Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Smart Bracelets Mutual Authentication System

Start from 2012, AI Technology was focused by the public again. As a sub-component, smart wearable devices are widely used with sensors for collecting the data about human body posture. Privacy and security problems are concerned by both users and developers in the using. Therefore, a reliable authentication technology with devices is necessary. This article is going to solve the problems of authentication using in the smart devices as smart bracelets. The article proposes an innovative method of smart bracelet authentication based on hand raising gesture and experiment the feasibility. Then, based on the research, a web-based program is designed and implemented using GIN framework to realize the bracelet authentication and user management. The sensors of the bracelets are used to record the three-axis accelerometer and the variation of the three-axis angular velocity of the arm movements. Features were extracted from the data set and classify them using a variety of algorithmic classifiers, such as multi-layer perceptron and random forest. Result indicators like TAR, FAR, and AUC are set to evaluate the effectiveness of the authentication method. In conclusion, authentication is feasible demonstrated by user identity web application but still need to be improved by diverse data in multiple environments.
Privacy-preserving People Identification and Tracking without Use of Facial Recognition

Nowadays, facial recognition technology is widely being used for people identification and tracking in both consumer and industrial applications. Because of near-perfect accuracy of such systems, these are being actively deployed worldwide especially in public safety and retail industries. However, there are serious privacy and civil rights concerns due to the inherent nature of analyzing the human face. As a result, several major cities (e.g. San Francisco) have banned use of facial recognition technology by the police and other agencies. In addition, several major technology companies have also announced plans to either quit facial recognition business altogether or being selective in who they supply this product to. In this paper, we propose an alternative approach for people identification for use in commercial premises such as productivity measurement in a factory or retail. Our proposed system consists of a camera to locate and track people anonymously and a wireless beacon transmitter for an opt-in-based people identification that does not require facial/gait or appearance attributes. By intelligently fusing this multimodal data and leveraging recent advances in deep learning, the proposed system locates every person in frame and identifies those who opted-in for people identification by equipping them with a wireless beacon transmitter.
Improved Unscented Kalman Filtering Algorithm Applied to On-vehicle Tracking System

In this paper, we study the problem of tracking aerial targets in a modified spherical coordinate system using an on-vehicle tracking system using infrared sensors. In this challenging scenario, to solve the problems of poor calculation stability and large amount of calculation of the traditional unscented kalman filter algorithm, this article introduces and adopts an improved unscented kalman filter algorithm. In Sigma sampling, the error variance matrix uses singular value decomposition (SVD) to select the simplex with the smallest skewness. Compared with the estimation algorithm based on the standard unscented kalman filter, the new algorithm effectively reduces the state estimation error and improves the tracking stability and tracking accuracy.
Efficient statistical estimation for a non-randomly distributed system with application to large-scale data neural network

This paper proposes a communication-efficient pilot sample surrogate loss function framework that is able to solve efficient statistical estimation for a non-randomly distributed system. Specifically, we first collect a small size pilot sample from worker machines, then approximate the global loss function by a surrogate one, which only relates to the pilot sample and the gradients of local datasets. Without any restrictive condition about randomness, the established asymptotical properties show that the resulting estimator obtained by minimizing the surrogate loss is equivalent with the global estimator. Since the pilot sample and gradients can easily be communicated between the master and worker machines, the communication cost is significantly reduced. What is more, as a specific application, we apply our new method to the neural network with large-scale data for fast and accurate optimization. Monte Carlo simulations and real-world application are also used to validate our method.
A Knowledge Transfer-based Fuzzy Broad Learning System for Modeling Nonlinear Systems

Fuzzy broad learning system is regarded as an effective algorithm to utilize the measured data for modeling nonlinear systems. However, due to the possible existence of data inadequate or data loss, it is a challenge to design a suitable fuzzy broad learning system with the data shortage issue for modeling. Therefore, a knowledge transfer-based fuzzy broad learning system is developed in this paper. First, the knowledge extracted from the process is used to construct the initial condition. Then, this model can obtain the precise parameter and structure. Second, a knowledge evaluation mechanism is employed to rebuild the knowledge by judging the correlation and discrepancy. Then, the knowledge can be preferably integrated. Third, a transfer gradient algorithm is employed to adjust the parameters of fuzzy broad learning system. Then, the modeling performance of knowledge transfer-based fuzzy broad learning system can be improved. Finally, a benchmark problem and a practical application are used to test the merits of knowledge transfer-based fuzzy broad learning system. The results demonstrate that this model can achieve superior modeling performance.
Application of artificial intelligence in computer network technology in the era of big data

This paper integrates the computer network and the artificial intelligence together, based on the big data, and expounds the relevant concepts of the big data and the artificial intelligence. By enhancing the information processing ability of the computer network, it explores the use of the artificial intelligence in the computer network, so that people can use the artificial intelligence more efficiently.
Localizing unknown radiation sources by unscented particle filtering based on divide-and-conquer sampling

Predictive estimation of radioactive sources using Bayesian theory is of great importance in nuclear emergency response. To this aim, we put forward an unscented particle filtering algorithm based on divide-and-conquer sampling. Our method exploits the information acquired by mobile detection robots to search for radioactive sources at unknown locations. Firstly, we assume a circular and extremely thin NaI (TI) scintillator detector, then analyze the geometric relationship between the detector cross-sectional area and the square of the radiation source distance, and finally establish a radiation source search model in accordance with Bayesian method. We also introduce a divide-and-conquer strategy to solve the problems of particle dilution and particle degradation, so as to achieve higher search accuracy and speed. Through simulation experiments, compared with standard particle filter and unscented particle filter, the algorithm can effectively improve particle diversity and successfully search radioactive sources in a shorter time.
Online Graph Learning From Time-Varying Structural Equation Models

Topology identification is an important problem across many disciplines, since it reveals pairwise interactions among entities and can be used to interpret graph data. In many scenarios, however, this (unknown) topology is time-varying, rendering the problem even harder. In this paper, we focus on a time-varying version of the structural equation modeling (SEM) framework, which is an umbrella of multivariate techniques widely adopted in econometrics, epidemiology and psychology. In particular, we view the linear SEM as a first-order diffusion of a signal over a graph whose topology changes over time. Our goal is to learn such time-varying topology from streaming data. To attain this goal, we propose a real-time algorithm, further accelerated by building on recent advances in time-varying optimization, which updates the time-varying solution as a new sample comes into the system. We augment the implementation steps with theoretical guarantees, and we show performances on synthetic and real datasets.
Rich Feature Deep Learning Classifier for Multiple Simultaneous Radio Signals

The ability to detect, classify, and characterize radio signal transmissions is an important task with diverse applications in defense, networking, and communications. This task is made complex when multiple simultaneous signals may be present. Finding useful signal features can go beyond just classifying the signal modulation type, to include estimates of: the signal-to-noise ratio (SNR), the confidence in the classification, and the presence of multiple signals. Such tasks have traditionally been done by human analysts, often relying on ad-hoc techniques. Recent advances have shown that applying deep learning methods to radio signal modulation classification tasks is feasible. But current recognition-only networks are limited in several ways. They can classify modulation for just one signal, produce limited feature outputs, and may give over-confident results in noisy environments. We have built a robust radio frequency signal classifier with multiple outputs, providing not just modulation classification, but also SNR estimation, classification confidence, and multiple signal detection (even when these signals are located at the same transmit frequency). Compared to the state-of-the-art deep learning classifier in the single signal case, our system obtains better accuracy (up to 91.6% accuracy at 20 dB SNR), while providing additional useful outputs.
Learning Temporal Dynamics from Cycles in Narrated Video

Learning to model how the world changes as time elapses has proven a challenging problem for the computer vision community. We introduce a self-supervised approach to this problem that solves a multi-modal temporal cycle consistency objective jointly in vision and language. This objective requires a model to learn modality-agnostic functions to predict the future and past that undo each other when composed. We hypothesize that a model trained on this objective will discover long-term temporal dynamics in video. We verify this hypothesis by using the resultant visual representations and predictive models as-is to solve a variety of downstream tasks. Our method outperforms state-of-the-art self-supervised video prediction methods on future action anticipation, temporal image ordering, and arrow-of-time classification tasks, without training on target datasets or their labels.
Inductive Learning of OWL 2 Property Chains

We present an algorithm to inductively learn Web Ontology Language (OWL) 2 property chains to be used in object subproperty axioms.For efficiency, it uses specialized encodings and data structures based on hash-maps and sparse matrices.The algorithm is based on the frequent pattern search principles and uses a novel measure called s-support.We prove soundness and termination of the algorithm, and report on evaluation where we mine axioms from DBpedia 2016-10.We extensively discuss the 36 mined axioms and conclude that 30 (83%) of them are correct and could be added to the ontology.
A user-space virtual device driver framework for Kubernetes

Virtualization and Kubernetes have been widely used now in cloud platform. SRIOV-enabled devices can simulate multiple virtual devices from the hardware layer, each of which can be bound to a Pod from Kubernetes. So multiple Pods can share hardware resources, and resource utilization can be increased greatly. However, the existing drivers of virtual devices are mainly implemented in the Linux kernel space. If the driver fails, it will affect all applications on the host. In this paper, a user-space virtual device driver framework for Kubernetes is proposed. In the framework, only the driver and virtualization of physical device are implemented in kernel space. The IO address of virtual device is mapped into user space through VFIO and the driver of the virtual device is implemented in the Pause container. Other application containers in the same Pod can communicate with drivers in the Pause container through the API interface. Based on the framework, the user only needs to write drivers of virtual devices in the user space, which reduces the difficulty of developing the drivers and improves the reliability of the system. At the same time, because drivers of virtual device run in Pause containers, they can be distributed with applications together, which increases driver’s capability of isolation and distribution.
Exploring the relationship between refactoring and code debt indicators

Abstract Refactoring, which aims to improve the internal structure of the software systems preserving their behavior, is the most common payment strategy for technical debt (TD) by removing the code smells. There exist many studies presenting code smell detection approaches/tools or investigating their impact on quality attributes. There are also studies that focus on refactoring techniques, their relation with quality attributes, tool supports, and opportunities for them. Although there are several studies addressing the gap between refactoring and TD indicators, the empirical evidence provided is still limited. In this study, we examine the distribution of 29 refactoring types among the different projects and their relation with code smells or faults. We explore the refactoring types that are most commonly performed together and other activities performed with refactorings. We conduct a large exploratory study with automatically detected 57,528 refactorings, 37,553 smells, 27,340 faults, and 134,812 commits of 33 Java projects. Results show that some refactoring types are more commonly applied by developers. Our analysis indicates that refactorings usually remove or do not affect the code smells, and this contradicts with the previous studies. Also, the commits in which refactoring(s) is performed are three times more fault inducing than those without refactoring.
Immuno-inspired management of halls of fame for embodied evolution

Evolutionary robotics employs unconventional techniques to continuously evolve controllers for robots, based on their fitness values. In most cases, a parent controller is subjected to mutation to evolve its offspring. If the offspring performs better than the parent, the former is made to replace the latter. This essentially results in a major loss in information learned by the parents over generations. One simple workaround to circumvent this problem is to maintain a Hall of Fame (HoF) comprising the best parent controllers for use in future generations. In embodied evolutionary robotic scenarios, caching a large number of controllers in an HoF would result in increased computational overheads while selecting the best out of them, resulting in a drastic reduction in performance. With no means to find an upper limit to the number of controllers that can populate an HoF a priori, devising a technique to dynamically regulate this population is imperative. In this work, a novel method to evict the non-performing controllers within an HoF based on the dynamics of the system, is proposed. We describe the evolution of such controllers using genetic operators that eventually form an Idiotypic Network. A constantly varying Resource, associated with each controller together with its concentration in the Idiotypic Network, helps decide its eviction from the HoF. Experiments performed using simulations and also a real robot indicate a marked improvement in the learning process due to the dynamic eviction policy.
A Preliminary Study of Convolutional Neural Network Architectures for Breast Cancer Image Classification

Breast cancer is one of the most common cancer with high mortality rate worldwide. Classification of breast cancer images is an important clinical issue related to accurate early diagnosis and treatment plan preparation. However, it is still uncertain which model is effective for classifying breast cancer images. For medical image analysis, deep learning models have proved to yield excellent outcomes in classification tasks. Hence, this study compared the performance of the most common deep learning models which is convolutional neural networks for breast cancer classification on the histopathology images. A total of 7,909 images were extracted from BreakHis database that comprised of 2,480 benign and 5,429 malignant samples. The images are of four different magnifications which are 40X, 100X, 200X and 400X. This study focused on comparing the state-of the-art architectures, namely, AlexNet, GoogleNet and ResNet 18 to evaluate the performance of model in classifying the breast cancer images. The models were examined through a multiclass classification analysis in terms of accuracy, sensitivity, specificity and F-Score. The experimental results indicated that ResNet18 was the most effective method with an accuracy of 94.8&#x0025; with 70 min 31 sec time taken for computation. The research findings are expected to facilitate the radiologist in classifying the breast cancer images and hence planning proper treatment for patients.
Viewpoint Alignment and Discriminative Parts Enhancement in 3D Space for Vehicle ReID

Vehicle Re-Identification is to find the same vehicle from images captured in different views under cross-camera scenarios. Traditional methods focus on depicting the holistic appearance of a vehicle, but they suffer from the hard samples with the same vehicle type and color. Recent works leverage the discriminative visual cues to solve this problem, where three challenges exist as follows. First, vehicle features are misaligned and distorted because of the viewpoint variance. Second, the discriminative visual cues are usually subtle, which is easy to be diluted by the large area of non-discriminative regions in subsequent average pooling modules. Third, these discriminative visual cues are dynamic for the same image when it compares with different vehicle images. To tackle the above problems, we project the vehicle images from 2D to 3D space and rotate them to the same view, and leverage the viewpoint aligned features to enhance the discriminative parts for vehicle ReID. In detail, our method consists of three sub-modules, 1) The 3D viewpoint alignment module restores the 3D information of the vehicle from a single vehicle image, and then rotates and re-renders it under fixed viewpoints. It enables fine-grained viewpoint alignment and relieves the distortion of the vehicle caused by the viewpoint variation. 2) The discriminative parts enhancement module performs feature enhancement guided by the prior distribution of distinctive parts. 3) The adaptive duplicated parts suppression module guides the network to focus on the most discriminative parts, which not only prevents the dilution of the high responses but also provides explainable evidence. The experimental results reveal our method achieves new state-of-the-art on large scale vehicle ReID dataset.
Characterising spectra of equivalences for event structures, logically

We present a logical characterisation of the equivalences in the spectrum for labelled Prime Event Structures (PESs) and use it for also studying how such spectrum changes when restricting to subclasses of event structures. We first show that a minor modification of the logic characterising hereditary history preserving bisimilarity induces PES isomorphism as logical equivalence. Then, we distill fragments of the logic that characterise all the equivalences in the aforementioned spectrum. In particular, we single out logics for interleaving/step/pomset trace equivalences and weak (pomset) history preserving bisimilarity, which were missing. Finally, we apply our logical characterisation to investigate how the spectrum simplifies when we restrict to subclasses of event structures: Coherence Spaces, where causality is absent, and Elementary Event Structures, where instead conflicts are not allowed. Inclusions between behavioural equivalences are proved by providing encodings between the corresponding sublogics, whereas the non-inclusion between equivalences is witnessed by using distinguishing formulae, i.e., by providing structures which are identified by an equivalence and distinguished by a formula in the logics of the other equivalence.
Emotion Cause Detection with a Hierarchical Network

Emotion cause detection plays a key role in many downstream sentiment analysis applications. Research shows that both knowledge from data and experience from linguistics help to improve the detection performance. In this paper, we propose an approach to combine them. We utilize a hierarchical framework to model emotional texts, in which the emotion is independently represented along with each word and clause in a document. We also employ linguistic features to help the model find the emotion cause. Such features by manual work help to describe deep semantic relations between emotions and their causes that are difficult to be cast by representation models. Experimental results show that the combination model helps to detect emotion cause within emotional texts with complex semantic relations.
Towards an Automatic Approach for Assessing Program Competencies

Skills analysis is an interdisciplinary area that studies labor market trends and provides recommendations for developing educational standards and re-skilling efforts. We leverage techniques in this area to develop a scalable approach that identifies and evaluates educational competencies. In this work, we developed a skills extraction algorithm that uses natural language processing and machine learning techniques. We evaluated our algorithm on a labeled dataset and found its performance to be competitive with state-of-the-art methods. Using this algorithm, we analyzed student skills, university course syllabi, and online job postings. Our cross-sector analysis provides an initial landscape of skill needs for specific job titles. Additionally, we conducted a within-sector analysis based on programming jobs, computer science curriculum, and undergraduate students. Our findings suggest that students have a variety of hard skills and soft skills, but they are not necessarily the ones that employers want. The data also suggests these courses teach skills that are somewhat different from industry needs, and there is a lack of emphasis on soft skills. These results provide an initial assessment of the program competencies for a computer science program. Future work includes more data gathering, improving the algorithm, and applying our method to assess additional educational programs.
Hate Speech as a Politico-Religious Tool in Contemporary Zimbabwe

Abstract The dawn of the new second Republic of Zimbabwe raised the hopes of the people of Zimbabwe for a moment. Zimbabwe has experienced diverse challenges, including socioeconomic meltdowns, a decline in the rule of law, and human rights abuse. To address these challenges politicians and religious leaders use ‘hate speech’ to express their ideologies and criticise their opponents’ struggle to win people. The availability of social media has made it easy for these attacking statements to reach a wider audience. Through documentary analysis, this article explores how politicians and religious leaders use hate speech as electioneering and a response to the problems bedeviling Zimbabwe. This study concludes that hate speech from politicians and religious leaders is socialising some Zimbabweans into violent, angry individuals, murderers, and vandals. The problem of hate speech is that it creates violence, causes psychological effects, dehumanises people, and conditions a negative national trait.