Reviewerly/exHarmony huggingface dataset:
Blockchain-based cross-domain authorization system for user-centric resource sharing

User-centric data sharing is essential to encourage citizens' active participation in the digital economy. One key to smart cities, a form of the digital economy, is the promotion of public use of citizen data. Nevertheless, it is not easy to utilize data without citizens' consent. In this study, we took a technological approach to these issues. User-managed access (UMA) is a well-known framework for delegating resource access rights to others on the Internet. In UMA, authorization mechanisms are designed to be centralized so that resource owners can centrally manage access rights for various resources stored in different domains. However, the lack of transparency in the authorization mechanism is a barrier to its implementation in large-scale systems such as smart cities. In this study, we developed a blockchain-based cross-domain authorization architecture that enables a resource-sharing ecosystem in which organizations that wish to utilize data can freely trade with each other. The proposed architecture solves the transparency problem that conventional authorization systems have had by designing the authorization mechanism on blockchain technology. We implemented the proposed architecture as smart contracts and evaluated its processing performance. The resultant time required for delegating access rights and accessing resources was less than 500 ​ms. Furthermore, we found that the fluctuation in the processing time overhead was small. Based on these results, we concluded that performance degradation with the proposed architecture is minor.
Google Maps Data Analysis of Clothing Brands in South Punjab, Pakistan

The Internet is a popular and first-hand source of data about products and services. Before buying a product, people try to gain quick insight by scanning through online reviews about a targeted product. However, searching for a product, collecting all the relevant information, and reaching a decision is a tedious task that needs to be automated. Such composed decision-assisting text data analysis systems are not conveniently available worldwide. Such systems are a dream for major cities of South Punjab, such as Bahawalpur, Multan, and Rahimyar khan. This scenario creates a gap that needs to be filled. In this work, the popularity of clothing brands in three cities of south Punjab has been assessed by analysing the brand's popularity using sentiment analysis by prioritizing brands based on organic feedback from their potential customers. This study uses a combination of quantitative and qualitative research to examine online reviews from Google Maps. The task is accomplished by applying machine learning techniques, Logistic Regression (LR), and Support Vector Machine (SVM), on Google Maps reviews data using the n-gram feature extraction approach. The SVM algorithm proved to be better than others with the uni-bi-trigram features extraction method, achieving an average of 80.93% accuracy.
VMLH: Efficient Video Moment Location via Hashing

Video-moment location by query is a hot topic in video understanding. However, most of the existing methods ignore the importance of location efficiency in practical application scenarios; video and query sentences have to be fed into the network at the same time during the retrieval, which leads to low efficiency. To address this issue, in this study, we propose an efficient video moment location via hashing (VMLH). In the proposed method, query sentences and video clips are, respectively, converted into hash codes and hash code sets, in which the semantic similarity between query sentences and video clips is preserved. The location prediction network is designed to predict the corresponding timestamp according to the similarity among hash codes, and the videos do not need to be fed into the network during the process of retrieval and location. Furthermore, different from the existing methods, which require complex interactions and fusion between video and query sentences, the proposed VMLH method only needs a simple XOR operation among codes to locate the video moment with high efficiency. This paper lays the foundation for fast video clip positioning and makes it possible to apply large-scale video clip positioning in practice. The experimental results on two public datasets demonstrate the effectiveness of the method.
An efficient hidden marking approach for forensic and contents verification of digital images

Abstract Recently, forensic tools have been presented for detecting the forged image and illegal image manipulations. This paper presents an efficient and simple image contents verifying approach acting as a forensic technique. The presented approach mechanism is built by adding a hidden mark in a secret images, this mark achieves image integrity verification and detecting the tampering or forgery in the secret/authentic images. The secret/authentic/authentic image is divided to two main partitions, each portion is segmented to small blocks. These blocks in one partition are used to mark the blocks of the second partition using data transform technique. Firstly, the sensitive image is marked according to a self-embedding method. Then, a transform domain is utilized in order to embed a block-based signature into another block of the same image. Common discrete transform domains like DWT, DCT, and DFT are examined individually. Different analyses and comparison measurements are employed. The DCT is proved to be the most suitable and efficient transform domain to be used with the proposed scheme. At the receiver side, the reverse process is performed to verify image integrity. As proved from the experiments, this mark-algorithm is not visible or observable and robust against various attacks.
Estimating the Number of Clusters in High-Dimensional Large Datasets

Clustering is a basic primer of exploratory tasks. In order to obtain valuable results, the parameters in the clustering algorithm, the number of clusters must be set appropriately. Existing methods for determining the number of clusters perform well on low-dimensional small datasets, but how to effectively determine the optimal number of clusters on large high-dimensional datasets is still a challenging problem. In this paper, the authors design a method for effectively estimating the optimal number of clusters on large-scale high-dimensional datasets that can overcome the shortcomings of existing estimation methods and accurately and quickly estimate the optimal number of clusters on large-scale high-dimensional datasets. Extensive experiments show that it (1) outperforms existing estimation methods in accuracy and efficiency, (2) generalizes across different datasets, and (3) is suitable for high-dimensional large datasets.
Data-driven evolutionary multi-task optimization for problems with complex solution spaces

Smoothing rugged and rough fitness landscapes by machine learning (ML) models is a feasible approach to improve the global optimization performance of evolutionary algorithms (EA). However, systematic studies on the smoothing performance of ML models are rare. Moreover, existing model management methods have a relatively low efficiency and can cause the error propagation problem. In this paper, we empirically evaluate and compare the performance of common ML models in smoothing high-dimensional fitness landscapes. Based on the experimental results, we design a novel framework called data-driven multi-task optimization (DDMTO) to enhance EAs’ search abilities in complex solution spaces with ML smoothing models. The proposed framework firstly smooths the fitness landscape by a ML model. Then, it models the original fitness landscape optimization and the smoothed fitness landscape optimization as a two-task optimization problem and solves them with an evolutionary multi-task optimizer (EMTO). Coordinated by EMTO, the easy task can help optimize the difficult task through the proposed knowledge transfer operator, and the error propagation problem can be solved by the proposed knowledge transfer control scheme. To prove the effectiveness of the proposed methodology, we design several example algorithms by embedding different EAs and ML models into the DDMTO framework and compare their optimization performance on high-dimensional continuous benchmark functions with rugged and rough fitness landscapes and a real-world optimization problem. Experimental results show that, by embedding an EA into the DDMTO framework with an appropriate smoothing model, its exploration ability and global optimization performance in complex solution spaces can be significantly enhanced without increasing the total computational cost.
Gender Impact on Emoji Sentiment Analysis Among Arabic Users in Digital Networks

Gender differences in usages and attitudes toward emoji have been reported previously in many different cultures, but not in Arabic. Here we examine the extent to which Arabic smart-device users, specifically females and males, interpret emoji sentiment differently. A manual sentiment annotation of 1034 emoji was applied by 28 Arabic native speakers (14 females, and 14 males) from seven different Arabic regions (Gulf, Egypt, Levant, Sudan, Magharib, Iraq, and Yemen). For the gender-based analysis, ten emoji groups were used to compare the differences in context-free emoji sentiment interpretation between Arabic women and men. Two approaches were used to perform the comparison: emoji sentiment score correlation coefficient and emoji sentiment score intensity-based clustering. The analysis reveals that, although the correlation results are moderate between the two genders' emoji sentiment interpretations, the clustering results reveal nuanced differences. Arabic females, mainly, indicate positivity through heart and facial expression emoji, while Arabic males indicate positivity through objects, nature, and some heart emoji. Regarding negativity, both genders tend to use facial expression emoji slightly more than any other type of emoji. Moreover, emoji representing flags are more neutral from the Arabic female perspective, while emoji representing symbols are more neutral from the Arabic male perspective.
Virtual Machine Migration for Infrastructure Service in Cloud Network

Cloud computing has the ability to work in a predestined manner. The main technology is virtualization. This generalizes the physical infrastructure. This helps us to manage easily. In this work, based on the needs the allocated resources are used. It supports the green computing concept. Dealing with the clients request is difficult for the interest of asset allotment. Virtual Machine is utilized for asset provisioning. By utilizing the virtualization climate will lessen the jib reaction time as well as it executes the undertaking as per the accessibility of assets. The viable and dynamic usage of the assets. This would help to balance the load and situations. The implementation uses the co-location approach. It is used to combine the small spaces and improve the performance of server. To reduce the wrong data based on time to live property. We use self-destruction approach. By developing the online prediction model we can estimate the sizes to reduce the task at run time.
Design and Development of Voice and Keypad Controlled Smart Wheelchair

Many difficulties confront elderly and physically challenged people, who must rely on the assistance of a third party to move from one location to another. Furthermore, deaf and dumb people need to communicate with others using sign language is also difficult. In order to satisfy either disabilities or a single disability, persons who can move independently using proposed wheelchair equipped with two control modes such voice as well as a keypad. The voice control is only available to physically challenged people, whereas the keypad control is available to both deaf and dumb. The movement of a wheelchair in the desired direction can be made much easier using voice commands using the Voice recognition module HM2007L, which is interfaced with the microcontroller. The proposed keypad control mechanism, which is linked to the voice module, not only controls the movement of the wheelchair, but it also has pre-programmed sentences that are used for day-to-day conversation, allowing it to easily interact with normal people. In addition to the automated wheelchair, the health parameters of the person are also monitored regularly through heart rate sensors, humidity and temperature sensors. The main aim of this paper is to provide enjoyable, productive and independent living for physically challenged/deaf and dumb people with a weight of up to 100 kg using a single-person, voice-operated wheelchair.
Travel Budget Prediction for Determining Tourism Objects Using Simple Additive Weighting (SAW) Algorithm

Predicting tourism cost planning is one of the most exciting activities to do before traveling, so that travel activities do not affect financial stability. On the one hand, the budget can be used as a reference for planning tours. Making an application to estimate or determine the selection of tours based on the budget for travel costs is not easy to do and requires consideration of several factors and involves a calculation process. In this study, determining tourist locations based on the travel budget was carried out by implementing a decision support system using the Simple Additive Weighting (SAW) method. The SAW algorithm is used in this study because it can provide an alternative prediction of tourist destination locations as the best trip based on ranking existing budget information. The experimental results in the study show that the SAW method uses three parameters (total budget costs, vacation duration, and the number of people) and a filtering process (filtering) on the budget data that has been stored based on the parameters of tourist destinations.
A Data Mining Analysis of Cognitive Science and Artificial Intelligence

Cognitive science borrows from fields such as Artificial Intelligence (AI) which helps in simulating and modeling the human brain. Recently, there has been an increase in the number of research and applications involving cognitive science and AI cooperation. Based on data extracted from the Scopus database. This paper uses the Visualization Of Similarities Method (VOS) between objects in VOSviewer 1.6.18 to look at, evaluate, and find relevant literature, trends, and the scope of research in the fields of cognitive science and AI. The results showed that the USA, the UK, China, Germany, and Canada are the top 5 most active countries in terms of publications. The University of Calgary came out on top of the active institution while the top funding source came from the National Science Foundation in the USA. The study's results will serve as a road map for future academics and researchers developing theory and practice in artificial intelligence and cognitive science.
An Improved Assessing Requirements Quality with ML Methods

This paper describes a method for classification and quality analysis of unspecified information technology (IT) requirements in enterprises using machine learning (ML). During classification, unspecified IT requirements are classified into predefined categories and chapters using a term frequency algorithm and a neural network (NN) based on Tensorflow, which makes a multiclass decision. It bases on the encoding of IT requirements into tensors. The unspecified IT requirements are subject to a quality analysis, and a stencil value is obtained by two algorithms: a pattern template and a Tensorflow model (NN). Both approaches use the Natural Language Toolkit (NLTK) and its Part-of-Speech (PoS) tags. A stencil algorithm uses a PoS-based stencil to compute the stencil value. The Tensorflow model uses other key performance indicators (KPI) and features based on the PoS to make a binary decision about the quality of the requirements. The template thus provides the requirement author with a quality analysis for a possible revision. The described methodology intends to support DevOps in requirements classification and help developers to develop valid artifacts from requirements with little interpretation effort. This paper presents a model driven engineering approach that provides automation and software engineering techniques for managing information in a system (PRIMS).
SCRT: A Secure and Efficient State-Channel-Based Resource Trading Scheme for Internet of Things

With the development of edge computing technology, the resource-limited Internet of Things (IoT) devices can offload computation-intensive artificial intelligence tasks, such as model training and inference to edge servers through resource trading. However, due to the increase in the number of intelligent applications and the rise of peer-to-peer (P2P) resource trading, the existing resource trading schemes based on the blockchain can no longer meet the needs of efficiency and security at the same time. In this article, a new state channels-based resource trading scheme is proposed for IoT, which can improve scalability without sacrificing security and fairness. In our scheme, most of the trading process could be completed off-chain, and the blockchain is used as an adjudicator to determine malicious behavior according to the users’ actions. Moreover, a method without being reliant on support from third parties is presented to defend against execution forks that must be faced when using the state channels. Finally, the feasibility and efficiency of our scheme are experimentally verified in the realistic testbed.
Predictors selection strategy based on stepwise random forests and logistic regression model

Random Forests (RF) are a popular machine learning method for developing variables selection models. However, it may suffer from a lack of better model interpretability compared with traditional models, such as Logistic Regression (LR). In this paper, we propose a predictors selection strategy based on Stepwise Random Forests and Logistic Regression model (SRFLR) and validate it using HCV data provided by UCI machine learning repository platform. The Synthetic Minority Oversampling Technique (SMOTE) algorithm is adopted to deal with the problem of imbalance class in the dataset. The results demonstrate that the proposed SRFLR can obtain more predictors while preserving better prediction ability than LR alone, which will offer some references for clinical researchers to select relevant disease predictors.
Mechanised Operational Reasoning for C11 Programs with Relaxed Dependencies

Verification techniques for C11 programs have advanced significantly in recent years with the development of operational semantics and associated logics for increasingly large fragments of C11. However, these semantics and logics have been developed in a restricted setting to avoid the thin-air-read problem. In this article, we propose an operational semantics that leverages an intra-thread partial order (called semantic dependencies ) induced by a recently developed denotational event-structure-based semantics. We prove that our operational semantics is sound and complete with respect to the denotational semantics. We present an associated logic that generalises a recent Owicki–Gries framework for RC11 RAR (repaired C11) with relaxed and release-acquire accesses. We describe the mechanisation of the logic in the Isabelle/HOL theorem prover, which we use to prove correctness of a number of examples.
Optimal control for maximally creating and maintaining a superposition state of a two‐level system under the influence of Markovian decoherence

Abstract Reducing decoherence is an essential step toward realizing general‐purpose quantum computers beyond the present noisy intermediate‐scale quantum (NISQ) computers. To this end, dynamical decoupling (DD) approaches in which external fields are applied to qubits are often adopted. We numerically study DD using a two‐level model system (qubit) under the influence of Markovian decoherence by using quantum optimal control theory with slightly modified settings, in which the physical objective is to maximally create and maintain a specified superposition state in a specified control period. An optimal pulse is numerically designed while systematically varying the values of dephasing, population decay, pulse fluence, and control period as well as using two kinds of objective functionals. The decrease in purity due to the decoherence limits the ability to maintain a coherent superposition state; we refer to the state of maximal purity that can be maintained as the saturated value. The optimally shaped pulse minimizes the negative effect of decoherence by gradually populating and continuously replenishing the state of saturated purity.
Automating NISQ Application Design with Meta Quantum Circuits with Constraints (MQCC)

Near-term intermediate scale quantum (NISQ) computers are likely to have very restricted hardware resources, where precisely controllable qubits are expensive, error-prone, and scarce. Programmers of such computers must therefore balance trade-offs among a large number of (potentially heterogeneous) factors specific to the targeted application and quantum hardware. To assist them, we propose Meta Quantum Circuits with Constraints (MQCC) , a meta-programming framework for quantum programs. Programmers express their application as a succinct collection of normal quantum circuits stitched together by a set of (manually or automatically) added meta-level choice variables, whose values are constrained according to a programmable set of quantitative optimization criteria. MQCC’s compiler generates the appropriate constraints and solves them via an SMT solver, producing an optimized, runnable program. We showcase a few MQCC’s applications for its generality including an automatic generation of efficient error syndrome extraction schemes for fault-tolerant quantum error correction with heterogeneous qubits and an approach to writing approximate quantum Fourier transformation and quantum phase estimation that smoothly trades off accuracy and resource use. We also illustrate that MQCC can easily encode prior one-off NISQ application designs- –multi-programming (MP) , crosstalk mitigation (CM) —as well as a combination of their optimization goals (i.e., a combined MP-CM).
Three-dimensional object detection network based on geometric information supplement strategy

Three-dimensional object detection from point clouds greatly advances autonomous driving. The real point clouds are unbalanced, where some occluded or distant objects with fewer points suffer from insufficient features, resulting in limited detection accuracy. To address this issue, we propose a novel three-dimensional object detection network based on a Geometric Information Supplement (GIS) strategy. Concretely, we design a novel shape completion model and a point attention fusion module to implement the GIS strategy. The shape completion model based on a structure-aware Transformer first recovers the complete shape points of objects within proposals. Then, the point attention fusion module supplies the recovered geometric shape cues to the original spatial features of objects. By the GIS strategy, the network learns enriched geometric features of objects for proposal refinement, thus, generating accurate detection boxes. Comparative experiments on KITTI dataset show that our method significantly outperforms other state-of-the-art methods while maintaining modest model complexity.