Reviewerly/exHarmony huggingface dataset:
Research on Histogram Equalization Algorithm Based on Optimized Adaptive Quadruple Segmentation and Cropping of Underwater Image (AQSCHE)

Due to the uncertain, diverse, and light-attenuating characteristics of the underwater environment, underwater images have low contrast and unclear problems. This paper proposes a histogram equalization algorithm based on optimized adaptive image quadruple segmentation and cropping (AQSCHE). Compared with the traditional histogram equalization underwater image enhancement algorithm, this algorithm introduces histogram quadruple segmentation and cropping technology. Using the exposure value and segmentation point calculation formula that optimizes the distribution range of the histogram, perform quadruple segmentation on the image to obtain a more refined histogram. The adaptive histogram clipping is realized by constructing the clipping parameter z to adjust the contrast and brightness of the image. The original image is enhanced by the equalization of the sub-histogram and the histogram of each channel. Finally, the simulation experiments verify the enhancement effect of the proposed algorithm AQSCHE on underwater images. The processed underwater image has higher contrast, clearer and more natural in subjective evaluation, and has better visual effect; in the image objective evaluation indicators, information entropy (Entropy), peak signal to noise ratio (PSNR), structural similarity index (SSIM) and universal color image quality evaluator (UCIQE), etc., this algorithm also outperforms other common algorithms such as HE and CLAHE.
Open-answer question with regular expression templates and string completion hinting

Open-answer questions are resistant to guesswork; they stimulate students' thinking.However, the number of correct answers rises quickly with the answer length both in natural and formal languages.One template can define many variants of correct answers.The article presents a question plugin Preg for the popular Moodle LMS, which uses Perl-compatible regular expressions as answer templates and can hint next correct character and lexeme.The software was used to study the effectiveness of templates and hint usage during online learning and in the learning process to teach programming languages, medicine, veterinary, English as a foreign language and so on.
A Knowledge Graph-Driven CNN for Radar Emitter Identification

In recent years, the rapid development of deep learning technology has brought new opportunities for specific emitter identification and has greatly improved the performance of radar emitter identification. The most specific emitter identification methods, based on deep learning, have focused more on studying network structures and data preprocessing. However, the data selection and utilization have a significant impact on the emitter recognition efficiency, and the method to adaptively determine the two parameters by a specific recognition model has yet to be studied. This paper proposes a knowledge graph-driven convolutional neural network (KG-1D-CNN) to solve this problem. The relationship network between radar data is modeled via the knowledge graph and uses 1D-CNN as the metric kernel to measure these relationships in the knowledge graph construction process. In the recognition process, a precise dataset is constructed based on the knowledge graph according to the task requirement. The network is designed to recognize target emitter individuals from easy to difficult by the precise dataset. In the experiments, most algorithms achieved good recognition results in the high SNR case (10–15 dB), while only the proposed method could achieve more than a 90% recognition rate in the low SNR case (0–5 dB). The experimental results demonstrate the efficacy of the proposed method.
Attribute-Based Encryption Schemes for Next Generation Wireless IoT Networks: A Comprehensive Survey

Most data nowadays are stored in the cloud; therefore, cloud computing and its extension-fog computing-are the most in-demand services at the present time. Cloud and fog computing platforms are largely used by Internet of Things (IoT) applications where various mobile devices, end users, PCs, and smart objects are connected to each other via the internet. IoT applications are common in several application areas, such as healthcare, smart cities, industries, logistics, agriculture, and many more. Due to this, there is an increasing need for new security and privacy techniques, with attribute-based encryption (ABE) being the most effective among them. ABE provides fine-grained access control, enables secure storage of data on unreliable storage, and is flexible enough to be used in different systems. In this paper, we survey ABE schemes, their features, methodologies, benefits/drawbacks, attacks on ABE, and how ABE can be used with IoT and its applications. This survey reviews ABE models suitable for IoT platforms, taking into account the desired features and characteristics. We also discuss various performance indicators used for ABE and how they affect efficiency. Furthermore, some selected schemes are analyzed through simulation to compare their efficiency in terms of different performance indicators. As a result, we find that some schemes simultaneously perform well in one or two performance indicators, whereas none shines in all of them at once. The work will help researchers identify the characteristics of different ABE schemes quickly and recognize whether they are suitable for specific IoT applications. Future work that may be helpful for ABE is also discussed.
Deep Neural Network Augmented Wireless Channel Estimation for Preamble-Based OFDM PHY on Zynq System on Chip

Reliable and fast channel estimation is crucial for next-generation wireless networks supporting a wide range of vehicular and low-latency services. Recently, deep learning (DL)-based channel estimation has been explored as an efficient alternative to conventional least-square (LS) and linear minimum mean square error (LMMSE) approaches. Most of these DL approaches have not been realized on system on chip (SoC), and preliminary study shows that their complexity exceeds the complexity of the entire physical layer (PHY). The high latency of DL is another concern. This article considers the design and implementation of deep neural network (DNN) augmented LS (LSDNN)-based channel estimation for preamble-based orthogonal frequency-division multiplexing (OFDM) PHY on SoC. We demonstrate the gain in performance compared with the conventional LS and LMMSE approaches. Via software–hardware codesign, word-length optimization, and reconfigurable architectures, we demonstrate the superiority of the LSDNN over LS and LMMSE for a wide range of signal-to-noise ratio (SNR), number of pilots, preamble types, and wireless channels. Furthermore, we evaluate the performance, power, and area (PPA) of the LS and LSDNN application-specific integrated circuit (ASIC) implementations in 45-nm technology. We demonstrate that word-length optimization can substantially improve PPA for the proposed architecture in ASIC implementations.
Waste Self-reporting for Software Development Productivity Improvement

Abstract Little research has been done on enabling software development teams to self-report waste to assist in productivity improvement. This study created a waste categorization and survey for teams to identify and quantify wasteful activities. Developers from a Swiss company used the survey for three weeks. Participants found the survey helpful for identifying waste but there was little evidence that self-reported waste correlated with improved performance.
An ROI Optimization Method Based on Dynamic Estimation Adjustment Model

An important research direction in the field of traffic light recognition of autonomous systems is to accurately obtain the region of interest (ROI) of the image through the multi-sensor assisted method. Dynamic evaluation of the performance of the multi-sensor (GNSS, IMU, and odometer) fusion positioning system to obtain the optimum size of the ROI is essential for further improvement of recognition accuracy. In this paper, we propose a dynamic estimation adjustment (DEA) model construction method to optimize the ROI. First, according to the residual variance of the integrated navigation system and the vehicle velocity, we divide the innovation into an approximate Gaussian fitting region (AGFR) and a Gaussian convergence region (GCR) and estimate them using variational Bayesian gated recurrent unit (VBGRU) networks and a Gaussian mixture model (GMM), respectively, to obtain the GNSS measurement uncertainty. Then, the relationship between the GNSS measurement uncertainty and the multi-sensor aided ROI acquisition error is deduced and analyzed in detail. Further, we build a dynamic estimation adjustment model to convert the innovation of the multi-sensor integrated navigation system into the optimal ROI size of the traffic lights online. Finally, we use the YOLOv4 model to detect and recognize the traffic lights in the ROI. Based on laboratory simulation and real road tests, we verify the performance of the DEA model. The experimental results show that the proposed algorithm is more suitable for the application of autonomous vehicles in complex urban road scenarios than the existing achievements.
Facial Recognition in Hexagonal Domain—A Frontier Approach

Many face-recognition (FR) methods have been proposed thus far. Although FR has achieved wisdom in square pixel-based image processing (SIP) due to many studies, this wisdom has not been transferred to Hexagonal pixel-based image processing (HIP) until now. This study presents HIP versions of the most basic texture extraction studies in SIP, namely Gray-Level-Co-occurrence-Matrices (GLCM), Local Binary Pattern (LBP), and our recent work, local-holistic graph-based descriptor (LHGPD). The images are first transformed from the SIP domain to the HIP domain. The HIP domain equivalences (HexGLCM, HexLBP, and HexLHGPD) of the SIP domain GLCM, LBP, and LHGPD are then established. Finally, the facial recognition performances of the SIP and HIP domain versions of GLCM, LBP, and LHGPD are evaluated and compared on the primary data sets. The results of the experiments reveal that HIP domain GLCM, LBP, and LHGPD show a par performance, surpassing them in places when compared to their counterparts in the SIP domain regarding face recognition accuracy.
Generalizing Simultaneously to Support Smooth Zooming: Case Study of Merging Area Objects

Abstract When users zoom in or out on a digital map, the map should change correspondingly to present geographical information at proper levels. A way to help map users better keep track of their interested objects is to change the map smoothly instead of discretely switching between several levels of detail. This paper focuses on the problem of providing smooth merging of area objects. We propose to merge multiple areas simultaneously to share their animation durations. In this way, each merging operation can be prolonged, and it is visually smoother. We present a greedy algorithm to decide which areas should be merged at each step. The merging process is pre-computed and is recorded into a space-scale cube (SSC). When a user accesses our web map, the SSC is sent to the client side so that the map can be generated by slicing the SSC in the graphics processing unit (GPU). We also explain how to snap the zooming to valid states so that the zooming will not stop halfway of the merging operations. Our case study shows that it is visually smoother to merge simultaneously than to sequentially merge each pair of areas.
Improvement of the Efficiency of Neural Cryptography-Based Secret Key Exchange Algorithm

Owing to new security threats and relevant changes in network computing environments, key exchange methods that replace conventional public key exchange algorithms are being researched. The neural cryptography-based key exchange algorithm proposed recently uses neural synchronization as an alternative to public-key methods. However, the learning-based synchronization method uses considerable communication resources to generate output values and share the results. The efficiency of this method depends on the type of algorithm and is affected by both the communication rounds for exchanging output values and the number of weight learning rounds. To improve its efficiency, this paper proposes 1-h random walk and batch scheme methods and verifies their efficiency and security.
Cyber Literacy Skills of Estonians: Activities and Policies For Encouraging Knowledge-Based Cyber Security Attitudes

With the advent of COVID-19, people spent more time at home. Countries, societies, companies and individuals suddenly became dependent on cyberspace overnight. Work, shopping and leisure meant we became more than ever weak to the risks of cyberspace. The human factor makes people the “key” to otherwise technically hardly penetrable systems – criminals play on people’s greed, emotions, happiness, etc.&#x0D; It is no surprise that Estonians are more active daily internet users compared to the rest of the Europeans. But what is being done in Estonia at the national level, as well as in cyber protection organisational level, to raise the awareness of residents about cyber security? What else should be done to reduce victims among ordinary citizens and how to protect various important public and private organisations from the consequences of the cyber-risky behaviour of their employees? What is the role of libraries in promoting cyber security awareness? A human error is a central target of cyber attacks, phishing scams, and data breaches. Cyber criminals are becoming more and more inventive. It is important to understand that cyber security risks can be managed and mitigated, but not completely eliminated. Increasing security awareness is the only factor that can help limit breaches caused by human frailty.
Multiparty quantum contract signing

Quantum contract signing has the advantage of unconditional security compared with classical one. However, the prior works focus on two clients’ case. In this paper, we give a multiparty quantum contract signing protocol, which allows multiparty clients to sign an electronic contract simultaneously. The analysis shows that it not only satisfies the requirements for optimism and fairness but also can be realized with current technology.
Trust, understanding, and machine translation: the task of translation and the responsibility of the translator

Abstract Could translation be fully automated? We must first acknowledge the complexity, ambiguity, and diversity of natural languages. These aspects of natural languages, when combined with a particular dilemma known as the computational dilemma, appear to imply that the machine translator faces certain obstacles that a human translator has already managed to overcome. At the same time, science has not yet solved the problem of how human brains process natural languages and how human beings come to acquire natural language understanding. We will then distinguish between the task of translation and the responsibility of the translator. Thereafter, we will conduct a survey of the methods of machine translation (viz. RBMT, SMT, NMT, foundation models or large language models). These methods will then be critically evaluated both in general and relative to Bar-Hillel’s hypothesis about the impossibility of fully automatic, high-quality machine translation (F ahqmt ). Some concluding remarks will be made about the scope, prospects, and limits of machine translation.
FPGA Centric Attention Based Deep Learning Network Evoked Chaotic Encryption to Mitigate Side Channel Attacks

Security concerns are growing, especially in applications where sensitive information is sent (such as health care devices, credit cards, and even Internet of Things (IoT) nodes), even while these developments bring in a modern revolution. This is due to the constant scaling of technologies, which has now been incredibly useful for delivering the new trend of development in the numerous domains such as communications, semiconductors, health care, and automation. Even when cutting-edge standard encryption algorithms are used in smart devices, the principal threats, such as Side-Channel Attacks (SCA) and Correlation Power Analysis (CPA), increase the susceptibility of encrypted cipher text to attacks. In order to defend against side channel assaults, the usefulness of deep learning algorithms combined with chaotic principles is examined in this paper. Scroll Mapping (SM) and Attention-Evoked Long Short-Term Memory (AE-LSTM) are used in the proposed study to effectively safeguard sensitive data while consuming little power. The most promising advancements in terms of all security metrics and hardware capabilities for obtaining high-speed performances are shown by experimental findings on an FPGA implementation of a suggested framework.
A doubly stochastic matrices-based approach to optimal qubit routing

Swap mapping is a quantum compiler optimization that, by introducing $$\text {SWAP}$$ gates, maps a logical quantum circuit to an equivalent physically implementable one. The physical implementability of a circuit is determined by the fulfillment of the hardware connectivity constraints. Therefore, the placement of the $$\text {SWAP}$$ gates can be interpreted as a discrete optimization process. In this work, we employ a structure called doubly stochastic matrix, which is defined as a convex combination of permutation matrices. The intuition is that of making the decision process smooth. Doubly stochastic matrices are contained in the Birkhoff polytope, in which the vertices represent single permutation matrices. In essence, the algorithm uses smooth constrained optimization to slide along the edges of the polytope toward the potential solutions on the vertices. In the experiments, we show that the proposed algorithm, at the cost of additional computation time, can deliver significant depth reduction when compared to the state-of -the-art algorithm SABRE.
DCP–NAS: Discrepant Child–Parent Neural Architecture Search for 1-bit CNNs

Neural architecture search (NAS) proves to be among the effective approaches for many tasks by generating an application-adaptive neural architecture, which is still challenged by high computational cost and memory consumption. At the same time, 1-bit convolutional neural networks (CNNs) with binary weights and activations show their potential for resource-limited embedded devices. One natural approach is to use 1-bit CNNs to reduce the computation and memory cost of NAS by taking advantage of the strengths of each in a unified framework, while searching the 1-bit CNNs is more challenging due to the more complicated processes involved. In this paper, we introduce Discrepant Child–Parent Neural Architecture Search (DCP–NAS) to efficiently search 1-bit CNNs, based on a new framework of searching the 1-bit model (Child) under the supervision of a real-valued model (Parent). Particularly, we first utilize a Parent model to calculate a tangent direction, based on which the tangent propagation method is introduced to search the optimized 1-bit Child. We further observe a coupling relationship between the weights and architecture parameters existing in such differentiable frameworks. To address the issue, we propose a decoupled optimization method to search an optimized architecture. Extensive experiments demonstrate that our DCP–NAS achieves much better results than prior arts on both CIFAR-10 and ImageNet datasets. In particular, the backbones achieved by our DCP–NAS achieve strong generalization performance on person re-identification and object detection.
Visual cue-aided human supervised robot navigation guidance in photometrically challenging environments using adaptive spatial-feature kernel-guided bilateral LPP

Abstract The objective of an effective human-robot collaborative (HRC) task is the maximization of human-robot competencies while ensuring the user’s convenience. In photometrically challenging and unstructured HRC environments, data obtained from vision sensors often tend to get degraded due to illumination irregularities and spatio-temporal complexities. To extract useful and discriminative features from the data under such situations, locality-sensitive methods like locality preserving projections (LPPs) become quite useful as it captures the local geometric structure of the high-dimensional data. In LPP, the local structural information is encoded in the form of weight values between two samples in the higher-dimensional Euclidean space. The weight values are learned in a regular and continuous manner which only depends on the spatial distribution of the data. Moreover, because of its weight dependency solely on the Euclidean distance, improper weight values can occur frequently, as the Euclidean distance is susceptible to noise, outliers, and different types of geometrical transformations. This paper proposes an adaptive weight learning method to be utilized in the weight computation of LPP, which allows it to adaptively select and extract more discriminative features from the higher-dimensional input data while preserving the intrinsic structural information of the data. Additionally, to alleviate the issues with spatial dependency, the concept of bilateral filtering that incorporates the range weights from the feature space along with the similarity weight in the Euclidean space has been utilized here. This paper proposes an augmented version of adaptive spatial-feature kernel-guided bilateral filtering inspired LPP which addresses two of these basic and fundamental issues of the conventional LPP.
Attribute disentanglement with gradient reversal for interactive fashion retrieval

Interactive fashion search is gaining more and more interest thanks to the rapid diffusion of online retailers. It allows users to browse fashion items and perform attribute manipulations, modifying parts or details of given garments. To successfully model and analyze garments at such a fine-grained level, it is necessary to obtain attribute-wise representations, separating information relative to different characteristics. In this work we propose an attribute disentanglement method based on attribute classifiers and the usage of gradient reversal layers. This combination allows us to learn attribute-specific features, removing unwanted details from each representation. We test the effectiveness of our learned features in a fashion attribute manipulation task, obtaining state of the art results. Furthermore, to favor training stability we present a novel loss balancing approach, preventing reversed losses to diverge during the optimization process.
JurisBERT: A New Approach that Converts a Classification Corpus into an STS One

We propose in this work a new approach that aims to transform a classification corpus into an STS (Semantic Textual Similarity) one. In that sense, we use BERT (Bidirectional Encoder Representations from Transformers) to validate our hypothesis, i.e., a multi-level classification dataset can be converted into an STS dataset which improves the fine-tuning step and evidences the proposed corpus. Also, in our approach, we trained from scratching a BERT model considering the legal texts, called JurisBert which reveals a considered improvement in fastness and precision, and it requires less computational resources than other approaches. JurisBERT uses the concept of sub-language, i.e., a model pre-trained in a language (Brazilian Portuguese) passes through refining (fine-tuning) to better attend to a specific domain, in our case, the legal field. JurisBERT uses 24k pairs of ementas with degrees of similarity varying from 0 to 3. We got this data from search mechanisms available on the court websites to validate the model with real-world data. Our experiments showed JurisBERT is better than other models such as multilingual BERT and BERTimbau with $$3.30\%$$ better precision ( $$F_1$$ ), 5 times reduced training time, and using accessible hardware, i.e., low-cost GPGPU architecture. The source code is available at https://github.com/alfaneo-ai/brazilian-legal-text-dataset and the model is here: https://huggingface.co/alfaneo .