Reviewerly/exHarmony huggingface dataset:
Distance Learning Entrepreneurship Education in the Time of Corona Virus - COVID-19 Challenges & Solution

This study aimed at identifying the most important challenges that female teachers working in Saudi public schools of the Eastern Region face while using distance-learning during the time of Corona, and then suggesting the appropriate solutions for these challenges from their own perspective. The descriptive approach was used, and a questionnaire of 43 items was distributed, covering 3 areas: the challenges of distance-learning in (1) teaching & learning, (2) infrastructure & the skills of using platforms, and (3) possible solutions for facing these challenges. Then, means and standard deviations were calculated to identify the participants' responses, with a total of 375 teachers. Independent-Samples T test was conducted for the differences' significance between 2 independent groups, as well as Kruskal-Walls test to identify the differences' significance between more than 2 independent groups. The results of the study showed that all the items related to the challenges teachers face during distance-learning in the time of Corona (in the teaching & learning area) got a mean of (2.66) with a medium difficulty degree. Also, all the items got a mean of (2.77) in the (infrastructure & the skills of using platforms area) with a medium difficulty degree, from the teachers' perspective each. There were statistically significant differences for the challenges related to infrastructure and the skills of using platforms, from the teachers' perspective, for the favor of having less experience (less than 5 years). All the suggested solutions have generally been of medium importance, making them worthy of application.
Design and Implementation of a Multi Agent Architecture to Communicate Reinforcement Learning Knowledge and Improve Agents’ Behavior

This research project presents a multi agent architecture which uses reinforcement learning. The goal is to design a system that able the agents to take advantage of its peers’ knowledge. The knowledge of the environment is obtained from the reinforcement learning algorithm, Q-learning. While, the multi agent architecture sets a communication model among the agents of the system. To reach the goal, the present research project takes advantage of the Q-learning characteristic, off-policy, incorporating a condition before the use of ε-greedy. This condition allows the agents not to explore a state that has already been sent by another agent, or itself. In the proposed multi agent architecture the agents work in pairs. Each pair of agents have two different behaviors allowing them to communicate and work on relevant states of the environment. The conditions to send the states depend on the environment, specifically, it depends on the circumstances which the agent obtains a reward from the environment. The results evidences that the number of agent-environment interactions to improve agent’s behavior is reduced by more than 90% through the proposed architecture.
How to filter hate speech from your social media feed

If there's content you don't want to see on your social media feeds, Consumer Reports explains a few ways to sift through the hate speech to get what you want out of sites like Facebook and Twitter.
Post communication data transmission by entangled photons having high data security

Post communication modulation by implementation of polarization entangled photons, or post transmission communication is a novel method by in which raw data photons is transmitted to the receiver and transmitter from an entangled photon source through two distinct polarization maintaining optical fiber. A pair of photons is generated in entangled photons source, one of twin photons e.g. |V&gt; is sent to the transmitter and its counterpart e.g. |H&gt; is sent to the receiver through the two links of polarization-maintaining optical fibers, having exactly the same length to guarantee reaching of photons to transmitter and receiver exactly at the same time. When one of entangled photons is ready to be polarization modulated in the transmitter, its twin photon is ready in the receiver to be read. We know the polarization of each one of twin entangled photons transmitted to receiver and transmitter, because the polarization is maintained through the fiber and by tuning and checking some photons in receiver and transmitter we can find the normal incidence polarization. When polarization of one of the photons in the transmitter is changed, this causes changing of photon counterpart in the receiver at zero-time delay. This makes zero-time communication possible ignoring transmission delay of optical fiber classical link. On the other hand, entangled photon enhancer is proposed here, because due to no-cloning theorem, it is impossible to use optical amplifier to amplify entangled photons.
Fake Online Reviews Detection Using Semi-Supervised and Supervised Learning

Today's business and commerce is greatly impacted by Online reviews. These Online reviews are helping the users in making decisions regarding the purchase of online products. Online reviews are a great source of reputation for the companies. These reviews also have a great impact on promotion and advertisement of products and services. Hence some individuals or groups are trying to manipulate these online reviews for their own interests. This paper gives some semi-supervised and supervised text mining models to detect the fake online reviews and compares the efficiency in both the techniques on a dataset containing the reviews of some hotels.
Graphing Website Relationships for Risk Prediction: Identifying Derived Threats to Users Based on Known Indicators

The hypothesis for the study was that the relationship based on referrer links and the number of hops to a malicious site could indicate the risk to another website. The researchers chose Receiver Operating Characteristics (ROC) analysis as the method of comparing true-positive and false-positive rates for captured web traffic to test the predictive capabilities of the created model. Known threat indicators were used as designators and leveraged with the Neo4j graph database to map the relationships between other websites based on referring links. Using the referring traffic, the researchers mapped user visits across websites with a known relationship to track the rate at which users progressed from a non-malicious website to a known threat. The results were grouped by the hop distance from the known threat to calculate the predictive rate. The results of the model produced true-positive rates between 58.59% and 63.45% and false-positive rates between 7.42% and 37.50%, respectively. The true and false-positive rates suggest an improved performance based on the closer proximity from the known threat, while an increased referring distance from the threat resulted in higher rates of false-positives.
Essential Question: ‘Equal or Equivalent Entities?’ About Two Things as Same, Similar, or Different

We discuss definitions of entities, equality, and equivalence as used by a transdisciplinary diversity of research fields including mathematics, statistics, computational linguistics, computer programming, knowledge engineering, and music theory. Declaring definitions for these concepts in the situational context of each domain specific field supports the essential question `Equal or equivalent entities?' about two things as same, similar, related, or different for that field. Pattern recognition performed by artificial intelligence applications can be described as the automated process of answering this fundamental question about the similarity or difference between two things.
Industrial Cyber-Physical Systems Engineering: The Educational Aspect

This work discusses issues and approaches to organizing training for specialists in the field of engineering of industrial cyber-physical systems (ICPS) at technical universities. An overview of the current state of research in the field of competence models of a future specialist, expectations of industrial enterprises is presented. This work proposes a novel educational approach T-CHAT and its application in education of engineers in the field of ICPS. Additionally, a comprehensive organization and provision of students are examined connecting the competency model, the architectural model of the “smart factory”, the formation of curricula, and individual educational trajectories of students. Furthermore, this work formulates recommendations for the use of the indicated practices and approaches in the training of specialists in the framework of consideration of the educational aspect of the engineering of industrial cyber-physical systems.
Deep Reinforcement Learning using Cyclical Learning Rates

Deep Reinforcement Learning (DRL) methods often rely on the meticulous tuning of hyperparameters to successfully resolve problems. One of the most influential parameters in optimization procedures based on stochastic gradient descent (SGD) is the learning rate. We investigate cyclical learning and propose a method for defining a general cyclical learning rate for various DRL problems. In this paper we present a method for cyclical learning applied to complex DRL problems. Our experiments show that, utilizing cyclical learning achieves similar or even better results than highly tuned fixed learning rates. This paper presents the first application of cyclical learning rates in DRL settings and is a step towards overcoming manual hyperparameter tuning.
Review of the calibration of a structured light system

The accuracy of a structured light system largely depends on its calibration precision. This paper makes a review of some representative calibration approaches in the literature. Based on the techniques used in the calibration process, these approaches are divided into four categories: the calibration methods based on well-designed calibration target, invariance of the cross ratio, pseudo camera and other methods. Then the four categories are introduced separately.
The versatility of augmented reality for the enhancement of cultural heritage.

Abstract All Virtual reality, augmented reality, mixed reality or straight-up video games can now be found in growing numbers within spaces dedicated to culture and the arts. The choice of such tools is trans-forming the traditional museum tour into a multifaceted and interactive experience to the point, in some cases, of flirting with entertainment. From our experience, the role of augmented reality hasn’t been to substitute itself to the « real » tour. On the contrary, AR invites the user to immerse themselves in an animated personal experience enriched with supplementary information. Elaborated with goals and features different from those of virtual reality, AR aims at strengthening direct contact with works of art as much as possible, inciting an active « live » use and a presence “on-site” more than “on-line”. The diversity of three logistical contexts in which we have worked — a large-scale monument, a historical palace and its heterogenous art collection, and finally an archeological complex encompassing a traditional museum and the remains of a Roman amphitheatre — has brought us to reflect upon the functional aspects that come to determine a personalized digital product, as tailored as possible to specific needs. Each of the three apps has permitted a deepening of each of their features in order to meet specific goals targeted by various museum institutions. In this paper we describe these features in more specific terms.
Search-and-Train: Two-Stage Model Compression and Acceleration

Convolutional neural networks have achieved great success in many fields. However, the practical application of convolutional neural networks is hindered due to the high consumption of memory and computational. In this paper, we propose a two-stage method for model compression and acceleration. More specifically, the training process mainly includes the search stage and train stage, the approach is abbreviated as ST. In the search stage, we first search and remove the unnecessary parts of a large pre-trained network (named supernet) by certain evaluation criteria to get a pruned network. Then the weights in the pruned network are initialized to get a small network (called a subnet). During the training stage, the supernet is untrainable, and the subnet will be trained under the supervision of the supernet. The knowledge extracted from the supernet will be transmitted to the subnet, then the subnet will be able to learn from the dataset and the knowledge at the same time. We have proved the effectiveness of our method through implement extensive experiments on several state-of-the-art CNN models (including VGGNet, ResNet, and DenseNet). The subnet only with 1/10 parameters and 1/2 calculations achieves more significant performance than the supernet.
Security of Enhancement of Adder in Stream Cipher System

The strength of the encryption system depends on the power of the key generator that produces a pseudorandom stream. The generator generates a pseudorandom sequence so passes several tests. The non-linear flow coding system that relies on the Adder function has several unwanted vulnerabilities. It leads to failure to face randomness tests and relative success in the correlation attack. In this paper, the Adder function was developed by adding a third offset register to improve its ability to bypass randomness tests. In addition to increasing the non-linear complexity of the generated chain, it is resistant to breaking the code using a link attack. Many sequences were applied to the improved Adder system, and good results were obtained.
Migration Guided by a Performance Index in Heterogeneous Island Models

Evolutionary Algorithms are efficient alternatives to solve complex optimization problems. The high computational cost of these algorithms commonly motivates their implementation to run in parallel computational environments. Island Model enables the parallel implementation of Evolutionary Algorithms relatively easily to incorporate the migration operation into the evolutionary process. The inclusion of new solutions in a population, previously evolved in another population, can contribute positively to the problem's solution quality. In this work, a performance index was added to the Island Model, aiming to indicate how efficiently each island's population is in solving the problem according to its algorithm. Islands with higher performance indexes receive more individuals in migrations. In this way, these algorithms become more active in the evolutionary process. The experiments demonstrated that the new model solutions were as good as the solutions from each problem's best algorithm. We also noticed that even if we remove the most efficient algorithm from the model, it still adapts and provides efficient solutions.
Research on Generalized Error Control Mechanism of Monocular Vision Ranging Method

Abstract Distance perception is the basis and necessary prerequisite of environment perception, attitude perception and obstacle avoidance of intelligent vehicle and unmanned vehicle. Monocular vision ranging method is one of the mainstream distance sensing methods at present. In order to improve the accuracy of monocular vision ranging, a monocular vision ranging method based on machine learning is proposed. The monocular vision ranging method studied in this paper has the advantages of high accuracy, simple training data, simple ranging formula and can be explained, but the uncontrollable generalization error is one of the disadvantages of this method. Therefore, in order to explore the relationship between generalization error and training error, according to the monocular vision ranging method based on the pinhole imaging principle and a large number of measured data, this paper uses the polynomial method in the curve fitting toolbox of MATLAB to fit the functional relationship between coordinates and image distance, so as to obtain the model parameters. Finally, the threshold value of the optimal model is 0.5%, 49 training data and 21 test data. The extreme value of the ratio of the generalization error to the threshold value of the training error is 1.68, which can be used to control the generalization error of the monocular vision ranging method.
Are People Ready for Unexpected Encounters With Social Robots

Recent developments in robotics are potentially changing the nature of service, and research in human-robot interaction has previously shown that humanoid robots could possibly work in public space ...
Lower Estimates for Distances from a Given Quantum Channel to Certain Classes of Quantum Channels

The tight, in a sense, lower estimates of diamond-norm distance from a given quantum channel to the sets of degradable, antidegradable and entanglement-breaking channels are obtained via the tight continuity bounds for quantum mutual information and for relative entropy of entanglement in finite-dimensional case. As an auxiliary result there are established lower bounds of trace-norm distance from a given bipartite state to the set of all separable states.
Pseudothermal Light Based Quantum Random Number Generator

The importance of quantum random number generators (QRNGs) is increasing day by day. An important aspect of any QRNG is the speed of the system. This paper introduces the theory behind a pseudothermal light based QRNG. This pseudothermal effect is provided by using an optical scatterer in the setup. Pseudothermal light is used as the local oscillator of the system instead of a conventional coherent or thermal light source where the pseudothermal light combines the advantages of both sources. This technique converts the Poissonian distribution of light to super-Poissonian to have better randomness characteristics and allow a faster bit generation rate.
Designing of Enterprise Architecture for Vocational High School Information System Using TOGAF Architecture Development Method

The purpose of this research is to design a blueprint that can be used to meet the needs of business functions the Vocational High School of XYZ.  In this research, the method used to design Enterprise Architecture was The Open Group Architecture Framework Architecture Development Method (TOGAF ADM). The phases of TOGAF ADM applied in this design are the Preliminary Phase, Architecture Vision Phase, Business Architecture Phase, Information System Architecture Phase, and Technology Phase. The results of this research in the form of preliminary phase up to the technology phase from the TOGAF ADM that can be used for information system development in the Vocational High School of XYZ so that the next research is expected can meet all the TOGAF ADM phases.
PRELIMINARY STUDY: KNOWLEDGE SHARING IN COLLABORATIVE E-COMMERCE

Recent technological advancement in internet speed has contributed to the growing number of collaborative e-commerce. Interaction between businesses cause intensive information sharing via knowledge transfer and collaboration. Thus, protection of information in e-commerce requires significant measures to adequately mitigate the impact to business, customer and society. This paper reviews the elements related to knowledge sharing in collaborative e-commerce. Intensive literature reviews are discussed and analyzed to discover issues and challenges face by e-commerce collaborators in sharing knowledge. The result shows trade secret is applicable in developing and maintaining trust partnership in collaborative e-commerce. Next future work is to measure knowledge sharing awareness among collaborators of e-commerce. There is an opportunity to investigate this paper further in the legal perspective.
3D Human Pose Estimation with 2D Human Pose and Depthmap

Three-dimensional human pose estimation models are conventionally based on RGB images or by assuming that accurately-estimated (near to ground truth) 2D human pose landmarks are available. Naturally, such data only contains information about two dimensions, while the 3D poses require the three dimensions of height, width, and depth. In this paper, we propose a new 3D human pose estimation model that takes an estimated 2D pose and the depthmap of the 2D pose as input to estimate 3D human pose. In our system, the estimated 2D pose is obtained from processing an RGB image using a 2D landmark detection network that produces noisy heatmap data. We compare our results with a Simple Linear Model (SLM) of other authors that takes accurately-estimated 2D pose landmarks as input and that has reached the state-of-the-art results for 3D human pose estimate using the Human3.6m dataset. Our results show that our model can achieve better performance than the SLM, and that our model can align the 2D landmark data with the depthmap automatically. We have also tested our network using estimated 2D poses and depthmaps separately. In our model, all three conditions (depthmap+2D pose, depthmap-only and 2D pose-only) are more accurate than the SLM with, surprisingly, the depthmap-only condition being comparable in accuracy with the depthmap+2D pose condition.
Image Annotation based on Semantic Structure and Graph Learning

Image annotation is an important method to mine semantic information of images. The current methods do not fully consider the semantic repetitiveness and the imbalance hidden in labels, resulting in the unsatisfied image annotation. To address those problems, a semantic-independent nearest-neighbor graph model is proposed based on semantic structure and graph learning. Specifically, graph learning is used for producing the pre-annotation of images on the basis of label propagation of nearest-neighbor images, which can improve accuracy of weak labels. Then, the semantic structure and the word graph are introduced to fine-tune the image annotation, which can reduce the redundancy of the predicted labels. Finally, two-representative datasets are used to evaluate the proposed method. The results show that the proposed method outperforms the compared methods in terms of precision, recall, F1 and N+.
A Behavior Tree Designing Tool for Online Evaluation

First presented as a tool to develop intelligent agents in computer games, Behavior Trees (BTs) are an alternative to Finite State Machines that later gained popularity in robotics, due to its modularity and how easy it makes to create intelligent reactive behaviors. Even with BTs being popular, the available tools to design it still lack features that allow validation without the end-application, whether it is a game or a robot. In this paper, a novel tool for BTs design is presented. This tool allows us to create BTs and also to evaluate then without any external software, making it easy to check all possible behaviors during the design stage. Also, the BTs created in the software can be directly used in BehaviorTree.CPP, a popular behavior tree library for robotics.
Algorithm Diversity for Resilient Systems

Diversity can significantly increase the resilience of systems, by reducing the prevalence of shared vulnerabilities and making vulnerabilities harder to exploit. Work on software diversity for security typically creates variants of a program using low-level code transformations. This paper is the first to study algorithm diversity for resilience. We first describe how a method based on high-level invariants and systematic incrementalization can be used to create algorithm variants. Executing multiple variants in parallel and comparing their outputs provides greater resilience than executing one variant. To prevent different parallel schedules from causing variants' behaviors to diverge, we present a synchronized execution algorithm for DistAlgo, an extension of Python for high-level, precise, executable specifications of distributed algorithms. We propose static and dynamic metrics for measuring diversity. An experimental evaluation of algorithm diversity combined with implementation-level diversity for several sequential algorithms and distributed algorithms shows the benefits of algorithm diversity.