. To obtain task-related sentence embedding, we embed the prior label information into the token embedding and adjust the sentence embedding for each relation type. The proposed method can also effectively deal with overlapping relational triples. Compared with 10 baselines, extensive experiments are conducted on two public datasets to verify the performance of our proposed network. The experimental results show that LAPREL outperforms the 10 baselines in extracting relational triples from complex text.
&lt;i&gt;run&lt;/i&gt;Data: Re-distributing Data via Piggybacking for Geo-distributed Data Analytics over Edges

Efficiently analyzing geo-distributed datasets is emerging as a major demand in a cloud-edge system. Since the datasets are often generated in closer proximity to end users, traditional works mainly focus on offloading proper tasks from those hotspot edges to the datacenter to decrease the overall completion time of submitted jobs in a one-shot manner. However, optimizing the completion time of current job alone is insufficient in a long-term scope since some datasets would be used multiple times. Instead, optimizing the data distribution is much more efficient and could directly benefit forthcoming jobs, although it may postpone the execution of current one. Unfortunately, due to the throwaway feature of data fetcher, existing data analytics systems fail to re-distribute corresponding data out of hotspot edges after the execution of data analytics. In order to minimize the overall completion time for a sequence of jobs as well as to guarantee the performance of current one, we propose to re-distribute the data along with task offloading, and formulate corresponding ε-bounded data-driven task scheduling problem over wide area network under the consideration of edge heterogeneity. We design an online schema <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">run</i> Data, which offloads proper tasks and related data via piggybacking to the datacenter based on delicately calculated probabilities. Through rigorous theoretical analysis, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">run</i> Data is proved concentrated on its optimum with high probability. We implement <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">run</i> Data based on Spark and HDFS. Both testbed results and trace-driven simulations show that run Data re-distributes proper data via piggybacking and achieves up to 37 percent reduction on average response time compared with state-of-the-art schemas.
Towards integrated, interactive, and extensible text data analytics with Leam

From tweets to product reviews, text is ubiquitous on the web and often contains valuable information for both enterprises and consumers.However, the online text is generally noisy and incomplete, requiring users to process and analyze the data to extract insights.While there are systems effective for different stages of text analysis, users lack extensible platforms to support interactive text analysis workflows end-to-end.To facilitate integrated text analytics, we introduce LEAM, which aims at combining the strengths of spreadsheets, computational notebooks, and interactive visualizations.LEAM supports interactive analysis via GUI-based interactions and provides a declarative specification language, implemented based on a visual text algebra, to enable user-guided analysis.We evaluate LEAM through two case studies using two popular Kaggle text analytics workflows to understand the strengths and weaknesses of the system.
COVID-19 challenges in organising teaching at a faculty of pharmacy

We come from Bosnia and Herzegovina, a small country in western Balkans. Our Faculty of Pharmacy at the University of Sarajevo was opened in 1973, but from then on there was no kind of online teaching. So when the COVID-19 pandemic broke out, and when the university decided to stop all kinds of ‘in-class’ teaching (12 March 2020),1 we were faced with something new.We work at the Department of Pharmaceutical Technology, where we teach regular courses on ‘Drug Formulation’ and ‘Industrial Pharmacy’. Students take classes in these subjects in the seventh, eighth and ninth semesters. The exercises are practical and last several hours. On 25 March 2020, classes in the lecture hall were suspended until further notice by the decision of the Senate of the University of Sarajevo,2 and then we realised that we must embark on the adventure of organising online classes.We carried out short research on available online teaching platforms to find basic information on the platform’s capabilities, identify their advantages and disadvantages, and check their commercial prices.We studied the following platforms for online courses: Google Meet , …
Learning English Through Duolingo: Narrating Students’ Experience During Covid-19 Pandemic Time

This study aims to narrate students experience during learning English in COVID-19 pandemic particularly by using Duolingo as a self-learning. Semi-structured interview is used to collect the data. By using the thematic analysis of Braun and Clark, two themes of findings indicate that during the use of Duolingo, the beginner students feel satisfied because it is easy to use and to help them in English basic structure, and for intermediate students, Duolingo did not really support them to improve their skill.
Comparative Analysis of Security Algorithms used in Cloud Computing

Cloud computing is a technique that enables the users to access applications to infrastructure through subscription methods on remote location. Cloud computing, being collective technique, possesses features of various technologies like Virtual Private Network (VPN), distributed technology, parallel computing, grid computing as well as ubiquitous computing. Cloud storage is the infrastructure offered by the cloud computing in order to facilitate users to upload and access their data on cloud space. Due to multitenancy of cloud storage, the data uploaded on it is the more prone to security breaches. To secure the data on cloud, cryptographic technique is used which works like safeguard against data leakage. This research work focuses on cryptographic algorithms from most basic to advance and also presents their comparative analysis. Moreover, the paper focuses on the nature of the algorithms in context of cloud that is homogeneous and heterogeneous nature. In the cloud computing, homogeneous nature algorithm is used only if the encryptors and decryptors both are same while heterogeneous refers to the situation in which encryptors and decryptors are different entities.
Potential User Prediction for Financial APP Based on Random Forest Model

In recent years, mobile applications have brought great convenience to people's lives. Most financial Apps need to bind user credit cards. Thus, if historical transaction records could be effectively used to predict the potential user groups of Apps that may be tied to credit cards in the future, financial institutions can take personalized recommendation and accurate marketing promotion for this part of potential users, which helps to expand their market share of mobile applications and increase corporate profits. Therefore, this paper proposes a method for predicting potential users of financial apps based on the random forest model, and confirms that this model has better classification prediction effects and higher accuracy when dealing with the problem of predicting potential users of financial apps.
Human activity recognition using penalized support vector machines and Hidden Markov Models in multimodal systems

Human activity detection has evolved due to the advances and developments of machine learning techniques, which have enabled solutions to new challenges without ignoring prevalent difficulties that need to be addressed. One of the challenges is the learning model’s sensitivity regarding the unbalanced, atypical, and overlapping information that directly affects the performance of the model. This article evaluates a methodology for the classification of human activities that penalizes defective information. The methodology is carried out through two redundant classifiers, a penalized support vector machine that detects the sub-movements (micro-movements) and the Marvok Hidden Model that predicts the activity given the micro- movements sequence. The performance of the method was compared with state-of-the-art techniques, and the findings suggested significative advance in the detection of micro-movements compared to the data obtained with non-penalized paradigms. In this research, an adequate performance is found in the classification of primitive movements, with hit rates of 95.15% for the Kinect One®, 96.86% for the IMU sensor network, and 67.51% for the EMG sensor network.
Towards Understanding and Mitigating Social Biases in Language Models

As machine learning methods are deployed in real-world settings such as healthcare, legal systems, and social science, it is crucial to recognize how they shape social biases and stereotypes in these sensitive decision-making processes. Among such real-world deployments are large-scale pretrained language models (LMs) that can be potentially dangerous in manifesting undesirable representational biases - harmful biases resulting from stereotyping that propagate negative generalizations involving gender, race, religion, and other social constructs. As a step towards improving the fairness of LMs, we carefully define several sources of representational biases before proposing new benchmarks and metrics to measure them. With these tools, we propose steps towards mitigating social biases during text generation. Our empirical results and human evaluation demonstrate effectiveness in mitigating bias while retaining crucial contextual information for high-fidelity text generation, thereby pushing forward the performance-fairness Pareto frontier.
Pattern Recognition and Related Topics of Olympiad Tasks

In most of tasks proposed for olympiads in informatics inital data are taken arbitrary from any ranges such that a “brute force” solution (close to immediate translation of condition of the task into an algorithmical one) can pass tests with initial data from narrow ranges, and the task itself is to improve that solution. We make a survey of types of tasks where inital data are too vast to use a “brute force” but they are announced to be connected and the task itself is to extract necessary information in an optimal way. We consider tasks on recognition and on restoration of data from a unified point of view. We also make an attempt to describe various types of tasks formally.