Reviewerly/exHarmony huggingface dataset:
Information Associations for Multi-Domain Applications

Multiple Independent Levels of Security (MILS) architectures provide strong information security through separation and isolation of security domains. This architecture is useful in environments where sensitive information needs to be tightly controlled at varying levels of security, e.g., in miltary environments that support multiple classifications for data. A drawback of MILS architectures is impaired functionality caused by information partitioning resulting in an inability to easily share and aggregate information. Cross domain solutions facilitate data transfer between security domains to overcome these limitations, often at the expense of security and resulting in duplication and/or reclassification of data. We describe an alternate approach to improving functionality in MILS architectures based on Information Associations, these are design patterns that allow information on different security domains to be linked together, without having to transfer data and instead rely on coordination between the separate domains. A military case study is described that uses these design patterns to limit cross domain transfers, whilst increasing overall functionality, and ultimately creating a more secure MILS architecture.
Real-world pedestrian detection method enhanced by semantic segmentation

Although pedestrian detection has been largely improved with the emergence of convolutional neural networks (CNN), the performance in autonomous driving still faces various challenges, which mainly include large-scale variation, illumination variation, and occlusion of different levels. A robust pedestrian detector enhanced by semantic segmentation is proposed. Inspired by the benefits of multitask learning, our main idea lies in integrating the task of semantic segmentation into the detection framework with auxiliary supervision, inheriting the merits of the two-stream network. Specifically, anchor boxes with various scales are paved on the feature maps of a base CNN; detection is performed based on bounding box classification and regression. On the other stream, semantic segmentation is also performed based on the same feature maps. Extensive experiments on the recently published large-scale pedestrian detection benchmark, i.e., CityPersons, show that the additional supervision from semantic segmentation can significantly improve the detection accuracy without extra computational burdens during inference, which demonstrates the superiority of the proposed method.
Switching on an Environmentally Friendly and Affordable Light in Africa: Evaluation of the Role of Natural Gas

The purpose of this article is to review the dynamics of natural gas resources in Africa and evaluate how it can help solve the power challenges of the continent. This article develops from a descriptive analysis and desk review on natural gas and power. The key finding is that despite the increased discovery of natural gas in Africa, it has had minimal impact on power production. This study provides a descriptive overview and is limited to only natural gas. It does not consider how other energy sources can contribute to solving Africa’s power challenges. This article draws the attention of both policymakers and the investment community to the opportunities in the ‘natural gas-power’ value chain and the need to invest in gas infrastructure. An overview of the power challenges and natural gas potential of Africa is provided.
Multiobjective Imperialist Competitive Algorithm for Solving Nonlinear Constrained Optimization Problems

Abstract Nonlinear constrained optimization problem (NCOP) has been arisen in a diverse range of sciences such as portfolio, economic management, airspace engineering and intelligence system etc. In this paper, a new multiobjective imperialist competitive algorithm for solving NCOP is proposed. First, we review some existing excellent algorithms for solving NOCP; then, the nonlinear constrained optimization problem is transformed into a biobjective optimization problem. Second, in order to improve the diversity of evolution country swarm, and help the evolution country swarm to approach or land into the feasible region of the search space, three kinds of different methods of colony moving toward their relevant imperialist are given. Thirdly, the new operator for exchanging position of the imperialist and colony is given similar as a recombination operator in genetic algorithm to enrich the exploration and exploitation abilities of the proposed algorithm. Fourth, a local search method is also presented in order to accelerate the convergence speed. At last, the new approach is tested on thirteen well-known NP-hard nonlinear constrained optimization functions, and the experiment evidences suggest that the proposed method is robust, efficient, and generic when solving nonlinear constrained optimization problem. Compared with some other state-of-the-art algorithms, the proposed algorithm has remarkable advantages in terms of the best, mean, and worst objective function value and the standard deviations.
Power Loss Classification on Shifts Based on SMS (Singlemode-Multimode-Singlemode) Structured Fiber Optic Using Gaussian Naïve Bayes Method

Singlemode-multimode-singlemode based optical fiber can be used as a good communication medium, but energy or power carried by light will be weakened (losses) due to leakage or due to lack of clarity or shift in optical fibers. In this study, power losses in fiber optics based on SMS will be classified based on changes in the values of power losses to shifts and divided them according to three classes, there are good, average, and bad. The shift will be used as a classification variable that is between 0 μm to 450 μm with an increment of 50 µm for every interval. The SMS optical fiber structure used is 5.5 with 25 attempts on different optical fibers. The classification method used is Naïve Bayes with a Gaussian distribution. Gaussian distribution is used in Naïve Bayes because the dataset will be processed in the form of continuous values. From the results of testing based on TP+TN = 6, FP = 6 FN = 6 on confusion matrix, the classification accuracy value was 42.86%. This indicates that this classification method is still less effective for classifying fiber optic power losses with an SMS structure. For further study, another classification methods can be used in the power loss classification to get better results.
Dual-context Modal Logic as Left Adjoint of Fitch-style Modal Logic

In the stream of studies on intuitionistic modal logic, we can find mainly three kinds of natural deduction systems. For logical aspects, adding axiom schemata is a simple and popular way to construct a system. The Curry-Howard correspondence, however, gives us a connection between logic and computer science. From the viewpoint of programming languages, two more important systems, called a dual-context system and a Fitch-style system, have been proposed. While dual-context systems for S4 are heavily used in the field of staged computation, a dual-context system for K is also studied more recently. In our previous studies, categorical semantics for Fitch-style modal logic is proposed and usefulness of levels is noticed. This paper observes an interesting fact that the box modality of the dual-context system is in fact a left adjoint of that of the Fitch-style system. In order to show the statement, we embed both the two systems, which are refined with levels, into the adjoint calculus that equips an adjunction a priori. Moreover, the adjunction is refined with polarity and the adjoint calculus is extended to polarized logic.
Scene Text Detection with a SSD and Encoder-Decoder Network Based Method

In this work, we propose a simple yet powerful method that yields effective text detection in natural scenes. We present a Text Localization Neural Network, which detects text in scene images with one forward propagation and a standard non-maximum suppression subsequently. In order to eliminate few scene background mistaken by Text Localization Neural Network, we propose a Text Verification Model based on the encoder-decoder network. Thus, precision of text detection can be further improved by recognizing text in our candidate text regions. We have evaluated the proposed method for text detection on our own constructed horizontal text detection dataset. Compared with previous approaches, our method achieves a highest recall rate of 0.784 and competitive precision rate in text detection.
Query Oriented Extractive-Abstractive Summarization System (QEASS)

This work proposes a query oriented extractive-abstractive summarization system where the query is synthesized and expanded from the novel details provided by the patent analyst and the domain ontology. Since the search and patent document retrieval using the formulated semantic query alone will not satisfy the user requirements, this work filters and summarizes the retrieved document set both extractively and abstractively. Summarization makes use of deep learning techniques as their structure mimics the human brain. The proposed work was evaluated using Google patent dataset. The retrieval results of semantic query expansion using domain ontology are compared with Google Prior-art search query results and WordNet based semantic query expansion retrieval sets. The summarization results of the retrieved document sets are compared with the human summaries.
Findings of the user study conducted to understand the training of rural ASHAs in India

Accredited Social Health Activists (ASHAs) are the essential link to healthy communities for Indian households. We present insights and findings of two focus group studies conducted with 16 ASHAs and 8 ASHA trainers across two health centers in Assam, India. We conducted two focus group studies to investigate challenges and opportunity areas in current health worker training ecosystem. Our objective was to identify insights that formulate design directions in order to develop technology solutions to train ASHAs effectively. We found challenges of poor training schedules, outdated information, inadequate contextual knowledge and field-based challenges, poor accessibility, and portability of the training material and low-confidence in communicating sensitive health information. We propose the need for technology intervention that supports practice-based learning, methods of role-playing and use of cultural songs and dance to increase engagement. We also propose the use of peer learning and sharing among ASHAs to increase adoption and acceptance.
GCM-Bench: A Benchmark for RDF Data Management System on Microorganism Data

The biological data is growing up to an unprecedented scale, such as microorganism knowledge graph organized by biologists, which is represented by Resource Description Framework (RDF) data model. In this paper, GCM-Bench, a new benchmark to evaluate the performance of general-purpose RDF data management systems on microorganism RDF data is proposed, which consists of microorganism RDF data generator, SPARQL query workloads and automatic test system, that can execute the testing workloads automatically and monitor the resource utilization. Five RDF data management systems are selected for evaluation on different sizes of data using automatic test system. We think GCM-Bench will help microbiologists and system developers to select their proper RDF data management system.
Exploring Recent Advances in Digital Video Steganography and Future Scope

In this digital era, with the advent of technology like 4G network and VOIP, video calling is the most cost effective and cutting-edge communication technique. Simultaneously, video sharing through social networking sites is very popular, as it can reach a wider public domain in seconds. This enormous use of video motivates the fact that as digital medium video can be effectively utilized for secret sharing. Using video steganography, any kind of secret data like text, image, audio, even a short video can be hidden inside another video object, which can be securely transmitted to the recipient over the internet. In this chapter, an effort has been made to relate various techniques of video steganography under a single header to identify future scope of research. Also, all possible quality metrics for videos and for measuring robustness have been studied, and different steganalysis attacks on video have been analyzed. The broad mission of this chapter is to be a quick reference to future researchers of video steganography.
Generalised vertical projection histograms using multi‐plane homology

Vertical projection histograms are an efficient shape representation for 2D binary silhouettes and have been widely used in pedestrian localisation for video surveillance. The weakness of this method is that it is not invariant to rotation. In this Letter, a generalised vertical projection histogram is proposed to solve this problem, in which the homology transformations of the foreground silhouettes, for a set of parallel planes, are warped to, and accumulated, in the original foreground map. Then a method, similar to the vertical projection histogram, is carried out to localise the pedestrians in the foreground silhouettes. The algorithm applies an integrated approach using both image projection and geometric projection. Its value is demonstrated in a case study on pedestrian localisation with cast shadows.
Adaptive ARMA Based Prediction of CPU Consumption of Servers into Datacenters

The optimization of the energy consumed by data centers is a major concern. Several techniques have tried in vain to overcome this issue for many years. In this panoply, predictive approaches start to emerge. They consist in predicting in advance the resource requirement of the Datacenter’s servers in order to reserve their right quantities at the right time and thus avoid either the waste caused by their over-supplying or the performance problems caused by their under-supplying. In this article, we explored the performance of ARMA models in the realization of this type of prediction. It appears that with good selection of parameters, the ARMA models produce reliable predictions but also about 30% higher than those performed with naive methods. These results could be used to feed virtual machine management algorithms into Cloud Datacenters, particularly in the decision-making of their placement or migration for the rationalization of provisioned resources.
Outliers in High Dimensional Data

This chapter addresses one of the research issues connected with the outlier detection problem, namely dimensionality of the data. More specifically, the focus is on detecting outliers embedded in subspaces of high dimensional categorical data. To this effect, some algorithms for unsupervised selection of feature subsets in categorical data domain are furnished here. A detailed discussion on devising necessary measures for assessing the relevance and redundancy of categorical attributes/features is presented. Experimental study of these algorithms on benchmark categorical data sets explores the efficacy of these algorithms towards outlier detection.
Ciphertext-Policy Attribute-Based Encryption for Circuits from Lattices Under Weak Security Model

The existing Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme from lattices can only support a simple threshold access structure, resulting in its limited application scenario. In order to improve the flexibility and expressiveness of the CP-ABE scheme, we present a CP-ABE for circuits from lattices in this paper. The new scheme generates secret key for each attribute of the attribute set by invoking the sampling algorithm from lattices and embeds the attribute set into the secret keys. Meanwhile, to associate the ciphertext with a circuit, we design a Secret Matrix Distribution Mechanism (SMDM) for circuits, which distributes a matrix with specific form to each node of the circuit, and the scheme can generate the ciphertexts by combining the matrices of the leaf nodes. In the decryption phase, the SMDM guarantees the user who satisfies the access structure can decrypt the ciphertexts correctly. Finally, we prove that our scheme is secure against chosen plaintext attack in the selective weak security model under the Learning with Errors (LWE) assumptions.
One-Class Quantification

This paper proposes one-class quantification, a new Machine Learning task. Quantification estimates the class distribution of an unlabeled sample of instances. Similarly to one-class classification, we assume that only a sample of examples of a single class is available for learning, and we are interested in counting the cases of such class in a test set. We formulate, for the first time, one-class quantification methods and assess them in a comprehensible open-set evaluation. In an open-set problem, several “subclasses” represent the negative class, and we cannot assume to have enough observations for all of them at training time. Therefore, new classes may appear after deployment, making this a challenging setup for existing quantification methods. We show that our proposals are simple and more accurate than the state-of-the-art in quantification. Finally, the approaches are very efficient, fitting batch and stream applications. Code related to this paper is available at: https://github.com/denismr/One-class-Quantification .
Performing Security on Digital Images

In image watermarking, information is embedded in the original image for many reasons, such as ownership proofing, alteration detection, and/or fingerprinting, but it can also be used for real-time services such as e-payment, broadcast monitoring, and surveillance systems. For these, the data embedded must be extractable even if the image is manipulated intentionally or unintentionally. In contrast, robust techniques are the kind of watermarking that could assure the authenticity and protect the copyright. Many robust image watermarking approaches have been proposed in the last few years, and the purpose of this chapter is to provide a survey about recent relevant robust image watermarking methods existing in the literature.
De-Noising of Binary Image Using Accelerated Local Median-Filtering Approach

In the last few decades huge amounts and diversified work has been witnessed in the domain of de-noising of binary images through the evolution of the classical techniques. These principally include analytical techniques and approaches. Although the scheme was working well, the principal drawback of these classical and analytical techniques are that the information regarding the noise characteristics is essential beforehand. In addition to that, time complexity of analytical works amounts to beyond practical applicability. Consequently, most of the recent works are based on heuristic-based techniques conceding to approximate solutions rather than the best ones. In this chapter, the authors propose a solution using an iterative neural network that applies iterative spatial filtering technology with critically varied size of the computation window. With critical variation of the window size, the authors are able to show noted acceleration in the filtering approach (i.e., obtaining better quality filtration with lesser number of iterations).
Content-Based Video Shot Boundary Detection Using Multiple Haar Transform Features

In the entire content-based video technologies, video shot boundary detection (VSBD) is a crucial task. VSBD is used to segment the video into shots. In this paper, we propose a VSBD technique which identifies the abrupt (cut) gradual transitions in video sequence. In our technique, we first extract the multiple feature vectors from video sequence using discrete Haar transform (DHT) for N = 4. The similarity between successive video frames is measured using extracted features, and a continuous signal is computed. To identify shot transition, procedure-based shot detection algorithm is applied on continuous signal. The proposed technique is evaluated on TRECVID test collection videos. The experimental results show that the proposed technique performs better than the existing method in identifying cuts and gradual transitions.
A compounded encoding and decoding algorithm for product tracing based on the information‐grayscale matrix

Abstract This paper proposes a compounded encoding and decoding algorithm based on the information‐grayscale matrix. Generally, it is very difficult to track the original information in the circulation of commodity. This algorithm is aimed to be applied to product tracing, especially fast‐moving consumer goods (FMCG). According to the traceability requirements of product information, we construct the structure of basic information code with information bits and check bits. The encoded information can be mapped into an information matrix through visible and invisible processes after extending the matrix, and we generate the corresponding grayscale matrix by calculating the relationship between the four‐dimensional matrices of each matrix cell. It is the information‐grayscale matrix that forms our product traceability information matrix. When the matrix is damaged in logistics transportation, we can get very high matrix information recovery rate through the decoding algorithm of the information‐grayscale matrix including self‐recovery, cross recovery, mutual recovery, and iteration; finally, the 2 × DA + DC structure can obtain 85% recovery rate when the damage rate reaches 70% for 10 million data volumes, and it can be extended into various structures. In addition, the algorithm can achieve a good balance between encoding rate and recovery rate and can be extended into different structures.
Expressive innovation: How first-year college students introduced emoticons to the curriculum

For the initiated, college may be remembered as a care-free and playful time. However, for contemporary college students the transition to college is challenging with only 1 in 3 returning for their second year of study, and the challenges are even greater for first-generation and low-income students. The interactive digital platform of the current study invited low-income first-year students to write about and reflect upon their transition to college. It was through the higher order cognitive process of writing, which involved deciding which words, symbols, and punctuation to use to express a particular thought to a particular audience, that the students made-sense of their experiences. Our analyses detail how students integrated new approaches for communicating their thoughts in writing and how these techniques changed over time. We show how first-year students used the affordances of the keyboard and an interactive blog to create a reflective and supportive digital writing style somewhere between the formality of the college essay and the freewheeling social media post. The emerging playful style was marked by the introduction of computer-mediated communication (CMC) cues typical of social media platforms like Twitter and Facebook, including emoticons and emojis, hashtags, repeated capital letters, repeated punctuation, and abbreviations such as lol, to the college course context. A path analysis of 209 blog posts and 161 comments over four time points, coupled with a qualitative case study showed that students developed their use of CMC cues first on the social level through comments and later on the individual level in their posts. Our analyses show how students integrated CMC cues to communicate humor and critique as they created a new genre of college writing. The social support and the CMC cues were concretely relevant to the students' development of knowledge and practice of what it meant to write in college and what it meant to become a college student.