Reviewerly/exHarmony huggingface dataset:
Mixed Reality and Gamification in Distance Learning Education: The Virtual Enterprise Planning Simulator to Learn ERP Strategies

The worldwide confinement due to Covid19 has boosted the development of creative learning activities in academia, including role-playing games and multidisciplinary workshops brought to students’ homes. Tecnologico de Monterrey, like most universities in the world, is delivering classes remotely using internally developed and external platforms designed to continue the practices that were done every day in laboratories, facilities, and partner companies and institutions. One of the biggest challenges of distance education is creating fun and practical learning. Integrating innovative technologies into the learning process is necessary to achieve this challenge. One of these technologies is Augmented Reality (AR), which allows virtual objects to be superimposed in the real world to “coexist” with the objects in real-world surroundings. In this research, we generated an ERP (Enterprise Resource Planning) simulator based on the educational platform “Tec21 Car Assembler” . Using Virtual Reality (VR), the students applied the virtual classroom’s theoretical learning to practice, guided by an academician collaborating with training partners from companies and institutions partnering with Tecnologico de Monterrey to transfer knowledge to students.
Autonomous Drone Path Scheduling and Management Strategy with Multi-agent Decision Support and Coordination

In a harsh and isolated environment with limited communication and human interaction, e.g., Antarctica, multiple unmanned vehicles or multiple drone systems can use fuel/battery faster than normal conditions. This has led to many drone failures. Preservation of fuel level becomes an essential factor for the management of drones while performing the required set of tasks within an acceptable time. The uncertainty of the environment further challenges the autonomous task allocation and path planning for drones. The existing literature algorithm was not tested or used in these severe conditions, which creates the major gaps in the autonomous drone’s management. This paper proposes a CBS-LSA algorithm, which is a combined algorithm of conflict-based search and linear sum assignment. This algorithm considers the drones’ fuel consumption before allocating the tasks uniformly and proportionately to all the available agents whenever the new sets of tasks were generated randomly. It can proportionately allocate tasks to drones, even if the number of drones is lesser than the number of tasks while maintaining the optimal task allocation process. Moreover, the algorithm optimizes the fuel consumption of the drones by allocating them to the nearest available agents and providing the best possible path for the agents that avoid static obstacles such as mountains and terrains.
Impact Analysis of Changing Education Forms on the Future Engineers’ Motivation in Connection with COVID-19

Currently, the situation with COVID-19 has changed our daily lives, creating serious problems in all spheres of society. This has also affected the system of engineering education, new forms of e-learning have appeared. In the current conditions, the integration of digital technologies into university education has become an integral component that meets the challenges of global digitalization. In this context, an important issue is the study of students' attitudes to new educational formats. This article presents some accumulated experience in the use of distance learning technologies by students of engineering specialties of the university. The main directions of influence on the increase of educational motivation in the process of digital learning are determined. The advantages and difficulties arising in the implementation of online forms of education, as well as socio-economic factors affecting the increase of educational motivation of engineering students are identified. The article considers virtual environments that have the greatest efficiency in terms of process management, quality of learning, student engagement and increasing their motivation.
How Oversea University Students Use Mobile Devices to Support Their Foreign Language Learning and Their Learning Motivation

International students have more difficulty than host nationals in dealing with a wide range of academic and social situations. The common challenges reported by students studying abroad encompassed the themes of language. Learning a new language is difficult for many students, who study abroad and are dependent on language to find their way around. If asked to identify the most significant influence on language learning, motivation would probably be high on most teachers’ and learners’ lists. Language teachers constantly use the term ‘motivation’ when they describe successful or unsuccessful learners. However, there is no doubt that motivation is the most complex and challenging issue faced by language learners, especially in such a digital age. A significant number of recent studies have been done on investigating the effectiveness of computer-assisted language learning, and the literature on psychology in language learning is very considerable. Relatively little work has been conducted about the psychological factor within a mobile-assisted language learning environment. Furthermore, few studies have explicitly focused on international students’ perceptions of their use of mobile devices in motivating their foreign language learning. This paper is intended as an investigation of Chinese oversea university students’ experience in Germany of using the MALL applications in motivating foreign language learning. The research investigated whether and with what tools or activities they perceive can affect their motivation of learning foreign languages in informal settings. The reasons behind the mobile devices motivating language use and specific types of motivation are also examined.
Remote Laboratory to Develop Hardware for IoT Applications

Nowadays monitoring of most of the appliances are being done using IoT (Internet of Things) applications. The challenge in developing these IoT applications is to measure the real time natural physical parameters like temperature, light, wind etc. using sensors and how to interface actuators to meet the requirements. This paper outlines the development of an innovative Remote access system that provides a unique platform for the user to interface the sensors and actuators. There is a provision for developing the code to verify the customized test cases. An important feature of this system is user can visualize the real time implementation of the project which will make the IoT application development faster.
Context-aware Urdu Information Retrieval System

World Wide Web (WWW) is playing a vital role for sharing dynamic knowledge in every field of life. The information on web comprises a huge amount of data in different forms such as structured, semi structured, or few is totally in unstructured format. Due to huge size of information, searching from larger textual data about the specific topic or getting precise information is a challenging task. All this leads to the problem of word sense ambiguity (WSA). Urdu language-based information retrieval system using different techniques related to Web Semantic Search Engine architecture is proposed to efficiently retrieve the relevant information and solve the problem of WSA. The proposed system has average precision ratio 96% as compared to average precision ratio of 74% and 75% average precision Google for single word query. For the long text queries, our system outperforms the existing famous search engines with 92% accuracy such as Bing and Google having 16.50% and 16% accuracy, respectively. Similarly, the proposed system for single word query, the recall ratio is 32.25% as compared to 25% and 25% of Bing and Google. The results of recall ratio for long text query are improved as well, showing 6.38% as compared to 6.20% and 4.8% of Bing and Google, respectively. The results showed that the proposed system gives better and efficient results as compared to the existing systems for Urdu language.
Securing medical transcription using homomorphic transform

In today's era, medical data is the most crucial statistic for an individual. With the increase in chronic diseases, medical experts must keep an eye on the disease trends. For early detection of the disease, it is necessary to blend information technology efficiency into the healthcare system. According to a survey conducted on healthcare providers, 65–70% of medical agencies use cloud services to store data. Machine learning algorithms made it easy for anybody to upload their medical information to the cloud and get medical suggestions or predictions about their disease. As medical data is getting outsourced, data privacy is important. For providing security to the data many encryption algorithms were used and were successful to a certain extent. In this paper, a homomorphic transform algorithm is proposed for encryption and decryption. The performance of the model is compared with other methods. The execution time is calculated for 10 KB, 50 KB, and 1 MB size of the file as input. Where the proposed method gives execution time of 10MS, 50MS, and 1400 MS respectively. The existing algorithms were given an execution time more than the proposed algorithm.
The Influence of Open Access on the Academic Purification Effect of Retracted Publications: A Causal Inference Analysis

ABSTRACT Recently, open access has gradually become an important publishing model in scientific research, while the academic purification effect of open access on flawed publications needs to be further explored. In this study, we use the methods of entropy balancing matching (EBM) and difference in difference (DID) in causal inference to examine the academic purification effect of different open access levels. The results show that in the retraction speed, limited open access papers had the slowest retraction speed, non‐open access had the second and full open access had the fastest, reflecting the best performance of full open access in the detection of problematic papers. As for post‐retraction citation changes, the citation of limited open access papers declined slightly slower than that of non‐open access papers after retraction, while the citation of full open access papers showed an increase rather than a decrease. The next step of the research is to discover what factors may cause the above results.
Ukrainian standard variants in the 20th century: stylometry to the rescue

Abstract In this study, we use the General Regionally Annotated Corpus of Ukrainian (GRAC, www.uacorpus.org ) as an experimental field for testing stylometric approaches for variationist analysis. While, in the last years, quantitative methods such as binomial mixed-effects regression models as well as machine-learning methods such as random forests have gained considerable popularity in corpus linguistics, methods from stylometry have not been used for variation-linguistic analysis very often. Using data from GRAC, we show that a stylometric approach can be useful to analyze the diachronic development of Standard Ukrainian in the 20th century. We take departure from the two main variants of Standard Ukrainian used in the interwar period in Soviet Ukraine, on the one hand, and Western Ukraine as it was part of the Polish republic, on the other. We ask: what can stylometry tell us about how these standards differed and about their subsequent fate in enlarged Soviet Ukraine after WWII? Our analysis shows that certain specifically Western Ukrainian features common during the first decades of the 20th century did not find their way into the post-WWII standard, while others were retained. Moreover, we show that, by and large, stylometry shows a stronger continuity of the Eastern than the Western standard. Methodologically, we demonstrate that stylometry can be used as a tool to start corpus-linguistic research from a bird’s-eye view and in an inductive manner, without formulating any hypotheses regarding particular variables, and later zoom in on hitherto unknown variables representing regional or diachronic differences.
BiTouch

We present a secure device pairing approach, called BiTouch, using the human body as a conductor to generate and transmit user-specific pairing keys for advancing touch-to-access policy. BiTouch is designed based on the observation that the human body responds uniquely to electrical signals flowing through it. Built-in microphones on devices are essentially used to capture ambient sound as entropy and convert it into an electrical signal, which is subsequently transmitted by the body for device pairing. We implement BiTouch using off-the-shelf microphones and evaluate it with 31 participants. The results demonstrate that BiTouch ensures the user's legitimacy and key transmission reliability, and achieves a pairing success rate of 97.74% and an equal error rate of 2.28%.
Inducing wireless chargers to voice out

Recent advances have demonstrated that voice assistants or speech recognition systems can be manipulated by malicious and inaudible voice commands. However, the previously proposed attacks require an acoustical generator (e.g., a speaker or a capacitor) to trigger mechanical vibrations at a microphone diaphragm. In this work, we investigate a new type of inaudible command attack using wireless chargers. Specifically, the magnetic interference generated by a wireless charger can induce an inaudible sound at a nearby microphone, without triggering any mechanical vibrations, even if the microphone is equipped with a Faraday cage and an internal electromagnetic interference filter already. By taking advantage of this new insight, we will present a novel inaudible command attack demo that can inject inaudible voice commands into smart devices that are being charged or near to a charger. We conduct extensive experiments with 17 victim devices (iPhone, Huawei, Samsung, etc.) and six types of voice assistants (Siri, Google STT, Bixby, etc.). Evaluation results demonstrate the feasibility of the proposed attack with commercial charging settings.
Enabling secure touch-to-access device pairing based on human body's electrical response

Recent efforts in reducing user involvement during device pairing have successfully introduced touch-to-access. To detect whether two devices are being held by the same person, existing touch-to-access solutions extract features from a shared information source to generate pairing keys. They focus on validating the device's authenticity by only requiring the user's simple touching of the device, however, ignore the device holder's legitimacy and pairing intent. Moreover, the pairing keys may be vulnerable to eavesdropping attacks since they are exchanged over an open wireless link (e.g., WiFi or Bluetooth). In this paper, we develop a secure device pairing mechanism that essentially uses the human body to generate and transmit user-specific pairing keys, ensuring the user's legitimacy and pairing intent, as well as improving key transmission reliability. Our work is based on the observation that the human body produces a unique response to the electrical signal flowing through it, and different bodies induce distinct responses to the signal. The built-in microphone on devices captures ambient sound as an entropy source and converts it into an electrical signal, which is subsequently processed and transmitted by the human body for device pairing. We build a prototype using off-the-shelf microphones and conduct extensive experiments with 31 participants to evaluate its security performance and usability. The results show that our system achieves a pairing success rate of 97.74% and an equal error rate of 2.28%.
Bodies in space: XR documentary in Australia

This article discusses a selection of interactive and immersive works from the past twenty years in Australia and argues that these have emerged from a specific cultural and geographical perspective in relation to space and place. In the context of settler colonial or migrant Australians, who have fraught and unresolved relationships to place, technologies that intervene with and implicate the audience can further expand documentary's capacity to frame, interpret and challenge these relationships. In this article, we discuss five specific interactive and immersive Australian documentary works. Each of these projects re-frames an encounter with space and place through the methods by which the participant-as-audience is situated in relation to the subject matter and virtual environment. The article explores the controversial asylum-seeker documentary game, Escape From Woomera ([2003]. Australia: EFW Collective); Lynette Wallworth's Collisions which recounts the atomic bomb testing in the desert (2012); Oscar Raby's examination of history, identity and witnessing, Assent (2013); Joan Ross's examination of the colonial relationship to the environment in Did you ask the river? (2019) and Tyson Mowarin's VR of the Ngarluma people of North Western Australia and the threats to their culture and land in the VR work Thalu: Dreamtime is Now (2018).
DoCam

Optical image stabilizers (OIS) are widely used in digital cameras to counteract motion blur caused by camera shakes in capturing videos and photos. In this paper, we sought to expand the applicability of the lens-shift OIS technology for metric depth estimation, i.e., let a RGB camera to achieve the similar function of a time-of-flight (ToF) camera. Instead of having to move the entire camera for depth estimation, we propose DoCam, which controls the lens motion in the OIS module to achieve 3D reconstruction. After controlling the lens motion by altering the MEMS gyroscopes readings through acoustic injection, we improve the traditional bundle adjustment algorithm by establishing additional constraints from the linearity of the lens control model for high-precision camera pose estimation. Then, we elaborate a dense depth reconstruction algorithm to compute depth maps at real-world scale from multiple captures with micro lens motion (i.e., ≤ 3 mm). Extensive experiments demonstrate that our proposed DoCam can enable a 2D color camera to estimate high-accuracy depth information of the captured scene by means of controlling lens motion in the OIS. DoCam is suitable for a variety of applications that require depth information of the scenes, especially when only a single color camera is available and located at a fixed position.
Computational Study of Dynamic Susceptibility and Phase-Matching Angle by Two-Photon Entangled Generation

Two-photon entangled generation is used to produce an entangled photon source which is a key and core element concerning the technology applications of quantum computing, quantum communication, and quantum precision measurement. In this work, we have deduced the formulas of dynamic susceptibility and phase-matching angle of two-photon entangled generation in nonlinear optical crystals. The formulas are employed to compute the susceptibilities and phase-matching angles of these optical processes for uniaxial and biaxial crystals. The susceptibility magnitude and phase-matching condition of two-photon entangled generation affect the performance of the source. The calculated results by these formulas are employed to study properties and estimate the performance of an entangled photon source. In this way, we discuss the phase matching among waves and working wavelength in an entangled source that affects the efficiency of satellite communication with the ground during the day and night.
A taxonomy for similarity metrics between Markov decision processes

Abstract Although the notion of task similarity is potentially interesting in a wide range of areas such as curriculum learning or automated planning, it has mostly been tied to transfer learning. Transfer is based on the idea of reusing the knowledge acquired in the learning of a set of source tasks to a new learning process in a target task, assuming that the target and source tasks are close enough . In recent years, transfer learning has succeeded in making reinforcement learning (RL) algorithms more efficient (e.g., by reducing the number of samples needed to achieve (near-)optimal performance). Transfer in RL is based on the core concept of similarity : whenever the tasks are similar , the transferred knowledge can be reused to solve the target task and significantly improve the learning performance. Therefore, the selection of good metrics to measure these similarities is a critical aspect when building transfer RL algorithms, especially when this knowledge is transferred from simulation to the real world. In the literature, there are many metrics to measure the similarity between MDPs, hence, many definitions of similarity or its complement distance have been considered. In this paper, we propose a categorization of these metrics and analyze the definitions of similarity proposed so far, taking into account such categorization. We also follow this taxonomy to survey the existing literature, as well as suggesting future directions for the construction of new metrics.
A note on the construction of unextendible maximally entangled bases in ℂd ⊗ ℂd′

We study the construction of unextendible maximally entangled bases in bipartite systems [Formula: see text] ([Formula: see text], [Formula: see text], [Formula: see text]). We provide a class of maximally entangled states in [Formula: see text] by constructing some unitary matrices in [Formula: see text]. In particular, we present two complete unextendible maximally entangled bases in [Formula: see text] which are mutually unbiased.
LOSC: A locality-optimized subgraph construction scheme for out-of-core graph processing

Big data applications increasingly rely on the analysis of large graphs. In recent years, a number of out-of-core graph processing systems have been proposed to process graphs with billions of edges on just one commodity computer, by efficiently using the secondary storage (e.g., hard disk, SSD). Unfortunately, these graph processing systems continue to suffer from poor performance, despite of many solutions proposed to address the disk I/O bottleneck problem, a commonly recognized root cause. However, our experimental results show that another root cause of the poor performance is the subgraph construction phase of graph processing, which induces a large number of random memory accesses that substantially weaken cache access locality and thus greatly degrade the performance. In this paper, we propose an efficient out-of-core graph processing system, LOSC, to substantially reduce the overheads of subgraph construction. LOSC proposes a locality-optimized subgraph construction scheme that significantly improves the in-memory data access locality of the subgraph construction phase. Furthermore, LOSC adopts a compact edge storage format and a lightweight replication of vertices to reduce I/O traffic and improve computation efficiency. Extensive evaluation results show that LOSC is respectively 9.4× and 5.1× faster than GraphChi and GridGraph, two representative out-of-core systems. In addition, LOSC outperforms other state-of-art out-of-core graph processing systems including FlashGraph, GraphZ, G-Store and NXGraph. For example, LOSC can be up to 6.9× faster than FlashGraph.