Reviewerly/exHarmony huggingface dataset:
Reinforcement Learning Rebirth, Techniques, Challenges, and Resolutions

Reinforcement learning (RL) is a new propitious research space that is well-known nowadays on the internet of things (IoT), media and social sensing computing are addressing a broad and pertinent task through making decisions sequentially by deterministic and stochastic evolutions. The IoTs extend world connectivity to physical devices like electronic devices network by use interconnect with others over the Internet with the possibility of remotely being supervised and meticulous. In this paper, we comprehensively survey an in-depth assessment of RL techniques in IoT systems focusing on the main known RL techniques like artificial neural network (ANN), Q-learning, Markov Decision Process (MDP), Learning Automata (LA). This study examines and analyses learning technique with focusing on challenges, models performance, similarities and the differences in IoTs accomplish with most correlated proposed state of the art models. The results obtained can be used as a foundation for designing, a model implementation based on the bottlenecks currently assessed with an evaluation of the most fashionable hands-on utility of current methods for reinforcement learning.
Do effects of visual contrast and font difficulty on readers’ eye movements interact with effects of word frequency or predictability?

The time a reader's eyes spend on a word is influenced by visual (e.g., contrast) as well as lexical (e.g., word frequency) and contextual (e.g., predictability) factors. Well-known visual word recognition models predict that visual and higher-level manipulations may have interactive effects on early eye movement measures, because of cascaded processing between levels. Previous eye movement studies provide conflicting evidence as to whether they do, possibly because of inconsistent manipulations or limited statistical power. In the present study, 2 highly powered experiments used sentences in which a target word's frequency and predictability were factorially manipulated. Experiment 1 also manipulated visual contrast, and Experiment 2 also manipulated font difficulty. Robust main effects of all manipulations were evident in both experiments. In Experiment 1, interactions between the effect of contrast and the effects of frequency and predictability were numerically small and statistically unreliable in both early (word skipping, first fixation duration) and later (gaze duration, go-past time) measures. In Experiment 2, frequency and predictability did demonstrate convincing interactions with font difficulty, but only in the later measures, possibly implicating a checking mechanism. We conclude that although the predicted interactions in early eye movement measures may exist, they are sufficiently weak that they are difficult to detect even in large eye movement experiments. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Comparison of Recent Metaheuristic Algorithms for Shape Detection in Images

Shape recognition in images represents one of the complex and hard-solving problems in computer vision due to its nonlinear, stochastic and incomplete nature.Classical image processing techniques have been normally used to solve this problem.Alternatively, shape recognition has also been conducted through metaheuristic algorithms.They have demonstrated to have a competitive performance in terms of robustness and accuracy.However, all of these schemes use old metaheuristic algorithms as the basis to identify geometrical structures in images.Original metaheuristic approaches experiment several limitations such as premature convergence and low diversity.Through the introduction of new models and evolutionary operators, recent metaheuristic methods have addressed these difficulties providing in general better results.This paper presents a comparative analysis on the application of five recent metaheuristic schemes to the shape recognition problem such as the Grey Wolf Optimizer (GWO), Whale Optimizer Algorithm (WOA), Crow Search Algorithm (CSA), Gravitational Search Algorithm (GSA) and Cuckoo Search (CS).Since such approaches have been successful in several new applications, the objective is to determine their efficiency when they face a complex problem such as shape detection.Numerical simulations, performed on a set of experiments composed of images with different difficulty levels, demonstrates the capacities of each approach.
StressNet: A deep convolutional neural network for recovering the stress field from isochromatic images

Extending photoelasticity studies to industrial applications is a complex process generally limited by the image acquisition assembly and the computational methods for demodulating the stress field wrapped into the color fringe patterns. In response to such drawbacks, this paper proposes an auto-encoder based on deep convolutional neural networks, called StressNet, to recover the stress map from one single isochromatic image. In this case, the public dataset of synthetic photoelasticity images `Isochromatic-art' was used for training and testing achieving an averaged performance of 0.95 +/- 0.04 according to the structural similarity index. With these results, the proposed network is capable of obtaining a continuous stress surface which represents a great opportunity toward developing real time stress evaluations.
Offline/Online Outsourced Attribute-Based Encryption with Partial Policy Hidden for the Internet of Things

In the Internet of Things (IoT) environment, the intelligent devices collect and share large-scale sensitive personal data for a wide range of application. However, the power of storage and computing of IoT devices is limited, so the mass perceived data will be encrypted and transmitted to a cloud platform-interconnected IoT devices. Therefore, the concern how to save the encryption/decryption cost and preserve the privacy of the sensitive data in IoT environment is an issue that deserves research. To mitigate these issues, an offline/online attribute-based encryption scheme that supports partial policy hidden and outsourcing decryption will be proposed. This scheme adopts offline/online attribute-based encryption algorithms; then, the key generation algorithm and encryption algorithm are divided into two stages: offline stage and online stage. Meanwhile, in order to solve the problem of policy disclosure under the cloud platform, the policy hidden is supported, that is, the attribute is divided into the attribute value and the attribute name. For the pairing operation involved in decryption process, a verifiable outsourced decryption is implemented. Our scheme is constructed based on composite bilinear groups, which meets full security under the standard model. Finally, by comparing with other schemes in terms of functionality and computational overhead, it is shown that the proposed scheme is more efficient and applicable to the mobile devices with limited computing and storage functions in the Internet of Things environment.
Fed+: A Family of Fusion Algorithms for Federated Learning

We present a class of methods for federated learning, which we call Fed+, pronounced FedPlus. The class of methods encompasses and unifies a number of recent algorithms proposed for federated learning and permits easily defining many new algorithms. The principal advantage of this class of methods is to better accommodate the real-world characteristics found in federated learning training, such as the lack of IID data across the parties in the federation. We demonstrate the use and benefits of this class of algorithms on standard benchmark datasets and a challenging real-world problem where catastrophic failure has a serious impact, namely in financial portfolio management.
Governance and Legal Framework of Blockchain Technology as a Digital Economic Finance

Blockchain (BC) provides an encrypted echo system to the users where they can securely mark their transactional entries in a distributed ledger. The ledger comprises of distributed and shared network of nodes that validate the authenticity of each transaction and keeps its authentic record for perusal. Keeping view of the enormous potential of this technology, numerous public and private entities are embarking the bandwagon of blockchain and integrating this technology for ensuring a transitioning in their digital finance operations. Apart from the financial realm, healthcare, agriculture, and education sectors are also benefiting from the security and reliability of BC. Incorporation of BC in the public sector at increased scale requires a review of existing governance and legislative structures and a recalibration of regulatory regimes. This paper presents a literature review on blockchain technology, its current usage trends, and its governance.
Geochemical and electrical characterization of heavy metals in contaminated soils

The present work is conducted in the industrial district south of greater Cairo (ElTabbin area). Some heavy metals like Mn, Co, Ni, Cu, Zn, Pb, Cr, Ba, Cd, Mo were determined in polluted soils during May, 2018. At the study area, results displayed that average heavy metals concentrations differ considerably. They are decreased from Mn to Cd (Mn > Ba > Zn > Cr > Ni > Co > Pb > Cu > Mo > Cd). The average ranges as follows: Mn (255.8–31448.2 ppm); Ba (145.2–17545.6 ppm); Zn (53.3–1589.9 ppm); Cr (26.7–311.3 ppm); Ni (29.7–114.1 ppm); Co (13.2–39.8 ppm); Pb (5.7–77.4 ppm); Cu (7.6–35.2 ppm); Mo (0.1–15.9 ppm) and Cd (0–1.5 ppm), respectively. ElTabbin area has heavy manufacturing activity at Egypt. ElTabbin area was chosen as a model for that contamination.Electrical characteristics of some specimens from El- Tebbin area, Egypt, were taken and measured electrically at frequency range (10−3 to 100 kHz). Major and heavy elements were measured at specimens. Specimens were classified to three categories according to electrical properties. The changes were the consequence of change in minor and major mineral composition in the specimens. Electrical properties were able to recognize different specimens according to different levels of contaminants in the specimens. Texture, tortuosity and minor elements of heavy elements are the controlling factors that control electrical properties at specimens. High values concentration of Mn, Ni, and Zn heavy elements increases conductivity while the high concentration of Pb and Cu heavy elements decreases the conductivity. The mixing up of these different minor and major minerals and elements at specimen may lead to the change of conductivity values. The main controlling factors of the major elements are the Fe2O3 and SiO2, while the main controlling factors of the minor elements are the Mn and Pb. Our main objective is to study mixing up of the different minor and major minerals and elements on electrical properties of rocks.
Police Functional Adaptation to the Digital or Post Digital Age: Discussions with Cybercrime Experts

This article examines the challenges of functional adaptation faced by the police in response to technologically driven changes in the nature of crime. It also recounts how research under the auspices of a ‘dark web’ research project resulted in a search for an effective approach to engaging with investigators dealing with cybercrime. In doing so it tested, as a research methodology, a standard change implementation tool (problem tree analysis) from the Disaster Management and Sustainable Development (DMSD) discipline. This in turn resulted in significant consideration being given to the physical space in which that methodology is used. It presents the results of a workshop held with cybercrime investigators (not all were police officers) in terms of the importance of four key organisational and cultural issues (management, leadership and institutional ethos within the police; the risks of over-complication and exaggerated distinctions between cyber and real world policing; ethics; and knowledge, training and development) alongside the development and acquisition of new technical capabilities.
A Holistic Model for Recognition of Handwritten Arabic Text Based on the Local Binary Pattern Technique

&lt;p class="0abstract"&gt;In this paper, we introduce a multi-stage offline holistic handwritten Arabic text recognition model using the Local Binary Pattern (LBP) technique and two machine-learning approaches; Support Vector Machines (SVM) and Artificial Neural Network (ANN). In this model, the LBP method is utilized for extracting the global text features without text segmentation. The suggested model was tested and utilized on version II of the IFN/ENIT database applying the polynomial, linear, and Gaussian SVM and ANN classifiers. Performance of the ANN was assessed using the Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) training methods. The classification outputs of the herein suggested model were compared and verified with the results obtained from two benchmark Arabic text recognition models (ATRSs) that are based on the Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA) methods using various normalization sizes of images of Arabic text. The classification outcomes of the suggested model are promising and better than the outcomes of the examined benchmarks models. The best classification accuracies of the suggested model (97.46% and 94.92%) are obtained using the polynomial SVM classifier and the BR ANN training methods, respectively.&lt;/p&gt;
Exploring the Effects of Scale and Color Differences on Users’ Perception for Everyday Mixed Reality (MR) Experience: Toward Comparative Analysis Using MR Devices

With continued technological innovations in the fields of mixed reality (MR), wearable type MR devices, such as head-mounted display (HMD), have been released and are frequently used in various fields, such as entertainment, training, education, and shopping. However, because each product has different parts and specifications in terms of design and manufacturing process, users feel that the virtual objects overlaying real environments in MR are visualized differently, depending on the scale and color used by the MR device. In this paper, we compare the effect of scale and color parameters on users’ perceptions in using different types of MR devices to improve their MR experiences in real life. We conducted two experiments (scale and color), and our experimental study showed that the subjects who participated in the scale perception experiment clearly tended to underestimate virtual objects, in comparison with real objects, and overestimated color in MR environments.
A Unified Framework for User-Preferred Multi-Level Ranking of Cloud Computing Services Based on Usability and Quality of Service Evaluation

As networking and Cloud computing technologies have evolved, a wide range of Cloud services has been introduced by different Cloud service providers. Many organizations and individuals are using these services as a part of their regular work. Thus, the performance of users' systems is significantly dependent upon the performance of the services they employ. Therefore, it becomes crucial for Cloud users to thoroughly evaluate and compare the available Cloud services to select the best of them. However, different Cloud service models, range of pricing and feature schemes, different performance attributes used by service providers, fuzzy nature of some of the performance attributes, etc. make Cloud service performance analysis and comparison a challenging task. In addition, different user preferences regarding Cloud service attributes make this analysis even more complex. This situation leads to ambiguity and indecisiveness in selecting a Cloud service that best matches the end-user's needs and thus leads to degraded performance of user's systems and financial losses. To this end, this article proposes a unified Cloud service measurement index to provide a single comprehensive framework for multi-level evaluation of Cloud services. For a detailed and effective performance evaluation, we identified 8 top-level attributes of Cloud services and 65 detailed key performance indicators to evaluate these attributes. For an analytical ranking of the target Cloud services, we employed “Multi-Attribute Global Inference of Quality”, which considers the hierarchical relationship of performance attributes. Our method considers user preferences for Cloud service attributes in terms of attribute weights and is flexible to select all or only user-preferred Cloud service attributes. We show the application of the proposed framework and the ranking method using a case study.
Context Clustering-based Recommender Systems

Recommender systems have gained lots of attention due to the rapid increase in the amount of data on the internet.Therefore, the demand for finding more advanced techniques to generate more useful recommendations becomes an urgent.The increasing need for generating more relevant recommendations led to the emergence of many novel recommendation systems, such as Context-aware Recommender System (CARS), which is based on incorporating the contextual information in recommendation systems.The goal of this paper is to propose new recommender systems that utilize the contextual information to find more relevant recommendations.In this paper, we propose CoCl, a novel Context Clusteringbased recommender system.We introduce two approaches which utilize the contextual information and KMeans clustering algorithm to generate new forms of user-item matrices.We show that the accuracy of CoCl which uses the new user-item matrices has been improved comparing with the accuracy of classical recommender system which uses the original user-item matrix.
Fast outlier detection for high-dimensional data of wireless sensor networks

This article addresses the problem of outlier detection for wireless sensor networks. As increasing amounts of observational data are tending to be high-dimensional and large scale, it is becoming increasingly difficult for existing techniques to perform outlier detection accurately and efficiently. Although dimensionality reduction tools (such as deep belief network) have been utilized to compress the high-dimensional data to support outlier detection, these methods may not achieve the desired performance due to the special distribution of the compressed data. Furthermore, because most existed classification methods must solve a quadratic optimization problem in their training stage, they cannot perform well in large-scale datasets. In this article, we developed a new form of classification model called “deep belief network online quarter-sphere support vector machine,” which combines deep belief network with online quarter-sphere one-class support vector machine. Based on this model, we first propose a model training method that learns the radius of the quarter sphere by a sorting method. Then, an online testing method is proposed to perform online outlier detection without supervision. Finally, we compare the proposed method with the state of the arts using extensive experiments. The experimental results show that our method not only reduces the computational cost by three orders of magnitude but also improves the detection accuracy by 3%–5%.
A Learning Model Proposal Focused On Challenge-Based Learning

This paper proposes a model that comprises a learning exercise based on challenges, which tends to promote non-technical skills such as oral expression, communication, resource management, leadership and problem solving among engineering students.Since engineers have been recognized throughout the world for their ability to creatively solve problems, using the technical knowledge developed in their professional career, it has been considered that these skills make it easier for them to reach results that involve the solution of mathematical problems, or those related to science, through the use of technical skills, research or analysis and synthesis.In this sense, the academia seems to have placed the emphasis on hard and technical skills, while, non-technical ones, have been left in a secondary stage in the case of engineers, ignoring their role on their professional development.This project proposes a model that will bring together more than 180 engineering students and 4 university professors, by involving them in a real challenge that they might solve by means of their technical and non-technical skills.The model is being applied at a university and their first results show the need to modify the engineering training processes, to include the development of appropriate competences in engineers, in response to the demands of a globalized world.
Contact Tracing in an African Megacity during COVID 19: Lessons Learned

Contact tracing is the process of identifying, assessing, and managing people who have been exposed to a disease to prevent onward transmission. It is an essential public health tool and a crucial component to the on-going COVID-19 pandemic response in Lagos State, Nigeria. This contact tracing exercise is the largest one to be conducted in the megacity and is leveraging on the expertise of professionals across different strata of the health care system. Following the confirmation of a positive case of COVID-19; the State's contact tracing team commenced investigations by identifying contacts and following them up daily for 14 days from the last point of exposure. In the process of conducting this large-scale exercise, several lessons that can improve contact tracing outcomes such as the need for community mobilizers and decentralization, the importance of technology and communication campaigns were learnt and can serve as good practice for other implementers.
UTM: A trajectory privacy evaluating model for online health monitoring

A huge amount of sensitive personal data is being collected by various online health monitoring applications. Although the data is anonymous, the personal trajectories (e.g., the chronological access records of small cells) could become the anchor of linkage attacks to re-identify the users. Focusing on trajectory privacy in online health monitoring, we propose the User Trajectory Model (UTM), a generic trajectory re-identification risk predicting model to reveal the underlying relationship between trajectory uniqueness and aggregated data (e.g., number of individuals covered by each small cell), and using the parameter combination of aggregated data to further mathematically derive the statistical characteristics of uniqueness (i.e., the expectation and the variance). Eventually, exhaustive simulations validate the effectiveness of the UTM in privacy risk evaluation, confirm our theoretical deductions and present counter-intuitive insights.
ML-CK-ELM: An efficient Multi-layer Extreme Learning Machine using Combined Kernels for Multi-label classification

Recently many neural network methods have been proposed for multi-label classification in the literature. One of these recent researches is the multi-layer extreme learning machines (ML-ELM) in which stack auto encoders have been used for tuning the weights. However, ML-ELM suffers from three primary drawbacks: First, input weights and biases are chosen randomly. Second, the pseudo-inverse solution for calculating output weights will cause to increase the reconstruction error. Third, memory and execution time of transformation matrices are proportional to the number of hidden layers. In this paper multi-layer kernel extreme learning machine, that uses a linear combination of base kernels in each layer, is proposed for multi-label classification. The proposed approach effectively addresses the above-mentioned drawbacks. Furthermore, multi-label classification data inherently have multi-modal aspects due to the variety of labels assigned to each instance. Applying a combination of different kernels is the added advantage of ML-CK-ELM that leads to implicitly assess the inherent multi-modal aspects of multi-label data; each kernel can be effectively used to cover one of the modals better than the other kernels. The empirical study indicates that ML-CK-ELM represents competitive performance against other state-of-the-art methods, and experimental results over multi-label datasets verify the feasibility of ML-CK-ELM.