Reviewerly/exHarmony huggingface dataset:
Path planning using deep reinforcement learning based on potential field in complex environment

Abstract This paper introduces a deep reinforcement learning path planning method based on potential field for complex environment. Based on the potential field model in the artificial potential field method, we define states, actions, rewards in reinforcement learning, and use Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithm for optimization. By training robots in the environment, our method can effectively plan the path in a complex environment with massive obstacles, and avoid trapping in the local minimum region of the potential field.
Correlative Analysis of Denoising Methods in Spectral Images Embedded with Different Noises

Digital image is one of the primary way of communication in the present digital world. During the acquiring process, the images may become noisy. Noise reduction is a demanding task during the image analysis process without dissimilating the important features. It is the procedure of restoring the original image by discarding unwanted noises and known as Image denoising. The main intention of any noise removal technique is to completely eradicate the noise from the image, such that the resulting image is better than the original image. In this digital era, remote sensing images are widely commercial for environmental monitoring. In this study, a correlative analysis of different noise removal methods using various filters in spectral images is performed. Spectral images are introduced with different types of noise and further filters are applied to denoise the image. The performances of the methods are evaluated using benchmarks: Signal-to-Noise Ratio (SNR) and Peak Signal-to-N oise Ratio (PSNR). Experimental results demonstrate that the SNR and PSNR measures were comparatively higher for all the filters when the image is introduced with Poisson noise.
Abstract Concept Learning in Cognitive Robots

Abstract Purpose of Review Understanding and manipulating abstract concepts is a fundamental characteristic of human intelligence that is currently missing in artificial agents. Without it, the ability of these robots to interact socially with humans while performing their tasks would be hindered. However, what is needed to empower our robots with such a capability? In this article, we discuss some recent attempts on cognitive robot modeling of these concepts underpinned by some neurophysiological principles. Recent Findings For advanced learning of abstract concepts, an artificial agent needs a (robotic) body, because abstract and concrete concepts are considered a continuum, and abstract concepts can be learned by linking them to concrete embodied perceptions. Pioneering studies provided valuable information about the simulation of artificial learning and demonstrated the value of the cognitive robotics approach to study aspects of abstract cognition. Summary There are a few successful examples of cognitive models of abstract knowledge based on connectionist and probabilistic modeling techniques. However, the modeling of abstract concept learning in robots is currently limited at narrow tasks. To make further progress, we argue that closer collaboration among multiple disciplines is required to share expertise and co-design future studies. Particularly important is to create and share benchmark datasets of human learning behavior.
Handwritten Arabic numerals recognition system using probabilistic neural networks

Abstract This paper presents a system for the recognition of the handwritten Arabic numerals zero to nine (0–9) using a probabilistic neural network (PNN) approach. This system can recognize handwritten input and externally imported Arabic numerals in real time, including two processes of image pre-processing and recognition. Image pre-processing involved normalization and expansion to enlarge the characteristics of the picture for easy recognition. Recognition process involves the calculation of mode distance, which can help obtain the similarity match between sample matrix and learning matrix. According to similarity matching the input feature matrix will be classified into one of the ten numbers. This study uses MATLAB to establish a graphical interface that is easy to operate. The system has good performance and strong scalability, which established a simple experimental platform for a more in-depth study of the recognition of handwritten Arabic numerals.
Implementation of a Virtual Reality Escape Room Game

The implementation of Virtual Reality (VR) has brought unprecedented meaning to various fields. The application of VR in different areas is bringing revolutionary changes. The gaming field is one of them. The realization of VR in gaming has added a new dimension to this important area. Games have been more interactive, and they have been more enhanced. The use of VR has made gaming more enjoyable. The application of games in education has played a vital role. Therefore, a VR escape room game is proposed to be developed using the Unity3D game engine. The game consists of puzzles that are to be solved to escape the rooms. The game aims to reduce the VR sickness of users while using different VR applications. The goal of the game is to use VR and make players use their skills and utilize their free time effectively. The unique feature of this game is using teleportation for in-game movement to reduce VR sickness. This game promotes testing and enhancing players' skills by making it more interactive with the use of VR. A flowchart, a use case diagram, and an activity diagram have also been designed to explain how the game works. This study analyzes the performance of the escape room game developed. The performance evaluation has been done based on the relationship between time to play the game and VR sickness felt by the users, with and without using teleportation for in-game movement. It includes the limitation of the study as well. Finally, it also presents a future direction for this study and explains how VR sickness can be reduced further in VR applications.
Assuring Security and Reliability of Emerging Non-Volatile Memories

At the end of Silicon roadmap, keeping the leakage power in tolerable limit has become one of the biggest challenges. Several promising Non-Volatile Memories (NVMs) offering high-density, high speed, and competitive reliability/endurance while eliminating leakage issues are being investigated. On one hand, the above-desired properties make emerging NVM suitable candidates to assist or replace conventional memories in memory hierarchy as well as to infuse compute capability to eliminate Von-Neumann bottleneck. On the other hand, their unique features such as high and asymmetric read/write current and persistence bring new threats to data security while compute-capability imposes new fundamentally different security challenges. Some of these memories are already deployed in full systems and as discrete chips. Therefore, it is utmost important to investigate the security issues of NVMs spanning the application space. This work makes pioneering contributions to this challenge through a holistic approach- from devices to circuits and systems using a combination of design and test methodologies to develop secure and resilient NVMs. The proposed attacks and countermeasures are validated on test boards using commercial NVM chips. Finally, this research has been tied to education by converting the test boards to design a modular and reproducible self-learning cybersecurity kit which has been piloted to train graduate and undergraduate students and K-12 teachers.
Linear open quantum systems with passive Hamiltonians and a single local dissipative process

We consider linear open quantum systems with passive Hamiltonians and a single, local dissipative process. Generally speaking, these systems are easier to implement than systems with active Hamiltonians and non-local dissipative processes. We parametrize the set of all covariance matrices corresponding to pure Gaussian steady states that can be achieved by this type of quantum system. Given such pure states, we parametrize the corresponding linear quantum systems with passive Hamiltonians and a single, local dissipative process that generate them.
Innovations in Knowledge Management Perspectives: An Empirical Study in the Jordanian Commercial and Islamic Banks

This research study aims at developing a knowledge management system to enhance Jordanian Banks Performance. The authors developed the system by examining three variables: knowledge processes, knowledge perspectives, and a balanced scorecard. The research sample included all 327 bank employees (directors, general directors, deputies, assistant managers &amp; chairpersons) working in six commercial and Islamic-commercial Jordanian banks, from which 193 completed the study questionnaire. AMOS software was used to treat and organize the collected data. Path analysis proved that the optimal study model excludes the process of knowledge diagnose from knowledge processes, and considers organizational culture perspective as the best moderating variable through which the system can be applied in Jordanian banks. There is a difference in the effect level of the knowledge management system to enhance performance in the Jordanian commercial and Islamic banks. The obtained results indicate that there is a strong relationship between information technology, knowledge management, and high business values. Banks managements were not keen to share knowledge with universities and research centers. They depended on existing knowledge, developed knowledge by experiments, or borrowed it from other financial institutions. This study confirmed that knowledge management practice contributes to increasing efficacy and experience, contributing in turn to enhancing human capital in knowledge-intensive business services in Spain. Knowledge management use in Jordan banks increased employee’s satisfaction toward using operating business and the quality of knowledge obtained. As a result, banking services and trust in banks increased. Based on the research results, it is recommended that banks managements develop, equip, and update bank systems, recruit professional employees, and seek experts and researcher’s assistance in this regard. Banks managements should adopt models that replace a diagnosis from the knowledge processes with organizational culture. Besides, management should develop banking knowledge and pay attention to other knowledge management processes. Keywords: knowledge processes, knowledge perspectives, balanced scorecard.
Classification of community opinion on the use of the Transjakarta bus based on twitter social network using naïve bayes method

Abstract At this time, social networking is becoming a very popular communication tool among Indonesians ranging from children to adults. One of them is Twitter, which has quite a lot of users. This social network is used by users to write their opinions on a matter. One of the topics discussed in the Twitter social network is the Trans-Jakarta bus. In this study, the opinions of Trans-Jakarta bus users listed on Twitter are only limited to positive and negative opinions. An application designed to predict positive or negative opinions using the Naïve Bayes algorithm. The Naïve Bayes algorithm was chosen because it only requires a small amount of training data to estimate averages and variable variations. The developed application can make it easier for decision-makers towards Trans-Jakarta to find out public opinion on Trans-Jakarta bus services. The resulting system accuracy is 73% because the amount of training data used is only 62.5% of the total data of 50 data. While the test data that has been used are only 30 data. The proportions of the training and testing data affect the level of accuracy, so if you want a higher level of accuracy you can reproduce the training data used.
Cyclic scope and processing difficulty in a Minimalist parser

A common view in the theoretical literature is that quantifier raising (QR) is a clause-bounded operation. But in a paper published in Glossa, Wurmbrand (2018) argues that (i) QR is not clause-bounded, and the apparent clause-boundedness of QR is due to the human parser’s difficulty in processing extraclausal QR; and (ii) the relative difficulty of extraclausal QR depends on the size of the embedded clause from which QR takes place. She then proposes a theory of scope processing in which parsing Logical Form (LF) movement is costly for the human parser, which in conjunction with independently motivated assumptions about A′-movement generates the desired results. In this paper, we accept Wurmbrand’s descriptive observations and proposed syntax but offer an alternative, rigorously defined metric of scope processing difficulty that makes precise quantitative predictions. Our proposal is formalized with Minimalist Grammars (Stabler 1997) and expands recent work by Kobele et al. (2013), among others, that uses this formalism to account for numerous processing phenomena. Our metric correctly handles Wurmbrand’s observations as well as cases that are problematic for her account, and points the way toward an explanatory theory of scope processing.
Collision-Based EM Analysis on ECDSA Hardware and a Countermeasure

This paper presents an EM analysis for extracting a signature generation key from ECDSA hardware. In particular, we presents a horizontal side-channel attack using EM waves radiated from compact ECDSA hardware based on Jacobian projective coordinates. The validity of the attack is demonstrated through an experiment using a prototype LSI. We also mention an efficient algorithm-level countermeasure against the attack.
Design of Online Course based on SPOC as a Signature Pedagogy in Music Teacher Education

The article introduces the possibilities of online learning education based on MOODLE for musical pedagogy in higher education for keeping students` motivation and stimulation their active learning through the educational process based on Signature Pedagogy. The main trend of research work is the signature pedagogies of music based on the study of L.Shulman. According to his works authors try to find key ideas of music teaching that will reflect the way of thinking of professional musicians and allow to establish the most progressive methods of learning for future music teachers.
Comparative Study of Supervised Machine Learning Color-Based Segmentation for Object Detection in X-Ray Baggage Images for Intelligent Transportation Systems

Aviation security screening systems have been considerably strengthened since the attacks of September 11, 2001. To date, almost all used baggage control systems are managed manually by human operators. The latter remains essential in this type of technology; however, it presents a number of risk factors such as distraction or errors of inattention, which constitutes a major risk. Therefore, the need for intelligent transportation systems is convenient. This paper presents an automatic color-based segmentation for object detection in X-ray baggage images at airports. It is based on the use of machine learning to perform the color-based segmentation of X-ray images of scanned baggage. A comparative study between eight different machine learning methods is carried out with the optimization of the hyperparameters for a better design of the different models. The results obtained over our private database (High Tech Detection Systems HDTS) composed by dual-energy X-ray luggage scan images showed a perfect object detection without practically any false detection.
Framing ethical considerations on artificial intelligence bias applied to voice interfaces

Digital ecosystem has transformed our lives in a connected living through multiple networks and technologies, five billion individuals in the world have access to the internet. The interaction with these technologies not only provides the immediate supply of any virtual content but increases the possibilities of personalization through artificial intelligence (AI). AI in all of its developments, is advancing faster than the capacity of institutions and organizations to provide legal and deontological responses. Finding solutions to its implications in new ecosystems where technology learns from human routines and interacts with users is a challenge that must be addressed from the academic, political and business fields. Technology advances show that algorithms and automatization have become omnipresent in contemporary times, increasingly determining the nature of the contents that reach the users. This work offers an interpretative descriptive framework based on two main objectives: firstly, describing the evolution of voice assistants as tools for the automated personalization of information contents —about which each individual becomes aware of his environment and makes decisions; and secondly, highlighting the ethical demands on AI when personalizing contents in voice interfaces. In result the study presented offers an open debate to the increasing concern that algorithms can actually influence people, replicating or amplifying existing biases, bringing a wide and current field for academics to explore.
Hand Gesture Communication using Deep Learning based on Relevance Theory

In recent years, social robots are widely research due to the advancement of electronics hardware and deep learning algorithms breakthrough. Hand gesture recognition playing an important role in human-robot interaction. However, to deploy state-of-art of deep learning methods for hand gesture recognition, researchers have to associate all hand gestures with their respective meanings (label). Re-train the deep learning model is required if a new gesture added for recognition. In addition, the meaning of hand gestures often depending on the environmental conditions. In this paper, we propose a framework that learns and recognizes hand gesture meaning based on surrounding objects. The proposed method consists of two sub frameworks: i) A deep learning model, Mask RCNN to detect and extract features of objects and hand gestures; ii) An incremental learning model, Declarative Memory Recurrent Neural Model to incrementally learn hand gestures meaning based on surrounding objects. The effectiveness of our proposed method is validated through several experiments.
Personalized Recommendation Algorithm for books and its implementation

Abstract In recent years, with the completion of the new library of Qinghai University, the collection of books in the library has greatly increased. The library has a total collection of 880,000 volumes, covering a dozen disciplines such as science, engineering, agriculture, literature, history, economics, philosophy, law, education, management and medicine. It is difficult for users to find the books they are interested in among the numerous materials. Based on the actual situation of the library of Qinghai University, the differences of different professional users and their personal interests, this paper chooses the item-based collaborative filtering algorithm to realize personalized recommendation. First of all, in the calculation of book similarity, the traditional user score data is not chosen to calculate the similarity, but to calculate the similarity between books and books according to the feature vector of book name. Secondly, in order to avoid the problem of cold start, the system recommends the users who have no borrowing record, but the most borrowed books in their department. The combination of the two realized the personalized recommendation of books. By comparing with other traditional recommendation algorithms, it is found that the algorithm adopted in this paper has better recommendation effect.
Safety and robustness for deep learning with provable guarantees

Computing systems are becoming ever more complex, with decisions increasingly often based on deep learning components. A wide variety of applications are being developed, many of them safety-critical, such as self-driving cars and medical diagnosis. Since deep learning is unstable with respect to adversarial perturbations, there is a need for rigorous software development methodologies that encompass machine learning components. This lecture will describe progress with developing automated verification and testing techniques for deep neural networks to ensure safety and robustness of their decisions with respect to bounded input perturbations. The techniques exploit Lipschitz continuity of the networks and aim to approximate, for a given set of inputs, the reachable set of network outputs in terms of lower and upper bounds, in anytime manner, with provable guarantees. We develop novel algorithms based on feature-guided search, games, global optimisation and Bayesian methods, and evaluate them on state-of-the-art networks. The lecture will conclude with an overview of the challenges in this field.
Measuring and modeling the impact of buffering and interrupts on streaming video quality of experience

Streaming video clients use playout buffers to smooth out variations in network bitrates, especially important for mobile devices. The playout buffer sizes trade-off: the time delay before a video starts playing with interrupts when a video stops playing in the middle. The best buffer size a client should choose for the video and network conditions depends upon the relative impacts of buffering delays and interrupts on the user quality of experience (QoE). We design user study experiments that isolate buffering delays and interrupts, allowing for direct, quantitative comparisons of the impact on QoE for buffering delays versus interrupts. In our user study, over thirty users watched and rated 17 videos with a broad range of content, encoded with varying amounts of buffering delays and interrupts. Analysis of the data reveals interrupts more costly to QoE than the corresponding amount of buffering by a factor of about 2 to 1. The data is used to construct an analytic model of QoE which incorporates the impacts of buffering delays and interrupts, a model that can be a tool for assessing and improving how streaming video clients pick buffer sizes to maximize user QoE.
Robotic Technology for Pandemic Situation

COVID-19 is a transmittable pandemic which is caused by novel coronavirus of SARs family. It spreads to humans primarily through saliva or nasal discharge from an infected person when he/she coughs, sneezes or speaks. As per guidelines from WHO, the only way to protect people is to break the transmission chain through social distancing, i.e., keeping away from the infected patients. In this pandemic situation, frontline people like sanitary workers, nurses and doctors play a key role in treating the patients with mild, medium and serious symptoms. They are exposed to the virus very easily which in turn makes them infected. All the countries around the globe are tirelessly looking at every possible medicine to cure the disease. They are also encouraged to adopt any kind of science and technology to fight the battle against the deadly virus. To make the medical staff’s work easy and to minimize the exposure and direct contact to the deadly virus, a feasible idea would be to employ robots that are fully automated in its functioning and is competent in performance. They can be used for different purposes like cleaning the floors and automatic cleaning of toilets. The robotic lavatory cleaning system can reduce the manual labor involved in the process. This would aid the procedure of maintaining sanitation in the isolation wards as the toilets used by the patients are found to be a source of high-risk transmission by ICMR. Robots can also be employed to visit patients at regular intervals of time to provide food, medicines, checking temperature, etc. This chapter imparts knowledge on such automatic robots that can be employed to maintain the protocol of social distancing that helps to break the chain of virus transmission.