Reviewerly/exHarmony huggingface dataset:
FUTURE OF INTERNET OF EVERYTHING (IOE)

In the world, the man matters, not the machine, people need to be care, not data. There is transition from information technology to human technology. The answer is internet of everything (IoE). The internet of everything (IoE) is a concept that extends the internet of things (IOT) by encompassing the machine-to-machine (M2M) communication, machine-to-people (M2P) and technology-assisted people-to-people (P2P) with expended digital features. It includes different types of devices, appliances and items that are connected to the global internet. The internet of everything (IoE) comprises four components, namely, people, process, data and things to make the networked connections more relevant and valuable. These components enable us to create new capabilities, wide experiences and economic opportunity for individuals and businesses. IoE connects people in more relevant and valuable ways, deliver the right information to the right people and/or machine at the right time, leverages data into more useful information for decision making and connect physical devices and objects with each other via the internet of things (IoT). The IoE includes user-generated communications and interactions associated with the global internet. The IoE is 5G technology that include evolution, elasticity, capacity, speed and agility. In this research work, we discuss the potentiality of IoE technology that includes the seamless interconnections and autonomous coordination among people, data and process through the global internet infrastructure. We will highlight the various applications of IoE in the area of privacy and security. Security and privacy are the major challenge in today world. There are various challenges like software and hardware compatibility, wired and wireless infrastructure, data analysis, data mining, big data and many other things that need to be solves globally. We will discuss the security aspects of IoE that ensure the securing the environment, the infrastructure and the process. Data collection is also the major challenge at the network edge. It includes multitude of endpoint devices and sensors that automatically collect, analyze and transmit the data on the massive scale and at very fast rate. Internet of everything (IoE) will have a huge impact on individuals and business in the next few years. It will be a radial shift to think that how we live, solve the problem, create value, secure environment to keep people, data, process and things under one umbrella. It will include devices and objects at the massive scale. The network capabilities will create the new experiences, capabilities and economic opportunities for individuals, businesses and governments. In short, the internet of everything (IoE) changes only one thing, which is everything.
A Motion Capture Data-Driven Automatic Labanotation Generation Model Using the Convolutional Neural Network Algorithm

All human movements can be effectively represented with labanotation, which is simple to read and preserve. However, manually recording the labanotation takes a long time, so figuring out how to use the labanotation to accurately and quickly record and preserve traditional dance movements is a key research question. An automatic labanotation generation algorithm based on DL (deep learning) is proposed in this study. The BVH file is first analyzed, and the data are then converted. On this foundation, a CNN (convolutional neural network) algorithm for generating the dance spectrum of human lower-limb movements is proposed, which is very good at learning action space information. The algorithm performs admirably in terms of classification and recognition. Finally, a spatial segmentation-based automatic labanotation generation algorithm is proposed. To begin, every frame of data is converted into a symbol sequence using spatial law, resulting in a very dense motion sequence. The motion sequence is then regulated according to the minimum beat of motion obtained through wavelet analysis. To arrive at the final result, the classifier is used to determine whether each symbol is reserved or not. As a result, we will be able to create more accurate dance music for simple human movements.
Cross-Lingual Transfer Learning for Arabic Task-Oriented Dialogue Systems Using Multilingual Transformer Model mT5

Due to the promising performance of pre-trained language models for task-oriented dialogue systems (DS) in English, some efforts to provide multilingual models for task-oriented DS in low-resource languages have emerged. These efforts still face a long-standing challenge due to the lack of high-quality data for these languages, especially Arabic. To circumvent the cost and time-intensive data collection and annotation, cross-lingual transfer learning can be used when few training data are available in the low-resource target language. Therefore, this study aims to explore the effectiveness of cross-lingual transfer learning in building an end-to-end Arabic task-oriented DS using the mT5 transformer model. We use the Arabic task-oriented dialogue dataset (Arabic-TOD) in the training and testing of the model. We present the cross-lingual transfer learning deployed with three different approaches: mSeq2Seq, Cross-lingual Pre-training (CPT), and Mixed-Language Pre-training (MLT). We obtain good results for our model compared to the literature for Chinese language using the same settings. Furthermore, cross-lingual transfer learning deployed with the MLT approach outperform the other two approaches. Finally, we show that our results can be improved by increasing the training dataset size.
Incorporation of Computational Thinking Practices to Enhance Learning in a Programming Course

The development of computational thinking skills is essential for information management, problem-solving, and understanding human behavior.Thus, the aim of the experience described here was to incorporate computational thinking practices to improve learning in a first Python programming course using programming tools such as PSeInt, CodingBat, and the turtle graphic library.A quasi-experimental methodological design was used in which the experimental and control groups are in different academic semesters.Exploratory mixed research was carried out.The control and experimental group consisted of 41 and 36 students, respectively.The results show that with the use of support programming tools, such as PSeInt, CodingBat, Python turtle graphic library, and the incorporation of computational thinking practices, the experimental group students obtained better learning results.It is concluded that student performance and motivation in university programming courses can be improved by using proper tools that help the understanding of programming concepts and the skills development related to computational thinking, such as abstraction and algorithmic thinking.
LPPS-AGC: Location Privacy Protection Strategy Based on Alt-Geohash Coding in Location-Based Services

The Internet of Things (IoT) connects billions of physical devices around the world to the Internet to collect and share massive data. Location privacy leakage has received considerable attention in the field of security. In order to implement the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>k</mi> </math> -anonymity location privacy protection mechanism, a previous work constructed an anonymous location set by retrieving historical request record database from the trusted third anonymous server, which advantageously protects user’s location privacy. However, the performance of location-based services (LBSs) is weakened by the time overhead of continual retrieving database. Moreover, with the increasingly flourishing of the positioning technique, user location can be accurate to the user’s altitude beyond the two-dimensional latitude and longitude coordinates. In this paper, we present a location privacy protection strategy in LBSs based on Alt-Geohash coding (LPP-AGC) method synthetically considering the altitude of user location and time overhead. Specifically, we give the Alt-Geohash coding algorithm (AGCA) to retrieve the historical request record database, which greatly reduces the time overhead and ensures the immediacy of LBSs. In addition, we propose the dummy location generating algorithm (DLGA) and location filtering algorithm (LFA) to provide users with autonomous <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>k</mi> </math> -anonymity location privacy protection. Extensive simulations are performed to verify the performance and security of the proposed strategy.
Beyond Cybercrime: New Perspectives on Crime, Harm and Digital Technologies

This special issue comprises 10 journal articles and one book review. Collectively, the contributions broaden our theoretical and conceptual understandings of the technology–harm nexus and provide criminologists with new ways of moving beyond cybercrime. The issue consists of two parts. The first part of the issue, entitled ‘Digital (in)Justices’, contains five manuscripts, each examining a particular intersection between digital technology and criminal justice agencies. The second part of the special issue—‘Rethinking the Technology–Harm Nexus’—includes five manuscripts that engage with a range of techno-social harms. The authors provide novel theoretical contributions that explore how the intersection of technology and harm can be problematised and reconceptualised.
Two-way ↔ quantum communication using four-qubit cluster state: Mutual exchange of quantum information

In the present study, we have proposed a scheme for two-way quantum communication in which the two legitimate participants mutually exchange their quantum information to each other by using a four-qubit cluster state as the quantum channel. Recently, by utilizing a four-qubit cluster state as the quantum channel, Kazemikhah et al. [Int. J. Theor. Phys., 60 (2021) 378] tried to design a scheme for the mutual exchange of quantum information between two legitimate participants. However, in the present study, it has been shown that in their scheme the transmission of quantum information cannot be realized because the two participants are not entangled to each other due to a trivial conceptual mistake made by Kazemikhah et al. in the description of the quantum channel. Here, we have shown that two legitimate participants can teleport quantum information states to each other by using a four-qubit cluster state as the quantum channel, provided they co-operate with each other and perform non-local controlled phase gate operation. If both participants do not co-operate with each other, then no one can reconstruct the information sent to them, and therefore the exchange of information is possible only when both participants are honest to each other.
Decomposing neural networks as mappings of correlation functions

Understanding the functional principles of information processing in deep neural networks continues to be a challenge, in particular for networks with trained and thus nonrandom weights. To address this issue, we study the mapping between probability distributions implemented by a deep feed-forward network. We characterize this mapping as an iterated transformation of distributions, where the nonlinearity in each layer transfers information between different orders of correlation functions. This allows us to identify essential statistics in the data, as well as different information representations that can be used by neural networks. Applied to an XOR task and to MNIST, we show that correlations up to second order predominantly capture the information processing in the internal layers, while the input layer also extracts higher-order correlations from the data. This analysis provides a quantitative and explainable perspective on classification.
Multiparameter estimation for qubit states with collective measurements: a case study

Abstract Quantum estimation involving multiple parameters remains an important problem of both theoretical and practical interest. In this work, we study the problem of simultaneous estimation of two parameters that are respectively associate with the length and direction of the Bloch vector for identically prepared qubit states that is confined to a plane, where in order to obtain the optimal estimation precision for both parameters, collective measurements on multiple qubits are necessary. Upon treating N qubits as an ensemble of spin-1/2 systems, we show that simultaneous optimal estimation for both parameters can be attained asymptotically with a simple collective measurement strategy—first, we estimate the length parameter by measuring the populations in spaces corresponding to different total angular momentum values j , then we estimate the direction parameter by performing a spin projection onto an optimal basis. Furthermore, we show that when the state is nearly pure, for sufficiently but not arbitrarily large N , most information will be captured in the largest three j -subspaces. Then, we study how the total angular-momentum measurement can be realized by observing output signatures from a Bell multiport setup, either exactly for N = 2, 3, or approximately when the qubits are nearly pure for other N values. We also obtain numerical results that suggest that using a Bell multiport setup, one can distinguish between projection onto the j = N /2 and j = N /2 − 1 subspaces from their respective interference signatures at the output.
Detecting Humans in Search and Rescue Operations Based on Ensemble Learning

Detection of humans accurately in aerial images is critical for various applications such as surveillance, detecting and tracking athletes on sports fields, and search and rescue operations (SAR). The goal of SAR is to assist, detect, and rescue people who have had accidents in the mountains or other hazardous environments. By using drones in SAR applications, it is desirable to minimize the cost and time spent on SAR operations. In this paper, we present a convolutional neural network-based model for the detection of humans in aerial images of mountain landscapes acquired by unmanned aerial vehicles (UAVs) used in search and rescue operations. Detection of humans in aerial images remains a complex task due to various challenges such as pose and scale variations of humans, low visibility, camouflaged environment, adverse weather conditions, motion blur, and high-resolution aerial images. Due to imaging from high altitudes, in most high-resolution aerial images captured by UAVs, only 0.1 to 0.2 percentage of the image represents humans. To solve the problem of low coverage of the object of interest in high-resolution aerial images, we propose to implement a deep learning-based object detection model. In this paper, we propose a novel method for the detection of humans in aerial images based on the EfficientDET architecture and ensemble learning. The method has been validated on the HERIDAL image dataset. By implementing the proposed methodologies, we achieved an mAP of 95.11%. To the best of our knowledge, this is the highest accuracy result for human detection on the HERIDAL dataset.
Designing a New XTS-AES Parallel Optimization Implementation Technique for Fast File Encryption

XTS-AES is a disk encryption mode of operation that uses the block cipher AES. Several studies have been conducted to improve the encryption speed using XTS-AES according to the increasing disk size. Among them, there are researches on parallel encryption of XTS-AES using GPU. Although these studies focus on parallel encryption of AES, optimization for the entire XTS mode has not been performed. The reason is that the <inline-formula> <tex-math notation="LaTeX">$\alpha ^{j}$ </tex-math></inline-formula> computation process included in XTS mode is not suitable for parallel operation. Therefore, in this paper, we proposed several techniques for high-speed encryption in GPU by modifying XTS-AES into a form that is advantageous for parallel operation. The core idea is to pre-calculate the <inline-formula> <tex-math notation="LaTeX">$\alpha ^{j}$ </tex-math></inline-formula> calculation on the CPU into a form that is easy to operate on the GPU. To achieve this goal, we analyzed the <inline-formula> <tex-math notation="LaTeX">$\alpha ^{j}$ </tex-math></inline-formula> calculation process and present the parts that can be optimized. First, we presented a method that can replace multiple operations with a single table reference through the analyzed <inline-formula> <tex-math notation="LaTeX">$\alpha ^{j}$ </tex-math></inline-formula> computation progress. Thereafter, we proposed a method that can be calculated by partially skipping the entire <inline-formula> <tex-math notation="LaTeX">$\alpha ^{j}$ </tex-math></inline-formula> computation process that must be sequentially calculated through the table reference technique. For the proposed optimization implementation, we presented various results for evaluating the optimal implementation. In addition, we compared the performance of XTS-AES OpenSSL implementation on CPU and our proposed optimization implementation on GPU.
An empirical evaluation of Lex/Yacc and ANTLR parser generation tools

Parsers are used in different software development scenarios such as compiler construction, data format processing, machine-level translation, and natural language processing. Due to the widespread usage of parsers, there exist different tools aimed at automizing their generation. Two of the most common parser generation tools are the classic Lex/Yacc and ANTLR. Even though ANTLR provides more advanced features, Lex/Yacc is still the preferred choice in many university courses. There exist different qualitative comparisons of the features provided by both approaches, but no study evaluates empirical features such as language implementor productivity and tool simplicity, intuitiveness, and maintainability. In this article, we present such an empirical study by conducting an experiment with undergraduate students of a Software Engineering degree. Two random groups of students implement the same language using a different parser generator, and we statistically compare their performance with different measures. Under the context of the academic study conducted, ANTLR has shown significant differences for most of the empirical features measured.
Improving Alzheimer's Disease Detection for Speech Based on Feature Purification Network

Alzheimer's disease (AD) is a neurodegenerative disease involving the decline of cognitive ability with illness progresses. At present, the diagnosis of AD mainly depends on the interviews between patients and doctors, which is slow, expensive, and subjective, so it is not a better solution to recognize AD using the currently available neuropsychological examinations and clinical diagnostic criteria. A recent study has indicated the potential of language analysis for AD diagnosis. In this study, we proposed a novel feature purification network that can improve the representation learning of transformer model further. Though transformer has made great progress in generating discriminative features because of its long-distance reasoning ability, there is still room for improvement. There exist many common features that are not indicative of any specific class, and we rule out the influence of common features from traditional features extracted by transformer encoder and can get more discriminative features for classification. We apply this method to improve transformer's performance on three public dementia datasets and get improved classification results markedly. Specifically, the method on Pitt datasets gets state-of-the-art (SOTA) result.
Centralized project-specific metadata platforms: toolkit provides new perspectives on open data management within multi-institution and multidisciplinary research projects

Open science and open data within scholarly research programs are growing both in popularity and by requirement from grant funding agencies and journal publishers. A central component of open data management, especially on collaborative, multidisciplinary, and multi-institutional science projects, is documentation of complete and accurate metadata, workflow, and source code in addition to access to raw data and data products to uphold FAIR (Findable, Accessible, Interoperable, Reusable) principles. Although best practice in data/metadata management is to use established internationally accepted metadata schemata, many of these standards are discipline-specific making it difficult to catalog multidisciplinary data and data products in a way that is easily findable and accessible. Consequently, scattered and incompatible metadata records create a barrier to scientific innovation, as researchers are burdened to find and link multidisciplinary datasets. One possible solution to increase data findability, accessibility, interoperability, reproducibility, and integrity within multi-institutional and interdisciplinary projects is a centralized and integrated data management platform. Overall, this type of interoperable framework supports reproducible open science and its dissemination to various stakeholders and the public in a FAIR manner by providing direct access to raw data and linking protocols, metadata and supporting workflow materials.
A Small Network MicronNet-BF of Traffic Sign Classification

One of a very significant computer vision task in many real-world applications is traffic sign recognition. With the development of deep neural networks, state-of-art performance traffic sign recognition has been provided in recent five years. Getting very high accuracy in object classification is not a dream any more. However, one of the key challenges is becoming making the deep neural network suitable for an embedded system. As a result, a small neural network with as less parameters as possible and high accuracy needs to be explored. In this paper, the MicronNet which is a small but powerful convolutional neural network is improved by batch normalization and factorization, and the proposed MicronNet-BN-Factorization (MicronNet-BF) takes advantages about reducing parameters and improving accuracy. The effect of image brightness is reduced for feature recognition by the elimination of mean and variance of each input layer in MicronNet via BN. A lower number of parameters are realized with the replacement of convolutional layers in MicronNet, which is the inspiration of factorization. In addition, data augmentation is also been changed to get higher accuracy. Most important, the experiment shows that the accuracy of MicronNet-BF is 99.383% on German traffic sign recognition benchmark (GTSRB) which is much higher than the original MicronNet (98.9%), and the most influence factor is batch normalization after the confirmation of orthogonal experimental. Furthermore, the handsome training efficiency and generality of MicronNet-BF indicate the wide application in embedded scenarios.
A reproducible POI recommendation framework: Works mapping and benchmark evaluation

This work is a companion reproducibility paper that presents a framework to reproduce our previous experiments and results reported in Werneck et al. (2021). In that previous paper, we introduced a systematic mapping process of points-of-interest (POI) recommendation methods and provided a uniform evaluation methodology based on metrics covering different aspects besides accuracy. Due to the lack of reproducible and extensible benchmarks, our work introduces a reproducibility framework for POI methods based on a collection of Python software libraries and a Docker image. Our proposal is composed of: (1) a package to perform a protocol that reproduces our systematic mapping process Werneck et al. (2021), containing all collected data, insightful views on current advances and opened challenges; and (2) an extensible benchmark to perform a protocol to reproduce experimental evaluations on POI recommendation, considering different datasets, metrics, and the strongest baselines in the literature. This work also demonstrates all processes required to instantiate its framework. Moreover, our work can be considered at least weakly reproducible, since we were able to reproduce the results of the previous paper, leading us to the same conclusions.