Reviewerly/exHarmony huggingface dataset:
Accessibility Support in Urban Space

This paper presents the development of a set of gadgets named Smart Mobility Kit (SMK), which aims to make the urban space inclusive for people with disabilities (or Disabled People - DP). In its first trial version, SMK presents an intuitive and low-cost solution and has two gadgets: a Smart Cap and a Smart Glove, both integrated across the data transmission layer and application layers. The functionalities developed for this Kit make it a convenient solution with a high level of usability by the DP, according to the tests performed. The SMK has been tested in the facilities of the National University of San Agustin de Arequipa (UNSA) in Peru, and it is the result of result of the Project entitled entitled "Testbed for Intelligent and Assistive Applications for UNSA Campuses".
Fall prevention system based on airbag protection and mechanical exoskeleton support

The aging of population is a worldwide social problem that all countries will face in the 21st century. The health and quality of life of the elderly will have a significant impact on the country and society. In fact, falls are the leading cause of accidental injury or death in the elderly. Fortunately, using inflatable airbags as a buffer to reduce the injuries caused by falls is currently the most effective means of fall protection. This paper designs an indoor protection device for elderly patients in the rehabilitation stage. It not only includes an accurate and effective fall detection system, but also can use airbags and mechanical exoskeleton to perform head, waist and hip joints on patients who are about to fall. Through experiments, the designed airbag can be ejected within a specified time, and the designed algorithm can accurately distinguish the fall of the human body from the daily behaviour of human body.
Research on the Role of Computer Network Technology in Online Business Japanese Teaching

Abstract Based on the background of the epidemic situation in 2020 and the “bottleneck” of local computer network practice teaching, feasible countermeasures to optimize the practice teaching link are proposed to promote the smooth implementation of the transformation and development of local computer network Online Japanese teaching. Cultivate students to master scientific methods and improve their practical ability, so that students can consolidate theoretical knowledge and cultivate high-quality engineers and technicians with innovative consciousness. Against the background of the transformation and development of online Japanese teaching, applied undergraduate online Japanese teaching should change the original practice teaching mode and train of thought, and establish a set of practice teaching mode consistent with its transformation and development path.
Functions and practice of instrument &amp; equipment sharing platform for talent training in higher education

In colleges and universities, the sharing system of instruments and equipment is beneficial to implement convenient service and management for all users. The administrators and teachers or students achieve unified information sharing through the platform no matter in on-line/off-line teaching, scientific research or entrepreneurship requirements. It also can contribute to realize the connection between network and database, the management end and mutual visit of database, and the communication between users and management end; then the dual purpose of “management and sharing” can be realized at last. Currently, large-scale equipment sharing platform is conducive to stimulate students’ enthusiasm for research and entrepreneurship activities by using advanced instruments, and then cultivate students’ practical ability, analysis and problem-solving ability. At the same time, the extensive use of similar systems among students is also an important way to create a university education environment and improve the quality of innovative talents training.
Interactive Knowledge Graph Attention Network for Recommender Systems

Recent progress in personalized recommendation has shown great potential in exploiting structure information provided by a knowledge graph (KG). As a heterogeneous information network, KG contains rich semantic relatedness among entities, which contributes to addressing notorious issues such as data sparsity and cold start. State-of-the-art KG-based recommendation approaches try to propagate information along KG links to encode long-range connectivities into hidden representations. However, most of them only model the user or item representation independently, lacking a focus on user-item interaction. To this end, we propose the Interactive Knowledge Graph Attention Network (IKGAT), which directly models user-item interaction and high-order structure information within KG. For the user representation, following an interactive attention mechanism, we use the item to attend over the user's neighbors and then propagate their information to update the representation. Such a process is extended to multi-hops away to obtain richer neighborhood information. Similarly, the item representation is updated under the supervision of the user. With that design, IKGAT can capture collaborative signals and user preferences effectively. Experiment results on three public datasets show that IKGAT consistently outperforms the state-of-the-art approaches, especially when the dataset is sparse.
Development of Mobile Application Incorporating Augmented Reality for E-education and Training Systems

Abstract A visual platform to help the learners to enhance their knowledge of understanding. The learners can use the application as an aiding source to increase their visual understanding of knowledge through immersive 3D objects (images) in the real-world. The 3D objects are displayed in a rendered perspective aided by multimedia to be shown to the user in the real-world environment. The end-user uses an application to view the Augmented images in the real-world. The 3D visuals give the users apparently an immersive experience through user interactive environments. Complex concepts in STEM education can be apparently be displayed in 3D visuals with ease, which can improve the understanding perspective of the individual concept displayed in Augmented Reality. In the education system, this style of learning can be drastically improvised using this source of modern technology. The product is a mobile application which can be accessed offline or online according to the need and requirements of the user. The existing educational system can be complemented with this solution to enhance the knowledge and understanding of the users with technical expertise.
Text Captcha Defense Algorithm Based on Overall Adversarial Perturbations

Abstract The traditional text captcha adds adversarial perturbation based on a single character, and it is necessary to joint and compound the captcha picture, so that the characters and the background are easy to segment. In order to strengthen the security of text captcha, an AECAPTCHA text captcha defense algorithm with overall adversarial perturbations is proposed. The algorithm uses multi-label classification training text captcha as a pre-training model. Therefore, the AECAPTCHA algorithm is used to generate multi-character overall adversarial perturbation, eliminate the steps of splicing, compositing, and adding colors, then superimpose to generate safe text captcha with adversarial examples properties. The experimental results show that text captcha with the overall adversarial perturbation reduced the accuracy to 0.06%, effectively improves the security of the text captcha, and does not affect the user’s rapid identification and use.
Abductive Recognition of Context-dependent Utterances in Human-robot Interaction

Context-dependent meaning recognition in natural language utterances is one of the key problems of computational pragmatics. Abductive reasoning seems apt for modeling and understanding these phenomena. In fact, it presents observations through hypotheses, allowing us to understand subtexts and implied meanings without exact deductions. For this reason in this paper, we are going to explore abductive reasoning and context modeling in human-robot interaction. Rather than a radical inferential approach, we assumed a conventional approach towards context-depending meanings, i.e, they are conventionally encoded rather than inferred from the utterances. In order to address the problem, a case study is presented, analyzing whether such a system could manage correctly these linguistic phenomena. The results obtained confirm the validity of a conventional approach in context modeling and, on this basis, further models are proposed to work around the limitations of the case study.
Pose-Robust Facial Expression Recognition by 3D Morphable Model Learning

Facial expression recognition (FER) plays a crucial role in human-computer interaction and is a challenging task due to the drastic face appearance variations across head poses. In order to classify different expressions under arbitrary poses, in this paper, we utilize an end-to-end encoder-decoder network by leveraging both 2D and 3D modalities for simultaneous facial expression recognition and 3D Morphable Model (3DMM) expression part reconstruction. Specifically, an encoder regresses expression representations from 2D images, and a decoder recovers 3DMM expression parts from corresponding expression representations. These two components are trained jointly with an expression classification loss being explicitly enforced over expression representations. For handling lack of non-frontal views in FER databases, we also generate the profile views of face image with out-of-plane rotation. Finally, the learned expression representations are desirably discriminative, generative and robust to pose variations. Within extended CK+ and Oulu-CASIA database, our proposed method outperforms ExpNet by 34.20% and 30.56% respectively, demonstrating the superiority of the proposed method.
From Euphemism to Verbal Aggression in British and Armenian Cultures

&#x0D; The topic of the present article concerns verbal aggression and focusses on the verbal expression of the emotional mind; specifically, the expression of negative feelings, emotions and attitudes. Since computer-mediated communication is widely used to shape and reshape public opinion, the analysis of hate speech on the material of internet discourse may shed light on the manipulative communicative tactics that are used in online media and social networking sites to spread hostility and negativity globally. Hence, the examination of the language strategies and tactics that are used to formulate hate speech becomes essential in communicatively oriented linguistic studies.&#x0D; &#x0D; &#x0D; The present article provides a comparative analysis of manifestations of hate speech and euphemisms in Armenian and British online media outlets and social sites targeted towards people's sexual orientation. The aim of the paper is to show the close connection between hate speech and culture. The research, which embarks on two basically different cultures – British and Armenian – is carried out within the framework of cross-cultural pragmatics and discourse analysis. A qualitative research method is applied to analyse samples of hate speech. Social sites and online media outlets were searched through search engines, using certain keywords (LGBT, sexual minorities, etc.). For the purpose of the study, language resources from English and Armenian – words, expressions, constructions, speech acts expressing hostile attitudes towards sexual orientation – have been picked out and analysed.&#x0D;
Uncovering the Hidden Dangers: Finding Unsafe Go Code in the Wild

The Go programming language aims to provide memory and thread safety through measures such as automated memory management with garbage collection and a strict type system. However, it also offers a way of circumventing this safety net through the use of the unsafe package. While there are legitimate use cases for unsafe, developers must exercise caution to avoid introducing vulnerabilities like buffer overflows or memory corruption in general. In this work, we present go-geiger, a novel tool for Go developers to quantify unsafe usages in a project's source code and all of its dependencies. Using go-geiger, we conducted a study on the usage of unsafe in the top 500 most popular open-source Go projects on GitHub, including a manual analysis of 1,400 code samples on how unsafe is used. From the projects using Go's module system, 38% directly contain at least one unsafe usage, and 91% contain at least one unsafe usage in the project itself or one of its transitive dependencies. Based on the usage patterns found, we present possible exploit vectors in different scenarios. Finally, we present go-safer, a novel static analysis tool to identify dangerous and common usage patterns that were previously undetected with existing tools.
Text Classification Model Based on Multi-level Topic Feature Extraction

The max pooling strategy of Recurrent Convolutional Neural Networks in text classification can only extract the influence of local features on text semantics, ignore global features. In order to solve that problem, this paper proposes a multiple level topic feature extraction model, which is based on Recurrent Convolutional Neural Networks and Latent Dirichlet Allocation. The Latent Dirichlet Allocation topic model can be used to model text, which can fully consider the global characteristics of the text and obtain the semantic information of different levels of text. Experimental results show that the proposed model is better than the classical Recurrent Convolutional Neural Networks and other models.
Generation of Gaussian sets for clustering methods assessment

Clustering methods are generally used to study the homogeneity in a set of observations. The results obtained from the clustering process differ from one method to another, to the extent that the same method or validity index gives different outcomes depending on the initial parameters. Analytical evaluation appears to be insufficient for studying the behavior of clustering methods due to its ad hoc nature. Even if the real data set is used in evaluating clustering methods, artificial data is fundamental for assessing the performance since it allows creating different scenarios of test with known structures. The main drawback of existing methods of artificial data is that they do not take into consideration the problem of sensitivity to the size of clusters. In this paper, we propose an automatic method: the high-dimensional artificial Gaussian mixture generator. By formally quantifying the overlap, the generator preserves the notion of the overlap rate between the mixture components. The advantages of this generator are its use of the notion of overlap rate, the unlimited number of mixture components, high-dimensionality of the observations, and the non-utilization of visual inspection as a criterion to quantify the overlap. In addition, we evaluate the k-means, fuzzy c-means (FCM), FCM-based splitting algorithm (FBSA), and expectation maximization (EM) in different dimensions. The results obtained confirm previous work and reveal new findings that are not pointed out when using 1D and 2D artificial data.1
HybridGNN-SR: Combining Unsupervised and Supervised Graph Learning for Session-based Recommendation

Session-based recommendation aims to predict the next item that a user may visit in the current session. By constructing a session graph, Graph Neural Networks (GNNs) are employed to capture the connectivity among items in the session graph for recommendation. The existing session-based recommendation methods with GNNs usually formulate the recommendation problem as the classification problem, and then use a specific uniform loss to learn session graph representations. Such supervised learning methods only consider the classification loss, which is insufficient to capture the node features from graph structured data. As unsupervised graph learning methods emphasize the graph structure, this paper proposes the HybridGNN-SR model to combine the unsupervised and supervised graph learning to represent the item transition pattern in a session from the view of graph. Specifically, in the part of unsupervised learning, we propose to combine Variational Graph Auto-Encoder (VGAE) with Mutual Information to represent nodes in a session graph; in the part of supervised learning, we employ a routing algorithm to extract higher conceptual features of a session for recommendation, which takes dependencies among items in the session into consideration. Through extensive experiments on three public datasets, we demonstrate that HybridGNN-SR outperforms a number of state-of-the-art methods on session-based recommendation by integrating the strengths of the unsupervised and supervised graph learning methods.
Enhancing Randomization Entropy of x86-64 Code while Preserving Semantic Consistency

Code randomization is considered as the basis of mitigation against code reuse attacks, fundamentally supporting some recent proposals such as execute-only memory (XOM) that aims at dynamic return-oriented programming (ROP) attacks. However, existing code randomization methods are hard to achieve a good balance between high-randomization entropy and semantic consistency. In particular, they always ignore code semantic consistency, incurring performance loss and incompatibility with current security schemes, e.g., control flow integrity (CFI). In this paper, we present an enhanced code randomization method termed as HCRESC, which can improve the randomization entropy significantly, meanwhile ensure the semantic consistency between variants and the original code. HCRESC reschedules instructions within the range of functions rather than basic blocks, thus producing more variants of the original code and preserving the code's semantic. We implement HCRESC on Linux platform of x86-64 architecture and demonstrate that HCRESC can increase the randomization entropy of x86-64 code over than 120% compared with existing methods while ensuring control flow and size of the code unaltered.
An integrated approach to the direct simulation of brasses in the performance environment

The present work deals with the synthesis of sounds produced by brass instruments through the direct physical modelling. The purpose is the development of an integrated methodology for the evaluation of the response of a wind instrument taking into account the properties of the surrounding environment. The identification of the frequency response of the resonator and the performing environment is obtained by means of a Boundary Integral Equation approach. The formulation produces the matrix transfer function between the inflow at the input section of the instrument bore and the signal evaluated at an arbitrary location, and can account for the response of any boundary and object present in the surroundings. The reflection function obtained from the above model is coupled to a simplified model of valve, used to represent the excitation mechanism behaviour. The exploited algorithm has demonstrated to be accurate and efficient in offline calculation, and the observed performance discloses the possibility to implement real–time applications.
Current trends in grammar writing

Abstract Over the last few decades, there has been a tremendous increase in the production of descriptive grammars for languages that had previously been poorly described or lacked description altogether. In most cases, these are minority languages, and many of them are endangered. To enhance the dialogue between grammar-writers and typologists, the conference Descriptive Grammars and Typology was held at the University of Helsinki (Finland) at the end of March, 2019. In this paper, we discuss three current trends in grammar writing that emerged from the conference plenaries as well as from many other presentations: (i) the target audience of grammars and the role of language communities in grammar writing, (ii) the digitalization of grammaticography and (iii) themes that have earlier been more or less neglected in grammars but that would bring more insight into typological research as well as into understanding the structures of individual languages: discourse, variation, diachrony and contact effect of languages.
The Power of D-hops in Matching Power-Law Graphs

This paper studies seeded graph matching for power-law graphs. Assume that two edge-correlated graphs are independently edge-sampled from a common parent graph with a power-law degree distribution. A set of correctly matched vertex-pairs is chosen at random and revealed as initial seeds. Our goal is to use the seeds to recover the remaining latent vertex correspondence between the two graphs. Departing from the existing approaches that focus on the use of high-degree seeds in $1$-hop neighborhoods, we develop an efficient algorithm that exploits the low-degree seeds in suitably-defined D-hop neighborhoods. Specifically, we first match a set of vertex-pairs with appropriate degrees (which we refer to as the first slice) based on the number of low-degree seeds in their D-hop neighborhoods. This approach significantly reduces the number of initial seeds needed to trigger a cascading process to match the rest of graphs. Under the Chung-Lu random graph model with n vertices, max degree Θ(√n), and the power-law exponent 2&lt;β&lt;3, we show that as soon as D&gt;4-β/3-β, by optimally choosing the first slice, with high probability our algorithm can correctly match a constant fraction of the true pairs without any error, provided with only Ω((log n)4-β) initial seeds. Our result achieves an exponential reduction in the seed size requirement, as the best previously known result requires n1/2+ε seeds (for any small constant ε^&gt;0). Performance evaluation with synthetic and real data further corroborates the improved performance of our algorithm.
Defending Against Localized Adversarial Attacks on Edge-Deployed Monocular Depth Estimators

Estimation of depth from a single image is an important scene understanding task in computer vision. With the advent of Deep Learning and Convolutional Neural Networks, staggeringly high accuracies have been achieved in this task. With advancements in model optimization, it has been possible to deploy these models on edge devices, allowing for efficient depth estimation in safety-critical applications in robots, rovers, drones and even self-driving vehicles. However, these models are susceptible to attacks from malicious adversaries, which aim to distort the output of the model for a seemingly clean image by adding minute perturbations. In the real-world scenario, the most plausible attack is the adversarial patch, which can be printed and used as a physical adversarial attack against Deep Learning models. In the case of Monocular Depth Estimation, we show that small adversarial patches, which range from 0.7% to 5% of the image size, greatly worsen model performance. It is thus essential that these models are made robust using defense mechanisms, to defend against malicious inputs while also not reducing performance on clean images. Moreover, it is essential that the defense mechanism be computationally efficient, for real-time inference on edge devices. In this work, we propose the first defense mechanism against adversarial patches for a regression network, in the context of Monocular Depth Estimation on an edge device. The defense mechanism adds very little overhead time of 38 milliseconds on a Raspberry Pi 3 Model B, maintaining performance on clean images while also achieving near clean image levels of performance on adversarial inputs.