Reviewerly/exHarmony huggingface dataset:
An Efficient and Secure Variant of RC4 Stream Cipher Scheme for Emerging Networks

Data Confidentiality (DC) is considered one of the most important security services. Currently, a set of existing cipher algorithms is being used to ensure DC. However still, designing and implementing a more efficient cipher scheme is always being sought. Moreover, various vulnerabilities are constantly being targeted by new kinds of attacks such as the physical ones. In addition, some cipher algorithms exhibit limitations in terms of latency and required resources, and hence cannot be preferred to constrained devices. This leads to a trade-off between system performance and security level. Towards solving these challenges, we propose a lightweight cipher scheme that ensures a high level of security with minimal latency and resource requirements compared to existing standards such as AES. Specifically, the proposed cipher scheme is based on the original RC4 but adapted and extended by introducing two different round functions that consist of substitutions, addition, and non-invertible diffusion operations to achieve the aforementioned goals. Experimental results indicate that the proposed cipher is a strong and promising stream cipher candidate that has high key sensitivity, high randomness degree, as well as periodicity properties.
Face Validation Based Anomaly Detection Using Variational Autoencoder

Abstract In unconstrained facial images, large visual variations concerning pose, scale, the presence of occlusions, expressions and lighting usually cause difficulties in discriminating faces from the background accurately. As a result, some non-face regions are recognized as faces (false positive) and that influences the effectiveness of face detection algorithms which is characterized by low false positive (FP) rate, high detection rate and high speed of processing. In order to reduce these non-face regions, they are considered as anomalies and then try to detect them. In this paper, we propose an anomaly detection method using reconstruction error from variational autoencoder (VAE), which is a generative machine learning model. We train VAE to learn reconstructing faces that are close to its original input faces using FDDB dataset, then the difference between the original input face and the reconstructed output is measured to obtain the reconstruction error which can be used as an anomaly score. Consequently, the regions resulting from faces detection algorithm with high reconstruction error are defined as anomalies or false positives.
CADENCE

Many forms of interaction between computer systems and users are recorded in the form of event records, such as login events, API call records, bank transaction records, etc. These records are often comprised of high-dimensional categorical variables, such as user name, zip code, autonomous system number, etc. In this work, we consider anomaly detection for such data sets, where each record consists of multi-dimensional, potentially very high-cardinality, categorical variables. Our proposed technique, named CADENCE, uses a combination of neural networks, low-dimensional representation learning and noise contrastive estimation. Our approach is based on estimating conditional probability density functions governing observed events, which are assumed to be mostly normal. This conditional modeling approach allows CADENCE to consider each event in its own context, thereby significantly improving its accuracy. We evaluate our proposed method using both synthetic and real world data sets. Our results show that CADENCE performs significantly better than existing methods at real-world anomaly detection tasks.
GizMO -- A Customizable Representation Model for Graph-Based Visualizations of Ontologies

Visualizations can support the development, exploration, communication, and sense-making of ontologies. Suitable visualizations, however, are highly dependent on individual use cases and targeted user groups. In this article, we present a methodology that enables customizable definitions for the visual representation of ontologies.
Convolutional Neural Network for Core Sections Identification in Scientific Research Publications

The overwhelming volume of data generated online continuous to grow at an exponential and unprecedented rate. Over 80% of such data is unstructured. Scientific research publications constitute a significant portion of such unstructured data. Systematic literature review (SLR) activity is a rigorous and challenging process. The key challenge in SLR is the automatic extraction of the relevant data from the sheer volume of research publications. Lack of a unified framework has been identified as the key problem. A canonical model, based on the structure of the papers was proposed as the framework for data extraction purposes in SLR. Implemented as a classification problem, traditional machine learning models were used to realise the canonical model. A good accuracy was reported in these traditional models. However, there is room for improvement. This paper presents the result of the work on the same problem using convolutional neural network (CNN), which is more sophisticated (deeper). The results show an improvement over the traditional machine learning models with an accuracy of 85%. Unlike the previous CNN NLP works, this work also demonstrates the application of CNN on a bigger NLP dataset such as the data from the scientific research publications. The result also shows that the CNN performs even better in NLP tasks with bigger datasets.
Deep Demosaicing Using ResNet-Bottleneck Architecture

Demosaicing is a fundamental step in a camera pipeline to construct a full RGB image from the bayer data captured by a camera sensor. The conventional signal processing algorithms fail to perform well on complex-pattern images giving rise to several artefacts like Moire, color and Zipper artefacts. The proposed deep learning based model removes such artefacts and generates visually superior quality images. The model performs well on both the sRGB (standard RGB color space) and the linear datasets without any need of retraining. It is based on Convolutional Neural Networks (CNNs) and uses a residual architecture with multiple 'Residual Bottleneck Blocks' each having 3 CNN layers. The use of 1 $$\times $$ 1 kernels allowed to increase the number of filters (width) of the model and hence, learned the inter-channel dependencies in a better way. The proposed network outperforms the state-of-the-art demosaicing methods on both sRGB and linear datasets.
Exploration of marine ship anomaly real-time monitoring system based on deep learning

Maritime transportation is the main traffic mode of globalization, accounting for about 90% of the proportion of global trade. Maritime safety is an important problem of marine transportation. Therefore, it is very important to set up the ship abnormal real-time monitoring system. In order to reali ze the real-time monitoring of abnormal data in the course of ship driving, a hybrid single classification framework based on depth learning is designed. DDBN-OCSVM framework uses the deep network to solve the problem that complex high-dimensional data are difficult to reduce and learn. The single classification algorithm is used to avoid the impact of data imbalance on the results of ship real-time monitoring anomaly detection. Finally, the experimental analysis and discussion of the data are carried out. The experimental results show that DDBN-OCSVM can effectively reduce the detection error under the accelerating effect of GPU and cuDNN. The DDBN-OCSVM algorithm proves that the unsupervised feature learning and hierarchical representation are effective and feasible. It is also proved that it is feasible to apply this deep learning mode to real-time monitoring of ship anomalies.
Reliable Demosaicing Detection for Image Forensics

Visually plausible image forgeries are easy to create even without particular knowledge or skills. However, most forgeries unknowingly alter the underlying statistics of an image. In particular, demosaicing artefacts created by the camera are usually destroyed or modified when the image is tampered. Most of the literature focus on detecting where these traces are destroyed, and generally do it in a way that still requires a visual interpretation. We introduce an a contrariomethod which detects global demosaicing parameters, and then checks for regions of the image which are inconsistent with these parameters. Detections are guaranteed in the form of a number of false alarms (NFA), which enables the user to control the false positive rate. Such a guarantee is a useful complement to existing methods, and enables inclusion into fully automatic image authentication processes. The source code and an online demo are provided with the article.
Object Detection and Trcacking Based on Convolutional Neural Networks for High-Resolution Optical Remote Sensing Video

Object detection algorithms, from high-resolution optical remote sensing images, have been booming from the last few years. However, object tracking for high-resolution optical remote sensing video is a challenging task due to the large number and small size of objects. In this paper, we propose an object detection and tracking method based on deep convolutional neural networks for wide swath high-resolution optical remote sensing videos. The proposed method firstly segments each frame of a video into sub-samples using a sliding window of fixed size. In order to detect the objects appearing at the edge of the sliding window efficiently, we use an overlapping sliding window sampling method. Further, we design a network fusing region of interests (RoIs) of the previous and current frames to track the objects occurred in the previous frames of the video. RoIs of previous frame are applied directly to the feature layer of the current frame. Finally, for each frame, we merge the detection and tracking results of sub-samples by non-maximum suppression (NMS) method. The experimental results on our dataset demonstrate the validity and generality of the proposed detection algorithm.
MBECN: Enabling ECN with Micro-burst Traffic in Multi-queue Data Center

Modern multi-queue data centers often use the standard Explicit Congestion Notification (ECN) scheme to achieve high network performance. However, one substantial drawback of this approach is that micro-burst traffic can cause the instantaneous queue length to exceed the ECN's threshold, resulting in numerous mismarkings. After enduring too many mismarkings, senders may overreact, leading to severe throughput loss. As a solution to this dilemma, we propose our own adaptationthe Micro-burst ECN (MBECN) scheme-to mitigate mismarking. MBECN finds a more appropriate threshold baseline for each queue to absorb micro-bursts, based on steady-state analysis and an ideal generalized processor sharing (GPS) model. By adopting a queue-occupation-based dynamically adjusting algorithm, MBECN effectively handles packet backlog without hurting latency. Through testbed experiments, we find that MBECN improves throughput by ~20% and reduces flow completion time (FCT) by ~40%. Using large scale simulations, we find that throughput can be improved by 1.5~2.4× with DCTCP and 1.26~1.35× with ECN*. We also measure network delay and find that latency only increases by 7.36%.
Deep Neural Networks

In Chap. 1 , our empirical analysis was based on neural networks with a single hidden layer. These networks, called shallow, are in theory universal approximators of any continuous function. Deep neural networks use instead a cascade of multiple layers of hidden neurons. Each successive layer uses the output from the previous layer as input. As with shallow networks, many issues can arise with naively trained deep networks. Two common issues are the overfitting of the training dataset and the increase of computation time. We present the techniques for limiting the computation time and the methods of regularization for avoiding the overfitting. We next explain why deep neural networks outperform shallow networks for approximating hierarchical binary functions. This chapter is concluded by a numerical illustration.
EURECA: epistemic uncertainty classification scheme for runtime information exchange in collaborative system groups

Collaborative embedded systems (CES) typically operate in highly dynamic contexts that cannot be completely predicted during design time. These systems are subject to a wide range of uncertainties occurring at runtime, which can be distinguished in aleatory or epistemic. While aleatory uncertainty refers to stochasticity that is present in natural or physical processes and systems, epistemic uncertainty refers to the knowledge that is available to the system, for example, in the form of an ontology, being insufficient for the functionalities that require certain knowledge. Even though both of these two kinds of uncertainties are relevant for CES, epistemic uncertainties are especially important, since forming collaborative system groups requires a structured exchange of information. In the autonomous driving domain for instance, the information exchange between different CES of different vehicles may be related to own or environmental behavior, goals or functionalities. By today, the systematic identification of epistemic uncertainties sourced in the information exchange is insufficiently explored, as only some specialized classifications for uncertainties in the area of self-adaptive systems exist. This paper contributes an epistemic uncertainty classification scheme for runtime information exchange (EURECA) in collaborative system groups. By using this classification scheme, it is possible to identify the relevant epistemic sources of uncertainties for a CES during requirements engineering.
The Function Of Technology And Device Laptop For Education Purpose

Abstract Education is one of the technological needs that must be needed. People’s lifestyle in the present era raises the emergence of developments in various fields and one of its technologies. The changes in learning patterns are required to perform the update following the dynamics of the changing times of increasingly rapid developments triggered by technology. Technology in education is very helpful in the world of education, one of them in the learning process between educators and students. So an education, teachers and students must use technology by mastering technology, one of which is laptop technology. Laptop is an advanced technology designed with various applications in it. The application in it is an application that has different uses that greatly help the human needs of one of the teachers and students in an education
Disambiguation Model for Bio-Medical Named Entity Recognition

Discovery of biomedical named entities is one of the preliminary steps for many biomedical texts mining task. In the biomedical domain, typical entities are present, including disease, chemical, gene, and protein. To find these entities, currently, a deep learning-based approach applied into the Biomedical Named Entity Recognition (Bio_NER) which gives prominent results. Although deep learning-based approach gives a satisfactory result, still a tremendous amount of data is required for training because a lack of data can be one of the barriers in the performance of Bio_NER. There is one more obstacle in the path of Bio_NER is polysemy or misclassification of the entity in bio-entity. Which means one biomedical entity might have a different meaning in different places, i.e., a gene named entity may be labeled as disease name. When Conditional Random Field combined with deep learning-based approach i.e. Bidirectional Long Short Term Memory (Bi-LSTM), It mistakenly labeled a gene entity “BRCA1” as a disease entity which is “BRCA1 abnormality” or “Braca1-deficient” present in the training dataset. Similarly, “VHL (Von Hippel-Lindau disease),” which is one of the genes named labeled as a disease by Bi-LSTM CRF Model. One more problem is addressed in this chapter, as bio-med domain, entities are long and complex like cell whose name is “A375M (B-Raf (V600E)) is a human melanoma cell line”, in this biomedical entity, multiple words are present, but still it is difficult to find the context information of this particular bio-entity. For lack of data and entity misclassification problem, this chapter embeds multiple Bio_NER models. In the proposed model, the model trained with different datasets is connected so that the targeted model obtained the information by combining another model, which reduce the false-positives rate. Recurrent Neural Network (RNN) which is dependent upon the Bi-LSTM gates are introduced to handle the long and complex range dependencies in biomedical entities. BioCreative II GM Corpus, Pubmed, Gold-standard dataset, and JNLPBA dataset are used in this research work.
Data-driven Summarization and Synchronized Second-screen Enrichment of Cycling Races

Traditional broadcasters of cycling races are experiencing hard times as the numbers of spectators are decreasing each year. Other ways of reporting are needed to keep the viewer interested. In this paper, two possible solutions are proposed that have been evaluated during the Grand Depart of the Tour de France 2019 in Brussels. The first innovation focuses on data-driven summarization and allows end-users to query for personalized stories of a race, tailored to their wishes (such as the length of the clip and the teams and/or riders that they are interested in). The second innovation follows the second screen trend and synchronizes cycling heritage multimedia data with the riders' live location during the race. Both rich, interactive TV experiences are based on a combination of data mining and computer vision techniques which can also be applied to other sports with similar characteristics. Evaluation by a test audience showed that there is certainly potential in both formats.
An attempt to adopt DevOps on embedded system development: empirical evidence

Abstract DevOps is a method that integrates and narrows two environments in software development process, developer side and operational side. DevOps becomes a solution that can be offered to improve SDLC (System Development Life Cycle) in current embedded system development process. This paper provides a concept and its implementation to prove that DevOps adoption on embedded system architecture is possible. After doing the research, we found out that it is possible to adopt DevOps on embedded system development, we also provide empirical evidence and analyze the weaknesses about the concept designed.
An Information Approach to Accuracy Comparison for Classification Schemes in an Ensemble of Data Sources

An accuracy of multiclass classifying the collections of objects taken from a given ensemble of data sources is investigated using the average mutual information between the datasets of the sources and a set of the classes. We consider two fusion schemes, namely WMV (Weighted Majority Vote) scheme based on a composition of decisions on the objects of the individual sources and GDM (General Dissimilarity Measure) scheme which uses a composition of metrics in datasets of the sources. For a given metric classification model, it is proved that the weighted mean value of the average mutual information per one source in WMV scheme is smaller to the similar mean in GDM scheme. Using a lower bound to the appropriate rate distortion function, it is shown that the lower bounded error probability in WMV scheme exceeds the similar error probability in GDM scheme. This theoretical result is confirmed by a computing experiment on face recognition of HSI color images giving the ensemble of H, S, and I sources.
Data Management and Analytics System for Online Flight Conformance Monitoring and Anomaly Detection

Air Navigation Service Providers (ANSP) worldwide have been making a considerable effort for the development of a better method to monitor conformance to the planned routes and detect anomalies within a particular airspace. Conformance monitoring and anomaly detection are crucial for a better managed airspace, both strategically and tactically, yielding a higher level of automation and thereby reducing the air traffic controller's workload. Although the prior approaches with limited amount of static air traffic data have been able to address the problem to some extent, data management and query processing of ever-increasing vast volume of streaming air traffic data at high rates for online conformance monitoring and anomaly detection still remain a challenge. In this paper, we present a novel data management and analytics system to continuously conformance monitor flights and accurately detect anomalies within the National Airspace System (NAS). The incoming Traffic Flow Management (TFM) data is streaming, big, uncorrelated and noisy. In the overall data pipeline, the system monitors flights and detects anomalies in 3 steps: In the preprocessing step, the system continuously processes the incoming raw flight data and makes it available for the next step where an interim Key-Value data store is created and maintained for efficient query processing. In the final step, the system learns from historical trajectories and pertinent weather parameters and builds a Long Short-Term Memory (LSTM) model. As the flights progress, the non-conforming trajectory segments as part of the live data stream are raised as anomalies. Evaluations on real air traffic and weather data in the U.S. verify that our system efficiently and accurately detects anomalies.
Gambling Behaviour in the Cryptocurrency Market

This article examines whether the investment strategies of cryptocurrency market involve high-risk gambling. Results show that the cryptocurrency risk premiums co-move closely with the return on CBOE Volatility Index (VIX). As such, the strategies of cryptocurrency trading closely resemble that of high-risk gambling. In other words, traders' expectations co-move closely (significantly) with the expected future payoffs from gambling. The co-movement is more pronounced when the gambling offers gains rather than losses and the payoffs are above average. VIX index returns significantly Granger-cause CSAD of returns (with and without Bitcoin) indicates that the cryptocurrency trading constitutes a form of gambling where the motivation for gambling comes from the amount of variation (i.e. riskiness) in the gambling payoffs. These findings warrant policymakers of countries to revisit the existing regulatory framework governing the conduct of electronic finance in the financial services industry.
Blended Teaching Mode Based on Rain Classroom in College English

Abstract The purpose of the study is to discuss the process of constructing the blended teaching mode based on Rain Classroom and explore the effects of blended teaching mode based on Rain Classroom on College English teaching. This paper concludes that the main components of the blended teaching mode based on Rain Classroom are pre-class study, during-class interaction, and after-class review and expansion. In addition, the blended teaching mode based on Rain Classroom has significant effect on improving college students’ comprehensive language level.
Sifar: An Attempt to Develop Interactive Machine Translation System for English to Hindi

The presently available machine translation systems are still far from being perfect, and to improve their performance the concept of interactive machine translation (IMT) was introduced. This paper proposes Sifar, an IMT system, which uses statistical machine translation and a bilingual corpus on which several algorithms (Word error rate, Position Independent Error Rate, Translation Error Rate, n-grams) are implemented to translate text from English to Hindi. The proposed system improves both the speed and productivity of the human translators as found through experiments.