Reviewerly/exHarmony huggingface dataset:
Compositional strategies with generative scores

Generative processes have found broad application in the design of innovative musical forms but it is only comparatively recently through the affordances of modern digital technologies that composers have been able to explore integrating such processes in the real-time creation of musical scores. This paper explores some of these affordances as well as a variety of approaches to some of the key aesthetic and compositional challenges through the analysis of recent works by composers Georg Hajdu, Ryan Ross Smith, Jason Freeman, David Kim-Boyle, and Arne Eigenfeldt.
An Algorithm Rapidly Segmenting Chinese Sentences into Individual Words

This paper proposes an improved Trie tree structure. The tree node records the position information of the characters participating in the word formation, and the child node uses the hash search mechanism. On this basis, the forward maximum matching algorithm of Chinese word segmentation is optimized. In the process of word segmentation, the automaton mechanism is used to judge whether it constitutes the longest word, and the problem that the forward maximum matching algorithm needs to adjust the string according to the word length is solved. The algorithm time complexity is 1.33, and the comparison test results show that there is a fast word segmentation speed. The forward maximum matching algorithm based on the improved Trie tree structure improves the Chinese word segmentation speed, especially when the dictionary structure needs to be updated in real time.
New classes of generalized zero-difference balanced functions and their applications

Generalized zero-difference balanced (G-ZDB) function is a generalization of zero-difference balanced (ZDB) function, which has many important applications in coding and cryptography. In this paper, three new classes of G-ZDB functions are presented based on the generalized cyclotomic classes. Our constructions give new parameters of G-ZDB functions not covered in the literature. As applications, frequency-hopping sequences with optimal Hamming autocorrelation are obtained by using the newly constructed G-ZDB functions.
Evolutionary Trends in the Collaborative Review Process of a Large Software System

In this paper, we study the evolutionary trends in the collaborative review process of a large open source software system. As expected, the number of reviews, the number of reviews commented on, as well as the number of reviewers, and the interactions between them show increasing trends over time. But unexpectedly, levels of clustering between developers in their interaction networks show a decreasing trend, even as connections between them increase. In the context of our study, clustering is an indicator of developer collaboration, whereas connection points to how intensely developers work together. Thus the trends we observe can inform how developer interactions become concentrated around specific units of work as the project progresses. The dichotomy between the simultaneous increase in connection and decrease in clustering also points to the interplay between collective and individual efforts in the review process, and the distinct nature of peer review in the software development life cycle.
Quantized Local Trio Patterns for Multimedia Image Retrieval System

In this paper, we propose a novel feature descriptor named quantized local trio pattern (QLTP) for multimedia image retrieval application. The QLTP extracts quantized edge information from the pixels in a specified neighborhood. QLTP integrates the quantization and trio patterns for image retrieval. Performance of the QLTP is evaluated by conducting experiments on Corel-10,000 databases. Experimental results exhibit an improvement in terms of avg. retrieval precision (ARP) and avg. retrieval rate (ARR) as compared to the other related methods.
Formulation of Fractional Derivative-Based De-Hazing Algorithm and Implementation on Mobile-Embedded Devices

This paper presents the modification of a previously developed algorithm using fractional order calculus and its implementation on mobile-embedded devices such as smartphones. The system performs enhancement on three categories of images such as those exhibiting uneven illumination, faded features/colors and hazy appearance. The key contributions include the simplified scheme for illumination correction, contrast enhancement and de-hazing using fractional derivative-based spatial filter kernels. These are achieved without resorting to logarithmic image processing, histogram-based statistics and complex de-hazing techniques employed by conventional algorithms. The simplified structure enables ease of implementation of the algorithm on mobile devices as an image processing application. Results indicate that the fractional order version of the algorithm yields good results relative to the integer order version and other algorithms from the literature.
A General Approach for Achieving Supervised Subspace Learning in Sparse Representation

Over the past few decades, a large family of subspace learning algorithms based on dictionary learning have been designed to provide different solutions to learn subspace feature. Most of them are unsupervised algorithms that are applied to data without label scenarios. It is worth noting that the label information is available in some application scenarios such as face recognition where the above-mentioned dimensionality reduction techniques cannot employ the label information to improve their performance. Therefore, under these labeled scenarios, it is necessary to transform an unsupervised subspace learning algorithm into the corresponding supervised algorithm to improve the performance. In this paper, we propose an approach which can be used as a general way for developing a corresponding supervised algorithm based on any unsupervised subspace learning algorithm using sparse representation. Moreover, by utilizing the proposed approach, we achieve a new supervised subspace learning algorithm named supervised principal coefficients embedding (SPCE). We show that SPCE establishes the advantages over the state-of-the-art supervised subspace learning algorithm.
Data Mining for Social Network Analysis Using a CLIQUE Algorithm

Many different areas of computer science have been developed for many years in the world. Data mining is one of the fields which many algorithms, methods, and models have been built and applied to many commercial applications and research successfully. Many social networks have been invested and developed in the strongest way for the recent years in the world because they have had many big benefits as follows: they have been used by lots of users in the world and they have been applied to many business fields successfully. Thus, a lot of different techniques for the social networks have been generated. Unsurprisingly, the social network analysis is crucial at the present time in the world. To support this process, in this book chapter we have presented many simple concepts about data mining and social networking. In addition, we have also displayed a novel model of the data mining for the social network analysis using a CLIQUE algorithm successfully.
View-Aware Person Re-identification

Appearance-based person re-identification (PRID) is currently an active and challenging research topic. Recently proposed approaches have mostly dealt with low- and middle-level processing of images. Furthermore, there is very limited research that has focused on view information. View variation limits the performance of most approaches because a person’s appearance from one view can be completely different from that of another view, which makes the re-identification challenging. In this work, we study the influence of the view on PRID and propose several fusion strategies that utilize multi-view information to handle the PRID problem. We perform experiments on a re-mapped version of Market-1501 dataset and an internal dataset. Our proposed multi-view strategy increases the recognition rate at rank-one by a large margin in comparison with that obtained via random view matching or multi-shot.
Quality Measures for Agent-Oriented Software

The popularity of software agents and multiagent systems has increased, and this is now one of the most active areas in informatics research and development. Agent-oriented software engineering is a new paradigm. But no paradigm would be complete without a means for measuring its quality. This work is the result of an investigation in order to evaluate characteristics of a software agent, measuring its most relevant characteristics. For this, we consider the three most important characteristics of a software agent: its social ability, autonomy and proactivity. Measurable attributes are defined for each feature and then are defined measures to evaluate each attribute, and from these values obtain the value of the characteristics considered are proposed. To validate these measures are applied to a case study.
Recognizing People in Blind Spots Based on Surrounding Behavior

Recent advances in computer vision have achieved remarkable performance improvements. These technologies mainly focus on recognition of visible targets. However, there are many invisible targets in blind spots in real situations. Humans may be able to recognize such invisible targets based on contexts (e.g. visible human behavior and environments) around the targets, and used such recognition to predict situations in blind spots on a daily basis. As the first step towards recognizing targets in blind spots captured in videos, we propose a convolutional neural network that recognizes whether or not there is a person in a blind spot. Based on the experiments that used the volleyball dataset, which includes various interactions of players, with artificial occlusions, our proposed method achieved 90.3% accuracy in the recognition.
Blockchain Technology Is a Boost to Cyber Security

The information in any real-time application is needed to be digitalized across the world. Since digitalization of data happens, there comes the role of privacy. Blockchain could address the security challenge that happens in the any real sector. There are a few more challenges that prevail in the industry such as integrity in data, traceability of stored records, and interoperability among organizations that share information. This chapter says what blockchain is and applications in which blockchain technology could solve the existing challenges where they lack security, privacy, integrity, and interoperability.
Trust in Cognitive Assistants

The main purpose of this research is to develop a framework of trust determinants in the interactions between people and cognitive assistants (CAs). CAs are defined as new decision tools that can provide people with high quality recommendations and help them make data-driven decisions to understand the environment around them. Trust is defined as the belief of people that CAs will help them reach a desired decision. An extensive review on trust in psychology, sociology, economics and policy making, organizational science, automation, and robotics was conducted to determine the factors that influence people's trust in CAs. On the basis of this review, a framework of trust determinants in people's interactions with CAs was developed where reliability, attractiveness, and emotional attachments positively influence people's trust in CAs. The framework also shows that relative advantages of innovativeness positively affect the intention to use CAs. Future research directions are suggested for developing and validating more concrete scales in measuring trust determinants in the interactions between people and CAs.
Low-temperature ground freezing methods for underground construction in urban areas

Artificial ground freezing (AGF) is a commonly used ground improvement technique. It can be applied in diverse construction projects in dense populated urban areas where other methods are inapplicable. In this article are discussed several methods of ground freezing, such as: “brine” freezing, liquid nitrogen freezing, solid carbon dioxide freezing. Their advantages and disadvantages are indicated. For solid carbon dioxide freezing, several approaches are presented. One of them is a brine “aftercooling” freezing method. They can significantly increase efficiency of ground freezing and reduce its cost.
Runtime Verification Method for Social Network Security Based on Source Code Instrumentation

Security of social network is a serious issue that cannot be ignored. In order to improve the situation that most traditional verification methods are not real-time and too complicated, this paper proposes a runtime verification method based on source code instrumentation for social network systems. First, a property related to a social network system’s characteristics is formalized as a three-valued propositional projection temporal logic (PPTL $$_3$$ ) formula, and based on the formula a monitor is constructed. Then, probes are instrumented into the source code of the system, which capture events and generate the execution trace of the system. The trace is dealt with by the monitor in real-time to check whether the system satisfies or violates the desired property. To illustrate the effectiveness of this method, a case study of an open-source social network system is provided.
Situational Awareness in Space Based Blockchain Wireless Networks

Blockchain is a new type of cryptographic distributed network transaction accounting system. Blockchain adopts some new security ideas, methods and technologies in its design to meet the real-world security requirements of various types of large-scale network transactions worldwide. In this paper, we discuss the state of art in space based blockchain wireless networks, and provide the future challenges in such scenario, which lead to the future researches in this area.
Dark Side of the Blockchain

Jude Umeh MBCS examines the dark side of the blockchain and any likely impact it may have on the future of this incredible achievement and opportunity.
Comparative Analysis of Different Clustering Techniques for Video Segmentation

Video segmentation is an extremely challenging and active area in the field of video processing and computer vision. Video segmentation techniques can be classified basically into two approaches: one approach for which there are preassigned thresholds and another clustering approach for which the number of clusters has been used, which is known. Here, we have studied and analyzed the cluster-based techniques such as mean-shift, K-means, and fuzzy C-means segmentation algorithms. We have evaluated and compared the performances of segmentation methods qualitatively and also quantitatively. To calculate the different quantitative metrics, the images and ground truth of the CDnet 2014 database have been used.
One-Shot Learned Priors in Augmented Active Appearance Models for Anatomical Landmark Tracking

In motion science, biology and robotics animal movement analyses are used for the detailed understanding of the human bipedal locomotion. For this investigations an immense amount of recorded image data has to be evaluated by biological experts. During this time-consuming evaluation single anatomical landmarks, for example bone ends, have to be located and annotated in each image. In this paper we show a reduction of this effort by automating the annotation with a minimum level of user interaction. Recent approaches, based on Active Appearance Models, are improved by priors based on anatomical knowledge and an online tracking method, requiring only a single labeled frame. In contrast, we propose a one-shot learned tracking-by-detection prior which overcomes the shortcomings of template drifts without increasing the number of training data. We evaluate our approach based on a variety of real-world X-ray locomotion datasets and show that our method outperforms recent state-of-the-art concepts for the task at hand.
Enterprise Integration With the Structural Services Architectural Style

The Service-Oriented Architecture (SOA) and Representational State Transfer (REST) architectural styles are the most used for the integration of enterprise applications. Each is more adequate to a different class of applications and exhibits advantages and disadvantages. This chapter performs a comparative study between them. It is shown that SOA and REST are dual architectural styles, one oriented towards behavior and the other towards state. This raises the question of whether it is possible to combine them to maximize the advantages and to minimize the disadvantages. A new architectural style, Structural Services, is proposed to obtain the best characteristics from SOA and REST. As in SOA, services are able to offer a variable set of operations and, as in REST, resources are allowed to have structure. This style uses structural interoperability, based on structural compliance and conformance. A service-oriented programming language is also introduced to instantiate this architectural style.
Failure Of The 21st Century Researchers In Solving National Economic Menace: A Necessity

Challenges of the 21st century in solving economic problems needs to be transformed. Myriads issues like economic turmoil, fast changing technological advances and many more needs refined changes. The role of research in combating this national challenge cannot be limited to policies from research institute and academic community, thus the need for this work. The main problem of these 21st researcher is to hone her professional skills such as critical thinking, communication skills, problem-solving skills, deep learning and collaborating with multidisciplinary team. In the light of this growing concern and improvement, there is a look at the role of past and present impactful research in scaling national growth and development. The focus of this report is to weigh in on problems limiting the growth of research to solve national economic problems and proffering healthy solutions.
Reversible Data Hiding Scheme in Encrypted-Image Based on Prediction and Compression Coding

For cloud storage management and security of images, more and more attention is paid to reversible data hiding in encrypted images (RDH-EI). This work proposes a RDH-EI method based on a novel compression coding that can vacate more space for data hiding. To improve the embedding capacity, several most significant bits (MSB) of an original image are firstly predicted in turn to generate the corresponding prediction bit-planes, and then the XOR operation and a shorter variable-length coding are designed to further decrease the coding length of the prediction bit-planes. The proposed scheme can achieve a larger embedding capacity and real reversibility and lossless decryption. Experimental results demonstrate that the performance of the proposed method outperforms other RDH-EI schemes, including embedding capacity, reversibility and the ability against the cipher-only attack.
Benchmarking Distributed Data Processing Systems for Machine Learning Workloads

Distributed data processing systems have been widely adopted to robustly scale out computations on massive data sets to many compute nodes in recent years. These systems are also popular choices to scale out the training of machine learning models. However, there is a lack of benchmarks to assess how efficiently data processing systems actually perform at executing machine learning algorithms at scale. For example, the learning algorithms chosen in the corresponding systems papers tend to be those that fit well onto the system’s paradigm rather than state of the art methods. Furthermore, experiments in those papers often neglect important aspects such as addressing all aspects of scalability. In this paper, we share our experience in evaluating novel data processing systems and present a core set of experiments of a benchmark for distributed data processing systems for machine learning workloads, a rationale for their necessity as well as an experimental evaluation.
Resource allocation through optimized three-phase scheduled VMs by grey wolf optimization and introspection security analysis

Even though, cloud computing reduces the operating cost by enabling adaptation of virtual machines, it has suffered in selection of optimal virtual machine due to shortage of resource or resource wastage, sudden changes in requirement so it requires optimal resource allocation. Resource allocation is the process of providing services and storage space to the particular task requested by the users. This is one of the important challenges in cloud computing environment and has variant level of issues like scheduling task, computational performance, reallocation, response time and cost efficiency. In this research work we introduce a three-phase scheduling method based on memory, energy and QOS in order to overcome the above issues which also yield low energy consumption, maximum storage and the high level Quality of Service (QoS). Biggest Memory First and Biggest Access First is introduced with NUMA scheduler and cache scheduler for memory scheduling and the optimal VM resulting from the three phases of scheduling is determined by Grey Wolf Optimization (GWO) algorithm. To carry the security level of optimized VMs, Streamline Security and Introspection security analysis are exhausted for detecting the malware VMs which results the secured and efficient VMs for further resource allocation. Our proposed methodology is implemented using the Cloud Sim tool and the experimental result shows the efficiency of our proposed method in terms of security, time consumption, and cost.
Establishing a Solar Monitoring Station with Auxiliary Measurements

A considerable amount of thought and planning should occur when considering the establishment of a solar monitoring station. One needs to understand the goals and limitations of the project and what can be accomplished with the instruments that fit within the budget. Other initial issues are: how the station will be maintained; how the data will be collected and analyzed; and how long the station will be operating. Of paramount importance is how the data are going to be used. For example, there is a considerable difference between monitoring to evaluate the long-term variability of the solar resource and using site data to provide the performance estimates to help obtain financing for a solar electric facility. This chapter consists of eleven sections. Section 1 will discuss overall considerations that set the basis for how the station should be configured. Section 2 will cover the instrumentation. The choice of location for the solar monitoring site is discussed next. Sections 5–7 cover the data logger and logistics such as maintenance and communications. Section 8 describes auxiliary measurements, and Sect. 9 will cover other useful instruments. Section 10 is on grounding, and Sect. 11 presents the physical layout of a hypothetical solar monitoring station.
Context Aware 3D Fully Convolutional Networks for Coronary Artery Segmentation

Cardiovascular disease caused by coronary artery disease (CAD) is one of the most common causes of death worldwide. Coronary artery segmentation has attracted increasing attention since it is useful for better visualization and diagnosis. Conventional lumen segmentation methods basically describe vessels by a rough tubular model, thus presenting inferiority on abnormal vascular structures and failing to distinguish exact coronary arteries from vessel-like structures. In this paper, we propose a context aware 3D fully convolutional network (FCN) for vessel enhancement and segmentation in coronary computed tomography angiography (CTA) volumes. Combining the superior capacity of CNN in extracting discriminative features and satisfactory suppression of vessel-like structures by spatial prior knowledge embedded, the proposed approach significantly outperforms conventional Hessian vesselness based approach on a dataset of 50 coronary CTA volumes.