Reviewerly/exHarmony huggingface dataset:
Authentication and password storing improvement using SXR algorithm with a hash function

Secure password storing is essential in systems working based on password authentication. In this paper, SXR algorithm (Split, Exclusive OR, and Replace) was proposed to improve secure password storing and could also be applied to current authentication systems. SXR algorithm consisted of four steps. First, the received password from users was hashed through a general hash function. Second, the ratio and the number of iterations from the secret key (username and password) were calculated. Third, the hashed password and ratio were computed, and the hashed password was divided based on the ratio (Split) into two values. Both the values were applied to XOR equation according to the number of iterations, resulting in two new values. Last, the obtained values were concatenated and stored in the database (Replace). On evaluating, complexity analyses and comparisons has shown that SXR algorithm could provide attack resistance with a stronger hashed password against the aforementioned attacks. Consequently, even if the hackers hacked the hashed password, it would be challenging and would consume more time to decrypt the actual one, because the pattern of the stored password is the same as the one that has been hashed through the general hash function.
Hybrid Model using Stack-Based Ensemble Classifier and Dictionary Classifier to Improve Classification Accuracy of Twitter Sentiment Analysis

Ensemble classifiers are widely used for the enhancement of accuracy of twitter sentiment classification.In the present research, a hybrid model based on stack based ensemble classifiers and dictionary based classifier is used for tweet classification as positive and negative.To enhance accuracy of classification, sentiment score retrieved from dictionary based classifier is added to the feature vector to get enhanced feature set and the hybrid stack based ensemble model is implemented on this enhanced feature set.Three machine learning classifiers svmRadial, C5.0, NB are used to build stacked based ensemble classifier using GLM and RF as Meta learners.Three data sets viz.Kaggle -US Airline Twitter Sentiment Data Set, Sentiment 140 Twitter Data Set, and Real time manually labeled data set related to 'Clean India Mission' are used for the implementation of the proposed model.Caret library of R Studio is used for creating the stack based ensemble of classifiers.The results show that the proposed hybrid model that used sentiment score as one of the features in feature set performed better with an accuracy of 0.8742223 for Kaggle -US Airline Twitter Sentiment Data Set, 0.8881453 for data set related to 'Clean India Mission' and 0.9953593 for Sentiment 140 Twitter Data Set, as compared to machine learning classifiers and other ensemble classifiers.
An IoT approach for efficient overtake detection of vehicles using H264/AVC video data

In this paper, a low-cost embedded autonomous system is presented. Its main device is a Raspberry Pi 2, which allows real-time analysis of traffic videos. However, the previously developed computer vision algorithms do not work at the pixel-level, meaning that they do not analyze each pixel on every frame of the video because this would entail working with such a large amount of information that it would not be possible to obtain real-time processing. Therefore, as an alternative, our system works on compressed video. In particular, our system takes as an input the H.264/AVC motion vector data produced in the encoder of the GPU embedded in Broadcom’s BCM2836 SoC. Furthermore, apart from working with very little information, the algorithms that are employed for video analysis must be efficient on their own. Thus, we propose algorithms that are capable of obtaining results directly from the analysis of a group of statistic measures, which are calculated based on a combination of input vectors. Experimentally, our system applies on the identification of overtakes, particularly those exceeding the maximum permitted speed, generating at the same time a report that is sent to a complaints e-mail account.
GENERATING SYNTHETIC TRAINING DATA FOR OBJECT DETECTION USING MULTI-TASK GENERATIVE ADVERSARIAL NETWORKS

Abstract. Nowadays, digitizing roadside objects, for instance traffic signs, is a necessary step for generating High Definition Maps (HD Map) which remains as an open challenge. Rapid development of deep learning technology using Convolutional Neural Networks (CNN) has achieved great success in computer vision field in recent years. However, performance of most deep learning algorithms highly depends on the quality of training data. Collecting the desired training dataset is a difficult task, especially for roadside objects due to their imbalanced numbers along roadside. Although, training the neural network using synthetic data have been proposed. The distribution gap between synthetic and real data still exists and could aggravate the performance. We propose to transfer the style between synthetic and real data using Multi-Task Generative Adversarial Networks (SYN-MTGAN) before training the neural network which conducts the detection of roadside objects. Experiments focusing on traffic signs show that our proposed method can reach mAP of 0.77 and is able to improve detection performance for objects whose training samples are difficult to collect.
TOWARDS STRUCTURELESS BUNDLE ADJUSTMENT WITH TWO- AND THREE-VIEW STRUCTURE APPROXIMATION

Abstract. The global approaches solve SfM problems by independently inferring relative motions, followed be a sequential estimation of global rotations and translations. It is a fast approach but not optimal because it relies only on pairs and triplets of images and it is not a joint optimisation. In this publication we present a methodology that increases the quality of global solutions without the usual computational burden tied to the bundle adjustment. We propose an efficient structure approximation approach that relies on relative motions known upfront. Using the approximated structure, we are capable of refining the initial poses at very low computational cost. Compared to different benchmark datasets and software solutions, our approach improves the processing times while maintaining good accuracy.
BUNDLE BLOCK ADJUSTMENT WITH CONSTRAINED RELATIVE ORIENTATIONS

Abstract. This paper deals with bundle adjustment with constrained cameras, i.e. where the orientation of certain cameras is expressed relatively to others, and these relative orientations are part of the unknowns. Despite the remarkable interest for oblique multi-camera systems, an empirical study on the effect of enforcing relative orientation constraints in bundle adjustment is still missing. We provide experimental evidence that indeed these constraints improve the accuracy of the results, while reducing the computational load as well. Moreover, we report for the first time in the literature the complete derivation of the Jacobian matrix for bundle adjustment with constrained cameras, to foster other implementations.
Feasibility and acceptability of a video library tool to support community health worker counseling in rural Afghan districts: a cross-sectional assessment

Rural Afghan populations have low skilled birth attendance rates and high maternal and infant mortality. Insecurity and armed conflict, geographic barriers, and cultural norms often hinder women's access to facility-based reproductive, maternal, newborn, and child health (RMNCH) services. Community health workers (CHWs) are critical agents for behavioral change in this and similarly fragile settings, where RMNCH information exposure is limited by low literacy and mass media access. We assessed the feasibility and acceptability of a computer tablet-based health video library (HVL) to enhance CHW counseling on RMNCH topics in three rural Afghan districts.The HVL was introduced by trained CHWs in 10 pilot communities within one rural district in each of Balkh, Herat, and Kandahar provinces. We used a mixed-methods study design to assess exposure to and perception of the HVL 6 months post-introduction. We surveyed married women (n = 473) and men (n = 468) with at least one child under 5 years and conducted in-depth interviews with CHWs and community leaders (shuras and Family Health Action groups) within pilot communities (n = 80). Program improvement needs were summarized using quantitative and qualitative data.Higher proportions of women in Balkh (60.3%) and Herat (67.3%) reported viewing at least one HVL video compared to women in Kandahar (15%), while male HVL exposure was low (8-17%) across all districts. Most HVL-exposed clients (85-93% of women and 74-92% of men) reported post-video counseling by CHWs. Nearly all (94-96% of women and 85-92% of men) were very interested in watching videos on other health topics in the future. Participants recommended increasing the number of videos and range of topics, using tablets with larger screens, and translating videos into additional local languages to improve the HVL program.The HVL was a highly acceptable tool for relaying health information, but coverage of female audiences in Kandahar and male audiences broadly was low. The HVL should better engage men and other key influencers to engineer local solutions that directly facilitate male HVL exposure, indirectly improve women's HVL access, and support collaborative spousal health decision-making. A larger efficacy trial is warranted to measure the HVL's effect on knowledge and health-related behavioral outcomes.
Invited Paper: Edge-based Provisioning of Holographic Content for Contextual and Personalized Augmented Reality

Mobile augmented reality (AR) has been attracting considerable attention from industry and academia due to its potential to provide vibrant immersive experiences that seamlessly blend physical and virtual worlds. In this paper we focus on creating contextual and personalized AR experiences via edge-based on-demand provisioning of holographic content most appropriate for the conditions and/or most matching user interests. We present edge-based hologram provisioning and pre-provisioning frameworks we developed for Google ARCore and Magic Leap One AR experiences, and describe open challenges and research directions associated with this approach to holographic content storage and transfer. The code we have developed for this paper is available online.
Psychological Aspects of Professional Suitability of Students at the Stage of Higher Education

The current socio-economic situation in the country and in the world places ever-higher demands on the professionalism of a person: a constant change in the nature and goals of work occurs, new professions arise, and the intensity and tension of work increases.The employee is required high professionalism, personal competitiveness, increased responsibility, the ability to work in conditions of economic and psychological uncertainty.The formation of professional suitability and competence of such a professional is impossible without reliance on the psychological qualities of a person.In this regard, the role of targeted career guidance work in the educational system has sharply increased, which should be based on a thorough knowledge of the entire system of the main factors that determine the future professional suitability of a specialist, including psychological ones.The article considers the psychological aspects of the professional suitability of students receiving higher education.On the basis of an empirical study, which was attended by 120 junior students receiving higher education in economics and finance, it is concluded that the professional inclinations and orientation, motivation for choosing a profession for the majority of students who took part in the study correspond to the choice of profession.At the same time, a certain part of students has been identified whose professional inclinations and motivation do not yet sufficiently correspond to the direction of study, and therefore this group requires in-depth psychological and pedagogical work in a higher educational institution.The article highlights the experience of the Russian regional university in building a career guidance system, shaping professional suitability and enhancing professional selfdetermination of student youth.
An Extensive Review on Recent Evolutions in Object Detection Algorithms

The domain of Computer Vision Algorithms has achieved exemplary development in the past few years.Owing to its wide range of applications, especially in object detection, image classification and image segmentation, it has gained its own significance in this technological era.This paper provides a significant remark on fractal and multifractal methods for object identification in images and also a keen review of various Computer Vision algorithms and their features for detecting objects.We have also significantly analyzed the previous contributions on various algorithms discussed for the object detection process.
DEVELOPMENT OF THE EDUCATION AND SCIENCE SYSTEM IN THE CONTEXT OF THE FORMATION OF INDUSTRY 4.0

The creation and dissemination of new technologies in modern production, economy and social sphere leads to significant changes in global markets. Today, the speed of development of new developments and their implementation is much higher than at any time in history, and continues to grow every day. In the coming years, this rapid development of technology may lead to the formation of new markets that can provide consumers with fundamentally new services. Even today, we can talk about the unfolding global industrial revolution, which was the result of recent achievements in biotechnology, artificial intelligence, robotics and information and communication technologies. The main perspective of this research is focused on the development of the education system in the era of Industry 4.0.
A Hybrid Approach to Reassemble Ancient Decorated Block Fragments through a 3D Puzzling Engine

The reassembling of severely damaged tangible heritage is a primordial task for archaeologists who not only aim to further study the past but also to preserve ruined ancient monuments. As a consequence, various researchers have proposed methods to automatically solve this problem by computing and matching geometric properties of counterpart fragments. Although their results are quite promising, experts still carry out this task manually by finding relationships between distinctive matching cues, such as type of decoration, remaining traces, inscriptions’ content, etc. The topic itself poses challenges to both automatic and manual approaches due to the high level of damage ancient broken fragments have undergone over the centuries. Therefore, this paper proposes a Puzzling Engine that combines crucial elements of automatic and manual methodologies to empower experts with registration tools for reassembling fragmented heritage. Unlike similar hybrid human-computer puzzling engines, our approach is capable of automatically proposing matches and rough alignments solely based on the geometry of fractured surfaces. Based on these initial solutions and a set of registration tools, experts can accurately solve the puzzle. The virtual environment has been used and verified to find pairwise puzzle-pieces of actual antique wall decorated fragments, resulting in new discoveries that experts could not have come up with by utilizing classic techniques. Concretely, the contributions are twofold, (i) a feature-based registration pipeline that is able to suggest both matches and alignments to the user and (ii) a virtual interface that integrates automatic and user-assisted techniques to accurately puzzle fragmented surfaces.
DeeDP: vulnerability detection and patching based on deep learning

We present the DeeDP system for automatic vulnerabilities detection and patch providing. DeeDP allows to detect vulnerabilities in C/C++ source code and generate patch for fixing the detected issue. This system uses deep learning methods to organize rules for deciding whether a code fragment is vulnerable. Patch generation processes can be performed based on neural network and rule-based approaches. The system uses the abstract syntax tree (AST) representations of the source code fragments.We have tested effectiveness of our approach on different open source projects. For example, Microsoft/Terminal (https://github.com/microsoft/Terminal) was analyzed with DeeDP: our system detected security issue and generated patch which was successfully approved and applied by Microsoft maintainers.
The Ergonomics of the Islamic Ablution: Exploring Considerations for the Elderly in the Mosque

A general observation on the state and condition of the ablution area at some mosques in the country has pictured the place as slippery and unsafe and not properly used and maintained. This has led to the assumptions that the design of the place had not thoroughly considered the ergonomic factors and right anthropometric of the users which has created discomfort in positioning oneself and accessing the fixtures during the act. The impact of the problems has never been rationalized and always overlooked particularly on how it may affect the elderly group that is has become very common and being repeated in the provision of the ablution area in other mosque’s design and development. In consideration of the pressing issues and needs for an appropriate ablution area as part of safety and cleanliness measures at Mosque or Surau, the design of the facility must be emphasized and be based on the needs and physical abilities of the users. This particular study on the ablution area is necessary as it has uncovered salient and common problem of the current design of the place. The methodology selected for the study was a combination of several methods. It encompassed onsite observation on the practical use and performance of the ablution area, interview survey on users’ practical experience and design assessment focusing on the ergonomic and anthropometric considerations of the place through measured drawing method. The results were triangulated to determine the overall performance of the present design of ablution area in facilitating spiritual obligation of the people. For the purpose, three different mosques in Kuala Nerus district namely Masjid UNiSZA (Universiti Sultan Zainal Abidin), Masjid UMT (Universiti Malaysia Terengganu) and Masjid Tok Jembal were selected as case studies. The study on the 3 sites of ablution area has displayed results indicating problems generally based on the location and proximity of water faucet and seating provision at the area which apparently were not based on the right ergonomic. This has created discomfort to the users and leaving messy and slippery ground after use.
ANALYSIS OF THE EFFECTS OF CELL TEMPERATURE ON THE PREDICTABILITY OF THE SOLAR PHOTOVOLTAIC POWER PRODUCTION

The use of intermittent power supplies, such as solar energy, has posed a complex conundrum when it comes to the prediction of the next days' supply. There have been several approaches developed to predict the power production using Machine Learning methods, such as Artificial Neural Networks (ANNs). In this work, we propose the use of weather variables, such as ambient temperature, solar irradiation, and wind speed, collected from a weather station of a Photovoltaic (PV) system located in Amman, Jordan. The objective is to substitute the aforementioned ambient temperature with the more realistic PV cell temperature with a desire of achieving better prediction results. To this aim, ten physics-based models have been investigated to determine the cell temperature, and those models have been validated using measured PV cell temperatures by computing the Root Mean Square Error (RMSE). Then, the model with the lowest RMSE has been adopted in training a data-driven prediction model. The proposed prediction model is to use an ANN compared to the well-known benchmark model from the literature, i.e., Multiple Linear Regression (MLR). The results obtained, using standard performance metrics, have displayed the importance of considering the cell temperature when predicting the PV power output. Keywords: Renewable Energy, Photovoltaic, Prediction, Cell temperature, Multiple Linear Regression, Artificial Neural Networks.JEL Classifications: C53, Q47.DOI: https://doi.org/10.32479/ijeep.9533
Time Series Predictive Models for Social Networking Media Usage Data: The Pragmatics and Projections

Aims: We have set forth three main objectives in the work presented in this paper, they are namely, to study how social networking media usage is surging over the time for three social media networks viz., Facebook, Twitter and LinkedIn, ii.to develop best fitting time series predictive models for predicting future usage of three network media and, iii. to make a comparative analysis to herald the ups and downs noticed in the usage across three network media considered.&#x0D; Study Design: Application of time series techniques for the analysis of social network user’s data. The main research question addressed by this work is to see how time series models augurs for time dependent data such as the one chosen in this research.&#x0D; Place and Duration of Study: Research Center, Department of Master of Computer Applications, Siddaganga Institute of Technology, Tumakuru, Karnataka, India, between January 2020- April 2020.&#x0D; Methodology: The work delved on collection three social network users (Facebook, LinkedIn, and Twitter) data for a span of nine years i.e., for the tenure 2011-2019. One dimensional, two dimensional and three dimensional visual analytics is made prior to time series analysis. Time series predictive analytics involved development of best fits for prediction. To select the best fits among linear, polynomial, exponential, power function and logarithmic models, mean absolute error and root mean square error metrics were used.&#x0D; Results: Linear, polynomial function trend lines proved to be the best for Facebook, LinkedIn and Twitter respectively with low values of MAE and RMSE and high values of regression coefficients as compared with other kinds of models. Apart from the error metrics, the Theil’s U-statistic values of 0.928, 1.008 and 1.21 for Facebook, Twitter and LinkedIn also heralded the fact that these functions are superior models when compared with other naïve models. It is also projected that by 2025, Facebook will see 10,000 billion, followed by LinkedIn at 1500 billion while Twitter would see 750 billion people if same kind of surge trend prevails in user numbers across three networks considered in this research.&#x0D; Conclusion: This paper presented a unique work which is supposedly deemed to be the first of its kind to the best of the knowledge of authors. The models come with a limitation that, they can provide accurate projection if the same trend prevails in the pattern of upheavals in usage.
Utilising Technology in Making the Nigerian Administration of Criminal Justice Act Effective for Criminal Trials

The 2015 Administration of Criminal Justice Act (ACJA) has set Nigeria on the path of adopting a more technology-driven criminal justice administration. Prior to its enactment after several years of consideration, there had been only episodic use of Information and Communication Technology (ICT) facilities in criminal proceedings. This paper examines Nigeria's Criminal Justice Administration (CJA) in light of the ACJA's innovative provisions with a view to furthering the application of ICT facilities. It is hoped that ICT might be integrated throughout the CJA as it currently operates in Nigeria so as to promote the effective and efficient automated administration of criminal justice. In the new era of social distancing given the COVID-19 pandemic, such reform is submitted to be imperative.