Reviewerly/exHarmony huggingface dataset:
Acceleration Feature Extraction of Human Body Based on Wearable Devices

Wearable devices used for human body monitoring has broad applications in smart home, sports, security and other fields. Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human body acceleration feature extraction method based on wearable devices is studied. Firstly, Butterworth filter is used to filter the data. Then, in order to ensure the extracted feature value more accurately, it is necessary to remove the abnormal data in the source. This paper combines Kalman filter algorithm with a genetic algorithm and use the genetic algorithm to code the parameters of the Kalman filter algorithm. We use Standard Deviation (SD), Interval of Peaks (IoP) and Difference between Adjacent Peaks and Troughs (DAPT) to analyze seven kinds of acceleration. At last, SisFall data set, which is a globally available data set for study and experiments, is used for experiments to verify the effectiveness of our method. Based on simulation results, we can conclude that our method can distinguish different activity clearly.
Evaluation of Recurrent Neural Network Based on Indonesian Speech Synthesis for Small Datasets

The recurrent neural network (RNN) application when it comes to audio and speech processing in the case of Indonesian-language voice data is rarely done now. This is important because Indonesian languages have different characteristics from foreign languages. So in this case we try to evaluate a number of methods in RNN to make speech synthesis in Indonesian. In this research we use objective measurements, the results we get that LSTM generally produces better sound quality than GRU. While the derivative of GRU, MGU2 gets the best results in the model training time.
Chapter 26. Approximate Model Counting

Model counting, or counting solutions of a set of constraints, is a fundamental problem in Computer Science with diverse applications. Since exact counting is computationally hard (#P complete), approximate counting techniques have received much attention over the past few decades. In this chapter, we focus on counting models of propositional formulas, and discuss in detail universal-hashing based approximate counting, which has emerged as the predominant paradigm for state-of-the-art approximate model counters. These counters are randomized algorithms that exploit properties of universal hash functions to provide rigorous approximation guarantees, while piggybacking on impressive advances in propositional satisfiability solving to scale up to problem instances with a million variables. We elaborate on various choices in designing such approximate counters and the implications of these choices. We also discuss variants of approximate model counting, such as DNF counting and weighted counting.
Improving Pixel Embedding Learning Through Intermediate Distance Regression Supervision for Instance Segmentation

As a proposal-free approach, instance segmentation through pixel embedding learning and clustering is gaining more emphasis. Compared with bounding box refinement approaches, such as Mask R-CNN, it has potential advantages in handling complex shapes and dense objects. In this work, we propose a simple, yet highly effective, architecture for object-aware embedding learning. A distance regression module is incorporated into our architecture to generate seeds for fast clustering. At the same time, we show that the features learned by the distance regression module are able to promote the accuracy of learned object-aware embeddings significantly. By simply concatenating features of the distance regression module to the images as inputs of the embedding module, the mSBD scores on the CVPPP Leaf Segmentation Challenge can be further improved by more than 8% compared to the identical set-up without concatenation, yielding the best overall result amongst the leaderboard at CodaLab.
A preliminary study of cyber parental control and its methods

The growing Internet accessibility from various places and devices makes a vast amount of web content, including objectionable materials, available to users. These objectionable contents, including, for example, pornography, cyberbullying, sexual abuse, violence, hatred, gambling, and drugs endanger children on the Internet. More than half of the Internet children users have accidentally seen objectionable web contents. This problem has motivated the academia and industry to develop parental control tools for web content filtering. A few studies demonstrate the significance of research in cyber parental control. However, there is a lack of studies on the methods, techniques, and datasets of this domain. The aim of this study is to present a preliminary study of the methods, techniques, and datasets of the field of cyber parental control. It also identifies the most effective methods and techniques, their strengths and limitations, determine the currently used datasets, and provides insights for the future work on the research of cyber parental control. Addressing such aspects of this field provide a better understanding of the methods, techniques, and dataset in this field not previously fully evaluated. Finally, this study presented several suggestions for proposing an effective and efficient system of cyber parental control.
Communication in Multi-Agent Reinforcement Learning: Intention Sharing

Communication is one of the core components for learning coordinated behavior in multi-agent systems. In this paper, we propose a new communication scheme named Intention Sharing (IS) for multi-agent reinforcement learning in order to enhance the coordination among agents. In the proposed IS scheme, each agent generates an imagined trajectory by modeling the environment dynamics and other agents' actions. The imagined trajectory is the simulated future trajectory of each agent based on the learned model of the environment dynamics and other agents and represents each agent's future action plan. Each agent compresses this imagined trajectory capturing its future action plan to generate its intention message for communication by applying an attention mechanism to learn the relative importance of the components in the imagined trajectory based on the received message from other agents. Numeral results show that the proposed IS scheme outperforms other communication schemes in multi-agent reinforcement learning.
Building Vehicle Autonomy: Sensors, Semiconductors, Software and U.S. Competitiveness

This paper describes the current state of driving automation, the components that go into autonomous vehicles, and U.S. firm participation in the sector. There are three main components that enable autonomous driving: sensors, semiconductors, and software. Sensors, including cameras, Light Detection and Ranging (LiDAR), and radar are used together to help vehicles see road conditions at various distances, and in different weather and lighting conditions. Semiconductors facilitate the processing of data gathered by sensors in order to make real time driving decisions. Machine learning and mapping software provide the tools to improve the operation and decision making of vehicles. U.S. firms, including vehicle manufacturers, parts suppliers, and tech companies are competing across all of the components of driving automation. As a new area of competition, there are opportunities for startups and for firms to move into new areas (e.g., vehicle manufacturers developing chips, and technology companies supplying automotive parts).
TransR*: Representation learning model by flexible translation and relation matrix projection

The TransR model solves the problem that TransE and TransH models are not sufficient for modeling in public spaces, and is considered a highly potential knowledge representation model. However, TransR still adopts the translation principles based on the TransE model, and the constraints are too strict, which makes the model’s ability to distinguish between very similar entities low. Therefore, we propose a representation learning model TransR* based on flexible translation and relational matrix projection. Firstly, we separate entities and relationships in different vector spaces; secondly, we combine our flexible translation strategy to make translation strategies more flexible. During model training, the quality of generating negative triples is improved by replacing semantically similar entities, and the prior probability of the relationship is used to distinguish the relationship of similar coding. Finally, we conducted link prediction experiments on the public data sets FB15K and WN18, and conducted triple classification experiments on the WN11, FB13, and FB15K data sets to analyze and verify the effectiveness of the proposed model. The evaluation results show that our method has a better improvement effect than TransR on Mean Rank, Hits@10 and ACC indicators.
How Well Generative Adversarial Networks Learn Distributions

This paper studies the rates of convergence for learning distributions implicitly with the adversarial framework and Generative Adversarial Networks (GAN), which subsume Wasserstein, Sobolev, MMD GAN, and Generalized/Simulated Method of Moments (GMM/SMM) as special cases. We study a wide range of parametric and nonparametric target distributions, under a host of objective evaluation metrics. We investigate how to obtain a good statistical guarantee for GANs through the lens of regularization. On the nonparametric end, we derive the optimal minimax rates for distribution estimation under the adversarial framework. On the parametric end, we establish a theory for general neural network classes (including deep leaky ReLU networks), that characterizes the interplay on the choice of generator and discriminator pair. We discover and isolate a new notion of regularization, called the generator-discriminator-pair regularization, that sheds light on the advantage of GANs compared to classical parametric and nonparametric approaches for explicit distribution estimation. We develop novel oracle inequalities as the main technical tools for analyzing GANs, which is of independent interest.
Implicit Feedback-based Group Recommender System for Internet of Things Applications

With the prevalence of Internet of Things (IoT)-based social media applications, the distance among people has been greatly shortened. As a result, recommender systems in IoT-based social media need to be developed oriented to groups of users rather than individual users. However, existing methods were highly dependent on explicit preference feedbacks, ignoring scenarios of implicit feedbacks. To remedy such gap, this paper proposes an implicit feedback-based group recommender system using probabilistic inference and non-cooperative game (GREPING) for IoT-based social media. Particularly, unknown process variables can be estimated from observable implicit feedbacks via Bayesian posterior probability inference. In addition, the globally optimal recommendation results can be calculated with the aid of non-cooperative game. Two groups of experiments are conducted to assess the GREPING from two aspects: efficiency and robustness. Experimental results show obvious promotion and considerable stability of the GREPING compared to baseline methods.
A Hierarchical Data Mining Process Ontology

Aiming to the problem of data mining processes building, we present a hierarchical data mining process ontology. The ontology is constructed based on CRISP-DM framework. Four layers of data mining process are described to solve the complexity of process synthesis. The ontology supports the management of DM processes, that assumes defining steps of the processes and selecting algorithms taking into account the input and the output data. An example of querying on the proposed ontology is presented.
Representing Partial Programs with Blended Abstract Semantics

Synthesizing programs from examples requires searching over a vast, combinatorial space of possible programs. In this search process, a key challenge is representing the behavior of a partially written program before it can be executed, to judge if it is on the right track and predict where to search next. We introduce a general technique for representing partially written programs in a program synthesis engine. We take inspiration from the technique of abstract interpretation, in which an approximate execution model is used to determine if an unfinished program will eventually satisfy a goal specification. Here we \emph{learn} an approximate execution model implemented as a modular neural network. By constructing compositional program representations that implicitly encode the interpretation semantics of the underlying programming language, we can represent partial programs using a flexible combination of concrete execution state and learned neural representations, using the learned approximate semantics when concrete semantics are not known (in unfinished parts of the program). We show that these hybrid neuro-symbolic representations enable execution-guided synthesizers to use more powerful language constructs, such as loops and higher-order functions, and can be used to synthesize programs more accurately for a given search budget than pure neural approaches in several domains.
Cost-optimal single-qubit gate synthesis in the Clifford hierarchy

For universal quantum computation, a major challenge to overcome for practical implementation is the large amount of resources required for fault-tolerant quantum information processing. An important aspect is implementing arbitrary unitary operators built from logical gates within the quantum error correction code. A synthesis algorithm can be used to approximate any unitary gate up to arbitrary precision by assembling sequences of logical gates chosen from a small set of universal gates that are fault-tolerantly performable while encoded in a quantum error-correction code. However, current procedures do not yet support individual assignment of base gate costs and many do not support extended sets of universal base gates. We analysed cost-optimal sequences using an exhaustive search based on Dijkstra’s pathfinding algorithm for the canonical Clifford+<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>T</mml:mi></mml:math>set of base gates and compared them to when additionally including<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Z</mml:mi></mml:math>-rotations from higher orders of the Clifford hierarchy. Two approaches of assigning base gate costs were used. First, costs were reduced to<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>T</mml:mi></mml:math>-counts by recursively applying a<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Z</mml:mi></mml:math>-rotation catalyst circuit. Second, costs were assigned as the average numbers of raw (i.e. physical level) magic states required to directly distil and implement the gates fault-tolerantly. We found that the average sequence cost decreases by up to<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>54</mml:mn><mml:mo>±</mml:mo><mml:mn>3</mml:mn><mml:mi mathvariant="normal">%</mml:mi></mml:math>when using the<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Z</mml:mi></mml:math>-rotation catalyst circuit approach and by up to<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>33</mml:mn><mml:mo>±</mml:mo><mml:mn>2</mml:mn><mml:mi mathvariant="normal">%</mml:mi></mml:math>when using the magic state distillation approach. In addition, we investigated observed limitations of certain assignments of base gate costs by developing an analytic model to estimate the proportion of sets of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Z</mml:mi></mml:math>-rotation gates from higher orders of the Clifford hierarchy that are found within sequences approximating random target gates.
Semi-Markov Based Dependability Modeling of Bitcoin Nodes Under Eclipse Attacks and State-Dependent Mitigation

The block chain technology has immense potential in many different applications, including but not limited to cryptocurrencies, financial services, smart contracts, supply chains, healthcare services, and energy trading. Due to the critical nature of these applications, it is pivotal to model and evaluate dependability of the block chain-based systems, contributing to their reliable and robust operation. This paper models and analyzes the dependability of Bitcoin nodes subject to Eclipse attacks and state-dependent mitigation activities. Built upon the block chain technology, the Bitcoin is a peer-to-peer cryptocurrency system enabling an individual user to trade freely without the involvement of banks or any other types of intermediate agents. However, a node in the Bitcoin is vulnerable to the Eclipse attack, which aims to monopolize the information flow of the victim node. A semi-Markov process (SMP) based approach is proposed to model the Eclipse attack behavior and possible mitigation activities that may prevent the attack from being successful during the attack process. The SMP model is then evaluated to determine the steady-state dependability of the Bitcoin node. Numerical examples are provided to demonstrate the influence of the time to restart the Bitcoin software and time to detect and delete the malicious message on the Bitcoin node dependability.
Joint space–time Bayesian disease mapping via quantification of disease risk association

Alzheimer’s disease is an increasingly prevalent neurological disorder with no effective therapies. Thus, there is a need to characterize the progression of Alzheimer’s disease risk in order to preclude its inception in patients. Characterizing Alzheimer’s disease risk can be accomplished at the population-level by the space–time modeling of Alzheimer’s disease incidence data. In this paper, we develop flexible Bayesian hierarchical models which can borrow risk information from conditions antecedent to Alzheimer’s disease, such as mild cognitive impairment, in an effort to better characterize Alzheimer’s disease risk over space and time. From an application of these models to real-world Alzheimer’s disease and mild cognitive impairment spatiotemporal incidence data, we found that our novel models provided improved model goodness of fit, and via a simulation study, we demonstrated the importance of diagnosing the label-switching problem for our models as well as the importance of model specification in order to best capture the contribution of time in modeling Alzheimer’s disease risk.
Using noise to augment synchronization among oscillators

Abstract Noise is expected to play an important role in the dynamics of analog systems such as coupled oscillators which have recently been explored as a hardware platform for application in computing. In this work, we experimentally investigate the effect of noise on the synchronization of relaxation oscillators and their computational properties. Specifically, in contrast to its typically expected adverse effect, we first demonstrate that a common white noise input induces frequency locking among uncoupled oscillators. Experiments show that the minimum noise voltage required to induce frequency locking increases linearly with the amplitude of the oscillator output whereas it decreases with increasing number of oscillators. Further, our work reveals that in a coupled system of oscillators—relevant to solving computational problems such as graph coloring, the injection of white noise helps reduce the minimum required capacitive coupling strength. With the injection of noise, the coupled system demonstrates frequency locking along with the desired phase-based computational properties at 5 × lower coupling strength than that required when no external noise is introduced. Consequently, this can reduce the footprint of the coupling element and the corresponding area-intensive coupling architecture. Our work shows that noise can be utilized as an effective knob to optimize the implementation of coupled oscillator-based computing platforms.
The relevance of this article is due to the need to form and develop algorithmic thinking of higher education students as the main requirement of the information society following 21st century skills and competences for new millennium learners. The purpose of the article is to consider algorithmic thinking as a new dimension of learning in higher education. The leading approach to the study of this problem is the analysis of methodological literature, and the experience of students, teachers, …

The relevance of this article is due to the need to form and develop algorithmic thinking of higher education students as the main requirement of the information society following 21st century skills and competences for new millennium learners. The purpose of the article is to consider algorithmic thinking as a new dimension of learning in higher education. The leading approach to the study of this problem is the analysis of methodological literature, and the experience of students, teachers, and academic staff. The article considers the essence, main properties, and characteristics of algorithmic thinking, suggests the universal sequence of algorithm development and model of algorithmic thinking as well as determines its importance for any subjects outside the information and communications technology area. The materials of the article can be useful for lecturers, professors, and other academic staff of universities and institutes when studying any subjects related to the basic and professional training of students.
An in-depth exploration of Bangla blog post classification

Bangla blog is increasing rapidly in the era of information, and consequently, the blog has a diverse layout and categorization. In such an aptitude, automated blog post classification is a comparatively more efficient solution in order to organize Bangla blog posts in a standard way so that users can easily find their required articles of interest. In this research, nine supervised learning models which are Support Vector Machine (SVM), multinomial naïve Bayes (MNB), multi-layer perceptron (MLP), k-nearest neighbours (k-NN), stochastic gradient descent (SGD), decision tree, perceptron, ridge classifier and random forest are utilized and compared for classification of Bangla blog post. Moreover, the performance on predicting blog posts against eight categories, three feature extraction techniques are applied, namely unigram TF-IDF (term frequency-inverse document frequency), bigram TF-IDF, and trigram TF-IDF. The majority of the classifiers show above 80% accuracy. Other performance evaluation metrics also show good results while comparing the selected classifiers.
3 Serial verb constructions and motion semantics

This chapter investigates the expression of associated motion and directional motion in the form of serial verb constructions (SVCs).In a sample of 124 languages with SVCs, 80% have motion SVCs.The most common types are directional SVCs, in which a path-of-motion verb combines with another motion verb, and prior associated motion SVCs expressing motion prior to the activity or state predicated by the other verb in the construction.Concurrent motion and subsequent motion are much less common.In a prior motion SVC, the motion verb nearly always precedes the other verb, and the figure on the path of motion is the subject.In a directional SVC, the path-of-motion verb nearly always follows the other verb, and the grammatical function of the figure on the path of motion can vary according to the semantics of the main verb in the construction.