Reviewerly/exHarmony huggingface dataset:
SURF-BRISK–Based Image Infilling Method for Terrain Classification of a Legged Robot

In this study, we propose adaptive locomotion for an autonomous multilegged walking robot, an image infilling method for terrain classification based on a combination of speeded up robust features, and binary robust invariant scalable keypoints (SURF-BRISK). The terrain classifier is based on the bag-of-words (BoW) model and SURF-BRISK, both of which are fast and accurate. The image infilling method is used for identifying terrain with obstacles and mixed terrain; their features are magnified to help with recognition of different complex terrains. Local image infilling is used to improve low accuracy caused by obstacles and super-pixel image infilling is employed for mixed terrain. A series of experiments including classification of terrain with obstacles and mixed terrain were conducted and the obtained results show that the proposed method can accurately identify all terrain types and achieve adaptive locomotion.
The Case for I/O-Device-as-a-Service

Many computer systems, especially mobile and IoT systems, use a large number of I/O devices. A contemporary OS acts as a security guard for these devices, which trust the OS to correctly implement the "perimeter defense." Moreover, the OS also trusts these devices and their drivers to be well-behaved and bug-free. This interwoven trust model complicates the security of the system as a single vulnerable component can undermine all security guarantees. Not surprising, this architecture has failed to achieve strong security as evident by attacks that have targeted I/O devices or their drivers. In this paper, we call for a radically new approach, called I/O-Device-as-a-Service (IDaaS), where each I/O device acts a separate service and is responsible for its own security. Inspired by Service-Oriented Architecture (SOA), IDaaS requires every device to be equipped with its own software stack and provide an externalizable API that can be safely exposed to untrusted software. We discuss the design decisions in IDaaS, highlight its security benefits and research challenges, and present a case study.
Privacy Protection for Audio Sensing Against Multi-Microphone Adversaries

Abstract Audio-based sensing enables fine-grained human activity detection, such as sensing hand gestures and contact-free estimation of the breathing rate. A passive adversary, equipped with microphones, can leverage the ongoing sensing to infer private information about individuals. Further, with multiple microphones, a beamforming-capable adversary can defeat the previously-proposed privacy protection obfuscation techniques. Such an adversary can isolate the obfuscation signal and cancel it, even when situated behind a wall. AudioSentry is the first to address the privacy problem in audio sensing by protecting the users against a multi-microphone adversary. It utilizes the commodity and audio-capable devices, already available in the user’s environment, to form a distributed obfuscator array. AudioSentry packs a novel technique to carefully generate obfuscation beams in different directions, preventing the multi-microphone adversary from canceling the obfuscation signal. AudioSentry follows by a dynamic channel estimation scheme to preserve authorized sensing under obfuscation. AudioSentry offers the advantages of being practical to deploy and effective against an adversary with a large number of microphones. Our extensive evaluations with commodity devices show that protects the user’s privacy against a 16-microphone adversary with only four commodity obfuscators, regardless of the adversary’s position. AudioSentry provides its privacy-preserving features with little overhead on the authorized sensor.
FUNCTIONS OF UNIVERSITY TEACHERS IN MODERN CONDITIONS

Education always responds to changes in society, and the teacher, in his turn, is its key figure. Society is changing, demands are changing. The teacher of the University is obliged to change, which requires continuous improvement both in the subject area and in the field of teaching methods, acquiring skills in information systems of educational process management. The relevance of the problem of clarifying the functions of professional activity of teachers of educational institutions of higher education is due to the need for rational allocation of time for quality performance of their work. The activity of university teachers in modern conditions is aimed at the training of competitive personnel, which requires significant intellectual, temporary efforts from these employees; readiness for effective social interaction with colleagues, students, partners; technological competence in solving problems in the information and educational environment and information management systems of the educational process associated with the organization of research, teaching, organizational, methodological and contact work. The article presents the results obtained by the following research methods: analysis and synthesis of scientific information, professional standard Teacher of professional training and the draft of the professional standard Scientist, the results of the research in the field of professional activity of teachers; generalization and systematization of scientific facts and provisions; survey of teachers of educational organizations (monographic sample was 20 people). Experimental work was to determine the time spent on the functions of the teaching staff of educational institutions of higher education, to determine the importance of each of the selected functions and to assess the satisfaction of teachers from the results of their functions.
Efficient Depth Estimation Using Sparse Stereo-Vision with Other Perception Techniques

The stereo vision system is one of the popular computer vision techniques. The idea here is to use the parallax error to our advantage. A single scene is recorded from two different viewing angles, and depth is estimated from the measure of parallax error. This technique is more than a century old and has proven useful in many applications. This field has made a lot of researchers and mathematicians to devise novel algorithms for the accurate output of the stereo systems. This system is particularly useful in the field of robotics. It provides them with the 3D understanding of the scene by giving them estimated object depths. This chapter, along with a complete overview of the stereo system, talks about the efficient estimation of the depth of the object. It stresses on the fact that if coupled with other perception techniques, stereo depth estimation can be made a lot more efficient than the current techniques. The idea revolves around the fact that stereo depth estimation is not necessary for all the pixels of the image. This fact opens room for more complex and accurate depth estimation techniques for the fewer regions of interest in the image scene. Further details about this idea are discussed in the subtopics that follow.
Tight Tradeoffs for Real-Time Approximation of Longest Palindromes in Streams

We consider computing a longest palindrome in the streaming model, where the symbols arrive one-by-one and we do not have random access to the input. While computing the answer exactly using sublinear space is not possible in such a setting, one can still hope for a good approximation guarantee. Our contribution is twofold. First, we provide lower bounds on the space requirements for randomized approximation algorithms processing inputs of length n. We rule out Las Vegas algorithms, as they cannot achieve sublinear space complexity. For Monte Carlo algorithms, we prove a lower bound of $$\varOmega ( M \log \min \{|\varSigma |,M\})$$ bits of memory; here $$M=n/E$$ for approximating the answer with additive error E, and $$M= \log n/\log (1+\varepsilon )$$ for approximating the answer with multiplicative error $$(1 + \varepsilon )$$ . Second, we design four real-time algorithms for this problem. Three of them are Monte Carlo approximation algorithms for additive error, “small” and “big” multiplicative errors, respectively. Each algorithm uses $$\mathcal {O}(M)$$ words of memory. Thus the obtained lower bounds are asymptotically tight up to a logarithmic factor. The fourth algorithm is deterministic and finds a longest palindrome exactly if it is short. This algorithm can be run in parallel with a Monte Carlo algorithm to obtain better results in practice. Overall, both the time and space complexity of finding a longest palindrome in a stream are essentially settled.
On verifying the authenticity of e-commercial crawling data by a semi-crosschecking method

Purpose Data crawling in e-commerce for market research often come with the risk of poor authenticity due to modification attacks. The purpose of this paper is to propose a novel data authentication model for such systems. Design/methodology/approach The data modification problem requires careful examinations in which the data are re-collected to verify their reliability by overlapping the two datasets. This approach is to use different anomaly detection techniques to determine which data are potential for frauds and to be re-collected. The paper also proposes a data selection model using their weights of importance in addition to anomaly detection. The target is to significantly reduce the amount of data in need of verification, but still guarantee that they achieve their high authenticity. Empirical experiments are conducted with real-world datasets to evaluate the efficiency of the proposed scheme. Findings The authors examine several techniques for detecting anomalies in the data of users and products, which give the accuracy of 80 per cent approximately. The integration with the weight selection model is also proved to be able to detect more than 80 per cent of the existing fraudulent ones while being careful not to accidentally include ones which are not, especially when the proportion of frauds is high. Originality/value With the rapid development of e-commerce fields, fraud detection on their data, as well as in Web crawling systems is new and necessary for research. This paper contributes a novel approach in crawling systems data authentication problem which has not been studied much.
Mazi

Playtime is an important activity for child development as it stimulates as well as predicts cognitive, motor, emotional and social skills. Children with autism may find it difficult to socialize, particularly initiating and maintaining human interactions. Consecutively, it is thought that playing with peers is often a challenge that many children avoid by simply playing in solitary mode. We present the design of Mazi, an e-textile sonic tangible user interface (TUI) designed with the aim of promoting basic social skills; stimulating spontaneous, independent and collaborative play; and providing sensory regulation opportunities. Mazi was tested in a Special Education Needs (SEN) School based in NorthEast London, with a group of five minimally verbal children with autism aged between 6 to 9. The results show great potentials for TUI implementation in educational settings as a way of promoting social skills through carefully designed playful and recreational activities.
Superpixel Segmentation Based on Anisotropic Edge Strength

Superpixel segmentation can benefit from the use of an appropriate method to measure edge strength. In this paper, we present such a method based on the first derivative of anisotropic Gaussian kernels. The kernels can capture the position, direction, prominence, and scale of the edge to be detected. We incorporate the anisotropic edge strength into the distance measure between neighboring superpixels, thereby improving the performance of an existing graph-based superpixel segmentation method. Experimental results validate the superiority of our method in generating superpixels over the competing methods. It is also illustrated that the proposed superpixel segmentation method can facilitate subsequent saliency detection.
About Subjectivity of a University Student in Educational Activity

The subjectivity of a university student in educational activities is considered as a significant factor of the compliance between the existing state of the university educational system and the requirements placed on it. The article focuses on the imbalance of information support of educational activities, the lack of personal characteristics of students in this provision. First of all, this refers to the indicators of students’ development processes, their value systems, and the choice of activity strategies. A review of the results of empirical studies that characterize students in the current educational situation is carried out. As part of a study aimed at identifying students’ individual academic strategies, a technique has been proposed to define the value-semantic determination of student activity. This method enables determining the student’s value preferences in the space of possible components of his activity. The student performs pair comparisons of the various components of his activity and in each pair chooses the component that is more important for him. According to the results of comparisons, a hierarchy for each student is determined which reflects his value preferences in the space of possible activity. The analysis of such hierarchies indicates that only 27,6% of students characterize the dominance of the processes of formation of their subjectivity. At the same time, 52,6% of students show incomplete personal subjectivity. And 19,8% of students in their activities are guided by the existing situation and external stimulation. The analysis of the general hierarchy reflecting the value preferences of the considered sample of students in the space of possible activities was performed.
Deep Neural Model for Point-of-Interest Recommendation Fused with Graph Embedding Representation

With the rapid popularity of smart mobile devices and the rapid development of location-based social networks (LBSNs), location-based recommendation has become an important method to help people find the attractive point-of-interest (POI). However, due to the sparsity of user-POI check-in data, the traditional recommendation model based on collaborative filtering cannot be well applied to the POI recommendation problem. In addition, location-based social networks are different from other recommendation scenarios, and users’ POI check-ins are closely related to social relations and geographical factors. Therefore, this paper proposes a neural networks POI recommendation model fused with social and geographical graph embedding representation(SG-NeuRec). Our model organically combines social and geographical graph embedding representations with user-POI interaction representation, and captures the latent interactions between users and POIs under the neural networks framework. Meanwhile, in order to improve the accuracy of POI recommendation, the relevance between users’ accessing time pattern and POI is modeled by the designed shallow network and unified under the same framework. Extensive experiments on two real location-based social networks datasets demonstrate the effectiveness of the proposed model.
Improvising Singular Value Decomposition by KNN for Use in Movie Recommender Systems

Online recommender systems are an integral part of e-commerce. There are a plethora of algorithms following different approaches. However, most of the approaches except the singular value decomposition (SVD), do not provide any insight into the underlying patterns/concepts used in item rating. SVD used underlying features of movies but are computationally resource-heavy and performs poorly when there is data sparsity. In this article, we perform a comparative study among several pre-processing algorithms on SVD. In the experiments, we have used the MovieLens 1M dataset to compare the performance of these algorithms. KNN-based approach was used to find out K-nearest neighbors of users and their ratings were then used to impute the missing values. Experiments were conducted using different distance measures, such as Jaccard and Euclidian. We found that when the missing values were imputed using the mean of similar users and the distance measure was Euclidean, the KNN-based (K-Nearest Neighbour) approach of pre-processing the SVD was performing the best. Based on our comparative study, data managers can choose to employ the algorithm best suited for their business.
AUTOHOME-ORCA at SemEval-2019 Task 8: Application of BERT for Fact-Checking in Community Forums

Fact checking is an important task for maintaining high quality posts and improving user experience in Community Question Answering forums.Therefore, the SemEval-2019 task 8 is aimed to identify factual question (subtask A) and detect true factual information from corresponding answers (subtask B).In order to address this task, we propose a system based on the BERT model with meta information of questions.For the subtask A, the outputs of fine-tuned BERT classification model are combined with the feature of length of questions to boost the performance.For the subtask B, the predictions of several variants of BERT model encoding the meta information are combined to create an ensemble model.Our system achieved competitive results with an accuracy of 0.82 in the subtask A and 0.83 in the subtask B. The experimental results validate the effectiveness of our system.
Optimized Measures of Bipartite Quantum Correlation

How can we characterize different types of correlation between quantum systems? Since correlations cannot be generated locally, we take any real function of a multipartite state which cannot increase under local operations to measure a correlation. Correlation measures that can be expressed as an optimization of a linear combination of entropies are particularly useful, since they can often be interpreted operationally. We systematically study such optimized linear entropic functions, and by enforcing monotonicity under local processing we identify four cones of correlation measures for bipartite quantum states. This yields two new optimized measures of bipartite quantum correlation that are particularly simple, which have the additional property of being additive.
Semantic Path Planning for Indoor Navigation and Household Tasks

Assisting people with daily living tasks in their own homes with a robot requires a navigation through a cluttered and varying environment. Sometimes the only possible path would be blocked by an obstacle which needs to be moved away but not into other obstructing regions like the space required for opening a door. This paper presents semantic assisted path planning in which a gridded semantic map is used to improve navigation among movable obstacles (NAMO) and partially plan simple household tasks like cleaning a carpet or moving objects to another location. Semantic planning allows the execution of tasks expressed in human-like form instead of mathematical concepts like coordinates. In our numerical experiments, spatial planning was completed well within a typical human-human dialogue response time, allowing for an immediate response by the robot.
Freshmens anxiety in an intensive listening class: A qualitative study

Listening skill is an ability to understand a spoken language.It is also one of the important skills that people actively use in their daily lives.For learning English language at the Faculty of Language and Arts (FLA) at Universitas Kristen Satya Wacana (UKSW) Salatiga, Indonesia, there are several listening classes.One of them is Intensive Listening class, which is often considered as one of the most difficult listening classes by freshmen.Freshmen seem to be quite anxious in attending this class, and because of that anxiety, it is hard for them to attend the class comfortably.Even though not all freshmen thought that way, listening is still often considered as a complex skill, even among sophomores or older students.Thus, this research aims to investigate students' sources of anxiety and ways to remedy it.The participants in this study were 2017 students.The population was 80 students of four parallel intensive listening classes and all the students were selected as the respondents.In other words, the whole population was used as the sample of this study.Data were collected through interviews and questionnaires.Findings revealed that 46 out of 80 students admitted that they felt anxious while the remaining 34 students admitted they did not feel anxious during the intensive listening.In this research, the symptoms, levels, and the reasons for being anxious in the intensive listening class are explained.
Adiabatic preparation of squeezed states of oscillators and large spin systems coupled to a two-level system

We study a single two-level system coupled resonantly to an oscillator mode or a large spin. By adiabatically turning on a linear driving term on the oscillator or the spin, the eigenstates of the system change character and its ground state evolves into squeezed states of the oscillator or the spin. The robust generation of such states is of interest in many experimental systems with applications for sensing and quantum information processing.
Diverse Exploration via Conjugate Policies for Policy Gradient Methods

We address the challenge of effective exploration while maintaining good performance in policy gradient methods. As a solution, we propose diverse exploration (DE) via conjugate policies. DE learns and deploys a set of conjugate policies which can be conveniently generated as a byproduct of conjugate gradient descent. We provide both theoretical and empirical results showing the effectiveness of DE at achieving exploration, improving policy performance, and the advantage of DE over exploration by random policy perturbations.
Learning Variations in Human Motion via Mix-and-Match Perturbation

Human motion prediction is a stochastic process: Given an observed sequence of poses, multiple future motions are plausible. Existing approaches to modeling this stochasticity typically combine a random noise vector with information about the previous poses. This combination, however, is done in a deterministic manner, which gives the network the flexibility to learn to ignore the random noise. In this paper, we introduce an approach to stochastically combine the root of variations with previous pose information, which forces the model to take the noise into account. We exploit this idea for motion prediction by incorporating it into a recurrent encoder-decoder network with a conditional variational autoencoder block that learns to exploit the perturbations. Our experiments demonstrate that our model yields high-quality pose sequences that are much more diverse than those from state-of-the-art stochastic motion prediction techniques.
A Note on Strong Axiomatization of Gödel Justification Logic

Justification logics are special kinds of modal logics which provide a framework for reasoning about epistemic justifications. For this, they extend classical boolean propositional logic by a family of necessity-style modal operators "t:", indexed over t by a corresponding set of justification terms, which thus explicitly encode the justification for the necessity assertion in the syntax. With these operators, one can therefore not only reason about modal effects on propositions but also about dynamics inside the justifications themselves. We replace this classical boolean base with G\"odel logic, one of the three most prominent fuzzy logics, i.e. special instances of many-valued logics, taking values in the unit interval [0,1], which are intended to model inference under vagueness. We extend the canonical possible-world semantics for justification logic to this fuzzy realm by considering fuzzy accessibility- and evaluation-functions evaluated over the minimum t-norm and establish strong completeness theorems for various fuzzy analogies of prominent extensions for basic justification logic.