Reviewerly/exHarmony huggingface dataset:
GPT-K: A GPT-based model for generation of text in Kannada

Large AI-based language models are changing how we work with language. They are becoming increasingly popular because they allow us to create complex linguistic structures without requiring a lot of resources. A language model must have access to a large corpus of linguistic data (e.g., word frequencies) to learn and generate new words. GPT-2, a language model, can generate coherent paragraphs independently, without any input on what to write about or guidance on grammar rules. Although multiple pre-trained GPT-2 models exist for English and other high-resource languages, there are few to no such models for Indic languages like Kannada. In this study, we propose GPT-K, a GPT-2 based model for language modeling in Kannada. GPT-K has been trained on a large corpus of Kannada text and can effectively perform language modeling tasks in Kannada. The model generated syntactically correct text in most cases.
Asynchronous Parallel Incremental Block-Coordinate Descent for Decentralized Machine Learning

Machine learning (ML) is a key technique for big-data-driven modelling and analysis of massive Internet of Things (IoT) based intelligent and ubiquitous computing. For fast-increasing applications and data amounts, distributed learning is a promising emerging paradigm since it is often impractical or inefficient to share/aggregate data to a centralized location from distinct ones. This paper studies the problem of training an ML model over decentralized systems, where data are distributed over many user devices and the learning algorithm run on-device, with the aim of relaxing the burden at a central entity/server. Although gossip-based approaches have been used for this purpose in different use cases, they suffer from high communication costs, especially when the number of devices is large. To mitigate this, incremental-based methods are proposed. We first introduce incremental block-coordinate descent (I-BCD) for the decentralized ML, which can reduce communication costs at the expense of running time. To accelerate the convergence speed, an asynchronous parallel incremental BCD (API-BCD) method is proposed, where multiple devices/agents are active in an asynchronous fashion. We derive convergence properties for the proposed methods. Simulation results also show that our API-BCD method outperforms state of the art in terms of running time and communication costs.
A Translation Model-Based Question Answering Approach over Cross-Lingual Knowledge Graphs

Question answering is a typical application of knowledge graphs and it develops fast recent years. However, there are still some difficulties in this topic like QA over cross-lingual knowledge graphs. CCKS2022 holds a benchmark competition on QA over cross-lingual knowledge graphs. In this paper, we present a three-stage approach leveraging translation model to this benchmark. Our approach outperforms in the benchmark, which reaches 0.9320 as the precision score ranking the first place on the leaderboard.
P-FCloHUS: A Parallel Approach for Mining Frequent Closed High-Utility Sequences on Multi-core Processors

Frequent closed high-utility (FCHU) sequences are preferable to frequent closed sequences. Not only because of their utility-based nature that considerately contributes to taking decisive business actions, FCHU sequences also preserve necessary information for re-constructing frequent high-utility sequences. Despite of their vital role, mining FCHU sequences is a time consuming task when facing with large-scale datasets, or especially when the input thresholds are relatively small. To contend with these difficulties, this paper proposes a parallel algorithm named P-FCloHUS for fast mining FCHU sequences by making good use of multi-core processors. By relying on a novel Single scan synchronization strategy that is facilitated by an efficiently Partitioned result space structure, P-FCloHUS successfully alleviates the communication cost between mining tasks and hence speeds up the parallel mining process. Experiments on both dense and sparse datasets show that P-FCloHUS outperforms the state-of-the-art FMaxCloHUSM in terms of runtime performance.
Prediction of Significant Bitcoin Price Changes Based on Deep Learning

Bitcoin is one of the most famous cryptocurrencies in the world and its price has changed frequently in recent years. Different from the traditional stock market, the changes of bitcoin's price are even more dramatic. Huge price movements have attracted not only investors but also more researchers to find different methods to predict bitcoin's price. In this research, we consider using change rate over previous a few days to predict the significant bitcoin price changes, which are defined as a three-category problem. Besides, we compare the predictive results using two deep learning models - LSTM and GRU, and then find LSTM perform better than GRU with the highest AUC value of 0.701 in most cases.
Virtual Network Function Embedding with Quantum Annealing

In recent years, the growing number of devices connected to the internet led network operators to continuously expand their own infrastructures. In order to simplify this scaling process, the research community is currently investigating the opportunity to move the complexity from a hardware to a software domain, through the introduction of a new paradigm, called Network Functions Virtualisation (NFV). It considers standard hardware platforms where many virtual instances are allocated to implement specific network services. However, despite the theoretical benefits, the mapping of the different virtual instances to the available physical resources represents a complex problem, difficult to be solved classically. The present work proposes a Quadratic Unconstrained Binary Optimisation (QUBO) formulation of this embedding process, exploring the implementation possibilities on D-Wave’s Quantum Annealers. Many test cases, with realistic constraints, have been considered to validate and characterise the potential of the model, and the promising results achieved are discussed throughout the document. The technical discussion is enriched with comparisons of the results obtained through heuristic algorithms, highlighting the strengths and the limitations in the resolution of the QUBO formulation proposed on current quantum machines.
Investigation of the Joint Simon Effect with Human, Robot, and Computer Interface Task Partners: A Pilot Study

This pilot study assessed whether there are fundamental differences in how people perceive human and nonhuman task partners using a joint Simon task. Fourteen participants completed the task with human, robot, and computer task partners. Results showed no joint Simon effect for any task partner, and the robot and computer were both perceived as not very humanlike. However, while the robot was seen as less accurate and slower than the human task partner, the computer was consistently viewed as more accurate and faster. This indicates that there may be differences in user’s perceptions of different types of technological task partners, and that for certain tasks, robots may be seen as inferior while computers are viewed as superior.
Contemporary topics of life long agrarian education under digital innovation conditions

The article highlights radical topics hindering the innovative development of additional professional agrarian education, foreground areas of focus for their solution having been offered. The purpose of the study is to consider topical problems in the system of continuous agricultural education and identify innovative approaches to solving these problems in the context of digitalization. The main challenges in the system of additional professional agrarian education are the quality of educational programs, their conception and pedagogical implementation. To overcome these problems, an integrated approach is desired to support the continuous agricultural education system and solve the questions of incorporating such spheres as agricultural science, education, production and business. The authors have noted that it is necessary to strengthen the organizational-pedagogical, practice-oriented and digital components of the continuous agricultural education system, to revise the structure and content of supplementary vocational education programs, taking into account digital innovations and Industry 4.0 requests. Teachers’ willingness to design short-term programs in supplementary vocational education considering digital innovations, digital didactics principles and labor market demands is still a pedagogical dilemma.
A Traceable and Revocable Attribute-based Encryption Scheme Based on Policy Hiding in Smart Healthcare Scenarios

The smart medical industry, which relies on advanced Internet of Things technology and cloud platform, has been developing rapidly. Smart healthcare is a way to realize information-based communication between patients and medical staff by creating an online medical information platform. To address the user privacy and data security issues involved, we use attribute-based encryption scheme (CP-ABE). However, the traditional CP-ABE scheme has the following problems: 1. user-sensitive information is embedded directly into the access structures without hidden operations; 2. the number of public parameters is not fixed and varies linearly with the size of the system’s attribute set; 3. it does not effectively fix the user attribute change problem; 4. it is impossible to trace the user. To solve the above issues, this paper proposes a scheme with the following special features: 1. using partial policy hiding; 2. using large attribute universe; 3. performing attribute revocation when user attributes are changed; 4. being able to trace back to the user after the user decryption key is leaked. Finally, we provide design details and do performance evaluation to prove its effectiveness.
Requirements for Developing the Technical Education and Vocational Training System in Republic of Yemen

Abstract in English: This study aims at investigating the requirements for developing the technical education and vocational training system in Yemen. It benefited from the experiences of world’s leading countries in the field of technical education and vocational training such as Germany, Austria, Switzerland and Finland. The researched used the comparative analytical descriptive methods. The results showed that there are failure areas of failure and obstacles facing the technical education and vocational training in Yemen. The most important of these issues are: duality and plurality source of legislation, weak funding almost depending on government, and weak infrastructure, classic curricula and teaching methods, several recommendations emerged as result of this study. First, researchers suggest unifying the legislations and policies in one ministry of education or even into two entities. Second, giving administrative and decision-making power with full control to the technical collages and vocational institutions. Third, supporting the technical education and vocational system with adequate funding and resources to enable it to improve its infrastructure and start new programs and specializations. Key words: Requirements of development- System- Technical Education-Vocational Training- Yemen الملخص بالعربية: هدف البحث إلى التعرف على متطلبات تطوير نظام التعليم الفني والتدريب المهني في الجمهورية اليمنية؛ بالاستفادة من تجارب أكثر الدول العالمية الرائدة في مجال التعليم الفني والتدريب المهني، وهي: (ألمانيا – النمسا - سويسرا – فنلندا)، واستخدم الباحثون المنهج الوصفي التحليلي المقارن، وتمثلت العينة في مجموعة من الوثائق الورقية والإلكترونية والدراسات خلال الأعوام الأخيرة، وبينت نتائج الدراسة وجود جوانب قصور ومشكلات تواجه نظام التعليم الفني والمهني في الجمهورية اليمنية، من أهمها: ازدواجية وتعددية مصادر التشريع، ضعف التمويل والاعتماد شبة الكلي على التمويل الحكومي، ضعف البنية التحتية، وتقليدية المناهج وأساليب وطرائق التدريس، واستنادا للنتائج قدم الباحثون مجموعة من التوصيات؛ وتضمنت أهم المتطلبات لتطوير النظام وأهمها: توحيد مصدر التشريعات والسياسات في وزارة واحدة، والتمكين الإداري للكليات الفنية والمعاهد المهنية، وإيجاد مصادر تمويل متعددة لدعم التعليم الفني والمهني، وفتح برامج دراسية وتخصصات حديثة. أضاف الباحثون في الأخير مقترحات بدراسات مكملة في الموضوع. الكلمات المفتاحية: متطلبات التطوير، نظام التعليم الفني – التدريب المهني _ اليمن، تجارب دولية.
A Comparative Analysis of Reinforcement Learning Approaches to Cryptocurrency Price Prediction

Nowadays, Machine Learning (ML) is present in a high number of application fields. Among these, there is also automatic trading in the financial sector. The research question underlying our research activities is as follows: can ML techniques provide added value in the prediction task in domains with high volatility such as the cryptocurrency financial market? To answer this question, we analyzed and compared different Reinforcement Learning (RL) algorithms on data publicly available online. Specifically, we tested some value-based and policy-based RL algorithms trained for different time intervals, with diverse hyperparameter values and reward functions. The agent that allowed us to achieve the best results was the Deep Recurrent Q-Network trained using the Sharpe ratio as a reward function.
Implementation of Clustering Techniques to Data Obtained from a Memory Match Game Oriented to the Cognitive Function of Attention

Serious games are software applications with an explicit educational objective that have been thoroughly thought out and designed as a learning instrument or tool. They allow the user to experience situations similar to real life and learn from their mistakes through immediate feedback. These games have been developed in various fields, including business, industry, marketing, health, government, among others. For instance, some are used for cognitive training in human beings, where attention and memory are fundamental axes during the various stages of the human life cycle. In this context, the “memory match game” is a card game where many pairs of cards are laid face down, and its objective is to match pairs in the lowest time and with the minimum number of wrong clicks. This information is registered and stored along with the player’s sociodemographic information. Thus, this article aims to analyze the dataset, applying clustering techniques in order to find behavioral patterns. The age variable was used to generate 4 age groups that served as the basis for applying the unsupervised machine learning algorithm, k-means. The results show the behavior of the data in relation to the age groups, it is evident that the more experience the players gain, times and scores improve, regardless of age.
Using Institutional Purposes to Enhance Openness of Multi-Agent Systems

In this paper, we consider a programmer who needs to develop an agent to publish information on different existing social networks. Several works in the literature allow the interaction of agents with a social network, but as far as we know, they are not focused on the interaction with several networks. Considering this problem, the paper highlights two options the programmer can take to enable the agent to interact with existing social networks and achieve its goal. The first option requires that the programmer knows the features present in the APIs used to interact with social networks, and the consequences that the functions can bring when executed in the system. The second option uses the institutional notion of purpose aiming to reduce the amount of previous knowledge required by the programmer. The paper aims to discuss the advantages and disadvantages of these options considering the development of open multi-agent system. A JaCaMo implementation of both options is used the help the evaluation.
Visualizing Memory Consumption with Vismep

Detecting and repairing memory issues is still a challenging task. One reason is that understanding a program's memory usage involves a diverse and related set of dynamic and static aspects. Over the years, multiple tools have been proposed to assist practitioners in these activities. However, detailed information about how a tool helps users when analyzing memory usage is missing.This article introduces Vismep, an interactive visualization prototype to help programmers analyze Python applications' memory usage, and presents an exploratory study to understand the behavior and perception of users when using Vismep. As a result, we reported five information needs when participants analyze memory consumption and how they use Vismep to satisfy these needs. Besides, participants positively perceived Vismep due to their valuable views and high overall usability.
Mangling Rules Generation With Density-Based Clustering for Password Guessing

Rule-based password generation is one of the most effective and often employed techniques in the highly compute-intensive password recovery process. However, it is challenging to design and maintain a practical password mangling ruleset, which is a time-consuming task requiring specialized expertise. This paper therefore introduced MDBSCAN (Modified Density-Based Spatial Clustering of Applications with Noise), a novel density-based cluster approach in machine learning, to build an automatic password mangling rule generator. To evaluate the proposed method, cross-checks across 4 different real-world password datasets leaked from popular Internet services and applications are adopted. The results indicate that the proposed generator could produce high-quality mangling rules with a better hit rate and enhance current mangling rules by identifying hidden or omitted rules. The proposed approach also shows strong interpretability and computational efficiency. When examining the RockYou password dataset with the top 77 rules, the hit rate may rise by 11% to 104% proportionally to other well-known solutions. Furthermore, by combining the top 77 rules generated by MDBSCAN with those from other rulesets, 3–12.67% more real-world passwords can be retrieved.
Prototype of Smart Garden System for Monitoring Holticulture Plants Based on LoRa Technology

This paper proposes a development of smart garden sistem based on LoRa technology. The basic principles of the control system in this study use a smart garden system that has the ability to send inputs in the form of data information that aims to monitor air temperature, air humidity, soil temperature, and automatic watering of ground kale plants which can be accessed through the Android application. The communication tool used is LoRa EBYTE E32. The LoRa EBYTE E32 module is used as a data communication module between the sensor node and the LoRa gateway, as well as the MQTT protocol as a data communication gateway. We introduce a new system monitoring plants based Internet of Things (IOT) which can be accesed through the android application on the smartphone. The Smart Garden system based on LoRa technology can work according to configuration because it can monitor air temperature, air humidity, soil temperature, soil humidity, and automatic watering of ground kale plants according to the configuration, namely the water pump is able to turn on when the humidity sensor has a range value below 80% is 20%-79% and the water pump shuts down when the humidity value exceeds 80%. Then, the communication coverage distance between the sensor node and the LoRa gateway after testing obtained the maximum distance is 440 meters in the Semi Line of Sight (LOS) area while in the Non Line of Sight (NLOS) area a lower maximum distance of 240 meters is obtained, this is because in the NLOS area there is an obstacle that hinders the coverage of the LoRa signal.
Multi-subspace Attention Graph Pooling

To effectively learn from different perspectives of a graph, we propose a new pooling mechanism based on joint attention scores of different representation subspaces of the graph, which we refer to as multi-head attention graph pooling. Instead of performing a single attention function over a graph, we propose to perform multiple attention functions that leverage information from different representation subspaces of both node features and graph topology. Each attention function is trained to attend to information from different representation subspaces, while the aggregation of attentions can exchange information globally on the entire graph. The results in graph classification experiments demonstrate that our method is comparable and often surpasses current state-of-the-art baselines on the benchmark datasets with fewer parameters (We release our code at https://github.com/caoyu-noob/MAGPool ).
Target Tracking and Motion Estimation of Fixed Wing UAVs Based on Vision Measurement

In view of the target tracking control and state estimation of unmanned aerial vehicle (UAV), a fixed wing unmanned aerial vehicle with a front fixed camera is used as the research object. The target is estimated by visual measurement and extended Kalman filter, and the 3D local coordinate system research guidance law. The dynamic equation of UAV and the state equation and observation equation of the target are set up, and the Kalman filter is designed to estimation the state of the ground moving target. The simulation results show that motion estimation based on EKF can achieve effective tracking of targets.
Deep face generation from a rough sketch using multi-level generative adversarial networks

Face generation from a rough sketch is a challenging task in computer vision, and most methods are based on conditional generative adversarial networks (cGANs) that use U-net as their backbone. They are helped by auxiliary information such as segmentation and parsing maps to generate visually pleasing facial images from sketches. However, it is hard to get the auxiliary information in reality when the input is only a rough sketch. In this paper, we propose deep face generation from a rough sketch using multi-level GANs. The proposed method does not need any auxiliary information for face generation except the input rough sketch. Instead, we generate a face image by the preceding generator and use it as auxiliary information for the succeeding generator along with the input sketch. The multilevel GANs progressively generates fine textures and contours in face images, thus leading to photo-realistic face components and hair even from a rough sketch. Various experiments show that the proposed method generates natural-looking face images and outperforms state-of-the-art methods in terms of visual quality and quantitative measurements.
Semi-supervised Medical Image Segmentation with Semantic Distance Distribution Consistency Learning

Semi-supervised medical image segmentation has attracted much attention due to the alleviation of expensive annotations. Recently, many existing semi-supervised methods incorporate unlabeled data via the consistency learning. However, those consistency learning methods usually utilize the mean teacher structure, resulting in the different feature distribution on the intermediate representations. As for semi-supervised medical image segmentation, the different feature distribution limits the efficiency of consistency. In this paper, we propose Semantic Distance Distribution (SDD) Consistency Learning method, which has the ability to maintain the same feature distribution on the intermediate representations. On the one hand, to model invariance on feature distribution, we consider the shared encoder instead of averaging model weights. On the other hand, we introduce a SDD Map for consistency learning on the intermediate representations, where SDD Map is closely related to the feature distribution. SDD Map is characterized with the set of distances between the feature on each voxel and the mean value of all features in intra-cluster. Extensive experiments on two popular medical datasets have demonstrated our proposed method achieves state-of-the-art results.