Reviewerly/exHarmony huggingface dataset:
A Nonlinear White-Box SM4 Implementation Applied to Edge IoT Agents

With the rapid development of power Internet of Things (IoT), the ubiquitous edge agents are frequently exposed in a risky environment, where the white-box attacker could steal all the internal information by full observation of dynamic execution of the cryptographic software. In this situation, a new table-based white-box cryptography implementation of SM4 algorithm is proposed to prevent the attacker from extracting the secret key, which hides the encryption and decryption process in obfuscated lookup tables. Aiming to improve the diversity and ambiguity of the lookup tables as well as resist different types of white-box attacks, the random bijective nonlinear mappings are applied as scrambling encodings of the lookup tables. Moreover, in order to make our implementation more practical in the resource-constrained edge IoT agent, elaborate design is proposed to make some tables reusability, leading to less memory occupation while guaranteeing the security. The validity and security of the proposed implementation will be illustrated through several evaluation indicators.
Dealing with data intrinsic difficulties by learning an interPretable Ensemble Rule Learning (PERL) model

One of the challenges of learning algorithms is data imbalance, especially data intrinsic difficulties such as borderline, rare, and outlier examples. In order to improve the performance of learning algorithms in such cases, various solutions have been proposed, one of which is ensemble learning. Despite the good results of ensemble learning, their results are not interpretable. In some domains, such as medicine, banking, and telecommunications, the interpretability of the final model results is very important. For this purpose, rule-based learning algorithms and decision trees are two appropriate approaches. Therefore, an interPretable Ensemble Rule Learning (PERL) model is proposed in this paper. PERL has three main phases: sampling, rule learning, and rule processing. We reduce the negative effects of data intrinsic difficulties in each phase by an appropriate solution. PERL is compared with several interpretable ensemble models on ten imbalanced real-world datasets. According to the obtained results, PERL outperforms significantly the other models based on the f1-score measure.
Three-Dimensional Video Super-Resolution Reconstruction Scheme Based on Histogram Matching and Recursive Bayesian Algorithms

Multimedia Super-Resolution (SR) reconstruction is an essential and mandatory process for different visualization functions. Recently, several schemes have been suggested for single- and multi-image SR reconstruction. This work presents an effective SR reconstruction scheme for visual quality and resolution enhancement of 3D Video (3DV) sequences. The idea behind the proposed 3DV SR reconstruction scheme is the utilization of a recursive Bayesian algorithm for improving the resolution of the degraded 3DV sequences with down-sampling, blurring, and noise effects. In addition, a significant stage of histogram matching based on a visual image with a better-distributed histogram is employed. The main aim of employing the histogram matching stage for enhancing the 3DV sequence is to introduce a dynamic range modification of each 3DV frame. Hence, it presents a 3DV sequence with an enhanced distribution of intensities. This improves the whole performance efficiency of the suggested scheme. The performance of the proposed SR reconstruction scheme is compared with that of the conventional bicubic interpolation scheme. Comparisons with recent and related SR reconstruction schemes are also introduced. Simulation results reveal that the proposed scheme achieves superior outcomes in terms of Structural Similarity (SSIM) index, local contrast, average gradient, Mean Square Error (MSE), edge intensity, entropy, and Peak Signal-to-Noise Ratio (PSNR) of the resulting 3DV frames.
A DQN-based agent for automatic software refactoring

Nowadays, technical debt has become a very important issue in software project management. The main mechanism to repay this debt is through refactoring. Refactoring software projects usually comes at a high cost. As a result, researchers have always looked for ways to minimize this cost, and a good potential candidate to reduce the cost of a process is to automate it. One of the automatic software refactoring methods that recently has received a lot of attention is based on search-based software engineering (SBSE) methods. Although because of comprehensiveness and versatility SBSE is considered an appropriate method for automatic refactoring, it has its downsides, the most important of which are the uncertainty of the results and the exponential execution time. In this research, a solution is proposed inspired by search-based refactoring while taking advantage of exploitation in reinforcement learning techniques. This work aims to solve the uncertainty problems and execution time for large programs. In the proposed approach, the problem of uncertainty is solved by targeting the selection of refactoring actions used in the search-based approach. Also, due to the reduction of the dependency between the choice of the appropriate refactoring and its execution time, the time problem in large software refactoring has been greatly improved. Amongst the performed evaluations and specifically for the refactoring of the largest case study, the proposed approach managed to increase the accuracy to more than twice of the SBSE refactoring approaches, while reducing the execution time of refactoring by more than 98%. The results of the tests show that with increasing the volume and size of the software, the performance of the proposed approach also improves compared to the methods based on SBSE, both in terms of reducing technical debt and speeding up the refactoring process.
A Pre-Trained CNN based framework for Handwritten Gujarati Digit Classification using Transfer Learning Approach

Digit recognition is a software problem to identify numerals of the specific language using computer system. Digit can be printed or handwritten. Handwritten digit recognition is complex task compare to printed because various writing style, thickness and different curve of handwritten digit are difficult to interpret. Numerous work is performed on the native script of India such as Hindi, Bangla, Gurumukhi, and Tamil. However, research efforts on Gujarati Handwritten digit or character recognition are reported very less. This paper aims to demonstrate the efficiency of transfer learning and utilization of a pre-trained model developed for ImageNet dataset to classify handwritten Gujarati digits from zero to nine. The proposed framework developed from the Convolutional and pooling layers of VGG-16, VGG-19, ResNet50, ResNet101, InceptionV3 and EfficentNet pre-trained CNN networks for the feature extraction and newly defined fully-connected layers and output layer for the classification. The proposed framework is investigated on self-created Gujarati Handwritten Digit Dataset. Experimental results show that EfficientNet achieved highest accuracy (training accuracy − 94.9% and testing accuracy − 94.98%) among six pre-trained networks using proposed framework.
Analysis of Software Sizing and Project Estimation prediction by Machine Learning Classification

In this study, the outcomes of trials with various projects are analyzed in detail. Estimators may decrease mistakes by combining several estimating strategies, which helps them maintain a close eye on the difference between their estimations and reality. An effort estimate is a method for estimating a model's correctness by calculating the total amount of effort needed. It's a major pain in the backside of software development. Several prediction methods have recently been created to find an appropriate estimate. The suggested SVM approach is utilized to reduce the estimation error for the project estimate to the lowest possible value. As a result, throughout the software sizing process, the ideal or exact forecast is achieved. Early in a model's development, the estimate is erroneous since the needs are not defined, but as the model evolves, it becomes more and more accurate. Because of this, it is critical to choose a precise estimate for each software model development. Observations and suggestions for further study of software sizing approaches are also included in the report.
Prototyping of Transparent and Flexible Electrochromic Displays

This course is a hands-on introduction to the fabrication of flexible, transparent free-form displays based on electrochromism for an audience with a variety of backgrounds, including artists and designers with no prior knowledge of physical prototyping. Besides prototyping using screen printing or ink-jet printing of electrochromic ink and an easy assembly process, participants will learn essentials for designing and controlling electrochromic displays.
GroupRec: Group Recommendation by Numerical Characteristics of Groups in Telegram

Today, recommender systems are used in many different businesses to find items of interest to users. The use of these systems is widely found in online economic systems and social networks. Therefore, using these systems in the messaging environment will cause changes and transformations for marketing. Telegram is a cloud-based messenger with more than 500 million monthly active users. This messenger has a relatively acceptable position compared to its other competitors, because the security and features provided in it, have made it different from other messengers and close to social networks. One of the most popular features of messengers is groups. Many marketers are looking for groups that fit their field. One of the main gaps in the messengers regarding advertising and marketing to expand businesses is the impossibility of finding social groups. In this paper, a new method for group recommendation in the Telegram is presented. This method, by receiving a set of users, analyzes their groups and recommends a list of ranked groups. The proposed method is created by combining the previous two methods in the field of group recommendation and computational modeling of numerical variables obtained from each group. This study is dependent on the information of all users due to the use of the membership graph, and the behavior of the system changes by the information extracted from the users. The results of experimental experiments show a significant reduction of RMSE and MAE in the proposed method compared to the previous two methods.
A Blockchain-Based Document Verification System for Employers

Academic institutions often maintain records and documents of the students enrolled within the institute. These records are greatly regarded by employers and often verified before giving employment offers. While all the other sectors in academia are moving towards automation, this specific use case is still quite primitive in its functioning. Even when this is a regular activity undergone by academic institutes, methodologies with vulnerabilities and reasonable overhead are employed. There is a need for a tamper-proof, reliable, and faster mode of document verification for employers which can be used on the go with complete reassurance for the authenticity of the provided documents. This paper puts forward a blockchain-based platform that allows academic institutes to offer a secure, dependable, cost-effective, and scalable verification of documents via a Web interface. The platform is built upon the logic written in solidity and utilizes the ethereum blockchain to enforce immutability of the document, and the backend is linked to the user interface using the web3.js library. Additionally, the platform offers a statistical comparison of these records to other students and assists the employer to assess the performance of the student being recruited.
Evaluation of Emergency Intelligence Capability of Major Public Health Events in Probabilistic Uncertain Language Environment

Emergency intelligence capability is an important index reflecting the intelligence level of emergency management. The accuracy of emergency intelligence capability evaluation is related to the scientific nature of emergency decision-making. By analyzing the operation process and mechanism of the emergency intelligence system for major public health events, this paper establishes the evaluation index system of the emergency intelligence capability. By using the decision method of VIKOR and considering the preference of experts in the evaluation process, this paper proposes the evaluation model based on the probabilistic uncertain language environment. The use of probabilistic uncertain linguistic term set (PULTS) to describe the uncertain information is helpful to improve the scientific and accuracy of emergency rescue decision-making of major public health events and then realize the organic unity of emergency information and scientific decision-making of public health events.
Identification of Mine Explosions Using Manifold Learning Techniques

In many regions of the world, mine activity accounts for the bulk of the total seismicity. Therefore, automatic identification of repeating mining events would significantly reduce the analyst’s workload at many seismological organizations. This work focuses on the problem of identifying mine blasts that originate from a specific mine, given a stream of seismic data. The mine pattern to identify is complex, as the mine spans several kilometers and the recorded blasts may be of a different type, magnitude, and location. Identification of such single blasts from ongoing recordings that contains diverse seismic events and noise is a challenging task, which is typically not executed in a fully automatic manner but is rather carried out by manual analysis. In this work, manifold learning techniques, which faithfully model high-dimensional data into a compact, low-dimensional space, are utilized. In particular, a modification of diffusion maps is applied for creating a compact representation of a large set of mine patterns, bypassing the need to select a representative subset of patterns. This representation is stable with respect to the changing time streams that hold new seismic data. In addition, manifold-based data fusion techniques are implemented for processing the three-channel seismic recordings. The capabilities of the model are presented on several different datasets and compared with deep learning techniques. The results highlight the method’s computational and explainable proprieties.
Video Visual Relation Detection via 3D Convolutional Neural Network

Video visual relation detection, which aims to detect the visual relations between objects in the form of relation triplet &#x003C;subject, predicate, object&#x003E; (<i>e.g.</i>, &#x201C;person-ride-bike&#x201D;, &#x201C;dog-toward-car&#x201D;, <i>etc.</i>), is a significant and fundamental task in computer vision. However, most of the existing works about visual relation instances are focused on static images. Modeling the non-static relationships in videos has drawn little attention due to lacking large-scale video dataset support. In our work, we propose a video dataset named Video Predicate Detection and Reasoning (VidPDR) for dynamic video visual relation detection, which consists of 1,000 videos with dense manually dynamic labeled annotations on 21 object classes and 37 predicates classes. Moreover, we propose a novel spatio-temporal feature extraction framework with 3D Convolutional Neural Networks (ST3DCNN), which includes three modules <b>1)</b> object trajectory, <b>2)</b> short-term relation prediction, and <b>3)</b> greedy relational association. We conducted appropriate experiments on public datasets and our own dataset (VidPDR). Results demonstrate that our proposed method has a great improvement in comparison to the state-of-the-art baselines.
A Modified Shamir Secret Sharing Scheme With Efficient Encoding

The Shamir secret sharing (SSS) scheme requires a Maximum Distance Separable (MDS) code, and in its most common implementation, a Reed-Solomon (RS) code is used. In this letter, we observe that the encoding procedure can be made simpler and faster by dropping the MDS condition and specifying the possible symbols that can be shared. In particular, the process can be made even faster by using array codes based on exclusive-or (XOR) operations instead of RS codes.
Alpha Lightweight Coreset for k-Means Clustering

The evolution of the Internet and personal devices has changed our modem world to a new age of data. The data now is not only big in volume and size but also huge in varieties and velocity. As a result, data scientists have to investigate and propose more methods to deal with big data. One of the common approaches is that instead of solving problems on the whole data with large-scale size, we can find the answer for the subset of this data; this result subsequently is used as the baseline for finding the actual solution for the original data set. To have the best final results, we have to find the best coreset, which is the subset that must be small enough for effectively reducing computational complexity but must keep all representative characteristics of original data. In this paper, based on the lightweight coreset, we propose a general coreset construction for k-means clustering named the &#x03B1; - lightweight coresets with a new adjustable parameter. Our experimental results have shown that this proposed method can create a good coreset for the k-means clustering problem.
Hyperparameter on-line learning of stochastic resonance based threshold networks

Aiming at training the feed-forward threshold neural network consisting of nondifferentiable activation functions, the approach of noise injection forms a stochastic resonance based threshold network that can be optimized by various gradient-based optimizers. The introduction of injected noise extends the noise level into the parameter space of the designed threshold network, but leads to a highly non-convex optimization landscape of the loss function. Thus, the hyperparameter on-line learning procedure with respective to network weights and noise levels becomes of challenge. It is shown that the Adam optimizer, as an adaptive variant of stochastic gradient descent, manifests its superior learning ability in training the stochastic resonance based threshold network effectively. Experimental results demonstrate the significant improvement of performance of the designed threshold network trained by the Adam optimizer for function approximation and image classification.
Effect of virtual reality guidance system on the overhaul teaching

Abstract Students' learning motivation and gains in the professional training are essential criteria for evaluating the vocational teaching level. This study designed the virtual reality guidance (VRG) system to teach the skills of urban railway vehicle overhaul. The VRG system aims to improve students' enthusiasm and gains from learning. The system has two modules, teaching and experiment modules, respectively. The teaching module added the independent design mode and evaluation mode to guide students to participate in and enjoy the teaching system actively. The experiment randomly divided 80 urban railway vehicle engineering students into the experimental and control groups. Participants underwent the entire pretest, teaching, and posttest process. The results show that the VRG system endows students with more effective operational skills learning rather than traditional teaching methods. Also, the novel system can guide students to discover their professional advantages and enhance their enthusiasm for professional learning.
Ranking Models in Unlabeled New Environments

Consider a scenario where we are supplied with a number of ready-to-use models trained on a certain source domain and hope to directly apply the most appropriate ones to different target domains based on the models’ relative performance. Ideally we should annotate a validation set for model performance assessment on each new target environment, but such annotations are often very expensive. Under this circumstance, we introduce the problem of ranking models in unlabeled new environments. For this problem, we propose to adopt a proxy dataset that 1) is fully labeled and 2) well reflects the true model rankings in a given target environment, and use the performance rankings on the proxy sets as surrogates. We first select labeled datasets as the proxy. Specifically, datasets that are more similar to the unlabeled target domain are found to better preserve the relative performance rankings. Motivated by this, we further propose to search the proxy set by sampling images from various datasets that have similar distributions as the target. We analyze the problem and its solutions on the person re-identification (re-ID) task, for which sufficient datasets are publicly available, and show that a carefully constructed proxy set effectively captures relative performance ranking in new environments. Code is avalible at https://github.com/sxzrt/Proxy-Set.
Towards the Resolution of Safety and Security Conflicts

Safety engineering and cyber security have complementary aims, but typically realise these using different techniques, risk assessment methods and cultural approaches. As a result, the integration of safety and cyber security concerns is a complex process, with potential for conflict. We present a generalized taxonomy of common conflict areas between safety and cyber security, oriented around the development and deployment lifecycle, and supplement this with a discussion of concepts and methodologies for resolution based on the shared principle of defence-in-depth.
The optimal projection intensities determination strategy for robust strip-edge detection in adaptive fringe pattern measurement

Adaptive digital fringe projection (AFP) technique is a traditional approach to measure the high dynamic range (HDR) surface. However, the projected light intensities are difficult to determine. And there are some discrepancies occurred in the location of detected strip-edge. In this paper, a projection intensities determination strategy was proposed to minimize the influence of camera response nonlinear distortion and improve the robustness of strip-edges detection. To determine the relationship between the projected gray level and the captured gray level, the mapping function between the projector and the camera is established. To evaluate the distortion degree of the captured image, the camera response nonlinear distortion model is proposed. Compared with the traditional AFP method, the proposed method is relatively simple and flexible. And the proposed method could be satisfying the different user demands. The experiment shows that the influence of nonlinearity of camera response was reduced and the robustness of strip-edge detection is improved.
Light Field Compression With Graph Learning and Dictionary-Guided Sparse Coding

Light field (LF) data are widely used in the immersive representations of the 3D world. To record the light rays along with different directions, an LF requires much larger storage space and transmission bandwidth than a conventional 2D image with similar spatial dimension. In this paper, we propose a novel framework for light field image compression that leverages graph learning and dictionary learning to remove structural redundancies between different views. Specifically, to significantly reduce the bit-rates, only a few key views are sampled and encoded, whereas the remaining non-key views are reconstructed via the graph adjacency matrix learned from the angular patch. Furthermore, dictionary-guided sparse coding is developed to compress the graph adjacency matrices and reduce the coding overheads. To our best knowledge, this paper is the first to achieve compact representation of cross-view structural information via adaptive learning on graphs. Experimental results demonstrate that the proposed framework achieves better performance than the standardized HEVC-based codec.