Miracle-dz/UniFAR-FTUs huggingface dataset:
Service Function Chain Composition, Placement, and Assignment in Data Centers

With the development of network function virtualization (NFV), service function chains (SFCs) are deployed via virtual network functions (VNFs). In general, the SFCs are served via composition and then deployed into data center infrastructures. However, most of the existing works neglect SFC composition. Furthermore, they consider that VNF instances are independently deployed for each SFC, which may underutilize the computational power of servers. We consider, for each required VNF in the chain, the operator can either place it on a new instance or assign it to an established instance if the residual resource of that instance is sufficient. Such a deployment scheme can leverage resources more efficiently and we define it as SFC placement and assignment. In this paper, we first combine SFC composition, placement and assignment together to enhance resource allocation. We present the system model and formulate the problem as 0–1 integer programming. We aim to improve the VNF instance utilization as well as reduce the link consumption. A heuristic approach called Jcap is developed to solve the problem in two stages. The simulations show that Jcap achieves competitive performance with the optimal results obtained from mathematical model.
Integration Review of National Remote Sensing Ground Station Based on Virtual Ground Station by Full Remote and Nearly Automation

National Institute of Aeronautics and Space (LAPAN) has long experience in Remote Sensing Ground Station operation, so that it is able to carry out its development and operation independently. LAPAN’s Remote Sensing has also received support from the government especially with the Roadmap, Law, and Government Regulation. Therefore, in accordance with the mandate of LAPAN in this matter represented by Technology and Data Center of Remote Sensing is required to make list, guidance, and supervision in development and operation of remote sensing ground station in Indonesia. The next stage to do is to integrate LAPAN’s Ground Station with all or some Remote Sensing Ground Stations operated by other Indonesian Ministries/Institutions in receiving remote sensing satellite data of low and medium resolution. It will use fully remote and nearly automation method in Remote Sensing Ground Station development, such as acquisition, processing and reporting stages Therefore, it is not necessary for operators in the ground station, so that it can be developed and operated optimally and efficiently according to the government’s mandate.
ASSESSMENT OF SMOKE INDUCED GENOTOXICITY IN SPRAGUE DAWLEY RATS

Objective: To investigate the genotoxic effect of cigarette smoking in Sprague Dawley rats. Study Design: Randomized experimental study. Place and Duration of Study: Study was conducted at Army Medical College Rawalpindi, from Jan 2013 to Jul 2013. Material and Methods: Seventy healthy Sprague Dawley rats were randomly divided into two groups at National Institute of Health (NIH). Group-I rats were not exposed while Group-II rats were exposed to cigarette smoke for 3 months. Cytokinesis Block Micronucleus Assay (CBMN) was done to determine genotoxicity. Data were subjected to statistical analysis using independent sample t-test. Results: The mean frequency of micronuclei in peripheral blood lymphocytes of control and smoker groups was 3.06 ± 0.639 and 6.77 ± 0.731 respectively. The difference was not statistically significant (p>0.05). Conclusion: The mean micronuclei frequency in peripheral blood lymphocytes of the smoke exposed rats was higher than that of the control rats however the difference was not significant.
Single sample face recognition based on orthogonal gradient binary pattern

To overcome the limitations of traditional face recognition methods for single sample,an improved gradient face algorithm named Orthogonal Gradient Binary Pattern( OGBP),which was robust to variations of illumination,face expression and posture,was proposed. Firstly,the features of the image samples were extracted by orthogonal gradient binary pattern. Then the feature vectors of each direction were concatenated into the general feature vector for face recognition. Finally the Principle Component Analysis( PCA) method was used to reduce dimensions and the nearest neighbor classifier was used for face image classification and recognition. The experimental results on YALE and AR face database indicate that the proposed method is simple,effective and better than the original gradient face algorithm,and also has better performance in face description for single sample.
Using PVM to Host CLIPS in Distributed Environments

Distributed Artificial Intelligence is important research field of Artificial Intelligence. It is need a generous of intelligence entity in distributed environments to solve problems together. Proceed from building high-speed distributed expert system, the realization technology is introduced of using PVM and CLIPS. The ready availability of both CLIPS and PVM is sufficient to provide essential message passing functions in CLIPS. It can construct a huge and portly distributed expert system, which can fill every net environment.
Speedy Palaeoanthropology: How virtual morphology, digital databases and open access policies boost research in human evolution

The last two decades have seen the development of virtual morphology (ViMo), which emerged during the late 20th century through the application of medical imaging techniques to the study of fossil hominins (Spoor et al. 1994, Zollikofer et al. 1995, Conroy et al. 1998). The ViMo workflow has evolved successively by first building digital databases of fossil hominins, followed by digital reference collections, through the development of virtual 3D geometric morphometrics and, more recently, also 3D-printing (Fig. 1; Bastir et al. 2019). ViMo-workflows have led to a renaissance of morphological studies of diversity in evolutionary Earth and life sciences. The aim of this presentation is to briefly present standard workflows in the Virtual Morphology Lab in the Museo Nacional de Ciencias Naturales, Madrid, and to show, more generally, how ViMo-technologies, together with paradigmatic changes in science (open access, digital data bases), contribute to boosting current research in human paleontology. The accidental discovery, in 2013, of fossil remains of a new human species, Homo naledi, in the Rising Star cave system, South Africa, has produced a large and important collection documenting early hominin diversity (Berger et al. 2015) . In the light of the huge amount of fossil material, a new research strategy was decided: different kinds of social media and an open-access policy were used for the organisation of a workshop focussed on the study of this new fossil collection and based on data sharing and global collaborations. Because of this modern strategy, H. naledi was published very soon after its discovery (Berger et al. 2015) and simultaneously, the digitized fossils were made available to the public via MorphoSource, an open-access database. As a consequence, only five years later, more than 30 scientific publications have yielded almost 600 citations. This productivity is much higher than in any other recently discovered hominin species. Thus, 13 years after “glasnost” was proclaimed for paleoanthropology (Weber 2001), H. naledi has provided the first real example illustrating how open-access to digital collections accelerates and modifies research and diffusion in human paleontology.
A trust management model for virtual communities

Virtual community is becoming a significant component in today s world of open, decentralized networks Trust management mechanism is crucial to fertilize the development of the virtual community. After the analysis of the features of virtual community, and various approaches for trust management model, we present a novel trust management model for virtual communities, which depend on both process-based and institutional-based, trust production. We propose some effective methods to address or alleviate some typical problems in virtual community trust management, such as the feedback incentive problem and malicious or false feedback problem. Our approach satisfies the time-dependence, history-dependence, and subjectivity properties of trust, especially the temporal feature of trust, and is suitable to the virtual community.
Convolutional neural networks for image processing

In this paper related to the Final Degree Project of Daniel Palacios Hidalgo under the supervision of Ramon Baldrich and with the support of the Autonomous University of Barcelona, the original Style Transfer Algorithm, a Denoising Convolutional Network and a Deep Dreaming network will be exposed in depth. The main purpose of this project is the understanding of how they work, how to implement them and finally, evaluate their results to compare them against the original implementations of their respective authors using Pytorch framework.
Exploration and Mining of Source Code Level Traceability Links on Stack Overflow

Test-to-Code traceability is a valid problem of software engineering that arises naturally in the development of larger software systems. Traceability links can be uncovered through various techniques including information retrieval. The immense amount of data shared daily on Stack Overflow behaves similarly in many aspects. In the current work, we endeavor to discover test-to-code connections in the code shared and propose some applications of the findings. Semantic connections can also be explored between different software systems, information retrieval can be used both in cross-post and in cross-system scenarios. The information can also be used to discover new testing possibilities and ideas and has the potential to contribute to the development and testing of new systems as well.
Reliability study of U-type multistage interconnection networks

Multistage Interconnection Networks (MINs) are used in wide applications of multiprocessor systems in order to set up connections between various nodes such as processors and memory modules. Reliability can be considered as one of the most challenging issues in these systems because this can degrade or cancel the network performance and consequently the effort of the whole system. Here, a type of multistage fabric called U-type is selected for study, particularly in terms of its reliability. This fabric turns out to be the most appropriate type for constructing parallel systems due to the particular features that it possesses. This study reveals that U-type fabrics are ideal devices as they present mostly high values of reliability and low values of latency compared to unidirectional fabrics. This behaviour becomes more intensive when it is used in a cluster that demands service that includes a high percentage of “local type traffic. ” Thus, U-type multistage devices can considerably enhance the connection points in constructing parallel systems, improving internal data flows.
Directions for future research on the integration of SOA, BPM, and BRM

Purpose Digital transformation has been changing the economic environment of enterprises in recent years. To remain competitive, they rely on an extensible software architecture, flexible workflow execution, and automated decision management. The concepts of service-oriented architectures (SOA), business process management (BPM), and business rules management (BRM) provide an adequate, yet isolated means of addressing many of these requirements. The paper aims to discuss these issues. Design/methodology/approach This study adapts from established research frameworks to structure the current state of research on the integration of SOA, BPM and BRM. The authors highlight the current research scope, point to disregarded topics and sketch out multidisciplinary research approaches. Findings While the three concepts are often discussed only in isolation or pairwise, approaches that integrate them are scarce. Against this backdrop, this study defines three types of research opportunities and identifies several directions for future research that should be explored from a technological as well as organizational perspective. Given the breadth of open questions, the authors present sources for each area of our research framework, which can serve as starting points for future investigations. Practical implications Except for well-established support for separate tasks and technologies, there is a lack of integrated standards, methods and platforms, which for example enable integrated decision-making across BPM and SOA. Originality/value Our contribution builds on established frameworks and clearly shows that the discussion on the integration of SOA, BPM and BRM cannot yet be regarded as sufficient. The research agenda highlights which areas explicitly benefit from a more precise consideration.
An overview of profiling side channel analysis based on machine learning

It has made a real issue in cryptographic security with the widespread use of them which attracts more attention now. This paper summarizes the most distinguishing profiling type attack in the side channel of cryptographic chips.The principle steps of the traditional profiling side channel analysis are introduced firstly,then the exposed problems are directed in discussion.It fits well to solve the problems using the algorithm in machine learning. So what undoubtedly added in is that the profiling side channel analysis is based on machine learning. Also the situation and the progress are both the significance in the paper.The disadvantages and the future of its development are mentioned at the end.
Programming and wireless control of a wafer manipulation SCARA robot using a mobile device

The research performed by the authors had the purpose of developing a control and programming application for a 3-axes SCARA robot designed for wafer manipulation and integrating it with a mobile device capable of wireless communication. The mobile device was then used to command axis movements, internal signals and for programming. The application was developed as a user-interface capable of communicating with robot’s controller and converting the user inputs into controller commands. In order to achieve position control, the direct kinematic model of the robot was developed. Furthermore, the application was integrated with the V-REP virtual simulation platform in order to achieve simulation and offline programming capabilities - the V-REP software provided the 3-D environment and the kinematics modelling functions required to perform the simulation while receiving commands from the developed application. Finally, a few demonstrative applications were configured in order to test the functionality of the system. The research has important implications in the field of industrial wireless communication, being a step towards more flexible robotic applications, full integration of remote operation technologies and easier access to remote production facilities.
Design and implementation of Area and Power efficient reconfigurable FIR filter with low complexity coefficients

This paper presents the design and implementation of area and power efficient reconfigurable finite impulse response (FIR) filter. We present a method for designing a reconfigurable filter with low binary complexity coefficients (LBCC) and thus to optimize the filter while satisfying the design specifications. The total number of non zero binary bits is taken as a measure of the binary complexity (BC) of a coefficient. We propose two implementation architectures namely signed-magnitude architecture (SMA) and signed-decimal architecture (SDA) which are based on 3-bit binary common sub expression elimination (BCSE) algorithm and vertical horizontal BCSE (VHBCSE) algorithm respectively. SMA and SDA reduce the redundant computations of the coefficient multiplications in the filter. The proposed filters are synthesized on tsmc 65nm CMOS technology. The synthesis results show that the proposed filters are area and power efficient when compared with the existing ones.
Test Suite Minimization of Evolving Software Systems: A Case Study

Test suite minimization ensures that an optimum set of test cases are selected to provide maximum coverage of requirements. In this paper, we discuss and evaluate techniques for test suite minimization of evolving software systems. As a case study, we have used an industrial tool, Static Code Analysis (SCAN) tool for Electronic Device Description Language (EDDL) as the System Under Test (SUT). We have used standard approaches including Greedy, Greedy Essential (GE) and Greedy Redundant Essential (GRE) for minimization of the test suite for a given set of requirements of the SUT. Further, we have proposed and implemented k-coverage variants of these approaches. The minimized test suite which is obtained as a result reduces testing effort and time during regression testing. The paper also addresses the need for choosing an appropriate level of granularity of requirements to efficiently cover all requirements. The paper demonstrates how fine grained requirements help in finding an optimal test suite to completely address the requirements and also help in detecting bugs in each version of the software. Finally, the results from different analyses have been presented and compared and it has been observed that GE heuristics performs the best (run time) under certain conditions.
Target Tracking Algorithm based on Context-Aware Deep Feature Compression

The main focus of target tracking is robustness and efficiency. Because of challenges such as background clutter, occlusion, and rotation, high robustness and efficiency cannot be achieved simultaneously. A tracking framework of perceptual correlation filter is improved to achieve high-speed computation between real-time trackers. The main contribution to high-speed computing speed comes from improved depth feature compression, which is realized by combining content-aware features with multiple automatic encoders. In the pre-training stage, an automatic encoder is trained for each class separately. In order to obtain the feature map suitable for target tracking, the orthogonal loss function is added to the training stage and the fine-tuning self-encoder stage. Experiments show that the improved algorithm demonstrates great improvement in accuracy and speed.
Effective Training Methods for Automatic Musical Genre Classification

Musical genres are labels created by human and based on mutual characteristics of songs, which are also called musical features. These features are key indicators for the content of the music. Rather than predictions by human decisions, developing an automatic solution for genre classification has been a significant issue over the last decade. In order to have automatic classification for songs, different approaches have been indicated by studying various datasets and part of songs. In this paper, we suggest an alternative genre classification method based on which part of songs have to be used to have a better accuracy level. Wide range of acoustic features are obtained at the end of the analysis and discussed whether using full versions or pieces of songs is better. Both alternatives are implemented and results are compared. The best accuracy level is 55% while considering the full version of songs. Besides, additional analysis for Turkish songs is also performed. All analysis, data, and results are visualized by a dynamic dashboard system, which is created specifically for the study.
A comparison of Satellite Image Compression methods in the Wavelet Domain

Better resolution of remotely sensed satellite images will make images clearer and interpretation easier but will increase the total volume of data that has to be managed. In order to reduce data volume for easier satellite communication transmission and reduce the total volume of data needed to be stores, the images should be compressed. Image compression in wavelet domain can be used for both lossy or lossless compression. Four major compression methods are available using the wavelet domain, i.e. CCSDS, Wavelet, Bandelet, and JPEG 2000. Some optical satellite images, were used as input data in simulation software which analyzed and compared the four compression methods in the wavelet domain. The result showed that the CCSDS method yielded the fastest compression and decompression time, but the Bandelet method retained better image quality when reconstructing original images or approximations of them compared to CCSDS. The JPEG 2000 method delivered better quality images than CCSDS for low bit rate. In summary at a rate of 0.25 bpp, CCSDS is 15 times faster than Bandelet and 3 times faster than JPEG2000. However, CCSDS quality is lower by up to 8.77% compared with Bandelet and up to 13.64% compared with JPEG2000.
Remote Sensing Image Super-Resolution Reconstruction based on Generative Adversarial Network

The super-resolution reconstruction algorithm based on generative adversarial network (GAN) can generate realistic texture in the super-resolution process of a single remote sensing image. In order to further improve the visual quality of the reconstructed image, this paper will improve the generation network, discrimination network, and perceptual loss of the generated confrontation network. Firstly, the batch normalization layer is removed and dense connections are used in the residual blocks, which effectively improves the performance of the generated network. Then, we use the relative discriminant network to learn more detailed texture. Finally, we obtain the perception loss before the activation function to maintain the consistency of brightness. In addition, transfer learning is used to solve the problem of insufficient remote sensing data. The experimental results show that the proposed algorithm has superiority in the super-resolution reconstruction of remote sensing images and can obtain better subjective visual effects
Neural attention model with keyword memory for abstractive document summarization

Abstractive summarization is the task of creating summaries by generating a set of novel sentences based on the information extracted from the original document, while most of summarization researches are based on extractive or compressive approaches. These approaches extract phrases from the original document and concatenate them by post‐processing and cannot truly encapsulate the contents of summaries, because they only reuse the phrases in the given document. Moreover, there are limits for paraphrasing and re‐organizing of the original contents with the current Natural Language Processing (NLP) techniques. With these reasons, we propose a novel abstractive summarization method. The main goal of our paper is to generate a long sequence of words with coherent sentences by reflecting the key concepts of the original document and the contents of summaries. To achieve this goal, we propose an attention mechanism that uses Document Content Memory for learning the language model effectively. To evaluate its effectiveness, the proposed methods are compared with other language models and an extractive summarization method. We demonstrate that our proposed methods improve summarization results in ROUGE score using ACL dataset. The experimental results show that our proposed methods using keyword memory are effective to generate long sequence summary.