Reviewerly/exHarmony huggingface dataset:
Boosted Sine Cosine Algorithm with Application to Medical Diagnosis

The sine cosine algorithm (SCA) was proposed for solving optimization tasks, of which the way to obtain the optimal solution is mainly through the continuous iteration of the sine and cosine update formulas. However, SCA also faces low population diversity and stagnation of locally optimal solutions. Hence, we try to eliminate these problems by proposing an enhanced version of SCA, named ESCA_PSO. ESCA_PSO is proposed based on hybrid SCA and particle swarm optimization (PSO) by incorporating multiple mutation strategies into the original SCA_PSO. To validate the effect of ESCA_PSO in handling global optimization problems, ESCA_PSO was compared with quality algorithms on various types of benchmark functions. In addition, the proposed ESCA_PSO was employed to tune the best parameters of support vector machines for dealing with medical diagnosis tasks. The results prove the efficiency of the proposed algorithms in solving optimization problems.
Style-woven Attention Network for Zero-shot Ink Wash Painting Style Transfer

Traditional Chinese painting is a unique form of artistic expression. Compared with western art painting, it pays more attention to the verve in visual effect, especially ink painting, which makes good use of lines and pays little attention to information such as texture. Some style transfer methods have recently begun to apply traditional Chinese painting style (such as ink wash style) to photorealistic. Ink stylization of different types of real-world photos in a dataset using these style transfer methods has some limitations. When the input images are animal types that have not been seen in the training set, the generated results retain some semantic features of the data in the training set, resulting in distortion. Therefore, in this paper, we attempt to separate the feature representations for styles and contents and propose a style-woven attention network to achieve zero-shot ink wash painting style transfer. Our model learns to disentangle the data representations in an unsupervised fashion and capture the semantic correlations of content and style. In addition, an ink style loss is added to improve the learning ability of the style encoder. In order to verify the ability of ink wash stylization, we augmented the publicly available dataset $ChipPhi$. Extensive experiments based on a wide validation set prove that our method achieves state-of-the-art results.
Decision Tree Algorithm in the Performance Evaluation of School-Enterprise Cooperation for Higher Vocational Education

With the development of China’s economy and the internet, machine learning has become one of the people’s favorite ways of working. This study aims to solve the problems of brain drain, inefficiency, and injustice in the performance appraisal of school-enterprise cooperation. Decision tree technology is used to establish an assessment system. After the assessment index data are segmented, and the fuzzy version of the C4.5 algorithm is used to calculate and count different data segments. Finally, different data types are used to classify and construct decision trees. The school-enterprise cooperation performance appraisal system has been established and perfected for higher vocational education. In this way, the performance appraisal system is optimized in the school-enterprise cooperation. The system improves work efficiency while reducing manual labor and improves the problems in the performance appraisal of other colleges and universities’ cooperation. Facts have proved that the establishment of decision trees can effectively solve the problems of duplication of indicators and complex calculations in performance appraisal. After being optimized, the C4.5 algorithm will increase the calculation accuracy to 95%. The overall speed of establishment is increased by 5% based on the algorithm before optimization. This breaks the traditional performance appraisal system and promotes the performance appraisal of school-enterprise cooperation to be more open, transparent, fair, and equal.
Evaluating the layout quality of UML class diagrams using machine learning

UML is the de facto standard notation for graphically representing software. UML diagrams are used in the analysis, construction, and maintenance of software systems. Mostly, UML diagrams capture an abstract view of a (piece of a) software system. A key purpose of UML diagrams is to share knowledge about the system among developers. The quality of the layout of UML diagrams plays a crucial role in their comprehension. In this paper, we present an automated method for evaluating the layout quality of UML class diagrams. We use machine learning based on features extracted from the class diagram images using image processing. Such an automated evaluator has several uses: (1) From an industrial perspective, this tool could be used for automated quality assurance for class diagrams (e.g., as part of a quality monitor integrated into a DevOps toolchain). For example, automated feedback can be generated once a UML diagram is checked in the project repository. (2) In an educational setting, the evaluator can grade the layout aspect of student assignments in courses on software modeling, analysis, and design. (3) In the field of algorithm design for graph layouts, our evaluator can assess the layouts generated by such algorithms. In this way, this evaluator opens up the road for using machine learning to learn good layouting algorithms. We use machine learning techniques to build (linear) regression models based on features extracted from the class diagram images using image processing. As ground truth, we use a dataset of 600+ UML Class Diagrams for which experts manually label the quality of the layout. This paper makes the following contributions: (1) We show the feasibility of the automatic evaluation of the layout quality of UML class diagrams. (2) We analyze which features of UML class diagrams are most strongly related to the quality of their layout. (3) We evaluate the performance of our layout evaluator. (4) We offer a dataset of labeled UML class diagrams. In this dataset, we supply for every diagram the following information: (a) a manually established ground truth of the quality of the layout, (b) an automatically established value for the layout-quality of the diagram (produced by our classifier), and (c) the values of key features of the layout of the diagram (obtained by image processing). This dataset can be used for replication of our study and others to build on and improve on this work. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board..
Selective Hypergraph Convolutional Networks for Skeleton-based Action Recognition

In skeleton-based action recognition, Graph Convolutional Networks (GCNs) have achieved remarkable performance since the skeleton representation of human action can be naturally modeled by the graph structure. Most of the existing GCN-based methods extract skeleton features by exploiting single-scale joint information, while neglecting the valuable multi-scale contextual information. Besides, the commonly used strided convolution in temporal dimension could evenly filters out the keyframes we expect to preserve and leads to the loss of keyframe information. To address these issues, we propose a novel Selective Hypergraph Convolution Network, dubbed Selective-HCN, which stacks two key modules: Selective-scale Hypergraph Convolution (SHC) and Selective-frame Temporal Convolution (STC). The SHC module represents the human skeleton as the graph and hypergraph to fully extract multi-scale information, and selectively fuse features at various scales. Instead of traditional strided temporal convolution, the STC module can adaptively select keyframes and filter redundant frames according to the importance of the frames. Extensive experiments on two challenging skeleton action benchmarks, i.e., NTU-RGB+D and Skeleton-Kinetics, demonstrate the superiority and effectiveness of our proposed method.
Mahalanobis distance based accuracy prediction models for Sentinel-2 Image Scene Classification

Over the years, due to the enrichment of paired-label datasets, supervised machine learning has become a prime component of any problem-solving. Examples include building classifiers for applications such as image/speech recognition, traffic prediction, product recommendation, virtual personal assistant (VPA), online fraud detection and many more. The performance of these developed classifiers is highly dependent upon the training dataset, and subsequently, without human intervention or true labels, the evaluation over unseen observations remains unknown. Using a statistical distance researchers did try to assess the model’s goodness-of-fit and compared multiple independent models. Nonetheless, given a train-test split and different classifiers built over the training set, the question ‘is it possible to find a prediction error using the relation between training and test set?’ remains unsolved. In this article, we propose a generalized statistical distance-based method measuring the prediction uncertainty at a new query point. To be specific, we propose a Mahalanobis distance-based Evidence Function Model to measure the misclassification caused by K-Nearest Neighbours (KNN), Extra Trees (ET), and Convolutional Neural Network (CNN) models when classifying Sentinel-2 image into six scene classes (Water, Shadow, Cirrus, Cloud, Snow, Other). The performance of the proposed method was assessed over two different datasets: (i) the test set, with an overall mean prediction uncertainty detection of 62.99%, 29.80% and 31.51%, leading to a mean micro-F1 performance of 67.89%, 39.30%, and 38.29% for KNN, ET, and CNN, respectively; (ii) a water-body set, with prediction uncertainty detection of 22.27%, 42.08%, and 27.67%, leading to a micro-F1 performance of 34.70%, 58.96%, and 43.32%, respectively.
A new classification model with a multi-layer dimensionality reduction approach

Documents categorization has evolved into a pertinent study topic, adding new challenges to data mining and information retrieval fields. However, classification algorithms suffer from the large sparse matrix generated from the processed dataset. Therefore, reducing data dimensionality is considered an essential phase in the whole process of classification. This phase aims to remove irrelevant features while enhancing the classifier&#x2019;s performance. Several feature extraction and selection methods have been introduced and discussed. Still, applying these methods in Arabic topic categorization is not well-studied and left behind in comparison with various languages like English. In this paper, we highlight the benefit of using a hybrid multi-layer dimension reduction approach, especially for Arabic topic classification. The procedure proposed includes four layers of feature extraction and selection. The first two layers apply linguistic methods, while the last two layers use statistical analyses. Then, a bagging classifier model is implemented using the Support Vector Machines (SVM) as its base model. Finally, we evaluated the classifier performance using several measurable criteria such as precision, recall, accuracy, and F-score. According to the literature, the presented model reached an average accuracy that is among the best results reported in previous studies.
xTag: Mitigating Use-After-Free Vulnerabilities via Software-Based Pointer Tagging on Intel x86-64

Memory safety in complex applications implemented in unsafe programming languages such as C/C++ is still an unresolved problem in practice. Such applications were often developed in an ad-hoc, security-ignorant fashion, and thus they contain many types of security issues. Many different types of defenses have been proposed in the past to mitigate these problems, some of which are even widely used in practice. However, advanced attacks are still able to circumvent these defenses, and the arms race is not (yet) over. On the defensive side, the most promising next step is a tighter integration of the hardware and software level: modern mitigation techniques are either accelerated using hardware extensions or implemented in the hardware by extensions of the instruction set architecture (ISA). In particular, memory tagging, as proposed by ARM or SPARC, promises to solve many issues for practical memory safety. Unfortunately, Intel x86-64, which represents the most important ISA for both the desktop and server domain, lacks support for hardware-accelerated memory tagging, so memory tagging is not considered practical for this platform. In this paper, we present the design and implementation of an efficient, software-only pointer tagging scheme for Intel x86-64 based on a novel metadata embedding scheme. The basic idea is to alias multiple virtual pages to one physical page so that we can efficiently embed tag bits into a pointer. Furthermore, we introduce several optimizations that significantly reduce the performance impact of this approach to memory tagging. Based on this scheme, we propose a novel use-after-free mitigation scheme, called xTag, that offers better performance and strong security properties compared to state-of-the-art methods. We also show how double-free vulnerabilities can be mitigated. Our approach is highly compatible, allowing pointers to be passed back and forth between instrumented and non-instrumented code without losing metadata, and it is even compatible with inline assembly. We conclude that building exploit mitigation mechanisms on top of our memory tagging scheme is feasible on Intel x86-64, as demonstrated by the effective prevention of use-after-free bugs in the Firefox web browser.
Cascaded Structure-Learning Network with Using Adversarial Training for Robust Facial Landmark Detection

Recently, great progress has been achieved on facial landmark detection based on convolutional neural network, while it is still challenging due to partial occlusion and extreme head pose. In this paper, we propose a Cascaded Structure-Learning Network (CSLN) with using adversarial training to improve the performance of 2D facial landmark detection by taking the structure of facial landmarks into account. In the first stage, we improve the original stacked hourglass network, which applies a multi-branch module to capture different scales of features, a progressive convolution structure to compensate for the missing structural features in hourglass networks, and a pyramid inception structure to expand the receptive field. Specially, by introducing a discriminator, we use the adversarial training strategy to urge the improved hourglass network for generating more accurate heatmaps. The second stage, which is based on attention mechanism, optimizes the spatial correlations between different facial landmarks by reusing the structural features. Moreover, we propose a novel region loss, which can adaptively allocate proper weights to different regions. In this way, the network can focus more on those occluded landmarks. The experimental results on several datasets, i.e. 300W, COFW, and AFLW, show that our proposed method achieves superior performance compared with the state-of-the-art methods.
Countering Steganographic Security with CNN

Accurate steganalysis is required for extracting secret data hidden using different steganographic techniques. A steganalysis technique using the "Yedroudj" network is proposed in this work. The common steganalysis process is followed using highlight extractors and classifiers. The suspected images are classified with Convolutional Neural Network(CNN) and tested. The accuracy of detecting a steganography image with the proposed network and strategy was around 86.36%. Extracting data from images that used steganography tools like Xiao and Open puff are tested using the yedroudj network by detecting the steganography information.
Evolving Gaussian on-line clustering in social network analysis

In this paper, we present an evolving data-based approach to automatically cluster Twitter users according to their behavior. The clustering method is based on the Gaussian probability density distribution combined with a Takagi–Sugeno fuzzy consequent part of order zero (eGauss0). This means that this method can be used as a classifier that is actually a mapping from the feature space to the class label space. The eGauss method is very flexible, is computed recursively, and the most important thing is that it starts learning "from scratch". The structure adapts to the new data using adding and merging mechanisms. The most important feature of the evolving method is that it can process data from thousands of Twitter profiles in real time, which can be characterized as a Big Data problem. The final clusters yield classes of Twitter profiles, which are represented as different activity levels of each profile. In this way, we could classify each member as ordinary, very active, influential and unusual user. The proposed method was also tested on the Iris and Breast Cancer Wisconsin datasets and compared with other methods. In both cases, the proposed method achieves high classification rates and shows competitive results.
Coherent Excitation of Bound Electron Quantum State With Quantum Electron Wavepackets

We present a fully quantum model for the excitation of a bound electron based on the “free-electron bound-electron resonant interaction” (FEBERI) scheme. The bound electron is modeled as a quantum two-level system (TLS) at any initial quantum (qubit) state, and the free electron is presented as a pre-shaped quantum electron wavepacket (QEW). In the case that the QEW is short or modulated at optical frequency, the TLS quantum state may be coherently controlled with multiple modulation-correlated QEWs. For this case, we derive the transition probability of the TLS due to interaction with a multi-particle beam based on an analytical approximate solution of the Schrodinger equation that amounts to using Born’s probabilistic interpretation of the quantum electron wavefunction. We verify the credibility of the analytical model at its validity ranges using a fully quantum density matrix computation procedure. It is shown that the transition probability can grow quadratically with the number of correlated QEWs and exhibit Rabi oscillation. The study indicates a possibility of engineering the quantum state of a TLS by utilizing a beam of shaped QEWs.
Pedestrian Tracking in Public Passageway by Single 3D Depth Sensor

We propose an approach to pedestrian tracking in a public passageway with various pedestrians, such as those carrying luggage and baby strollers and family groups close to each other, based on 3D point cloud data captured by a single 3D depth sensor. Since we assume a wall-attached sensor, which is easy to deploy in passageways, pedestrians walking nearby the sensor frequently occlude the others behind. This causes a severe error in pedestrian segmentation in the 3D point cloud and Kalman-filter-based tracking. We introduce a new technique to spatially complement the missing part of segments in KF-based multi-object tracking to cope with this issue. We have evaluated our method using the 3D point cloud data capturing pedestrians at the entrance of an existing commercial facility (shopping small), as well as the one collected in our laboratory space. As a result, the tracking accuracy index (MOTA) for multiple objects is 0.914, with severe and frequent occlusions.
HARM: Hardware-Assisted Continuous Re-randomization for Microcontrollers

Microcontroller-based embedded systems have become ubiquitous with the emergence of IoT technology. Given its critical roles in many applications, its security is becoming increasingly important. Unfortunately, MCU devices are especially vulnerable. Code reuse attacks are particularly noteworthy since the memory address of firmware code is static. This work seeks to combat code reuse attacks, including ROP and more advanced JIT-ROP via continuous randomization. Previous proposals are geared towards full-fledged OSs with rich runtime environments, and therefore cannot be applied to MCUs. We propose the first solution for ARM-based MCUs. Our system, named HARM, comprises a secure runtime and a binary analysis tool with rewriting module. The secure runtime, protected inside the secure world, proactively triggers and performs non-bypassable randomization to the firmware running in a sandbox in the normal world. Our system does not rely on any firmware feature, and therefore is generally applicable to both bare-metal and RTOS-powered firmware. We have implemented a prototype on a development board. Our evaluation results indicate that HARM can effectively thaw code reuse attacks while keeping the performance and energy overhead low.
Embedding causal team languages into predicate logic

Causal team semantics ([2]) supports causal-observational languages, which enrich the languages for deterministic causation ([11], [18]) with dependencies and other team-specific operators. Handling the causal aspects of these languages requires a richer semantics than propositional team semantics; nonetheless, in this paper we show that the causal-observational languages considered in [2] can be embedded into first-order dependence logic by means of a translation and a careful choice of models. We show that, in some significant cases, the translation can be refined to an embedding into the Bernays-Schönfinkel-Ramsey fragment of dependence logic or, in the restricted case of recursive causal models, into the existential fragment. As an application, we use the embeddings to show the decidability of a satisfiability problem for the causal-observational languages. Along the way, we question the correctness of the semantics for interventionist counterfactuals proposed by Halpern ([18]) and propose an alternative one which behaves as usual in the uncontroversial recursive case.
Hierarchical classification for account code suggestion

As part of invoice processing, businesses are required to manually classify each line item on an invoice to a specific financial account. This can be time-consuming and challenging when there is a large set of account codes to choose from. Failing to select the correct account will lead to errors in financial reporting which can be misleading for stakeholders and have adverse effects during a financial audit. The emergence of cloud-based accounting platforms has introduced potential areas for automated support across invoice processing tasks such as helping bookkeepers to select the correct financial account. Traditionally, account code suggestion is framed as a multi-class classification task, however, we explore the applicability of using a hierarchical single-label classifier. Account codes can be expressed as taxonomic classes, either explicitly through the chart of accounts or implicitly through associations with reconciled bank accounts. Our research provides evidence to support that the exploitation of hierarchical information from induced taxonomies can be highly advantageous for improving classification and recommendation performance. Furthermore, we introduce Top-K Parent Boosting, a novel post-processing strategy. We demonstrate the suitability of Top-K Parent Boosting for DAG structures and highlight its superiority in improving recommendation performance over former strategies.