Reviewerly/exHarmony huggingface dataset:
Application of IoT in the COVID-19 Pandemic

As we all know, the Internet has altered everything, and the Internet of Things has given us hope for a bright future of the Internet with Machine-to-Machine (M2M) connectivity. This review study demonstrated that smart systems based on the Internet of Things are feasible and economical to build (IoT). In the field of healthcare, the Internet of Things has made significant progress. This article examines how the Internet of Things (IoT) is revolutionising the healthcare industry by giving huge healthcare advantages to humanity through accessible and practical healthcare solutions specially during the hard coronavirus situation around the world. The purpose of this study is to address the function of IoT in smart hospitals, as well as its importance in dealing with pandemics. Various smart gadgets that can provide a variety of features, such as adequate monitoring of high-risk patients, tracking their bio-metric measurements, and gathering real-time data, can be used to serve community-specific demands during pandemic spread. We've also looked into other plans that can detect unforeseen events utilising a variety of sensors and display the information gathered on an LED display. The results of observational studies have indicated a high level of agreement with the hypothetical claims.
Interoperable Dynamic Procedure Interactions on Semantic Augmented Reality Browsers

This article presents a framework that aims to assist users in their daily tasks by creating and exploiting reusable procedures for semantic augmented reality browsers using the ontology of our research. The contribution aims to fill the gap in current semantic augmented reality browsers to allow users to interact dynamically with virtual content through the performance of procedures or sequence of steps in the real environment. These capabilities will have an impact in several areas, for example, in Industry 4.0 contexts, such as the creation of a sequence of tasks to be performed by an intelligent operator in a Smart factory or in the increase of tasks to be done with an IoT device/equipment in the plant, as well as its potential use in massive contexts such as the growth of IOT devices in augmented home contexts.
Modeling time series by aggregating multiple fuzzy cognitive maps

The real time series is affected by various combinations of influences, consequently, it has a variety of variation modality. It is hard to reflect the variation characteristic of the time series accurately when simulating time series only by a single model. Most of the existing methods focused on numerical prediction of time series. Also, the forecast uncertainty of time series is resolved by the interval prediction. However, few researches focus on making the model interpretable and easily comprehended by humans.To overcome this limitation, a new prediction modelling methodology based on fuzzy cognitive maps is proposed. The bootstrap method is adopted to select multiple sub-sequences at first. As a result, the variation modality are contained in these sub-sequences. Then, the fuzzy cognitive maps are constructed in terms of these sub-sequences, respectively. Furthermore, these fuzzy cognitive maps models are merged by means of granular computing. The established model not only performs well in numerical and interval predictions but also has better interpretability.Experimental studies involving both synthetic and real-life datasets demonstrate the usefulness and satisfactory efficiency of the proposed approach.
Adaptive Node Embedding Propagation for Semi-supervised Classification

Graph Convolutional Networks (GCNs) are state-of-the-art approaches for semi-supervised node classification task. By increasing the number of layers, GCNs utilize high-order relations between nodes that are more than two hops away from each other. However, GCNs with many layers face three drawbacks: (1) over-fitting due to the increasing number of parameters, (2) over-smoothing in which embeddings converge to similar values, and (3) the difficulty in selecting the appropriate number of propagation hops. In this paper, we propose ANEPN that effectively utilizes high-order relations between nodes by overcoming the above drawbacks of GCNs. First, we introduce Embedding Propagation Loss which increases the number of propagation hops while keeping the number of parameters constant for mitigating over-fitting. Second, we propose Anti-Smoothness Loss (ASL) that prevents embeddings from converging to similar values for avoiding over-smoothing. Third, we introduce a metric for predicted class labels for adaptively controlling the number of propagation hops. We show that ANEPN outperforms ten state-of-the-art approaches on three standard datasets.
ErfAct and Pserf: Non-monotonic Smooth Trainable Activation Functions

An activation function is a crucial component of a neural network that introduces non-linearity in the network. The state-of-the-art performance of a neural network depends also on the perfect choice of an activation function. We propose two novel non-monotonic smooth trainable activation functions, called ErfAct and Pserf. Experiments suggest that the proposed functions improve the network performance significantly compared to the widely used activations like ReLU, Swish, and Mish. Replacing ReLU by ErfAct and Pserf, we have 5.68% and 5.42% improvement for top-1 accuracy on Shufflenet V2 (2.0x) network in CIFAR100 dataset, 2.11% and 1.96% improvement for top-1 accuracy on Shufflenet V2 (2.0x) network in CIFAR10 dataset, 1.0%, and 1.0% improvement on mean average precision (mAP) on SSD300 model in Pascal VOC dataset.
Distributed Optimization with Application to Power Systems and Control

In many engineering domains, systems are composed of partially independent subsystems—power systems are composed of distribution and transmission systems, teams of robots are composed of individual robots, and chemical process systems are composed of vessels, heat exchangers and reactors. Often, these subsystems should reach a common goal such as satisfying a power demand with minimum cost, flying in a formation, or reaching an optimal set-point. At the same time, limited information exchange is desirable—for confidentiality reasons but also due to communication constraints. Moreover, a fast and reliable decision process is key as applications might be safety-critical.Mathematical optimization techniques are among the most successful tools for controlling systems optimally with feasibility guarantees. Yet, they are often centralized—all data has to be collected in one central and computationally powerful entity. Methods from distributed optimization control the subsystems in a distributed or decentralized fashion, reducing or avoiding central coordination. These methods have a long and successful history. Classical distributed optimization algorithms, however, are typically designed for convex problems. Hence, they are only partially applicable in the above domains since many of them lead to optimization problems with non-convex constraints. This thesis develops one of the first frameworks for distributed and decentralized optimization with non-convex constraints.Based on the Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) algorithm, a bi-level distributed ALADIN framework is presented, solving the coordination step of ALADIN in a decentralized fashion. This framework can handle various decentralized inner algorithms, two of which we develop here: a decentralized variant of the Alternating Direction Method of Multipliers (ADMM) and a novel decentralized Conjugate Gradient algorithm. Decentralized conjugate gradient is to the best of our knowledge the first decentralized algorithm with a guarantee of convergence to the exact solution in a finite number of iterates. Sufficient conditions for fast local convergence of bi-level ALADIN are derived. Bi-level ALADIN strongly reduces the communication and coordination effort of ALADIN and preserves its fast convergence guarantees. We illustrate these properties on challenging problems from power systems and control, and compare performance to the widely used ADMM.The developed methods are implemented in the open-source MATLAB toolbox ALADIN-𝛼—one of the first toolboxes for decentralized non-convex optimization. ALADIN-𝛼 comes with a rich set of application examples from different domains showing its broad applicability. As an additional contribution, this thesis provides new insights why state-of-the-art distributed algorithms might encounter issues for constrained problems.
Semi-supervised associative classification using ant colony optimization algorithm

Labeled data is the main ingredient for classification tasks. Labeled data is not always available and free. Semi-supervised learning solves the problem of labeling the unlabeled instances through heuristics. Self-training is one of the most widely-used comprehensible approaches for labeling data. Traditional self-training approaches tend to show low classification accuracy when the majority of the data is unlabeled. A novel approach named Self-Training using Associative Classification using Ant Colony Optimization (ST-AC-ACO) has been proposed in this article to label and classify the unlabeled data instances to improve self-training classification accuracy by exploiting the association among attribute values (terms) and between a set of terms and class labels of the labeled instances. Ant Colony Optimization (ACO) has been employed to construct associative classification rules based on labeled and pseudo-labeled instances. Experiments demonstrate the superiority of the proposed associative self-training approach to its competing traditional self-training approaches.
A New Worldview at the Practice of Formalizing the Abilities of Natural Intelligence

The tasks of formalizing the properties of natural intelligence are being solved to organize the harmony of external forms of representing the results of the activity of mind and sensible mind. The external form of the mind is the context of movement along the synthesis of forms of the developing space as a methodological means, in which the content is organized into external forms of the sensible mind as rules for organizing the actions of discourse. Content is not notions, but systems of concept. The concept of “organization” should have a form capable of self-development.
Monitoring Student Attendance Using A Smart System at Taif University

The university system in the Kingdom of Saudi Arabia is concerned with student attendance for lectures, and it is the responsibility of lecturers to monitor student attendance for each lecture. By the end of the semester, students get an attendance register indicating which lectures the student has attended and it reports the calculated percentage for each student’s attendance in each course. Universities have regulated the mechanisms and the acceptable percentages of student absence. The process for a lecturer to manually check student attendance consumes a lot of time and effort, either during the lecture or when in the process of emptying absenteeism and inserting it into the university’s electronic system. Therefore, Saudi universities compete to find modern methods of checking student attendance that will avoid the disadvantages of manually taking attendance. For this reason, they have produced electronic attendance systems, for example, using a student's fingerprint, an eye recognition system, or a mobile phone system to read a QR code designed for the same purpose. All of these systems have the disadvantage that they consume a lot of time, as all students have to line up at the fingerprint reader or the eye detector for identification. Therefore, the problem of the consumption of lecture time is still present, even with these modern systems. Therefore, the aim of this research is to propose a smart mobile application that is able to check the attendance of students without having to consume lecture time or require any effort from the lecturer. The system automatically recognizes the attendance of students through their university ID cards. Each lecturer would use his/ her own mobile phone to use the proposed system to check the attendance of students instead of using manual method to register the attendance of students and the students’ ID cards that are detected by coming within range of the lecturer reader would represent present students, and missing student ID cards represent absent students.
Unsupervised Approach for Knowledge-Graph Creation from Conversation: The Use of Intent Supervision for Slot Filling

In this paper, we propose an unsupervised approach for knowledge graph (KG) creation from conversational data. We make use of intent classification and slot-filling, the two important components of any dialogue agent, exploit their interconnectedness, and finally construct a KG. We build a supervised intent classifier to extract the intent classes, and then on top of this we run our occlusion based slot-information extraction algorithm. Our algorithm is able to make use of supervised training of intent classifiers for extracting the relevant slot-information in an unsupervised way. To test the effectiveness of our system, we perform both automatic and manual evaluation of our intent-classifier and slot-filling system on three dialog datasets. Finally, we construct a knowledge graph from the dialogue conversation using an algorithm that makes use of our occlusion based slot-information extraction module. Empirical evaluation shows that our occlusion based method is able to successfully extract slot information from conversations, resulting in a high-quality KG.
Green Human Resource Management and its Impact on Sustainable Tourism Development (An Exploratory Study of the Green Parks of the Mayoralty of Baghdad)

The researcher aims, through the current study, to clarify the relationship between green human resources management and the extent of its impact on sustainable tourism development in the green parks of the Municipality of Baghdad.The study started with a major problem that included several questions centered on the nature of the influential relationship between the studied variables as well as clarifying the relationship between the variables by analyzing opinions The higher and middle administration responsible for managing the green parks in the city of Baghdad, which reached (16) departments, with (150) questionnaires distributed to them. In order to achieve the goal of the study mentioned above, answer questions and test the validity of the hypotheses. The researcher relied in the study mainly on (the questionnaire), which is the main tool for collecting data and information, in addition to using another method, which is (the personal interview). For a number of managers and their assistants and heads of departments in the surveyed departments, It was the use of descriptive and analytical approach scientific methodology in the current study, and was reached by the study to a set of conclusions was the most prominent, has achieved green human resources management a positive indirect impact on sustainable tourism development in the green parks of the Secretariat of Baghdad.
Convexity of Division Property Transitions: Theory, Algorithms and Compact Models

Integral cryptanalysis is a powerful tool for attacking symmetric primitives, and division property is a state-of-the-art framework for finding integral distinguishers.This work describes new theoretical and practical insights into traditional bit-based division property. We focus on analyzing and exploiting monotonicity/convexity of division property and its relation to the graph indicator. In particular, our investigation leads to a new compact representation of propagation, which allows CNF/MILP modeling for larger S-Boxes, such as 16-bit Super-Sboxes of lightweight block ciphers or even 32-bit random S-boxes. This solves the challenge posed by Derbez and Fouque (ToSC 2020), who questioned the possibility of SAT/SMT/MILP modeling of 16-bit Super-Sboxes. As a proof-of-concept, we model the Super-Sboxes of the 8-round LED by CNF formulas, which was not feasible by any previous approach.Our analysis is further supported by an elegant algorithmic framework. We describe simple algorithms for computing division property of a set of n-bit vectors in time \(O(n2^n)\), reducing such sets to minimal/maximal elements in time \(O(n2^n)\), computing division property propagation table of an \(n\times m\)-bit S-box and its compact representation in time \(O((n+m)2^{n+m})\). In addition, we develop an advanced algorithm tailored to “heavy” bijections, allowing to model, for example, a randomly generated 32-bit S-box.
Implementation of Modified Dual-Coupled Linear Congruential Generator in Data Encryption Standard Algorithm

Data transmission in cryptography is held by encipher and decipher processes. Data Encryption Standard is one of the symmetric key encryption algorithms. Data Encryption Standard (DES) is one of the simplest cryptography algorithms. Modified Dual Coupled LCG (MD-CLCG) is an essential element of Pseudorandom Bit Generator (PRBG) because it requires less area and it is more secured compared to previously executed different algorithmic techniques of linear congruential generator (LCG) family and other pseudorandom bit generators. In cryptographic schemes key generation makes an important role. This paper has implemented a modified dual-CLCG for the key generation with the utilization of shift register in Data Encryption Standard cryptographic technique. Usage of Modified Dual-CLCG in Data Encryption Standard algorithm is designed and coded by the Verilog-HDL language and prototyped on FPGA device Spartan3E XC3S500E.
Onward Migration from Italy to the UK: Reshaped Linguistic Repertoires and the Role of English

This chapter presents a discussion on the role of English in the linguistic repertoires of the second generation of onward-migrating families from Italy to the UK. Participants reported on their language use, language maintenance, and language attitudes, both in their early life in Italy and in the UK. The second generation maintain Italian with same-age peer friendships and older siblings. They view the language as linguistic capital to enhance their future career prospects in the UK or support a return to Italy. Italian is also maintained in conversations with parents often in the form of code-switching. Parents struggle with English after a long period of residence in Italy and children are not fluent in the heritage languages. English is considered the most important language and, together with a British education to improve their children’s life chances, is the main pull factor for families in the decision to migrate onward. Onward migration allows these families to restart language shift towards English (which was interrupted during the years of stay in Italy) in a parallel way to language shift towards English taking place in their countries of origin.
Scaling-up Disentanglement for Image Translation.

Image translation methods typically aim to manipulate a set of labeled attributes (given as supervision at training time e.g. domain label) while leaving the unlabeled attributes intact. Current methods achieve either: (i) disentanglement, which exhibits low visual fidelity and can only be satisfied where the attributes are perfectly uncorrelated. (ii) visually-plausible translations, which are clearly not disentangled. In this work, we propose OverLORD, a single framework for disentangling labeled and unlabeled attributes as well as synthesizing high-fidelity images, which is composed of two stages; (i) Disentanglement: Learning disentangled representations with latent optimization. Differently from previous approaches, we do not rely on adversarial training or any architectural biases. (ii) Synthesis: Training feed-forward encoders for inferring the learned attributes and tuning the generator in an adversarial manner to increase the perceptual quality. When the labeled and unlabeled attributes are correlated, we model an additional representation that accounts for the correlated attributes and improves disentanglement. We highlight that our flexible framework covers multiple image translation settings e.g. attribute manipulation, pose-appearance translation, segmentation-guided synthesis and shape-texture transfer. In an extensive evaluation, we present significantly better disentanglement with higher translation quality and greater output diversity than state-of-the-art methods.
Progressively Inpainting Images Based on a Forked-Then-Fused Decoder Network

Image inpainting aims to fill in corrupted regions with visually realistic and semantically plausible contents. In this paper, we propose a progressive image inpainting method, which is based on a forked-then-fused decoder network. A unit called PC-RN, which is the combination of partial convolution and region normalization, serves as the basic component to construct inpainting network. The PC-RN unit can extract useful features from the valid surroundings and can suppress incompleteness-caused interference at the same time. The forked-then-fused decoder network consists of a local reception branch, a long-range attention branch, and a squeeze-and-excitation-based fusing module. Two multi-scale contextual attention modules are deployed into the long-range attention branch for adaptively borrowing features from distant spatial positions. Progressive inpainting strategy allows the attention modules to use the previously filled region to reduce the risk of allocating wrong attention. We conduct extensive experiments on three benchmark databases: Places2, Paris StreetView, and CelebA. Qualitative and quantitative results show that the proposed inpainting model is superior to state-of-the-art works. Moreover, we perform ablation studies to reveal the functionality of each module for the image inpainting task.
An optimal ablation time prediction model based on minimizing the relapse risk

Percutaneous microwave ablation is an essential and safe method for the treatment of liver cancer. As one therapeutic dose, ablation time is crucial to the treatment effect determined by the physicians. However, due to the different experiences of physicians and the significant individual differences of patients, the final treatment effect is also different, which makes it difficult for the ablation time recorded in the electronic health records (EHRs) to follow the same pattern. To solve this problem, we propose a data mining method based on historical treatment data recorded in EHR, which uses a robust relapse risk as strong supervision to correct the ablation time. The prediction results of this method are closer to the situation of patients without relapse, which can provide physicians with reference. In the proposed method, we introduce the optimization method to iteratively minimize the postoperative relapse risk and utilize gradient propagation between the risk and ablation time during iteration to correct the latter. We also apply a self-attention mechanism to find the global dependencies between each feature in EHR to improve the final prediction performance of the model. Comparative experimental results show that compared with other baseline model, the proposed model achieves better performance on R-square, MAE, and MSE metric. The results of ablation experiments show that the integration of label correction and self-attention mechanism can improve the model performance. We using relapse risk as strong supervision related to the ablation time can effectively correct the deviation of the ablation time as weak supervision. The self-attention mechanism in the proposed model can significantly improve the prediction performance.