Reviewerly/exHarmony huggingface dataset:
Graduates Profile Mapping based on Job Vacancy Information Clustering

Nowadays, an industry's expectation that's often not fulfilled by job applicants require companies to actively cooperate with universities, one of the reasons is through employees that is considered to have good performance to find talents within their alma mater. This research aims to analyze job vacancy information uploaded by graduates for juniors in their university that can be mapped into a graduate's profile and evaluation materials in making a curriculum. Collected job vacancy information from several communication media are generally unstructured data which requires it to be preprocessed first through a data mining convention to produce several terms ready to be processed, continued with implementation of TF-IDF, feature extraction using PCA, and grouping using k-Means algorithm. The clustering analysis found 3 job clusters i.e. developer, teacher and researcher/lecturer as job vacancies that frequently shared by graduates. This result obtained from clustering analysis using 10 words as a minimum document frequency based on Elbow Method and Silhouette Coefficient analysis.
News Aggregator from Telegram Channels Using Thematic Text Analysis

This research observes the process of creating a telegram bot that will simulate a news feed from Russian-language telegram channels based on the interests of the user. Among the tasks of the research were the implementation of comparison of documents similarity, developing parsing system, the design of the bot and algorithms for selecting articles using thematic analysis, which is carried out using a multinomial naive Bayesian classifier. As a result, the analogue of news and posted messages feed in telegram was created. In addition, analysis of performance and complexity of algorithms were executed.
Implications of the COVID-19 pandemic on the Internet traffic

In this paper, we report on a measurement study by researchers from several institutions that collected and analyzed network data to assess the impact of the first wave of COVID-19 (February-June 2020) on the Internet traffic. The datasets from Internet Service Providers, Internet Exchange Points, and academic networks, primarily in Europe, provide a unique view on the changes of Internet traffic due to pandemic and the lockdown that forced hundreds of millions of citizens to stay and work from home. The analysis shows that the increase of Internet traffic was about 15-20 % within a couple of weeks, an increase that is typically spread over multiple months under typical operation. However, traffic during peak hours does not increase by more than 5 %. The increase was noticeably higher for specific applications, e.g., remote work applications, teleconferencing, video on demand; in some cases up to 200 %. However, overall, the Internet reacted well to these unprecedented times.
Multi-dimensional QoS Cloud Computing Task Scheduling Strategy Based on Improved Ant Colony Algorithm

Abstract This paper proposed a multi-dimensional QoS cloud computing task scheduling algorithm based on improved ant colony algorithm, considering QoS demand of users and load balancing of cloud platform comprehensively. First, this paper defines a QoS model composed of the completion time and execution cost of tasks, and defines the cloud platform load balancing constraint function. Secondly, in view of the shortcomings of ant colony algorithm such as slow convergence speed and easy to fall into local optimum, the pheromone update method and expected heuristic function are improved, and the pheromone strength is dynamically changed. Finally, the simulation is carried out in cloudsim and compared with the ACS algorithm and the MMAS algorithm. Experimental results show that the algorithm in this paper is better than these two algorithms in terms of user satisfaction and cloud platform load.
Motion Parameters Estimation of a Moving Target Based on Instantaneous Frequency Estimates Using a Passive Acoustic Sensor

This paper studies a method for estimating the motion parameters [Formula: see text] using the instantaneous frequency estimates of the acoustical signal emitted from a moving target in the air and received by an acoustic sensor placed on the ground. Here, [Formula: see text] is the target’s travel speed, [Formula: see text] is the closest point of approach slant range, [Formula: see text] is the source acoustical frequency, and [Formula: see text] is the time at the target get to the closest point of approach to the sensor. A novel relationship formula between the instantaneous frequency and motion parameters is derived and an algorithm for estimating the motion parameters based on the solution of quadratic nonlinear optimization from the instantaneous frequency estimates is proposed. The instantaneous frequency is estimated by searching the peak of a polynomial Wigner–Ville distribution. An approach for calculating the optimal window width in a polynomial Wigner–Ville distribution is proposed. Numerical examples are provided for validating our theoretic claims.
Dynamic and Decentralized Trust Management for the Internet of Things (IoT) Paradigm

Trust is an invisible behavior of any entity. An entity could be a living being or a cyber-physical system. The Internet of Things (IoT) is a connected network of smart objects or things where trusted relationships are crucial. Trust in an entity can increase or decrease based on different parameters and properties of the specific entity. Trusted relationships can dynamically reach based on contextual data collected over time. The heterogeneous behavior of IoT devices makes trust measurement more difficult. The massive deployment of IoT devices and related innovative IoT applications leads to exploring new trust management frameworks for the IoT paradigm. Emerging IoT applications need to trust entities deployed by third-party providers. Innovative external IoT applications need to be dynamically trusted by the IoT devices and IoT gateways. Dynamic trust achievement is a complex process when an entity is new within the network. In this article, we have defined the trust management for IoT and discussed the need for trusted architecture for dynamic IoT infrastructure, and elaborated the requirements of trust management policies. We have also heightened the need for decentralized architecture for trust management for the Internet of Things (IoT). A new edge-centric multi-agent-based dynamic and decentralized trust management model is proposed and simulated to solve the aforementioned issues. The results of this work are useful for further research in the field of trust management for IoT.
Covid-19: a catalyst for cybercrime?

Abstract While the second wave of the Covid-19 pandemic is keeping the world on tenterhooks, the last few months have also led to a new wave of cybercrime. The following article analyzes the background and manifestations of pandemic-related cybercrimes and shows how our criminal law systems are able to deal with current challenges in the age of the coronavirus.
Cooperative Navigation Based on Bearing and Range Measurements to Different Vehicles

This paper presents a navigation solution for a vehicle operating in cooperation with two other. The vehicle is assumed to measure bearing to one of the aiding vehicles and range to the other. An observer with globally exponentially stable error dynamics is designed by obtaining an equivalent observable linear time-varying system using an artificial output and state augmentation. The observer relies on local measurements, as well as limited communication between the vehicles. Simulations are performed to assess the proposed solution.
VisRec: A Hands-on Tutorial on Deep Learning for Visual Recommender Systems

This tutorial serves as an introduction to deep learning approaches to build visual recommendation systems. Deep learning models can be used as feature extractors, and perform extremely well in visual recommender systems to create representations of visual items. This tutorial covers the foundations of convolutional neural networks and then how to use them to build state-of-the-art personalized recommendation systems. The tutorial is designed as a hands-on experience, focused on providing both theoretical knowledge as well as practical experience on the topics of the course.
MultiAdapt: A Neural Network Adaptation For Pruning Filters Base on Multi-layers Group

Abstract Deep convolutional neural networks have been widely used in various AI applications. The most advanced neural networks are becoming deeper and wider, which has caused some large convolutional neural networks to exceed the size limit of the server or application. The pruning algorithm provides a way to reduce the size of the neural network while keeping the accuracy as high as possible. The automatic progressive pruning algorithm is one of the widely used pruning algorithms. The progressive pruning algorithm prunes a certain layer of the network in each iteration to reduce the sparsity while preserving the accuracy as much as possible. In this article, we design a new automatic progressive pruning algorithm named MultiAdapt. MultiAdapt combines the combination method and the greedy algorithm. This multi-layers progressive pruning method greatly increases the search space of the greedy algorithm, making it possible to obtain a better pruning network. We use MultiAdapt to prune large neural networks VGG-16 and ResNet. The experimental results show that the MultiAdapt algorithm is better than other mainstream methods in the balance of neural network model size and accuracy. For image classification tasks on the ImageNet dataset, our method achieved 88.72% and 90.55% TOP-5 accuracy on the 50% sparsity VGG-16 and ResNet, while obtaining nearly 2×reduction in parameters and floating point numbers. The operation is reduced, and the reduction is higher than the recent popular method.
Disaster Recognition System for Risk Management in Socio-Technical Systems

Socio-technological risks have sparked a lot of interest all over the world. The subject of risk perpetrates and translates to more dynamic conceptualizations and approaches. The necessity for improvement from time to time has proved scantiness within the current establishment. This paper will explore the methodologies in modeling and inert socio-technical technique capable of addressing present-day underperformances. A cocktail of System Dynamics (SD) and Event trees (ET), Fault Trees (FT), and Artificial Neural Networks (ANN) has prospects of quenching the autonomous needs of various simple existing models. SD models are abstract in nature. Their insufficiency in narrowing risk components right down to the bone marrow leaves a lot to be desired about their implementation. However, their power is in computational speed, and given a less complex scenario, they could however suffice. Probabilistic Risk Analysis (PRA) methods lack autonomy and the models are somehow vague leaves a lot to be desired. The flexibility to expose, adjust, and prescribe is key to the core of modern-day technical-evolution. The hybrid model in context here strives for autonomy and scalability with time. This paradigm shift for propagating a sufficiently operative socio-technical risk management approach is a sure stride to exhaustively benefit from the PRA methods.
Development of mobile learning integration with scientific approaches on electrolyte solution and redox reaction

Abstract This study aims to develop innovative learning media that is mobile learning through scientific approaches on Electrolyte and Redox and to find out the feasibility of media produced. This research was conducted at SMAN 39 Jakarta and SMAN 98 Jakarta from January to May 2019. The research method used is Research and Development (Borg and Gall) by modifying five stages, namely: needs analysis, product development, validation, and product test. The resulting mobile learning media is called “Electrolyte and Redox.apk” that is compatible with Android devices and provides a summary, videos, simulations, quizzes, and educational games. Feasibility test by topic and language expert acquired 83%-100% with 0.808 of reliability. Feasibility tests by media experts acquired 91%-100% with 0.812 of reliability. Media trials by chemistry teachers acquired 88% -100% with “very good” criteria. Media trials by small scale students received 88%, and a large scale gained 92% with “very good” standards. Based on the results, it can be concluded that mobile learning media through the scientific approach in Electrolyte and Redox topic proper to use as a learning media and suitable for students and teachers needed.
A Book Recommendation Algorithm Based on Data Cleaning and Association Rules

Using data mining technology can extract valuable information from users' book borrowing data, obtain users' borrowing behavior, and provide personalized book recommendation service for users. The traditional association rule algorithms don't carry out data cleaning before use, resulting in a single user's single lending records becoming outliers in the overall data set, which makes the running time of the Apriori algorithm increase significantly. In this paper, according to the support threshold, confidence threshold and filtering threshold of the data set firstly, then the Apriori algorithm is used to analyze the association rules of the cleaned data set. The experimental results show that in the case of both large and small amount of data, the analysis time of Apriori algorithm with data cleaning is shorter, the strong association rules are stronger, and the effect is remarkable in the field of personalized book recommendation.
Hybrid Collaborative Filtering Recommendation Algorithm for ALS Model Based on a Big Data Platform

Aiming at the advantages of parallel computing on a big data platform, an improved algorithm for the ALS model under a big data platform is proposed. According to the number of users' visits, the hybrid recommendation is carried out, and a similar user reconstruction matrix model is used for optimization. Experiments show that: the ALS model hybrid collaborative filtering recommendation algorithm based on big data platform can improve the recommendation accuracy compared with the traditional K-means clustering recommendation algorithm, the article based recommendation algorithm, and the tag-based recommendation algorithm, and the acceleration ratio increases significantly with the increase of nodes when the big data set are running.
Enhancement Techniques for Analysis of Satellite Images through ERDAS Software

With a view of accomplishing the needs for further analysis on image, Image pre-processing methods are applied on an image for its betterment. Image enhancement is a pivotal part in digital image techniques. The main objective behind image enhancement is to apply an algorithm to original image, so that the resultant image is well suited for a specific task. Digital image enhancement is apt in providing diverse methods for better visualization of image for purpose of object detection and recognition by the machine. Enhancement techniques help us to view the image more efficiently by integrating color and intensity in it. This paper provides detail analysis of different basic enhancement techniques like Min-Max Filter, Gaussian filter, Invert filter, Histogram equalization and Contrast stretching for enrichment of image using ERDAS software. After the analysis an enhanced image is achieved using histogram equalization due to the dynamic range of pixels can grow best for better visualization of an image for better analysis.
Electromyogram Based Bot-assistive Device for Paralyzed People

Electromyogram based wheelchair (EBW) is considered as the best module for the handicapped and paralyzed people in the medical arena. In the developing countries, the people are not even aware of such kind of innovations in the field of wheelchairs. This is due to the cost and complexity in the technological part of the equipment. During the preparation of such biomedical equipment, the designer cannot neglect the inculcation of the basic component such as the strength of the material through which the products going to be designed. However, the need of EMG based wheelchair is basically useful for paralyzed people. Those people have to face difficulty in suffering pain until the end of their life. Further, the drawback in such system is its cost and availability even in the remote and for the middle class people. Hence, this device provides the paralyzed people an easy way of accessing the wheelchair by using their muscles in their own way. The modified device works in the fashion where the user can move the wheelchair by their own muscle movements. This article reports the ease of use in the wheelchair with comfortable muscular movements.
Constructing a Data Middle Platform for Promoting Renewable Energy Accommodation Capacity

Abstract Constructing a data middle platform for renewable energy accommodation can provide essential support for the safe and efficient accommodation and the orderly development of the renewable energy industry. Based on the connotation and technical characteristics, the data middle platform is constructed for renewable energy accommodation from four dimensions: data source layer, data asset layer, analysis layer and application layer. Furthermore, the paper analyses the application scenarios and corresponding key technologies of the data middle platform, which plays a vital role in the critical reference for promoting the accommodation of renewable energy.
Efficient Object Removal and Region Filling Image Refurbishing Approach

Inpainting is a technique to restore damaged portions in images such that it is imperceivable to the naked eye. The challenge is to fill the damaged regions of images such that it looks more discernible and visually plausible while restoring the unity of the picture. Inpainting has various applications such as restoration of damaged photographic images, paintings and removal of particular objects from photographs and images. In this paper, a novel inpainting approach has been introduced that removes objects from images in an indistinguishable manner. Although the exemplar-based traditional object removal approach has certain plus points such as ability to preserve textural, structural details of an image and also preserves lucidity in images, it does not preserve the visual quality of inpainted images. This paper proposes a robust “image refurbishing” algorithm for both synthetic and real unshaded textured images wherein the local information from synthesized patch is used to reconstruct the target area in the image in pixel-by-pixel manner, thus propagating structural as well as textural information simultaneously using a single efficient algorithm. Furthermore, the state-of-art inpainting methods available till date use air-brush masking method; however, in the proposed approach, masking is automatically done which helps in reducing the inpainting time. The inpainted images obtained using the proposed approach are visually appealing which depict the efficiency of the proposed method in removing desired large objects from photographs and other images. The experimental results of the proposed method compare favorably to the state-of-the-art object removal approaches, showing supremacy of the proposed technique over others in terms of computational efficiency, visual quality of inpainted images and inpainting time.
UI/UX Design of Educational Game for Earthquake Mitigation

Earthquake activity in Indonesia is extremely high in which there are 400 earthquakes every month on average. Most victims are children because they are lack of knowledge about earthquake mitigation. Through the educational game, it was expected that children could optimally absorb the knowledge provided. This game is intended for children with an age range of 6-9 years old because, at that age, children have understood enough to play the game with a narrative so that they can absorb the information delivered through this game. The UI design in this game refers to the four menu displays, namely start, help, setting, and exit. Regarding how to play this game, it is started from the players to enter the same room code and will be directed to the game board, namely red, blue, and green boards. Alternately, the player will roll the dice, if the players are on the red board, then they will get information. If they are on the blue board, they will choose whether the image provided is under the narrative or not, if it is wrong, they will return to the menu start. If they arrive at the green board, they will be asked a question then choose which image matches the narrative given, if it is wrong, the player will return to the start menu. Then the testing of this game is based on UI / UX using the User Experience Questionnaire (UEQ) and respondents' perceptions about the Earthquake Mitigation Educational Game. Earthquake mitigation educational games show positive results where the mean and variance results produce a green up arrow in table format and the box bar graph format are in a positive area. Besides, the UEQ benchmark from the six UEQ scales obtained excellent results. This indicates that users have got their needs both in terms of functionality and ease of use.
SSR: Joint Optimization of Recommendation and Adaptive Bitrate Streaming for Short-form Video Feed

Short-form video feed has become one of the most popular ways for billions of users to interact with content, where users watch short-form videos of a few seconds one-by-one in a session. The common solution to improve the quality of experience (QoE) for short-form video feed is to treat it as a common sequential item recommendation problem and maximize its click-through rate prediction. However, the QoE of short-form video streaming under dynamic network conditions is jointly determined by both recommendation accuracy and streaming efficiency, and thus merely considering recommendation will lead to the degradation of the QoE of the streaming system for the audience. In this paper, we propose SSR, namely the short-form video streaming and recommendation system, which consists of a Transformer-based recommendation module and a reinforcement learning (RL) based bitrate adaptation streaming module. Specifically, we use Transformer to encode the session into a representation vector and recommend proper short-form videos based on the user's recent interest and the timeliness characteristics of short-form video contents. Then, the RL module combines the representation of session and other observations within the playback, and yields the appropriate bitrate allocation for the next short-form video to optimize a given QoE objective. Trace-driven emulations verify the efficiency of SSR compared to several state-of-the-art recommender systems and streaming strategies with at least 10%-15% QoE improvement under various QoE objectives.