Reviewerly/exHarmony huggingface dataset:
Video Big Data Analytics in the Cloud: Research Issues and Challenges

On the rise of distributed computing technologies, video big data analytics in the cloud have attracted researchers and practitioners’ attention. The current technology and market trends demand an efficient framework for video big data analytics. However, the current work is too limited to provide an architecture on video big data analytics in the cloud, including managing and analyzing video big data, the challenges, and opportunities. This study proposes a service-oriented layered reference architecture for intelligent video big data analytics in the cloud. Finally, we identify and articulate several open research issues and challenges, which have been raised by the deployment of big data technologies in the cloud for video big data analytics. This paper provides the research studies and technologies advancing the video analyses in the era of big data and cloud computing. This is the first study that presents the generalized view of the video big data analytics in the cloud to the best of our knowledge.
Forming Analytical Competence of International Economics Students in the Process of Learning Foreign Languages

The author explains the need in forming of analytical thinking among International Economics students in order to build up their professional competence as an important condition of job competitiveness, responsiveness to new market changes, successful intercultural communication in the business world. The author highlights the meaning of the analytical thinking definition and its place in the process of analytical performance and competence development. The article presents the algorithm of analytical thinking building as an activity which starts with identifying a problem, describing an aim, tasks, subject matter and methods to solve it. The article presents the components of analytical competence and describes necessary conditions for its development within professional language training with the focus on aim and tasks specified by the state documents. The author emphasizes the unique role of foreign languages and their place in the development of future specialist professional competence. In conclusion the author notes that developed analytical competence is the main part of professionally oriented personality becoming with high level of competitiveness and deep understanding of intercultural communication.
Towards a Symmetric Crypto Algorithm: The HAJ

In symmetric encryption, the algorithm and secret key determine the security factor. This paper presents the idea to create a multiple (7 times) and block-separated encryption algorithm. To achieve this, we will use the Hill Cipher and Gauss-Jacques methods. In addition to the above, our most significant contribution will be to obtain large secret keys, which will allow us to obtain as a possible result a meaningful approximation to the Shannon perfect secrecy, as well as the reduction of computational complexity and the verification of security through anti-bot mechanisms such as code breakers.
AGRIBUSINESS MANAGEMENT: A Community-based Learning Module

The Agribusiness Management Module is written for faculty, agribusiness manager, and students (soon to be an agribusiness manager). There are tremendous career opportunities in the food and agribusiness industries. In this course, you will be exposed first to the breadth of these opportunities, from research and development to a logistics manager for a major food retail organization. You need to understand the marketplace and some of the specific institutional aspects of the food processing and marketing system before beginning A career as a director in agribusiness. Preparing for a career in agribusiness management also requires that you understand management's fundamentals â€”planning, organizing, directing, controlling, and the basic functions of marketing, finance, operations and logistics, and human resource management. In this course, you will find all of these topics covered straightforwardly. We hope that many food and agribusiness examples and case studies will bring these ideas to life for you..This is a module you will continue to use as a reference as your managerial career unfolds in the field of agricultural-based business.
Attributed Heterogeneous Network Embedding Based on Graph Convolutional Neural Network

Network embedding transforms a network into a low-dimensional space, in which the information of network can be stored, it is an effective approach to solve the problems of network analysis. However, most of the networks in real scenario are attributed heterogeneous network (AHN), which contains multiple types of nodes and edges. In addition, different types of nodes in AHN have different attribute information. To deal with the heterogeneity of topological structure and node attributes of AHN while preserving the rich information of AHN as much as possible in embedding space, is main difficulty in AHN embedding. To solve this problem, this paper proposes a novel AHN embedding model, namely AHN2Vec (Attributed heterogeneous network to vector), which can preserve node attributes, k-order neighborhood attributes, semantic information and topological structure information in embedding space. Experimental results on two real-word datasets not only show that the overall performance of AHN2Vec is better than the state-of-the-art methods, but also demonstrate AHN2Vec is able to generate high-quality node embeddings for AHN and can achieve excellent performance in downstream tasks.
Independence-friendly logic without Henkin quantification

Abstract We analyze the expressive resources of $$\mathrm {IF}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>IF</mml:mi> </mml:math> logic that do not stem from Henkin (partially-ordered) quantification. When one restricts attention to regular $$\mathrm {IF}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>IF</mml:mi> </mml:math> sentences, this amounts to the study of the fragment of $$\mathrm {IF}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>IF</mml:mi> </mml:math> logic which is individuated by the game-theoretical property of action recall (AR). We prove that the fragment of prenex AR sentences can express all existential second-order properties. We then show that the same can be achieved in the non-prenex fragment of AR, by using “signalling by disjunction” instead of Henkin or signalling patterns. We also study irregular IF logic (in which requantification of variables is allowed) and analyze its correspondence to regular IF logic. By using new methods, we prove that the game-theoretical property of knowledge memory is a first-order syntactical constraint also for irregular sentences, and we identify another new first-order fragment. Finally we discover that irregular prefixes behave quite differently in finite and infinite models. In particular, we show that, over infinite structures, every irregular prefix is equivalent to a regular one; and we present an irregular prefix which is second order on finite models but collapses to a first-order prefix on infinite models.
Steerable Pyramid for Texture Classification of Photographic Paper

In this work, we describe the application of the ‘steerable pyramid’ (Portilla and Simoncelli, 2000), a high-dimensional texture descriptor, to the discrimination and classification of a large collection of photographic paper textures. This work is part of a broader effort to bring precise physical measurements to the characterization of photographic papers, and our intended audience includes photograph conservators, curators, and collectors. For our purposes, it is important both to enable fine-grained local comparisons of very similar textures and to identify salient groupings across the entire landscape of photographic papers. The steerable pyramid was developed with the explicit goal of synthesizing textures; as such, it is an overcomplete representation. Its high native dimensionality is an advantage for highly precise local comparisons of texture, as in nearest neighbor search; it is, however, a disadvantage for grouping tasks, which require a well-structured global topology. For grouping tasks, we describe a number of compression and grouping approaches designed to meet a variety of analytical needs. Finally, we validate the steerable pyramid descriptor numerically using ‘one versus all’ classification on an augmented data set that includes multiple samples per paper type.
Antonio Pareja-Lora, María Blume, Barbara C. Lust &amp; Christian Chiarcos (eds.), Development of linguistic linked open data resources for collaborative data-intensive research in the language sciences. Cambridge: The MIT Press, 2019. Pp. xxi + 247.

Antonio Pareja-Lora, María Blume, Barbara C. Lust &amp; Christian Chiarcos (eds.), Development of linguistic linked open data resources for collaborative data-intensive research in the language sciences. Cambridge: The MIT Press, 2019. Pp. xxi + 247. - Volume 57 Issue 3
Style Scanner—Personalized Visual Search and Recommendations

In this paper, we present a visual search and recommendation system that supports typical shopping behaviour. We present a unified convolutional neural network architecture, to learn embeddings, which is a way to capture notion of similarity. We will introduce the concept of embeddings with respect to similarity and show how we try to achieve required embeddings with various loss functions. We demonstrate various model architectures based on availability of data. We also demonstrate a semiautomatic way of creating labelled dataset for training. We will talk about the concept of accuracy with respect to similarity, which is complicated as similarity is subjective. Finally, we present an end-to-end system for deployment.
Exploration of the Teaching Model of Chinese as a Second Language Based on the Production-oriented Approach

With the increase of China's strength, its influence has been greatly improved in the world, and now studying Chinese has become a new trend in many countries. For the development of Chinese teaching as a second language, it is necessary to actively improve the model of Chinese language teaching, build a good educational environment and add Chinese contexts. In terms of the transformation of teaching methods, the teaching concept “Production-oriented Approach” has become the focus of exploration in recent years. Starting from the concept of Production-oriented Approach, the author discussed how to improve the level of teaching Chinese as a second language, with the purpose to promote the reform of teaching model.
Evolutionary Computation of Facial Composites for Suspect Identification in Forensic Sciences

Suspect identification is an integral and significant part of forensic sciences. The detection of face is an important issue when the face of a suspect is described by a witness. The article aims to simplify the process of creating facial composites and thereby generate the images of facial likeness by utilizing an advanced genetic algorithm. The disclosed procedure is comfortable for a forensic technician and natural for a witness at the same time. The proposed genetic algorithm evolves from the original algorithm by employing a feedback-loop mechanism, wherein the most optimal solution is obtained for the generated facial composite.
Do Hacker Groups Pose a Risk to Organizations? Study on Financial Institutions Targeted by Hacktivists

As organizations are increasingly engaged in the digital world with greater dependency on data, crime and activism have shifted from the streets to the internet. In this paper, we study the impact of activist hacking campaigns on financial institutions. We look into how target institutions’ deep web and dark web exposure in terms of different risk categories is affected by hacking campaigns, and the interactions of the risk categories during the campaigns. On average, hacking campaigns raise the target institutions’ deep web and dark web exposure by 62 percent per year during the first two years after the campaigns’ start date. Small financial institutions are more vulnerable to the campaigns than large institutions. Further, leaked employee passwords amplify the campaign effect substantially, which allows us to forecast the institutions’ cyber exposure changes during and after the campaigns.
Cryptography Using Multi-Dimensional Objects

In today’s era, protection of data has become a major issue due to vulnerable information that is increasing significantly, because of active & passive attack. So, in this era finding ways to make a data secure has now become one of the most demanding aspects of orthodox cryptography in order to secure communication. In this project, the authors have analyzed and designed a new algorithm based on multi-dimensional objects, to bring a revolutionary change in cryptography encryption and decryption by increasing the bits of the encryption key, rate of confusion and rate of diffusion significantly. The algorithmic approach that is to be used in encryption of data, will be rotation of bits of data on different vertices in place of an index, using the concept of transposition technique, where it will serve all the basic purpose of cryptography that are authentication, data confidentiality, data integrity, and non-repudiation. This will help to tackle security issues regarding message and data encryption which are soon going to play a crucial role in our lives.
Properties of the full class of quaternary bent-functions of two variables

The development of modern cryptography methods is related in many respects to the research of perfect algebraic constructions which are characterized by the highest level of cryptographic strength indicators. The most important among these structures are bent-sequences, which have a maximum level of nonlinearity. This paper is devoted to the actual issue of research of such perfect algebraic constructions as quaternary bent-sequences. It is shown that in the quaternary case the complete class of affine functions and Vilenkin-Chrestenson functions are different algebraic constructions. The degeneracy of the full class of quaternary bent-functions of one variable is established, and the algorithm for the synthesis of the full class of quaternary bent-functions of two variables is proposed. The structural properties of the full class of quaternary bent-sequences were discovered, on the basis of which the 4428 different IV-sets were found that can be used to construct pseudorandom key sequence generators. The algebraic degrees of nonlinearity of all existing quaternary bent-functions of two variables are determined.
Lip Language Recognition System Based on AR Glasses

With the rapid development of science and technology, researches on AR, speech recognition, and lip recognition are more mature. Speech recognition is used in many fields; however, the problem exists in which speech recognition is not accurate in noisy environments. To solve the problem, lip recognition can be used as an aid, but receiving real-time image information is difficult. AR is easy to carry and can obtain image information in real time. But few people currently combine lip recognition with AR. This primary goal of this research is the combination of lip recognition and AR glasses. The image information is obtained through the camera module of the AR glasses, next face detection is performed based on OpenCV, and then, the lip language information is compared with the corpus by image processing to form text and finally reflected on the screen of the AR glasses. We have found that on the premise of reasonable recognition distance, using AR glasses as a carrier, the lip-reading text is displayed in a three-dimensional scene, real-time mapping is realized, and people can see the results of lip recognition more intuitively. It is concluded that lip recognition based on AR glasses works well, is robust, and has strong real-time performance, after a large number of experiments.
Image denoising based on learning sparse coding network

This work presents a novel image denoising architecture based on learning sparse coding network. Our network is inspired by learning iterate soft thresholds algorithm (LISTA) and sparse coding network (SCN). By doing this, the training parameters are reduced effectively, and training process is speeded up. We attempt to use this architecture to get a clear image from the noisy overlapping image patches. Finally, we used adaptive weights to fuse the image patches as whole image. Compared with existing denoising methods, the proposed method achieves state-of-the-art performance in image denoising
A Recommendation Approach to Diversify the Collaboration Scenario in Global Software Development Contexts

Finding developers to assist with project issues is essential in Global Software Development (GSD) contexts, where various individuals with distinct characteristics are involved. Several recommending approaches lead to identifying the same group of individuals who end up work overloaded. Aiming to diversify the recommendation process, we introduced DRecSys, a diversity-based recommendation system. Our approach seeks to identify individuals with characteristics similar to those previously requested to collaborate. To that end, we proposed a hybrid process composed of supervised (classification) and unsupervised (clustering) techniques. We provided evidence that DRecSys is able to recommend suitable non-obvious developers to assist with project issues.
Query- Focused Content Retrieval from Surveillance videos: Techniques and Challenges

The rocketing demand of security and safety has made video surveillance to be an indispensable part of the research. Over few decades a remarkable technological growth has been observed in the area of security and safety. A huge number of surveillance cameras are mounted everywhere such as hospitals and financial institutions. The growth of artificial intelligence and machine Learning is giving a digital brain and the capability to analyze the videos captured by those cameras. Contents of the video can be retrieved using some queries and the queries can be an object, a text, an example, a shot or a clip. The content based retrieval is mainly categorized into two. Intelligent content Based retrieval and Non Intelligent content Based retrieval. The Intelligent video retrieval uses a neural network that studies the pattern in the videos and the classify them accordingly. Non Intelligent systems uses some primitive methods such as text, clip etc to retrieve the contents from the videos. This paper analyses various techniques to retrieve the contents from the videos. Convolutional Neural Network, Genetic Algorithm, Pattern Recognition,YOLO-3,Darknet
A note on Quebbemann’s extremal lattices of rank <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>64</mml:mn></mml:math>

By constructing explicit examples, we show that the method of Quebbemann yields many isomorphism classes of extremal lattices of rank 64. Many of these examples have no non-trivial automorphisms.
Worldwide Research Trends on Smart Homes

Research in smart homes is still quite recent; however, there is no doubt that it will become a pervasive research topic in the near future. This chapter analyzes the whole research production on smart homes indexed in the Scopus database from 1985 to 2019, yielding a total of 11,000 studies. One of the goals of this chapter is to identify the main countries and institutions that have published on this topic and what their interest has been throughout the time. Four out of 116 countries stand out in this field, namely China, USA, India, and South Korea. In terms of the main institutions, the three with the highest scientific output are Ulster University (UK), CNRS Centre National de la Recherche Scientifique (France), and Universite Grenoble Alpes (France). Another aim of the chapter is to determine the research fields and subfields investigated about smart homes. The publications are mainly focused on two scientific fields, that is, computer science and engineering, accounting for 64% of the overall scientific production. In an aggregate analysis of all publications, four main clusters have been identified, namely Internet of Things, Activity Recognition, Security, and Energy.
A Variant Q-Sorting Methodology for Building Diagnostic Trees

Diagnostic theories are fundamental to information system (IS) practice and are represented as trees. While there are approaches for validating diagnostic trees, these validate the overall performance of the tree rather than identifying ways incorrect diagnoses can occur. It is important to fully validate diagnostic trees because even if the tree gives the correct decision “most of the time,” it is possible for incorrect decisions traveling down little-used branches of the tree to result in catastrophic decisions. In this article, we describe the process of using a variant of q-sorting to validate diagnostic trees. In this methodology, diagnostic trees that independent experts develop are transformed into a quantitative form, and that quantitative form is tested to determine the inter-rater reliability of the individual branches in the tree. The trees are then successively transformed to incrementally test if they branch in the same way. The results help researchers not only identify quality items for use in a diagnostic tree but also facilitate diagnoses of problems with those items and facilitate the reconciliation of discrepant trees by experts. The methodology validates not only the whole tree but also its subparts.
A Study on Implementing Gizduino as an Effective Humidity and Temperature Sensor

Humidity and temperature sensors are essential in ensuring products are stored under controlled conditions to prolong shelf life. In this study, the researchers discussed the design, prototyping, and testing of a Gizduino microcontroller as a humidity and temperature sensor. The accuracy of the product was proven by comparing the measurements from the Gizduino Sensor to an actual thermometer and hygrometer. Based upon the t-test analysis, the group concluded that the difference between the data gathered from both devices was insignificant therefore, the Gizduino sensor can be used as an effective humidity and temperature sensor. Moreover, the group also conducted an online survey with 201 participants to identify the factors that could influence consumers' intention to use the product. A Structural Equation Modeling (SEM) was used to analyze the data and determine the relationship between the factors involved. Results showed that consumers are most likely to use devices that are user-friendly or easy to operate. It also showed that their perception of the product influences their attitude or intention towards using it. They prefer devices that are efficient and in line with the present technology. In this way, they would also recommend these products to other owners who might find them useful. The study could be beneficial to owners or entrepreneurs who plan to invest in low-cost but accurate temperature and humidity sensors.