Reviewerly/exHarmony huggingface dataset:
Hacker and the Treat for National Security: Challenges in Law Enforcement

Globalization has become the driving force behind the birth of the era of information technology development. The development of this technology is very fast and has spread to all corners of the world. The development of information technology is not only felt by developed countries but developing countries also feel the development of information technology, so that information technology gets an important position for the progress of society in this modern era. The need for computer network technology is increasing. Apart from being a medium for providing information, through the internet, commercial community activities are also the largest and growing rapidly and penetrate various national borders. Even through this network market activities in the world can be known for 24 hours. Through the world of the internet or also called cyber space, anything can be done. The positive side of this virtual world of course adds to the trend of world technology development with all forms of human creativity. However, the negative impact cannot be avoided. When pornography is rife on the internet, people can't do much. Along with the development of internet technology, causing the emergence of a crime called cyber crime or crime through the internet network or cyberspace. The emergence of several cases of cyber crime in Indonesia, such as credit card theft, hacking of several sites, tapping other people's data transmissions, such as e-mail and manipulating data by preparing unwanted commands into computer programmers. So that in computer crimes it is possible to have formal offenses and material offenses. Formal offense is the act of someone entering someone else's computer without permission, while material offense is an act that causes harm to other people. The existence of cyber crime has become a threat to stability, so it is difficult for the government to balance the techniques of crime committed with computer technology, especially in the internet network.
Graph-Wise Common Latent Factor Extraction for Unsupervised Graph Representation Learning

Unsupervised graph-level representation learning plays a crucial role in a variety of tasks such as molecular property prediction and community analysis, especially when data annotation is expensive. Currently, most of the best-performing graph embedding methods are based on Infomax principle. The performance of these methods highly depends on the selection of negative samples and hurt the performance, if the samples were not carefully selected. Inter-graph similarity-based methods also suffer if the selected set of graphs for similarity matching is low in quality. To address this, we focus only on utilizing the current input graph for embedding learning. We are motivated by an observation from real-world graph generation processes where the graphs are formed based on one or more global factors which are common to all elements of the graph (e.g., topic of a discussion thread, solubility level of a molecule). We hypothesize extracting these common factors could be highly beneficial. Hence, this work proposes a new principle for unsupervised graph representation learning: Graph-wise Common latent Factor EXtraction (GCFX). We further propose a deep model for GCFX, deepGCFX, based on the idea of reversing the above-mentioned graph generation process which could explicitly extract common latent factors from an input graph and achieve improved results on downstream tasks to the current state-of-the-art. Through extensive experiments and analysis, we demonstrate that, while extracting common latent factors is beneficial for graph-level tasks to alleviate distractions caused by local variations of individual nodes or local neighbourhoods, it also benefits node-level tasks by enabling long-range node dependencies, especially for disassortative graphs.
Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks

Meta reinforcement learning (meta-RL) aims to learn a policy solving a set of training tasks simultaneously and quickly adapting to new tasks. It requires massive amounts of data drawn from training tasks to infer the common structure shared among tasks. Without heavy reward engineering, the sparse rewards in long-horizon tasks exacerbate the problem of sample efficiency in meta-RL. Another challenge in meta-RL is the discrepancy of difficulty level among tasks, which might cause one easy task dominating learning of the shared policy and thus preclude policy adaptation to new tasks. This work introduces a novel objective function to learn an action translator among training tasks. We theoretically verify that the value of the transferred policy with the action translator can be close to the value of the source policy and our objective function (approximately) upper bounds the value difference. We propose to combine the action translator with context-based meta-RL algorithms for better data collection and moreefficient exploration during meta-training. Our approach em-pirically improves the sample efficiency and performance ofmeta-RL algorithms on sparse-reward tasks.
Active Sampling for Text Classification with Subinstance Level Queries

Active learning algorithms are effective in identifying the salient and exemplar samples from large amounts of unlabeled data. This tremendously reduces the human annotation effort in inducing a machine learning model as only a few samples, which are identified by the algorithm, need to be labeled manually. In problem domains like text mining and video classification, human oracles peruse the data instances incrementally to derive an opinion about their class labels (such as reading a movie review progressively to assess its sentiment). In such applications, it is not necessary for the human oracles to review an unlabeled sample end-to-end in order to provide a label; it may be more efficient to identify an optimal subinstance size (percentage of the sample from the start) for each unlabeled sample, and request the human annotator to label the sample by analyzing only the subinstance, instead of the whole data sample. In this paper, we propose a novel framework to address this challenging problem, in an effort to further reduce the labeling burden on the human oracles and utilize the available labeling budget more efficiently. We pose the sample and subinstance size selection as a constrained optimization problem and derive a linear programming relaxation to select a batch of exemplar samples, together with the optimal subinstance size of each, which can potentially augment maximal information to the underlying classification model. Our extensive empirical studies on six challenging datasets from the text mining domain corroborate the practical usefulness of our framework over competing baselines.
Single Input Single Head CNN-GRU-LSTM Architecture for Recognition of Human Activities

Due to its applications for the betterment of human life, human activity recognition has attracted more researchers in the recent past. Anticipation of intension behind the motion and behaviour recognition are intensive applications for research inside human activity recognition. Gyroscope, accelerometer, and magnetometer sensors are heavily used to obtain the data in time series for every timestep. The selection of temporal features is required for the successful recognition of human motion primitives. Different data pre-processing and feature extraction techniques were used in most past approaches with the constraint of sufficient domain knowledge. These approaches are heavily dependent on the quality of handcrafted features and are also time-consuming and not generalized. In this paper, a single head deep neural network-based approach with the combination of a convolutional neural network, Gated recurrent unit, and Long Short Term memory is proposed. The raw data from wearable sensors are used with minimum pre-processing steps and without the involvement of any feature extraction method. 93.48 % and 98.51% accuracy are obtained on UCI-HAR and WISDM datasets. This single-head deep neural network-based model shows higher classification performance over other architectures under deep neural networks.
FPGA Design of Real Time Hardware for Face Detection

This paper proposes the hardware architecture of face detection FPGA hardware system using the AdaBoost algorithm. The proposed structure of face detection hardware system is possible to work in 30 frames per second and in real time. And the AdaBoost algorithm is adopted to learn and generate the characteristics of the face data by MATLAB, and finally detected the face using this data. This paper describes the face detection hardware structure composed of image scaler, integral image extraction, face comparing, memory interface, data grouper and detected result display. The proposed circuit is so designed to process one point in one cycle that the proposed design can process full HD (1920x1080) image at 70MHz, which is approximate 2316087 x 30 cycle.
Class Impression for Data-Free Incremental Learning

Standard deep learning-based classification approaches require collecting all samples from all classes in advance and are trained offline. This paradigm may not be practical in real-world clinical applications, where new classes are incrementally introduced through the addition of new data. Class incremental learning is a strategy allowing learning from such data. However, a major challenge is catastrophic forgetting, i.e., performance degradation on previous classes when adapting a trained model to new data. To alleviate this challenge, prior methodologies save a portion of training data that require perpetual storage, which may introduce privacy issues. Here, we propose a novel data-free class incremental learning framework that first synthesizes data from the model trained on previous classes to generate a Class Impression. Subsequently, it updates the model by combining the synthesized data with new class data. Furthermore, we incorporate a cosine normalized Cross-entropy loss to mitigate the adverse effects of the imbalance, a margin loss to increase separation among previous classes and new ones, and an intra-domain contrastive loss to generalize the model trained on the synthesized data to real data. We compare our proposed framework with state-of-the-art methods in class incremental learning, where we demonstrate improvement in accuracy for the classification of 11,062 echocardiography cine series of patients. Code is available at https://github.com/ sanaAyrml/Class-Impresion-for-Data-free-Incremental-Learning
Coarse-to-fine multiscale fusion network for single image deraining

Cameras convert optical signals into electronic information to enable image acquisition. However, cameras are susceptible to environmental factors such as light and weather conditions. The information captured by cameras in rainy condition tends to degrade, resulting in reduced contrast and visibility of the images. Deraining aims to restore the image degraded by raindrops and rain accumulation in rainy weather. Most state-of-the-art, end-to-end neural network-based deraining methods have achieved satisfactory results. By considering the features from different scales, we proposed a further improved multiscale information exchange module to extract features from different scales. As a result, the structure achieves a competitive effect on rain mask detection. Moreover, experiments have found that the rain layer-based prediction models tend to leave rain streak residuals in specific image patches. Therefore, we further developed the Feature Attention Compensation module (FAC) to better utilize the deraining image and rain layer information obtained from the multiscale model, which is demonstrated to boost deraining performance. To summarize, we design a coarse-to-fine rain removal model, starting with a rain detection network to get a coarse rain-free image, which is further refined with the FAC module to eventually create a fine rain-free image. We conduct our experiments on both synthetic and real-world datasets. Quantitative and qualitative experimental results demonstrate that the proposed method outperforms the state-of-the-art deraining methods. Source code will be available at https://github.com/Jstar-s/CFMFN
Biographical Semi-Supervised Relation Extraction Dataset

Extracting biographical information from online documents is a popular research topic among the information extraction (IE) community. Various natural language processing (NLP) techniques such as text classification, text summarisation and relation extraction are commonly used to achieve this. Among these techniques, RE is the most common since it can be directly used to build biographical knowledge graphs. RE is usually framed as a supervised machine learning (ML) problem, where ML models are trained on annotated datasets. However, there are few annotated datasets for RE since the annotation process can be costly and time-consuming. To address this, we developedBiographical, the first semi-supervised dataset for RE. The dataset, which is aimed towards digital humanities (DH) and historical research, is automatically compiled by aligning sentences from Wikipedia articles with matching structured data from sources including Pantheon and Wikidata. By exploiting the structure of Wikipedia articles and robust named entity recognition (NER), we match information with relatively high precision in order to compile annotated relation pairs for ten different relations that are important in the DH domain. Furthermore, we demonstrate the effectiveness of the dataset by training a state-of-the-art neural model to classify relation pairs, and evaluate it on a manually annotated gold standard set.Biographical is primarily aimed at training neural models for RE within the domain of digital humanities and history, but as we discuss at the end of this paper, it can be useful for other purposes as well.
A survey on methods, datasets and implementations for scene text spotting

Text Spotting is the union of the tasks of detection and transcription of the text that is present in images. Due to the various problems often found when retrieving text, such as orientation, aspect ratio, vertical text or multiple languages in the same image, this can be a challenging task. In this paper, the most recent methods and publications in this field are analysed and compared. Apart from presenting features already seen in other surveys, such as their architectures and performance on different datasets, novel perspectives for comparison are also included, such as the hardware, software, backbone architectures, main problems to solve, or programming languages of the algorithms. The review highlights information often omitted in other studies, providing a better understanding of the current state of research in Text Spotting, from 2016 to 2022, current problems and future trends, as well as establishing a baseline for future methods development, comparison of results and serving as guideline for choosing the most appropriate method to solve a particular problem.
RecDelta

In this demonstration, we present RecDelta, an interactive tool for the cross-model evaluation of top-k recommendation. RecDelta is a web-based information system where people visually compare the performance of various recommendation algorithms and their recommended items. In the proposed system, we visualize the distribution of the δ scores between algorithms--a distance metric measuring the intersection between recommendation lists. Such visualization allows for rapid identification of users for whom the items recommended by different algorithms diverge or vice versa; then, one can further select the desired user to present the relationship between recommended items and his/her historical behavior. RecDelta benefits both academics and practitioners by enhancing model explainability as they develop recommendation algorithms with their newly gained insights. Note that while the system is now online at https://cfda.csie.org/recdelta, we also provide a video recording at https://tinyurl.com/RecDelta to introduce the concept and the usage of our system.
Case Study: Code of Ethics for Facial Recognition Technology

Facial Recognition Technology (FRT) is a way of identifying an individual by comparing their facial signature to a database of known faces. It has many current uses, and this is only expected to grow due to its many potential and promising applications. However, this technology presents a lot of ethical issues that need to be addressed. This paper examines current literature to find the current ethical issues of FRT such as its usage to infringe on one’s privacy, use an individual’s data without their consent and misidentify individuals. Based on these issues, a code of ethics will be created that will ensure an individual's privacy and data security and minimize the biases and misidentifications in FRT. This code of ethics is applied and assessed via a case study about the usage of live facial recognition technology in the New Zealand Police.
Virtual Mouse And Keyboard For Computer Interaction

Mouse and keyboard are the wonderful inventions of Human-Computer Interaction (HCI) technology. Currently, wireless mouse or a Bluetooth mouse still uses devices and is not free of devices completely since it uses a battery for power and a dongle to connect it to the PC. In the proposed virtual mouse and keyboard system, this limitation can be overcome by employing webcam or a built-in camera for capturing of hand gestures and coloured object. Method used for Hand gesture recognition is based on Convolutional Neural Networks (CNNs).The computer can be controlled virtually and can perform left click, right click, scrolling functions,volumecontrol,brightness control and other computer cursor function without the use of a physical mouse.A virtual keyboard is controlled by tracking a coloured object and it is based on image processing technique. The proposed system will mostly can use on the conference to present topics using projector and avoid COVID-19 spread by eliminating the human intervention and dependency of devices to control the computer.Main goal is to make the interaction between human and computer as natural as the interactionbetween humans.
Pre-Service Social Educators’ Professional Competences: An Inclusive Education Context

The quality of education is increasingly a priority area for state development. In the context of globalisation, the mission of the education system has become to create conditions for the achievement of greater stability in society, by ensuring equal rights and opportunities for all citizens, including individuals with disabilities. Amid efforts towards fully inclusive education, the professional competences of specialists addressing the needs of those with learning disabilities are crucial. High-quality training is required for qualified educator –particularly for social educators – in this field. It aimed the research at identifying the necessary professional competences and personal qualities of pre-service social educators to work in an inclusive education, as well as to identify the difficulties university lecturers face in the training of social educators. A qualitative research design was applied in the study. Fifty-one respondents-university lecturers participated in it. Its results showed that, alongside central competences, emotional-volitional qualities have played a significant role in the social educators’ engagement in inclusive education. The research enabled to identify that the professional difficulties have the highest indicator among the difficulties that lecturers face when training pre-service social educators. It can be assumed that these findings will advance improvements in the development of social educators’ professional competences and solutions to obstacles during their university-level studies.
A Comprehensive Framework for Learning Declarative Action Models

A declarative action model is a compact representation of the state transitions of dynamic systems that generalizes over world objects. The specification of declarative action models is often a complex hand-crafted task. In this paper we formulate declarative action models via state constraints, and present the learning of such models as a combinatorial search. The comprehensive framework presented here allows us to connect the learning of declarative action models to well-known problem solving tasks. In addition, our framework allows us to characterize the existing work in the literature according to four dimensions: (1) the target action models, in terms of the state transitions they define; (2) the available learning examples; (3) the functions used to guide the learning process, and to evaluate the quality of the learned action models; (4) the learning algorithm. Last, the paper lists relevant successful applications of the learning of declarative actions models and discusses some open challenges with the aim of encouraging future research work.
Self-Supervised RF Signal Representation Learning for NextG Signal Classification With Deep Learning

Deep learning (DL) finds rich applications in the wireless domain to improve spectrum awareness. Typically, DL models are either randomly initialized following a statistical distribution or pretrained on tasks from other domains in the form of transfer learning without accounting for the unique characteristics of wireless signals. Self-supervised learning (SSL) enables the learning of useful representations from Radio Frequency (RF) signals themselves even when only limited training data samples with labels are available. We present a self-supervised RF signal representation learning method and apply it to the automatic modulation recognition (AMR) task by specifically formulating a set of transformations to capture the wireless signal characteristics. We show that the sample efficiency (the number of labeled samples needed to achieve a certain performance) of AMR can be significantly increased (almost an order of magnitude) by learning signal representations with SSL. This translates to substantial time and cost savings. Furthermore, SSL increases the model accuracy compared to the state-of-the-art DL methods and maintains high accuracy when limited training data is available.
DEVELOPMENT OF CLEANING EVALUATION APPLICATION USING HUE CHANGE AS A PARAMETER

In the field of cleaning, there is a method to evaluate the amount of soiling by processing the color signal of an image of a soiled sample using the Kubelka-Munka formula, but this method could not be adapted to the case where the hue changes due to cleaning or washing. In this study, we aimed to construct a system to quantify the amount of soiling corresponding to the hue change.We conducted a cleaning experiment using a sample of iron oxide, which changes from yellowish-brown to black by reduction, as a model soiling on fabric and evaluated the accuracy of the image analysis method by comparing it with the results of XRF analysis of the sample after cleaning.As a result, it was found that the change in the hue of iron oxides due to reduction could be clearly captured as a color signal, and the degree of reduction of iron oxides could be determined from the xy value. Using the results, we were able to construct an algorithm that can accurately determine the amount of dirt adhesion by recalculating the hue change as a variable.This achievement has greatly expanded the range of objects for which the amount of soiling from the exterior can be evaluated.