Reviewerly/exHarmony huggingface dataset:
Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning

Federated learning learns a neural network model by aggregating the knowledge from a group of distributed clients under the privacy-preserving constraint. In this work, we show that this paradigm might inherit the adversarial vulnerability of the centralized neural network, i.e., it has deteriorated performance on adversarial examples when the model is deployed. This is even more alarming when federated learning paradigm is designed to approximate the updating behavior of a centralized neural network. To solve this problem, we propose an adversarially robust federated learning framework, named Fed_BVA, with improved server and client update mechanisms. This is motivated by our observation that the generalization error in federated learning can be naturally decomposed into the bias and variance triggered by multiple clients' predictions. Thus, we propose to generate the adversarial examples via maximizing the bias and variance during server update, and learn the adversarially robust model updates with those examples during client update. As a result, an adversarially robust neural network can be aggregated from these improved local clients' model updates. The experiments are conducted on multiple benchmark data sets using several prevalent neural network models, and the empirical results show that our framework is robust against white-box and black-box adversarial corruptions under both IID and non-IID settings.
Assessment of National Cybersecurity Capacity for Countries in a Transitional Phase: The Spring Land Case Study

Cybersecurity capacity building has emerged as a notable matter for numerous jurisdictions. Cyber-related threats are posing an ever-greater risk to national security for all countries, irrespective of whether they are developed or in the midst of transitioning. This paper presents the results of two qualitative studies using the Cybersecurity Capacity Maturity Model (CCMM) for nations: (1) Interactive Management (IM) and (2) focus groups to analyse the current state of Spring Land’s cybersecurity capacity. A total of 26 participants from government agencies and five national experts from the Spring Land National Cybersecurity Authority (NCSA) contributed to this study. The results show that Spring Land has many issues such as lack of cybersecurity culture and collaborative road-map across government sectors which results in instability within the country. The assessments feed into the requirement analysis of the National Cybersecurity Capacity Building Framework that can be utilised to organise and test the cybersecurity for nations.
A Survey on various Machine Learning Approaches for thalassemia detection and classification

Thalassemia is a genetic blood disease caused by a deficiency in the production of hemoglobin, the central protein found in red blood cells and responsible for carrying oxygen from the lungs to the parts of the body. Hemoglobin consists of two types of protein chains, two alpha chains and two beta chains. In diagnosing thalassemia, doctors rely on two types of tests: a complete blood count (CBC) and a special hemoglobin test (Hemoglobin Electrophoresis). A complete blood count, or CBC, measures the amount of hemoglobin and various types of blood cells, such as red blood cells, in a blood sample. In this study present a survey of different method based on artificial intelligence to classify and detection thalassemia using the variable (parameter) of the CBC test which include RBC, HGB, MCV, HTC, HB . To distinguish between thalassemia minor alpha and thalassemia major beta patients. Decision tree, Naive Bayes, support vector machine (SVM), and neural network classification method are used.
Research on the Implementation of E-learning Tools by Bulgarian and Foreign Universities

Over the past two years, the pandemic caused by the COVID-19 virus has put to the test all spheres of life not only in Bulgaria but also worldwide. It can be claimed that the educational sector has acted adequately and has been able to deal with the challenge. The article presents research on the implementation of various e-learning tools by Bulgarian and foreign universities. The research was conducted at the beginning of the pandemic caused by COVID-19 by surveying students and lecturers. It turns out that despite the plethora of e-learning tools, at many universities, even in technologically advanced countries such as China, in most cases, education is delivered through distance learning and course management systems, for instance Moodle. The research objective is to ascertain the degree of implementation of various e-learning tools at Bulgarian and foreign universities. The research subject is the degree of implementation of e-learning tools, whereas the research object is students and lecturers from Bulgarian and foreign universities. The hypothesis, which is proven, is that the implementation of diverse e-learning tools is insufficient, despite the fact that their importance is acknowledged at all levels of learning and teaching.
Patient Monitoring System for Smart Multi Speciality Healthcare Centre using Internet of Things IoT

A healthcare centre is an organization in which effective treatment is given to patient for their health problems using specialized staff and equipments. Here patient’s information is stored in computer databases and retrieved when the further treatment is needed. At present all the information are stored into database and retrieved with human intervention. The human part of this system is error prone and often leads to inconsistencies in treatment. It may create severe problems in patient’s health related problems. With the advent of technology in the modern era these problems can be eradicated and improvements can be made in patient care by eliminating the unnecessary burden. Even though modern technology is often used in hospitals for many purposes, it is not concentrated in the reduction of patients waiting time. The waiting time cannot be taken lightly as it leads to increase in their blood pressure and mismanage their valuable time, also increasing healthcare centre’s expenses in an indirect manner by providing water facility, air conditioning facility etc. The manual token issuing system maintained in many healthcare centres is obsolete due to the improper handling of the queue by hospital staff. If it is automated through IoT, these problems will be sorted out completely. If a system is designed to reduce the patient’s unnecessary waiting time, it will be of immense importance for both the healthcare centre management and the public. So, this paper provides a solution to eradicate this problem
Context-Tree-Based Lossy Compression and Its Application to CSI Representation

We propose novel compression algorithms for time-varying channel state information (CSI) in wireless communications. The proposed scheme combines (lossy) vector quantisation and (lossless) compression. First, the new vector quantisation technique is based on a class of parametrised companders applied on each component of the normalised CSI vector. Our algorithm chooses a suitable compander in an intuitively simple way whenever empirical data are available. Then, the sequences of quantisation indices are compressed using a context-tree-based approach. Essentially, we update the estimate of the conditional distribution of the source at each instant and encode the current symbol with the estimated distribution. The algorithms have low complexity, are linear-time in both the spatial dimension and time duration, and can be implemented in an online fashion. We run simulations to demonstrate the effectiveness of the proposed algorithms in such scenarios.
How to Leverage Multimodal EHR Data for Better Medical Predictions?

Healthcare is becoming a more and more important research topic recently. With the growing data in the healthcare domain, it offers a great opportunity for deep learning to improve the quality of medical service. However, the complexity of electronic health records (EHR) data is a challenge for the application of deep learning. Specifically, the data produced in the hospital admissions are monitored by the EHR system, which includes structured data like daily body temperature, and unstructured data like free text and laboratory measurements. Although there are some preprocessing frameworks proposed for specific EHR data, the clinical notes that contain significant clinical value are beyond the realm of their consideration. Besides, whether these different data from various views are all beneficial to the medical tasks and how to best utilize these data remain unclear. Therefore, in this paper, we first extract the accompanying clinical notes from EHR and propose a method to integrate these data, we also comprehensively study the different models and the data leverage methods for better medical task prediction. The results on two medical prediction tasks show that our fused model with different data outperforms the state-of-the-art method that without clinical notes, which illustrates the importance of our fusion method and the value of clinical note features. Our code is available at https: //github.com/emnlp-mimic/mimic.
Micro Batch Streaming: Allowing the Training of DNN Models to Use a large Batch Size in Memory Constrained Environments

Recent deep learning models are difficult to train using a large batch size, because commodity machines may not have enough memory to accommodate both the model and a large data batch size. The batch size is one of the hyper-parameters used in the training model, and it is dependent on and is limited by the target machine memory capacity because the batch size can only fit into the remaining memory after the model is uploaded. Moreover, the data item size is also an important factor because if each data item size is larger then the batch size that can fit into the remaining memory becomes smaller. This paper proposes a framework called Micro-Batch Streaming (MBS) to address this problem. This method helps deep learning models to train by providing a batch streaming method that splits a batch into a size that can fit in the remaining memory and streams them sequentially. A loss normalization algorithm based on the gradient accumulation is used to maintain the performance. The purpose of our method is to allow deep learning models to train using larger batch sizes that exceed the memory capacity of a system without increasing the memory size or using multiple devices (GPUs).
Pengaruh Dongeng terhadap Keterampilan Menulis Narasi Siswa Kelas 5 Sekolah Dasar

This study aimed to test the presence and absence of the influence of the method of storytelling to narrative writing skills and a confident attitude of students in grade 5 of primary school. The approach used in this study is a quantitative approach, a quasi-experimental methods, to design pretest-posttest control group design. The research was carried through the preliminary study phase, test research instruments, implementation of learning by using fairy tales as well as disclosure of the final study to determine whether there is any effect on the ability of storytelling method to write narrative and confident attitude. Research results show; 1. The ability to write narrative learners in general are in the moderate category, 2. Circumstances confident attitude of students in general are in the medium category, 3. Learning to write narrative through fairy tales are hypothetical and empirical predicted to affect the ability to write narrative learners, 3. Methods Storytelling can affect the attitude of confident learners in learning. Learning to write by using a method of downloading a fairytale implemented in schools in an effort to improve the ability to write narrative and confident attitude of learners.
The Impact of Foreign Direct Investment (FDI) on Economic Growth (EG) : Evidence from Djibouti

Abstract: The achievement of EG and development is considered the core objective for both Developing Countires (DCs) and Least Developed Countries (LDCs), so countries try to get adequate funding to achieve this goal through optimal macroeconomic policies and different strategies. Countries prefer other mechanisms with less burden and cost to achieve economic growth, such as FDI flows. International development-oriented institutions such as WB and IMF recommend and consider FDI flows are the most important factors of the modern technology transfer, management, and know-how, which is necessarily needed in the local investment projects in poor countries, so FDI represents optimal external sources of growth. The objective of this study is to explain the impact of FDI on the EG of Djibouti. To achieve this objective the study used a secondary annual time series data for the period 1985-2019 by the method of Ordinary Least Square (OLS). The study results showed that FDI in the case of Djibouti tends to be statistically insignificant effects and a limited impact on Djibouti‘s EG, Moreover,other factors such as the Human Development Index(HDI), and Gross Fixed Capital Formation(GFCF), Trade Openness(TOP) shows significant effects on the Gross Domestic Product (GDP). Finally, the Consumer Price Index (CPI) has no significance in the EG of Djibouti. The findings provide critical information to Djibouti policy decision-makers to make an informed decision with regard to attracting investment sectors and policies in encouraging foreign investors to invest in the country. KEYWORDS: Foreign Direct Investment, Economic Growth, Djibouti, Empirical Analysis.
Modeling the Block Verification Time of Zcash

An important aspect of the propagation delay in blockchain networks is the block verification time, which is also responsible for the so-called verifier’s dilemma. Models for the block verification time can help to understand and improve the verification process. Moreover, modeling the verification time is necessary for blockchain network simulations. In this paper, we present JOIST, a new model for the block verification time of Zcash. We identify computationally complex operations in the verification process of Zcash, and derive our model based on characteristic transaction features. We evaluate JOIST and show that the model is consistently more accurate than existing models, which consider the block size only.
MANDERA: Malicious Node Detection in Federated Learning via Ranking

Byzantine attacks hinder the deployment of federated learning algorithms. Although we know that the benign gradients and Byzantine attacked gradients are distributed differently, to detect the malicious gradients is challenging due to (1) the gradient is high-dimensional and each dimension has its unique distribution and (2) the benign gradients and the attacked gradients are always mixed (two-sample test methods cannot apply directly). To address the above, for the first time, we propose MANDERA which is theoretically guaranteed to efficiently detect all malicious gradients under Byzantine attacks with no prior knowledge or history about the number of attacked nodes. More specifically, we transfer the original updating gradient space into a ranking matrix. By such an operation, the scales of different dimensions of the gradients in the ranking space become identical. The high-dimensional benign gradients and the malicious gradients can be easily separated. The effectiveness of MANDERA is further confirmed by experimentation on four Byzantine attack implementations (Gaussian, Zero Gradient, Sign Flipping, Shifted Mean), comparing with state-of-the-art defenses. The experiments cover both IID and Non-IID datasets.
Evaluation of Human Resource Information System by Using HOT-Fit Model

Today’s competitive business environment, many organization give more attention to enhance effectiveness and efficiency of employee. PT Pembangkitan Jawa Bali as a subsidiary of PT PLN (Persero) also continues to improve business processes of Human Resource Management through the application of Human Resource Information System (HRIS). This application has been developed since 2016 and is still implemented 67% of the HRIS PJB model design. This certainly raises questions about the development of HRIS PJB implementation, which is need for an evaluation of the implementation of HRIS PJB applications. HRIS Manager has never conducted an evaluation of HRIS PJB that can strengthen the benefits of its use for employees. Therefore, this study was conducted to evaluate the use of HRIS PJB applications in this case the focus on the Personal Management module in order to assess its usefulness to the needs of users and organizations by using Human Organization Technology (HOT) Fit Model. Primary data were obtained through a survey method by distributing questionnaires to PT PJB employees as application users. Data analysis method used is Partial Least Square using SmartPLS. The results of this study shows the suitability of this application benefits. If there is a gap, alternative solutions are needed to improve and develop applications in the future. Decision making through Borda Count Method is used to get the right solution with the current conditions and can be used as improvements priority and development of HRIS PJB applications for both application managers and management of PT PJB.
АНАЛИЗ МЕТОДОВ И ПРАКТИЧЕСКОЕ ПРИМЕНЕНИЕ ХЕШ-ФУНКЦИЙ

In recent years, there has been an extraordinary increase in the need for secure hash functions. In addition, hashing functions play a special and central role in modern cryptography. The article offers an analysis of the current state of security and structures of cryptographic hash functions. The results of practical application of algorithms for cryptographic hash functions and their implementation on various platforms are presented. The features of the use of hash functions by areas of application are shown.
UPCYCLING JUNK ART AND CRAFT MODULE TO NURTURE CREATIVITY OF CHILDREN

Up-cycling is a sustainable alternative incorporating the prevalent 3Rs of Recycle, Reuse, and Reduce of used items in campaigns to save the environment. The ‘DIY Art and Craft Activity Module’ is used as a scaffolding tool to enhance environmental awareness and its impact among primary school children through Visual Arts Education. This research utilised the Design Development Research Method (DDR) comprising of three phases namely; Phase 1: Needs Analysis, Phase 2: Design and Development, and Phase 3: Implementation and Evaluation. The data was collected by interviewing Visual Art teachers on the usability of the module and document analysis on the collection of artworks produced by students. Findings of this study demonstrated positive feedback from the teachers and students on the usability of the Junk Art module as supplementary learning material in order to enhance recycling awareness among Primary School students through Visual Art Education.
Design and Implementation of File Tracking System for Registry Department of Bayero University Kano

In present situation, in any organization file work is mandatory. So every time files are moving from one department to other departments manually is somewhat difficult. In this paper, we developed and implemented a file tracking system for the Registry Department of Bayero University Kano. XAMPP, PHP, MySQL, JavaScript, CSS and JQuery were used for the design. 1.8GHz processor, 1GB RAM and 10GB HDD were server configuration while client configuration was 1GHz processor, 512MB RAM and 1GB HDD which were the hardware requirement for the design of this file tracking system. This paper explained the design, development, and implementation strategy that was adopted to implement a web-based file-tracking system for Bayero University Kano. It reviewed the existing file flow system in the Registry Department of Bayero University Kano, discussed the proposed file-tracking system and its working. Through a design outlay, this paper gave an insight about its working including user screens, roles of users and reports. The system was implemented and found efficient and consistent in file records, resource management and quality of administration, transparency and accountability,thereby helped to reduce turnaround time, and processing delays of files. Keywords: File tracking, Web application, Form, Markup-language, Open source software.
Modular Action Concept Grounding in Semantic Video Prediction

Recent works in video prediction have mainly focused on passive forecasting and low-level action-conditional prediction, which sidesteps the learning of interaction between agents and objects. We introduce the task of semantic action-conditional video prediction, which uses semantic action labels to describe those interactions and can be regarded as an inverse problem of action recognition. The challenge of this new task primarily lies in how to effectively inform the model of semantic action information. Inspired by the idea of Mixture of Experts, we embody each abstract label by a structured combination of various visual concept learners and propose a novel video prediction model, Modular Action Concept Network (MAC). Our method is evaluated on two newly designed synthetic datasets, CLEVR-Building-Blocks and Sapien-Kitchen, and one real-world dataset called Tower-Creation. Extensive experiments demonstrate that MAC can correctly condition on given instructions and generate corresponding future frames without need of bounding boxes. We further show that the trained model can make out-of-distribution generalization, be quickly adapted to new object categories and exploit its learnt features for object detection, showing the progression towards higher-level cognitive abilities. More visualizations can be found at http://www.pair.toronto.edu/mac/.
Application of yang transform in cryptography

Cryptography is the science of secret communication. Mathematics algorithms to encrypt and decrypt a data play an important role in cryptography. Integral transforms plays an important role in the field of Applied mathematics. In this paper, we introduced an encryption and decryption algorithm based on Yang transform and affine ciphers.
PROCESS OF MANAGING INCOMING AND OUTGOING LETTER IN BAPPEDA NAGAN RAYA REGENCY

This study aims to determine how the process of managing incoming and outgoing letters at the Nagan Raya Bappeda Office. This study uses a qualitative descriptive method that draws whatever happens in the field without changing it. This study uses primary data and secondary data. Primary data were obtained from interviews with Bappeda Nagan Raya employees. While secondary data obtained from the web. Respondents from this study were 5 Bappeda employees. The results of this study indicate that the process of managing incoming and outgoing mail has not been carried out effectively. This is because the process of managing incoming and outgoing mail is still using manual procedures so that sometimes errors occur in the process.
Performance Analysis with Differential Evolution Optimization for Moving Object Detection Using Back Ground Subtraction

Detecting and recognizing objects automatically in digital videos is a major challenge in video analysis. As part of this paper, we faced the difficult problem of segmenting objects in videos with constantly moving backgrounds. These are situations that occur when, for example, you film rivers, the sky, or a scene containing smoke, rain, etc. This is a subject little studied in the literature because very often the scenes treated are rather static and only a few parts move, for example the leaves because of the wind. Another source of movement relates to changes in light. The main difficulty, in the case of scenes with a moving background, is to be able to differentiate the movements of the object from those of the background which can sometimes be very similar. Indeed, for example, an object in a river can move at the same rate as water. The algorithms of the literature extracting fields of displacement then fail and those based on background modelings generate very many errors. It is therefore within this complicated framework that we have tried to provide solutions.