Reviewerly/exHarmony huggingface dataset:
Improve estimated time-on-task calculation in a Virtual Learning Environment

The main aim of this paper is to present results of an experimental test focused on the validity and effectiveness of composed methodology aimed at increasing the student's attention in Virtual Learning Environment. Areas of presented methodology which were subject of our research is students behavior during learning. The behavioral part of methodology is focused on calculating estimated time for reading/learning specific learning material based on mathematical model. The model has been created as linear regression model trained and validated with student's access logs from Moodle instance used at the university. The methodology created by us, which is the subject of the experiment, uses the mentioned mathematical model in order to more optimally calculate the time-on-task and thus assume the time required for the correct understanding of the presented curriculum in the form of e-learning. In this paper, we will focus on confirming the didactic effectiveness of our proposed methodology for monitoring student attention. We describe an experiment with which we verified the proposed methodology. Methodology itself is focused on improving students learning experiences while using e-learning platforms like Moodle.
Drone Detection by Neural Network Using GLCM and SURF Features

This paper presents a vision-based drone detection method. There are a number of researches on object detection which includes different feature extraction methods – all of those are used distinctly for the experiments. But in the proposed model, a hybrid feature extraction method using SURF and GLCM is used to detect object by Neural Network which has never been experimented before. Both are very popular ways of feature extraction. Speeded-up Robust Feature (SURF) is a blob detection algorithm which extracts the points of interest from an integral image, thus converts the image into a 2D vector. The Gray-Level Co-Occurrence Matrix (GLCM) calculates the number of occurrences of consecutive pixels in same spatial relationship and represents it in a new vector- 8 × 8 matrix of best possible attributes of an image. SURF is a popular method of feature extraction and fast matching of images, whereas, GLCM method extracts the best attributes of the images. In the proposed model, the images were processed first to fit our feature extraction methods, then the SURF method was implemented to extract the features from those images into a 2D vector. Then for our next step GLCM was implemented which extracted the best possible features out of the previous vector, into a 8 × 8 matrix. Thus, image is processed in to a 2D vector and feature extracted from the combination of both SURF and GLCM methods ensures the quality of the training dataset by not just extracting features faster (with SURF) but also extracting the best of the point of interests (with GLCM). The extracted featured related to the pattern are used in the neural network for training and testing. Pattern recognition algorithm has been used as a machine learning tool for the training and testing of the model. In the experimental evaluation, the performance of proposed model is examined by cross entropy for each instance and percentage error. For the tested drone dataset, experimental results demonstrate improved performance over the state-of-art models by exhibiting less cross entropy and percentage error.
Shadow elimination based on multiple feature differences and glvq

Aimed at problem of that shadow elimination methods based on model or single feature are all having inherent drawbacks. A shadow removal algorithm based on multiple feature differences between pixels and the reference background pixels is proposed. The multi feature differences of the current pixel and the corresponding pixel of the background are inputted into GLVQ to classify the current pixel.The experiments proved the proposed method has better performance than that of some other methods.
The Organisational Structure of an Agent-Based Model for the Management of Wastewater Systems

Water managers have to deal with complex problems due to the intertwined characteristics of processes, in particular those that occur in wastewater systems. Existing modelling approaches are usually centred in the physical, chemical and biological aspects of the individual processes, excluding the social and organisational context that will generate the global behaviour. These also include the responsibilities and decision making of different actors in the system. This paper proposes an agent-based model with the novelty to integrate the social and organisational structure of the wastewater system from which emerges the global behaviour of the system. The modelling process allows considering the legal regulations and the technical limits that would drive the decision making. The instantiation of the model, implementing a small system, evidenced the usefulness of this approach to manage the complexity of wastewater systems and its possible contribution to prevent environmental problems.
Semantics Exploration for Automatic Bangla Speech Recognition

ASRBangla is a proposed model that can recognize Bangla speech or voice automatically by using the Mel Frequency Cepstral Coefficient (MFCC) and Recurrent Neural Network (RNN). Speech Recognition is the power of a process of an instrument or program that puts word and phrases into words and converts them into a machine-meaningful arrangement. ASR are already implemented in many other language but not in Bengali language properly. In this worlds about 15% population which is almost one billion who are physically disabled. For this physically disabled people we used this technology like blind, handicapped, deaf. This really reduces their dependency. In this research work we pursuits at building a model in deep learning way to deal with Automatic Speech Recognition in Bangla. Our proposed methodology there used MFCC ‘for feature extraction, RNN ’ for training dataset. This proposed model we trained a LSTM model to know the most possible phonemes. Our proposed model get 20% error rate in word detection on Bangla-Word from audio dataset.
What are language learners looking for in a collocation consultation system? Identifying collocation look-up patterns with user query data

Abstract Corpus consultation with concordancers has been recognized as a promising way for learners to study and explore language features such as collocations at their own pace and in their own time. This study examined 1.5 million search queries sent to a collocation consultation tool called FlaxCLS (Flexible Language Acquisition Collocation Learning System; http://flax.nzdl.org ) over a period of two years to identify learners’ collocation look-up patterns. This paper examines and characterizes learners’ look-up patterns as they entered search queries, clicked on the query formation aids provided by the system, and navigated through the different levels of collocation information returned by the system to support collocation learning. We looked at how learners formulated query terms, and we analyzed the characteristics of query words learners entered, the characteristics of collocations they preferred, and the sample sentences they checked. Our collocation look-up pattern analyses, similar to traditional user query analyses of the web, provide interesting and revealing insights that are hard to obtain from small-scale user studies. The findings provide valuable information and pedagogical implications for data-driven learning (DDL) researchers and language teachers in designing tailored collocation consultation systems and activities. This paper also presents multidimensional analyses of learner query data, which, to the best of our knowledge, have not been explored in DDL research.
Towards Data Storage, Availability and Scalability with the Aid of Blockchain

Advent of Internet of things (IoT) and the increase of induced data from IoT’s ecosystem has led towards an exponential increase in data storage in the cloud. IoT provides a quick and easy access to the data and hence maintaining the security of data is of paramount importance. In this sensitive scenario, blockchain plays a crucial role in securing and preventing the data from being tempered or forged. Besides, all this demand of scaling up of data in each sector is increasing due to increased amount of data required in market. This paper presents a novel system architecture to visualize a means to store data with scalability as well as availability with the security of blockchain in sync. Furthermore, this paper explains the inevitability of blockchain in solving today’s security issues and provides future research directions in this context.
Pengembangan Media Pembelajaran Tematik Berbasis Android “Kisah TAYA” untuk Meningkatkan Hasil Belajar Siswa Kelas VI SD

To answer the situation in Indonesia and the challenges of 21st century education, this study aims to develop an interactive thematic learning media based on the Android-based Kisah TAYA to improve learning outcomes for students in grade VI SD. The research used is a type of research development or R&amp;D. The subjects of this study were students of grade VI elementary school, but the research only stopped until the expert test and did not practice using the media in the field. This research instrument is in the form of validation sheets for material experts, media experts, and learning design experts. After the validation test was carried out, the results of the material expert validation obtained a score of 75% and was included in the high category. The results of the validation of the instructional media expert obtained a score of 84.2% and it was included in the very high category. And the results of the validation of learning media obtained a score of 81.9% and were included in the very high category. And the conclusion from the results of these categories can be stated that the thematic interactive learning media based on the Android-based TAYA Story is suitable to use.
Consensus in Sentiment Analysis

The objective of this chapter is to present a method applicable in group decision-making where computing the opinion of the majority of participants is key. In this article, we present a method that makes use of Induced Ordered Weighted Averaging (IOWA) operators to aggregate a majority opinion out of a number of Sentiment Analysis (SA) classification systems. The numerical output of each SA classification method is used as input to a carefully chosen IOWA operator that is semantically equivalent to the fuzzy linguistic quantifier ‘most of’. The object of the aggregation will be the intensity of the previously determined sentence polarity in such a way that the results represent what the majority thinks.
VMware ESXi: Virtual Web Server performance evaluation with weighttp Benchmark

Virtual webserver is handle several numbers of requests from clients throughout the world that host different webpages, application etc. Which support to cloud computing, it is essential component of cloud service model to deploy application as well as services because of that it is necessary to find out performance level of virtual webserver. In this article, virtual webserver is evaluated with the help of weighttp benchmark that generates HTTP requests with multi-threaded ability for multi core (CPU) processor and experiments were conducted at server side i.e. against Thread-1, Thread-2 and Thread-4 amenity. VMware ESXi application used for virtualization and Apache for webserver, the results stated that single threaded users performed better as compared to multi-threaded users and this test performed with weighttp benchmark.
Flight data anomaly detection and diagnosis with variable association change

Aircraft sensors generate multivariate time series during flights, where each sensor corresponds to one variable. During normal operation mode, the associations (dependencies) among variables are mainly stationary. One type of flight anomaly that is of interest relates to variable association change. Detection and diagnosis of such type of anomaly need to pinpoint the time series, i.e., variables related to association change, which helps in understanding the underlying mechanisms of anomalies. However, it is hard to detect such change because the variable associations are usually unknown and complicated, and the anomalous samples are usually insufficient for learning the substandard association. In this work, we present a neural network that can 1) detect this type of anomalies given multivariate time series as input; 2) locate the association change by learning the nonlinear variable associations from both normal data and the detected anomalies. Specifically, we leverage the learned model from normal data to learn the faulty association of the anomalies. Experiments using simulated and real-world flight data show that our model outperforms existing methods in flight anomaly detection and diagnosis.
Accelerate Distributed Stochastic Descent for Nonconvex Optimization with Momentum

Momentum method has been used extensively in optimizers for deep learning. Recent studies show that distributed training through K-step averaging has many nice properties. We propose a momentum method for such model averaging approaches. At each individual learner level traditional stochastic gradient is applied. At the meta-level (global learner level), one momentum term is applied and we call it block momentum. We analyze the convergence and scaling properties of such momentum methods. Our experimental results show that block momentum not only accelerates training, but also achieves better results.
The General Data Protection Regulation and Log Pseudonymization

The General Data Protection Regulation (GDPR) demands the use of various protection levels to ensure that personal data meet information security requirements. One of the techniques the GDPR recommends to protect personal data is pseudonymization, which consists of replacing real data with pseudonyms. Although databases and documents contain much of the personal data that should be protected, several types of log files contain data (like IP addresses, e-mails, and usernames) that may lead to the (direct or indirect) identification of a person. Therefore, log files must also be processed to achieve regulatory compliance with the principle of accountability imposed by the regulation. In this work, we deal with the pseudonymization of log data. We identify and discuss pseudonymization strategies in terms of the log processing phase and management architecture. We experimentally evaluate such strategies using three implementation alternatives, providing conclusions and helpful insights on their usage.
Opinion Mining: Is Feature Engineering Still Relevant?

This paper manifests the experimentation with sentiment polarity detection over Stanford's IMDB movie review dataset using a Support Vector Machine classifier (SVM). Our prime motivation was to find out the best possible combinations of classic features and preprocessing techniques for the classification of positive and negative opinions. We also explored two variants of kernels with numerous parameter settings for the classifier in the hope of getting the best SVM model. Our best model achieved an accuracy score of 85.45%. The results indicate that a model with a non-linear Radial Basis Function (RBF) kernel leads to the highest accuracy. The features that contributed the most are stemmed word n-grams.
Overview On Public Wi-Fi Security Threat Evil Twin Attack Detection

Wi-Fi is widely used internet source which use to provide internet access in many areas such as Stores, Cafes, University campuses, Restaurants and so on. Due to transmission of data over the air which makes network vulnerable so it becomes prone to various threats such as Evil Twin and etc. The Evil Twin is a kind of adversary which impersonates a legitimate access point (LAP) as it can happen by spoofing the name (SSID) and MAC address (BSSID) of a legitimate access point (LAP). And this attack can cause many threats such as MITM, Service Interruption, Access point service blocking. In this paper we review various Evil Twin Attack Detection Techniques and proposed methods with their comparisons.
Human Sentiments and Associated Physical Actions Detection in Disasters with Deep Learning

In the study of emotions and activities, the increasingly growing development of social networks and the tendency of users to share their physical activities, opinions, expressions and perspectives in text, visual and audio content have opened up new possibilities and challenges. While the literature has widely examined sentiment and action interpretation of text streams, it is comparatively recent but challenging to evaluate sentiment and physical actions from visuals such as photos and videos together. This paper focuses human emotion with connected physical activity analysis in a socially critical area, namely social media disaster/catastrophe analysis. For disaster-related videos and photos in occluded areas, we propose multi-tagging sentiment and associated action analysis. We believe that the proposed approach will lead to more viable communities by benefiting multiple stakeholders, such as news broadcasters, emergency relief services, and the public in general.
Efficient and High-Quality Seeded Graph Matching: Employing Higher-order Structural Information

Driven by many real applications, we study the problem of seeded graph matching. Given two graphs <?TeX $G_1 = (V_1, E_1)$?> and <?TeX $G_2 = (V_2, E_2)$?> , and a small set <?TeX $S$?> of pre-matched node pairs <?TeX $[u, v]$?> where <?TeX $u \in V_1$?> and <?TeX $v \in V_2$?> , the problem is to identify a matching between <?TeX $V_1$?> and <?TeX $V_2$?> growing from <?TeX $S$?> , such that each pair in the matching corresponds to the same underlying entity. Recent studies on efficient and effective seeded graph matching have drawn a great deal of attention and many popular methods are largely based on exploring the similarity between local structures to identify matching pairs. While these recent techniques work provably well on random graphs, their accuracy is low over many real networks. In this work, we propose to utilize higher-order neighboring information to improve the matching accuracy and efficiency. As a result, a new framework of seeded graph matching is proposed, which employs Personalized PageRank (PPR) to quantify the matching score of each node pair. To further boost the matching accuracy, we propose a novel postponing strategy, which postpones the selection of pairs that have competitors with similar matching scores. We show that the postpone strategy indeed significantly improves the matching accuracy. To improve the scalability of matching large graphs, we also propose efficient approximation techniques based on algorithms for computing PPR heavy hitters. Our comprehensive experimental studies on large-scale real datasets demonstrate that, compared with state-of-the-art approaches, our framework not only increases the precision and recall both by a significant margin but also achieves speed-up up to more than one order of magnitude.
Kernel-based LIME with feature dependency sampling

While deep learning makes significant achievements in Artificial Intelligence (AI), the lack of transparency has limited its broad application in various vertical domains. Explainability is not only a gateway between AI and society, but also a powerful feature to detect flaws of the models and bias of the data. Local Interpretable Model-agnostic Explanation (LIME) is a widely-accepted technique that explains the predictions of any classifier faithfully by learning an interpretable model locally around the predicted instance. However, the sampling operation in the standard implementation of LIME is defective. Perturbed samples are generated from an uniform distribution, ignoring the complicated correlation between features. Moreover, as the local decision boundary is non-linear for most complex networks, linear approximation may produce serious errors. This paper proposes an high-interpretability and high-fidelity local explanation method, known as Kernel-based LIME with Feature Dependency Sampling (KLFDS). KLFDS enhances interpretability by feature sampling with intrinsic dependency. Besides, KLFDS improves the local explanation fidelity by approximating nonlinear boundary of local decision. We evaluate our method on image classification tasks and results show that KLFDS's explanation of the black-box model achieves much better performance than original LIME.
Forensic Analysis of digital evidence extracted from Amazon Echo

The IoT ecosystem is encompassing new devices at a constant rate. And now there are around twenty billion of these devices on the planet. One category of IoT devices, known as Home Assistants, find themselves being used in a lot of households. Amazon Echo, being the most popular device in this category also has a great potential for storing valuable evidence when a crime occurs. The biggest problem lies in retrieval of this evidence. This article deeply compares and contrasts the methodologies and findings from three major works entirely focused on forensics of Amazon Echo and tries to find out the evidentiary value of data retrieved and how this could prove relevant for a crime investigation We also make our own contribution by explaining the significance of timestamps.
Learn from Object Counting: Crowd Counting with Meta‐learning

The objective of crowd counting is to learn a counter that can estimate the number of people in a single image. So far, most of the proposed work evaluates the crowd density by fitting the constructed density map corresponding to the sample. The performance of those algorithms depends on a large amount of carefully prepared data. However, a significant problem with crowd data sets is the difficulty of labeling. To address such a situation, utilizing object counting data in few-shot scenes is considered and an efficient algorithm to extract the meta-information is proposed, thus improving the accuracy and convergence rate of the crowd counting tasks. Specifically, the counting network is trained with only object counting tasks constructed on different domains during the meta-training phase. Then, the meta-counter is testing on crowd counting tasks in the meta-testing stage. Experimentally, it is demonstrated that the above way improves the converge rate and accuracy of crowd counting tasks on three crowd counting datasets when meta-training on ten-type object counting tasks.
In Search of the Performance- and Energy-Efficient CNN Accelerators

In this paper, starting from the algorithm, a performance- and energy-efficient 3D structure or shape of the Tensor Processing Engine (TPE) for CNN acceleration is systematically searched and evaluated. An optimal accelerator's shape maximizes the number of concurrent MAC operations per clock cycle while minimizes the number of redundant operations. The proposed 3D vector-parallel TPE architecture with an optimal shape can be very efficiently used for considerable CNN acceleration. Due to inter-block image data independency, it is possible to use multiple of such TPEs for the additional CNN acceleration. Moreover, it was shown that proposed TPE can also be uniformly used for acceleration of the different CNN models such as VGG, ResNet, YOLO and SSD.
A quantum protocol for private substitution problem

Private substitution (PS) is a problem for replacing one party’s binary string with the other party’s binary string in a privacy preserving manner. It is a new application of secure multi-party computation. In this paper, a protocol for PS using Pauli gates X, Z and SWAP gate is put forward. A quantum secure direct communication protocol is also used to transmit some classic information. Correctness analysis shows that parties can get the correct substitution results. The security of the proposed protocol is also analyzed, which shows that the protocol can resist attacks from outside and participants. The quantum private substitution protocol can be used to solve quantum private set intersection problem and quantum private set union problem.