Reviewerly/exHarmony huggingface dataset:
A Light-Weight and Accurate Method of Static Integer-Overflow-to-Buffer-Overflow Vulnerability Detection

The Integer-Overflow-to-Buffer-Overflow (IO2BO) vulnerability is an underrated source of security threats. Despite many works have been done to mitigate integer overflow, existing tools either report large number of false positives or introduce unacceptable time consumption. To address this problem, in this paper we present a new static analysis framework. It first utilizes inter-procedural dataflow analysis and taint analysis to accurately identify potential IO2BO vulnerabilities. Then it uses a light-weight method to further filter out false positives. Specifically, it generates constraints representing the conditions under which a potential IO2BO vulnerability can be triggered, and feeds the constraints to SMT solver to decide their satisfiability. We have implemented a prototype system LAID based on LLVM, and evaluated it on 228 programs of the NIST’s SAMATE Juliet test suite and 6 known IO2BO vulnerabilities in real world. The experiment results show that our system can effectively and efficiently detect all known IO2BO vulnerabilities.
Image generation via latent space learning using improved combination

Many researches have brought progress in learning a good generative model by combing the advantages of GAN and VAE, where latent space learning is always important for generating high-quality images. But these existing works mainly seek to impose the latent space a given distribution in advance or to obey a Gaussian distribution with KL divergence penalty, which leads to the difficulties of deciding a suitable prior distribution corresponding to different datasets. Thus in this paper we develop a two-stage combining method of AE and GAN under unsupervised and supervised conditions respectively, each stage performed to improve the effect of modeling latent distribution. In the first stage, an adversarial procedure achieves to match the latent distribution with real data distribution determined by arbitrary dataset without having access to a pre-set prior distribution. In the second stage, besides one adversarial procedure trained for outputting images, the other adversarial procedure is designed to attain the goal of optimizing the latent distribution of the first stage via back-propagation. Therefore, loop optimization of the network parameters during training will at last allow the framework to map an input noise to a high quality image. Extensive experiments are conducted to verify the performance of representing latent space and generating images on different datasets including MNIST, fashion-MNIST, CIFAR-10, and CelebA. The code and tutorials already release at https://github.com/TwistedW/CAE-CGAN.
Basics of Python and Scikit Image

Doing image processing without using a programing language is like counting the number of stars as you stare at the night sky. There are so many complex methodologies that, even if we try to do it manually, it’s not at all possible. But, if we use programing languages such as Python, R, C++, MATLAB, and so on, the same work can be done in a jiffy.
A Deep Learning Inference Accelerator Based on Model Compression on FPGA

Convolutional neural networks (CNN) have demonstrated state-of-the-art accuracy in image classification and object detection owing to the increase in data and computation capacity of hardware. However, this state-of-the-art achievement depends heavily on the DSP floating-point computing capability of the device, which increases the power dissipation and cost of the device. In order to solve the problem, we made the first attempt to implement a CNN computing accelerator based on shift operation on FPGA. In this accelerator, an efficient Incremental Network Quantization (INQ) method was applied to compress the CNN model from full precision to 4-bit integer, which represents values of either zero or power of two. Then the multiply and accumulate (MAC) operations for convolution layer and fully-connected layer was converted to shift and accumulation (SAC) operations, and SAC could be easily implemented by the logic elements of FPGA. Consequently, parallelism of CNN inference process can be further expanded. For the SqueezeNet model, single image processing latency was 0.673ms on Intel Arria 10 FPGA (Inspur F10A board) showing a slightly better result than on NVIDIA Tesla P4, and the compute capacity of FPGA increased by 1.77 times at least.
Ontologies for Data Science: On Its Application to Data Pipelines

Ontologies are usually applied to drive intelligent applications and also as a resource for integrating or extracting information, as in the case of Natural Language Processing (NLP) tasks. Further, ontologies as the Gene Ontology (GO) are used as an artifact for very specific research aims. However, the value of ontologies for data analysis tasks may also go beyond these uses and span supporting the reuse and composition of data acquisition, integration and fusion code. This requires that both data and code artifacts support meta-descriptions using shared conceptualizations. In this paper, we discuss the different concerns in semantically describing data pipelines as a key reusable artifact that could be retrieved, compared and reused with a degree of automation if semantically consistent descriptions are provided. Concretely, we propose attaching semantic descriptions for data and analytic transformations to current backend-independent distributed processing frameworks as Apache Beam, as these already abstract out the specificity of supporting execution engines.
A novel method for statistical pattern recognition using the network theory and a new hybrid system of machine learning

The increase in wear resistance of cast irons after laser treatment is due not only to the corresponding structural and phase composition, but also to the improvement in the friction conditions due to the graphite retained in the laser impact zone.Also, laser hardening increases the wear resistance of steels and some other alloys in terms of the friction in alkaline and acidic environments.In this article we present a new method for a hybrid system of machine learning using a new method for statistical pattern recognition through network theory in robot laser hardening (RLH).We combined the method of multiple regression, the method of a support vector machine and the method of a neural network.For statistical pattern recognition we use the topological properties of network theory.The even distribution of the topological property16-300 triads throughout the various levels of the organization and network in the microstructure of RLH indicates that there is a strong linkage across the network and an active connection among the needles of martensite.
Cartoon Image Encryption Algorithm by a Fractional-Order Memristive Hyperchaos

Based on the Adomian decomposition method and Lyapunov stability theory, this paper constructs a fractional-order memristive hyperchaos. Then, the 0–1 test analysis is applied to detect random nature of chaotic sequences exhibited by the fractional-order systems. Comparing with the corresponding integer-order hyperchaotic system, the fractional-order hyperchaos possesses higher complexity. Finally, an image encryption algorithm is proposed based on the fractional-order memristive hyperchaos. Security and performance analysis indicates that the proposed chaos-based image encryption algorithm is highly resistant to statistical attacks.
DESAIN KATALOG PRODUK MENGGUNAKAN ADOBE PHOTOSHOP CS6 SEBAGAI MEDIA PROMOSI UNTUK MENINGKATKAN KEPUTUSAN PEMBELIAN DI CV BAGUS AGRISETA MANDIRI

Bagus Agriseta did the promotional activities using brochures, name tags, instagram, facebook, exhibitions, and blog which were considered not very effective because of the contents listed was not complete. Therefore, the company needed to develop a promotion media, that is a catalog. This research was an action research to develop a catalog design using Adobe Photoshop CS6 application to create a catalog design for Bagus Agriseta. To know the catalog design effectivity questionnaire of EPIC model was used. The purposive sampling technique was used to select 33 respondents, involving 1 owner of the company, 2 design experts, 2 marketing experts and 28 prospective customers. The data analysis used was descriptive qualitative analysis method. The result of data analysis showed the average score of emphaty dimension was 4,27, the persuasive dimension was 4,30, the impact dimension was 4,23, and the communication dimension was 4,39. The score showed that the catalog of Bagus Agriseta was highly effective in the scale of EPIC Rate and feasible to be used and posted as an additional promotional media.
Evaluation of Packet Tracer Application Effectiveness in Computer Design Networking Subject

The dynamics of technological progress in various fields indirectly affect the world of education. One of the indications can be seen in the use of media as a learning tool. This study aims; 1) to investigate the effectiveness of the packet tracer learning media application, 2) to investigate the responses of the students to such applications, 3) to determine the appropriate media used in a learning process. The research is carried out in the subject of Computer Design Networking. The evaluation research is employed by using a mixed method of qualitative and quantitative approach. The quantitative analysis involved 58 students, while the qualitative analysis involved one productive teacher, one curriculum representative, and four students. Data are analysed by using quantitative and qualitative data analysis techniques. The results show that, the effectiveness of learning media with packet tracer is high i.e., 82.76%, which covers three aspects; software engineering, learning aspects, and display aspects. This means that this packet tracer learning media can be applied as a solution to the limitation of facilities and infrastructures of network practices by considering certain learning situations and conditions. The results indicate that this application is very useful to deal with the high cost of practising tools. It makes the students more enthusiastic and motivated during the learning process. Hence, by implementing this learning method, the interaction and the learning outcomes of the student can be increased.
Robust Visual Tracking With Spatial Regularization Kernelized Correlation Filter Constrained by a Learning Spatial Reliability Map

As a basic research topic in computer vision, visual tracking is still challenging because of the complexity of the tracking problems, such as abrupt motion, out-of-view, deformation, and heavy occlusion. In this paper, we extend the kernelized correlation filter (CF) for robust tracking by introducing spatial regularization components to penalize the CF coefficients. To afford a more confident prediction, we construct a spatial reliability map based on the color histogram to enforce the detecting samples near the target center. The feature fusion and the model update mechanism are further employed to improve the effectiveness of tracking. The extensive experiments are executed on the OTB-2013, OTB-2015, and Temple Color-128 datasets. The comprehensive results demonstrate the superiority of our proposed method comparing to the representative tracks on these datasets.
Factors Impacting on CMMI Acceptance Among Software Development Firms: A Qualitative Assessment

Productive firms try to deliver high-quality products to be globally competitive. Therefore, software development firms need to adhere to a set of best practices that improve their processes. Capability maturity model integration (CMMI) comprehensively assesses the maturity of a firm&amp;#39;s processes. Representing a major departure from the traditional method of running quality management in software development firms, the adoption of CMMI has major ramifications and long-lasting effects on a company&amp;rsquo;s quality procedures. Unfortunately, the literature lacks information as to how firms should implement CMMI. Our research involved conducting an exploratory study examining the major factors that influenced CMMI adoption for Jordanian software development firms. Quality managers from eighteen software development organizations took an open-ended survey. The results show that the main factors in CMMI implementation in Jordanian software development firms were issues of its being too costly, having no time, dealing with market scope, and lack of top management support. Conclusions are also presented.
3D printing and patent law – a UK perspective: apt and ready?

The impact of 3D printing on business models that are based on protection by design rights and copyright has been widely acknowledged. Since the technology is rapidly developing, its effects may also be felt within industries that rely on patent protection. This chapter traces how the law of patent infringement in the United Kingdom applies to 3D printing scenarios. It analyses the different stages of 3D printing and whether these may lead to direct and indirect infringement. It also sheds light on how exceptions to patent infringement currently apply to 3D printing. The chapter concludes that the law of patents in the UK is currently better equipped to deal with impact of 3D printing technology than other intellectual property rights but argues for interpretive clarifications by the courts as well as possible legislative action in the near future.
Remedies for digital patent infringement: a perspective from the USA

With the rapid advancement of additive manufacturing technologies, colloquially known as 3D printing, current US patent law will encounter a new set of challenges that it is inadequately equipped to resolve. Unlike copyright owners, patent owners lack robust protection for computer-aided design (CAD) files, which facilitate the 3D printing of an object, because of the dispersed nature of infringement and the knowledge requirements for indirect patent infringement. One potential solution is allowing liability for selling or offering to sell the CAD file itself. If one embraces this or other forms of digital patent infringement, then the appropriate remedy becomes important. This chapter addresses how US patent law would address issues of injunctive relief and compensatory damages in the context of digital patent infringement.
Security Storage of Sensitive Information in Cloud Computing Data Center

In order to increase the security of sensitive information, we need to research the security storage of sensitive information in CCDC (Cloud Computing Data Center).The traditional algorithm cannot guarantee the security of sensitive information when the hacker attacks the data center.Therefore, this paper proposes the three-dimensional CCDC sensitive information security storage algorithm.This algorithm uses the feature combination to filter the sensitive information and uses the sensitive information phase extraction technology to encrypt the sensitive information that was screened.This algorithm also uses three dimensional storage principle for the security storage of sensitive information.Experimental results show that this algorithm can effectively enhance the security of the sensitive information of CCDC.
Bootstrapping Method for Developing Part-Of-Speech Tagged Corpus in Low Resource Languages Tagset- A Focus on an African IGBO

Most languages, especially in Africa, have fewer or no established part-of-speech (POS) tagged corpus.However, POS tagged corpus is essential for natural language processing (NLP) to support advanced researches such as machine translation, speech recognition, etc.Even in cases where there is no POS tagged corpus, there are some languages for which parallel texts are available online.The task of POS tagging a new language corpus with a new tagset usually face a bootstrapping problem at the initial stages of the annotation process.The unavailability of automatic taggers to help the human annotator makes the annotation process to appear infeasible to quickly produce adequate amounts of POS tagged corpus for advanced NLP research and training the taggers.In this paper, we demonstrate the efficacy of a POS annotation method that employed the services of two automatic approaches to assist POS tagged corpus creation for a novel language in NLP.The two approaches are cross-lingual and monolingual POS tags projection.We used cross-lingual to automatically create an initial 'errorful' tagged corpus for a target language via word-alignment.The resources for creating this are derived from a source language rich in NLP resources.A monolingual method is applied to clean the induce noise via an alignment process and to transform the source language tags to the target language tags.We used English and Igbo as our case study.This is possible because there are parallel texts that exist between English and Igbo, and the source language English has available NLP resources.The results of the experiment show a steady improvement in accuracy and rate of tags transformation with score ranges of 6.13% to 83.79% and 8.67% to 98.37% respectively.The rate of tags transformation evaluates the rate at which source language tags are translated to target language tags.
A Generative 3D Facial Model by Adversarial Training.

Data-driven generative 3D face models are used to compactly encode facial shape data into meaningful parametric representations. A desirable property of these models is their ability to effectively decouple natural sources of variation, in particular identity and expression. While factorized representations have been proposed for that purpose, they are still limited in the variability they can capture and may present modeling artifacts when applied to tasks such as expression transfer. In this work, we explore a new direction with Generative Adversarial Networks and show that they contribute to better face modeling performances, especially in decoupling natural factors, while also achieving more diverse samples. To train the model we introduce a novel architecture that combines a 3D generator with a 2D discriminator that leverages conventional CNNs, where the two components are bridged by a geometry mapping layer. We further present a training scheme, based on auxiliary classifiers, to explicitly disentangle identity and expression attributes. Through quantitative and qualitative results on standard face datasets, we illustrate the benefits of our model and demonstrate that it outperforms competing state of the art methods in terms of decoupling and diversity.
Research of SWNMF with New Iteration Rules for Facial Feature Extraction and Recognition

Weighted nonnegative matrix factorization (WNMF) is a technology for feature extraction, which can extract the feature of face dataset, and then the feature can be recognized by the classifier. To improve the performance of WNMF for feature extraction, a new iteration rule is proposed in this paper. Meanwhile, the base matrix U is sparse based on the threshold, and the new method is named sparse weighted nonnegative matrix factorization (SWNMF). The new iteration rules are based on the smaller iteration steps, thus, the search is more precise, therefore, the recognition rate can be improved. In addition, the sparse method based on the threshold is adopted to update the base matrix U, which can make the extracted feature more sparse and concentrate, and then easier to recognize. The SWNMF method is applied on the ORL and JAFEE datasets, and from the experiment results we can find that the recognition rates are improved extensively based on the new iteration rules proposed in this paper. The recognition rate of new SWNMF method reached 98% for ORL face database and 100% for JAFEE face database, respectively, which are higher than the PCA method, the sparse nonnegative matrix factorization (SNMF) method, the convex non-negative matrix factorization (CNMF) method and multi-layer NMF method.
Video-Rate Video Inpainting

This paper considers the problem of video inpainting, i.e., to remove specified objects from an input video. Many methods have been developed for the problem so far, in which there is a trade-off between image quality and computational time. There was no method that can generate high-quality images in video rate. The key to video inpainting is how to establish correspondences from scene regions occluded in a frame to those observed in other frames. To break the trade-off, we propose to use CNNs as a solution to this key problem. We extend existing CNNs for the standard task of optical flow estimation to be able to estimate the flow of occluded background regions. The extension includes augmentation of their architecture and changes of their training method. We experimentally show that this approach works well despite its simplicity, and that a simple video inpainting method integrating this flow estimator runs in video rate (e.g., 32 fps for 832 × 448 pixel videos on a standard PC with a GPU) while achieving image quality close to the state-of-the-art.
The effect of using “Google classroom” on the achievement of computer department students in “image processing” subject, and their attitudes toward E-learning

The research aims to identify the effect of using "google classroom" on the achievement of computer department students in "image processing" subject, and their attitudes toward E-learning.To achieve its objectives, the following hypotheses are formulated:
Amortized Variational Inference with Graph Convolutional Networks for Gaussian Processes

GP Inference on large datasets is computationally expensive, especially when the observation likelihood is non-Gaussian. To reduce the computation, many recent variational inference methods define the variational distribution based on a small number of inducing points. These methods have a hard tradeoff between distribution flexibility and computational efficiency. In this paper, we focus on the approximation of GP posterior at a local level: we define a reusable template to approximate the posterior at neighborhoods while maintaining a global approximation. We first construct a variational distribution such that the inference for a data point considers only its neighborhood, thereby separating the calculation for each data point. We then train Graph Convolutional Networks as a reusable model to run inference for each data point. Comparing to previous methods, our method greatly reduces the number of parameters and also the number of optimization iterations. In empirical evaluations, the proposed method significantly speeds up the inference and often gets more accurate results than competing methods.
Who Will Use This and Why? User Stories and Use Cases

User stories and use cases help focus any development project on those who stand to benefit, i.e. the project’s stakeholders, and can guard simultaneously against insufficient planning and software bloat. And the concepts, though most often thought of with respect to large-scale projects, apply in all circumstances, from the smallest feature request to an existing system to the redesign of a complex system.
Robustness Guarantees for Density Clustering

Despite the practical relevance of density-based clustering algorithms, there is little understanding in its statistical robustness properties under possibly adversarial contamination of the input data. We show both robustness and consistency guarantees for a simple modification of the popular DBSCAN algorithm. We then give experimental results which suggest that this method may be relevant in practice.