Reviewerly/exHarmony huggingface dataset:
Proof of Storage Algorithm for a Decentralized Cloud System

This paper proposes a new proof-of-storage algorithm for decentralized cloud systems. The algorithm allows an untrusted host to prove data storage and integrity, while the auditing host receives reliable proof of data storage and integrity. The algorithm is based on time-distributed random sampling of data segments from the checked file, their conversion and comparison with the original. The algorithm is implemented in an integrated development environment, analyzed the accuracy of the algorithm, performance and tamper resistance. The efficiency of the algorithm is compared with the Merkle cryptographic protocol and the "seed planting" method currently used in decentralized storage systems. The proposed algorithm, in contrast to the considered algorithms, provides evidence of data storage without resource-intensive operations, such as dividing files into parts, calculating checksums of these parts, and transferring parts to the checking host. Hacking resistance is evaluated by calculating the probability of a successful "brute-force" attack. The algorithm showed high-performance and high hacking resistance.
The Viability of Using Online Prediction to Perform Extra Work while Executing BSP Applications

A fundamental problem in parallel processing is the difficulty in efficiently partitioning work: the result is that much of a parallel program's execution time is often spent idle or performing overhead operations. We propose to improve the efficiency of system resource utilization by having idle processes execute extra work. We develop a method whereby the execution of extra work is optimized through performance prediction and the setting of limits (a deadline) on the duration of the extra work execution. In our preliminary experiments of proxy BSP applications on a production supercomputer we find that this approach is promising with all five applications benefiting from this approach, with an average of 12 % improvement.
Parallel Computing with DNA Forensics Data

High-throughput sequencing (HTS) of single nu-cleotide polymorphisms (SNPs) provides advanced DNA forensics capabilities including complex mixture analysis. This paper describes a scalable pipeline for large DNA forensics data which can either be utilized on a standalone system or can also be used on high performance computing systems. This pipeline enables parallelization of processing of multiple samples. Surveillance modules detect completed sequencing datasets on both Illumina and Ion Torrent platforms. GrigoraSNPs is used for automated SNP allele calling from FASTQ files. These results are automatically loaded into the IdPrism DNA mixture analysis system. HTS SNP data analysis typically completes in roughly 7 minutes for 100M sequences, including SNP allele calling, enabling rapid access to the results within the IdPrism system for identification and complex mixture analysis of multiplexed samples.
Kv2vec: A Distributed Representation Method for Key-value Pairs from Metadata Attributes

Distributed representation methods for words have been developed for years, and numerous methods exist, such as word2vec, GloVe, and fastText. However, they are not designed for key-value pairs, which is an important data pattern and widely used in many scenarios. For example, metadata attributes of scientific files consist of a collection of key-value pairs. In this research, we propose kv2vec, a method that captures relationships between keys and values and represents key-value pairs in dense vectors. The fundamental idea of the kv2vec method is utilizing recurrent neural networks (RNNs) with long short-term memory (LSTM) hidden units to convert each key-value pair to a distributed vector representation. This new method overcomes the weaknesses of existing embedding models for representing key-value pairs as vectors. Moreover, it can be integrated into dataset search solutions through querying metadata attributes for self-describing file formats that are widely used in HPC systems. We evaluate the kv2vec method with multiple real-world datasets, and the results show that kv2vec outperforms existing models.
Detection of leaks through exception mechanisms

A timing attack is a security exploit that allows an attacker to discover vulnerabilities in the security of a computer or network system by analysing the execution time of algorithms. This is because each operation in a program takes time to be executed, and this time may vary depending on its inputs and the characteristics of the microprocessor on which it runs. With accurate time measurements for each operation, it may be possible for an attacker to discover secrets through the analysis of the execution time of a program. This paper presents an automatable approach for detecting information leakage in programs through timing information. It is based on the Z3-SMT solver. It allows to detect vulnerabilities in a software code according to a given security specification and target architecture. This paper also features some research issues that will be addressed during my thesis.
Performance Analysis of Deep Learning Models for Sweet Potato Image Recognition

Current technological developments make human work increasingly automated. Computer vision has widely used deep learning to help humans recognize objects. TensorFlow is a form of CNN model that is widely used to implement computer vision. In this research, the performance of four TensorFlow models was tested to recognize yellow sweet potatoes and Cilembu, which have many similarities and are not easily distinguished by ordinary people. These two types of sweet potatoes need to be determined because they significantly differ in economic value. The four TensorFlow models tested were MobileNetV1 FPN SSD, MobileNetV2 SSD, MobileNetV2 FPNLITE SSD, and EfficientDet-D0. Based on the test results, the MobileNetV1 FPN SSD model has the best precision in all classes and has good accuracy in the yellow sweet potato class. But the performance is too lame on Cilembu sweet potato and requires the longest training time. Meanwhile, the most stable performance based on precision, accuracy, and recall is the EfficientDet-D0 model. The training process is also faster than the MobileNetV1 FPN SSD.
Is the U-Net Directional-Relationship Aware?

CNNs are often assumed to be capable of using contextual information about distinct objects (such as their directional relations) inside their receptive field. However, the nature and limits of this capacity has never been explored in full. We explore a specific type of relationship~-- directional~-- using a standard U-Net trained to optimize a cross-entropy loss function for segmentation. We train this network on a pretext segmentation task requiring directional relation reasoning for success and state that, with enough data and a sufficiently large receptive field, it succeeds to learn the proposed task. We further explore what the network has learned by analysing scenarios where the directional relationships are perturbed, and show that the network has learned to reason using these relationships.
Automatic Visual Monitoring System for Data Center Device Management

One of the core responsibilities of a data center is to provide stability and availability for online services. But the data center frequently faces infrastructure failures and unpredicted irregularities. In order to minimize hardware related failures, data centers must monitor the housing devices for 24 hours and 365 days. Due to various physical and software related failure causes, monitoring has always been a challenging task. A visual inspection by human operators is a reliable method which is widely used for checking device's status. However, growth of number of devices in data centers causes exponential cost growth for visual checks and it is almost impossible to detect anomaly occurrences in real time. Therefore, we have proposed an automatic visual monitoring system for data centers to substitute human visual monitoring. In this paper, we present the overview of the proposed system and show experimental results to evaluate LED detection performance of the system.
A voting method for image binarization of text-based documents

Binarization is an essential stage of image pre-processing before text recognition. This process improves the performance of the obtained text and decreases the effort required for post-processing to find possible mistakes. The main direction for this work is to improve the binarization step in a Document Image Analysis System to increase the text’s accuracy and combine a series of thresholding algorithms into a voting solution.
The impact of uncorrelated noise on echo state networks

The bottleneck of modern neural network implementations is unstable power consumption and relatively low speed. This can be solved by analog hardware network implementations, but the influence of analog neuron noise, its propagation and accumulation threaten to make this approach inefficient. This paper studies the impact of uncorrelated additive and multiplicative noise on recurrent network by the case of echo state network. It is shown here that the activation function affects the noise accumulation in the same way as in deep networks. The noise impact is studied in terms of signal-to-noise ratio and variance. Both dependences are getting focusing points and the dependency view changes caused by nonlinearity. Moreover, uncorrelated noise can be suppressed by the system itself if the connectivity satisfies some conditions.
Changes in Statistical Measures of Cross-Recurrence Quantification Analysis of EEG after Sound Exposure

During the analysis of experimental records of intracranial EEGs of rats in sleep, wakefulness and after sound exposure, we have shown that statistical measures characterizing the features of individual and collective dynamics of the electrical activity of the brain in the δ-frequency range make it possible to classify the state of the animal. The factor determining such changes in EEGs is a change in the rate of cerebral fluid drainage, which is confirmed, in particular, by the similarity of the properties of low-frequency electrical activity of the brain during sleep and after auditory exposure, in contrast to the state of wakefulness.
Prevalence of neuromyths among pre-service teachers

The article discusses the prevalence of neuromyths among future teachers of mathematics, computer science and physics. A brief description of the considered problem in world studies is given. The results of a survey conducted among students of six Russian universities are described.
Machine learning methods application for outliers detection in epileptic EEG

In this paper possibility of machine learning meth-ods applications for epileptic activity detection in records of electroencephalography is studied for further application in medical decision-support system.
Usage of Morphotranslation in Data Augmentation of the Training Dataset for “TurkLang-7” Project on MT Systems Creation

The paper discusses an approach to solving the problem of low-resource languages in the development of neural network machine translation systems for Turkic language pairs by artificially increasing training data in form of parallel corpora. The basis of this approach lies in usage of toolset of the multilingual portal “Turkic Morpheme” for morphological analysis and generation of word forms. The issues of creating machine translation systems for Turkic languages are considered, a hypothesis about possibility of using the presented approach to improve the results of machine learning is formulated, and the rationale for using morphological analysis methods is given. The developed algorithm for artificial augmentation of training data is presented.
Open Source OPC UA Data Traffic Characteristic and Anomaly Detection using Image-Encoding based Convolutional Neural Network

The development of OT and IT technology and industrial growth requires the design of automation systems that can accommodate scalability and interoperability between devices. OPC UA is a communication protocol that bridges data exchange between devices using different communication platforms and protocols. OPC UA can also connect devices between levels in automation pyramids. As an open platform, open-source OPC UAs such as open62541, OpenOPCUA, and FreeOpcUa is currently being developed by several developer communities. Implementing open-source OPC UA is an attractive option if cost is a significant consideration. However, the primary purpose of implementing OPC UA is to communicate or exchange information effectively and reliably; information about the characteristics and performance of open-source OPC is needed before designing a particular open-source OPC-based automation system platform. This paper utilized FreeOpcUa, a Python OPC UA library, to determine communication traffic features between client and server and perform anomaly detection on the traffic. The results showed that when all clients read server data simultaneously, there was duplication (up to 9%) and loss (up to 5%) of some data packets. Otherwise, the server could read all clients’ transmitted data. Anomaly detection testing with an image-encoding CNN also showed promising results, with accuracy, precision, recall, and F-score values approaching one.
Role of Education in Modern Cyber Culture its Advantageous and Disadvantageous

Role of Education in Modern Cyber Culture its Advantageous and Disadvantageous
Foreword

prefatory Share on Foreword Author: Ruth Aylett View Profile Authors Info & Claims The Handbook on Socially Interactive Agents: 20 years of Research on Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics Volume 2: Interactivity, Platforms, ApplicationOctober 2022 Pages xvii–xxivhttps://doi.org/10.1145/3563659.3563660Published:03 November 2022Publication History 0citation3DownloadsMetricsTotal Citations0Total Downloads3Last 12 Months3Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
More on spectral sum rules

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MENINGKATKAN PERCAYA DIRI BERBICARA BAHASA INGGRIS DI CAHAYA ANAK NEGERI BEKASI

Self-confidence is a very important aspect for a person to develop his potential. The higher the confidence to speak in English, the smoother it will be to get the expected results. If you liken it, self-confidence is a key to open the door of doubt that has been a hindrance when you are trying to find a way out. The training method carried out is adjusted to the goal to be achieved, namely to increase self-confidence in speaking English. According to the needs of children, the material that needs to be developed at the Bekasi State Children's Light Community Service place was chosen, the theme "Self-Introduction and PostCard Writing in English" was chosen. Speech practice is usually one of the stages that is considered the most strenuous. It needs to be trained continuously to increase self-confidence in children when speaking English.
Pelatihan Pembuatan Website Dengan CMS (Content Management System) Di Smk Muhammadiyah Karanganyar

Smk Muhammadiyah 02 Karangayar is one of the schools that follows technological developments by visiting schools through the website. SMK Muhammadiyah 02 Karangayar has a bold business and marketing major so that every student needs expertise in website creation as a form of keeping up with technological developments in the digital marketing era. Smk Muhammadiyah 02 Karangayar requires supporting training that improves students' ability to create websites.
E-Commerce Information System Design in Web-Based UD Anggun Shoe Store

E-commerce or online shopping is a concept that is quite developed in the internet world. The use of this system can benefit many aspects, including consumers, producers, and sellers. The information system is a combination of work procedures, information, staffing, and information technology that is organized to achieve organizational goals. Information systems have become an urgent need in all areas of life including business. One ofthe companies engaged in trading, namely the Anggun Shoe Store, really needs information system technology that provides sales data recap functions and sales profit reports because consumers have difficulty getting detailed information on product specifications offered and employees find it difficult to offer products sold to consumers outside the city. Web-Based E-Commerce Information System Design (Anggun Shoe Store). An information system is needed by this company as well as other companies that run a similar business model, to facilitate sales reporting sothat their business can be measured. This system also makes it easier for business owners to view daily sales reports so they can determine marketing strategies for the future.
Design of Vending Machine using FPGA

The vending machine is an automated device that dispenses a variety of products to customers when cash or a credit card is placed, including food, beverages, newspapers, tickets, and more. Contrary to traditional purchase methods, vending machines are more convenient and available. Today, the market for vending machines generates enormous annual revenues for developed countries like the United States, Japan, China, and some other Asian nations, including India. The goal of the paper is to create a vending machine that can dispense three different items at varying costs with the added functionality of "return change" when a coin of a higher denomination is input and "return money" when a request is cancelled. Coins of the values of five and ten are accepted by the machine. The design of vending machines uses the finite state machine (FSM) methodology. The design is accomplished by using behavioural modelling to create the Verilog code for the FSM-based machine and the Xilinx ISE tool to simulate the test-bench for three items.
Automated modeling and reasoning approach for context in smart homes based on stochastic analysis of sensory data

Purpose The primary goal of smart homes is to fulfil convenient life for human residents by providing needed services and proper applications.The key for the smart home technology is to build an intelligent contextawareness computing (CAC) system which fascinates to recognize a current context.It proceeds in two processes: 1) modeling contexts and 2) reasoning contexts based on modeled contexts.As Bettini et al. have shown in their survey (2010), various modeling/reasoning approaches have been proposed.However, most approaches required manual configurations for context models, which needed a human-aided context reasoning process.The lack of automation degrades usability of CAC systems and increases the occurrence rate of errors in modeling and reasoning contexts due to possibility of mistakes by humans.Therefore, we proposed an approach to automate modeling and reasoning contexts by stochastically analyzing sensor data.Method In the prior phase of the approach, sensor data is derived to context models by using three machine learning techniques.This approach precedently needs to define a couple of components for contexts.First, Lee and Helal defined a state space to describe status of a smart space by using status of sensors and IoT (2022).A context represents a meaningful state space among a set of state spaces.To automatically define such contexts, all state spaces are clustered by the Kmeans clustering method.Since a centroid of each cluster has the minimum average distance to other state spaces, it is representative and considered as a context.This method requires the number of contexts(K), which is obtained by statistical analysis.In our current proposed approach, we utilized conditional probabilities of all state spaces, which enable to estimate how many contexts may happen with a high probability.Then, sensors which are significant in each context are determined by applying the Principal Component Analysis (PCA).With the sensors, context models finally are defined and used for next reasoning phase.In the reasoning process, a most similar context to a current status of space is discovered.For this purpose, two computational methods for similarity -Euclidean distance and Cosine similarity, are applied.Results and Discussion This approach achieves to fully automate modeling and reasoning contexts by analyzing sensor data.To validate it, we examined that the approach consistently showed as good results as a non-automated approach did.For the comparison, data were collected from the Gator Tech Smart House (Figure 2).The contexts modeled/reasoned by applying the automated approach were compared contexts manually configured in Persim 3D (Lee et al., 2015).The experiential validation showed that the proposed approach was acceptable with positive results.Based on the meaningful progress, we will enhance the approach, which can 1) figure impermissible contexts, 2) find unknown contexts, and 3) map the present context to the most relevant services for human residents in a smart space.
Antecedents of the adoption of blockchain to enhance patients’ health information management in South Africa

Background: Currently, the South African public healthcare system is hampered by a lack of effective patient data management. This leads to, amongst other challenges, a lack of transparency in the management of patients’ health information and unsecure medical records. Blockchain, on the other hand, can make healthcare records more secure, easily auditable, and hence more reliable. These advantages, among others, make Blockchain an appealing technology for managing patients’ health information in the South African context.Objectives: The study investigated the antecedents of the adoption of blockchain technology as a tool to improve the management of patients’ health information in the South African public healthcare sector.Method: The four pillars of a health information system (strong leadership, contingency plan, security and privacy, and IT or vendor support) were adopted as the theoretical grounding for the study. Qualitative data were collected through interviews with IT specialists and healthcare professionals working in the public healthcare sector.Results: The study identified 11 antecedents that can be related to the four pillars of a health information system. These antecedents must be addressed for blockchain to contribute meaningfully to enhancing patients’ health information management in South Africa.Conclusion: Although blockchain can contribute meaningfully to addressing health information management challenges in South Africa, contextual factors need to be considered for it to fulfil its promises. It is anticipated that the study findings will help stakeholders in the South African public healthcare sector to make informed decisions in their quest to deploy technology innovations like blockchain.