Reviewerly/exHarmony huggingface dataset:
Design Dimensions for Holographic Intelligent Agents: A Comparative Analysis.

Humanoid intelligent agents, or ‘Holographic AIs’, as we prefer, are trending, promising improved delivery of personalized services on smart glasses and in Augmented Reality. Lacking clarity of the concept and missing recommendations for their features, however, pose a challenge to developers of these novel, embodied agents. In this paper, we therefore conduct a comparative analysis of nine intelligent agents who can interact with both physical and virtual surroundings. We identify, select, and investigate four distinct types of nonplayer game characters, chatbot agents, simulation agents, and intelligent tutors in order to, subsequently, develop a framework of features and affordances for holographic AIs along the axes of appearance, behavior, intelligence, and responsiveness. Through our analysis, we derive preliminary recommendations for developers of Holographic AIs: the use case determines appearance; dialogue management is key; awareness and adaptation are equally important for successful personalization; and environmental responsiveness to events both in the virtual and digital ream is needed for a seamless experience.
Configuring the Perturbation Operations of an Iterated Local Search Algorithm for Cross-domain Search: A Probabilistic Learning Approach

Hyper-heuristics are general-purpose heuristic search methodologies for solving combinatorial optimization problems (COPs). Research findings have revealed that hyper-heuristics still suffer generalization issues as different strategies vary in performance from an instance of a COP to another. In this paper, an approach based on Iterated Local Search (ILS) is proposed to raise the level of generality of hyper-heuristics on the problem domains of the HyFlex framework. The proposed approach utilizes a probabilistic learning technique to automatically configure the behavior of the ILS algorithm during the perturbation stage of the optimization process. In the proposed method, the mutation and ruin-recreate heuristics are treated as distinct entities and the learning layer automatically determines the level of utilization of these heuristic categories depending on the problem domain being solved. The concept of double shaking is also presented where a solution can be perturbed twice before the intensification phase. Experimental results reveal the level of generality achieved by the proposed method as it recorded a minimum formula one score of 30.0 on each tested problem domain. Direct comparison with a state-of-the-art ILS-based approach also establishes the significance of the learning layer added to the perturbation stage of the ILS metaheuristic. Finally, analysis of the perturbation behavior of the hyper-heuristic leads to an interesting conclusion concerning the type of low-level heuristics that are highly beneficial and non-beneficial to a given problem domain.
A Ladder of Causal Distances

Causal discovery, the task of automatically constructing a causal model from data, is of major significance across the sciences. Evaluating the performance of causal discovery algorithms should ideally involve comparing the inferred models to ground-truth models available for benchmark datasets, which in turn requires a notion of distance between causal models. While such distances have been proposed previously, they are limited by focusing on graphical properties of the causal models being compared. Here, we overcome this limitation by defining distances derived from the causal distributions induced by the models, rather than exclusively from their graphical structure. Pearl and Mackenzie [2018] have arranged the properties of causal models in a hierarchy called the ``ladder of causation'' spanning three rungs: observational, interventional, and counterfactual. Following this organization, we introduce a hierarchy of three distances, one for each rung of the ladder. Our definitions are intuitively appealing as well as efficient to compute approximately. We put our causal distances to use by benchmarking standard causal discovery systems on both synthetic and real-world datasets for which ground-truth causal models are available.
Technical and Nontechnical Prioritization Schema for Technical Debt: Voice of TD-Experienced Practitioners

Technical debt (TD) can be injected at any stage of software development, spreading across other phases and causing various problems. This article presents a model for conceptualizing TD causes, effects, payment practices, and reasons for payment avoidance, with a prioritization schema for technical and nontechnical roles.
OVERCOMING AUGMENTED REALITY ADOPTION BARRIERS IN DESIGN: A MIXED PROTOTYPING CONTENT AUTHORING TOOL SUPPORTED BY COMPUTER VISION

Abstract Enhancing the appearance of physical prototypes with digital elements, also known as mixed prototyping, has demonstrated to be a valuable approach in the product development process. However, the adoption is limited also due to the high time and competence required for authoring the digital contents. This paper presents a content authoring tool that aims to improve the user acceptance by reducing the specific competence required, which is needed for segmentation and UV mapping of the 3D model used to implement a mixed prototype. Part of the tasks related to 3D modelling software, in fact, has been transferred to simpler manual tasks applied onto the physical prototype. Moreover, the proposed tool can recognise these manual inputs thanks to a computer-vision algorithm and automatically manage the segmentation and UV mapping tasks, freeing time for the user in a task that otherwise would require complete engagement. To preliminarily evaluate effectiveness and potential of the tool, it has been used in a case study to build up the mixed prototype of a coffee machine. The result demonstrated that the tool can correctly segment the 3D model of a physical prototype in its relevant parts and generate their corresponding UV maps.
File Fragment Classification using Content Based Analysis

One of the major components in Digital Forensics is the extraction of files from a criminal’s hard drives. To achieve this, several techniques are used. One of these techniques is using file carvers. File carvers are used when the system metadata or the file table is damaged but the contents of the hard drive are still intact. File carvers work on the raw fragments in the hard disk and reconstruct files by classifying the fragments and then reassembling them to form the complete file. Hence the classification of file fragments has been an important problem in the field of digital forensics. The work on this problem has mainly relied on finding the specific byte sequences in the file header and footer. However, classification based on header and footer is not reliable as they may be modified or missing. In this project, the goal is to present a machine learningbased approach for content-based analysis to recognize the file types of file fragments. It does so by training a Feed-Forward Neural Network with a 2-byte sequence histogram feature vector which is calculated for each file. These files are obtained from a publicly available file corpus named Govdocs1. The results show that content-based analysis is more reliable than relying on the header and footer data of files.
Security Analysis of Block Withholding Attacks in Blockchain

Blockchain technology has gained growing popularity in recent years. While the technology works well for most applications, the vulnerability of the blockchain-based system is not well understood. The core part of a blockchain-based system is the consensus algorithm. It can be regarded as a protocol used by all network users to make decisions on the growth of the chain. Malicious users can exploit the vulnerabilities of the consensus algorithm to launch block withholding attacks to earn more profit by increasing their winning possibilities, which are significant threats to the security of blockchain-based applications. In this paper, we describe some scenarios of block withholding attacks and suggest effective mitigation methods accordingly. We propose two hybrid consensus algorithms against the block withholding attacks and evaluate the effectiveness of these algorithms, both analytically and through simulation. Simulation results from the SimBlock simulator verified the effectiveness of the proposed algorithms.
Surrogate-Assisted Genetic Programming with Diverse Transfer for the Uncertain Capacitated Arc Routing Problem

The Uncertain Capacited Arc Routing Problem (UCARP) is an important routing problem that can model uncertainties of real-world scenarios. Genetic Programming (GP) is a powerful method for evolving routing policies for vehicles to enable them make real-time decisions and handle environmental uncertainties. When facing various problem domains, knowledge transfer can improve the effectiveness of the GP training. Previous studies have demonstrated that due to the existence of duplicated GP individuals in the source domain, the existing transfer learning methods do not perform satisfactorily for UCARP. To address this issue, in this work, we propose a method for detecting duplicates in the source domain and initialising the GP population in the target domain with phenotypically unique individuals. Additionally, since the presence of duplicates can limit the number of good GP individuals, we propose a surrogate-assisted initialisation approach that is able to generate much more diversely distributed initial individuals in the target domain. Our experiments demonstrate that our proposed transfer learning method can significantly improve the effectiveness of GP for training new UCARP routing policies. Compared with the state-of-the-art GP with knowledge transfer, the proposed approach can obtain significantly better solutions on a wide range of UCRP instances, in terms of both initial and final quality.
Video Annotation for Visual Tracking via Selection and Refinement

Deep learning based visual trackers entail offline pre-training on large volumes of video datasets with accurate bounding box annotations that are labor-expensive to achieve. We present a new framework to facilitate bounding box annotations for video sequences, which investigates a selection-and-refinement strategy to automatically improve the preliminary annotations generated by tracking algorithms. A temporal assessment network (T-Assess Net) is proposed which is able to capture the temporal coherence of target locations and select reliable tracking results by measuring their quality. Meanwhile, a visual-geometry refinement network (VG-Refine Net) is also designed to further enhance the selected tracking results by considering both target appearance and temporal geometry constraints, allowing inaccurate tracking results to be corrected. The combination of the above two networks provides a principled approach to ensure the quality of automatic video annotation. Experiments on large scale tracking benchmarks demonstrate that our method can deliver highly accurate bounding box annotations and significantly reduce human labor by 94.0%, yielding an effective means to further boost tracking performance with augmented training data.
Deep Learning Model with GA-based Visual Feature Selection and Context Integration

Deep learning models have been very successful in computer vision and image processing applications. Since its inception, Convolutional Neural Network (CNN)-based deep learning models have consistently outperformed other machine learning methods on many significant image processing benchmarks. Many top-performing methods for image segmentation are also based on deep CNN models. However, deep CNN models fail to integrate global and local context alongside visual features despite having complex multi-layer architectures. We propose a novel three-layered deep learning model that learns independently global and local contextual information alongside visual features, and visual feature selection based on a genetic algorithm. The novelty of the proposed model is that One-vs-All binary class-based learners are introduced to learn Genetic Algorithm (GA) optimized features in the visual layer, followed by the contextual layer that learns global and local contexts of an image, and finally the third layer integrates all the information optimally to obtain the final class label. Stanford Background and CamVid benchmark image parsing datasets were used for our model evaluation, and our model shows promising results. The empirical analysis reveals that optimized visual features with global and local contextual information play a significant role to improve accuracy and produce stable predictions comparable to state-of-the-art deep CNN models.
Comparison of feature extraction methods by color analysis and image recognition for photos on tourism websites

Many people go sightseeing based on the information available on tourism websites. There is much information available for famous tourist destinations and little information for unknown tourist destinations. In particular, for foreign travelers, it is difficult to find their favorite tourist destinations, so we proposed a personal adaptive tourism recommendation system in former research. During the system development, the method of tourism feature extraction is a key issue. Via a questionnaire, we showed the importance of photo information on tourism websites. As the first step of the tourism feature extraction of photos on tourism websites, we propose two methods of analysis: color analysis and image recognition. Comparing the two methods through experiments, we confirmed that each method had different characteristics and the combination of these methods exhibited the best accuracy in distinguishing between the ratio of artificial and natural objects in the photos.
Academic Silver Linings in a Philippine State University Amid the Early Stages of Pandemic Cases

The primary role of the academe is knowledge building, however, due to the prevailing digital divide, some institutions of higher learning were not able to offer even Emergency Online Teaching (EOT) for continuous formal education during the early stages of the pandemic. This article highlights diversified ways that a state university from a developing country (Philippines) responded to the crisis to offer assistance towards the social development of the stakeholders amid the COVID-19 pandemic.&#x0D;
THE CORRELATION BETWEEN EFL LEARNERS COHESION AND THEIR READING COMPREHENSION

Reading simply can be defined as a process to understand and get information from a text. Cohesion is the meaning relation that appears within the text in order to build connection among the different parts of the text. Understanding the cohesion can bring the students to get good comprehension in reading. Therefore, in this research, the researchers want to prove that students cohesion understanding can be correlated with students reading comprehension. The purpose of this research is to investigate and to find out the correlation between EFL Learners cohesion and their reading comprehension. The subjects of this research were the fourth semester students at English education department of State Islamic Institute of Metro in the Academic Year of 2019/2020. To investigate and calculate the correlation between those variables, the researchers applies Product-moment Pearson- correlation. The result of data analysis shows that r observed for correlation between cohesion understanding and reading comprehension is 0.728, after considering the Product Moment table by using df 30 it can be found that the critical value of r Product Moment for the 5% level is 0.361 and for the 1% level is 0.463. The data confirmed that r observed is higher than r table. Therefore, it can be inferred that there is a positive and significant correlation between the students cohesion understanding and reading comprehension among the fourth semester students at English education department of State Islamic Institute of Metro in the Academic Year of 2019/2020.
Encrypted Multitask Traffic Classification via Multimodal Deep Learning

Traffic Classification (TC), i.e. the collection of procedures for inferring applications and/or services generating network traffic, represents the workhorse for service management and the enabler for valuable profiling information. Sadly, the growing trend toward encrypted protocols (e.g. TLS) and the evolving nature of network traffic make TC design solutions based on payload-inspection and machine learning, respectively, unsuitable. Conversely, Deep Learning (DL) is currently foreseen as a viable means to design traffic classifiers based on automatically-extracted features, reflecting the complex patterns distilled from the multifaceted (encrypted) traffic nature, implicitly carrying information in "multimodal" fashion. To this end, in this paper a novel multimodal DL approach for multitask TC is explored. The latter is able to capitalize traffic data heterogeneity (by learning both intra- and inter-modality dependencies), overcome performance limitations of existing (myopic) single-modality DL-based TC proposals, and solve different traffic categorization problems associated with different providers’ desiderata. Based on a real dataset of encrypted traffic, we report performance gains of our proposal over (a) state-of-art multitask DL architectures and (b) multitask extensions of single-task DL baselines (both based on single-modality philosophy).
Homomorphic Encryption for Cloud Computing and Its Challenges

Cloud Computing has been envisioned as the next-generation architecture for Information Technology enterprises. The Cloud Computing concept offers scalable resources provisioned as a service over the Internet. Economic benefits are the main driver for the Cloud Computing since it promises reduction of capital expenditure and operational expenditure. In order for this to become reality, however, there are still some challenges that need to be solved. Most important among these challenges are the security and trust issues, since the user’s data has to be released to the Cloud and thus leaves the protection sphere of the data owner. Homomorphic Encryption is proposed to ensure data privacy, confidentiality, and integrity. Unlike traditional cryptosystem, Homomorphic Encryption allows computation delegation to the Cloud Service provider. However, there are significant security issues associated to the implementation of such scheme. The client (data owner) is unable to track the data once the data is being outsourced and by the same token, the client is also unable to authenticate the operations that are being applied to the encrypted data. This paper introduces a brief survey of cloud computing security issues and shows that Homomorphic Encryption alone is not adequate to provide indistinguishable ciphertext, under the adaptive chosen-ciphertext attacks. The paper concludes by providing possible suggestions as solution to the identified problem.
Indian Sign Language Recognition Using a Novel Feature Extraction Technique

Sign language is a means for communication between deaf mute people and people with normal speaking and hearing abilities. There is no way other than the sign language to be useful for deaf mute community. Roughly, there is around 5% of global community is using sign language; there is a need for automatic recognition of sign language. The Indian subcontinent community using sign language for communication purposes is known as Indian sign language. In this paper, a system for automatic conversion of isolated character-level ISL signs to text is presented. A total of 2600 input images, 100 images per ISL single-handed English character are used in the research. In experimental work, hierarchical centroid feature extraction technique with k-nearest neighbor and Naïve Bayes classifiers is used. A satisfactory result of around 97% accuracy has been attended in the experiments.
Decision Support System to determine students who are eligible to receive the scholarship of Indonesian Smart Card (KIP) By Using Fuzzy Sugeno

Smart Indonesia Card (KIP) Is a state guarantee card for the continuity of college by providing freedom of tuition fees, fees given to underprivileged students so that they can continue their education. The problem that occurs in the provision of kip scholarships is that it is difficult to analyze students who are entitled to receive the scholarship so that standard criteria are needed such as proof of kip card, photocopy of report card, certificate of graduation, photo of student's house and photocopy of family card so that the scholarship given is right on target for students who entitled to receive such assistance. This study uses the Sugeno method by taking the Max value (the highest value) and using the or operator. The benefits of this research can be used as a decision-making system.
A Meta Learner Autoencoder for Channel State Information Feedback in Massive MIMO Systems

In massive multiple-input multiple-output frequency division duplexing systems, the user equipment should independently estimate the massive downlink channels state information and then feed them back to the base station. This process results in a large signaling overhead. Deep learning approaches tried to overcome this challenge using neural networks as an autoencoder to learn the mapping between the input and corresponding output. However, this type of learning consumes massive training datasets to learn. Also, it can not make use of the learning through the internal information within the tasks and thus, it can not reach the convergence quickly as its parameters are randomly initialized. In this paper, we introduce a meta learner-based autoencoder for tackling the feedback overhead. The proposed approach is mainly based on finding a good initialization of the parameters of the autoencoder to adapt rapidly to new tasks with a few number of samples. The results show that the proposed autoencoder based on the meta-learner method outperforms the state of the art with a margin.
Individual Educational Trajectory as a Way to Reveal the Potential of a Future Teacher

New social needs in education at the beginning of the XXI century are primarily associated with the peculiarities of the development of higher education, which places high demands on the personality and professional activity of a physical education teacher, in difficult conditions, when the world is at a difficult stage of development, indicated by the search for an optimal socio-economic format of coexistence in the global space. The new concept of education requires the improvement of the pedagogical activity of future teachers and their achievement of the development of a high level of personal and professional potential, taking into account the socio-economic environment in which the national concepts of preparing young people for life in a changing and unpredictable world order compete, fighting for their right to dominance. The main purpose of the article is to form an algorithm for developing an individual educational trajectory, which is decisive in the formation of personal and professional potential of the future physical education teacher. To simplify and systematize this individual trajectory as much as possible, we used the IDEF0 functional modelling methodology, which allows us to visually see the algorithm of functioning of the studied process and the main elements that will be needed.