Reviewerly/exHarmony huggingface dataset:
Blockchain: a catalyst in fintech future revolution

Blockchain technology is a trending subject of research and development. As a result of the blockchain proof of concept, the banking industry also accepted the technology and altered the fundamental financial concepts. In the Fintech sector, difficulties such as missed objectives, lengthy fund-raising cycles, and rising losses are typical, and they often happen as a result of not strong management. Blockchain has created inclusive, open, and secure corporate networks that enable the rapid issuance of digital security at lower unit prices and with a higher degree of customization. In recent years, blockchain technology has evolved in the financial sector, exhibiting numerous benefits. According to analysts, blockchain technology will help the banking industry become more accessible, efficient, secure, and user-friendly in the upcoming years. This article reviews how blockchain technology is employed by the financial services industry and also the applications of blockchain, as an emerging field, in Fintech to find new gateways for this field.
Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks

White blood cells (WBCs) in the human immune system defend against infection and protect the body from external hazardous objects. They are comprised of neutrophils, eosinophils, basophils, monocytes, and lymphocytes, whereby each accounts for a distinct percentage and performs specific functions. Traditionally, the clinical laboratory procedure for quantifying the specific types of white blood cells is an integral part of a complete blood count (CBC) test, which aids in monitoring the health of people. With the advancements in deep learning, blood film images can be classified in less time and with high accuracy using various algorithms. This paper exploits a number of state-of-the-art deep learning models and their variations based on CNN architecture. A comparative study on model performance based on accuracy, F1-score, recall, precision, number of parameters, and time was conducted, and DenseNet161 was found to demonstrate a superior performance among its counterparts. In addition, advanced optimization techniques such as normalization, mixed-up augmentation, and label smoothing were also employed on DenseNet to further refine its performance.
Pre-service teachers’ perceptions of data protection in primary education

The protection of personal data and privacy are important issues closely related to use of social media, information and communication technologies, and the Internet in the area of education. The treatment of academic information and use of tools and programs for instruction, communication, and learning have revealed the handling of a significant volume of personal data from different sources. It is essential to protect this information from possible privacy violations. This descriptive study, which is of transversal nonexperimental design, focuses on how 384 pre-service teachers’ enrolled in educational technology courses in their education programs view the protection of personal data. The goals are to describe and analyze how these teachers perceive the risks associated with protection of data on the Internet and what they know about protection of data in primary education. We administered a questionnaire within the framework of an educational activity that focused on digital competence in data protection in education. The results show a high perception of risk in topics such as accepting cookies when surfing the Internet or transferring banking information. The knowledge the students claim to have shown a lack of information on the protection of minors’ data in issues related to the development and schooling of primary school students, as well as their health, background, and family environment. Curricular treatment of these areas that includes content, practices on regulations, and adopts a situated, critical, and responsible approach in pre-service teacher education is recommended.
BioByGANS: biomedical named entity recognition by fusing contextual and syntactic features through graph attention network in node classification framework

Automatic and accurate recognition of various biomedical named entities from literature is an important task of biomedical text mining, which is the foundation of extracting biomedical knowledge from unstructured texts into structured formats. Using the sequence labeling framework and deep neural networks to implement biomedical named entity recognition (BioNER) is a common method at present. However, the above method often underutilizes syntactic features such as dependencies and topology of sentences. Therefore, it is an urgent problem to be solved to integrate semantic and syntactic features into the BioNER model.In this paper, we propose a novel biomedical named entity recognition model, named BioByGANS (BioBERT/SpaCy-Graph Attention Network-Softmax), which uses a graph to model the dependencies and topology of a sentence and formulate the BioNER task as a node classification problem. This formulation can introduce more topological features of language and no longer be only concerned about the distance between words in the sequence. First, we use periods to segment sentences and spaces and symbols to segment words. Second, contextual features are encoded by BioBERT, and syntactic features such as part of speeches, dependencies and topology are preprocessed by SpaCy respectively. A graph attention network is then used to generate a fusing representation considering both the contextual features and syntactic features. Last, a softmax function is used to calculate the probabilities and get the results. We conduct experiments on 8 benchmark datasets, and our proposed model outperforms existing BioNER state-of-the-art methods on the BC2GM, JNLPBA, BC4CHEMD, BC5CDR-chem, BC5CDR-disease, NCBI-disease, Species-800, and LINNAEUS datasets, and achieves F1-scores of 85.15%, 78.16%, 92.97%, 94.74%, 87.74%, 91.57%, 75.01%, 90.99%, respectively.The experimental results on 8 biomedical benchmark datasets demonstrate the effectiveness of our model, and indicate that formulating the BioNER task into a node classification problem and combining syntactic features into the graph attention networks can significantly improve model performance.
Achieving Adaptive Visual Multi-Object Tracking with Unscented Kalman Filter

As an essential part of intelligent monitoring, behavior recognition, automatic driving, and others, the challenge of multi-object tracking is still to ensure tracking accuracy and robustness, especially in complex occlusion environments. Aiming at the issues of the occlusion, background noise, and motion state violent change for multi-object in a complex scene, an improved DeepSORT algorithm based on YOLOv5 is proposed for multi-object tracking to enhance the speed and accuracy of tracking. Firstly, a general object motion model is devised, which is similar to the variable acceleration motion model, and a multi-object tracking framework with the general motion model is established. Then, the latest YOLOv5 algorithm, which has satisfactory detection accuracy, is utilized to obtain the object information as the input of multi-object tracking. An unscented Kalman filter (UKF) is proposed to estimate the motion state of multi-object to solve nonlinear errors. In addition, the adaptive factor is introduced to evaluate observation noise and detect abnormal observations so as to adaptively adjust the innovation covariance matrix. Finally, an improved DeepSORT algorithm for multi-object tracking is formed to promote robustness and accuracy. Extensive experiments are carried out on the MOT16 data set, and we compare the proposed algorithm with the DeepSORT algorithm. The results indicate that the speed and precision of the improved DeepSORT are increased by 4.75% and 2.30%, respectively. Especially in the MOT16 of the dynamic camera, the improved DeepSORT shows better performance.
Don’t Learn What You Already Know

Over the past few years, deep-learning-based attacks have emerged as a de facto standard, thanks to their ability to break implementations of cryptographic primitives without pre-processing, even against widely used counter-measures such as hiding and masking. However, the recent works of Bronchain and Standaert at Tches 2020 questioned the soundness of such tools if used in an uninformed setting to evaluate implementations protected with higher-order masking. On the opposite, worst-case evaluations may be seen as possibly far from what a real-world adversary could do, thereby leading to too conservative security bounds. In this paper, we propose a new threat model that we name scheme-aware benefiting from a trade-off between uninformed and worst-case models. Our scheme-aware model is closer to a real-world adversary, in the sense that it does not need to have access to the random nonces used by masking during the profiling phase like in a worst-case model, while it does not need to learn the masking scheme as implicitly done by an uninformed adversary. We show how to combine the power of deep learning with the prior knowledge of scheme-aware modeling. As a result, we show on simulations and experiments on public datasets how it sometimes allows to reduce by an order of magnitude the profiling complexity, i.e., the number of profiling traces needed to satisfyingly train a model, compared to a fully uninformed adversary.
Security Level Significance in DApps Blockchain-Based Document Authentication

In the development of the Industrial revolution 4.0 to improve and modify the world's industry by integrating production lines, and extraordinary results in the field of technology and information marked its emergence. It can be used to enhance document security systems using Blockchain technology. Blockchain Innovation Authentication has attracted great attention in the world of science and capital markets. The persistent problems of the many available digital currencies and the various tricks of early coin offerings also welcome the well-known discussion of emerging innovations in the field of education. The importance of this paper follows the improvement of the blockchain framework to reveal the importance of decentralized applications (dApps) and blockchain on the future value in education. This study uses a descriptive method, which is a research method used to describe problems that occur in the present or ongoing, aiming to describe what happened as it should when the research was conducted. the novelty of cutting-edge dApps and talk about the title of blockchain progress to meet the positive attributes of future dApps. Readers will come to the conclusion of dApp research and know the continuous improvement in blockchain .
A Critical Review of Deep Learning-Based Multi-Sensor Fusion Techniques

In this review, we provide a detailed coverage of multi-sensor fusion techniques that use RGB stereo images and a sparse LiDAR-projected depth map as input data to output a dense depth map prediction. We cover state-of-the-art fusion techniques which, in recent years, have been deep learning-based methods that are end-to-end trainable. We then conduct a comparative evaluation of the state-of-the-art techniques and provide a detailed analysis of their strengths and limitations as well as the applications they are best suited for.
Control of Localized Single- and Many-Body Dark States in Waveguide QED

Subradiant states in a finite chain of two-level quantum emitters coupled to a one-dimensional reservoir are a resource for superior photon storage and their controlled release. As one can maximally store one energy quantum per emitter, storing multiple excitations requires delocalized states, which typically exhibit fermionic correlations and antisymmetric wave functions, thus making them hard to access experimentally. Here we identify a new class of quasilocalized dark states with up to half of the qubits excited, which only appear for lattice constants of an integer multiple of the wavelength. These states allow for a high-fidelity preparation and minimally invasive readout in state-of-the-art setups. In particular, we suggest an experimental implementation using a coplanar waveguide coupled to superconducting transmon qubits on a chip. With minimal free space and intrinsic losses, virtually perfect dark states can be achieved for a low number of qubits featuring fast preparation and precise manipulation.
Fraud, corruption, and collusion in public procurement activities, a systematic literature review on data-driven methods

Abstract Fraud, corruption, and collusion are the most common types of crime in public procurement processes; they produce significant monetary losses, inefficiency, and misuse of the public treasury. However, empirical research in this area to detect these crimes is still insufficient. This article presents a systematic literature review focusing on the most contemporary data-driven techniques applied to crime detection in public procurement. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology was adopted to identify typical elements that lead to crimes in public contracting. We collected scientific papers and analyzed the selected research using the Scopus repository. We evaluated and summarized findings related to crime detection techniques based mainly on machine learning and network science, as well as studies using fraud risk indices. Some methodologies presented promising results in identifying crimes, especially those using labeled data and machine learning techniques. However, due to the frequent unavailability of pre-labeled data on past cases, analysis through network science tools has become more evident and relevant in exploratory research.
Optimal nonequilibrium thermometry in Markovian environments

What is the minimum time required to take a temperature? In this paper, we solve this question for a large class of processes where temperature is inferred by measuring a probe (the thermometer) weakly coupled to the sample of interest, so that the probe's evolution is well described by a quantum Markovian master equation. Considering the most general control strategy on the probe (adaptive measurements, arbitrary control on the probe's state and Hamiltonian), we provide bounds on the achievable measurement precision in a finite amount of time, and show that in many scenarios these fundamental limits can be saturated with a relatively simple experiment. We find that for a general class of sample-probe interactions the scaling of the measurement uncertainty is inversely proportional to the time of the process, a shot-noise like behaviour that arises due to the dissipative nature of thermometry. As a side result, we show that the Lamb shift induced by the probe-sample interaction can play a relevant role in thermometry, allowing for finite measurement resolution in the low-temperature regime. More precisely, the measurement uncertainty decays polynomially with the temperature as $T\rightarrow 0$, in contrast to the usual exponential decay with $T^{-1}$. We illustrate these general results for (i) a qubit probe interacting with a bosonic sample, where the role of the Lamb shift is highlighted, and (ii) a collective superradiant coupling between a $N$-qubit probe and a sample, which enables a quadratic decay with $N$ of the measurement uncertainty.
Remaining Useful Life Prediction Based on Multi-Representation Domain Adaptation

All current deep learning-based prediction methods for remaining useful life (RUL) assume that training and testing data have similar distributions, but the existence of various operating conditions, failure modes, and noise lead to insufficient data with similar distributions during the training process, thereby reducing RUL prediction performance. Domain adaptation can effectively solve this problem by learning the cross-domain invariant features of the source domain and target domain to reduce the distribution difference. However, most domain adaptive methods extract the source domain and target domain features into a single space for feature alignment, which may leave out effective information and affect the accuracy of prediction. To address this problem, we propose a data-driven approach named long short-term memory network and multi-representation domain adaptation (LSTM-MRAN). We standardize and process the degraded sensor data with a sliding time window, use LSTM to extract features from the degraded data, and mine the time series information between the data. Then, we use multiple substructures in multi-representation domain adaptation to extract features of the source domain and target domain from different spaces and align features by minimizing conditional maximum mean difference (CMMD) loss functions. The effectiveness of the method is verified by the CMAPSS dataset. Compared with methods without domain adaptation and other transfer learning methods, the proposed method provides more reliable RUL prediction results under datasets with different operating conditions and failure modes.
Quantum repeaters: From quantum networks to the quantum internet

A quantum internet is the holy grail of quantum information processing, enabling the deployment of a broad range of quantum technologies and protocols on a global scale. However, numerous challenges exist before the quantum internet can become a reality. Perhaps the most crucial of these is the realization of a quantum repeater, an essential component in the long-distance transmission of quantum information. As the analog of a classical repeater, extender, or booster, the quantum repeater works to overcome loss and noise in the quantum channels comprising a quantum network. Here, we review the conceptual frameworks and architectures for quantum repeaters, as well as the experimental progress towards their realization. We also discuss the various near-term proposals to overcome the limits to the communication rates set by point-to-point quantum communication. Finally, we overview how quantum repeaters fit within the broader challenge of designing and implementing a quantum internet.
Dual-rail encoding with superconducting cavities

The design of quantum hardware that reduces and mitigates errors is essential for practical quantum error correction (QEC) and useful quantum computation. To this end, we introduce the circuit-Quantum Electrodynamics (QED) dual-rail qubit in which our physical qubit is encoded in the single-photon subspace of two superconducting microwave cavities. The dominant photon loss errors can be detected and converted into erasure errors, which are in general much easier to correct. In contrast to linear optics, a circuit-QED implementation of the dual-rail code offers unique capabilities. Using just one additional transmon ancilla per dual-rail qubit, we describe how to perform a gate-based set of universal operations that includes state preparation, logical readout, and parametrizable single and two-qubit gates. Moreover, first-order hardware errors in the cavities and the transmon can be detected and converted to erasure errors in all operations, leaving background Pauli errors that are orders of magnitude smaller. Hence, the dual-rail cavity qubit exhibits a favorable hierarchy of error rates and is expected to perform well below the relevant QEC thresholds with today's coherence times.
MARSHAL MCLUHAN’S TECHNOLOGICAL DETERMINISM THEORY IN THE ARENA OF SOCIAL MEDIA

McLuhan (1964) proposed that mediated technologies ensure culture diffusion in a society which in turn helps change human behavior. He states, "We shape our tools, and they in turn shape us." Most of the scholars in the field talked about radio and television etc. as mediated technologies but McLuhan perceived a bit differently by including numbers, games and money as mediated. Regarding numbers, he held that every individual in a theater enjoys all those others present. This creates a mass mind which let elites to establish a profile of the crowd. The phenomenon in turn homogenizes the masses that are easily influenced. McLuhan stated that games are media of interpersonal communication and extension of human social self. Games according to him “allow for people to simultaneously participate in an activity that is fun and that reflect who they are.” To him money is power that facilitates access. It is money that empowers people to travel the world and serve as transmitter of information, knowledge and culture. These mediated tools according to McLuhan turn the world into a global village. &#x0D; The media of social networking exactly play the same role attributed by McLuhan with number, game, money along with traditional media of radio and television. Social Media (SM) users make use of identical applications and undertake almost similar activities that turn them into a homogeneous mass. Likewise, online profiles reflect identity features and most of them consume SM to have fun. SM connects people across cultures, religions, and boundaries and let them feel members of a single community. SM has not only converted the world into a small village but also shaped every aspect of human social life.
The relationship of online resource use and academic writing of students

Online resources have become the trend in terms of making references accessible to students. The relevance of which has been indispensable with innovations in technology. This research was initiated to determine the relationship between online resource utilization and academic writing. The study employed a correlation method that involved 61 students. Results show that Online resource utilization was very high. In addition, the academic writing of students was high. It was also observed that a significant relationship between Online resource utilization and academic writing exists.
Improving Zero-Shot Cross-Lingual Hate Speech Detection with Pseudo-Label Fine-Tuning of Transformer Language Models

Hate speech has proliferated on social media platforms in recent years. While this has been the focus of many studies, most works have exclusively focused on a single language, generally English. Low-resourced languages have been neglected due to the dearth of labeled resources. These languages, however, represent an important portion of the data due to the multilingual nature of social media. This work presents a novel zero-shot, cross-lingual transfer learning pipeline based on pseudo-label fine-tuning of Transformer Language Models for automatic hate speech detection. We employ our pipeline on benchmark datasets covering English (source) and 6 different non-English (target) languages written in 3 different scripts. Our pipeline achieves an average improvement of 7.6% (in terms of macro-F1) over previous zero-shot, cross-lingual models. This demonstrates the feasibility of high accuracy automatic hate speech detection for low-resource languages. We release our code and models at https://github.com/harisbinzia/ZeroshotCrosslingualHateSpeech.
Constructive Post-Quantum Reductions

Is it possible to convert classical reductions into post-quantum ones? It is customary to argue that while this is problematic in the interactive setting, non-interactive reductions do carry over. However, when considering quantum auxiliary input, this conversion results in a non-constructive post-quantum reduction that requires duplicating the quantum auxiliary input, which is in general inefficient or even impossible. This violates the win-win premise of provable cryptography: an attack against a cryptographic primitive should lead to an algorithmic advantage. We initiate the study of constructive quantum reductions and present positive and negative results for converting large classes of classical reductions to the post-quantum setting in a constructive manner. We show that any non-interactive non-adaptive reduction from assumptions with a polynomial solution space (such as decision assumptions) can be made post-quantum constructive. In contrast, assumptions with super-polynomial solution space (such as general search assumptions) cannot be generally converted. Along the way, we make several additional contributions: