Reviewerly/exHarmony huggingface dataset:
Bayesian evaluation of dynamic signatures in operational conditions

Forensic handwriting examiners (FHE) activities are focused on comparative analysis of handwritten objects such as signatures. Their role is to provide and evaluate evidence for and against the authenticity of a questioned signature. In recent years, cases involving handwritten signatures captured on electronic devices have become more commonplace. These so-called 'dynamic signatures' (also known as 'digitally captured signatures') are much different from paper-based signatures. Not only does the medium of recording differ, but also the type, volume of data and features are different from the pattern-based evidence that makes up paper-based signatures. Recent developments in forensic science - including signature examination - have led to the adoption of evaluative probabilistic methodologies in many disciplines [see, e.g. ENFSI 1915 Guidelines]. In the current paper, a probabilistic model to evaluate signature evidence in the form of multivariate data, as proposed and described in Wacom Europe GmbH (2019), is adopted. Topics like data sparsity, joint evaluation of multiple features and feature selection are investigated. Performed experimental studies showed an accuracy rate above 90% even when a limited number (5) of reference signatures was available. The performances of a multivariate approach are compared with those characterizing a so-called multiplicative approach where variables (features) are taken as independent and the Bayes' factor (BF) is obtained as the product of univariate BFs associated to each selected feature. The simplicity of this latter approach is, however, accompanied by severe issues about the reliability of results. The use of a multivariate approach is therefore highly recommended. Finally, the evidential values in correspondence of alternative feature sets are compared. Results suggest that discriminative features are writer-related and necessitate a case-specific selection.
DetectPMFL: Privacy-Preserving Momentum Federated Learning Considering Unreliable Industrial Agents

Federated learning (FL) as an emerging learning paradigm, has been achieved widespread attention since it allows distributed industrial agents to collaboratively develop a global model while keeping their data locally. Although various FL-based algorithms were proposed to solve engineering tasks in industrial cyber-physical systems, existing works rarely study a practical problem that the training samples collected by certain industrial agents (called unreliable industrial agents) may be of low quality. Obviously, the unreliable industrial agent would degrade the model accuracy. In this article, we propose a privacy-preserving momentum federated learning considering unreliable industrial agents, named DetectPMFL. In DetectPMFL, we design a detection method to alleviate the adverse effect of the unreliable agents. In addition, the privacy issues are analyzed by the mathematical description, especially for the convolution neural network. Based on this, Cheon-Kim-Kim-Song (CKKS) homomorphic encryption is used to protect the private information of the agents. The proposed approach is evaluated by two common datasets for recognition tasks. The security analysis and experiment results indicate that DetectPMFL is robust against unreliable industrial agents, and achieves high accuracy while preserving privacy.
Genre-based Automated Writing Evaluation

Genre-based automated writing evaluation (GBAWE) is an emergent domain of inquiry and praxis motivated by the need to assist students in advanced writing contexts to acquire the specialized discourses of their target disciplinary communities.
On the Edge of the Perceptible: Microacting, imaginary worlds and cognitive estrangement in virtual performance

Microacting is a performance technique originated by the author in 2009 in collaboration with the jazz vibraphonist/actor Krystof Krasa, with subsequent contribution from neuroscientist Inna Tsirlin. It plays with the irregularities in human perception to build collectively imagined spaces, usually in a domestic setting. This text – part scholarly article, part research-as-practice reflection – contextualizes the latest series of experiments in perception and microacting brought about by the framework of the COVID-19 pandemic. We build imaginary worlds by inducing processes of cognitive deterritorialization. In the absence of normally available techniques due to the pandemic, we have been experimenting with how far our audience can carry the imaginative process on their own. During a series of virtual workshops conducted during the pandemic, the participants were guided via Zoom by an unseen voice through a perceptional de/reconstruction of their homes. The resulting observations along with the imaginary maps created by the participants are presented in the article.
Knowledge-Based Paranoia Search

This paper proposes knowledge-based paranoia search (KBPS) to find forced wins in the card game Skat; an internationally played card game, and likely one of the most interesting card games for three players. It combines efficient partial information game-tree search with knowledge representation and reasoning. This worst-case analysis, initiated after a small number of tricks, leads to a prioritized choice of cards. We provide variants of KBPS for the declarer and the opponents, and an approximation to find a forced win against most worlds in the belief space. Replaying thousands of expert games, our evaluation indicates that the AIs with the new algorithms perform better than humans in their play, achieving an average score of over 1,000 points in the agreed standard for evaluating Skat tournaments, the extended Seeger system.
Digital Qubits for FPGA-based Homogenous Quantum Coprocessor

Real quantum computers are analog and probabilistic devices by their nature. But their FPGA-based digital versions also exist. A digital quantum coprocessor has already been created, which has 1024 digital qubits. It can already be used to work out various quantum algorithms, algorithms for the interaction of a classic computer and its quantum coprocessor, as well as for research various options for building digital qubits. The effect of digital qubit state code length on the probability of obtaining correct results is studied in the work. For the study, a homogeneous digital quantum coprocessor with 32 digital qubits which can perform quantum Fourier transformation is selected. It was shown that probability is improved with increase of the qubit state code length, that is, with increase of coprocessor hardware cost.
Curriculum System of Preschool Education under the Background of AR Intelligence

With the advent of the "Internet +" era, the rapid development of mobile handheld devices such as smartphones and iPads provides a suitable platform for the development of augmented reality technology. In the field of education, the study of enriching teaching scenarios and deepening students' understanding through AR technology has attracted the attention of many experts and scholars at home and abroad. Among them, AR technology has great room for development in the field of preschool education. The article analyzes the literature, compares its research content and methods, specifically explores the application examples of AR technology in the field of preschool education, summarizes the three main directions of AR in the development of early childhood education (children books, children educational games and classroom practice), and focuses on AR in The characteristics and advantages of early childhood education applications, so as to design learning tools suitable for children's learning.
Research on Pedestrian Detection Technology Based on Deep Learning Network Complexity

Pedestrian detection (P-D) is an significant part in the field of machine vision. The development of P-D is allong with the whole process of target detection. The P-D's objective is to accurately find the moving suspected people in the video, return its classification results. Due to the particularity of its detection targets, for the past few years, P-D technology has been broadly applied in monitoring equipment, unmanned driving, smart home systems and other fields, which has attracted widespread attention from the academic community and society. However, the human body itself is a non-rigid target, and when a limb movement occurs, the extracted target features are time-varying. This leads to the machine learning method based on hand-designed features, which has the disadvantages of robustness and low noise immunity.
Semantic segmentation guided feature point classification and seam fusion for image stitching

Image stitching can be employed to stitch images taken from different times, perspectives, or devices into a panorama with a wider view. However, the imaging specification of images to be stitched is strict. If the imaging specification is not satisfied, artefacts caused by inaccurate alignment and unnatural distortion will occur. Semantic segmentation can solve the classification problem at the pixel level; however, image stitching significantly depends on the accuracy of feature points. Therefore, this paper proposes an image stitching algorithm based on semantic segmentation to guide feature point classification and seam fusion. First, the images are recognized by a cascade semantic segmentation network, and the image feature points are classified. Thereafter, the corresponding homography transformations are calculated using different class feature points, and the best homography mapping for the entire target image is selected. Finally, a seam-cutting algorithm based on semantic segmentation is used to compute the seam, and a feathering Poisson fusion with distance transformation is used to eliminate artefacts and light differences. Experiments show that the algorithm can generate transitional natural and perceptual stitching results even under the influence of perspective and light differences.
A Joint Entity-Relation Extraction Method with Sparse Parameter Sharing Architecture

The existing parameter sharing joint entity-relation extraction models cannot learn task-specific features for named entity recognition and relation classification subtasks. To address the problem, this work proposed a sparse parameter sharing joint entity-relation extraction method. The proposed method incorporates a sparse parameter sharing architecture with the existing parameter sharing joint entity-relation extraction model SpERT to learn subnetworks for the two subtasks. The subnetworks inherit and partially share the base network parameters, which provide the implicit feature interaction learning ability and task-specific feature learning ability for the model. Experiments are conducted on the ADE, SciERC, and CoNLL04 datasets. The experimental results show that the proposed method outperforms the baseline model SpERT by up to 1.40% in the F1 value. In addition, the performance on datasets SciERC and ADE can exceed the state-of-the-art results.
A Method of Shadowsocks(R) Traffic Identification Based on Protocol Analysis

Shadowsocks (R) is a proxy software based on Socks5, which is the collective name of Shadowsocks and Shadowsocks (R). In order to improves the shortcomings of other softwares breaking internet censorship, Shadowsocks (R) is a private protocol without handshake negotiation mechanism, and pays more attention to high camouflage and high concealment of traffic, which makes it difficult to use common methods(fingerprint identification, port filtering)to identify Shadowsocks (R) traffic[1], This paper proposes a method of Shadowsocks (R) traffic identification from the perspective of the protocol, which extracts features from four dimensions, and inputs features into the machine learning model for training. This method performs secondary identification and statistical filtering on the output of the machine learning model, and finally identifys Shadowsocks (R) traffic. This method has been very well validated in the laboratory environment.
A Discrete Missing Data Imputation Method Based on Improved Multi-layer Perceptron

Data have become an important factor of production in various fields. While there are many missing values due to some irresistible reasons, which may lead to incorrect results and conclusions. A set of imputation methods have been proposed for filling continuous data, while only a few works focus on discrete missing data. In this paper, we propose an improved multi-layer perceptron (IMLP) imputation method based on the momentum gradient descent (MGD) algorithm to impute the discrete missing data via the one-hot encoding technique. To verify the performance of this method, comparisons have been made of mode, K-nearest neighbor, and auto-encoder imputation methods on different missing patterns and missing rates. The simulation result shows that the IMLP has a good imputation performance on discrete data with a high missing rate.
Effects of Demographics and Photometric Normalization on Image Translation GANs for Cross-Spectral Face Recognition

This paper focuses on thermal-to-visible face matching through image synthesis. Most of the legacy face image datasets are composed of visible band data. Thermal band as well as dual band, i.e. visible and thermal face datasets, are limited. Operating in the thermal band and therefore working on visible thermal face recognition (FR) systems can be beneficial in various scenarios. The challenge is cross-spectral matching, i.e. matching gallery, visible band, face images against thermal ones. To address this problem, we train and test two of the most popular image-to-image translation Generative Adversarial Networks (GANs). These are Pix2pix and StarGAN2. In this work, the two aforementioned GAN trained models are tested, and the visible face images generated are matched against the ground truth visible faces using one of the most powerful visible-to-visible face matching algorithms, namely Facenet. We also perform an ablation study where the original thermal and visible images are photometrically normalized before training the image synthesis-specific models. The main outcomes of our study are that the FR accuracy from the pix2pix model did not vary significantly; when using StarGAN2, the original face images yield much higher accuracy compared to the photometrically normalized ones; finally, we observe that, when using the pix2pix model for image synthesis, bearded and non-Caucasian generated face images suffer the most from different noise factors. Specifically, the FR accuracy when using pix2pix after image synthesis yields a face verification area under curve (AUC) of 58.3%, while the same models when tested on data excluding bearded and non-Caucasian faces yields an accuracy of 68.6%.
AlphaGo, Locked Strategies, and Eco-cognitive Openness

In 2015, Google DeepMind's program AlphaGo (able to perform the famous Go game) beat Fan Hui, the European Go champion and a 2 dan (out of 9 dan) professional, five times out of five with no handicap on a full size 19 $$\times $$ 19 board.
Image processing-based braille document copying system as additional feature for ITS’s braille printer

Nowadays, there are huge numbers of old braille documents found in libraries which are getting obsolete and needs reproducing. Unfortunately, they don’t have their original alphabetical text so that can’t be re-printed by any braille printer unless we have to translate them manually one by one. This research develops braille duplicator system as Institut Teknologi Sepuluh Nopember (ITS) braille printer’s additional feature. It works by photographing braille on smartphone, translating it into text, and sending result wirelessly to add-on device installed on braille printer’s interface. Translation is done by image processing. Test result shows its accuracy reach 99%. Thus, braille document can be duplicated without needing its original text.
Stereoscopic Views Improve Spatial Presence but Not Spatial Learning in VR Games

Abstract A key affordance of virtual reality is the capability of immersive VR to prompt spatial presence resulting from the stereoscopic lenses in the head-mounted display (HMD). We investigated the effect of a stereoscopic view of a game, Cellverse, on users’ perceived spatial presence, knowledge of cells, and learning in three levels of spatial knowledge: route, landmark, and survey knowledge. Fifty-one participants played the game using the same game controllers but with different views; 28 had a stereoscopic view (HMD), and 23 had a non-stereoscopic view (computer monitor). Participants explored a diseased cell for clues to diagnose the disease type and recommend a therapy. We gathered surveys, drawings, and spatial tasks conducted in the game environment to gauge learning. Participants’ spatial knowledge of the cell environment and knowledge of cell concepts improved after gameplay in both conditions. Spatial presence scores in the stereoscopic condition were higher than the non-stereoscopic condition with a large effect size; however, there was no significant difference in levels of spatial knowledge between the two groups. Most drawings showed a change in cell knowledge; yet some participants only changed in spatial knowledge of the cell, and some changed in both cell knowledge and spatial knowledge. Evidence suggests that a stereoscopic view has a significant effect on users’ experience of spatial presence, but that increased presence does not directly translate into spatial learning.
Precise Control for Deep Driving using Dual Critic based DRL Approaches

Autonomous driving problems related to vehicle control using deep reinforcement learning (DRL) techniques, are still unsolved. DRL approaches have achieved notable results, its dependability on reward functions and defining the type of control actions are dominating factors of the objective, that controls its success. Several DRL approaches applied in the past consider a finite set of available actions to be controlled by the agent hence, it performs sharp actions. While real driving requires precision control capabilities that tend to apply safer and smoother actions. For incorporating such precision control capabilities, this paper considers the driving problem as a continuous control problem. For this, the gym-highway environments are used as these environments are controllable and customizable to simulate diverse driving scenarios. The simulation setup for parking is updated to resemble the complex scenario and for highway driving a novel reward function is designed to handle continuous actions. Dual critic based DRL approaches are applied as these approaches have shown remarkable performance in robotic locomotion control problems. The video results demonstrate the way different policies fulfil the objective.
Attacking Pathways of Health Information System (HIS)

The health information system (HIS) has been targeted by the hackers especially during the pandemics of COVID 19. This paper is motivated by the recent cyber incidents happened to healthcare organisations. This study was conducted to demonstrate how the HIS can be hacked and provide some recommendations to protect the HIS. We created a simulated virtual environment by implementing an open-source medical system. We then followed the NIST pen-testing methodology to perform ethical hacking. The hacking was successful, and we have managed to exploit several vulnerabilities of the simulated HIS. We then proposed cyber security recommendations to protect the HIS. Future work will consider demonstrating attacks to a specialized cardiac diagnosis medical system, e.g. the arrhythmia detection and classification in ambulatory ECGs, and explore how the core of its algorithms can be hacked and protected.
An Optimized VM Placement Approach to Reduce Energy Consumption in Green Cloud Computing

As a global digitization advancement, there is a massive need of cloud-based solutions and data centers. Another reason behind excessive need of data centers is because of increasing number of internet users. Increasing demand of data centers simultaneously need huge amount of energy for data center operation and on other end emit enormous amount of CO2. Several approaches have been proposed to reduce energy consumption, but major concern is by looking at one parameter or criteria they must compromise on other. Our proposed approach MIPS-Aware VM Placement in combination with searching of best capable host helps to reduce VM migration and increase mean time for better performance and save energy. Proposed approach identifies overloaded and underloaded hosts and to improve system performance algorithm does not allow to allocate additional workload, which will also help to reduce energy and get better QoS. Proposed approach significantly decreases VM migration and increase mean time before VM migration which in turns helps to reduce energy and associated cost. By using proposed MIPS-Aware VM Placement approach, we can reduce upto 25% more energy consumption compared to traditional approaches.
Hot topic detection and evaluation of multi-relation effects

With the growth of social media, Twitter has become one of the most popularly used microblogging communication platforms between people. Due to the wide preference of Twitter, popular issues in public, events like local or global news and daily life stories can immediately publish on Twitter. Thus, a substantial number of hot topics are created by Twitter users in real-time. These topics can exhibit every incident of everyday life. Therefore, detection of hot topics can be used in many applications such as observing public judgment, product recommendation, and incidence detection. In this paper, we propose a method for detecting Twitter hot topics and evaluate the effect of multi-relations such as retweets and hashtags on hot topics. The dataset was generated by fetching tweets for a certain time and location by using GetOldTweets3 API. Then using the LDA topic modeling algorithm the hot topics were identified for each multi relation. Finally, the effect of each relation is described by using the coherence scores)
Comparing Grammatical Theories of Code-Mixing

Code-mixed text generation systems have found applications in many downstream tasks, including speech recognition, translation and dialogue.A paradigm of these generation systems relies on well-defined grammatical theories of code-mixing, and there is a lack of comparison of these theories.We present a largescale human evaluation of two popular grammatical theories, Matrix-Embedded Language (ML) and Equivalence Constraint (EC).We compare them against three heuristic-based models and quantitatively demonstrate the effectiveness of the two grammatical theories.