Miracle-dz/UniFAR-FTUs huggingface dataset:
Synchronization Games in P2P Energy Trading

The rise of distributed energy generation technologies along with grid constraints, and conventional non-consumer centric business models, is leading many to explore alternative configurations of the energy system. Particularly popular are peer-to-peer energy trading models in which the role of the energy company is replaced with a trustless transaction layer based on a public blockchain. However, to ensure stable operation of microgrids, an energy company is required to constantly balance supply and demand. In this paper, we study the problem that arises from the conflicting goals of prosumers (to make money) and network operators (to keep the network stable) that have to co-exist in future energy systems. We show that prosumers can play large-scale synchronization games to benefit from the system. If they synchronize their actions to artificially increase energy demand on the market, the resulting power peaks will force the microgrid operator to use backup generation capacities and, as a consequence, contribute to the increased profit margins for prosumers. We study synchronization games from a game-theoretical point of view and argue that even non-cooperative selfish prosumers can learn to play synchronization games independently and enforce undesired outcomes for consumers and the grid. We build a simple model where prosumers independently run Q-learning algorithms to learn their most profitable strategies and show that synchronization games constitute a Nash equilibrium. We discuss implications of our findings and argue the necessity of appropriate mechanism design for stable microgrid operation.
Plagiarism in Low-Stakes Unproctored Internet Testing for Programming Aptitude

Unproctored Internet Testing (UIT) is a popular form of assessment due to its flexibility and low cost. Results from a UIT may be invalid if there is excessive cheating, but this risk can be acceptably small for certain low-stakes tests. In this paper, we analyze the occurrence of one form of cheating (source-code plagiarism) in a low-stakes UIT for programming aptitude conducted in India with 25,814 test takers. Although we find that the extent of plagiarism is sufficient to threaten the validity of this particular test's results, our analysis suggests feasible ways in which such a UIT can modified to enhance validity by reducing the occurrence of plagiarism.
A Context-Aware Privacy-Preserving Solution for Location-Based Services

Although there are many approaches and methods to protect user privacy, most of them work best for normal cases. In several particular situations, they would lead to privacy leak. Our approach is to overcome these exceptional scenarios by being aware of user context. In this paper, we propose a context-aware privacy-preserving solution for location-based services. With our system architecture as the backbone, we present our privacy-protecting strategy to deal with privacy breach by using cache and hybrid obfuscation. Furthermore, we analyze the contexts of privacy leak, even with privacy-preserving techniques, and show that our solution can get over them by providing either full or probabilistic privacy protection. The former gives no privacy risk to the end-user while the latter completely preserves the attack probability at least 1/k for all cases.
Effective Online Assessment and Evaluation though Online Laboratory Work Submission for Technology Enhanced Learning

This paper aims to explain the effective use of Google Classroom for online submission of laboratory work. The practical knowledge is one of the main graduate attributes and is efficiently achieved through the laboratory work. The purpose of this paper is to emphasize on the improvement in the quality of the laboratory work through online submission of the lab work by the students and the effective and transparent implementation of the lab rubrics by the lab course teacher. Also a study of comparison of two different classes one with the online lab submission and the other with traditional approach on the basis of grades and originality in the lab work submitted is also discussed, along with a few more advantages.
Joint Learning with Keyword Extraction for Event Detection in Social Media

Event detection for social media is an important social media analytics task in security domain, which can provide valuable information for decision making, intelligence analysis and public management. Traditional event detection methods either rely on simple term frequency based features, or take the bag of words assumption. Recently, some deep neural network based event detection methods are proposed. However, these methods still have some drawbacks. Firstly, they do not provide an effective way to learn the connection between message representation and event representation, but simply use average of message representations as the event representation. Secondly, representations in the hidden space lack interpretability compared to the traditional event keyword representation. To deal with these weaknesses, we propose an event detection approach joint learning with keyword extraction. We provide an episode learning strategy to enable the training of event representation update. In addition, by joint learning with keyword extraction, the model is more explainable, and can achieve a better performance. As the selection of a set of keywords is a combinatorial problem and non-differential, we also employ reinforcement learning in our approach. Experiments on a public available dataset show the superiority of our approach compared with baseline methods.
Smart X-Ray Scanners Using Artificial Neural Networks

Object detection and classification in Artificial Neural Networks (ANN) can play an important part in finding solutions for various tasks that are considered critical or time consuming if executed by humans. Also, object detection can be used in several applications and domains. One of which is physical security, where many illegal items, that should not pass through checkpoints, exist. In this sense, smart X-ray scanners can help in detecting any restricted object like weapons, knives or even drugs within a bag or held by an individual. This paper aims to provide a first step towards smart scanners where the MultiLayer Perceptron (MLP) is used to classify and detect two different types of illegal objects. Moreover, training of the network is performed using the Back Propagation algorithm, one of the most widely used algorithms in the MLP.
Optimized GPU Implementation of JPEG 2000 for Satellite Image Decompression

JPEG 2000 is a powerful yet computationally complex image compression algorithm which is widely used in remote sensing applications. In this work, we focus on speeding-up the decompression algorithm by applying various GPU optimization techniques. We have conducted numerous experiments on high-resolution satellite images in two operational modes; a syn-chronous mode and an asynchronous batch mode. We share our experiment results and make performance evaluations regarding each operational mode and optimization method separately. Finally we propose an optimized GPU architecture for satellite image decompression and compare the achieved performance with a multi-threaded CPU architecture.
Individual Semantics Building for HFLTS Possibility Distribution With Applications in Domain-Specific Collaborative Decision Making

Collaborative decision making (CDM) with linguistic computational techniques has recently achieved significant advancements. Due to the widespread use of sophisticated linguistic constructions, such as generalized comparative linguistic expressions (GCLEs), additional information associated with subjective appraisals has been exploited with the aim of addressing accuracy improvements in multifarious CDM, given that partial information loss is almost inevitable while dealing with complex linguistic comprehension. This paper brings an innovative perspective into CDM from COmponent ANalysis with GCLEs (COANG) to formalize problems involved in making optimal choices, mainly in CDM problems with participants who are usually characterized by domain specificity. Consequently, the focus of this paper is on the domain-specific CDM (DSCDM) in which individual semantics should be built predominantly to model various implications of their decision appraisals with heterogeneity in the knowledgeable domain for the effort of computational reinforcements. The attitude orientation and strength are crucial decompositions to incorporate COANG into DSCDM to establish an elastic paradigm that puts forward individual perception comprehension ahead of exerting collective efforts. The DSCDM based on COANG model enables agents to turn complex challenges of sophisticated linguistic constructions into substantial opportunities by translating them into customized individual semantics (CIS), and CIS into useful insights for making better decisions and improving results. The potential advantages of the proposed COANG-based DSCDM framework are validated with a clinical psychological practice related to the severity assessment of symptoms of schizophrenia.
ADSL: An Embedded Domain-Specific Language for Constraint-Based Distributed Self-Management

Resilience is an important property of cyber defence systems operating in complex, adversarial environments. In previous work, we have developed the AWaRE approach for enabling cyber resilience via distributed, agent-oriented, constraint-based self-management. A key requirement and feature of AWaRE is the programmatic definition of constraint problems and their mapping to suitable coordination architectures in a way that supports run-time self-representation and self-organization. This can be satisfied via a domain-specific language (DSL); however, existing DSLs, e.g. for automated deployment and self-adaptivity, are not suitable for this purpose. In this paper, we present an expressive, Java-embedded DSL named ADSL that enables the design-time specification of system components, inter-component relationships and constraints, constraint problem decompositions, managing agent architectures, and sub-problem to agent mappings – and the run-time use of these specifications to achieve self-management in accordance with the models@run.time paradigm. Our initial validation of ADSL in the context of an enterprise scenario demonstrates the suitability of the language.
Gamification in Requirements Engineering: A Systematic Review

Gamification has been applied to a large diversity of settings in recent years, with encouraging and promising results. In the Software Engineering context, there are some research works that focused on the application of gamification in Requirements Engineering that need to be better analyzed and understood. Does gamification impact RE? What are the gamification elements that have been used? What sub-processes and RE tasks have been the subject of gamification? The objective of this paper is to carry out a systematic review to characterize the state of the art of gamification in RE, answering those and other questions, identifying gaps and opportunities for further research. It was conducted a systematic review to find the primary studies in the existent literature which objectives is applying gamification to RE and its sub-processes. As a result of the systematic review, 8 primary studies were analyzed. They were published between 2012 and 2017 and most of them focus on the application of gamification onto elicitation, negotiation and prioritization.
A Low Cost System to Optimize Pesticide Application Based on Mobile Technologies and Computer Vision

Over the past few years, agriculture has undergone profound changes due to technological advances. The application of agrochemicals revolutionized agriculture by reducing manpower, increasing the productivity and providing a greater control of spontaneous plants. However, the application of pesticides using boom sprayers remain the same even though the advances in autonomous tractors. As crops are usually planted in rows, this paper optimizes and automates the application of pesticides through an autonomous system based on local information using computer vision. The system is implemented on smartphones and is capable of detecting rows in plantations, activating the related sprayer nozzles. Additionally, the system is easy to install in many types of tractor-mounted boom sprayers. Experimental results are obtained in an onion family farming where the system detects planting lines and controls the nozzles.
Online Battery Impedance Spectrum Measurement Based On Cross-correlation

Electrochemical impedance spectroscopy is an important parameter to characterize the performance of the battery and evaluate the state of health. However, the current methods of EIS measurement take too much time and require specific equipment of high cost. In this paper, an automatic method through three-section measurement for online EIS measurement is proposed, which can be obtained directly through cross-correlation and digital control. In the battery charging system, the impedance spectrum of the system can be obtained directly by integrating the frequency response measurement capability to the digital controller of tuck converter. This paper demonstrates the validity of this method by mathematical formula derivation and proves the feasibility by simulation on MATLAB.
Usage Monitoring Control in Radio Access Network

Multi-access Edge Computing is a promising research domain in 5G networks, as it is aimed at improving network efficiency by distributing cloud computing capabilities at the network edge. In this paper, we present a new mobile edge service for usage monitoring control. Usage monitoring control is defined as a part of Policy and Charging Control functionality in the core network. Moving the usage monitoring function close to the end user enables efficient control on data traffic. The proposed mobile edge service is described by typical use cases, data model and interface definition. As proof of concept, we propose resource state models as seen by mobile edge applications and platform, which are formally described and verified.
The Personalized Recommendation Algorithms in Educational Application

The rapid emergence of intelligent interaction technology has sparked a great deal of research interest in the field of contemporary education. Teaching students in accordance with their aptitude and satisfying the individualized development of students have become the core of individualistic education. There are a large number of educational information resources which increase the difficulty of selection for learners and further bring the problem of "information trek". As an effective tool to address those challenges, the research of personalized recommendation system has become a hotspot in the field of educational services. In this paper, we first classify and summarize the recommendation algorithms which are related to the personalized educational recommendation system. Then we conduct a detailed analysis on the advantages and disadvantages of existing algorithms. Finally, some evaluation indicators are outlined to analyze the performance of current personalized recommendation systems thoroughly.
Using PCA and K-Means to Predict Likeable Songs from Playlist Information

Most recommendation systems for music rely on individual song ratings. Current song recommendation software that uses playlists has shown to either be inaccurate or suggest songs that are extremely like those in the playlist already. Furthermore, this recommendation software tends to rely very large numbers of records. AI models are used to overcome these limitations using substantially less data. A collaborative filtering approach using two different models (K- means and hierarchical clustering) is used to separate playlist data into clusters for comparison. After the data has been clustered, a Euclidean distance measure is used between the songs in the cluster and the average values of the songs in a single users playlist to make the final predictions. The use of normalisation and PCA enabled the K-means and hierarchical clustering models to form clusters efficiently. When tested on a small sample of users, the system recommended songs that were considered likeable by the users 60% of the time, while still finding songs that were generally diverse.
A Method of Action Recognition in Ego-Centric Videos by Using Object-Hand Relations

We present a system for integrating the neural networks' inference by using context and relation for complicated action recognition. In recent years, first person point of view which called as ego-centric video analysis draw a high attention to better understanding human activity and for being used to law enforcement, life logging and home automation. However, action recognition of ego-centric video is fundamental problem, and it is based on some complicating feature inference. In order to overcome these problems, we propose the context based inference for complicated action recognition. In realistic scene, people manipulate objects as a natural part of performing an activity, and these object manipulations are important part of the visual evidence that should be considered as context. Thus, we take account of such context for action recognition. Our system is consist of rule base architecture of bi-directional associative memory to use context of object-hand relationship for inference. We evaluate our method on benchmark first person video dataset, and empirical results illustrate the efficiency of our model.
Proposal and Evaluation of Secure Device Pairing Method with Camera and Accelerometer

With the advancement of wireless short-range technology, the number of mobile devices is also increasing. Consequently, devices often exchange information using wireless communications, but this communication has vulnerabilities, such as eavesdropping and man-in-the-middle attack. Therefore, it is necessary to perform secure pairing between devices before wireless communication begin. In this paper, we assume that devices are securely paired in limited space, such as a room using wireless communication. The method for secure pairing using Received signal strength (RSS) is simple, but RSS is easy to change due to environmental factors. Recently, pairing methods using camera and accelerometer are studied. However, these methods do not directly detect inclination of the device. We propose the one of secure device pairing method using camera and accelerometer. Our proposed method make the camera read a marker on the display of the device. The marker's inclination corresponds to the device's inclination, hence, we assume that the accuracy is higher than that of existing methods.
Opponent Resource Prediction in StarCraft Using Imperfect Information

The real-time strategy (RTS) game StarCraft has recently become a focus of research on game AI. A major challenge in RTS gameplay is making decisions using imperfect information about the opponent's state and actions. One approach that has proven rewarding is to apply machine learning techniques to replays of games between skilled human players. We consider the problem of estimating the number of resources gathered by the opponent during a StarCraft match. We introduce and evaluate two techniques for opponent resource prediction using supervised learning on match replays. Our first method uses multiple linear regression on observable features of the game state. Our second method uses naïve Bayes classification to form imprecise but accurate predictions.
An Adaptive Demotion Policy with Considering Temporal Locality

In the upper-level caches like as L1 caches, the temporal locality mainly affects the performance of the caches. Although the Adaptive Demotion Policy (ADP) can achieve the performance improvement at the upper-level caches compared to the conventional hardware suitable replacement policies, the most recently used block will be evicted when all the blocks in the accessed set have the same priority value. Hence, there is a possibility of decreasing the performance of the cache by spoiling the temporal locality. To avoid this problem, this paper proposes a cache replacement policy which focuses on preventing the eviction of the most recently used block, and the policy is called Adaptive Demotion Policy with considering Temporal Locality (ADPTL). At a cache hit, if the priority values of all the blocks in the accessed set except the hit block are the highest value, the ADPTL decreases these priority values by one. This operation prevents the eviction of the most recently used block. The performance evaluation shows that the ADPTL achieves the reduction of the cache misses and the IPC improvement at the L1 caches compared to the LRU policy and the ADP.
Finnish Dancers' Attitudes Toward Folk, Competitive Ballroom, Ballet, and Modern Dance

One would like to think that dancers hold positive attitudes toward many forms of dance, not just their own. Maybe this is a reason why researchers have not been interested in studying the attitudes of dancers toward dance. However, there have always been conflicts (e.g., which teachers, dancers, dance schools, dance genres should be supported by grants and awards) among dancers in Finland, thus Finnish dance-policy groups have held discussions about improving cooperation both among dancers representing different dance forms and between dancers and artists in other fields. As a result the Dance Council of Finland was founded in 1980 as an organization with board members from various art dance organizations, folk dance organizations and the Finnish Dance Sport Association. The aims of the Dance Council were, among others, to increase cohesion among dancers and to improve the public perception, appreciation and funding of dance. The Dance Council initiated a dance magazine, Tanssi (Dance). However, according to Laine (1991), who was chair of the Dance Council and director of the Finnish National Ballet, the process was problematic and slow.
A Modified One Cycle Control of VIENNA Rectifier for Neutral Point Voltage Balancing Control Based on Cycle-by-cycle Correction

This paper presents a modified one cycle control (OCC) for VIENNA rectifier to balance neutral point (NP) voltage and suppress its oscillation by introducing the concept of redundant vector in space vector pulse width modulation (SVPWM) into OCC. By changing the phase of the drive signals, an additional switching state is introduced to minimize the average neutral current over a switching cycle and it can be proved that the additional switching state does not introduce additional harmonics to AC circuit by analysis the inductance voltage of the OCC-based VIENNA rectifier. Unlike SVPWM, all the calculations require only three-phase currents and capacitance voltages as parameters and it can be achieved in abc coordinate system, so that the proposed control strategy can be easily implemented by a digital controller without complex calculations. The feasibility and performance of the modified OCC strategy has been validated by simulations in MATLAB/Simulink and experimental results.