Reviewerly/exHarmony huggingface dataset:
Combining Movement Model with Finger-Stroke Level Model Towards Designing a Security Enhancing Mobile Friendly CAPTCHA

Although many solutions have been proposed for peripheral device controlled desktop applications to separate Human-Bot using CAPTCHAs, no significant work has addressed the same issue for handheld touch-sensitive devices. In this work, we propose a novel CAPTCHA system based on flicking or dragging segmented sub-images to a specific output box to regenerate a sample image. The successful matching of sample and output images can be considered to be submitted by humans only if the movement data of dragging object shows a human-like pattern and threshold crossing mismatch with the pattern for dragging performed by the bot. We designed the prototype of our proposed CAPTCHA for both PC and Mobile platform to underline the subtle, yet inevitable difference between key-stroke and finger-stroke. After comparing and analyzing evaluation factors like solving time, accuracy and reat-tempt requirements from 31 users with 3 different physical issues, we concluded our paper with two important discussions. First, the inclusion of extreme noise may become a handful in first-order bot identification, but an alarming drop of accuracy for specific users with vision and motor disabilities forced us to keep noise level in a moderated state. And finally, the task completion time of required action factors associated with unit tasks to solve the puzzle for both mobile and desktop versions indicates that our proposed finger-stroke based CAPTCHA performs faster than a key-stroke based traditional model.
Information Technology for Medical Data Stream Mining

In this paper information technology for medical data stream analysis under conditions of uncertainty is proposed. In this case, the uncertainty is defined like unknown total number of patients, of the initial number of medical features and diagnoses that can be changed during diagnostic process, the data need to be processed sequentially in online-mode (Data Stream processing). Information technology consists of three modules: controlled learning module (Data Stream consists of sufficient number of marked samples - representative dataset), active learning module (Data Stream consists of a few number of marked samples - unrepresentative dataset) and self-learning module (Data Stream consists of only unmarked samples). As a result of the operation of information technology, we obtain a diagnosis of each patient in sequential online mode.
Image Encryption using a Modified Pseudo-Random Generator

This paper presents a new image encryption algorithm, that accepts an initial secret key and an initialization vector, to produce the encryption/decryption keys using a modified pseudo-random generator. This key is used to encrypt the input images using an XOR cipher. The mathematical inability of the attacker to determine the values of the seeds, due to their logical discreteness from the output of the pseudorandom generator, and thus the encryption algorithm, is the key feature that ensures the security of the algorithm. The proposed algorithm is used to encrypt the standard test images, and the obtained results are scrutinized with respect to the following metrics - histogram, correlation, entropy, NPCR, and UACI. Theoretical and experimental analyses are performed to justify the performance and security offered by the proposed algorithm.
Vehicle Key Information Detection Algorithm Based on Improved SSD

With the rapid development of technology and economy, the number of cars is increasing rapidly, which brings a series of traffic problems. To solve these traffic problems, the development of intelligent transportation systems are accelerated in many cities. While vehicles and their detailed information detection are great significance to the development of urban intelligent transportation system, the traditional vehicle detection algorithm is not satisfactory in the case of complex environment and high real-time requirement. The vehicle detection algorithm based on motion information is unable to detect the stationary vehicles in video. At present, the application of deep learning method in the task of target detection effectively improves the existing problems in traditional algorithms. However, there are few dataset for vehicles detailed information, i.e. driver, car inspection sign, copilot, plate and vehicle object, which are key information for intelligent transportation. This paper constructs a deep learning dataset containing 10,000 representative images about vehicles and their key information detection. Then, the SSD (Single Shot MultiBox Detector) target detection algorithm is improved and the improved algorithm is applied to the video surveillance system. The detection accuracy of small targets is improved by adding deconvolution modules to the detection network. The experimental results show that the proposed method can detect the vehicle, driver, car inspection sign, copilot and plate, which are vehicle key information, at the same time, and the improved algorithm in this paper has achieved better results in the accuracy and real-time performance of video surveillance than the SSD algorithm.
Face Recognition System Based on Fusion Features of Local Methods Using CCA

Information fusion is a solution espoused for enhancing a pattern recognition system’s performance. A single representation précises the information and presents a single cue on the data; thus, information fusion is said to be more prolific as every feature set depicts a different outlook on the actual dataset. This paper recommends a face recognition system by utilizing fusion features of two local descriptor approaches. Firstly, blending of two most effective local face features, namely Gabor transform features and Local Binary Pattern (LBP), renders significantly improved performance compared to either individually: they complement each other wherein small appearance details are captured by LBP, while Gabor includes encoding facial shape for a wider range of scales. Secondly, to the combined feature vector, applying of the Canonical Correlation Analysis method (CCA) is done in order to extract discriminant characteristics for recognition. Lastly, a support vector machine (SVM) is deployed for classification, and K-nearest neighbor (K-NN) is utilized for feature matching. The technique is assessed against many challenging face datasets such as Yale database, with encouraging outcomes.
Elgamal cryptoalgorithm on the basis of the vector-module method of modular exponentiation and multiplication

This paper presents the implementation of the ELGamal cryptoalgorithm for information flows encryption / decryption, which is based on the application of the vector-modular method of modular exponentiation and multiplication. This allows us to replace the complex operation of the modular exponentiation with multiplication and the last one with addition that increases the speed of the cryptosystem. In accordance with this, the application of the vector-modular method allows us to reduce the modular exponentiation and multiplication temporal complexity in comparison with the classical one.
MoVIE: A Measurement Tool for Mobile Video Streaming on Smartphones

Mobile video streaming is becoming increasingly popular. In this paper, we describe the design and implementation of a cross-platform measurement tool called MoVIE (Mobile Video Information Extraction) for video streaming on mobile devices. MoVIE is a client-side traffic analyzer that studies smartphone video streaming from different viewpoints. It collects information about network-level packet traffic, transport-layer flows, and application-level video player activities. Then it identifies relationships within the collected data to make mobile video streaming activities transparent. MoVIE is an open-source tool with a graphical user interface. In addition to network traffic measurement, MoVIE supports objective Quality of Experience (QoE) evaluation of video streaming. These features make MoVIE a powerful tool for network traffic measurement, multimedia streaming studies, and privacy analysis. We illustrate MoVIE' scapabilities with a small case study of streaming 360° videos.
Hybrid Filtering for a Class of Nonlinear Quantum Systems Subject to Classical Stochastic Disturbances

A hybrid quantum-classical filtering problem, where a qubit system is disturbed by a classical stochastic process, is investigated. The strategy is to model the classical disturbance by using an optical cavity. The relations between classical disturbances and the cavity analog system are analyzed. The dynamics of the enlarged quantum network system, which includes a qubit system and a cavity system, are derived. A stochastic master equation for the qubit-cavity hybrid system is given, based on which estimates for the state of the cavity system and the classical signal are obtained. The quantum-extended Kalman filter is employed to achieve efficient computation. The numerical results are presented to illustrate the effectiveness of our methods.
Image quality assessment by an efficient correlation‐based metric

Summary Image quality can be measured visually. In the human visual system, a compressed image can be judged by the human eye. Image quality may not be perceived to decline in a region with low compression. However, image quality clearly declines in a region with high compression. As image compression increases, image quality gradually transitions from visually lossless to lossy. In this study, we aim to explain this phenomenon. A few images from different datasets were selected and compressed using JJ2000 and Apollo, which are well‐known image compression algorithms. Then, error‐based and correlation‐based metrics were applied to these images. The correlation‐based metrics agree with human‐vision evaluations in experiments, but the error‐based metrics do not. Inspired by the positive result of the correlation‐based metrics, a new metric named the simple correlation factor (SCF) was proposed to explain the aforementioned phenomenon. The results of the SCF show good consistency with human‐vision results for several datasets. In addition, the computation efficiency of the SCF is better than that of the existing correlation‐based metrics.
Research data management in Turkey: A survey to build an effective national data repository

Research data management is an important topic for funding agencies, universities and researchers. In this context, the main aim of this study is to collect preliminary information for Aperta, which is being developed by the Scientific and Technological Research Council of Turkey, to fulfil the following goals: determine the research data management awareness levels of researchers in Turkey; understand current research data management practices in their research environments; and find out their experiences of policy issues. For this, a questionnaire was distributed to 37,223 researchers, with 1577 researchers completing it. The results indicated that researchers who spend more time with data have more concerns about data management issues. The levels of experience of creating a data management plan were quite low. The importance of this study lies in how it is able to show the current research data management practices of Turkish scholars during the new repository’s foundational development stage.
Exponential triplet loss

This paper introduces a novel variant of the Triplet Loss function that converges faster and gives better results. This function can separate class instances homogeneously through the whole embedding space. With Exponential Triplet Loss function we also introduce a novel type of embedding space regularization Unit-Range and Unit-Bounce that utilizes euclidean space more efficiently and resembles features of the cosine distance. We also examined factors for choosing the best embedding vector size for specific embedding spaces. Finally, we also demonstrate how new function can train models for one-shot learning and re-identification tasks.
The Exact Form of the “Ockham Factor” in Model Selection

We explore the arguments for maximizing the “evidence” as an algorithm for model selection. We show, using a new definition of model complexity which we term “flexibility,” that maximizing the evidence should appeal to both Bayesian and frequentist statisticians. This is due to flexibility’s unique position in the exact decomposition of log-evidence into log-fit minus flexibility. In the Gaussian linear model, flexibility is asymptotically equal to the Bayesian information criterion (BIC) penalty, but we caution against using BIC in place of flexibility for model selection.
Design and model checking of timed automata oriented architecture for Internet of thing

The architecture model of the Internet of thing system is the primary foundation for the design and implementation of the Internet of thing system. This article discusses the method and practice of time automaton modeling and model checking for the architecture of the Internet of thing system from the state and time dimensions. This article introduces the theory and method of modeling using time automata. And then, combined with the actual need of the elderly health cabin Internet of thing system, a dynamic and fault-tolerant time automaton model is established through a relatively complete architecture modeling. The model checking method verifies that the designed Internet of thing system has no deadlock system activity, service correctness, and timeliness correctness. The results of modeling experiments and model validation show that the reference model of time automata Internet of thing architecture established in this article can better reflect the nature of interaction with the physical world, heterogeneity and large-scale, dynamic, and incompleteness of the Internet of thing system.
Short-Term Photovoltaic Power Forecast Based on Long Short-Term Memory Network

The randomness and fluctuation of photovoltaic (PV) power brings new challenges to power system operation. Accurate PV power forecasting is critical to system dispatch. This paper applies long short-term memory network (LSTM) to forecast short-term PV power. First, Pearson correlation analysis is applied to identify features affecting PV power. The features with high correlation coefficient are selected as LSTM inputs. Short-term LSTM PV power forecasting models are then established according to different seasons and weather types. Case study is performed using PV power and numerical weather prediction (NWP) of a practical PV station in northwest China. The results obtained indicate that the forecasting models can effectively improve the forecast accuracy of short-term PV power.
A Novel Way of Project Management to Ensure Engagement for Successful Digital Transformation.

The cultural step-change for a successful digital transformation in the Oil &amp; Gas industry is a significant struggle. Under the pressure of executing projects with a lower budget and in time during this low oil price era, project management becomes a complex challenge. In addition, the endeavor in digital technology imposes a transition from traditional project execution to agile project execution which makes the transformation even more challenging. Thus, the objective of this paper is to present a novel way of project management called "Open Project Management" (OPM) using a digital platform that helps team members and employees to engage in the digital transformation process using social networking technology. The Open Project Management (OPM) approach is planned and organized to bridge the traditional project management (TPM) processes (plan-execution-control/monitoring-closing cycle), and the agile project management (APM) processes (short sprint iterations of planning-analysis-design-test-acceptance cycle), by focusing on the basic project building blocks: project deliverables. In the OPM platform, all project members and management processes are directly connected thru the project deliverables (either working packages or sprints). The OPM is flexible enough to switch between the traditional, the agile, or a blended approach. The open approach is aimed to empower employees by offering leadership through a self-management and ease execution work by sharing and collaborating with other project team members and project managers. To implement the OPM approach, a digital collaborative workspace platform is built to mimic familiar social media networks so that employees can perform their work within the system with minimal training. The OPM platform is comprised of 3 frameworks: i) project management, ii) project visualization and iii) project communications. These three digital frameworks combinedly allow every member to work in a collaborative way within the platform by integrating their work process and get quick feedback. The novelty of the OPM approach lies in creating autonomy, increasing transparency and more importantly engagement without face-to-face interaction for all stakeholders of the project execution. The OPM platform is an intelligent digital eco-system (iDES) that works like the traditional social media platforms, which allows employees bringing what they use in their personal lifestyle into work without adopting a new system. The workspace offers to create their work-image into the workplace, enhancing their motivation towards work and empower them. Such a project management strategy and the tool for successful implementation are essential to provide a transitional phase for employees to trigger the cultural step change required to implement digital transformation objectives.
Sentiment and Emotion Analyses for Malaysian Mobile Digital Payment Applications

1. Sentiment and emotion analyses provide a quick and easy way to infer users' perceptions regarding products, services, topics and events, and thus rendering it useful to businesses and government bodies for effective decision making. In this paper, we describe the outcomes of sentiment and emotion analyses performed on a mobile payment app, Boost, which is available in the Google Play Store. A total of 2463 text reviews were gathered, however, after pre-processing, 1054 of these reviews were annotated and used for sentiment and emotion analyses. Four supervised learning algorithms, namely, Support Vector Machine, Naïve Bayes, Decision Tree and Random Forest were compared using Python. Accuracy and F1 scores indicate Random Forest to have outperformed all the other algorithms for both sentiment and emotion analyses. A vast majority of the reviews were found to contain anger for the negative sentiments, whereas joy was observed for the positive reviews.
Adjacent Vertices Can be Hard to Find by Quantum Walks

Quantum walks have been useful for designing quantum algorithms that outperform their classical versions for a variety of search problems. Most of the papers, however, consider a search space containing a single marked element. We show that if the search space contains more than one marked element, their placement may drastically affect the performance of the search. More specifically, we study search by quantum walks on general graphs and show a wide class of configurations of marked vertices, for which search by quantum walk needs Ω(N) steps, that is, it has no speed-up over the classical exhaustive search. The demonstrated configurations occur for certain placements of two or more adjacent marked vertices. The analysis is done for the two-dimensional grid and hypercube, and then is generalized for any graph. Additionally, we consider an algorithmic application of the found effect. We investigate a problem of detection of a perfect matching in a bipartite graph. We use the found effect as an algorithmic building block and construct quantum algorithm which, for a specific class of graphs, outperforms its classical analogs.
Building a Cybersecurity Culture

Cybersecurity Culture (CSC) is unique for an organization and not replicable for others, being based on its specific characteristics, in terms of technologies, processes, and people’s values. To be effective CSC has to be considered as an integral part of the Organizational Culture (OC), since it can reinforce the cultural cohesion within organizations and lead people to realize that security is not just an IT department problem, but an issue which involves everyone. The building of CSC requires adopting a clear strategy and a set of resources, methods and tools. The premise is that a cooperative approach is better than imposing procedures, given that people do not often follow rules which are imposed and, above all, that they do not understand. The first and foremost issue for building CSC in an organization is an assessment phase, an internal “diagnosis” in order to evaluate the current state of the maturity of its security culture. This step is essential to avoid adopting security awareness programmes which do not meet the actual needs of the organization. As a good recipe depends on its ingredients, an effective CSC depends mainly on the active commitment of people. In this chapter implications of the construction of a CSC are discussed, together with the main framework and tools available in literature; moreover, some simple recommendations, such as the real involvement of top management, are provided in order to steer the work in the right way. Finally, to achieve positive outcomes, we should ensure a receptive and healthy environment; we can assume that when people perceive a safe work environment, they are more likely to carry out their tasks and to adopt a secure behaviour.
Resource Utilization in Data Center by Applying ARIMA Approach

Resource administration is basically an important dire function in the data center that may be affect Service level agreement and operation cost provided by the data center known as OPEX and SLA. Efficient resource is the key factor of resource utilization and provided guarantee SLA to each application to maximize the resources in data center. Accurate prediction support each application in data center is the key requirement to provision an efficient resource management. However, Under-estimating and Overestimation in the application workload result shows the resource under provision or overestimating. In this paper, our approach to apply ARIMA model for workload applications in data center, it is forecasting technique and capture autocorrelation in the series by modeling it directly. The key concept of ARIMA model is ordering and differencing only with linear data capturing when data graph in straight line. We applies different operation model to fit applications workload time series. Performance of ARIMA can be tested by MATLAB simulation. We are using the ARIMA model parameters to find out prediction errors during the day and month calculated (i.e. Day = 7.01% and Month = 6.73%) to provide accuracy of ARIMA prediction model.
Modeling POS Tagging for the Urdu Language

This paper presents a Parts-of-Speech (POS) tagger for a low resourced “Urdu” language. POS tagging is a primary preprocessing step in many natural language processing tasks such as sentiment classification, syntactic parsing and named-entity recognition. The proposed taggers make use of the two state-of-the-art models widely used for sequential tagging: Conditional Random Field (CRF) and the Bidirectional long short-term memory CRF (BiLSTM CRF). This work is the first instance of applying BiLSTM CRF model for POS tagging in the Urdu language. Both models achieved the F1 score of 96% on the test data, thus outperforming existing Urdu POS tagger with a significant margin.