Reviewerly/exHarmony huggingface dataset:
Selecting Informative Data Samples for Model Learning Through Symbolic Regression

Continual model learning for nonlinear dynamic systems, such as autonomous robots, presents several challenges. First, it tends to be computationally expensive as the amount of data collected by the robot quickly grows in time. Second, the model accuracy is impaired when data from repetitive motions prevail in the training set and outweigh scarcer samples that also capture interesting properties of the system. It is not known in advance which samples will be useful for model learning. Therefore, effective methods need to be employed to select informative training samples from the continuous data stream collected by the robot. Existing literature does not give any guidelines as to which of the available sample-selection methods are suitable for such a task. In this paper, we compare five sample-selection methods, including a novel method using the model prediction error. We integrate these methods into a model learning framework based on symbolic regression, which allows for learning accurate models in the form of analytic equations. Unlike the currently popular data-hungry deep learning methods, symbolic regression is able to build models even from very small training data sets. We demonstrate the approach on two real robots: the TurtleBot mobile robot and the Parrot Bebop drone. The results show that an accurate model can be constructed even from training sets as small as 24 samples. Informed sample-selection techniques based on prediction error and model variance clearly outperform uninformed methods, such as sequential or random selection.
Interactive Apparel Coordination Recommendation System Reflecting Situation and Preference

This study proposes an apparel coordination recommendation system, which reflects the user's situation of wearing clothing and individual preference, based on the Interactive Evolutionary Computation. The proposed system is a practical method in three respects that are not found in previous studies. First, the images of the actual product were used so that the user could use in practice. Secondly, using the latest data from SNS, we generated the outfits that the system presents outfits based on the type and color of clothing that are actually paid attention. Thirdly, the subjective evaluation of the user is accumulated as an individual preference, and the outfit is presented by a simple procedure for daily use. From results of the experiments, it was confirmed that the proposed system was effective in clarifying and reconfirming the user's preference of outfits.
El videoling: uso educativo del vídeo en la enseñanza de la gramática

This research is about a task based experience working ICT on language projects carried out in the Master’s Degree in Secondary Education at the University of Valencia during last five years. The main aim is that future teachers learn to elaborate educational materials on grammar with ICT to apply in Secondary Education and Baccalaureate. The methodological approach deals with a language task-based learning stated, introducing ICT tools in a grammatical content in Secondary Education and Baccalaureate in language and literature in an educational stage. A sample of projects carried out by our students is going to be introduced, dealing with ICT tools for implementing some grammatical aspects taken from the Secondary Education curriculum. They have been adapted to work on some grammatical aspects of the secondary curriculum. In this research a project named videoling is introduced, based on audiovisual capsules dealing with linguistics issues which have been disseminated on Youtube. With the use of ICT in grammar projects it is intended to reinforce the linguistic learning of the students and offer resources to encourage self-learning.
Topic Modelling pada Sentimen Terhadap Headline Berita Online Berbahasa Indonesia Menggunakan LDA dan LSTM

The online mass media is the source of the fastest and up-to-date information. A model that can provide mapping will help in sorting out information more precisely. In this study, the authors applied topic modeling to the results of sentiment analysis on online news headlines in Indonesian. Sources of data in this study were obtained from online mass media in Indonesian. The data collected were analyzed for sentiment using the Long Short-term Memory (LSTM) method, in order to obtain news headlines with positive, negative, and neutral sentiments. The classification obtained from the results of the sentiment analysis process is continued with the topic modeling process using the Latent Dirichlet Allocation (LDA) method and visualized in the form of wordcloud and intertopic distance map (pyLDAVis) to determine the relationship between one topic and another. The result of sentiment analysis is a model with 71.13% of accuracy level and the results of topic modeling are in the form of some topics that are easy to interpret.
Security Assessment of Large-Scale IT Infrastructure

Due to today’s online interactions, the security of IT infrastructure components is important for organizations. The literature survey revealed that evaluation of security of an IT infrastructure has not received as much attention from the research communities as that of application security. This paper examined an example of Saudi IT infrastructure to identify the challenges that threaten security, along with recommendations to address these challenges. Different qualitative methods were used in data collection, including focus groups, direct meetings, observations, and archival data/documents. Key categories of security threats are found to be networking, (e.g., violation of the principles of secure design), systems and storage (e.g., patching management), and information/endpoint (e.g., operation procedures). The lessons learned indicated that these infrastructure security risks can be addressed through various means, including infrastructure management (e.g., monitoring, documentation, and compliance with project management practices), software business activities (e.g., renewal of vendor support service), network redesigning (e.g., avoiding single point of failure structure), and incident response procedures (through developing and implementing clear, formal procedures). Some kinds of infrastructure security threats, such as cascading threats, are difficult to discover and evaluate. This study will assist security requirements engineers, systems managers, and security compliance officers.
New Image Encryption Based on Pixel Mixing and Generating Chaos System

There was suggested new utilizing of the dispersion principle, based on the Lorenz Map, and chaos theory is known as perfect for using cryptography applications for their properties like unpredictability and sensitive initial states as one of the complex encryption approaches. to disengage the color of pixels (R, G, B). Was created this suggest with mixing all each color pixel to swap horizontally, vertically, or diagonally, finally using XOR operation. only milliseconds to encryption and decryption image, because the behavior of block cipher that leads to the period for the image to be decrypted fairly close as the images. The experiments demonstrate that the information security capabilities will be both safe more efficient, the result of our analysis of picture quality evaluation, differential attacks (NPSR), (UACI), Pixels correlation shows the quality and strength of encryption processing, Entropy show good results and closely to 8.
Agile-Stage-Gate Approach: Exploratory Research on the Structure, Roles, and Responsibilities

One of the most prominent methodologies gaining recognition in recent years is the Agile-Stage-Gate approach. The relatively new hybrid model is a combination of the Stage-Gate and the Agile-Scrum models. Several companies already using the Stage-Gate process have recently adopted the Agile-Stage-Gate approach to improve product development and project management. However, each of these companies has added their adjustments and modifications to the new approach in order to accommodate their specialized needs and to achieve the goals that are unique to their company. Therefore, no fixed structure or standardized features have been explicitly assigned to this hybrid approach. Instead, each company has added on different features and manipulated the new approach to fit whatever needs arise from moment to moment. This ambiguity leads to the question of whether the structure, roles, and responsibilities of this new approach can be defined and if so, how these clear and consistent definitions can improve productivity, efficiency, communication and market response time. The purpose of this study is to explore the structure, roles, and responsibilities within this new approach and to identify patterns that emerge during the product development and project management processes. The research questions presented were administered and examined through a qualitative survey. Fifty-two complete responses were collected from experienced individuals who have varying degrees of experience with the Agile-Stage-Gate approach. These findings revealed many similarities and differences between the structure, roles, and responsibilities of this approach, which were also dependent on the product and project type being considered. The most apparent similarities between roles and responsibilities were found in the case of software products and new product development projects. The Agile-Stage-Gate approach has also had a significant and undeniable impact on team communication and performance and was shown to improve overall quality and productivity. Keywords: agile, stage-gate, customer, scrum, sprint, hybrid, ideation, combination products, backlog, retrospective
ANALYSIS AND DESIGN OF USER INTERFACE/USER EXPERIENCE WITH THE DESIGN THINKING METHOD IN THE ACADEMIC INFORMATION SYSTEM OF JENDERAL SOEDIRMAN UNIVERSITY

User Interface/User Experience (UI/UX) design is very important because with a good design that meets user needs, it can make users feel comfortable when using a product. One example is the Academic Information System (SIA), if the design of the SIA is not user friendly, it will have an impact on both the user and the system. Such as input errors, missing information, difficulties in using it. The purpose of conducting UI / UX analysis and design at SIA Universitas Jenderal Soedirman is to solve the problems experienced by users today. Users involved in this research are students. Design is made using the Design Thinking method and for the testing using Usability Testing.
Learning Better Structured Representations Using Low-rank Adaptive Label Smoothing

Training with soft targets instead of hard targets has been shown to improve performance and calibration of deep neural networks. Label smoothing is a popular way of computing soft targets, where one-hot encoding of a class is smoothed with a uniform distribution. Owing to its simplicity, label smoothing has found wide-spread use for training deep neural networks on a wide variety of tasks, ranging from image and text classification to machine translation and semantic parsing. Complementing recent empirical justification for label smoothing, we obtain PAC-Bayesian generalization bounds for label smoothing and show that the generalization error depends on the choice of the noise (smoothing) distribution. Then we propose low-rank adaptive label smoothing (LORAS): a simple yet novel method for training with learned soft targets that generalizes label smoothing and adapts to the latent structure of the label space in structured prediction tasks. Specifically, we evaluate our method on semantic parsing tasks and show that training with appropriately smoothed soft targets can significantly improve accuracy and model calibration, especially in low-resource settings. Used in conjunction with pre-trained sequence-to-sequence models, our method achieves state of the art performance on four semantic parsing data sets. LORAS can be used with any model, improves performance and implicit model calibration without increasing the number of model parameters, and can be scaled to problems with large label spaces containing tens of thousands of labels.
Analysis of Security of Machine Learning and a proposition of assessment pattern to deal with adversarial attacks

Today, Machine Learning is being rolled out in a variety of areas. It is a promising field that can offer several assets and can revolutionize several aspects of technology. Nevertheless, despite the advantages of machine learning technologies, learning algorithms can be exploited by attackers to carry out illicit activities. Therefore, the field of security of machine learning is deriving attention in these times so as to meet this challenge and develop secure learning models. In this paper, we overview a taxonomy that will help us understand and analyze the security of machine learning models. In the next sections, we conduct a comparative study of most widespread adversarial attacks then, we analyze common methods that were advanced to protect systems built on Machine learning models from adversaries. Finally, we discuss a proposition of a pattern designed to ensure a security assessment of machine learning models.
Dynamic backup workers for parallel machine learning

The most popular framework for distributed training of machine learning models is the (synchronous) parameter server (PS). This paradigm consists of n workers, which iteratively compute updates of the model parameters, and a stateful PS, which waits and aggregates all updates to generate a new estimate of model parameters and sends it back to the workers for a new iteration. Transient computation slowdowns or transmission delays can intolerably lengthen the time of each iteration. An efficient way to mitigate this problem is to let the PS wait only for the fastest n−b updates, before generating the new parameters. The slowest b workers are called backup workers. The correct choice of the number b of backup workers depends on the cluster configuration and workload, but also (as we show in this paper) on the hyper-parameters of the learning algorithm and the current stage of the training. We propose DBW, an algorithm that dynamically decides the number of backup workers during the training process to maximize the convergence speed at each iteration. Our experiments show that DBW (1) removes the necessity to tune b by preliminary time-consuming experiments, and (2) makes the training up to a factor 3 faster than the optimal static configuration.
Dataset Search: A lightweight, community-built tool to support research data discovery

Objective: Promoting discovery of research data helps archived data realize its potential to advance knowledge. Montana State University (MSU) Dataset Search aims to support discovery and reporting for research datasets created by researchers at institutions. Methods and Results: The Dataset Search application consists of five core features: a streamlined browse and search interface, a data model based on dataset discovery, a harvesting process for finding and vetting datasets stored in external repositories, an administrative interface for managing the creation, ingest, and maintenance of dataset records, and a dataset visualization interface to demonstrate how data is produced and used by MSU researchers. Conclusion: The Dataset Search application is designed to be easily customized and implemented by other institutions. Indexes like Dataset Search can improve search and discovery for content archived in data repositories, therefore amplifying the impact and benefits of archived data.
UTILIZING NOVEL IN THE READING CLASS TO EXPLORE STUDENTS’ VIEWPOINT OF ITS EFFECTIVENESS

The use of novel on reading skills was important as an efficient and effective media. The novel helped students evaluate their reading skills. This research aimed to investigate student’s perceptions of the use of novels on reading skills. This research was a descriptive qualitative type. The subject of this research was English education students whose numbers were adjusted to the needs of the research, 22 students. This study was conducted at Universitas Teknokrat Indonesia, Bandar Lampung. The instrument of data collection used a questionnaire and interview. The data analysis method was descriptive qualitative. The results showed participants had a positive perception toward using interactive instruction with authentic literary texts, such as novels, on their reading ability. Hence, it can be concluded that the use of English novels can be an alternative way to improve their reading skills in the English learning process.
Pattern Mining and Detection of Malicious SQL Queries on Anonymization Mechanism

With the striking development of big data, individual privacy and data security obtain unprecedented importance. Database anonymization mechanism is created for protecting individual privacy by adding noise to the result of a query, which finds a tradeoff between the privacy and utility of personal data. However, corresponding attacks are emerging continuously resulting in a high risk of individual identification. In this paper, we learn patterns of malicious SQL queries and propose a novel detection method. Association rules are used to mine patterns and features of noise-exploitation attacks, and parse trees are applied to the feature extraction of SQL, thereby we construct feature vectors and input them into the classifiers. At the same time, we also propose a SQL generation method to generate query samples based on a real database for model training and testing. Experiments show that our detection method can significantly prevent noise-exploitation attacks including almost all differential attacks and 91% cloning attacks based on the synthetic dataset, which ensures a strong degree of data utility.
Features of e-learning organization in a Modern University

One of the most important areas of the educational system today is the widespread use of e-learning methods based on advanced information and telecommunication methods, modern pedagogical technologies The article discusses the relevance of e-learning, its possibilities in the formation of a large number of students skills necessary for their successful professional development. The necessity of using the e-learning management system in order to provide high-quality education is shown in the example of the developed e-course Foreign language for students future engineers. Besides the article shows the possibilities of LMS (Learning Management System) of the Moodle educational content management system for implementing the priority goal of the higher education system. Moreover, both positive and negative sides of using LMS are summarized in the paper. Then it gives practical suggestions for high-quality integration of e-learning in the process of teaching and learning in the educational institution of higher education Samara State Transport University on the basis of effective management of the e-learning process and good management.
Towards Self-Adaptive Cyber Deception for Defense

Cyber attackers currently benefit from an asymmetric advantage, leveraging both features and flaws of networking protocols and software to discover and exploit vulnerabilities with impunity.Although significant work has been done to automate various cyber defenses, novel research remains in combining autonomic reasoning and defensive cyber deception.While many difficulties remain in creating a robust system, we have actively explored the utility of such systems for achieving effective cyber defense.Our current approach applies autonomic reasoning to the task of interfering with an attacker's movement through the Cyber Kill Chain R [1] by employing deceptive countermeasures.In this paper, we explore the integration of autonomic computing with insights from game theory and cognitive and behavioral psychology to create a system for adaptive cyber defense using deception.
Election Infrastructure Security: Grants and Reimbursement to the States for Usage of their National Guards in State Active Duty Status to Provide Cybersecurity for Federal Elections

Because presidential and congressional elections (hereinafter Federal elections) are State-administered activities with a Federal nexus, the Federal government should both
The Environmentalization of space and listening

This paper aims to analyze the relationship between listening techniques and technologies and forms of subjectivation in our current auditory culture compared to mid-20th century practices. Applying a media archaeological approach in order to unearth underlying histories of knowledge of the discussed technologies and practices offers a way of understanding how subjectivations and the constitution of environments in the context of large power regimes are intertwined. Against the theoretical backdrop of Gilles Deleuze’s text on “societies of control” and Erich Hörl’s notion of “Environmentalization”, the paper outlines conceptualizations of environments in different forms of sonic control that are inherent in practices and technologies of noise-cancelling headphones and specifi c Spotify playlists. The listening spaces that emerge in the analyzed practices/technologies reveal continuities as well as discontinuities when compared to their historical predecessors. Both the current phenomena are characterized by a process of advancing cybernetization and thus the formation of controllable environments. The depicted transformation corresponds to Deleuze’s observation of a new paradigm of power which he characterized as a shift from “molding” to “modulation”, i.e. a shift from a form-imposing to a self-regulating mode of power. Spotify’s concentration playlists and noisecancelling headphones both operate based on the principle of modulation and represent modes of environmental technologies. In the consideration of the subject-environment relationship on the other hand, current forms of subjectivation become apparent in cybernetic visions of control and environmental power. It is thus shown that listening spaces offer an approach to analyzing power and subjectivation.
Privacy Protection in Prosumer Energy Management Based on Federated Learning

With the booming development of prosumers, there is an urgent need for a prosumer energy management system to take full advantage of the flexibility of prosumers and take into account the interests of other parties. However, building such a system will undoubtedly reveal users' privacy. In this paper, by solving the non-independent and identical distribution of data (Non-IID) problem in federated learning with federated cluster average (FedClusAvg) algorithm, prosumers' information can efficiently participate in the intelligent decision making of the system without revealing privacy. In the proposed FedClusAvg algorithm, each client performs cluster stratified sampling and multiple iterations. Then, the average weight of the parameters of the sub-server is determined according to the degree of deviation of the parameter from the average parameter. Finally, the sub-server multiple local iterations and updates, and then upload to the main server. The advantages of FedClusAvg algorithm are the following two parts. First, the accuracy of the model in the case of Non-IID is improved through the method of clustering and parameter weighted average. Second, local multiple iterations and three-tier framework can effectively reduce communication rounds.
Securing Cryptocurrency Wallet Seed Phrase Digitally with Blind Key Encryption

A cryptographic method of digitally securing cryptocurrency wallet seed phrase through Blind Key Encryption is discussed wherein two blind keys random in nature are generated and used to produce two ciphertexts. The mathematical algorithm used in blind key encryption is described in detail and also an explanation is provided as to how the encryption defeats hackers even after they could successfully compromise a ciphertext of the seed phrase along with its decryption key. Different scenarios of storing the ciphertexts are documented.