Reviewerly/exHarmony huggingface dataset:
Zero shot augmentation learning in internet of biometric things for health signal processing

In recent years, the number of Internet of Things (IoT) devices has increased rapidly. The Internet of Biometric Things (IoBT) can process biometrics and health signals, and it will greatly extend the range of biometric applications. The analysis of health signals in the IoBT can use computer-aided diagnosis techniques. However, most of the existing computer-aided diagnosis methods are developed for common diseases and are not suitable for rare diseases. Zero shot learning is a potential method for the computer-aided diagnosis of rare diseases because it can identify objects of unknown categories. However, the existing zero shot learning methods are based on attribute learning and rely on an attribute dataset. There is no attribute dataset for health signal processing. Therefore, the existing zero shot learning methods are not suitable for health signal processing. Based on the above background, we propose a zero shot augmentation learning model (ZSAL) in the IoBT for health signal processing. First, an expert doctor identifies the contour of a lesion and selects a background image without a lesion. Second, the computer automatically generates virtual images using zero shot augmentation technology. Finally, the generated virtual dataset is used to train a convolutional classifier, and then we apply the classifier to the computer-aided diagnosis of actual medical images. The experiment shows the efficiency and effectiveness of our method.
Generalized elastic net optimal scoring problem for feature selection

Linear discriminant analysis (LDA) is a well-known tool for classification and dimensionality reduction. As an equivalent form of LDA, optimal scoring is presented to solve the classification problem by regression. In this paper, we propose a generalized elastic net optimal scoring problem (GenOS) to find sparse discriminant vectors, where the generalized elastic et is the combination of the ℓ2-norm and ℓq-norm (0<q⩽1). In GenOS, ℓq-norm is imposed to confer sparsity to discriminant vectors and ℓ2-norm is added to enhance the performance of the classifier. Then, a new efficient algorithm based on block coordinate descent (BCD) method is developed to solve GenOS approximately and the convergence is also established. Simulated data and real-world data are used to empirically illustrate the effectiveness of the proposed model.
Software Ticks Need No Specifications

Software bugs cost time, money, and lives. They drive software research and development efforts, and are central to modern software engineering. Yet we lack a clear and general definition of what bugs are. Some bugs are defects, clearly defined as failures to meet some requirement or specification. However, there are many forms of undesirable program behaviour that are completely compatible with a typical program's specification. In this paper, we argue that the lack of a criterion for identifying non-defect bugs is hampering the development of tools that find and fix bugs. We propose such a criterion, based on the idea of wasted effort, discuss how bugs that meet our definition of software ticks can complement defects, and sketch how our definition can help guide future work on software tools.
Using Under-Trained Deep Ensembles to Learn Under Extreme Label Noise: A Case Study for Sleep Apnea Detection

Sleep Apnea (SA) is a severely under-diagnosed sleep related breathing disorder which affects a large percentage of the population.Recent advances in Machine Learning (ML) have enabled a wide variety of applications that originally were previously not efficient or feasible.In this thesis, we utilize these recent innovations to improve the ML classification process when performing unattended sleep monitoring and SA detection at home.We use several ML techniques on a variety of physiological signals to provide low-cost solutions for a variety of problems that arise when attempting to detect SA.Specifically, we investigate ML-based classification solutions to perform classification of sleep apnea events and measure the apnea severity of an individual.We show the viability of using such solutions for this task, on various sensor brands with different cost and quality.As such, we establish that low-cost sensors can reliably be used in conjunction with ML for efficient SA detection.To further understand how viable general ML solutions are for SA detection, we compare with a state-of-the-art highly-tuned SA diagnosis system called Noxturnal.Noxturnal is well-tailored for the high-cost Nox T3 monitor.We show high agreement between the two solutions.In this thesis, we use ML methods to resolve additional problems relevant to the aforementioned classification process.We investigate whether we can address data-scarcity occuring due to regulations and labelling bottlenecks with the use of generative models.As such we design generative solutions which can address or circumvent these problems and show promising results (e.g., consistent increases in sensitivity when using dataset augmentation techniques based on the generative adversarial networks framework).Furthermore, we address the problem of domain shift which occurs due to different individuals, populations, sensors environmental factors among others.We design a domain adaptation solution that is tailored for situations where the regulatory framework does not allow data sharing and we achieve personalization in such a setting.We showcase that such a technique can yield noticeable performance improvements, when generalizing in the presence of such a domain shift (e.g., kappa coefficient improvement from 0.012 to 0.242 compared to the baseline, for the task of SA detection).Finally, we address the problem of probabilistic errors or inconsistencies during labelling.We develop a ML-based solution which, given the appropriate conditions, can provide corrections or assistive opinions on a given labelling schema.Again, we obtain good results (performance improvements in kappa ranging from 0.02 up-to 0.55) which showcase the feasibility of the proposed method in both realistic and randomized settings.
Content-based Analysis of the Cultural Differences between TikTok and Douyin

Short-form video social media shifts away from the traditional media paradigm by telling the audience a dynamic story to attract their attention. In particular, different combinations of everyday objects can be employed to represent a unique scene that is both interesting and understandable. Offered by the same company, TikTok and Douyin are popular examples of such new media that has become popular in recent years, while being tailored for different markets (e.g. the United States and China). The hypothesis that they express cultural differences together with media fashion and social idiosyncrasy is the primary target of our research. To that end, we first employ the Faster Regional Convolutional Neural Network (Faster R-CNN) pre-trained with the Microsoft Common Objects in COntext (MS-COCO) dataset to perform object detection. Based on a suite of objects detected from videos, we perform statistical analysis including label statistics, label similarity, and label-person distribution. We further use the Two-Stream Inflated 3D ConvNet (I3D) pre-trained with the Kinetics dataset to categorize and analyze human actions. By comparing the distributional results of TikTok and Douyin, we uncover a wealth of similarity and contrast between the two closely related video social media platforms along the content dimensions of object quantity, object categories, and human action categories.
Indonesian Ethnicity Recognition Based on Face Image Using Gray Level Co-occurrence Matrix and Color Histogram

Abstract Facial and ethnic recognition became a popular research topic. Different from face recognition, ethnic recognition classifies faces according to the general features of certain ethnic groups. Ethnic recognition in face image is increasingly becoming a necessity and is used in various fields. This paper proposed the Indonesian ethnic recognition system based on periorbital features on facial images. We use the five largest ethnic groups in Indonesia in this study, namely Banjar, Bugis, Javanese, Malay, and Sundanese. Gray Level Co-occurrence Matrix and Color Histogram were used as methods, and Random Forest was used as a classifier. Based on - cross-validation tests with optimal k values, the model achieved 98.65% accuracy.
Modulation of Bodily Self-Consciousness by Self and External Touch

The full body illusion (FBI) is a bodily illusion based on the application of multisensory conflicts that induce changes in bodily self-consciousness (BSC). This has been used to study cognitive brain mechanisms underlying body ownership and related aspects of self-consciousness. Typically, such paradigms employ external passive multisensory stimulation, thus neglecting the possible contributions of self-generated action and haptic cues to body ownership. In this article, the effects of both external and voluntary self-touch on BSC were examined with a robotics-based FBI paradigm. We compared the effects of classical passive visuotactile stimulation and active self-touch (in which experimental participants had a sense of agency over the tactile stimulation) on the FBI. We evaluated these effects using a questionnaire, crossmodal congruency task, and measurements of changes in self-location. The results indicated that both synchronous passive visuotactile stimulation and synchronous active self-touch induced illusory ownership over a virtual body, without significant differences in their magnitudes. However, the FBI induced by active self-touch was associated with a larger drift in self-location towards the virtual body. These results show that movement-related signals arising from self-touch impact the BSC not only for hand ownership but also for torso-centered body ownership and related aspects of BSC.
Learning fused features with parallel training for person re-identification

The single-image representation (SIR) matching and cross-image representation (CIR) classification are two significant solutions in the person re-identification (Re-ID) task. Combining the two categories of methods has been regarded as an effective solution to improve discriminative performances in Re-ID. Previous combination methods mainly focus on fusing the SIR learning and CIR learning losses, representing the cross-image features with limitations and not available to different network structures for feature learning. This paper proposed an efficient joint SIR and CIR learning strategy — cross-image features fusion strategy (CFFS), which fuses cross-image information through parallel training. Precisely, CFFS consists of one shared sub-network and two branches, one branch for learning single-image feature and the other for fusing cross-image features. CFFS requires pairwise data parallelism training for each identity to learn the CIR, applied to metric learning. Therefore, the cross-image features would be fused better, and the performance of Re-ID would be improved. Experiments on Market-1501, DukeMTMC-reid, and CUHK03 datasets show that the proposed method can achieve favorable accuracy while compared with state-of-the-arts.
It is like taking a ball for a walk: on boundary work in software development

Abstract In this paper, we explore how the choices of boundary work in software development influence the team autonomy enacted by team members. Boundary work is when people protect their professional individual autonomy, when they downplay that autonomy to collaborate over professional boundaries, and when they create new boundaries. Team autonomy is here defined as a team using their autonomy to collaborate in deciding their own output. We use an action research design, with varied methodologies carried out through three action cycles. Our findings show that when collective, collaborative boundary work is not performed, a sort of individualized zone occurs where individuals either try to do collaborative boundary work by themselves or seek individual autonomy. We propose that individual autonomy can be divided into professional individual autonomy and situationally dependent individual autonomy. This research contributes theoretically by showing how the absence of collaborative boundary work can lead to an individualized zone. Practically, it can improve team autonomy by enhancing the understanding of why teams should perform collaborative boundary work. The value of the concept of boundary work used in this setting involves studying the intentions for collaboration, not whether collaboration actually takes place.
Lightweight Cross-Fusion Network on Human Pose Estimation for Edge Device

The deployment of human pose estimation on edge devices are essential task in computer vision. Due to memory and storage space limitations, it is difficult for edge devices to maintain implementing Convolutional Neural Networks, which deployed large-scale terminal platforms with abundant computing resources. This paper proposed novel Lightweight Cross-fusion Network on Human Pose Estimation with information sharing. Using state-of-the-art efficient neural architecture, and Ghost Net, as the backbone, which are gradually applying a cross-information fusion network for key points extraction in the baseline and strengthen phases. As a result, the computational cost significantly reduces, while maintaining feature confidence more accurate and predicting key points heatmaps more precisely. Our network model can entirely execute on edge devices, and extensive self-comparison experiments have evaluated the architecture’s effectiveness. The MS COCO 2017 dataset proved that the cross-fusion network is superior than other lightweight structures for pose estimation.
Observation-assisted heuristic synthesis of covert attackers against unknown supervisors

In this work, we address the problem of synthesis of covert attackers in the setup where the model of the plant is available, but the model of the supervisor is unknown, to the adversary. To compensate the lack of knowledge on the supervisor, we assume that the adversary has recorded a (prefix-closed) finite set of observations of the runs of the closed-loop system, which can be used for assisting the synthesis. We present a heuristic algorithm for the synthesis of covert damage-reachable attackers, based on the model of the plant and the (finite) set of observations, by a transformation into solving an instance of the partial-observation supervisor synthesis problem. The heuristic algorithm developed in this paper may allow the adversary to synthesize covert attackers without having to know the model of the supervisor, which could be hard to obtain in practice. For simplicity, we shall only consider covert attackers that are able to carry out sensor replacement attacks and actuator disablement attacks. The effectiveness of our approach is illustrated on a water tank example adapted from the literature.
A Novel Fractional-Order Variational Approach for Image Restoration Based on Fuzzy Membership Degrees

We propose a new fractional-order (space and time) total variation regularized model for multiplicative noise removal in this research article. We use the regularly varying fuzzy membership degrees to characterize the likelihood of a pixel related to edges, texture regions, and flat regions to improve model efficiency. This approach is capable of maintaining edges, textures, and other image information while significantly reducing the blocky effect. We opt for the option of local actions. In order to efficiently find the minimizer of the prescribed energy function, the semi-implicit gradient descent approach is used (which derives the corresponding fractional-order Euler-Lagrange equations). The existence and uniqueness of a solution to the suggested variational model are proved. Experimental results show the efficiency of the suggested model in visual enhancement, preserving details and reducing the blocky effect while extracting noise as well as an increase in the PSNR (dB), SSIM, relative error, and less CPU time(s) comparing to other schemes.
A Sensitivity Analysis of Evolutionary Algorithms in Generating Secure Configurations

The growth of Cyber-physical Systems (CPS) has been increased in recent years. This has led to the coupling of highly complex cyber-physical components. With the integration of such complex components, new security challenges have emerged. Studies involving security issues in CPS have been quite difficult to be generalized due to the presence of heterogeneity and the diversity of the CPS components. These systems are subject to various vulnerabilities, threats and attacks, as a consequence of complex versions of CPS being introduced over time. This paper deals with vulnerabilities caused due to improper configurations in the software component of cyber-physical systems. Evolutionary algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) can be employed to adequately test the underlying software for certain categories of vulnerabilities. This paper provides a detailed sensitivity analysis of these evolutionary algorithms in order to find out whether changing parameters involved in tuning these algorithms affect the overall performance. This analysis is based on the estimate of the number of generation of secure vulnerability pattern vectors under the variation of different parameters. The results indicate that while there is no evidence of influential parameters in Genetic Algorithms (i.e., mutation rate and population size), changes in the parameters involved in Particle Swarm Optimization algorithms (i.e., velocity rate and fitness range) have some positive impacts on the number of secure configurations generated.
X-View: Non-Egocentric Multi-View 3D Object Detector

3D object detection algorithms for autonomous driving reason about 3D obstacles either from 3D birds-eye view or perspective view or both. Recent works attempt to improve the detection performance via mining and fusing from multiple egocentric views. Although the egocentric perspective view alleviates some weaknesses of the birds-eye view, the sectored grid partition becomes so coarse in the distance that the targets and surrounding context mix together, which makes the features less discriminative. In this paper, we generalize the research on 3D multi-view learning and propose a novel multi-view-based 3D detection method, named X-view, to overcome the drawbacks of the multi-view methods. Specifically, X-view breaks through the traditional limitation about the perspective view whose original point must be consistent with the 3D Cartesian coordinate. X-view is designed as a general paradigm that can be applied on almost any 3D detectors based on LiDAR with only little increment of running time, no matter it is voxel/grid-based or raw-point-based. We conduct experiments on KITTI [1] and NuScenes [2] datasets to demonstrate the robustness and effectiveness of our proposed X-view. The results show that X-view obtains consistent improvements when combined with mainstream state-of-the-art 3D methods.
Evaluating the Accuracy of Cloud NLP Services Using Ground-Truth Experiments

Cloud services for natural language processing (NLP) increasingly establish as viable alternatives to self-maintained and self-trained NLP pipelines. In particular, they feature low access barriers and management overhead, a pay-as-you-go pricing model, and elastic scalability allowing to process large amounts of natural language data ad hoc. Any deliberation about employing cloud NLP services in practice does, however, face the challenge that so far, little is known about the accuracy provided by such services as well as about how to conduct respective quality assessments.In this paper, we therefore present a method for evaluating the accuracy provided by cloud NLP services and apply it to cloud services for three prominent NLP tasks offered by Amazon, Google, Microsoft, and IBM. Our results show significantly different accuracies as well as different dependencies on the specifics of input data among the covered providers. Our insights therefore allow for a more evidence-based quality-driven choice of the provider to be used for NLP in practice. Furthermore, the general approach employed may also serve as a blueprint for additional future evaluations of cloud NLP services for other tasks or offered by other providers.
Faster Quantum-inspired Algorithms for Solving Linear Systems

We establish an improved classical algorithm for solving linear systems in a model analogous to the QRAM that is used by quantum linear solvers. Precisely, for the linear system \( A{\bf x}= {\bf b} \) , we show that there is a classical algorithm that outputs a data structure for \( {\bf x} \) allowing sampling and querying to the entries, where \( {\bf x} \) is such that \( \Vert {\bf x}- A^{+}{\bf b}\Vert \le \epsilon \Vert A^{+}{\bf b}\Vert \) . This output can be viewed as a classical analogue to the output of quantum linear solvers. The complexity of our algorithm is \( \widetilde{O}(\kappa _F^6 \kappa ^2/\epsilon ^2) \) , where \( \kappa _F = \Vert A\Vert _F\Vert A^{+}\Vert \) and \( \kappa = \Vert A\Vert \Vert A^{+}\Vert \) . This improves the previous best algorithm [Gilyén, Song and Tang, arXiv:2009.07268] of complexity \( \widetilde{O}(\kappa _F^6 \kappa ^6/\epsilon ^4) \) . Our algorithm is based on the randomized Kaczmarz method, which is a particular case of stochastic gradient descent. We also find that when A is row sparse, this method already returns an approximate solution \( {\bf x} \) in time \( \widetilde{O}(\kappa _F^2) \) , while the best quantum algorithm known returns \( | {\bf x} \rangle \) in time \( \widetilde{O}(\kappa _F) \) when A is stored in the QRAM data structure. As a result, assuming access to QRAM and if A is row sparse, the speedup based on current quantum algorithms is quadratic.
Performance Evaluations of Channel Estimation Using Deep-learning Based Super-resolution

Thanks to breakthrough and evolution of deep learning in computer vision areas, adaptation of deep learning into communication systems are getting lots of attention to researchers. Recently, a channel estimation method by using a deep learning-based image super-resolution (SR) technique, namely ChannelNet, has been proposed. Inspired by this research, in this paper, we propose a deep SR based channel estimation method by applying more accurate deep learning-based SR network architecture, EDSR. In order to enhance intelligibility and reliability of deep SR based channel estimation methods, we evaluate the performance of several deep SR based channel estimation methods (SRCNN, ChannelNet and EDSR) by carrying out practical 5G simulations. From the evaluations, the results conclude that the deep SR based channel estimation methods can potentially improve accuracy of channel estimation and reduce BER characteristics.
Quantum Bayesian Decision-Making

As a compact representation of joint probability distributions over a dependence graph of random variables, and a tool for modelling and reasoning in the presence of uncertainty, Bayesian networks are of great importance for artificial intelligence to combine domain knowledge, capture causal relationships, or learn from incomplete datasets. Known as a NP-hard problem in a classical setting, Bayesian inference pops up as a class of algorithms worth to explore in a quantum framework. This paper explores such a research direction and improves on previous proposals by a judicious use of the utility function in an entangled configuration. It proposes a completely quantum mechanical decision-making process with a proven computational advantage. A prototype implementation in Qiskit (a Python-based program development kit for the IBM Q machine) is discussed as a proof-of-concept.