Reviewerly/exHarmony huggingface dataset:
An Efficient Brain MR Images Segmentation Hardware Using Kernel Fuzzy C-Means

This paper presents an efficient hardware-based approach for segmentation of brain MR images utilizing kernel Fuzzy-C-Means (KFCM). First, the performance of the FCM algorithm for MR image segmentation is evaluated on 217 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> 181 images of BrainWeb dataset by a software implementation of it. Experiments demonstrated that the proposed algorithm provides a clustering accuracy of 96%; an appropriate result compared to other methods. After asserting the validity of the algorithm, the hardware implementation of the algorithm is conducted. Here, an overall architecture is proposed and designed toward being implemented on Xilinx FPGA Virtex7. Based on synthesis results, this design occupies approximately 19% of the hardware resources of xa7al00tcsg324-2I. Base obtained clock frequency of the designed system was 108MHz; then it is increased to 254 MHz by applying optimization techniques such as pipelining and reducing critical path delay. In the final proposed design, the amount of speed up factor compared to software implementation is 101.06. This improvement in speed is the aim of optimized hardware implementation.
ADN for object detection

Owing to large‐scale diversity and location uncertainty in object detection, how to enrich semantic information has become an important issue that attracts a lot of concern. In this study, the authors propose a novel attentional detection network (ADN) to enrich semantic information of feature maps by adding an extra attention branch to the classic detection network. Compared to previous methods (e.g. feature pyramid network (FPN), single shot multibox detector (SSD)) that producing massive anchors in different layers of feature maps to detect objects with different scales and aspect ratios, which is very time‐consuming, their network is lightweight and do not need to produce extra anchors. Furthermore, ADN can be applied to different object detectors with little computational cost. Extensive experiments indicate that ADN has good detection performance on different datasets without bells and whistles.
Efficient Inner Product Functional Encryption with Full-Hiding Security

Inner product functional encryption (IPFE) is a subclass of functional encryption (FE), whose function class is limited to inner product. We construct an efficient private-key IPFE scheme with full-hiding security, where confidentiality is assured for not only encrypted data but also functions associated with secret keys. Recently, Datta et al. presented such a scheme in PKC 2016 and this is the only scheme that achieves full-hiding security. Our scheme has an advantage over their scheme for the two aspects. More efficient: keys and ciphertexts of our scheme are almost half the size of those of their scheme. Weaker assumption: our scheme is secure under the k-linear (k-Lin) assumption, while their scheme is secure under a stronger assumption, namely, the symmetric external Diffie-Hellman (SXDH) assumption. It is well-known that the k-Lin assumption is equivalent to the SXDH assumption when k=1 and becomes weak as k increases.
Credit Card Fraud Detection: A Systematic Review

Due to the tremendous growth of technology, digitalization has become the key aspect in the banking sector. As online transaction increases, the fraud rate grows simultaneously. Even though many techniques are available to identify the fraudulent transaction, the fraudsters adapt their own paradigm. This review intends to present the research studies accomplished on Credit Card Fraud Detection (CCFD) by highlighting the challenge of class imbalance and the various Machine Learning techniques, it also extends the efficient evaluation metrics particularly for CCFD. As the dataset is more sensitive and less available we have outlined the web sources of available datasets and trending software tools used in the deployment of CCFD.
Efficient AES implementation on Sunway TaihuLight supercomputer: A systematic approach

Encryption is an important technique to improve information security for many real-world applications. The Advanced Encryption Standard (AES) is a widely-used efficient cryptographic algorithm. Although AES is fast both in software and hardware, it is time-consuming to do data encryption especially for large amount of data. Therefore, it is a lasting effort to accelerate AES operations. This paper presents SW-AES, a parallel AES implementation on the Sunway TaihuLight, one of the fastest supercomputers in the world that takes the SW26010 processor as the basic building block. According to the architectural features of SW26010, SW-AES exploits parallelism from different levels, including (1) inter-CPE (Computing Processing Element) data parallelism that distributes tasks among the 256 on-chip CPEs, (2) intra-CPE data parallelism enabled by the Single-Instruction Multiple-Data (SIMD) instructions inside each CPE, and (3) instruction-level parallelism that pipelines memory access and the computation. In addition, corresponding to the two application scenarios, SW-AES presents scalable ways to efficiently run AES on many nodes. As a result, SW-AES can gain a maximum throughput of 13.50 GB/s on a single SW26010 node, which is 216.23× higher than the latest parallel AES implementation on the Sunway TaihuLight, and about 37.3% higher than the latest AES implementation on the GTX 480 GPU. When running on 1024 computing nodes with each one processing 1 GB data, SW-AES can achieve a throughput of 13819.25 GB/s. On the contrast, only a throughput of 63.91 GB/s can be achieved by the latest related work on the Sunway TaihuLight.
Analysing Effect of Vehicle Lean Using Cybernetic Model of Motion Sickness

Objective and quantitative evaluation of ride comfort can be achieved owing to recent advances in cybernetic models. Therefore, we developed a mathematical model for analyzing motion sickness and utilized it to evaluate comfort in vehicle riding. It is known that the inclination of the head toward the gravito-inertial force (GIF) decreases the severity of motion sickness; this research focused on the motion sickness incidence (MSI) as an evaluation index for ride comfort. Thus, we hypothesized that those vehicles that have a function to tilt driver/passenger seats toward the GIF direction, which is called the lean function, decrease motion sickness and increase comfort. Therefore, the purpose of this research was to investigate the comfort of a four-wheeled vehicle with a lean function. It was revealed that by adding the lean function, the head could be tilted in the GIF direction; the MSI was calculated using the mathematical model and subjective motion sickness decreased. The correlation analysis of head-roll angular velocity and vehicle centrifugal acceleration suggested that drivers tilted their heads before the occurrence of the vehicle lateral acceleration.
Multimedia-Based Visual Analysis As a Promotional Media At Raharja Internet Cafe (RIC)

The development of visual communication media is currently widely used as a means of promotion and information technology. By following the development of information technology today, the delivery of information is no longer presented in the conventional form, but now it has been widely applied in electronic form. One of the results of the development of information technology that can be used as a medium for delivering interesting information can be in the form of Graphic Design such as Online Posters and images uploaded on social media. Raharja Internet Cafe or often called RIC is one of the facilities located at Raharja University. Providing all personal needs Raharja in carrying out lecture activities such as Print, Scan, install iPad, also provides accessories for technological devices such as computers and iPads, besides that RIC also serves computer and iPad services. However, RIC still has problems in presenting information relevant to existing developments. Millennials today tend to get and search for information by accessing social media compared to accessing a website. This is one of the important things that must be applied to Raharja Internet Cafe in conducting socialization and marketing. Therefore, the author was given the opportunity to be able to do the Job Training Lecture (KKP) on Raharja Internet Cafe to apply the knowledge gained in the lecture curriculum. Then the author also gained a lot of knowledge about the world of work that had not previously been obtained in the lecture process. Which in the end, each task and the work that has been done by the author while carrying out the Job Training Lecture (KKP), will be summarized in the Job Training Lecture report.
Smart Recommendation System Based on Product Reviews Using Random Forest

Social network, e-commerce sites, blogs are new emerging platforms for people to express their opinion. These sites contain huge amount of text which can be used for different purpose like Sentiment Analysis. Sentiment Analysis is a growing field in natural language processing. Sentiment analysis is major focused on company's improvement. But sentiment analysis can be useful in recommendation system also. Based on various performance measures, this paper compares the results of machine learning algorithms like Multinomial Naive Bayes algorithm, Logistic Regression, SVM Classifier, Decision Tree and Random Forest. These algorithms are used for sentiment analysis of reviews and in turn for product recommendation. In proposed system, Random Forest shows outstanding performance. To create suitable recommendations using the analysis of emotions, there is a need to use polarity obtained through the reviews.
No-Reference Video Quality Assessment Based on Visual Memory Modeling

Objective video quality assessment plays an important role in a variety of video processing applications such as video compression, transmission, visualization, and display. This paper proposes a no-reference video quality assessment model based on visual memory understanding. Inspired by the findings of neuroscience researchers, who argue there is a large overlap between the active human brain area when performing video quality assessment and saliency detection tasks, saliency maps are employed here to assist the quality assessment. To this end, we first generate the saliency maps using CLBP (Complete Local Binary Patterns) features of the residual frames. Then, a model of visual memory is created from the statistics of saliency maps. This is followed by learning the video quality from the visual memory, saliency, and frame features through a support vector regression pipeline. The experimental results on the state-of-the-art LIVE and SJTU video datasets indicate that the proposed no-reference video quality assessment algorithm is effective and performs statistically better than several other state-of-the-art approaches.
Real Time Traffic Light Detection and Classification using Deep Learning

Traffic light detection and classification represent a major issue for autonomous driving. Although a number of works have been published on this topic, providing a real-time processing solution is still a challenging task. In this paper, we show, by experimenting three models, namely “Faster R-CNN”, “R-FCN” and “SSD” on and two datasets, namely “Bosch Small Traffic Light Dataset” and “Lisa Traffic Light Dataset”, that we can achieve a higher accuracy while reducing the detection and recognition time. In order to improve the overall performance and take the best score of the trained models, we used the ensembling modeling technique. The obtained results outperform the state-of-the-art.
Micro Expression Classification using Facial Color and Deep Learning Methods

Micro emotions are a unique type of facial expression as they are involuntary and very brief. They usually occur when a person attempts to suppress or hide their emotions. Lasting less than 500 ms, they can be very hard to detect even for the human eye, however, since they reveal a person's true feelings, they are of extreme interest in many fields of study. Most approaches to automatic detection and classification of micro emotions rely on detecting the small residual facial movements. In this paper, we propose to exploit an aspect of the human face which is much harder to subdue, namely, facial color change due to blood flow during expression of emotion. We propose a system that evaluates color change during micro emotion expression and successfully classifies the emotion type. This approach is unique in that it disregards the motion related aspects of the expression, and relies entirely on the facial color. We show that our system improves over movement based approaches.
Viewport Forecasting in 360° Virtual Reality Videos with Machine Learning

Objective. Virtual reality (VR) cloud gaming and 360° video streaming are on the rise. With a VR headset, viewers can individually choose the perspective they see on the head-mounted display by turning their head, which creates the illusion of being in a virtual room. In this experimental study, we applied machine learning methods to anticipate future head rotations (a) from preceding head and eye motions, and (b) from the statistics of other spherical video viewers. Approach. Ten study participants watched each 3 1/3 hours of spherical video clips, while head and eye gaze motions were tracked, using a VR headset with a built-in eye tracker. Machine learning models were trained on the recorded head and gaze trajectories to predict (a) changes of head orientation and (b) the viewport from population statistics. Results. We assembled a dataset of head and gaze trajectories of spherical video viewers with great stimulus variability. We extracted statistical features from these time series and showed that a Support Vector Machine can classify the range of future head movements with a time horizon of up to one second with good accuracy. Even population statistics among only ten subjects show prediction success above chance level. %Both approaches resulted in a considerable amount of prediction success using head movements, but using gaze movement did not contribute to prediction performance in a meaningful way. Even basic machine learning models can successfully predict head movement and aspects thereof, while being naive to visual content. Significance. Viewport forecasting opens up various avenues to optimize VR rendering and transmission. While the viewer can see only a section of the surrounding 360° sphere, the entire panorama has typically to be rendered and/or broadcast. The reason is rooted in the transmission delay, which has to be taken into account in order to avoid simulator sickness due to motion-to-photon latencies. Knowing in advance, where the viewer is going to look at may help to make cloud rendering and video streaming of VR content more efficient and, ultimately, the VR experience more appealing.
Private Streaming With Convolutional Codes

Recently, information-theoretic private information retrieval (PIR) from coded storage systems has gained a lot of attention, and a general star product PIR scheme was proposed. In this paper, the star product scheme is adopted, with appropriate modifications, to the case of private (e.g., video) streaming. It is assumed that the files to be streamed are stored on n servers in a coded form, and the streaming is carried out via a convolutional code. The star product scheme is defined for this special case, and various properties are analyzed for two channel models related to straggling and Byzantine servers, both in the baseline case as well as with colluding servers. The achieved PIR rates for the given models are derived and, for the cases where the capacity is known, the first model is shown to be asymptotically optimal, when the number of stripes in a file is large. The second scheme introduced in this work is shown to be the equivalent of block convolutional codes in the PIR setting. For the Byzantine server model, it is shown to outperform the trivial scheme of downloading stripes of the desired file separately without memory.
Applying Digital Forensics to Service Oriented Architecture

Digital forensics is an emerging research field involving critical technologies for obtaining evidence in digital crime investigations. Several methodologies, tools, and techniques have been developed to deal with the acquisition, preservation, examination, analysis, and presentation of digital evidence from different sources. However, new emerging infrastructures such as service-oriented architecture has brought new serious challenges for digital forensic research to ensure that evidence will be neutral, comprehensive, and reliable in such complex environment is a challenging research task. To address this issue, the authors propose in this article a generic conceptual model for digital forensics methodologies to enable their application in a service-oriented architecture. Challenges and requirements to construct a forensically sound evidence management framework for these environments are also discussed. Finally, the authors show how digital forensics standards and recommendations can be mapped to service-oriented architecture.
Enhancing Visual Embeddings through Weakly Supervised Captioning for Zero-Shot Learning

Visual features designed for image classification have shown to be useful in zero-shot learning (ZSL) when generalizing towards classes not seen during training. In this paper, we argue that a more effective way of building visual features for ZSL is to extract them through captioning, in order not just to classify an image but, instead, to describe it. However, modern captioning models rely on a massive level of supervision, e.g up to 15 extended descriptions per instance provided by humans, which is simply not available for ZSL benchmarks. In the latter in fact, the available annotations inform about the presence/absence of attributes within a fixed list only. Worse, attributes are seldom annotated at the image level, but rather, at the class level only: because of this, the annotation cannot be visually grounded. In this paper, we deal with such a weakly supervised regime to train an end-to-end LSTM captioner, whose backbone CNN image encoder can provide better features for ZSL. Our enhancement of visual features, called "VisEn", is compatible with any generic ZSL method, without requiring changes in its pipeline (a part from adapting hyper-parameters). Experimentally, VisEn is capable of sharply improving recognition performance on unseen classes, as we demonstrate thorough an ablation study which encompasses different ZSL approaches. Further, on the challenging fine-grained CUB dataset, VisEn improves by margin state-of-the-art methods, by using visual descriptors of one order of magnitude smaller.
An Improved and Adaptive Approach in ANFIS to Predict Knee Diseases

Artificial intelligence is emerging as a persuasive tool in the field of medical science. This research work also primarily focuses on the development of a tool to automate the diagnosis of inflammatory diseases of the knee joint. The tool will also assist the physicians and medical practitioners for diagnosis. The diseases considered for this research under inflammatory category are osteoarthritis, rheumatoid arthritis and osteonecrosis. A five-layer adaptive neuro-fuzzy (ANFIS) architecture was used to model the system. The ANFIS system works by mapping input parameters to the input membership functions, input membership functions are mapped to the rules generated by the ANFIS model which are further mapped to the output membership function. A comparative performance analysis of fuzzy system and ANFIS system is also done and results generated shows that the ANFIS system outperformed fuzzy system in terms of testing accuracy, sensitivity and specificity.
Web Vulnerability Assessment and Maturity Model Analysis on Indonesia Higher Education

College websites are websites that are used as media and means of campus information. Since the website is widely accessible, the level of security on the website must always be maintained. To see the level of web security, it can be done by testing the security vulnerability of the web. The test results using the tools Nessus and Skipfish, on the websites of several universities in Jakarta, show that there are still several vulnerabilities. This vulnerability will affect the maturity level of the web site security. The results of vulnerability testing show that as many as 60% of the total 33 web sites have a maturity level below number 3. This indicates that the level of vulnerability on the web site is still high.
BanFEL: A Blockchain Based Smart Contract for Fair and Efficient Lottery Scheme

Lottery is a game with many people's dreams. But corruptions of lottery centers make the lottery unfair. To address this unfair issue, fair lottery schemes have been studied for several years. In these schemes, delay functions or aggregation protocols can be used to generate the winning numbers fairly. However, to the best of our knowledge, none of the existing schemes can achieve the winning number generated randomly, while the randomness can be verified fairly. In this article, we first propose a [B]lockchain based sm[a]rt co[n]tract for [F]air and [E]fficient [L]ottery (BanFEL) scheme. We further present a winning number random generation smart contract, by which players submit the purchased numbers and the non-tampering property is protected. Security analysis and experiments show that the fairness of BanFEL is protected better than the existing works. Meanwhile, the verification cost of this scheme are at least 0.03s lower than the traditional Grumbach's scheme between 1000 and 10000 players.
Decisions and Their Making in OSS Development: An Exploratory Study Using the Hibernate Developer Mailing List

Stakeholders make various decisions regarding requirements, architectural design, project management, etc. during the life cycle of software development. They have an interest in making high-quality and appropriate decisions that meet project objectives and maximize system benefits. However, decision-making in software development is not a trivial task and the decisions made are often not well documented. In this paper, we analyzed 9006 posts from the Hibernate developer mailing list, in order to explore (1) decision expression and (2) classification, (3) the rationale behind decision-making, (4) approaches employed in decision-making, (5) related software artifacts of decision-making, and (6) the trend of decision-making over time. The results show that (1) all decisions are expressed as Information Giving, Solution Proposal, and Feature Request; (2) the main categories of decisions are Design Decision and Requirement Decision; (3) the most common rationale behind decision-making is Non-functional Requirement; (4) the most commonly used decision-making approach is Problem Structuring; (5) the main categories of software artifacts related to decision-making are Design Document and Requirement Document; and (6) decision-making runs through the whole software development life cycle.