Reviewerly/exHarmony huggingface dataset:
Use of Different Channels for User Awareness and Education Related to Fraud and Phishing in a Banking Institution

Each person faces the risk of falling to fraud not only as an individual but also as a customer of bank, insurance company, mobile operator, government services and many others. To tackle the fraud risk holistically it is not enough to implement fraud-risk related processes into daily operations and deploy fraud detection and prevention tools and systems. The weakest point is usually the person - employee or customer, especially the uneducated one. Therefore, it is essential complement the above measures with awareness and education. In this paper we reviewed different channels available for fraud prevention awareness campaigns and their characteristics (reach, relative cost, age group and supported message format) which impact their applicability. We also analyzed historical occurrences of fraud related messaging as well as fraud awareness posts distributed through one of the “youngest” channel - Twitter (social network) by the banks in the UAE. We analyzed the tweets of selected banks in UAE to observe, how they use this channel for communication with their customers and more specifically - how they use the channel for the purpose of fraud awareness.
A domain-based access control mechanism using smart contracts for the internet of things

This paper addresses the scalability in the cost of smart contracts, which prevent illegal resource access (control access) in the Internet of Things. In previous studies, smart contracts have been used to avoid having a single point of failure, while maintaining tamper resistance. However, a high cost (called gas) is necessary when the number of subject-object pairs becomes large. In order to reduce cost, this paper proposes an access control method using smart contracts with a hierarchical name space. The proposed method scales well in terms of cost-decisive factors, namely, the complexity of functions and the number of smart contracts, leading to scalability in cost.
Personalized Travel Product Recommendation Based on Embedding of Multi-Behavior Interaction Network and Product Information Knowledge Graph

In this paper, we proposed a hybrid recommendation model to tackle two challenges in the recommendation system. First, many of the products have been browsed frequently but may not consequentially be ordered. As a result, the users’ actions may not directly be considered as the preference on a specific item. Second, the popularity of sold products has a highly skewed distribution which results in the cold start problem in the recommendation. In order to extract knowledge from users’ implicit feedback, we develop the neighborhood structure of users and behaviors on products in the multi-behavior interaction network (MBIN) that incorporates the multiple behaviors simultaneously. To deal with the cold product issue and the skewed distribution problem, we take the product information into consideration by using the metadata of products and extracting more features from the textual contents to form a knowledge graph. By applying embedding algorithms to the multi-behavior interaction network and the knowledge graph, we are able to catch the user’s preference from the collaborative implicit feedback aspect and the product information aspect. To evaluate the performance of our model, we conduct extensive experiments on the real-world dataset. The result of our approaches outperforms several widely used methods for the recommendation systems.
Creating Visual Tools to Support Human-Centered Design Methods

Visual tools, such as illustrations, are increasingly being used as part of Human-Centred Design (HCD) methods (e.g. during the requirements gathering phase). However, many HCD practitioners, who do not have visual communication design skills, often find it difficult to determine which existing visual tools to use, or how to create new tools for their own design projects. As such, it is becoming necessary for HCD practitioners to work with visual communication designers to co-create visual tools for specific projects. Here, we provide a multidisciplinary design approach to guide the process of co-creating such visual tools. We also discuss the use of this process in a case study, which has required creating visual tools for field interviews.
Encryption and Decryption of Images using GGH Algorithm: Proposed

Abstract Images are considered as important data used in many fields such as military operations, medical imaging, and astronomy researches. For sending images through an insecure network, it is necessary to develop a secure encryption algorithm when transmitting the images. Three main properties of security services (i.e., confidentiality, integrity, and availability), the confidentiality is the most essential feature for exchanging images. The Goldreich Goldwasser Halevi (GGH) algorithm can be a good method for encrypting images as both the algorithm and sensitive data are represented in numeric matrices. Additionally, the GGH algorithm does not increase the size of the image. However, one of the disadvantages of using the GGH algorithm is the attacking of GGH cryptosystem mainly relied on special properties of the lattice generated. This shortcoming of GGH algorithm has been taken to proposed lattice reduction algorithms. A lattice basis reduction algorithm to get a better basis which is used into encryption and decryption approach.
An Efficient Confidence Measure-Based Evaluation Metric for Breast Cancer Screening Using Bayesian Neural Networks

In this paper, we propose a confidence measure-based evaluation metric for breast cancer screening using a modular network architecture; we use a traditional neural network as a feature extractor with transfer learning, followed by a Bayesian neural network. We show that by providing medical practitioners with a tool to tune two hyperparameters of the Bayesian neural network (fraction of sampled number of networks and minimum probability), the framework can be adapted as needed. We argue that instead of a single number like accuracy, a tuple (accuracy, coverage, sampled no. of networks, minimum probability) can be used as an evaluation metric. We provide experimental results on the CBIS-DDSM dataset, showing accuracy-coverage tradeoff trends while tuning the hyperparameters. To make the proposed framework deployable, we provide source code with reproducible results at https://git.io/JvRqE.
Problems of Organizing the Educational Process in Small Academic Groups at the University and Ways to Solve them (On the Materials of Kabardino-Balkarian State University)

Purpose: The purpose of the research of this problem is to obtain relevant information about the features of student education in small groups, as well as to develop recommendations for improving the educational process. Design /Methodology/Approach: The article considers methodological issues of organizing the educational process in small academic groups for bachelors and specialists (masters) in the conditions of reducing classroom classes. In recent years, groups of students with small numbers have appeared in higher education in some areas of training in senior courses. Practice shows that conducting classes in a small group causes a number of difficulties. Despite the conditions of almost individual training, students quickly get tired; they are disconnected from academic work; the quality of education is reduced; there is a need to adapt previous methods and forms of education to a new situation, and from the point of view of organizing the educational process economic problems for the university administration arise. Findings: The authors presented the reasons for the appearance of small groups at the university, the economic feasibility of training small groups and approaches to solving this problem. Guidelines for optimizing financial costs in the education of small academic groups have also been identified Originality/Value: The novelty of the research consists in generalization and application of the known and innovative methods of education to underfilled groups. The increasing role of the teacher and level of his professionalism which directly depends on extent of mastering the electronic information and education environment, innovative methods of training and ability to apply them in educational process taking into account specifics of work in relation to low-complete groups is established.
A User Study of a Gaze Window User Interface

We have proposed a proof of concept of a gaze window interface. A gaze window interface uses the user's gaze point to show the relevant content nearby the current gaze point. A system of a gaze window interface has been implemented that allows users to manage looking at multiple objects of interest on the screen while the system is responsive to the user's gaze points with relevant information shown in the gaze window. We also conducted user studies to investigate the effects of the interface on user performance and behaviors. In this paper, we report on a user study in which the gaze window was compared with a mouse window for supporting single user data entry tasks. We describe details of the study design and conduction and present the results.
Algorithms for Splitting Telemetric Data Frames into Strictly Homogeneous Areas for the Compression Problem

Problem statement: the telemetry data stream received from different sources of one complex telemetry object, presented in the difference-bit form, is characterized by high stationarity, and this, in turn, indicates that most of the bits in frames after the delta coding procedure will be zero. And also, bits of the same type should be located so that they form some geometric groups. Thus, the issue of developing effective algorithms for splitting the structure of telemetry frames into strictly homogeneous areas and their compact description is acute enough for effective compression of telemetry data. Purpose of the study: development of effective algorithms for splitting telemetry data frames into strictly homogeneous areas to improve the efficiency of telemetry data transmission in telemetry systems. Results: for all the proposed algorithms, the behavior of the averaged compression ratio was investigated depending on the properties of the streams of telemetric data frames. The experimental results presented show that the proposed algorithms provide a sufficiently high overall efficiency of compression of data frames of various types, and their further development can be associated with the optimization of the description of the areas obtained as a result of splitting.
Task schedule algorithm based on double-objective particle swarm optimization under cloud computing

In cloud computing environment, how to reasonably carry out the resource scheduling and reduce the task execution time and task execution energy consumption has become a hot issue in the researches. In this paper, the task processing time and energy consumption were used as the optimization objectives, and a Double-Objective Particle Swarm Optimization (DOPSO) was used to perform the optimal scheduling of task completion time and energy consumption. Finally, the results obtained in DOPSO were simulated in CloudSim, and results showed that compared with existing scheduling algorithms, the proposed algorithm (DOPSO) had obvious advantages in the integrated scheduling performance.
Realization of Ball on Plate Control using PID Controller and Webcam

Abstract This study aims to apply the PID controller to the ball on plate control realisation to reach a stable position of ball on plate. The ball on plate props adjust the position at the desired coordinates on a flat square plate. The targeted position is set as set point coordinates in the form of X pixels and Y pixels. The input used is in the form of coordinates obtained from the webcam through image processing methods and sent serially to the microcontroller. The output from the microcontroller is fed into the actuator, namely the servo motor. From the experiment results and its implementation, it is shown that ball on plate can move towards to the desired set point. For PWM signal set point, the value referred to centre of plate is 250 and the response of X-Y axes have errors 3.64% and 8%, respectively.
Exploration on Cloud Resource Platform-Based Cultivation Pattern for the Lifelong Learning of Vocational Teaching Staff

The cultivation of the lifelong learning literacy is an irresistible trend and requirement of promoting the individual competitiveness of teachers in vocational institutions, as well the overall development level of the vocational education. By taking the program of numerical control programming (NC programming) as an example, this paper studied the lifelong learning pattern based on the cloud platform of Internet resources. Cloud resource platform ran through full cultivation process of the teaching staff in their development stage from students to teachers. It is of great significance to achieve the integrated studies and cultivation for individual vocational education in terms of knowledge, skills and qualities, and promote the integration between production and education. Its application effectively mobilized the initiatives of students, teachers of vocational schools and enterprises, and developed a new channel for the lifelong learning of normal university students oriented for vocational schools and vocational school teachers.
Cosine and Soft Cosine Similarity-Based Anti-Phishing Model

Phishing attack has posed a greater threat to user information over network. In addition to the existence of various disguise illegal URL’s, instances had been seen when users are redirected to phishing URL’s that challenges their privacy concern. In the current work, author tried to develop an effective anti-phishing method based on hybrid similarity approach combining cosine and soft cosine similarity that measures the resemblance between user query and database. The strength of the proposed hybrid approach is further enhanced with the incorporation of feed forward backpropagation neural network (FFBPNN) so as to validate the similarity-based predictions. The model evaluated against 3000 sample files demonstrated to effectively detect phishing attacks with positive predictive value, true positive rate and F-measure of 71.9%, 72.6% and 72.23%, respectively.
Image Super-Resolution Using Light-Weight Deep Learning Methods

Recently, design of neural networks with compact model size, light computation cost and high performance, has attracted much attention due to the need of applications on mobile devices. Inspired by knowledge distillation, we design a multi-level supervised compact network (MSCN) for SR with lightweight parameters and computational complexity. In the proposed MSCN, we employ a compact model which is not only supervised by the HR label, but also supervised by the intermediate representation of a cumbersome model. We comprehensively evaluate our MSCN on benchmark datasets with two widely used image quality metrics: PSNR and SSIM. The results show that our proposed method outperforms the compact network which is without multi-level supervisions by a comparable margin in terms of PSNR and SSIM.
A Learning and Practicing System to Support Effective Poetry Generation Based on Neural Network

Chinese ancient poetry has been a favorite literary form and is still very popular after thousands of years. As opposed to free language, poetry has the characteristics of aestheticism and conciseness. People can easily judge the quality of ancient poetry, so the generation of ancient poetry can be used as an important method for beginners to learn NLG models and judge the performance of the models. Therefore, we present a novel system called LiBai to facilitate the comprehensive generation and analysis of poetry. The system LiBai includes two main functionality modules - a versatile model library and a user-friendly and interactive studio. LiBai can help user: 1) learn popular poetry generation models systematically, including the model introduction, network structures and performances; 2) through adjusting various model parameters, interactive training, predicting and directly apply these models on real data easily and 3) simply and quickly analyse the generated poetry.
Playing AI Dots-And-Boxes Using Endgame Theory and Nimstring Sprague-Grundy Values

In a previous work, endgame theory was developed on counting the final score of simple endgame positions in dots-and-boxes, notably in positions consisting only of chains and loops. We make use of the theory in actual gameplay to improve the performance of monte-carlo search methods for the game. Specifically, endgame results can be used to terminate the search tree early when it can be shown one side has a definitive lead. Furthermore, Sprague-Grundy values, which apply to the related game of nimstring, can be used as an evaluation function for dots-and-boxes positions, and as a heuristic for choosing moves. In doing so, we significantly improve the performance of the monte-carlo tree search algorithm for playing dots-and-boxes.
Link Prediction in Knowledge Graphs with Numeric Triples Using Clustering

Knowledge graphs (KG) include large amounts of structured data in many different domains. Knowledge or information is captured by entities and relationships between them in KG. One of the open problems in knowledge graphs area is "link prediction", that is predicting new relationships or links between the given existing entities in KG. A recent approach in graph-based learning problems is "graph embedding", in which graphs are represented as low-dimensional vectors. Then, it is easier to make link predictions using these vector representations. We also use graph embedding for graph representations. A sub-problem of link prediction in KG is the link prediction in the presence of literal values, and specifically numeric values, on the receiving end of links. This is a harder problem because of the numeric literal values taking arbitrary values. For such entries link prediction models cannot work, because numeric entities are not embedded in the vector space. There are several studies in this area, but they are all complex approaches. In this study, we propose a novel approach for link prediction in KG in the presence of numerical values. To overcome the embedding problem of numeric values, we used a clustering approach for clustering these numerical values in a knowledge graph and then used the clusters for performing link prediction. Then we clustered the numerical values to enhance the prediction rates and evaluated our method on a part of Freebase knowledge graph, which includes entities, relations, and numerical literals. Test results show that a considerable increase in link prediction rate can be achieved in comparison to previous studies.
Deep Neural Network-based Active Region Magnetogram Patch Super Resolution

Image super-resolution is a branch of image processing that is concerned with enhancing the spatial resolution and quality of images by learning the intrinsic details and relations between the lower resolution input and the higher resolution output images. It is widely accepted as an ill-posed problem, which has seen tremendous advancements with deep learning-based models. In this work, we present two super resolution models, Sub-Pixel Convolutional Neural Network (CNN) and Enhanced Deep Residual Networks (ResNet), which can be used for improving the spatial resolution of solar magnetograms. While the ill-posed nature of problem is still a challenge, there are several application areas, including space weather prediction, which can greatly benefit from the improved spatial resolution of solar magnetograms. Along with classical raster inputs we try to improve the model objective by giving HMI Active Region Patches. We show that through our experimental evaluation our models perform better than baselines and CNN-based super resolution model provides viable results for magnetogram super resolution.
Review Paper on Sentimental Analysis for Recommendation System

Sentimental analysis is the method of computationally identifying and categorizing opinion expressed in a piece of text. These opinions are categories good or positive, bad or negative, and neutral, the process of identifying differences of opinions, and group them in these categories. Take the opinion of public and their liking in specific text. Opinion mining identifies the emotional nature behind a body of text or music and builds the resources accordingly. The unique approach by performing abstract level of sentimental analysis of user’s reviews using sentiment classification. This sorting used entropy for machine learning systems.
Fusion of Texture and Optical Flow Using Convolutional Neural Networks for Gender Classification in Videos

Automatic Gender Classification (AGC) is an essential problem due to its growing demand in commercial applications, including social media and security environments such as the airport. AGC is a well-researched topic both in the field of Computer Vision and Biometrics. In this paper, we propose the use of decision-level fusion for AGC in videos. Our approach does a decision-level fusion of labels obtained from two fine-tuned deep-networks based on a color image and optical-flow image, respectively, based on Resnet-18 architecture. We compare our proposed method with handcrafted features, which includes the concatenation of the Histogram of Optical Flow (HOF) and Histogram of Oriented Gradients (HOG). We compare it with deep-networks, which includes pre-trained & fine-tuned Resnet-18 based on a color image, and pre-trained & fine-tuned Resnet-18 based on optical flow image. Our fusion-based approach considerably outperforms both the handcrafted features, and the deep-networks previously mentioned. Another advantage of our proposed method is that it can work when the visual features are hidden. We used 98 videos from the HMDB51 action recognition dataset, specifically from the cart-wheel action with an almost 50% training, and testing split without validation set. We achieve an overall accuracy of 79.59% with Resnet-18 network architecture with the proposed method, compared to fine-tuned single-stream Resnet-18 for Color-Stream at 65.30%, and Optical-Flow at 55.10% respectively.
100kW grid-connected renewable energy system design for an educational campus in Tarlac: A step towards green energy campus development

Abstract This paper discussed the design of a grid-connected renewable energy system for an education institution. One-year energy consumption and the hourly load of the institution were determined to design the energy system. HOMER Pro and What’s Best! 16.0 for Microsoft Excel optimization tools were used in the design, consisting mainly of wind turbines and solar PV. Weibull probability function and the simplified irradiance model were used to predict the wind turbine and solar PV power output in the Microsoft Excel spreadsheet. Results showed that the mean velocity at 50 m reference height is 3.886 m/s. The annual solar PV system energy produced based on the HOMER Pro simulation and What’s Best! was found to be 170,939 kWh/yr and 189,454 kWh/yr, respectively. HOMER Pro and What’sBest! iteration results showed that the optimum number of components are 303 and 0 for solar PV and wind turbine, with net present costs of Php 23,388,612 and Php 22,769,478, respectively. With this optimum result, emissions can be reduced by up to 51%, a financial saving of at least Php 1, 970, 145 per year can be achieved with payback period of 5 years.