Reviewerly/exHarmony huggingface dataset:
Digital Competence: A Study from the Perspective of Pre-service Teachers in Turkey

This study aims to know the opinions of pre-service teachers on their digital competence and to determine whether these opinions vary according to gender, branch and perceived level of digital competence. In this study, a cross-sectional survey model was used. In such a context, the study was conducted with 518 pre-service teachers who were studying in different provinces of Turkey. The study used a digital competence questionnaire as a data collection tool. When the results were evaluated, it can be said that the digital competence of pre-service teachers is moderate and that it varies significantly according to gender, branch and perceived level of digital competence. It is thought that the results of this study will guide the researchers as they reveal the needs of the pre-service teachers and contain information about what should be focused on the training or activities to be organized to meet these needs.
Energy and economic analysis of a 5 MW photovoltaic system in northern Jordan

The study focuses on the monitoring of the performance of a photovoltaic system, influenced by the climatic characteristics of a particular geographical area, in which the photovoltaic system is installed. This paper presents the results obtained by monitoring, from February 2017 to January 2018, the 5 MWp photovoltaic system that was installed on the campus of Jordan University of Science and Technology (JUST), Jordan. The tilt angle is 15°, and the azimuth angle is 0°. The values of energy and power generated, final yield, reference yield, photovoltaic system efficiency, performance ratio, and cell temperature losses were analyzed and correlated to the climatic characteristics of the site, such as insolation, ambient temperature, and wind speed. System Advisor Model was employed to give model simulation results. A comparison of the actual performance and the simulated one was given. An economic analysis was also carried out and showed that the annual internal rate of return was 30.11%, and the payback period was 4.32 years.
Experimental Setup Employed in the Operating Room Based on Virtual and Mixed Reality: Analysis of Pros and Cons in Open Abdomen Surgery

Currently, surgeons in operating rooms are forced to focus their attention both on the patient’s body and on flat low-quality surgical monitors, in order to get all the information needed to successfully complete surgeries. The way the data are displayed leads to disturbances of the surgeon’s visuals, which may affect his performances, besides the fact that other members of the surgical team do not have proper visual tools able to aid him. The idea underlying this paper is to exploit mixed reality to support surgeons during surgical procedures. In particular, the proposed experimental setup, employed in the operating room, is based on an architecture that put together the Microsoft HoloLens, a Digital Imaging and Communications in Medicine (DICOM) player and a mixed reality visualization tool (i.e., Spectator View) developed by using the Mixed Reality Toolkit in Unity with Windows 10 SDK. The suggested approach enables visual information on the patient’s body as well as information on the results of medical screenings to be visualized on the surgeon’s headsets. Additionally, the architecture enables any data and details to be shared by the team members or by external users during surgical operations. The paper analyses in detail advantages and drawbacks that the surgeons have found when they wore the Microsoft HoloLens headset during all the ten open abdomen surgeries conducted at the IRCCS Hospital “Giovanni Paolo II” in the city of Bari (Italy). A survey based on Likert scale demonstrates how the use of the suggested tools can increase the execution speed by allowing multitasking procedures, i.e., by checking medical images at high resolution without leaving the operating table and the patient. On the other hand, the survey also reveals an increase in the physical stress and reduced comfort due to the weight of the Microsoft HoloLens device, along with drawbacks due to the battery autonomy. Additionally, the survey seems to encourage the use of DICOM Viewer and Spectator View both for surgical education and for improving surgery outcomes. Note that the real use of the conceived platform in the operating room represents a remarkable feature of this paper, since most if not all the studies conducted so far in literature exploit mixed reality only in simulated environments and not in real operating rooms. In conclusion, the study clearly highlights that, despite the challenges required in the forthcoming years to improve the current technology, mixed reality represents a promising technique that will soon enter the operating rooms to support surgeons during surgical procedures in many hospitals across the world.
Front Vehicle Detection Algorithm for Smart Car Based on Improved SSD Model

Vehicle detection is an indispensable part of environmental perception technology for smart cars. Aiming at the issues that conventional vehicle detection can be easily restricted by environmental conditions and cannot have accuracy and real-time performance, this article proposes a front vehicle detection algorithm for smart car based on improved SSD model. Single shot multibox detector (SSD) is one of the current mainstream object detection frameworks based on deep learning. This work first briefly introduces the SSD network model and analyzes and summarizes its problems and shortcomings in vehicle detection. Then, targeted improvements are performed to the SSD network model, including major advancements to the basic structure of the SSD model, the use of weighted mask in network training, and enhancement to the loss function. Finally, vehicle detection experiments are carried out on the basis of the KITTI vision benchmark suite and self-made vehicle dataset to observe the algorithm performance in different complicated environments and weather conditions. The test results based on the KITTI dataset show that the mAP value reaches 92.18%, and the average processing time per frame is 15 ms. Compared with the existing deep learning-based detection methods, the proposed algorithm can obtain accuracy and real-time performance simultaneously. Meanwhile, the algorithm has excellent robustness and environmental adaptability for complicated traffic environments and anti-jamming capabilities for bad weather conditions. These factors are of great significance to ensure the accurate and efficient operation of smart cars in real traffic scenarios and are beneficial to vastly reduce the incidence of traffic accidents and fully protect people's lives and property.
Nonvolatile magneto-optical switches integrated with a magnet stripe array

Nonvolatile optical switches are promising components for low-power photonic integrated circuits with multiple functionalities. In this study, we experimentally demonstrate magneto-optical switches integrated with a magnet array. Optical switches in both microring and Mach-Zehnder configurations are fabricated on a high-quality single-crystalline magneto-optical material Ce:YIG. The switched state is alternated by a current-induced magnetic field from an integrated electromagnet and remained without any external power supply owing to the nonvolatile magnetization of thin-film magnets. Subsequently, the arbitrary level control of optical transmission is demonstrated by changing the magnetization state of integrated thin-film magnets with a current applied in the microring gate switch, and a maximum switching ratio over 25 dB is achieved in the Mach-Zehnder switch. The latching operation is presented with a 1-µs pulsed voltage.
Geothermal resource potential assessment utilizing GIS - based multi criteria decision analysis method

The accurate determination of potential geothermal resource locations is of great significance for efficient exploration and development. We identified five contributing criteria relevant to geothermal resources in northeastern China’s Changbai Mountain region include shallow ground temperature, fault lines, hot springs location, basalt distribution, and isogravity lines. These criteria were used to generate five grid maps: temperature distribution, distance to fault lines, distance to hot springs, distance to basalts, and distance to grabens. After mapping each criterion, the weights of each were determined using an analytic hierarchy process and entropy weight method, and the geothermal potential area was generated using geographic information system - based multi-criteria decision analysis method. The map of the geothermal favorable area was divided into six grades. One geothermal well is located at the junction of the extremely high favorable area and very high favorable area; the other two geothermal wells are located in the very high favorable area. These three geothermal wells are helpful in verifying the correctness of the results.
Engineering two-photon wavefunction and exchange statistics in a semiconductor chip

High-dimensional entangled states of light provide novel possibilities for quantum information, from fundamental tests of quantum mechanics to enhanced computation and communication protocols. In this context, the frequency degree of freedom combines the assets of robustness to propagation and easy handling with standard telecommunication components. Here we use an integrated semiconductor chip to engineer the wavefunction and exchange statistics of frequency-entangled photon pairs directly at the generation stage, without post-manipulation. Tuning the spatial properties of the pump beam allows to generate frequency-anticorrelated, correlated and separable states, and to control the symmetry of the spectral wavefunction to induce either bosonic or fermionic behaviors. These results, supported by analytical and numerical calculations, open promising perspectives for the quantum simulation of fermionic problems with photons on an integrated platform, as well as for communication and computation protocols exploiting antisymmetric high-dimensional quantum states.
Improving coordination in small-scale multi-agent deep reinforcement learning through memory-driven communication

Abstract Deep reinforcement learning algorithms have recently been used to train multiple interacting agents in a centralised manner whilst keeping their execution decentralised. When the agents can only acquire partial observations and are faced with tasks requiring coordination and synchronisation skills, inter-agent communication plays an essential role. In this work, we propose a framework for multi-agent training using deep deterministic policy gradients that enables concurrent, end-to-end learning of an explicit communication protocol through a memory device. During training, the agents learn to perform read and write operations enabling them to infer a shared representation of the world. We empirically demonstrate that concurrent learning of the communication device and individual policies can improve inter-agent coordination and performance in small-scale systems. Our experimental results show that the proposed method achieves superior performance in scenarios with up to six agents. We illustrate how different communication patterns can emerge on six different tasks of increasing complexity. Furthermore, we study the effects of corrupting the communication channel, provide a visualisation of the time-varying memory content as the underlying task is being solved and validate the building blocks of the proposed memory device through ablation studies.
Sentiment analysis for e-commerce product reviews by deep learning model of Bert-BiGRU-Softmax

Sentiment analysis of e-commerce reviews is the hot topic in the e-commerce product quality management, from which manufacturers are able to learn the public sentiment about products being sold on e-commerce websites. Meanwhile, customers can know other people's attitudes about the same products. This paper proposes the deep learning model of Bert-BiGRU-Softmax with hybrid masking, review extraction and attention mechanism, which applies sentiment Bert model as the input layer to extract multi-dimensional product feature from e-commerce reviews, Bidirectional GRU model as the hidden layer to obtain semantic codes and calculate sentiment weights of reviews, and Softmax with attention mechanism as the output layer to classify the positive or negative nuance. A series of experiments are conducted on the large-scale dataset involving over 500 thousand product reviews. The results show that the proposed model outperforms the other deep learning models, including RNN, BiGRU, and Bert-BiLSTM, which can reach over 95.5% of accuracy and retain a lower loss for the e-commerce reviews.
Global Context Networks

The non-local network (NLNet) presents a pioneering approach for capturing long-range dependencies within an image, via aggregating query-specific global context to each query position. However, through a rigorous empirical analysis, we have found that the global contexts modeled by the non-local network are almost the same for different query positions. In this paper, we take advantage of this finding to create a simplified network based on a query-independent formulation, which maintains the accuracy of NLNet but with significantly less computation. We further replace the one-layer transformation function of the non-local block by a two-layer bottleneck, which further reduces the parameter number considerably. The resulting network element, called the global context (GC) block, effectively models global context in a lightweight manner, allowing it to be applied at multiple layers of a backbone network to form a global context network (GCNet). Experiments show that GCNet generally outperforms NLNet on major benchmarks for various recognition tasks. The code and network configurations are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/xvjiarui/GCNet</uri> .
Learning the dynamics of open quantum systems from their steady states

Abstract Recent works have shown that generic local Hamiltonians can be efficiently inferred from local measurements performed on their eigenstates or thermal states. Realistic quantum systems are often affected by dissipation and decoherence due to coupling to an external environment. This raises the question whether the steady states of such open quantum systems contain sufficient information allowing for full and efficient reconstruction of the system’s dynamics. We find that such a reconstruction is possible for generic local Markovian dynamics. We propose a recovery method that uses only local measurements; for systems with finite-range interactions, the method recovers the Lindbladian acting on each spatial domain using only observables within that domain. We numerically study the accuracy of the reconstruction as a function of the number of measurements, type of open-system dynamics and system size. Interestingly, we show that couplings to external environments can in fact facilitate the reconstruction of Hamiltonians composed of commuting terms.
Sobel Edge Detection Based on Weighted Nuclear Norm Minimization Image Denoising

As a classic and effective edge detection operator, the Sobel operator has been widely used in image segmentation and other image processing technologies. This operator has obvious advantages in the speed of extracting the edge of images, but it also has the disadvantage that the detection effect is not ideal when the image contains noise. In order to solve this problem, this paper proposes an optimized scheme for edge detection. In this scheme, the weighted nuclear norm minimization (WNNM) image denoising algorithm is combined with the Sobel edge detection algorithm, and the excellent denoising performance of the WNNM algorithm in a noise environment is utilized to improve the anti-noise performance of the Sobel operator. The experimental results show that the optimization algorithm can obtain better detection results when processing noisy images, and the advantages of the algorithm become more obvious with the increase of noise intensity.
Cyber risk management: History and future research directions

Abstract Cybersecurity research started in the late 1960s and has continuously evolved under different names such as computer security and information security. This article briefly covers that history but will especially focus on the latest incarnation known as “cyber risk management,” which includes both technical and economic/management dimensions. The main focus of the article is to review research on individual steps of the cyber risk management process and on the overall process to highlight gaps and determine research directions. Two main findings are that cyber risk is difficult to include in the overall enterprise risk management process and that a move toward cyber resilience is necessary to deal with such a complex risk. Both findings require a level of interdisciplinary collaboration that is currently lacking.
Hyper-Chaotic Color Image Encryption Based on Transformed Zigzag Diffusion and RNA Operation

With increasing utilization of digital multimedia and the Internet, protection on this digital information from cracks has become a hot topic in the communication field. As a path for protecting digital visual information, image encryption plays a crucial role in modern society. In this paper, a novel six-dimensional (6D) hyper-chaotic encryption scheme with three-dimensional (3D) transformed Zigzag diffusion and RNA operation (HCZRNA) is proposed for color images. For this HCZRNA scheme, four phases are included. First, three pseudo-random matrices are generated from the 6D hyper-chaotic system. Second, plaintext color image would be permuted by using the first pseudo-random matrix to convert to an initial cipher image. Third, the initial cipher image is placed on cube for 3D transformed Zigzag diffusion using the second pseudo-random matrix. Finally, the diffused image is converted to RNA codons array and updated through RNA codons tables, which are generated by codons and the third pseudo-random matrix. After four phases, a cipher image is obtained, and the experimental results show that HCZRNA has high resistance against well-known attacks and it is superior to other schemes.
Naive Bayes and Entropy based Analysis and Classification of Humans and Chat Bots

Internet users are largely threatened by abuse and manipulation of several automated chat service programs called as chat bots. Malware and spam is distributed by the popular chat networks using chat bots. The commercial chat network is surveyed in this paper with a series of measurements. A series of 15 advanced to simple chatbots are used for this purpose. When compared to the bot behavior, the complexity of human behavior is high. A classification system is proposed for accurate distinguishing between human user and chatbots based on the measurements obtained from the study. Naïve Bayes Classifier and entropy classifier are used for the purpose of classification. Chat bot detection is performed with improved efficiency and accuracy using these classifiers. The speed of Naïve Bayes Classifier and accuracy of entropy classifier compliments each other in the process of detection of chat bots. The improved efficiency of the proposed system is proved by testing and comparison with the existing schemes.
Detecting and Quantifying Wash Trading on Decentralized Cryptocurrency Exchanges

Cryptoassets such as cryptocurrencies and tokens are increasingly traded on decentralized exchanges. The advantage for users is that the funds are not in custody of a centralized external entity. However, these exchanges are prone to manipulative behavior. In this paper, we illustrate how wash trading activity can be identified on two of the first popular limit order book-based decentralized exchanges on the Ethereum blockchain, IDEX and EtherDelta. We identify a lower bound of accounts and trading structures that meet the legal definitions of wash trading, discovering that they are responsible for a wash trading volume in equivalent of 159 million U.S. Dollars. While self-trades and two-account structures are predominant, complex forms also occur. We quantify these activities, finding that on both exchanges, more than 30% of all traded tokens have been subject to wash trading activity. On EtherDelta, 10% of the tokens have almost exclusively been wash traded. All data is made available for future research. Our findings underpin the need for countermeasures that are applicable in decentralized systems.
Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach

Fine-tuned pre-trained language models (LMs) have achieved enormous success in many natural language processing (NLP) tasks, but they still require excessive labeled data in the finetuning stage.We study the problem of finetuning pre-trained LMs using only weak supervision, without any labeled data.This problem is challenging because the high capacity of LMs makes them prone to overfitting the noisy labels generated by weak supervision.To address this problem, we develop a contrastive self-training framework, COSINE, to enable fine-tuning LMs with weak supervision.Underpinned by contrastive regularization and confidence-based reweighting, our framework gradually improves model fitting while effectively suppressing error propagation.Experiments on sequence, token, and sentence pair classification tasks show that our model outperforms the strongest baseline by large margins and achieves competitive performance with fully-supervised fine-tuning methods.Our implementation is available on https:// github.com/yueyu1030/COSINE.
Training Networks in Null Space of Feature Covariance for Continual Learning

In the setting of continual learning, a network is trained on a sequence of tasks, and suffers from catastrophic forgetting. To balance plasticity and stability of network in continual learning, in this paper, we propose a novel network training algorithm Adam-NSCL which sequentially optimizes network parameters in the null space of all previous tasks. We first propose two mathematical conditions respectively for achieving network stability and plasticity in continual learning. Based on them, the network training for sequential tasks without forgetting can be simply achieved by projecting the candidate parameter update into the approximate null space of all previous tasks in the network training process, where the candidate parameter update can be generated by Adam. The approximate null space can be derived by applying singular value decomposition to the un-centered covariance matrix of all input features of previous tasks for each linear layer. For efficiency, the uncentered covariance matrix can be incrementally computed after learning each task. We also empirically verify the rationality of the approximate null space at each linear layer. We apply our approach to training networks for continual learning on benchmark datasets of CIFAR-100 and TinyImageNet, and the results suggest that the proposed approach outperforms or matches the state-ot-the-art continual learning approaches.