Reviewerly/exHarmony huggingface dataset:
Increasing Adversarial Uncertainty to Scale Private Similarity Testing

Social media and other platforms rely on automated detection of abusive content to help combat disinformation, harassment, and abuse. One common approach is to check user content for similarity against a server-side database of problematic items. However, this method fundamentally endangers user privacy. Instead, we target client-side detection, notifying only the users when such matches occur to warn them against abusive content. Our solution is based on privacy-preserving similarity testing. Existing approaches rely on expensive cryptographic protocols that do not scale well to large databases and may sacrifice the correctness of the matching. To contend with this challenge, we propose and formalize the concept of similarity-based bucketization~(SBB). With SBB, a client reveals a small amount of information to a database-holding server so that it can generate a bucket of potentially similar items. The bucket is small enough for efficient application of privacy-preserving protocols for similarity. To analyze the privacy risk of the revealed information, we introduce a framework for measuring an adversary's confidence in inferring a predicate about the client input correctly. We develop a practical SBB protocol for image content, and evaluate its client privacy guarantee with real-world social media data. We then combine SBB with various similarity protocols, showing that the combination with SBB provides a speedup of at least 29x on large-scale databases compared to that without, while retaining correctness of over 95%.
A REVIEW ON GAS CONSUMPTION AND GAS PREDICTION IN ETHEREUM SMART CONTRACTS

This paper is a review of some real-time issues associated with the development of Ethereum smart contracts like out of gas exception and gas inefficient code patterns and focuses on the methods and solutions presented in recent years. With the help of this paper, we are trying to summarize the methods which can be used in future researches for gas prediction with the help of old transaction data and machine learning algorithms and have a look at old researches which are trying to predict with the help of regression algorithms and their efficiency. KEYWORDS: Blockchain, Etherium, Gas Consumption, Gas Prediction, Transaction
Privacy Risk Assessment for Text Data Based on Semantic Correlation Learning

Privacy risk assessment determines the extent to which generalization and obfuscation should be applied to the sensitive data. In this paper, we propose PriTxt for evaluating the privacy risk associated with text data by exploiting the semantic correlation. Using definitions derived from the General Data Protection Regulation (GDPR), PriTxt first defines the private features that related to individual privacy. By using the word2vec algorithm, a word-embedding model is further constructed to identify the quasi-sensitive words. The privacy risk of a given text is finally evaluated by aggregating the weighted risks of the sensitive and the quasi-sensitive words in the text.
Software Vulnerability Detection via Deep Learning over Disaggregated Code Graph Representation

Identifying vulnerable code is a precautionary measure to counter software security breaches. Tedious expert effort has been spent to build static analyzers, yet insecure patterns are barely fully enumerated. This work explores a deep learning approach to automatically learn the insecure patterns from code corpora. Because code naturally admits graph structures with parsing, we develop a novel graph neural network (GNN) to exploit both the semantic context and structural regularity of a program, in order to improve prediction performance. Compared with a generic GNN, our enhancements include a synthesis of multiple representations learned from the several parsed graphs of a program, and a new training loss metric that leverages the fine granularity of labeling. Our model outperforms multiple text, image and graph-based approaches, across two real-world datasets.
Analisis Kepuasan Pengguna Sistem Informasi Administrasi Rumah Sakit (SIARS) dengan Model Delone and Mclean

The Mother and Child Srikandi IBI Jember Hospital is one of the private hospitals in Jember district. Currently the Mother and Child Hospital uses the Hospital Administration Information System (HAIS) in providing services. There are several problems related to HAIS, including data that is not stored properly, the information generated by the hospital information system is still incomplete and users still have difficulty inputting data. The purpose of this study is to analyze system quality, information quality and service quality on user satisfaction. This type of research is quantitative, where there are 3 independent variables and 1 dependent variable. The results obtained are there is a moderate and unidirectional significant relationship of 0.519 between system quality and user satisfaction. There is a moderate and unidirectional significant relationship of 0.425 between information quality and user satisfaction. There is a strong and unidirectional significant relationship of 0.780 between service quality and user satisfaction. From these results it can be concluded that system quality, information quality and service quality have an influence on HAIS user satisfaction
Design and Implementation of Secure Cryptographic Algorithm Using Vedic Mathematics

In the world of digitization, security is the main aspect and security of data plays an important role in communication and the field of electronics. One of the highly flexible security standard algorithm, AES provides a secure communication over the network. This paper explains the importance of the AES with the combination of Vedic-mathematics. There are mainly four steps in AES, which include add-round key, subbytes, shift-rows, mix-columns. This paper mainly concentrates on mix-columns. The analysis and simulation is done using MATLAB, Modelsim tools. This method of implementing AES using Vedicmathematics improves the performance in terms of speed, power and area.
How to Inject Backdoors with Better Consistency: Logit Anchoring on Clean Data

Since training a large-scale backdoored model from scratch requires a large training dataset, several recent attacks have considered to inject backdoors into a trained clean model without altering model behaviors on the clean data. Previous work finds that backdoors can be injected into a trained clean model with Adversarial Weight Perturbation (AWP). Here AWPs refers to the variations of parameters that are small in backdoor learning. In this work, we observe an interesting phenomenon that the variations of parameters are always AWPs when tuning the trained clean model to inject backdoors. We further provide theoretical analysis to explain this phenomenon. We formulate the behavior of maintaining accuracy on clean data as the consistency of backdoored models, which includes both global consistency and instance-wise consistency. We extensively analyze the effects of AWPs on the consistency of backdoored models. In order to achieve better consistency, we propose a novel anchoring loss to anchor or freeze the model behaviors on the clean data, with a theoretical guarantee. Both the analytical and the empirical results validate the effectiveness of the anchoring loss in improving the consistency, especially the instance-wise consistency.
Radio Frequency Transient Segment Detection Based on Akaike Information Criterion

The precise interpreting of RF data starts from retrieving or knowing the exact time instant at which moment the sender is turned on, this challenge implies two important issues; prevent manipulating redundant information such as unavoidable background noise which speed up the processing and the other issue is to study the exact behavior of that sender. A method has been developed to automatically catch the onset in transient of Bluetooth signal using of the Akaike Information Criterion (AIC). Present method has been examined on real world data taken from the most common cellular phones brands by different ways with variation of signal to noise ratio. The AIC algorithm shows robustness in the existence of relatively a high-amplitude random noise.
PENGGUNAAN METODE PEMBELAJARAN MULTIMEDIA BERBASIS OBJECT-ORIENTED PROGRAMMING (ADOBE-FLASH) PADA MATA PELAJARAN BAHASA INGGRIS

The aim of the research are (1) to know an influence on the development of Adobe-Flash multimedia programs on the ability of students to improve vocabulary skills in English. (2) to find the percentage of the influence of the development of multimedia programs on the ability of students to improve vocabulary skills in English. This research is an experimental research. The program implementation technique for this research is a preliminary test used to measure students' vocabulary abilities of the material being taught while the final test is carried out to determine student learning outcomes after being treated using Multimedia-based Object-Oriented Programming (AdobeFlash) learning methods in English. Keywords: Multimedia-Based Learning Method, Vocabulary Skills, English.
Plan-then-Generate: Controlled Data-to-Text Generation via Planning

Recent developments in neural networks have led to the advance in data-to-text generation. However, the lack of ability of neural models to control the structure of generated output can be limiting in certain real-world applications. In this study, we propose a novel Plan-then-Generate (PlanGen) framework to improve the controllability of neural data-to-text models. Extensive experiments and analyses are conducted on two benchmark datasets, ToTTo and WebNLG. The results show that our model is able to control both the intra-sentence and inter-sentence structure of the generated output. Furthermore, empirical comparisons against previous state-of-the-art methods show that our model improves the generation quality as well as the output diversity as judged by human and automatic evaluations.
Language Model Metrics and Procrustes Analysis for Improved Vector Transformation of NLP Embeddings.

Artificial Neural networks are mathematical models at their core. This truismpresents some fundamental difficulty when networks are tasked with Natural Language Processing. A key problem lies in measuring the similarity or distance among vectors in NLP embedding space, since the mathematical concept of distance does not always agree with the linguistic concept. We suggest that the best way to measure linguistic distance among vectors is by employing the Language Model (LM) that created them. We introduce Language Model Distance (LMD) for measuring accuracy of vector transformations based on the Distributional Hypothesis ( LMD Accuracy ). We show the efficacy of this metric by applying it to a simple neural network learning the Procrustes algorithm for bilingual word mapping.
Binary Code based Hash Embedding for Web-scale Applications

Nowadays, deep learning models are widely adopted in web-scale applications such as recommender systems, and online advertising. In these applications, embedding learning of categorical features is crucial to the success of deep learning models. In these models, a standard method is that each categorical feature value is assigned a unique embedding vector which can be learned and optimized. Although this method can well capture the characteristics of the categorical features and promise good performance, it can incur a huge memory cost to store the embedding table, especially for those web-scale applications. Such a huge memory cost significantly holds back the effectiveness and usability of EDRMs. In this paper, we propose a binary code based hash embedding method which allows the size of the embedding table to be reduced in arbitrary scale without compromising too much performance. Experimental evaluation results show that one can still achieve 99\% performance even if the embedding table size is reduced 1000$\times$ smaller than the original one with our proposed method.
The Features of Deep Learning Algorithms

Deep learning is a type of machine learning that trains a computer to perform human-like tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing. This paper aims to illustrate some of the different deep learning algorithms and methods which can be applied to artificial intelligence analysis, as well as the opportunities provided by the application in various decision making domains.
An Effective Web Page Personalization model using Weighted Clustering and Improved Whale Optimization Algorithm

Web page personalization is the procedure of customizing a web page based on the requirements of every particular user or a group of users by utilizing the knowledge attained from the investigation of user’s navigational actions. Personalized recommendation predicts the browsing nature of the user by the use of data mining techniques received significant interest in the domain of web personalization research area. This paper presents a clustering with similarity measure based web page personalization model. Here, the web page personalization takes place by formulating queries and profiling by the WordNet ontology. To begin with, the needed data is gathered from various web sources and are grouped by the use of Weighted Clustering (WC) technique. The WC groups the web pages with respect to the fields which are learned by the user learning process. Using the set of four similarity metrics, data resemblance will be computed among the created word net and trained dataset. Among them, the highest resemblance based data is offered by the Improved Whale Optimization algorithm (WOA), where the WOA algorithm is extended by the concept of tumbling effect. A series of simulations were carried out to highlight the betterment of the presented model under various aspects.
Audio and Video Bimodal Emotion Recognition in Social Networks Based on Improved AlexNet Network and Attention Mechanism

In the task of continuous dimension emotion recognition, the parts that highlight the emotional expression are not the same in each mode, and the influences of different modes on the emotional state is also different. Therefore, this paper studies the fusion of the two most important modes in emotional recognition (voice and visual expression), and proposes a two-mode dual-modal emotion recognition method combined with the attention mechanism of the improved AlexNet network. After a simple preprocessing of the audio signal and the video signal, respectively, the first step is to use the prior knowledge to realize the extraction of audio characteristics. Then, facial expression features are extracted by the improved AlexNet network. Finally, the multimodal attention mechanism is used to fuse facial expression features and audio features, and the improved loss function is used to optimize the modal missing problem, so as to improve the robustness of the model and the performance of emotion recognition. The experimental results show that the concordance coefficient of the proposed model in the two dimensions of arousal and valence (concordance correlation coefficient) were 0.729 and 0.718, respectively, which are superior to several comparative algorithms.
Entropic Associative Memory for Manuscript Symbols

Abstract Manuscript symbols can be stored, recognized and retrieved from an entropic digital memory that is associative and distributed but yet declarative; memory retrieval is a constructive operation; symbols not contained in the memory are rejected directly without search; and memory operations can be performed through parallel computations. Manuscript symbols, both letters and numerals, are stored in Associative Memory Registers that have an associated entropy. The memory recognition operation obeys an entropy trade-off between precision and recall, and the entropy level impacts on the quality of the objects recovered through the memory retrieval operation. We discuss the operational characteristics of the system for retrieving objects with both complete and incomplete information, such as severe occlusions. The experiments reported in this paper add evidence that supports the scalability of the framework and its potential for developing practical applications. We also compare the present entropic associative memories to Hopfield’s paradigm and discuss its potential for the study of natural memory.
Рівень оплати праці освітян: конструктивний чи деструктивний фактор трансформації системи вищої освіти в Україні?

International scientific and practical conference «Pedagogy, psychology and teaching methods: international experience» : conference proceedings, July 16–17, 2021. Riga, Latvia : «Baltija Publishing», 2021. 295 pages.
ARTTIRILMIŞ GERÇEKLİĞE DAYALI BİR OYUN KULLANARAK KARIŞTIRILMIŞ ÖĞRENME MODELİ DENEYİMİ

Augmented Reality (AR) is a cutting-edge technology used for various learning media including for English learning. The advantage of AR in teaching is its ability to add multi-media objects to traditional teaching materials that motivate students to learn. The purpose of the study was to experiment with the use of AR-based game as additional material to the face-to-face teaching and learning in the blended learning model to improve English reading skills in secondary schools. This study employed a quasi-experimental method based on a pre-test and post-test design deploying statistical analysis. The whole research project involved a questionnaire and Focus Group Discussion (FGD) data collection to be presented separately. The results of the experiment showed that the results of the correlation test respectively show the Pair 1 number = .406 at the significance level of 0.17; Pair 2 = .288 at the significance level of 0.99 'Pair 3 = .030 at the significance value of 8.86, and the results of the Paired Sample Test' showed the value of "Paired Differences" of -5.569. This value indicated the difference between the pre-test and post-test results with a difference between -6,674 to -4,464 at the 95% confidence interval. The results of this experiment rejected hypothesis 0 which suggests that as the ArReadSpeed game is combined with teaching in the context of classroom learning result in good learning outcomes, making it a good blended learning model. The results of this experiment confirmed that the use of AR-based games for blended-learning contexts is an appropriate learning model.
Predictive Optimization with Zero-Shot Domain Adaptation

Prediction in a new domain without any training sample, called zero-shot domain adaptation (ZSDA), is an important task in domain adaptation. While prediction in a new domain has gained much attention in recent years, in this paper, we investigate another potential of ZSDA. Specifically, instead of predicting responses in a new domain, we find a description of a new domain given a prediction. The task is regarded as predictive optimization, but existing predictive optimization methods have not been extended to handling multiple domains. We propose a simple framework for predictive optimization with ZSDA and analyze the condition in which the optimization problem becomes convex optimization. We also discuss how to handle the interaction of characteristics of a domain in predictive optimization. Through numerical experiments, we demonstrate the potential usefulness of our proposed framework.
Does Playing Against An Error Prone Opponent Influence Learning in Nim?

When learning to play a game well, does it help to play against an opponent who makes the same sort of mistakes one tends to make or is it better to play against a procedurally rational algorithm, which never makes mistakes? This paper investigates subject performance in the game of Nim. We find evidence that subject performance improves more when playing against a human opponent than against a procedurally rational algorithm. We also find that subjects learn to recognize certain heuristics that improve their overall performance in more complex games.
Latent Tree Decomposition Parsers for AMR-to-Text Generation

Graph encoders in AMR-to-text generation models often rely on neighborhood convolutions or global vertex attention. While these approaches apply to general graphs, AMRs may be amenable to encoders that target their tree-like structure. By clustering edges into a hierarchy, a tree decomposition summarizes graph structure. Our model encodes a derivation forest of tree decompositions and extracts an expected tree. From tree node embeddings, it builds graph edge features used in vertex attention of the graph encoder. Encoding TD forests instead of shortest-pairwise paths in a self-attentive baseline raises BLEU by 0.7 and chrF++ by 0.3. The forest encoder also surpasses a convolutional baseline for molecular property prediction by 1.92% ROC-AUC.
Analyzing course affecting the results of inferential statistics students in UiTM Seremban Campus using association rule mining / Dayang Mas Rafisha Awang Raffi, Nur Asyikin Mohd Mustafa and Nursyaza Nisa Sazali

The purpose of the study was to analyze courses taken in semester l until semester 3 that affecting the results of Inferential Statistics. Inferential Statistics was recognized as high failure rate statistics course taken by the students of Degree in Statistics. Inferential Statistics known as a most difficult course compared to the other courses since majority the students failed for the course. Lectures of University Technology Mara (UiTM) Seremban Campus faced problems with high failure rate for this course which is more than 10% students who failed for the course in every semester. Association Rule Mining was a method adopted in this study in order to achieve the objective. The second purposes of this study was to determine the interesting rules form by the independent and dependent variables .Besides , this study interested on using data mining method to extract a meaningful information and to develop relationship among the variables stored in large data sets. An Association Rule Mining was used to graduated students from matriculation and foundation and out of 427 data generated, but only 126 dataset were employed for the purpose of this study. Results showed that 239 rules have been obtained during the process of association rule mining . Afterremoving the redundancy , only 106 rules that are most appropriate to be associated with the objectives of this research. Moreover, this study were prepared a visualization that include grouped matrix , graph-based technique and parallel coordinates plots to make it more easier in understanding the analysis. Thus , this study focused to providean effective mechanism for lecturers in order to improve students' performance in highfailure rate statistics courses.It is important for the university administration to improvein terms of the teaching and learning method in order to produce a good result for theiracademic performance.