Reviewerly/exHarmony huggingface dataset:
Is it feasible to detect FLOSS version release events from textual messages? A case study on Stack Overflow

Topic Detection and Tracking (TDT) is a very active research question within the area of text mining, generally applied to news feeds and Twitter datasets, where topics and events are detected. The notion of "event" is broad, but typically it applies to occurrences that can be detected from a single post or a message. Little attention has been drawn to what we call "micro-events", which, due to their nature, cannot be detected from a single piece of textual information. The study investigates the feasibility of micro-event detection on textual data using a sample of messages from the Stack Overflow Q&A platform and Free/Libre Open Source Software (FLOSS) version releases from Libraries.io dataset. We build pipelines for detection of micro-events using three different estimators whose parameters are optimized using a grid search approach. We consider two feature spaces: LDA topic modeling with sentiment analysis, and hSBM topics with sentiment analysis. The feature spaces are optimized using the recursive feature elimination with cross validation (RFECV) strategy. In our experiments we investigate whether there is a characteristic change in the topics distribution or sentiment features before or after micro-events take place and we thoroughly evaluate the capacity of each variant of our analysis pipeline to detect micro-events. Additionally, we perform a detailed statistical analysis of the models, including influential cases, variance inflation factors, validation of the linearity assumption, pseudo R squared measures and no-information rate. Finally, in order to study limits of micro-event detection, we design a method for generating micro-event synthetic datasets with similar properties to the real-world data, and use them to identify the micro-event detectability threshold for each of the evaluated classifiers.
Impact of AI Application on Digital Education Focused on STE(A)M

Digitization is omnipresent and digital transformations are thus constantly taking place in all areas of society. Of course, education is also subject to these changes. Since it is an absolute prerequisite for both the future security of society and the development of the individual, it must be innovated continuously in order to guarantee the future opportunities of future generations. To ensure that these developments can be planned and shaped systematically, there are varieties of initiatives by education stakeholders. Current studies emphasize that the further digitisation of education is a very complex and demanding process, although successful theories, concepts and models already exist.Although the key role of the digitisation of education is taken into account, the new challenge of the interplay between human and artificial intelligence is emerging. Artificial intelligence has been researched and taught for many years, especially in the context of knowledge-based systems], but with digitisation it is gaining a completely new status and an exploding range of applications. The individual field of artificial intelligence is increasingly linked with other scientific fields in an interdisciplinary way, resulting in new methodological, technological, social and ethical challenges. Existing target systems in research and teaching are not to be replaced completely, but should rather be questioned and further developed in a very comprehensive way. For this reason, the proven problem-solving processes are being made more dynamic and agile. They will be optimised with the methods and means of digitisation as well as artificial intelligence. Due to the omnipresence of digitization and artificial intelligence, all processes, structures and functions must also be reviewed and adapted in education.A prerequisite for this is a renaissance of the interaction of science, technology, engineering, mathematics, and their combination with areas of humanities, economics and social sciences. Applications of artificial intelligence are finding their way into all the above-mentioned scientific fields and promote their networking. These developments are already becoming visible in complex research and education projects, making it possible to demonstrate the sustainability of the new approaches in an exemplary manner.However, the rapid development of digitalisation in general and of artificial intelligence in particular is generating distortions in the systems. Seeking solutions for them is also on the agenda of research and teaching. This paper contributes to this debate by applying a systemic approach to develop a new understanding of the relationships between digitalisation and artificial intelligence. First, an overview of digitalisation in education as well as of smart systems based on human and artificial intelligence will be presented. The next section of this paper explains the theoretical basis of this research before discussing a specific example of STEAM influence on education. Finally, ethical borders of digitalisation and AI in education are highlighted.
Speech-to-Speech Conversion

A novel approach to build a speech-to-speech conversion (STSC) system for individuals with speech impairment dysarthria is described. STSC system takes impaired speech having inherent disturbance as input and produces a synthesized output speech with good pronunciation and noise free utterance. The STSC system involves two stages, namely automatic speech recognition (ASR) and automatic speech synthesis. ASR transforms speech into text, while automatic speech synthesis (or text-to-speech [TTS]) performs the reverse task. At present, the recognition system is developed for a small vocabulary of 50 words and the accuracy of 94% is achieved for normal speakers and 88% for speakers with dysarthria. The output speech of TTS system has achieved a MOS value of 4.5 out of 5 as obtained by averaging the response of 20 listeners. This method of STSC would be an augmentative and alternative communication aid for speakers with dysarthria.
The application of fuzzy PID control in the process temperature and humidity control of cigarette factory

The production and storage areas of cigarette factories have strict temperature and humidity control standards to meet the requirements of process production and storage. Generally, large-scale process air conditioners are equipped to achieve high-precision constant temperature and humidity control. Under the typical hardware and software configuration and classic PID control algorithm, because the process air conditioner has different tuning requirements for PID tuning parameters in the early, middle and late stages of temperature and humidity control, it is difficult to achieve good PID tuning parameters at different stages. Dynamic response performance. In this paper, through the design of the fuzzy PID controller, the PID parameters are optimized and adjusted in each stage of the system operation. Matlab simulation experimental data based on the transfer function of a typical large-scale process air conditioner shows that the design can greatly improve the dynamic response performance of each stage of temperature and humidity control.
Sentiment Analysis of Hinglish Text and Sarcasm Detection

Today the term "Sentiment Analysis" is no newer to the world. It falls under the umbrella of Natural Language Processing which is a very interesting and creative field of artificial intelligence. One important aspect which needs to take in consideration before going for Sentiment Analysis is the kind of the language of the data which is supposed to be processed. People in urban areas of the northern part of India used to communicate in the mixed language of Hindi- English which is commonly termed as "Hinglish". While doing sentiment analysis one needs resources like Dictionary containing polarity for words, part of speech tagger for both of the languages. A lot of resources were developed for the English language, but this does not hold true for Hinglish. The aim of this research is not only to carry out sentiment analysis and sarcasm detection but also to contribute to the resource development for the Hinglish language. In this paper, Sentiment Analysis is done to classify sentences as positive, negative, sarcastic and non–sarcastic. This is done using extended sentiwordnet 3.0 and naïve Bayes classifier. From the current study it is analyzed that, sentiment analysis using SentiWordNet gives a better precision than the Naïve Bayes whereas the latter successfully classified the sentences into sarcastic and non-sarcastic.
Policy-Driven Attack: Learning to Query for Hard-label Black-box Adversarial Examples

To craft black-box adversarial examples, adversaries need to query the victim model and take proper advantage of its feedback. Existing black-box attacks generally suffer from high query complexity, especially when only the top-1 decision (i.e., the hard-label prediction) of the victim model is available. In this paper, we propose a novel hard-label black-box attack named Policy-Driven Attack, to reduce the query complexity. Our core idea is to learn promising search directions of the adversarial examples using a well-designed policy network in a novel reinforcement learning formulation, in which the queries become more sensible. Experimental results demonstrate that our method can significantly reduce the query complexity in comparison with existing state-of-the-art hard-label black-box attacks on various image classification benchmark datasets. Anonymous code and models for reproducing our results are available in the supplementary material.
DeepFake Face Video Detection Using Hybrid Deep Residual Networks and LSTM Architecture

Millions of images and videos are daily being shared on internet due to recent advances in social media and multimedia technology. However, many of them are manipulated using readily available image and video editing software and apps. Specifically, “DeepFake” videos, which are created by swapping an individual’s face with the face of other individual by utilising deep learning techniques, may constitute a substantial risk either to attack character of public figures or to fool automated face recognition systems. In this paper, a hybrid and high-confidence DeepFake face video detection framework is proposed to discriminate manipulated face videos from non-manipulated face videos. The proposed framework is composed of three main stages: face detection, deep feature extraction, and long short-term memory (LSTM) classification. The face detection is achieved by a well-known Viola–Jones detector. The detected face images are rescaled to make them compatible with an input of the pre-trained convolutional neural network model. The residual network with 50 layers is employed for deep feature extraction. Finally, the LSTM model is considered in classification to gather final verdict: bona fide or DeepFake. The considered LSTM network contains seven layers such as input layer, two biLSTM layers, dropout layer, fully connected layer, softmax layer, and classification layer. The proposed framework is capable of spotting DeepFake face videos with high accuracy that is substantiated via experimental analysis on two diverse and publicly available datasets (i.e., Celeb-DF and DeepFakeTIMIT).
Vehicle Detection in Aerial Images Based on YOLOv3

In the twenty-first century, traffic problems are still troubling people. The concept of intelligent transportation was proposed to solve this problem. The main problem in intelligent traffic is to obtain information about the vehicles on the road and use this information to manage traffic problems. Most of the data now comes from the driveway monitor. The camera cannot focus on everywhere. Therefore, when some accident occurs, UAV can collect information as fast as possible. UAV is more inflexible than a fixed camera. In terms of real time, YOLOv3 is the best choice, because YOLOv3 is extremely fast. Thus, this study has important significance in promoting the development of intelligent transportation.
Remote Sensing Image Registration based on Attention and Residual Network

Multi-view remote sensing registration has important applications in ground target recognition, image aided navigation, missile image guidance and so on. In order to improve the registration accuracy of remote sensing image with change of view angle, a registration method combining attention mechanism and residual network is proposed. The residual network is used as the backbone structure of feature extraction to improve the abstract ability of the model for complex features. At the same time, the attention mechanism based on channel and spatial is introduced into the feature extraction network to improve the distinguish and location ability of the model to image features. Finally, in the feature matching stage, a mutual correlation operation that improve the performance of feature matching is proposed. Compared with the comparison method, the registration accuracy is improved by 10% on average, Experiments show that this method improves the accuracy of multi view remote sensing image registration.
One-shot action recognition towards novel assistive therapies.

One-shot action recognition is a challenging problem, especially when the target video can contain one, more or none repetitions of the target action. Solutions to this problem can be used in many real world applications that require automated processing of activity videos. In particular, this work is motivated by the automated analysis of medical therapies that involve action imitation games. The presented approach incorporates a pre-processing step that standardizes heterogeneous motion data conditions and generates descriptive movement representations with a Temporal Convolutional Network for a final one-shot (or few-shot) action recognition. Our method achieves state-of-the-art results on the public NTU-120 one-shot action recognition challenge. Besides, we evaluate the approach on a real use-case of automated video analysis for therapy support with autistic people. The promising results prove its suitability for this kind of application in the wild, providing both quantitative and qualitative measures, essential for the patient evaluation and monitoring.
CFLMEC: Cooperative Federated Learning for Mobile Edge Computing

We investigate a cooperative federated learning framework among devices for mobile edge computing,named (CFLMEC), where devices co-exist in a shared spectrum with interference. Keeping in view the time-average network throughput of cooperative federated learning framework and spectrum scarcity, we focus on maximize the admission data to the edge server or the near devices, which fills the gap of communication resource allocation for devices with federated learning. In CFLMEC,devices can transmit local models to the corresponding devices or the edge server in a relay race manner, and we use a decomposition approach to solve resource optimization problem by considering maximum data rate on sub-channel, channel reuse and wireless resource allocation in which establishes a primal-dual learning framework and batch gradient decent to learn the dynamic network with outdated information and predict the sub-channel condition. With aim at maximizing throughput of devices, we propose communication resource allocation algorithms with and without sufficient sub-channels for strong reliance on edge servers (SRs) in cellular link, and interference aware communication resource allocation algorithm for less reliance on edge servers (LRs) in D2D link. Extensive simulation results demonstrate the CFLMEC can achieve the highest throughput of local devices comparing with existing works, meanwhile limiting the number of the sub-channels.
Uniform-Precision Neural Network Quantization via Neural Channel Expansion

Uniform-precision neural network quantization has gained popularity thanks to its simple arithmetic unit densely packed for high computing capability. However, it ignores heterogeneous sensitivity to the impact of quantization across the layers, resulting in sub-optimal inference accuracy. This work proposes a novel approach to adjust the network structure to alleviate the impact of uniform-precision quantization. The proposed neural architecture search selectively expands channels for the quantization sensitive layers while satisfying hardware constraints (e.g., FLOPs). We provide substantial insights and empirical evidence that the proposed search method called neural channel expansion can adapt several popular networks' channels to achieve superior 2-bit quantization accuracy on CIFAR10 and ImageNet. In particular, we demonstrate the best-to-date Top-1/Top-5 accuracy for 2-bit ResNet50 with smaller FLOPs and the parameter size.
Towards an ontology of digital arts. Media environments, interactive processes and effects of presence

During the Nineties, the diffusion of information and communication technologies allowed a dramatic transformation in art practices. Radically new aesthetic experiences, such as tele-presence, immersivity, responsivity, hyper-mediacy and multimediality, emerge in the framework of the digital arts and call into question not only the traditional status of the work of art but also the fundamental relation with the beholder. The aim of this paper is to define a conceptual framework for the ontology of digital arts by identifying some ontological features that are distinctive to digital idioms. Such an analysis tries to outline how aesthetic and technical innovations affect our cognitive and sensorial relationship with technological artifacts. In the first part, the relation between technogenesis and ontology, as well as the key topics of the ontology of digital arts are discussed. The second part deals with the notion of presence. Despite traditional understandings of digital arts, mainly focused on immateriality, simulation and mediation, my analysis demonstrate how the notion of presence can provide an original perspective in order to understand the ontological status of the mediatized artistic practices. In the last three decades the generalisation of information and telecommunication technologies has played a major role in the transformation of the arts, opening the field to important experiments in the domain of computer graphics, digital audio, robotics and motion capture systems… (Dixon 2007). Peculiar forms of aesthetic experience such as tele-presence, immersivity, responsivity, hyper-mediation and multimediality, progressively arise from digital arts and question not only the status of artwork but also, more generally, the foundational relationship between this latter and the recipient.
Adaptive Hyperparameter for Face Recognition

One of the widely used prominent biometric techniques for identity authentication is Face Recognition. It plays an essential role in many areas, such as daily life, public security, finance, the military, and the smart school. The facial recognition task is identifying or verifying the identity of a person base on their face. The first step is face detection, which detects and locates human faces in images and videos. The face match process then finds an identity of the detected face. In recent years there have been many face recognition systems improving the performance based on deep learning models. Deep learning learns representations of the face based on multiple processing layers with multiple levels of feature extraction. This approach has made sufficient improvement in face recognition since 2014, launched by the breakthroughs of Deep Face and Deep ID. However, finding a way to choose the best hyperparameters remains an open question. In this paper, we introduce a method for adaptive hyperparameters selection to improve recognition accuracy. The proposed method achieves improvements on three datasets.
Learning Flexible Classifiers with Shot-CONditional Episodic (SCONE) Training

Early few-shot classification work advocates for episodic training, i.e. training over learning episodes each posing a few-shot classification task. However, the role of this training regime remains poorly understood, and its usefulness is still debated. Standard classification training methods (``pre-training'') followed by episodic fine-tuning have recently achieved strong results. This work aims to understand the role of this episodic fine-tuning phase through an exploration of the effect of the ``shot'' setting (number of examples per class) that is used during fine-tuning. We discover that fine-tuning on episodes of a particular shot can specialize the pre-trained model to solving episodes of that shot at the expense of performance on other shots, in agreement with a trade-off recently observed in the context of end-to-end episodic training. To amend this, we propose a shot-conditional form of episodic fine-tuning, inspired from recent work that trains a single model on a distribution of losses. Our investigation shows that this improves overall performance, without suffering disproportionately on any shot. We also examine the usefulness of this approach on the large-scale Meta-Dataset benchmark where test episodes exhibit varying shots and imbalanced classes. We find that our flexible model improves performance in that challenging environment.
A Semantic Approach to Multi-parameter Personalisation of E-Learning Systems

This paper presents a model framework for achieving semantic adaptation in e-learning systems using multiple parameters for personalisation. The proposed model, which utilises semantic technologies, aims to boost learning experiences and outcomes within the process of learning. This is often achieved through a mechanism that adapts the educational contents of a course in keeping with student’s preferences expressed by multiple parameters (such as their learning styles, media preferences, level of data, language, etc.) The variation process involves real-time mapping of learning resources and student data, semantic annotation, metadata enrichment of learning resources, creation of student profile with relevant preferences, and personalisation of every course in step with the foremost suitable (or preferred) parameters. Achieving this entailed the creation of an ontology and several other modules that work in the background of the adaptive process.
Analisis Kebijakan Penggunaan Smartphone Di SMA Kabupaten Simalungun

Law number 11 of 2019 concerning the National System and Knowledge and Technology has been regulated in such a way as to include technology in using smartphones to advance the quality of an education. The cause of the failure of the Law No. 11 of 2019 is the absence of strict regulations imposed by schools. Likewise, in state high schools in Simalungun Regency. The purpose of this study was to determine the implementation of the policy of smartphone use in Simalungun Regency High School by using the policy theory of David L. Weimer and Aidan R. Vining. By using the method of data analysis needed taken through a questionnaire distributed to 250 students. The results showed that using the policy theory of David L. Weimer and Aidan R. Vining are: a) the logic factors needed from a smartphone usage policy, b) there are factors that influence the success of the policy, namely: environmental factors; and the ability of the policy implementer
Tutorial: Programmable Integrated Photonics

Programmable photonic circuits are optical chips whose functionality can be (re)configured through electronics and software. This programmability enables new functionality, and opens the path to general-purpose photonic chips, similar to electronic FPGAs. This tutorial discusses the operational principles and technology stack for these chips.