Reviewerly/exHarmony huggingface dataset:
Using adversarial images to improve outcomes of federated learning for non-IID data

One of the important problems in federated learning is how to deal with unbalanced data. This contribution introduces a novel technique designed to deal with label skewed non-IID data, using adversarial inputs, created by the I-FGSM method. Adversarial inputs guide the training process and allow the Weighted Federated Averaging to give more importance to clients with 'selected' local label distributions. Experimental results, gathered from image classification tasks, for MNIST and CIFAR-10 datasets, are reported and analyzed.
CLEF. A Linked Open Data native system for Crowdsourcing

Collaborative data collection initiatives are increasingly becoming pivotal to cultural institutions and scholars, to boost the population of born-digital archives. For over a decade, organisations have been leveraging Semantic Web technologies to design their workflows, ensure data quality, and a means for sharing and reusing (Linked Data). Crucially, scholarly projects that leverage cultural heritage data to collaboratively develop new resources would benefit from agile solutions to simplify the Linked Data production workflow via user-friendly interfaces. To date, only a few pioneers have abandoned legacy cataloguing and archiving systems to fully embrace the Linked Open Data (LOD) paradigm and manage their catalogues or research products through LOD-native management systems. In this article we present Crowdsourcing Linked Entities via web Form (CLEF), an agile LOD-native platform for collaborative data collection, peer-review, and publication. We detail design choices as motivated by two case studies, from the Cultural Heritage and scholarly domains respectively, and we discuss benefits of our solution in the light of prior works. In particular, the strong focus on user-friendly interfaces for producing FAIR data, the provenance-aware editorial process, and the integration with consolidated data management workflows, distinguish CLEF as a novel attempt to develop Linked Data platforms for cultural heritage.
Nonwords Pronunciation Classification in Language Development Tests for Preschool Children

This work aims to automatically evaluate whether the language development of children is age-appropriate. Validated speech and language tests are used for this purpose to test the auditory memory. In this work, the task is to determine whether spoken nonwords have been uttered correctly. We compare different approaches that are motivated to model specific language structures: Low-level features (FFT), speaker embeddings (ECAPA-TDNN), grapheme-motivated embeddings (wav2vec 2.0), and phonetic embeddings in form of senones (ASR acoustic model). Each of the approaches provides input for VGG-like 5-layer CNN classifiers. We also examine the adaptation per nonword. The evaluation of the proposed systems was performed using recordings from different kindergartens of spoken nonwords. ECAPA-TDNN and low-level FFT features do not explicitly model phonetic information; wav2vec2.0 is trained on grapheme labels, our ASR acoustic model features contain (sub-)phonetic information. We found that the more granular the phonetic modeling is, the higher are the achieved recognition rates. The best system trained on ASR acoustic model features with VTLN achieved an accuracy of 89.4% and an area under the ROC (Receiver Operating Characteristic) curve (AUC) of 0.923. This corresponds to an improvement in accuracy of 20.2% and AUC of 0.309 relative compared to the FFT-baseline.
THE IMPLEMENTATION OF GUIDED QUESTIONS TECHNIQUE IN IMPROVING STUDENTS’ ABILITY IN WRITING DESCRIPTIVE TEXT

This research aims at finding out whether the implementation of guided questions technique can improve the students’ ability in writing descriptive text or not. The researcher applied a quasi-experimental research design which involved experimental class and control class. The population of this research was the second-grade students of SMP Negeri 1 Toribulu. The sample was VIII B as the control class and VIII C as the experimental class that consist of 20 students for each class. The researcher applied the purposive sampling technique. The instrument used to collect the data was test. The pre-test was administered to measure the students’ ability in writing descriptive text before the treatment and the post-test was to measure the students’ ability after the treatment. The data gathered from the test were analyzed statistically. The result of the data analysis shows that the hypothesis of the research was accepted regarding the analysis that the tcounted (2,076) was higher than the ttable (2,0042). The degree of freedom (df) of the table is Nx+Ny – 2 = 20+20-2 = 38 with the level of significance at 0.05. it means that guided questions technique can improve the students’ ability in writing descriptive text.
ResNorm: Tackling Long-tailed Degree Distribution Issue in Graph Neural Networks via Normalization

Graph Neural Networks (GNNs) have attracted much attention due to their ability in learning representations from graph-structured data. Despite the successful applications of GNNs in many domains, the optimization of GNNs is less well studied, and the performance on node classification heavily suffers from the long-tailed node degree distribution. This paper focuses on improving the performance of GNNs via normalization. In detail, by studying the long-tailed distribution of node degrees in the graph, we propose a novel normalization method for GNNs, which is termed ResNorm (\textbf{Res}haping the long-tailed distribution into a normal-like distribution via \textbf{norm}alization). The $scale$ operation of ResNorm reshapes the node-wise standard deviation (NStd) distribution so as to improve the accuracy of tail nodes (\textit{i}.\textit{e}., low-degree nodes). We provide a theoretical interpretation and empirical evidence for understanding the mechanism of the above $scale$. In addition to the long-tailed distribution issue, over-smoothing is also a fundamental issue plaguing the community. To this end, we analyze the behavior of the standard shift and prove that the standard shift serves as a preconditioner on the weight matrix, increasing the risk of over-smoothing. With the over-smoothing issue in mind, we design a $shift$ operation for ResNorm that simulates the degree-specific parameter strategy in a low-cost manner. Extensive experiments have validated the effectiveness of ResNorm on several node classification benchmark datasets.
THE EFFECTIVENESS OF THE READ-ASK-PARAPHRASE (RAP) STRATEGY TO IMPROVE STUDENTS’ READING COMPREHENSION

The aim of this research was to improve students' reading comprehension at the X grade of SMA Negeri 1 Kasimbar. The method applied was the quasi-experimental design with no equivalent control group, the design involved two groups of class. One group was treated as the experimental class, and another class group was the control class. In the experimental class, the researcher applied The RAP strategy and for the control class, the researcher does not apply this strategy. The population of this research was the X grade of SMA Negeri 1 Kasimbar which consisted of 168 students. The sample of this research consisted of 20 students, with the selected date by using the purposive sampling technique, 10 students for X IPA 3 as experimental class and 10 students from X IPA 2 as control class. The instrument used in this research was a reading test. The test was used in the pre-test and post-test. The implementation of this research was to improve the reading comprehension of the students by the (RAP) strategy. The mean score on the reading test before treatment with the (RAP) strategy read ask paraphrase was 60 and after treatment, the mean score was improved by 79.5. It means that the read-ask paraphrase (RAP) strategy to improve students' reading comprehension at the X grade of SMA Negeri 1 Kasimbar is effective.
A conceptual framework for assessing information security management practices in selected universities in Uganda

The purpose of this paper is to present a conceptual framework for assessing managerial level information security practices, governance, and activities in selected university institutions in Uganda. Extant literature was drawn from existing information security management practices in different organizations. The proposed conceptual framework consisted of four manageable areas, namely, information security governance practices, information security practices, personnel management practices, and physical security practices. These areas are further subdivided into 25 categories that provide a formal checklist for assessing existing information security management practices in university institutions in Uganda.
STUDENTS’ PERCEPTION OF ONLINE LEARNING USING WHATSAPP DURING COVID-19 PANDEMIC

The purpose of this study is to find out students' perceptions of online learning during the covid-19 pandemic by using WhatsApp. This research used quantitative descriptive research. The population of this research was 67 people, while the research sample used was simple random sampling based on the solving formula, so the sample in this study was 57 second semester of the English Tadris Department at UIN Datokarma Palu. The instrument used was a questionnaire. The results of the study indicate that online learning using the WhatsApp application has been going well or it can be interpreted that online learning during the pandemic using the WhatsApp application was very helpful. The percentage obtained in this study is; positive (4.38%), while the negative obtained (4.01%), and neutral (1.59%). Based on the percentages above, we can see that online learning using the WhatsApp application during the pandemic really helped students in the learning process even though they did not have face-to-face classes.
An Efficient Student ID Lookup System by Intelligent Key in Radix Tree

The college ID card is one of the very important archives for the student, since it is the only way to prove the student's identity interior the college and during exams.In this study a lookup system is proposed by examine ability of applying the radix search tree approach.So ID number is classified into five parts each one represented by one or more digit that indicate group of fields.Many bushes are built, each node includes part of ID number, the last node in the leaves contains students information, thus when searching process is applied every ID number can be tracked according to desired path through its tree until reach the leaf, where the student's information is stored.
Workshop Evaluasi Pembelajaran Daring bagi Guru Sekolah Dasar Mitra PGSD UNM Kampus VI Bone

Online learning during Covid-19 pandemic allows teaching and learning activities to occur without having to meet physically and at a distance by utilizing a computer or smartphone. However, the digital skills of Indonesian teachers are still low. So that the evaluation of online learning outcomes is less effective and does not characterize evaluations that measure cognitive, affective and psychomotor abilities. Evaluation is mostly done by giving assignments via Whatsapp, not measurements or tests. While there are many applications that can be used to measure student learning progress, such as Google Forms, Kahoot, Quizizz, and Wordwall. Therefore, community service was carried out in the form of a Workshop of online learning evaluation for elementary school teachers: partners of PGSD UNM Campus VI Bone. This community service is carried out by means of lectures, demonstrations or practices, and evaluation of the results of the training. The result shows that teachers master the use of Google Forms as an effective evaluation platform. Partner teachers are expected to be able to train colleagues both through workshops at the school level and at the Teacher Working Group to improve the digital skills of Indonesian teachers.&#x0D;
Interaction-Grounded Learning with Action-inclusive Feedback

Consider the problem setting of Interaction-Grounded Learning (IGL), in which a learner's goal is to optimally interact with the environment with no explicit reward to ground its policies. The agent observes a context vector, takes an action, and receives a feedback vector, using this information to effectively optimize a policy with respect to a latent reward function. Prior analyzed approaches fail when the feedback vector contains the action, which significantly limits IGL's success in many potential scenarios such as Brain-computer interface (BCI) or Human-computer interface (HCI) applications. We address this by creating an algorithm and analysis which allows IGL to work even when the feedback vector contains the action, encoded in any fashion. We provide theoretical guarantees and large-scale experiments based on supervised datasets to demonstrate the effectiveness of the new approach.
Taxonomy of Benchmarks in Graph Representation Learning

Graph Neural Networks (GNNs) extend the success of neural networks to graph-structured data by accounting for their intrinsic geometry. While extensive research has been done on developing GNN models with superior performance according to a collection of graph representation learning benchmarks, it is currently not well understood what aspects of a given model are probed by them. For example, to what extent do they test the ability of a model to leverage graph structure vs. node features? Here, we develop a principled approach to taxonomize benchmarking datasets according to a $\textit{sensitivity profile}$ that is based on how much GNN performance changes due to a collection of graph perturbations. Our data-driven analysis provides a deeper understanding of which benchmarking data characteristics are leveraged by GNNs. Consequently, our taxonomy can aid in selection and development of adequate graph benchmarks, and better informed evaluation of future GNN methods. Finally, our approach and implementation in $\texttt{GTaxoGym}$ package are extendable to multiple graph prediction task types and future datasets.
Unifying Framework for Optimizations in non-boolean Formalisms

Search-optimization problems are plentiful in scientific and engineering domains. Artificial intelligence has long contributed to the development of search algorithms and declarative programming languages geared towards solving and modeling search-optimization problems. Automated reasoning and knowledge representation are the subfields of AI that are particularly vested in these developments. Many popular automated reasoning paradigms provide users with languages supporting optimization statements. Recall integer linear programming, MaxSAT, optimization satisfiability modulo theory, and (constraint) answer set programming. These paradigms vary significantly in their languages in ways they express quality conditions on computed solutions. Here we propose a unifying framework of so called extended weight systems that eliminates syntactic distinctions between paradigms. They allow us to see essential similarities and differences between optimization statements provided by distinct automated reasoning languages. We also study formal properties of the proposed systems that immediately translate into formal properties of paradigms that can be captured within our framework. Under consideration in Theory and Practice of Logic Programming (TPLP).
Design and Manufacture of a New Debris Retrieval Robot

This is a report on the No.2 debris retrieval robot that has been developed. While unit 1 was a machine built to demonstrate basic ideas, such as the arm mechanism, the robot that was designed and manufactured this time was developed with an emphasis on its structure and the functions that can be used at the decommissioning site. Only the ideas of a crawler that can run through a thin pipe and a telescopic arm mechanism to access debris were carried over from unit 1; all other parts for debris retrieval robot No.2 were redesigned. The crawler was made from metal with significantly increased rigidity to improve the running performance of the robot. In addition, to improve maneuverability, an omnidirectional camera unit was installed. As the view of this camera is not limited to the direction of travel of the robot, it makes it possible for the operator to have a better grasp of situations that the robot may encounter. The debris collection mechanism was changed to a more reliable mechanism that is not affected by the shape of the debris. The arm expansion and contraction mechanisms were also changed to a more reliable structure.
Compression of Cyber Learning Images Based on DLDCT

Dealing with the picture database with minimal garage complexity, minimum computational complexity and ultimate nice is an important work. To obtain these solutions, many image-processing techniques are advanced. Now days, E-learning assets are widely used across the internet based totally expertise sharing environments. Within the cyber learning surroundings, multiple sorts of records sources are controlled. Particularly, organizing the photos is challenge that is more vital where pictures appeared in the cyber learning network databases. This trouble expects solutions from powerful image compression techniques. Block truncation coding technique is offer beneficial and easy implementations of cyber learning to know primarily based picture compression platform. On this regard, this proposed system develops a Dual Layered Deep Classification and Truncation (DLDCT) approach. This proposed system has applied the DLDCT and as compared with existing works with admire to considerable overall performance parameters.
Research on Enterprise Strategy Based on Block Chain Security Sharing Mechanism

As an emerging electronic information technology, block chain is gradually entering the public’s field of vision. The two major advantages of this emerging technology are security and efficient sharing of information. It is known for its data immutability, traceability, distributed ledger, joint maintenance of each node, and cost reduction. At present, most enterprises still use the traditional data ledger recording mode and the way of sharing the data of the alliance enterprises. Therefore, some enterprises still face problems such as large data errors and artificial tampering with the contents of the ledger, which damages the interests of the company and slows down the company development. In terms of information sharing, enterprises adopt traditional methods to transmit and share data, but also face data distortion and slow data sharing efficiency, which is not conducive to the company’s strategic deployment. Therefore, the use of block chain technology is introduced. Under the security sharing mechanism of the block chain, we use data screening algorithms such as genetic algorithms and sorting calculation algorithms such as entropy algorithms to process the information on the block chain to get the desired part. In the experimental part of this paper, the traditional method and the method using block chain technology in this paper are used to compare the relevant factors affecting the enterprise strategy. Finally, the strategic tendency of the two enterprises is analyzed according to the intuitive graph obtained by calculation, and the method of this paper is highlighted.
Forecasting the Sum of New College Students with Linear Regression Approach

Forecasting models are the result of developments in the field of science and technology that provide convenience in predicting future events. This paper aims to develop a linear regression model to predict the number of new students in the next year. The data to be used in this study is the total of students majoring in informatics engineering and information management during the last 5 years. Based on result obtained the number of student for department of Informatics Engineering is 198 people with a MAPE (Mean Absolute Percentage Error) score of 16.5%, and for the new students department of Informatic Management is 8 people with a MAPE score of 16.1%.
The Relationship of Digital Literacy with The Help of Anyflip.com to The Learning Motivation of Elementary School Students

The research objective is to determine the relationship between Digital Literacy assisted by Anyflip.com on the Learning Motivation of Class IV students at SDN Buran 03 Karanganyar. The method of collecting research data is quantitative. The total population is 132 students. Meanwhile, the study sample was 29 fourth-grade students with information obtained from data collection tools in questionnaires and documentation. The research data analysis was consulted with the prerequisite analysis test, then continued on the scale validity test using the product-moment correlation. The research concludes that the relationship between digital literacy assisted by Anyflip.com on the learning motivation of fourth-grade students at SDN Buran 3 Karanganyar is significant. Decision-making is based on the product-moment test, namely r arithmetic &gt; r table 0.632 &gt; 0.3009, so that Ha is accepted and Ho is rejected. This fact means that the use of digital literacy assisted by anyflip.com is not only digital information obtained by students, but students can explore, create, and be more active in online learning so that educational interactions occur between students and teachers.
A SCADA Expert System of Fuzzy Knowledge-based System for Fault Tracing of Marine Applications

SCADA systems run global mission critical infrastructures and industrial systems.The purpose of the development of a SCADA expert system relies in the necessity to preserve the human knowledge acquired in several maintenance tasks.The security and vulnerabilities of the knowledge-based system is important because compromise or destruction of the system would impact multiple areas of society far removed from the original comprise.As mechanical conditions, maintenance processes of marine diesel engine should be monitored periodically, for diagnosis and prognosis purposes.A rich functionality of the SCADA expert system and its extensive developments facilities enhances reliability, efficiency, economics, and robustness.
CCTV Victim Tracker using Images and Videos

Face recognition system has become an integral a part of our modern-day to life.Varied applications of face recognition system.Digital image process could be an evolving field with growing applications in science and engineering.Image process holds the chance of developing the final word machine that might perform the visual perform all living beings It is employed in video surveillance, human pc interface and image management.Image or Video process has become a serious demand in current world.this method is majorly accustomed sight, acknowledge and track varied objects.Face sighting and following is that the section wherever we tend to detect someone's face from a image or in video sequence and track him/her throughout the video.It plays very important role in video corrections, police work, military following thus on.