Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Quantum Key Distribution Technology for Power Business

Considering the complexity of power grid environments and the diversity of power communication transmission losses, this study proposes a quantum key distribution (QKD) network structure suitable for power business scenarios. Through simulating the power communication transmission environment, performance indicators of quantum channels and data interaction channels in power QKD systems are tested and evaluated from six aspects, such as distance loss, galloping loss, splice loss, data traffic, encryption algorithm and system stability. In the actual environment, this study combines the production business to build a QKD network suitable for power scenarios, and conducts performance analyses. The experimental results show that power QKD technologies can meet the operation index requirements of power businesses, as well as provide a reference for large-scale applications of the technology.
Analysis and Optimization of Advanced Encryption Standard for the Internet of Things

The Internet of Things creates advanced application systems requiring minimum human interaction by integrating entities having unique addressing mechanisms with digital processing platforms by communicating in a network. The Internet of Things devices being resource constrained are prone to attacks and security breaches. Conventional cryptographic procedures are not viable for Internet of Things and embedded applications as the computations involved with them require higher processing power, memory and energy. Lightweight cryptographic techniques can be used in resource constrained Internet of Things devices to mitigate the security issues. These lightweight cryptographic solutions use smaller key sizes, smaller block sizes and lesser number of rounds. This paper uses a lightweight cryptographic algorithm, AES (Advanced Encryption Algorithm) which is one of the efficient and robust algorithms, to secure communication in the wireless sensor networks. The experimentation uses UDP protocol for transmissions and Routing Protocol for Low-Power and Lossy networks (RPL) for packet delivery from source to destination. Later, the performance of different variants of AES is checked by performing the power analysis. The results demonstrate that AES-192 and AES-256 with 8 rounds show optimized results in terms of power, thus making it suitable for power constrained devices.
Using the electronic information and educational environment of the university in the training of tourism industry workers

The availability of electronic information and educational environment of an educational institution today is the most important requirement of the Federal state educational standards of higher education in all areas of preparation for the conditions of implementation of educational programs by universities. The purpose of the study is to rationale for use in modern educational process of the University electronic information-educational environment and study of its use in training future employees of the tourism industry. Research methodology: system-structural, competence-based, informational approaches; research methods: theoretical (analysis of normative documents, local acts of the University, experience in implementing the electronic information and educational environment at the University, comparison, synthesis of educational and methodological knowledge on the research problem, the method of analogy, forecasting; empirical (observation, study of educational products, description). The research was carried out on the basis of the faculty of service and tourism of the Kuban state University of physical culture, sport and tourism. Results: the necessity of using the modern educational process of higher school of electronic information and educational environment, discloses the functionality and experience of using electronic information-educational environment in the professional training of tourism industry personnel; the structural-functional components and design principles of the electronic information-educational environment of the University; the ways of implementing forms of educational activities with the use of e-learning technologies in the process of training future employees of the tourism industry and their advantages are revealed. Conclusions: the electronic information and educational environment of a University is a set of tools and resources based on information and communication technologies aimed at ensuring the requirements for the implementation of educational activities; in the training of future employees of the tourist industry used the following electronic information-educational environment: electronic library system; e-library of the University; module "qualifying work" on the platform MEGAPRO, Date Express LLC, management system training courses (virtual learning environments); information system "Document of the University"; the software package "Plans"; system "Antiplagiat.VUZ " electronic portfolio and other components; the advantages of using electronic information and educational environment are: the ability to build the educational process with active interaction of all subjects of the educational system; prompt informing of participants of educational activities about changes in the educational process; the ability to form various virtual communities, etc.
How to Teach Pre-Service Teachers to Make a Didactic Program? The Collaborative Learning Associated with Mobile Devices

Today, pedagogical proposals are increasingly moving away from purely traditional approaches, with a proliferation of active methodologies in the teaching–learning processes. This research aims to find out the effectiveness of the collaborative method in mobile learning, as opposed to traditional methodology, for university students in the learning of the didactic program. The research methodology is quantitative, applying a quasi-experimental design with a control group and experimental group. The results show that the collaborative learning teaching method associated with mobile learning mainly increases motivation, the relationships between teachers and students, the relationships between students and content, the relationships between students and students, autonomy in learning, pedagogical collaboration between students, problem-solving, and the sense of time in the training process. It is concluded that the collaborative learning method associated with mobile learning is more effective for learning didactic programming than the traditional method.
Reinforcement Learning Over Knowledge Graphs for Explainable Dialogue Intent Mining

In light of the millions of households that have adopted intelligent assistant powered devices, multi-turn dialogue has become an important field of inquiry.Most current methods identify the underlying intent in the dialogue using opaque classification techniques that fail to provide any interpretable basis for the classification.To address this, we propose a scheme to interpret the intent in multi-turn dialogue based on specific characteristics of the dialogue text.We rely on policy-guided reinforcement learning to identify paths in a graph to confirm concrete paths of inference that serve as interpretable explanations.The graph is induced based on the multi-turn dialogue user utterances, the intents, i.e., standard queries of the dialogues, and the sub-intents associated with the dialogues.Our reinforcement learning method then discerns the characteristics of the dialogue in chronological order as the basis for multi-turn dialogue path selection.Finally, we consider a wide range of recently proposed knowledge graph-based recommender systems as baselines, mostly based on deep reinforcement learning and our method performs best.
Internationality at university level

Abstract University level indicators of internationality have been compiled and presented. 18 universities from three countries, Hungary, Israel and Sweden were selected as sample. “Breadth” and “depth” of internationality were distinguished and characterized by bibliometric and webometric tools.
The Usefulness of Sparsifiable Inputs: How to Avoid Subexponential iO

We consider the problem of removing subexponential reductions to indistinguishability obfuscation (iO) in the context of obfuscating probabilistic programs. Specifically, we show how to apply complexity absorption (Zhandry Crypto 2016) to the recent notion of probabilistic indistinguishability obfuscation (piO, Canetti et al. TCC 2015). As a result, we obtain a variant of piO which allows to obfuscate a large class of probabilistic programs, from polynomially secure indistinguishability obfuscation and extremely lossy functions. Particularly, our piO variant is able to obfuscate circuits with specific input domains regardless of the performed computation. We then revisit several (direct or indirect) applications of piO, and obtain – a fully homomorphic encryption scheme (without circular security assumptions), – a multi-key fully homomorphic encryption scheme with threshold decryption, – an encryption scheme secure under arbitrary key-dependent messages, – a spooky encryption scheme for all circuits, – a function secret sharing scheme with additive reconstruction for all circuits, all from polynomially secure iO, extremely lossy functions, and, depending on the scheme, also other (but polynomial and comparatively mild) assumptions. All of these assumptions are implied by polynomially secure iO and the (non-polynomial, but very well-investigated) exponential DDH assumption. Previously, all the above applications required to assume the subexponential security of iO (and more standard assumptions).
LFR face dataset:Left-Front-Right dataset for pose-invariant face recognition in the wild

In this work, a new multitask convolutional neural network (CNN) is proposed aiming for the recognition of face under pose variations. Furthermore, the combination of pose estimation for each corresponding pose in a separate fashion allows robust face recognition in presence of various facial expressions as well as low illuminations. First, a CNN model for pose estimation is proposed. The pose estimation model is trained using a self-collected dataset built from three popular datasets including FLW, CEP, and CASIA-WebFace using three categories of face image capture such as Left side, Frontal and right side. Experimental evaluation has been conducted using two datasets: Pointing'04 and Schneiderman. Results reveal the robustness of the proposed pose estimation model. Moreover, the proposed face pose estimation is applied on three datasets to widen the dataset and make it bigger for training and testing deep learning models.
An Efficient Feature Pyramid Network for Object Detection in Remote Sensing Imagery

Scale diversity, small target, and power limitation have made remote sensing imagery a challenging field in object detection on satellites. Aiming at the aspects of scale diversity and small target, this paper provides a novel feature pyramid network with Adaptive Residual Spatial Bi-Fusion (ARSF) as a solution. ARSF nets introduce a robust fusion of multi-scale semantic information and fine spatial details. A spatial feature fusion module designed in networks with ARSF adapts to object size variation by learning the most crucial feature maps. Comparing to the original feature pyramid network, a shorter critical path for information transmission is formed in our method. Experiments show that a validation instance of YOLOv3-ARSF can achieve a state-of-the-art performance of 85.8 mAP on the NWPU-VHR10 dataset. YOLOv3-ARSF only 3MB larger than YOLOv3 but far exceeds YOLOv3 by 2.3% mAP, which shows our ARSF is efficient. As for the last challenge, two lightweight versions, ARSF(lite) and ARSF(lite+) are also validated for future research of online object detection on satellites in aerospace engineering. Visualizations and details are provided for a more comprehensive understanding.
Hierarchical Classification Method for Radio Frequency Interference Recognition and Characterization in Satcom

The Quality of Service (QoS) and security of Satellite Communication (Satcom) are crucial as Satcom plays a significant role in a wide range of applications, such as direct broadcast satellite, earth observation, navigation, and government/military systems. Therefore, it is necessary to ensure that transmissions are incorruptible, particularly in the presence of challenges such as Radio Frequency Interference (RFI), which is of primary concern for the efficiency of communications. The security of a wireless communication system can be improved using a robust RFI detection method, which could, in turn, lead to an effective mitigation process. This paper presents a new method to recognize received signal characteristics using a hierarchical classification in a multi-layer perceptron (MLP) neural network. The considered characteristics are signal modulation and the type of RFI. In the experiments, a real-time video stream transmitted in the direct broadcast satellite is utilized with four modulation types, namely, QPSK, 8APSK, 16APSK, and 32APSK. Moreover, it is assumed that the communication signal can be combined with one of the three significant types of interference, namely, Continuous Wave Interference (CWI), Multiple CWI (MCWI), and Chirp Interference (CI). In addition, two robust feature selection techniques have been developed to select more informative features, which leads to improving the classification precision. Furthermore, the robustness of the trained techniques is assessed to predict unknown signals at different Signal to Noise Ratios (SNRs).
Socially-Aware Robot Planning via Bandit Human Feedback

In this paper, we consider the problem of designing collision-free, dynamically feasible, and socially-aware trajectories for robots operating in environments populated by humans. We define trajectories to be social-aware if they do not interfere with humans in any way that causes discomfort. In this paper, discomfort is defined broadly and, depending on specific individuals, it can result from the robot being too close to a human or from interfering with human sight or tasks. Moreover, we assume that human feedback is a bandit feedback indicating a complaint or no complaint on the part of the robot trajectory that interferes with the humans, and it does not reveal any contextual information about the locations of the humans or the reason for a complaint. Finally, we assume that humans can move in the obstacle-free space and, as a result, human utility can change. We formulate this planning problem as an online optimization problem that minimizes the social value of the time-varying robot trajectory, defined by the total number of incurred human complaints. As the human utility is unknown, we employ zeroth order, or derivative-free, optimization methods to solve this problem, which we combine with off-the-shelf motion planners to satisfy the dynamic feasibility and collision-free specifications of the resulting trajectories. To the best of our knowledge, this is a new framework for socially-aware robot planning that is not restricted to avoiding collisions with humans but, instead, focuses on increasing the social value of the robot trajectories using only bandit human feedback.
Exploring communication protocols and centralized critics in multi-agent deep learning

Tackling multi-agent environments where each agent has a local limited observation of the global state is a non-trivial task that often requires hand-tuned solutions. A team of agents coordinating in such scenarios must handle the complex underlying environment, while each agent only has partial knowledge about the environment. Deep reinforcement learning has been shown to achieve super-human performance in single-agent environments, and has since been adapted to the multi-agent paradigm. This paper proposes A3C3, a multi-agent deep learning algorithm, where agents are evaluated by a centralized referee during the learning phase, but remain independent from each other in actual execution. This referee’s neural network is augmented with a permutation invariance architecture to increase its scalability to large teams. A3C3 also allows agents to learn communication protocols with which agents share relevant information to their team members, allowing them to overcome their limited knowledge, and achieve coordination. A3C3 and its permutation invariant augmentation is evaluated in multiple multi-agent test-beds, which include partially-observable scenarios, swarm environments, and complex 3D soccer simulations.
Speech Recognition of Oral English Teaching Based on Deep Belief Network

The oral English teaching faces several common problems: the teaching method is very inefficient, and the learners are poor in oral English. The development of computer-aided language learning offers a possible solution to these problems. Based on techniques of speech recognition, cloud computing and deep learning, this paper applies the deep belief network (DBN) to recognize the speeches in oral English teaching, and establishes a multi-parameter evaluation model for the pronunciation quality of oral English among college students. The model combines the merits of subjective and objective evaluations, and assesses the pronunciation from four aspects: pitch, speech rate, rhythm and intonation. Finally, the proposed model was verified through speech recognition and pronunciation evaluation experiments on 26 non-English majors from a college. The results show that the proposed evaluation model output credible results, which are consistent with those of experts, as evidenced by consistency, neighbourhood consistency and Pearson correlation coefficient. The research provides a feasible way to evaluate the oral English proficiency of learners, laying the basis for improving the teaching and learning efficiency of oral English.
Process Automation and Blockchain in Intelligence and Investigation Units: An Approach

In the context of combating crime, government institutions in several countries have instituted units specialized in investigation and intelligence activities to act in different areas and expertise. However, due to the considerable complexity and specificity of these activities, as well as a significant concern with security and other related aspects, there are challenges regarding the location and adoption of approaches aimed at applying process automation in the context of these units. Motivated by this scenario, this work presents an approach that adopts concepts of process automation in order to assist researchers and professionals interested in studies and practices aimed at simplification and/or automation of processes in the context of intelligence and investigation units. Exploring the main characteristics of blockchain technology, this paper also presents an overview of different application trends of blockchain technology and proposes the use of this technology as a support mechanism in the management, storage, and sharing of generated digital assets. On the other hand, to analyze the feasibility of applying the approach, a survey was carried out with specialists from specialized units and a real case scenario experience of use was performed. Results show evidence of the feasibility of use and suitability of the approach for the given context, and that it helps interested parties regarding the application of process automation in the scenario of intelligence and investigation units.
A Predictive Model of Youth Bystanders’ Helping Attitudes

One of the direct effect of current COVID-19 pandemic’ social distancing on an unspecified period of time urges us to compensate our lack of physical connection with our readiness in the online environments, especially social media platforms. Present research investigates to which extent the internet content awareness construct, difficulties in emotional regulation and online duality have an impact on predicting future helping attitudes of bystanders. In order to investigate the eligibility of this prediction model, our team has advanced the Erasmus+ funded project Hate’s Journey. In 2019, our research team has designed a multiple specific sections online questionnaire addressing 206 youth from Latvia, Turkey, Spain and Romania. We have used a multiple linear regression analysis. The obtained results validate our hypothesis, confirming that if an individual is characterized by a decreased internet content awareness, a high level of difficulties in emotional regulation and increased online duality, then there is a 37% probability that the youth bystanders will develop a lack of helping attitudes towards the victims when facing a digital hate speech context. Conclusions and implications regarding to the current pandemic social distancing and digital closeness effects over the prosocial behaviour are discussed.
Outsourcing Attributed-Based Ranked Searchable Encryption With Revocation for Cloud Storage

With the rapid growth of the cloud computing and strengthening of security requirements, encrypted cloud services are of importance and benefit. For the huge ciphertext data stored in the cloud, many secure searchable methods based on cryptography with keywords are introduced. In all the methods, attribute-based searchable encryption is considered as the truthful and efficient method since it supports the flexible access policy. However, the attribute-based system suffers from two defects when applied in the cloud storage. One of them is that the huge data in the cloud makes the users process all the relevant files related to the certain keyword. For the other side, the users and users' attributes inevitably change frequently. Therefore, attribute revocation is also an important problem in the system. To overcome these drawbacks, an attribute-based ranked searchable encryption scheme with revocation is proposed. We rank the ciphertext documents according to the TF×IDF principle, and then only return the relevant top-k files. Besides the decryption sever, an encryption sever is also introduced. And a large number of computations are outsourced to the encryption server and decryption server, which reduces the computing overhead of the client. In addition, the proposed scheme uses a real-time revocation method to achieve attribute revocation and delegates most of the update tasks to the cloud, which also reduces the calculation overhead of the user side. The performance evaluations show the scheme is feasible and more efficient than the available ones.
Gulliver’s virtual travels: active embodiment in extreme body sizes for modulating our body representations

It is noted that the perceptual experience of body and space can be modulated by changing the action capabilities or by manipulating the perceived body dimensions through a multisensory stimulation. This study adds to pre-existing literature by investigating the alterations in bodily experience following embodiment to both enlarged and shrunked bodies, while participants actively navigated in a virtual environment. A normal-sized body served as a reference condition. After each embodied navigation, participants estimated the height and width of three different body parts. Results revealed that the embodiment over shrunked body induced a significant reduction in participants' body image, while no changes were reported after the embodiment over the enlarged body. Findings were discussed in terms of previous literature exploring the constraints implicated in the ownership over different bodies.