Reviewerly/exHarmony huggingface dataset:
The Impact of Self-Representation and Consistency in Collaborative Virtual Environments

This paper explores the impact of self-representation (full body Self Avatar vs. Just Controllers) in a Collaborate Virtual Environment (CVE) and the consistency of self-representation between the users. We conducted two studies: Study 1 between a confederate and a participant, Study 2 between two participants. In both studies, participants were asked to play a collaborative game, and we investigated the effect on trust with a questionnaire, money invested in a trust game, and performance data. Study 1 suggested that having a Self Avatar made the participant give more positive marks to the confederate and that when the confederate was without an avatar, they received more trust (measured by money). Study 2 showed that consistency led to more trust and better productivity. Overall, results imply consistency improves trust only when in an equal social dynamic in CVE, and that the use of confederate could shift the social dynamics.
Vision-Based Real-time Human Malicious Behavior Detection

Human detection and behavior analysis from surveillance videos is an active area of research in computer vision. Authorities and security administrators need a system that can detect human malicious behavior to take immediate necessary actions. In this paper, we propose an approach to detect the anomalous/malicious behavior of humans in the surveillance videos. The proposed approach models the human behavior using human joint motion information from skeleton sequence. We have divided the proposed approach into four sub-modules i.e. human detection and skeleton estimation, human ID assignment, feature extraction and classification. The proposed approach is evaluated on publicly available CASIA dataset in offline mode and accuracy of 90.81% has been achieved. The experimental results indicates that it can be exploited in real-time applications with low computational cost of 18 frames per second.
Detection of Constellation-Modulated Wireless Covert Channel Based on Adjusted CNN Model

With the development of wireless communication technology, more and more information leakage is realized through a wireless covert channel, which brings great challenges to the security of wireless communication. Compared with the wireless covert channel on the upper layer, the wireless covert channel based on the physical layer (WCC-P) has better concealment and greater capacity. As the most widely used scheme of WCC-P, the wireless covert channel with the modulation of the constellation point (WCC-MC) has attracted more and more attention. In this paper, a deep learning scheme based on amplitude-phase characteristics is proposed to detect and classify the WCC-MC scheme. We first extract the amplitude and phase characteristic of error vector magnitude (EVM) and constellation points and then map the amplitude and phase characteristic to the grayscale image, respectively. Finally, the generated feature images are trained, detected, and classified with the adjusted convolution neural network. The experimental results show that the detection accuracy of our proposed scheme can reach 98.5%, and the classification accuracy can reach 81.7%.
Twin neural network regression is a semi-supervised regression algorithm

Twin neural network regression (TNNR) is a semi-supervised regression algorithm, it can be trained on unlabelled data points as long as other, labelled anchor data points, are present. TNNR is trained to predict differences between the target values of two different data points rather than the targets themselves. By ensembling predicted differences between the targets of an unseen data point and all training data points, it is possible to obtain a very accurate prediction for the original regression problem. Since any loop of predicted differences should sum to zero, loops can be supplied to the training data, even if the data points themselves within loops are unlabelled. Semi-supervised training improves TNNR performance, which is already state of the art, significantly.
Automated neural network identification of cirques

Morphological characteristics of cirques have been studied for decades; however, no repeatable set of metrics has been derived that can consistently identify them. Perhaps more importantly, there is no consensus definition of the form that distinguishes cirques and clusters of cirques from non-cirques. In our approach, we use Shuttle Radar Topography Mission (SRTM) digital elevation models (DEMs) in a Convolutional Neural Network (CNN) framework to identify cirques in 20 mountain ranges globally. We extracted bounding boxes of cirques in 19 of these study areas and used them to develop a training set for a cirque identification model. The trained model was applied to the Sierra Nevada California to assess whether this algorithmic approach derived from a global dataset could produce consistent results in complex terrain with mutually interacting cirque forms. Using commonalities revealed using this approach, we find that there is a basic, recognizable and morphometrically quantifiable cirque form. This approach can be used to automate the identification of cirque locations and to guide the quantification of cirque form independent of the subjective definitions of individual workers. The approach can also be used to understand cirque form under different environmental conditions, including similar forms on Mars.
A scalable helium gas cooling system for trapped-ion applications

Abstract Microfabricated ion-trap devices offer a promising pathway towards scalable quantum computing. Research efforts have begun to focus on the engineering challenges associated with developing large-scale ion-trap arrays and networks. However, increasing the size of the array and integrating on-chip electronics can drastically increase the power dissipation within the ion-trap chips. This leads to an increase in the operating temperature of the ion-trap and limits the device performance. Therefore, effective thermal management is an essential consideration for any large-scale architecture. Presented here is the development of a modular cooling system designed for use with multiple ion-trapping experiments simultaneously. The system includes an extensible cryostat that permits scaling of the cooling power to meet the demands of a large network. Following experimental testing on two independent ion-trap experiments, the cooling system is expected to deliver a net cooling power of 111 W at ∼70 K to up to four experiments. The cooling system is a step towards meeting the practical challenges of operating large-scale quantum computers with many qubits.
Fast One-class Classification using Class Boundary-preserving Random Projections

Several applications, like malicious URL detection and web spam detection, require classification on very high-dimensional data. In such cases anomalous data is hard to find but normal data is easily available. As such it is increasingly common to use a one-class classifier (OCC). Unfortunately, most OCC algorithms cannot scale to datasets with extremely high dimensions. In this paper, we present Fast Random projection-based One-Class Classification (FROCC), an extremely efficient, scalable and easily parallelizable method for one-class classification with provable theoretical guarantees. Our method is based on the simple idea of transforming the training data by projecting it onto a set of random unit vectors that are chosen uniformly and independently from the unit sphere, and bounding the regions based on separation of the data. FROCC can be naturally extended with kernels. We provide a new theoretical framework to prove that that FROCC generalizes well in the sense that it is stable and has low bias for some parameter settings. We then develop a fast scalable approximation of FROCC using vectorization, exploiting data sparsity and parallelism to develop a new implementation called ParDFROCC. ParDFROCC achieves up to 2 percent points better ROC than the next best baseline, with up to 12× speedup in training and test times over a range of state-of-the-art benchmarks for the OCC task.
DeepOnKHATT: An End-to-End Arabic Online Handwriting Recognition System

The importance of online handwriting recognition technology has steadily increased in recent years. This importance stems from the rapid increase in the number of handheld devices with digital pens and styluses. In addition, a large number of currently available communication software have been designed to support handwriting boards. During the past decade, most innovation in online handwriting recognition technology was geared towards supporting languages using the Latin alphabet. There has been a lack of sufficient development of Arabic online handwriting recognition (AOHR) systems, especially ones that perform recognition at the sentence level. In this paper, we present DeepOnKHATT, an end-to-end AOHR based on bidirectional long short-term memory and the connectionist temporal classification (BLSTM-CTC). DeepOnKHATT is capable of performing recognition at the sentence level in real-time. We evaluated our system utilizing two open access databases: CHAW and Online-KHATT. Our model achieved a 4.08% character error rate (CER) and a 14.65% word error rate (WER) against the CHAW dataset and a 12.24% CER and a 28.35% WER against the Online-KHATT dataset. A Comparison of the functionality of our proposed model to that of other existing systems showed that DeepOnKHATT had outperformed these systems.
Hybrid Label Noise Correction Algorithm For Medical Auxiliary Diagnosis

In the context of the continuous development of Internet of Things (IoT) technology and Machine learning (ML) technology, its application in the medical field is becoming more and more extensive. However, with a dramatic increase in medical data obtained from the IoT-based medical auxiliary diagnosis system, the impact of label noise problems is also increasing. When training a machine learning algorithm for a supervised-learning task in some clinical applications, uncertainty in the labels of some patients may adversely affect the performance of the algorithm. For example, due to ambiguous patient conditions or poor reliability of diagnostic criteria, even clinical experts may lack confidence in making medical diagnoses for some patients. As a result, some samples used in algorithm training may be mislabeled, which adversely affects the performance of the algorithm. In this paper, we study a classification problem of sample labels with random damage. We propose a new hybrid label noise correction model that generalizes many learning problems, including supervised, unsupervised and semi-supervised learning. This hybrid model can withstand the negative effects of random noise and various non-random label noise. Extensive experimental results using real-world datasets from UCI machine learning repository are provided, the empirical study shows that our approach successfully improves data quality in many cases, in terms of classification accuracy, over existing label noise correction methods.
User Experience and Engagement in Augmented Reality Systems for the Cultural Heritage Domain

This chapter reviews the different models of user experience (UX) and user engagement (UE) proposed for Augmented Reality (AR) systems and discusses their applicability to the Cultural Heritage (CH). Traditional models of UX and UE are not totally adaptable to the current trends in the AR continuum for the CH domain. Thus, an important HCI research area that requires investigation is the evaluation of UX and UE factors for AR systems in the CH field. This chapter proposes a conceptual framework model for assessing UX and UE in AR systems. Initially, the UX categories (such as instrumental, cognitive, emotional, sensory, social and motivational) are investigated thoroughly to have a deep understanding of all the related components. Further, the UE factors (such as aesthetics, interest, goal, novelty, interactivity, gamification and learning) are identified and categorised. Twenty AR systems in the CH domain (AR-CH) have been selected based on pre-defined criteria and evaluated against a list of derived AR characteristics. The gaps in current literature have been considered to formulate a comprehensive framework for the assessment of UX and UE factors in AR-CH systems. Metrics and methods are investigated and identified for the measurement of the UX and UE factors. This chapter lays a solid foundation for the assessment of UX and UE factors in AR-CH systems, which has the potential to help AR system developers with identifying and improving the most UX and UE influential factors in their systems.
Logo Detection with Artificial Intelligent

A logo detector is a device that detects illegal produced or counterfeit brand products. The focus of this research is to detect the product logo and consider the likeness of the sample product logo. Building the detector's product logo detector, we used the image detection process with a darknet framework and YOLO algorithm. Through this process, the logo of the copyright products is being set as sample product data for having a dataset. Furthermore, OpenCV image classification by DNN module is used in Python language to read our dataset and work in Windows OS platform, to create a Graphical User Interface (GUI) simply including the creating a function to support the various application. The YOLO algorithm will be the main variable in this research. With this precision, we can detect the fake logo with 97% confidence scores and for the authentic logo with 99% confidence scores.
An ensemble‐based predictive mutation testing approach that considers impact of unreached mutants

Summary Predictive mutation testing (PMT) is a technique to predict whether a mutant is killed, using machine learning approaches. Researchers have proposed various methods for PMT over the years. However, the impact of unreached mutants on PMT is not fully addressed. A mutant is unreached if the statement on which the mutant is generated is not executed by any test cases. We aim at showing that unreached mutants can inflate PMT results. Moreover, we propose an alternative approach to PMT, suggesting a different interpretation for PMT. To this end, we replicated the previous PMT research. We empirically evaluated the suggested approach on 654 Java projects provided by prior literature. Our results indicate that the performance of PMT drastically decreases in terms of area under a receiver operating characteristic curve (AUC) from 0.833 to 0.517. Furthermore, PMT performs worse than random guesses on 27% of the projects. The proposed approach improves the PMT results, achieving the average AUC value of 0.613. As a result, we recommend researchers to remove unreached mutants when reporting the results.
Distributed Boosting Classifiers over Noisy Channels

We present a principled framework to address resource allocation for realizing boosting algorithms on substrates with communication noise. Boosting classifiers (e.g., AdaBoost) make a final decision via a weighted vote from local decisions of many base classifiers (weak classifiers). Suppose the base classifiers' outputs are communicated over noisy channels; these noisy outputs will degrade the final classification accuracy. We show this degradation can be effectively reduced by allocating more system resources for more important base classifiers. We formulate resource optimization problems in terms of importance metrics for boosting. Moreover, we show that the optimized noisy boosting classifiers can be more robust than bagging for noise during inference (test stage). We provide numerical evidence to demonstrate the benefits of our approach.
KB-QA based on multi-task learning and negative sample generation

Question answering over knowledge base (KB-QA) has recently become a popular research topic in NLP. One popular approach to solve the KB-QA problem is to use a pipeline of several NLP modules. Most recent works show promising results in the KB-QA task through improving the accuracy of the subtasks. However, KB-QA is still a challenging problem. First, KB-QA is a pipeline of several NLP modules. Therefore, the results of the previous module will affect the prediction of next module, inevitably resulting in the problem of error propagation. Second, in relation detection, we often need to generate negative samples according to the question to help the model distinguish the correct relations. Existing methods usually employ simple sampling methods to generate incorrect relations. However, these methods do not have a powerful ability to fit the space of fake relations, so the negative samples generated by these methods are poor and degrade the performance of the model. Therefore, to solve these problems, we propose a new multi-task learning framework for KB-QA and design a distance loss to solve the error propagation problem. Additionally, to improve the quality of negative samples in relation detection, we propose a general sample generator.
Turning biases into hypotheses through method: A logic of scientific discovery for machine learning

Machine learning (ML) systems have shown great potential for performing or supporting inferential reasoning through analyzing large data sets, thereby potentially facilitating more informed decision-making. However, a hindrance to such use of ML systems is that the predictive models created through ML are often complex, opaque, and poorly understood, even if the programs “learning” the models are simple, transparent, and well understood. ML models become difficult to trust, since lay-people, specialists, and even researchers have difficulties gauging the reasonableness, correctness, and reliability of the inferences performed. In this article, we argue that bridging this gap in the understanding of ML models and their reasonableness requires a focus on developing an improved methodology for their creation. This process has been likened to “alchemy” and criticized for involving a large degree of “black art,” owing to its reliance on poorly understood “best practices”. We soften this critique and argue that the seeming arbitrariness often is the result of a lack of explicit hypothesizing stemming from an empiricist and myopic focus on optimizing for predictive performance rather than from an occult or mystical process. We present some of the problems resulting from the excessive focus on optimizing generalization performance at the cost of hypothesizing about the selection of data and biases. We suggest embedding ML in a general logic of scientific discovery similar to the one presented by Charles Sanders Peirce, and present a recontextualized version of Peirce’s scientific hypothesis adjusted to ML.
HEDAcc: FPGA-based Accelerator for High-order Epistasis Detection

The manifestation of important genetic diseases is often a consequence of the interactions between Single Nucleotide Polymorphisms (SNPs), also known as epistasis. Detecting epistasis for high-order interactions results in a huge computational complexity, as the number of SNP combinations to be evaluated exponentially grows with the interaction order. To address this challenge, state-of-the-art exhaustive search-based methods for epistasis detection rely on GPUs and FPGAs to provide high-performance solutions tailored for a specific order (second and rarely third-order interactions) and/or specific data-set sizes. In this paper, a novel parameterizable architecture is proposed that enables the deployment of FPGA-based accelerators targeting any order of interactions and any data-set size. By relying on a set of algorithmic and architecture optimizations, the proposed accelerator showed to outperform current FPGA accelerators for second and third-order interactions by as much as 4.6× and 9.5×, respectively. The proposed solution also showed comparable performance to current GPGPU third-order implementations, while consuming up to 8.2× less energy. Finally, the proposed architecture allowed for the implementation of a fourth-order epistasis detection accelerator in an FPGA platform.
A Real-Time Portable IoT System for Telework Tracking

Telework has become a universal working style under the background of COVID-19. With the increased time of working at home, problems, such as lack of physical activities and prolonged sedentary behavior become more prominent. In this situation, a self-managing working pattern regulation may be the most practical way to maintain worker's well-being. To this end, this paper validated the idea of using an Internet of Things (IoT) system (a smartphone and the accompanying smartwatch) to monitor the working status in real-time so as to record the working pattern and nudge the user to have a behavior change. By using the accelerometer and gyroscope enclosed in the smartwatch worn on the right wrist, nine-channel data streams of the two sensors were sent to the paired smartphone for data preprocessing, and action recognition in real time. By considering the cooperativity and orthogonality of the data streams, a shallow convolutional neural network (CNN) model was constructed to recognize the working status from a common working routine. As preliminary research, the results of the CNN model show accurate performance [5-fold cross-validation: 0.97 recall and 0.98 precision; leave-one-out validation: 0.95 recall and 0.94 precision; (support vector machine (SVM): 0.89 recall and 0.90 precision; random forest: 0.95 recall and 0.93 precision)] for the recognition of working status, suggesting the feasibility of this fully online method. Although further validation in a more realistic working scenario should be conducted for this method, this proof-of-concept study clarifies the prospect of a user-friendly online working tracking system. With a tailored working pattern guidance, this method is expected to contribute to the workers' wellness not only during the COVID-19 pandemic but also take effect in the post-COVID-19 era.
Generate classical Chinese poems with theme-style from images

The automatic generation of poems from images is a classic task in natural language processing. Recently, it has gained tremendous research interest due to the help of neural sequence-to-sequence networks. The current methods are not qualified to generate poems under the joint control of the theme and style conditions, while these two key factors are critical to the quality of Chinese poetry. This paper proposes an image-based Chinese poem generation network (ICPGN), which can obtain the holistic content and sentiments in images and transform them into theme and style representations. A poem theme and style control module (PTSC) is designed to ensure that the generated poems meet the theme and style requirements simultaneously. A new dataset CQC2020 is constructed for training and testing of the proposed network and other related methods. Extensive manual and machine experiment results demonstrate the effectiveness and competitiveness of the proposed method.
Multi-Modality Image Manipulation Detection

State-of-the-art multi-stream methods for image manipulation detection suffer from gaps between features. Moreover, the rapid development of GANs makes it an emerging method of image tampering. However, existing natural scene image tampered datasets are limited to manual tampering. In this paper, we address these two issues. Firstly, we propose a novel two-stream multi-modality image manipulation detection model (MM-net) that abandons the way of fusion. The main idea of the proposed model is to exploit one stream to guide the learning of the other stream through attention mechanism. Therefore, our approach enjoys the benefits of the multi-stream methods while avoiding the semantic gaps caused by bridging gaps between different streams. Secondly, we build the first tampered dataset of natural images based on GANs, pushing manipulation detection toward more realistic and challenging scenarios. Extensive experimental results demonstrate that our model outperforms state-of-the-art approaches on both manually and GANs tampered images.