Reviewerly/exHarmony huggingface dataset:
IoT Ambient Assisted Living: Scalable Analytics Architecture and Flexible Process

With the recent advances in IoT, Ambient Assisted Living (AAL) became an active field that attempts to assist individuals in their Activities of Daily Living (ADL). One researched venue deriving from these advances is how the technology and analytics could benefit the prevention and treatment of chronic diseases in the escalating number of elderly people experiencing health issues. Many architectures are proposed in the literature, but they lack modularity and flexibility for different types of sensors and do not have a way of selecting the appropriate algorithms to perform a given task. In this paper, we propose a four layered and highly modular architecture for health analytics of elderly people. Moreover, we propose a novel automated process for selecting the appropriate algorithms for a task at hand. In the final analysis, we evaluate the approach by implementing part of the architecture on fog nodes and the cloud. Finally, we deploy affordable consumer grade sensors in an apartment in order to move toward the use of the system proposed.
Greedy Motzkin–Kaczmarz methods for solving linear systems

Abstract The famous greedy randomized Kaczmarz (GRK) method uses the greedy selection rule on maximum distance to determine a subset of the indices of working rows. In this paper, with the greedy selection rule on maximum residual, we propose the greedy randomized Motzkin–Kaczmarz (GRMK) method for linear systems. The block version of the new method is also presented. We analyze the convergence of the two methods and provide the corresponding convergence factors. The computational complexities of the proposed methods are also given. Extensive numerical experiments show that the GRMK method has almost the same performance as the GRK method for dense matrices and the former performs better in computing time for some sparse matrices and some realistic practical problems, and their block versions have almost the same performance for most of cases.
Gamesy: How Videogames Serve as a Better Replacement for School?

Games have existed since time immemorial and have proved to significantly change people's mentality and attitudes towards countless scenarios. Games can be either physical or mental, for example, sports and chess. Games have developed over the past decades, having gone through significant changes via digitization. Games nowadays are at everyone's disposal since almost everyone possesses a smartphone. Videogame such as PUBG, Call of Duty mobile, Fortnite, and more are very addictive games that consume a lot of our time daily. Although games are `time wasters', we should acknowledge the one thing that games offer: this is the constant testing, and learning a simple game engages the brain proves that games are an asset in mental development and thus gamification can be used to provide a fun learning environment. In this article, we discuss how gamification has been implemented in various applications over the years, people's take on gamification and gamified apps from a survey and interview conducted, and thereafter design a student-oriented gamified study app, Gamesy, that makes use of game elements to improve performance in school and change/improve study habits in a game environment via progressive game design. With this, better performance may be realized in the tertiary education level, university, and college.
Comparing and Weighting Imperfect Models Using D-Probabilities

We propose a new approach for assigning weights to models using a divergence-based method ({\em D-probabilities}), relying on evaluating parametric models relative to a nonparametric Bayesian reference using Kullback-Leibler divergence. D-probabilities are useful in goodness-of-fit assessments, in comparing imperfect models, and in providing model weights to be used in model aggregation. D-probabilities avoid some of the disadvantages of Bayesian model probabilities, such as large sensitivity to prior choice, and tend to place higher weight on a greater diversity of models. In an application to linear model selection against a Gaussian process reference, we provide simple analytic forms for routine implementation and show that D-probabilities automatically penalize model complexity. Some asymptotic properties are described, and we provide interesting probabilistic interpretations of the proposed model weights. The framework is illustrated through simulation examples and an ozone data application.
Next generation automated software evolution refactoring at scale

Despite progress in providing software engineers with tools that automate an increasing number of development tasks, complex activities like redesigning and reengineering existing software remain resource intensive or are supported by tools that are error prone. Complex, but common tasks in industry, like evolving large codebases (1M+ SLOC) to meet changing needs, still rely on costly manual efforts and incur significant technical risk. In one example, an organization that we work with estimated 14,000 hours of development work alone (excluding integration and testing) to isolate a feature from the underlying hardware platform. These examples are pervasive in industry. Software engineering research has taken providing effective tools for software evolution for granted for far too long. The time is right for research to take advantage of advances in search-based software engineering and create the next generation of industry-relevant automated software evolution tools. This paper lays out a vision for automated refactoring at scale towards this goal.
Non-Invasive Challenge Response Authentication for Voice Transactions with Smart Home Behavior

Smart speakers, such as Alexa and Google Home, support daily activities in smart home environments. Even though voice commands enable friction-less interactions, existing financial transaction authorization mechanisms hinder usability. A non-invasive authorization by leveraging presence and light sensors’ data is proposed in order to replace invasive procedure through smartphone notification. The Coloured Petri Net model was created for synthetic data generation, and one month data were collected in test bed with real users. Random Forest machine learning models were used for smart home behavior information retrieval. The LSTM prediction model was evaluated while using test bed data, and an open dataset from CASAS. The proposed authorization mechanism is based on Physical Unclonable Function usage as a random number generator seed in a Challenge Response protocol. The simulations indicate that the proposed scheme with specialized autonomous device could halve the total response time for low value financial transactions triggered by voice, from 7.3 to 3.5 s in a non-invasive manner, maintaining authorization security.
Deep video code for efficient face video retrieval

In this paper, we address one specific video retrieval problem in terms of human face. Given one query in forms of either a frame or a sequence from a person, we search the database and return the most relevant face videos, i.e., ones have the same class label with the query. Such problem is very challenging due to the large intra-class variations and the high request on the efficiency of video representations in terms of both time and space. To handle such challenges, this paper proposes a novel Deep Video Code (DVC) method which encodes video faces into compact binary codes. Specifically, we devise an end-to-end convolutional neural network (CNN) framework that takes face videos as training inputs, models each of them as a unified representation by temporal feature pooling operation, and finally projects the high-dimensional representations of both frames and videos into Hamming space to generate binary codes. In such Hamming space, distance of dissimilar pairs is larger than that of similar pairs by a margin. To this end, a novel bounded triplet hashing loss is elaborately designed, which takes all dissimilar pairs into consideration for each anchor point in a mini-batch, and the optimization of the loss function is smoother and more stable. Extensive experiments on challenging video face databases and general image/video datasets with comparison to the state-of-the-arts verify the effectiveness of our method in different kinds of retrieval scenarios.
Draco: Architectural and Operating System Support for System Call Security

System call checking is extensively used to protect the operating system kernel from user attacks. However, existing solutions such as Seccomp execute lengthy rule-based checking programs against system calls and their arguments, leading to substantial execution overhead.To minimize checking overhead, this paper proposes Draco, a new architecture that caches system call IDs and argument values after they have been checked and validated. System calls are first looked-up in a special cache and, on a hit, skip all checks. We present both a software and a hardware implementation of Draco. The latter introduces a System Call Lookaside Buffer (SLB) to keep recently-validated system calls, and a System Call Target Buffer to preload the SLB in advance. In our evaluation, we find that the average execution time of macro and micro benchmarks with conventional Seccomp checking is 1.14× and 1.25× higher, respectively, than on an insecure baseline that performs no security checks. With our software Draco, the average execution time reduces to 1.10× and 1.18× higher, respectively, than on the insecure baseline. With our hardware Draco, the execution time is within 1% of the insecure baseline.
Effect of Social Anxiety on the Adoption of Robotic Training Partner

With the development of artificial intelligence technologies, robotic training partner is becoming a reality, which is a substitute for human training partner. Socially anxious individuals feel uncomfortable in front of unfamiliar people or when being observed by others. Playing with robotic training partners can avoid face-to-face interaction with other people. It is unclear whether social anxiety affects the adoption of robotic training partners. This study investigates the effect of social anxiety on the adoption of robotic training partners among university students. Study 1 confirmed that university students with higher social anxiety are more likely to choose robotic training partners than human training partners. Mediation analysis in Study 2 supported the mediating role of sense of relaxation with robotic training partner in the positive effect of social anxiety on the adoption of robotic training partner. This study shows that developing training partner robots is a meaningful thing for corporate profits and the health of socially anxious people.
The dark triad and insider threats in cyber security

Tracing the relationship between pathological personality traits and insider cyber sabotage.
SpringNet: Transformer and Spring DTW for Time Series Forecasting

In this paper, we present SpringNet, a novel deep learning approach for time series forecasting, and demonstrate its performance in a case study for solar power forecasting. SpringNet is based on the Transformer architecture but uses a Spring DTW attention layer to consider the local context of the time series data. Firstly, it captures the local shape of the time series with Spring DTW attention layers, dealing with data fluctuations. Secondly, it uses a batch version of the Spring DTW algorithm for efficient computation on GPU, to facilitate applications to big time series data. We comprehensively evaluate the performance of SpringNet on two large solar power data sets, showing that SpringNet is an effective method, outperforming the state-of-the-art DeepAR and LogSparse Transformer methods.
The prior probability in the batch classification of imbalanced data streams

In the diversity of contemporary decision-making tasks, where the data is no longer static and changes over time, data stream processing has become an important issue in the field of pattern recognition. In addition, most of the real problems are not balanced, representing their classes in various improportions. Following paper proposes the Prior Imbalance Compensation method, modifying on-the-fly predictions made by the base classifier, aiming at mapping prior probability in the statistics of assigned classes. It is intended to be a less computationally complex competition for popular algorithms such as smote , solving this problem by oversampling the training set. The proposed method has been tested using computer experiments on the example of a set of various data streams, leading to promising results, suggesting its usefulness in solving this type of problems.
Hybrid Embedded-Systems-Based Approach to in-Driver Drunk Status Detection Using Image Processing and Sensor Networks

Car drivers under the influence of alcohol is one of the most common causes of road traffic accidents. To tackle this issue, an emerging, suitable alternative is the use of intelligent systems -traditionally based on either sensor networks or artificial vision- that are aimed to prevent starting the car when drunk status on the car driver is detected. In such vein, this paper introduces a system whose main objective is identifying a person having alcohol in the blood through supervised classification of sensor-generated and computer-vision-based data. To do so, some drunk-status criteria are considered, namely: the concentration of alcohol in the car environment, the facial temperature of the driver and the pupil width. Specifically, for data acquisition purposes, the proposed system incorporates a gas sensor, temperature sensor and a digital camera. Acquired data are analyzed into a two-stages machine learning system consisting of feature selection and supervised classification algorithms. Both acquisition and analysis stages are to be performed into a embedded system, and therefore all procedures and algorithms are designed to work at low-computational resources. As a remarkable outcome, due mainly to the incorporation of feature selection and relevance analysis stages, proposed approach reaches a classification performance of 98% while ensures adequate operation conditions for the embedded system.
Testing the Robustness of AutoML Systems

Automated machine learning (AutoML) systems aim at finding the best machine learning (ML) pipeline that automatically matches the task and data at hand.We investigate the robustness of machine learning pipelines generated with three AutoML systems, TPOT, H2O, and AutoKeras.In particular, we study the influence of dirty data on accuracy, and consider how using dirty training data may help create more robust solutions.Furthermore, we also analyze how the structure of the generated pipelines differs in different cases.
Redundancy Removing Aggregation Network With Distance Calibration for Video Face Recognition

Attention-based techniques have been successfully used for rating image quality, and have been widely employed for set-based face recognition. Nevertheless, for video face recognition, where the base convolutional neural network (CNN) trained on large-scale data already provides discriminative features, fusing features with only predicted quality scores to generate representation are likely to cause duplicate sample dominant problem, and degrade performance correspondingly. To resolve the problem mentioned above, we propose a redundancy removing aggregation network (RRAN) for video face recognition. Compared with other quality-aware aggregation schemes, RRAN can take advantage of similarity information to tackle the noise introduced by redundant video frames. By leveraging metric learning, RRAN introduces a distance calibration scheme to align distance distributions of negative pairs of different video representations, which improves the accuracy under a uniform threshold. A series of experiments is conducted on multiple realistic data sets to evaluate the performance of RRAN, including YouTube Faces, IJB-A, and IJB-C. In comprehensive experiments, we demonstrate that our method can diminish the overall influence of poor quality components with large proportion in the video and further improve the overall recognition performance with individual difference. Specifically, RRAN achieves a 96.84% accuracy on YouTube Face, outperforming all existing aggregation schemes.
Experimental Demonstration of Quantum Fully Homomorphic Encryption with Application in a Two-Party Secure Protocol

A fully homomorphic encryption system hides data from unauthorized parties, while still allowing them to perform computations on the encrypted data. Aside from the straightforward benefit of allowing users to delegate computations to a more powerful server without revealing their inputs, a fully homomorphic cryptosystem can be used as a building block in the construction of a number of cryptographic functionalities. Designing such a scheme remained an open problem until 2009, decades after the idea was first conceived, and the past few years have seen the generalization of this functionality to the world of quantum machines. Quantum schemes prior to the one implemented here were able to replicate some features in particular use-cases often associated with homomorphic encryption but lacked other crucial properties, for example, relying on continual interaction to perform a computation or leaking information about the encrypted data. We present the first experimental realisation of a quantum fully homomorphic encryption scheme. We further present a toy two-party secure computation task enabled by our scheme. Finally, as part of our implementation, we also demonstrate a post-selective two-qubit linear optical controlled-phase gate with a much higher post-selection success probability (1/2) when compared to alternate implementations, e.g. with post-selective controlled-$Z$ or controlled-$X$ gates (1/9).
Multi-generator GAN learning disconnected manifolds with mutual information

Original data usually lies on a set of disconnected manifolds rather than a smooth connected manifold. This causes the problem of mode collapse in the training of vanilla Generative Adversarial Network (GAN). There are many existing GAN variants that attempt to address this problem, but they result in limitations. The existing variants either produce simulated instances with low quality or generate identical simulated instances. In this study, we propose a new approach to training GAN utilizing multiple generators, a classifier and a discriminator to address mode collapse. The classifier outputs the statistical probabilities of generated data belonging to a specific category. These probabilities implicitly reflect which manifolds are captured by generators, and the correlation between generators is quantified by mutual information. Our idea views the mutual information values as a constraint to guide generators in learning different manifolds. Specifically, we traverse the generators, calculating the mutual information between each generator and the others. The calculated values are integrated into the generator loss to form a new generator loss and to update the corresponding generator's parameters, using back-propagation. We minimize the mutual information to reduce the correlation between generators while also minimizing the generator loss. This ensures generators capture different manifolds while updating their parameters. A new minimax formula is established to train our approach in a similar spirit to vanilla GAN. We term our approach Mutual Information Multi-generator GAN (MIM-GAN). We conduct extensive experiments utilizing the MNIST, CIFAR10 and CelebA datasets to demonstrate the significant performance improvement of MIM-GAN in both achieving the highest Inception Scores and producing diverse generated data at different resolutions.
Deep Generative Model-Driven Multimodal Prostate Segmentation in Radiotherapy

Deep learning has shown unprecedented success in a variety of applications, such as computer vision and medical image analysis. However, there is still potential to improve segmentation in multimodal images by embedding prior knowledge via learning-based shape modeling and registration to learn the modality invariant anatomical structure of organs. For example, in radiotherapy automatic prostate segmentation is essential in prostate cancer diagnosis, therapy, and post-therapy assessment from T2-weighted MR or CT images. In this paper, we present a fully automatic deep generative model-driven multimodal prostate segmentation method using convolutional neural network (DGMNet). The novelty of our method comes with its embedded generative neural network for learning-based shape modeling and its ability to adapt for different imaging modalities via learning-based registration. The proposed method includes a multi-task learning framework that combines a convolutional feature extraction and an embedded regression and classification based shape modeling. This enables the network to predict the deformable shape of an organ. We show that generative neural network-based shape modeling trained on a reliable contrast imaging modality (such as MRI) can be directly applied to low contrast imaging modality (such as CT) to achieve accurate prostate segmentation. The method was evaluated on MRI and CT datasets acquired from different clinical centers with large variations in contrast and scanning protocols. Experimental results reveal that our method can be used to automatically and accurately segment the prostate gland in different imaging modalities.
Advanced Time-Frequency Analysis and Machine Learning for Pathological Voice Detection

This paper discusses an advanced positive time-frequency analysis method with accurate zero-, first- and second-order moments combined with machine learning for detection of pathological voice signals. The time-frequency analysis is based on the Wigner-Ville distribution. The transfer learning approach is used to train a convolutional neural network. Specifically, the GoogLeNet network is trained using the normal and pathological voices of the KAY database and the results are presented.
Interval counterexamples for loop invariant learning

Loop invariant generation has long been a challenging problem. Black-box learning has recently emerged as a promising method for inferring loop invariants. However, the performance depends heavily on the quality of collected examples. In many cases, only after tens or even hundreds of constraint queries, can a feasible invariant be successfully inferred.
GRUNGE: A Grand Unified ATP Challenge

This paper describes a large set of related theorem proving problems obtained by translating theorems from the HOL4 standard library into multiple logical formalisms. The formalisms are in higher-order logic (with and without type variables) and first-order logic (possibly with types, and possibly with type variables). The resultant problem sets allow us to run automated theorem provers that support different logical formalisms on corresponding problems, and compare their performances. This also results in a new “grand unified” large theory benchmark that emulates the ITP/ATP hammer setting, where systems and metasystems can use multiple formalisms in complementary ways, and jointly learn from the accumulated knowledge.