Reviewerly/exHarmony huggingface dataset:
A novel group based cryptosystem based on electromagnetic rotor machine

In this paper, an algorithm is aimed to make a cryptosystem for gray level images based on voice features, secret sharing scheme and electromagnetic rotor machine. Here, Shamir secret sharing (k n) threshold scheme is used to secure a key along with voice features of (n k) users. Keystream is molded by coefficients of a voice sample, using this key stream, rotor machines rotating cylinders positions are initialized and internal wiring is decided by pseudo random number of Henon chaotic map, where initial seed for chaotic system is chosen from keystream. And furthermore, shares of key stream are distributed among users. Speech processing is fused with electromagnetic machine to provide authentication as well as group based encryption. Perceptual linear predication (PLP) coefficients are utilized for formation of secret key. Simulation experiments and statistical analysis demonstrate that the proposed algorithm is sensitive to initial secret keystream, entropy, mean value analysis and histogram of the encrypted image is admirable. Hence, the proposed scheme is resistible to any vulnerable situation.
RESIVOZ: Dialogue System for Voice-based Information Registration in Eldercare

RESIVOZ is a spoken dialogue system aimed at helping geriatric nurses easily register resident caring information. Compared to the traditional use of computers installed at specific control points for information recording, RESIVOZ's hands-free and mobile nature allows nurses to enter their activities in a natural way, when and where needed. Besides the core spoken dialogue component, the presented prototype system also includes an administration panel and a mobile phone App designed to visualise and edit resident caring information.
AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data

Developing high-performing predictive models for large tabular data sets is a challenging task. The state-of-the-art methods are based on expert-developed model ensembles from different supervised learning methods. Recently, automated machine learning (AutoML) is emerging as a promising approach to automate predictive model development. Neural architecture search (NAS) is an AutoML approach that generates and evaluates multiple neural network architectures concurrently and improves the accuracy of the generated models iteratively. A key issue in NAS, particularly for large data sets, is the large computation time required to evaluate each generated architecture. While data-parallel training is a promising approach that can address this issue, its use within NAS is difficult. For different data sets, the data-parallel training settings such as the number of parallel processes, learning rate, and batch size need to be adapted to achieve high accuracy and reduction in training time. To that end, we have developed AgEBO-Tabular, an approach to combine aging evolution (AgE), a parallel NAS method that searches over neural architecture space, and an asynchronous Bayesian optimization method for tuning the hyperparameters of the data-parallel training simultaneously. We demonstrate the efficacy of the proposed method to generate high-performing neural network models for large tabular benchmark data sets. Furthermore, we demonstrate that the automatically discovered neural network models using our method outperform the state-of-the-art AutoML ensemble models in inference speed by two orders of magnitude while reaching similar accuracy values.
Conversational AI as Question Answering system implementation using BERT

Conversational AI refers the interaction of users throughmessaging apps or virtual assistants. Such communicationsthrough virtual agents or chat bots are designed to provide personalizeduser experience. Chat bot interactions are driven bylong tail messages, queries or two-way interaction with the privateaudiences. Real power of conversational AI is in its abilityto provide highly personalized interactions with huge numberof customers all together. As a business use case, conversationalAI can transform traditional ways of communication by facilitatingdepth engagement with users. One powerful applicationof conversational AI is question answering system. Either itwould be a virtual assistant or chat bot, or any support systemdesigned by any company, question-answering model plays acrucial role to provide such services. An automated questionanswering system enable a venture to provide its users a moredynamic and personalized experience. Practically, it’s difficultfor a human being to answer all the queries asked by users atone time. If anyone wants to do so, more resources will beneeded to be engaged with customers to solve their issues inservices or products offered. Automated question answeringsystem, not only saves a lot of time but also it acts efficienttool to understand customer needs and their behaviour. GeneralArchitecture of question answering process is to take inputfrom users, query analysis, information retrieval from the database, extraction of the most relevant answer and then outputthe desired answer. It is not as easy as it seems. Why? It is dueto the several challenges faced during the modelling. In thissession, we will discuss several challenges faced to design aquestion answering system like data availability, quality of thedata, it’s interpretability and language barrier. Though servalalgorithms have been implanted to design most efficient questionanswering system, BERT outperform all. We will discusshow Bi-directional Encoder Representation from Transformer[2] algorithm helps in solving mentioned challenges and providean optional solution. This tutorial will focus on designingquestion answering model by using human’s favourite tool forcommunication: Natural language processing. For this session,I will assume familiarity with basic terms of natural languageprocessing and python. The body of the talk will focus on implementationof BERT for designing question answering model,feature extraction techniques for data.
Monitoring-based Differential Privacy Mechanism Against Query-Flooding Parameter Duplication Attack

Public intelligent services enabled by machine learning algorithms are vulnerable to model extraction attacks that can steal confidential information of the learning models through public queries. Though there are some protection options such as differential privacy (DP) and monitoring, which are considered promising techniques to mitigate this attack, we still find that the vulnerability persists. In this paper, we propose an adaptive query-flooding parameter duplication (QPD) attack. The adversary can infer the model information with black-box access and no prior knowledge of any model parameters or training data via QPD. We also develop a defense strategy using DP called monitoring-based DP (MDP) against this new attack. In MDP, we first propose a novel real-time model extraction status assessment scheme called Monitor to evaluate the situation of the model. Then, we design a method to guide the differential privacy budget allocation called APBA adaptively. Finally, all DP-based defenses with MDP could dynamically adjust the amount of noise added in the model response according to the result from Monitor and effectively defends the QPD attack. Furthermore, we thoroughly evaluate and compare the QPD attack and MDP defense performance on real-world models with DP and monitoring protection.
Two Principles of Cultivating Young Marxists Among College Students

The principle of cultivating young Marxists among college students means that the training work follows the policies and guidelines established by the characteristics and growth laws of young Marxists among college students. The cultivation of young Marxists among college students should adhere to the following two principles: one is to focus on combining the need for stimulating belief with the rational choice of guiding belief; the other is to focus on combining dynamic selection and classified cultivation.
Deep Active Learning with Augmentation-based Consistency Estimation

In active learning, the focus is mainly on the selection strategy of unlabeled data for enhancing the generalization capability of the next learning cycle. For this, various uncertainty measurement methods have been proposed. On the other hand, with the advent of data augmentation metrics as the regularizer on general deep learning, we notice that there can be a mutual influence between the method of unlabeled data selection and the data augmentation-based regularization techniques in active learning scenarios. Through various experiments, we confirmed that consistency-based regularization from analytical learning theory could affect the generalization capability of the classifier in combination with the existing uncertainty measurement method. By this fact, we propose a methodology to improve generalization ability, by applying data augmentation-based techniques to an active learning scenario. For the data augmentation-based regularization loss, we redefined cutout (co) and cutmix (cm) strategies as quantitative metrics and applied at both model training and unlabeled data selection steps. We have shown that the augmentation-based regularizer can lead to improved performance on the training step of active learning, while that same approach can be effectively combined with the uncertainty measurement metrics proposed so far. We used datasets such as FashionMNIST, CIFAR10, CIFAR100, and STL10 to verify the performance of the proposed active learning technique for multiple image classification tasks. Our experiments show consistent performance gains for each dataset and budget scenario.
LSTM for Model-Based Anomaly Detection in Cyber-Physical Systems

Anomaly detection is the task of detecting data which differs from the normal behaviour of a system in a given context. In order to approach this problem, data-driven models can be learned to predict current or future observations. Oftentimes, anomalous behaviour depends on the internal dynamics of the system and looks normal in a static context. To address this problem, the model should also operate depending on state. Long Short-Term Memory (LSTM) neural networks have been shown to be particularly useful to learn time sequences with varying length of temporal dependencies and are therefore an interesting general purpose approach to learn the behaviour of arbitrarily complex Cyber-Physical Systems. In order to perform anomaly detection, we slightly modify the standard norm 2 error to incorporate an estimate of model uncertainty. We analyse the approach on artificial and real data.
Management of the collections of the Library Collections and Historical Collections Department of the National Library of Ukraine of V.I. Vernadsky, attribution of book marks in printed catalog materials

The article deals with the problems of managing the collections of the Library Collections and Historical Collections department of the Library's Institute of Book Studies of the Vernadsky National Library of Ukraine, the study and attribution of book marks from the funds of this department.The library collections and collections in the aforementioned department are divided into libraries of spiritual institutions of pre-revolutionary Russia, libraries of higher and secondary educational institutions of the Russian Empire, fund of pre-revolutionary religious and secular periodicals, collection of certain types of documents. Printed catalogs provide extensive book material for the study and attribution of book marks from the holdings of that department.
Digital Transaction Analysis on Game Call of Duty (COD) Mobile using Apriori Algorithm

The Call of Duty (COD) Mobile is a popular game which has reached 100 million downloads within a week after it was released in September 2019.T his game earned 28 billion Rupiah in 3 days after released and many local developers want to follow the success by making similar games.T herefore, local game developers need to know the player's habit in buying virtual product.T his research examines digital transaction patterns in used in Cash Point.Cash Points is the currency which used in the COD Mobile Game.T hus, we need a data collection algorithm that has better accuracy, namely the Apriori Algorithm.This algorithm analyzes transactions which often occur in the COD Mobile Game.T he results of this study prove that most of the player purchase Cash Point to buy a Battle Pass and the remaining balance is saved to buy a Battle Pass in the next sessions.Utilizing a battle pass with attractive prizes will increase player interest in buying the Cash Point.
Diagnostics Issues Of Innovativethinking Levels Of Future Pedagogues

The article considers the concept of innovative thinking and its structure, describes diagnostic methods that reveal the formation of innovative thinking of a future teacher in order to determine its initial level and further development for more effective implementation of professional activities.The authors draw attention to the problems that arise in the diagnosis of levels of this quality in students.It is noted that the professional activities of teachers are specific, related to the need to form the specified type of thinking.Manifestations of the innovative type of thinking, based on which criteria for the levels of formation of the type under study are formulated, correspond to the expectations of an innovatively thinking person: originality and high quality of ideas.Based on the characteristics of the type of thinking under consideration, two levels of its formation are distinguished: high and ordinary, for conducting diagnostic exercises with studentsfuture teachers.The results of an experiment to determine the levels of formation of innovative thinking in students studying in the field of education "Psychology pedagogy of primary education" of Togliatti State University are presented.In the process of experimental work, depending on the style of thinking, respondents were divided into 2 groups: innovators and adapters.The authors found that the levels of formation of innovative thinking depend not only on personal characteristics, but also on the conditions in which the educational and, later, professional activities of future teachers are implemented.
Context Dependent RNNLM for Automatic Transcription of Conversations

Conversational speech, while being unstructured at an utterance level, typically has a macro topic which provides larger context spanning multiple utterances.The current language models in speech recognition systems using recurrent neural networks (RNNLM) rely mainly on the local context and exclude the larger context.In order to model the long term dependencies of words across multiple sentences, we propose a novel architecture where the words from prior utterances are converted to an embedding.The relevance of these embeddings for the prediction of next word in the current sentence is found using a gating network.The relevance weighted context embedding vector is combined in the language model to improve the next word prediction, and the entire model including the context embedding and the relevance weighting layers is jointly learned for a conversational language modeling task.Experiments are performed on two conversational datasets -AMI corpus and the Switchboard corpus.In these tasks, we illustrate that the proposed approach yields significant improvements in language model perplexity over the RNNLM baseline.In addition, the use of proposed conversational LM for ASR rescoring results in absolute WER reduction of 1.2% on Switchboard dataset and 1.0% on AMI dataset over the RNNLM based ASR baseline.
Browser Security Attacks and Detection Techniques: A Case of Tabnabbing

Phishing is a malicious and deliberate act of sending counterfeit messages or mimicking a webpage. The goal is either to steal sensitive credentials like login information and credit card details or to install malware on a victim’s machine. Browser-based cyber threats have become one of the biggest concerns in networked architectures. The most prolific form of browser attack is tabnabbing which happens in inactive browser tabs. In a tabnabbing attack, a fake page disguises itself as a genuine page to steal data. This paper presents a multi agent based tabnabbing detection technique. The method detects heuristic changes in a webpage when a tabnabbing attack happens and give a warning to the user. Experimental results show that the method performs better when compared with state of the art tabnabbing detection techniques.
SOCIALLY CONSTRUCTIVIST APPROACH IN TEACHING INFORMATICS AT SCHOOL

The article considers features of organization of teaching computer science at school on the basis of social constructivist approach. The main idea of this approach is the construction by students of their knowledge of computer science in the process of collaboration through solving problem tasks in a network community. The organization of student activities in the network community on the basis of social constructivist approach includes the organization of joint community management, creation of conditions for communication and interaction of students, solution of problem tasks in computer science, and self-regulation of educational activities. Joint management of the network community contributes to a deeper understanding of differentiation of access rights in the network, functional possibilities of a community; gaining experience of organization activities of other people; increasing responsibility for their learning. Solving problem tasks in computer science contributes to the development of interest to the subject, formation of the ability to independent information research, its analysis, and a more confident acquirement of ICT tools. In the process of discussing problem situations in online communities, students master social norms, rules and values of these communities that leads to a successful functionality in these communities. Self-regulation of educational activity allows students to make a decision about the effectiveness of cognitive activity and the need for its correction.
Modeling and scheduling engineering biotechnology building using bim and microsoft project

The development of education today is required to reflect specificity in future development achievements, the needs of infrastructure related to Biomass and Bioenergy shall be facilitated well. Related to this goal shall be supported with facilities and infrastructure that is integrated laboratory building. The construction activity of the laboratory that develops massively made complexity of work that needs quick method IT and project management based to monitoring and minimalize mistakes in development process. The performance of integrated project management overall is obtained using Building Information Modeling (BIM) concept. This article is using one of the features from BIM program that is Revit Architecture because it is capable to give 2D work picture output, 3D modelling and the volume of each work. The aim of this article is to model the building using BIM and to arrange scheduling using Ms. Project. Modelling only consider the structure and architecture work. The study case of this research is IsDB Engineering Biotechnology. Validation is done to check the volume of work from the result of modelling. The result of validation obtained is error (%). The result of Revit Architecture modelling that is done to the whole work has volume that corresponds to DEDC volume except to the reinforcement work. This happened because of the bending and hook reinforcement settings are using default setting so that the error is more than 0%. The result of Revit Architecture modelling is used as the guidelines for scheduling using Ms. Project. The length of time needed in building the IsDB Engineering Biotechnology building is 232 days.
System Identification via Meta-Learning in Linear Time-Varying Environments

System identification is a fundamental problem in reinforcement learning, control theory and signal processing, and the non-asymptotic analysis of the corresponding sample complexity is challenging and elusive, even for linear time-varying (LTV) systems. To tackle this challenge, we develop an episodic block model for the LTV system where the model parameters remain constant within each block but change from block to block. Based on the observation that the model parameters across different blocks are related, we treat each episodic block as a learning task and then run meta-learning over many blocks for system identification, using two steps, namely offline meta-learning and online adaptation. We carry out a comprehensive non-asymptotic analysis of the performance of meta-learning based system identification. To deal with the technical challenges rooted in the sample correlation and small sample sizes in each block, we devise a new two-scale martingale small-ball approach for offline meta-learning, for arbitrary model correlation structure across blocks. We then quantify the finite time error of online adaptation by leveraging recent advances in linear stochastic approximation with correlated samples.
Cyber Space Crimes and IT Laws in opposition of Cyber Offence

In today's scenario where each activity takes place on Internet, from paying bills to buying groceries and to booking movie tickets to online banking. It is necessary to have the proper knowledge about the trending security threats and laws, in reference of those threats. Use and users of internet is increasing on daily basis, which makes this virtual field, all the more perfect for cybercrime. In this paper, the discussion is based on what are the security threats we face on daily basis, their specific types and nature also what are the Cyber Laws we have against cyber-crimes.
PHASE VOLUME OF THE PERCEPTION TO REALITY: DETERMINANTS AND FACILITIES OF THE EXPRESSION

The article is devoted to problem of revision of the borders of the range (the phase volume) and adequacy of the perception of the world in speaker long disheveled locks of universe-social evolution of the man. It is shown that in man’s perception volume is conditioned by the physiological organization as well as by its place in the cosmos and the Earth biosphere. The accent is made on discovery determinant conditioning given range of the perception and facility of the expression available perception to information. It is revealed that the modeling of reality becomes more abstract and clear, but less concrete, adequate to the «ultimate reality» with its blurred forms.
(Mis)Measuring People's Attitudes from Social Media

Activities of people, recorded via digital devices or online environments, offer increasingly comprehensive pictures of both individual and group-level behavior, potentially allowing inferences within and outside the platforms. These digital traces are often in the form of textual units such as tweets or Reddit posts or comments. Compared to solicited survey responses, social media posts are the organic, unsolicited thoughts of people on a variety of topics, and the language in these posts are a key to their attitudes, beliefs and values. Notwithstanding the many promises of digital traces, recent studies have begun to discuss the errors that can occur when digital traces are used to learn about social phenomena. In this thesis, I propose to first, diagnose and characterize issues in the measurement of people's attitudes at scale, and second, mitigate these errors through theory-driven solutions. To critically study and record errors and biases in using digital traces for measuring human behavior, we propose a systematic framework, named 'Total Error Framework for Digital Traces' (TED). TED is inspired by and adapted from the Total Survey Error Framework, developed and employed in survey methodology to assess the validity and reliability of survey-based studies. To mitigate errors unearthed by examining Computational Social Science through TED, we apply several domain specific solutions, such as using linguistic theories to understand people's attitudes. This thesis contributes in improving the reliability and validity of attitude measurement from digital traces.