Reviewerly/exHarmony huggingface dataset:
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout

Federated Learning (FL) has been gaining significant traction across different ML tasks, ranging from vision to keyboard predictions. In large-scale deployments, client heterogeneity is a fact, and constitutes a primary problem for fairness, training performance and accuracy. Although significant efforts have been made into tackling statistical data heterogeneity, the diversity in the processing capabilities and network bandwidth of clients, termed as system heterogeneity, has remained largely unexplored. Current solutions either disregard a large portion of available devices or set a uniform limit on the model's capacity, restricted by the least capable participants. In this work, we introduce Ordered Dropout, a mechanism that achieves an ordered, nested representation of knowledge in Neural Networks and enables the extraction of lower footprint submodels without the need of retraining. We further show that for linear maps our Ordered Dropout is equivalent to SVD. We employ this technique, along with a self-distillation methodology, in the realm of FL in a framework called FjORD. FjORD alleviates the problem of client system heterogeneity by tailoring the model width to the client's capabilities. Extensive evaluation on both CNNs and RNNs across diverse modalities shows that FjORD consistently leads to significant performance gains over state-of-the-art baselines, while maintaining its nested structure.
Certifying Robustness to Programmable Data Bias in Decision Trees

Datasets can be biased due to societal inequities, human biases, under-representation of minorities, etc. Our goal is to certify that models produced by a learning algorithm are pointwise-robust to potential dataset biases. This is a challenging problem: it entails learning models for a large, or even infinite, number of datasets, ensuring that they all produce the same prediction. We focus on decision-tree learning due to the interpretable nature of the models. Our approach allows programmatically specifying bias models across a variety of dimensions (e.g., missing data for minorities), composing types of bias, and targeting bias towards a specific group. To certify robustness, we use a novel symbolic technique to evaluate a decision-tree learner on a large, or infinite, number of datasets, certifying that each and every dataset produces the same prediction for a specific test point. We evaluate our approach on datasets that are commonly used in the fairness literature, and demonstrate our approach's viability on a range of bias models.
Memory-Hard Functions: When Theory Meets Practice

Author(s): Chen, Binyi | Advisor(s): Tessaro, Stefano; Lin, Huijia | Abstract: Memory-hard functions (MHFs) is a class of hash functions whose fast evaluation requires the heavy use of memory, and an evaluation that spends less memory has to incur a much larger time penalty. Memory-hardness is particularly useful in the setting of password hashing and cryptocurrencies, as memory cost is platform-independent and efficient special-purpose hardware for brute-forcing attacks becomes much harder to be built. Since its first proposal by Colin Percival in 2009, many memory-hard hash heuristics were proposed, and the notion/design of MHFs has received a considerable amount of theoretical scrutiny as well. However, a large gap still exists when theory meets practice. On the one hand, most of the practical schemes are only heuristics without formal analysis, and attacks do exist for some of them; on the other hand, theoretical analyses are usually based on unrealistic assumptions: they consider MHFs as modes of operation of some underlying hash function H, modeled as a random oracle. Unfortunately, in practice, this is never the case as H is usually a heuristic design built from simpler primitives.This dissertation makes progress in addressing both of the problems. Our main contributions are threefold. First, we prove that a widely-used MHF candidate, called scrypt, is provably and optimally memory-hard, thus shedding light on the confidence of its wide application. Second, we model simple cryptographic tools (e.g. AES) as the underlying ideal primitives and present a generic and provably-secure MHF construction from hard-to-pebble graphs. The resulting scheme significantly decreases the efficiency gap between legitimate users and ASICs-equipped attackers. Finally, given the practice demands for H to have large outputs (to increase memory hardness without changing the description size of MHFs), we go back to the framework of constructing MHFs from H (with large output). Different from previous work, we take finer-granularity of the hash function H into account and provide the first provably secure design of H from simpler primitives (e.g. fixed-key AES).
Optimization in Artificial Intelligence-Based Devices and Algorithms for Efficient Training: A Survey

In recent years, artificial intelligence (AI) is the nest of intrigue both in the industry and the academic community. The massive success of AI-based technologies in smartphones, autonomous driving cars, smart agriculture, smart robots, and more is because of its ability to make complex decisions like humans by learning from data. With the significant breakthrough in AI applications, data explosion, and learning models getting deeper and complex, the challenges are growing resource requirements like memory, processing capacity, and longer processing times, which affects the cost and user experience. Hence, the need for efficient optimization techniques arises to address these cost performance challenges. A salient element of the research developments in AI is optimization to enable the provision of more and more features to users at a lesser price. This paper offers a comprehensive study of optimization in AI-based devices. It approaches optimization from an AI device perspective. The objective is to design a system that optimizes a set of cost-performance metrics subject to resource constraints. This survey’s scope is an in-depth study and review of optimization in AI-based systems, the process and conditions, the challenges, optimization approaches, and algorithms for efficient training of deep learning models.
Model Selection for Bayesian Autoencoders

We develop a novel method for carrying out model selection for Bayesian autoencoders (BAEs) by means of prior hyper-parameter optimization. Inspired by the common practice of type-II maximum likelihood optimization and its equivalence to Kullback-Leibler divergence minimization, we propose to optimize the distributional sliced-Wasserstein distance (DSWD) between the output of the autoencoder and the empirical data distribution. The advantages of this formulation are that we can estimate the DSWD based on samples and handle high-dimensional problems. We carry out posterior estimation of the BAE parameters via stochastic gradient Hamiltonian Monte Carlo and turn our BAE into a generative model by fitting a flexible Dirichlet mixture model in the latent space. Consequently, we obtain a powerful alternative to variational autoencoders, which are the preferred choice in modern applications of autoencoders for representation learning with uncertainty. We evaluate our approach qualitatively and quantitatively using a vast experimental campaign on a number of unsupervised learning tasks and show that, in small-data regimes where priors matter, our approach provides state-of-the-art results, outperforming multiple competitive baselines.
Analysis Of The Effect Of Economic Performance And Population On Natural Disaster Death In Indonesia

Natural disaster will always have a detrimental impact on human life. Losses due to natural disasters can in form of loss of property or loss of life. Indonesia’s geographical position in the meeting of two rings of fire makes Indonesia very vulnerable to natural disaster. Departing from these thoughts, this study tries to analyze more deeply about some of the variables thought to be able to influence the number of deaths caused by natural disasters in Indonesia. Those variables are income per capita and numbers of population. This research is using shock absorber model approach which can accommodate shock variable in the shortrun. By using time series data from 1972 to 2018, This research came to the conclusion that income per capita and shock variable have positive and significant relationship to natural disaster death. Numbers of population have a positive but not significant relationship to natural disaster death. furthermore, natural disaster death in this research also determine by previous disaster.
An Empirical Study on Leveraging Position Embeddings for Target-oriented Opinion Words Extraction

Target-oriented opinion words extraction (TOWE) (Fan et al., 2019b) is a new subtask of target-oriented sentiment analysis that aims to extract opinion words for a given aspect in text. Current state-of-the-art methods leverage position embeddings to capture the relative position of a word to the target. However, the performance of these methods depends on the ability to incorporate this information into word representations. In this paper, we explore a variety of text encoders based on pretrained word embeddings or language models that leverage part-of-speech and position embeddings, aiming to examine the actual contribution of each component in TOWE. We also adapt a graph convolutional network (GCN) to enhance word representations by incorporating syntactic information. Our experimental results demonstrate that BiLSTM-based models can effectively encode position information into word representations while using a GCN only achieves marginal gains. Interestingly, our simple methods outperform several state-of-the-art complex neural structures.
Retrospection on Various Regression Testing Approaches

Today’s world is technology world. Softwares are used in numerous forms in everyday life. A software is developed to condense the work load and upsurge efficiency of human. SDLC is used for software development and to ensure the correctness of software, software testing is conducted. Testing is a vital for software development to check the extent to which it meets user’s requirements, observe the defects in the software code and take essential steps to rectify the errors to make software worthwhile. Time to time software’s need amendments according to changing user’s requirement. For validation of modifications in software, regression testing has to be applied. Regression testing is a sort of software testing which is used when some changes are embedded in software and ensures that changes do not upset the other part of software. Regression testing may run entire test suite again to ensure modifications have no negative effect, but this will increase the intricacy in the system and make it time consuming and costly. Various approaches that are used for resolving regression testing issues are (i) test case minimization (ii) test case prioritization and (iii) test case selection. This paper summarizes the research verdicts of numerous researchers in regression testing area.
IMPLEMENTASI METODE FUZZY LOGIC PADA SISTEM PAKAR PENDETEKSI KECERDASAN ANAK

The multiple intelligences proposed by Dr. Howard Gardner in his book entitled Frame of Mind states that every child has unique and different characteristics and does not only focus on one intelligence. Parents who don't know how to improve their children's intelligence from all categories of intelligence, and only focus on language and logic intelligence, are considered as intelligent benchmarks for children. This expert system application aims to facilitate parents in knowing the level of intelligence of children so that children get the opportunity to develop at least from various dimensions such as musical, kinesthetic, intra and interpersonal, etc. This study applies calculations using the Mamdani fuzzy logic method. The results of this study are able to identify children's intelligence by designing a fuzzy expert system where input variables and output variables are generated from interviews with experts who produce 45 input variables and 9 outputs. For example, the calculation in this study uses 5 input variables and 1 output variable with the number of rules generated, namely 243 rules. The results obtained from the manual calculation of the fuzzy logic method are 50.68 with a fairly intelligent level of medium level. The system development method used in this research is the extreme programming method with the Laravel framework. This website-based expert system application is also a place for information on multiple intelligences where this information will be useful to find out how to improve children's intelligence from all categories of intelligence.
Sistema de gestión de aprendizaje y su variante en tiempos de covid-19 en el Ecuador

The learning management system is a tool that allows to support virtual classes and therefore has a pedagogical component that differentiates its methodology from a simple content emulator, however everything begins in December 2019 where an unknown virus emerged which was taking the name SARS-CoV-2 and later denominating it as Covid-19 which gave rise to this whole situation of shocks worldwide where countries did not have the capacity to fight with this virus that after a time became a pandemic , but it was affecting all levels of education in Ecuador, it was possible to use technological resources but the great deficiency that not in all parts of the country there were no connections due to the few transmitting antennas that are used as signal receivers and the little infrastructure. A verification of the stages that the learning management system has evolved will be carried out and analyze the results that we have with google meet that is used by the Technical University of Babahoyo to give asynchronous and synchronous support to its students that allows improving the quality of education in our country and hoping that the changes that the covit-19 has no more affect higher education and be able to contribute with new teaching methodologies to the national educational System.
Uma implementação do esquema de multi-assinaturas MuSig no cenário m-de-n com árvores de Merkle e suas aplicações ao bitcoin

This work presents a proof-of-concept implementation of the multi-signature scheme MuSigin the m-of-n scenario with aggregated key validation using Merkle trees, expanding theoriginal MuSig description in this scenario. Python was the programming language ofchoice and elliptic curves (mainly secp256k1) were used as the basis of the implementa-tion. The multi-signatures generated by the scheme can keep the same size as a singleSchnorr signature and can be verified with a single aggregated public key computed fromthe individual public keys of the signers, this scenario brings positive expectations for per-formance and privacy improvements in Bitcoin. Future works includes a proof-of-conceptimplementation that integrates the MuSig scheme directly into the Bitcoin protocol andthe implementation of an interactive signature scheme (IAS) with the MuSig scheme asits basis.
Software Defect Prediction Using Synthetic Minority Over-sampling Technique and Extreme Learning Machine

Software testing is one of the crucial processes in software development life cycle which will influence the software quality. One of the strategies to help testing process is predicting the part or module of software which is prone to defect. Then, the testing process can be more focused to those parts. In this research a classifier model for predicting software defect was built. One of the most important problems in software defect prediction is imbalance data distribution between samples of positive class (prone to defect) and of negative class. Therefore, in this research SMOTE is implemented to handle imbalance data problem and extreme learning machine is implemented as a classification algorithm. As a comparison to SMOTE-ELM, a modification of ELM which directly copes with imbalance problem, weighted-ELM, is also observed. This research used NASA MDP dataset PC1, PC2, PC3 and PC4. The results of experiment using 10-fold cross validation show that directly classification using ELM obtain the worse result compared to SMOTE-ELM and weighted-ELM. When the value of imbalance ratio is not very small, the SMOTE-ELM is better than weighted-ELM. When the value of imbalance ratio is very small, the g-mean of weighted-ELM is higher than the g-mean of SMOTE-ELM, but the accuracy of weighted-ELM is lower than the accuracy of SMOTE-ELM. Therefore, in this software defect prediction case it can be concluded that SMOTE is effective to increase the generalization performance of classifier in minority class as long as the value of imbalance ratio is not very small.
Expert Methods for Developing University Graduates’ Professional Competencies

The article addresses the expert methods for developing the graduates’ professional competencies in specialty programs in the conditions of predetermined specializations within particular specialties in the absence of professional standards. The authors analyze the features of obtaining and processing data from experts, which are specialists in this field of professional activity. Special attention is paid to expert commission forming using the expert competence criteria and the cost of the survey. Based on the developed expert method, a comparative analysis on the importance of professional competencies of specialists in the chosen field of activity is carried out. The authors propose a method of matching the experts’ assessments obtained as a result of the survey. The article finds that it is necessary to include formalized procedures for the development of the graduates’ professional competencies in the indicative basic educational program.
العلاقة بين أبعاد البراعة التدريبية وتطوير ديناميكية الأداء الوظيفي لفرق العمل في ضوء نظرية التعليم التجريبي : دراسة ميدانية على العاملين في البنوك التجارية الأردنية = The Relationship between the Dimensions of Ambidexterity Training and the Development of Work Teams Dynamics within the Experiential Learning Theory : Expermintal Study on Jordanian Commercial Bank Employees

البراعة التدريبية | ديناميكية الأداء الوظيفي | فرق العمل | نظرية التعليم التجريبي | دراسة ميدانية | Ambidexterity Training | Work Teams Dynamics | Experiential Learning Theory | Jordanian Commercial Bank Employees | Empirical Study
One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective

A deep hashing model typically has two main learning objectives: to make the learned binary hash codes discriminative and to minimize a quantization error. With further constraints such as bit balance and code orthogonality, it is not uncommon for existing models to employ a large number (>4) of losses. This leads to difficulties in model training and subsequently impedes their effectiveness. In this work, we propose a novel deep hashing model with only a single learning objective. Specifically, we show that maximizing the cosine similarity between the continuous codes and their corresponding binary orthogonal codes can ensure both hash code discriminativeness and quantization error minimization. Further, with this learning objective, code balancing can be achieved by simply using a Batch Normalization (BN) layer and multi-label classification is also straightforward with label smoothing. The result is an one-loss deep hashing model that removes all the hassles of tuning the weights of various losses. Importantly, extensive experiments show that our model is highly effective, outperforming the state-of-the-art multi-loss hashing models on three large-scale instance retrieval benchmarks, often by significant margins. Code is available at this https URL
LibGuides: School of Engineering and Built Environment's Libguide: Home

This LibGuide has been created to guide users in the Engineering and IT field through library services and resources that are available to support teaching, learning and research.
Website Development of Accreditation Information System in Higher Education

The problem regarding archives that are often experienced when preparing for study program accreditation is that many documents cannot be recovered, so they have to create new documents to replace lost documents. These problems are always repeated so that the accreditation committee has to lose a lot of time to make documents that should already exist. Previous research has succeeded in producing a description of the needs that support the archival system to be built and creating an archival system design that can guarantee the availability of information for study programs and departments related to data that supports accreditation. Next stage of research is the stage of developing an archive management system. This research was conducted at the Faculty of Economics and Business, Jenderal Sudirman University. Research and development adapt the Borg & Gall model and prototype model in software development. The software development method begins with requirements analysis, development design, flowchart design, implementation, and testing. As a result, an accreditation information system website was developed based on the Study Program Accreditation Instrument, which consists of nine university accreditation standards. Keywords: Archive, Digital, Accreditation
Monitoring of Internet Communications in Criminal Proceedings

The present paper reviews the Georgian Legislation and international standards related to secret investigative actions of obtaining internet communications. Due to rapid development of modern technologies, protection of privacy in the field of electronic surveillance has become the significant challenge. Since the Constitutional Court of Georgia, under the judgement of April 14, 2016 recognized as unconstitutional certain provisions regulating secret investigative actions of obtaining communications in real time, this issue has acquired a special importance in Georgia. Taking into consideration the above mentioned, the aim of the present paper is to discuss the legal standards established by the Constitutional Court of Georgia and amendments into the legislation, to analyze certain problematic issues with regard to monitoring of internet communications and to demonstrate the best international practice developed in this field.
Research on the Application of Game Method in Children’s English Teaching Activities

with the rapid development of economy, English education for children has attracted attention in today’s society.  To improve the quality of children’s English teaching, and allow children to truly master the knowledge they have learned through English teaching activities, which has always been a problem for children’s English teachers.  The application of game teaching methods has played a vital role in the teaching of children’s English.  This paper analyzes and studies the value of games in children’s English teaching activities and the main categories of games in children’s English teaching.