Reviewerly/exHarmony huggingface dataset:
Assessing the Impact of Video Compression on Background Subtraction

Video cameras are deployed in large numbers for surveillance. Video is compressed and transmitted over bandwidth limited communication channels. Researches have increasingly focused on automated analysis algorithms to understand video. This raises the question “Does compression have an effect on automated analysis algorithms?”. This is a vital question in understanding the reliability of automated analysis algorithms. In this paper, we evaluated background subtraction algorithm’s performance on videos acquired using a typical surveillance camera under different scenarios. The videos in the dataset are collected from an IP-based surveillance camera and compressed using different bitrates and quantization levels. The experimental results provide insights on the different compression settings and their impact on the expected performance of background subtraction algorithms. Furthermore, we train a classifier that utilizes video quality to predict the performance of an algorithm.
Energy-Efficient Hybrid Firefly–Crow Optimization Algorithm for VM Consolidation

Virtual machine consolidation (VMC) is a successful approach to enhance resource utilization and reduce energy consumption by minimizing the number of active physical machines in a cloud data center. It can be implemented in a centralized or a distributed fashion. In this paper, an efficient multi-objective-based VM consolidation using hybrid firefly-crow optimization algorithm (HFCOA) is proposed. The proposed HFCOA is a novel approach developed by combining firefly optimization algorithm (FA) with crow search optimization algorithm (CSA). A new multi-objective fitness function is derived based on energy consumption, migration cost, and memory utilization. To analyze the performance of the algorithm, the simulation is carried out in the ClousdSim simulator. The proposed HFCOA is compared with FA and CSA in the same simulation environment. Experimental results show that the proposed hybrid algorithm significantly outperforms the original firefly and crow search algorithms.
ANALYSIS OF NOVEL FEATURE SELECTION CRITERION BASED ON INTERACTIONS OF HIGHER ORDER IN CASE OF PRODUCTION PLANT DATA

Feature selection plays vital role in the processing pipeline of today’s data science applications and is a crucial step of the overall modeling process. Due to multitude of possibilities for extracting large and highly structured data in various fields, this is a serious issue in the area of machine learning without any optimal solution proposed so far. In recent years, methods based on concepts derived from information theory attracted particular attention, introducing eventually general framework to follow. The criterion developed by author et al., namely IIFS (Interaction Information Feature Selection), extended state-of-the-art methods by adopting interactions of higher order, both 3-way and 4-way. In this article, careful selection of data from industrial site was made in order to benchmark such approach with others. Results clearly show that including side effects in IIFS can reorder output set of features significantly and improve overall estimate of error for the selected classifier.
Reconstructing Sets of Latin Squares, Linear and Equivalent to Linear Codes

We consider the reconstruction problem concerning latin squares, sets of mutually orthogonal latin squares as well as linear and equivalent to linear codes in a vector space over a finite field. With regard to this problem we discuss related topics on intersection of combinatorial objects of the same class. An algorithm reconstructing latin squares and its sets is presented. By this way it is shown for q ∈ {3, 4, 5, 7} that the Bose complete set of mutually orthogonal latin squares of order q with 2q - 1 or less blank entries in each square at the same places is reconstructed uniquely. We also point some recently obtained results on the maximum intersection of linear and equivalent to linear codes, and their applications as well.
Real-time head pose estimation based on face geometry

This paper presents a novel head pose estimation technique based on face-geometry. The approach involves performing nose detection and threshold-based segmentation over near-infrared (NIR) input. NIR's inherent invariance to illumination changes makes it an ideal fit within automotive, avionics, mining, and many more such applications where the inconsistent lighting leaves visible light RGB cameras impractical. The pose angles are inferred from the shape of the segmented face and its nose location. Validation of the proposed approach is performed in a two-fold manner since annotated public NIR-based head-pose datasets are scarce. Firstly, the pure yaw and pitch angles are justified using the UPNA RGB head pose dataset. Secondly, an in-house captured and annotated NIR dataset is used for corroborating the pure roll angle. Comparison of the proposed approach with the two more commonly used head pose estimation algorithms viz., DLIB and OpenVINO over the NIR dataset reveal that the proposed approach outperforms the latter two with better accuracy, maximum range coverage, and lesser computation time, thereby, making it a suitable choice in applications such as driver monitoring and surveillance systems, especially during the night/low-light scenarios.
A Novel Approach to Training Artificial Neural Networks for Automatic Indexing of Locality Sensitive Text Documents

Automatic Indexing of documents using paragraph vectors is a popular unsupervised method for learning distributed representations of texts. This method learns embedding of words with document vectors for document classification. In addition, this method can be leveraged for sentiment analysis. However, while the results presented in the original Doc2Vec study were promising, the overall proof of concept was rather narrow. In this study, we extend the Doc2Vec method, and enhance it to classify locality sensitive documents, i.e. domain-based documents which are largely similar with marginal differences. In particular, we use the enhanced Doc2Vec technique to classify similar documents describing the Internet of Things (IoT) patterns. We observed that our enhanced locality sensitive Doc2Vec technique performs significantly well to improve embedding quality. The model performance is in par with state-of-the-art results and can be qualified as a benchmark for similar vector space models.
Input Pattern Classification Based on the Markov Property of the IMBT with Related Equations and Contingency Tables

In this contribution, we provide a detailed analysis of the search operation for the Interval Merging Binary Tree (IMBT), an efficient data structure proposed earlier to handle typical anomalies in the transmission of data packets. A framework is provided to decide under which conditions IMBT outperforms other data structures typically used in the field, as a function of the statistical characteristics of the commonly occurring anomalies in the arrival of data packets. We use in the modeling Bernstein theorem, Markov property, Fibonacci sequences, bipartite multi-graphs, and contingency tables.
COMMUNICATION MOBILITY WITHIN THE STRUCTURE OF PROFESSIONAL COMPETENCE

The article considers communication mobility as a characteristic which significantly affects the development of professional competence. The author analyses the meaning of the term professional competence, proves that it is important to develop universal (generic) competences, allocates communication mobility in the system of generic competences. On the basis of labour market analysis the author concludes on the need to develop communication mobility as a component of professional competence. Communication mobility is described as verbal mobility connected to mental mobility. It is based on speaking skills and the ability to respond in a flexible way, to perceive and analyze interlocutors utterance quickly, to communicate in accordance with the context. The indicators of communication mobility are responsiveness, flexibility, critical thinking and reasoning, ambiguity tolerance, social tolerance and emotional self-control. The author gives recommendations on the methodology of communication mobility development. The research methods applied in the paper are the following: analysis and synthesis of the information presented in scientific papers of Russian and foreign researchers, content analysis of educational standards, functional and semantic analysis of terms (mobility, communication mobility, professional competence, communication competence). The results of the analysis can be used in the system of higher education to develop academic programmes and assessment tools. The article is targeted at the researchers involved in solving the problems connected with professional competence, communication competence and communication mobility, as well as university lecturers and specialists dealing with optimization and adaptation of training content and teaching methodology to labour market needs.
Application of the Computer Database Technology in the Sports Competition Information Management

In the information management, the advantage of the database technology is most obvious. By introducing the technology and characteristics of the computer database, this paper expounds the application status of the computer database technology in sports competition information management, and probes into the effective strategies of strengthening the application of the computer database technology in the sports competition information management, aiming at providing the relevant personnel with the computer database technology and its characteristics, and the computer database technology in sports competition information management. The application status of the computer database technology in the sports competition information management mechanism is analyzed, and some ideas are provided.
High-Frequency Feature Learning in Image Super-Resolution with Sub-Pixel Convolutional Neural Network

Sub-pixel convolutional neural network is efficient for image super-resolution. However, the images generated are relatively smooth. Improving the learning ability of high-frequency features is of great significance for sub-pixel convolutional neural network to get better performance. In the paper, we propose an improved algorithm of sub-pixel convolutional neural network based on high-frequency feature learning for image super-resolution, it optimizes the traditional sub-pixel convolutional structure. Firstly we introduce a residual convolutional layer in the generation net. it assigns the residual factor to each sub-pixel feature map and forces each pixel feature map to adaptively use the input information. Furthermore, a method for high frequency feature mapping is proposed. During image super-resolution training stage, the multi-task learning function, combining the pixel-level loss function with high-frequency contrast loss function, make the generation images getting closer to the target super-resolution images in high-frequency domain. The experiments on CelebA dataset show that our proposed method can effectively improve the quality of super-resolution images by contrast to the traditional sub-pixel convolutional neural network.
Towards Privacy Framework in Software Development Projects and Applications: An Integrated Framework

Privacy has become a critical attribute in information security. Essentially, it is the responsibility of service providers who have access to personally identifiable information. General Data Protection Regulation (GDPR) is an indicator that privacy is being considered as an important regulatory requirement. This paper reviews and presents privacy frameworks within software development and its applications. The reviewed results would be consolidated and used for settling integrated privacy framework for software development projects. The proposed framework is demonstrated by examining through SDLC and project management processes.
Generative Image Steganography Based on GANs

According to the embedding method of secret information, steganography can be divided into: cover modification, selection and synthesis. In view of the problem that the cover modification will leave the modification trace, the cover selection is difficult and the load is too low, this paper proposes a generative image steganography scheme based on GANs, which combines with cover synthesis. Based on GAN, the scheme uses secret information as the driver and directly generates encrypted images for transmission, which can effectively resist the detection of steganalysis algorithms. The security of the scheme is based on the key of the encryption algorithm. Even if the attacker obtains the transmitted information, only the meaningless result will be obtained without the key. Experiments were carried out on the data set of CelebA, and the results verified the feasibility and security of the scheme.
Innovations in parallel corpus alignment and retrieval

In this chapter, we give an overview of parallel corpus annotation, alignment and retrieval. We present standard annotation methods such as Part-of-Speech tagging, lemmatization and dependency parsing, but we also introduce language-specific methods, for example for dealing with split verbs or truncated compounds in German. Our corpus annotation includes the identification of code-switching within sentences as a special case of language identification. We argue for careful sentence and word alignment for parallel corpora. And we explain how word alignment is the basis for a wide range of applications from translation variant ranking to lemma disambiguation.
Classification of Visual Attention Level During Target Gazing Using Microsaccades

In the previous researches on microsaccades, a typical and basic experimental method involves the following: a dot is displayed as a visual target in the middle or near the middle of a monitor, and eye movements of the subject are measured, and then the number of microsaccades is extracted from the measured eye movements. However, it is difficult to determine whether or not the subject is paying visual attention while gazing at the target, and the degree of visual attention paid by the subject. This paper proposes a system that uses microsaccades to classify visual attention levels during visual target gazing. In our experiment, ten subjects performed three tasks requiring different levels of visual attention. Microsaccades were measured and the number of microsaccades was extracted for each task. Statistical analysis showed that the number of microsaccades differed among the tasks. Our results suggest that visual attention level can be classified by the number of microsaccades.
Margin Constraint for Low-Shot Learning

Low-shot learning aims to recognize novel visual categories with limited examples, which is mimicking the human visual system and remains a challenging research problem. In this paper, we introduce the margin constraint in loss function for the low-shot learning field to enhance the model’s discriminative power. Additionally, we adopt the novel categories’ normalized feature vectors as the corresponding classification weight vectors directly, in order to provide an instant classification performance on the novel categories without retraining. Experiments show that our method provides a better generalization and outperforms the previous methods on the low-shot leaning benchmarks.
Portal to knowledge: a virtual library using marker-less augmented reality system for mobile devices

Since exceedingly efficient hand-held devices became readily available to the world, while not being a relatively recent topic, Augmented Reality (AR) has rapidly become one of the most prominent research subjects. These robust devices could compute copious amounts of data in a mere blink of an eye. Making it feasible to overlap computer generated, interactive, graphics over the real world images in real-time to enhance the comprehensive immersive experience of the user. In this paper, we present a novel mobile application which allows the users to explore and interact with a virtual library in their physical space using marker-less AR. Digital versions of books are represented by 3D book objects on bookcases similar to an actual library. Using an in-app gaze controller, the user’s gaze is tracked and mapped into the virtual library. This allows the users to select (via gaze) a digital version of any book and download it for their perusal. To complement the immersive user experience, a continuity is maintained using the concept of Portals while making any transition from AR to immersive VR or vice-versa, corresponding to transitioning from a "physical" to a virtual space. The use of portals makes these transitions simple and seamless for the user. The presented application was implemented using Google AR Core SDK and Unity 3D, and will serve as a handy tool to spawn a virtual library anytime and anywhere, giving the user an imminent mixed sense of being in an actual traditional library while having the digital version of any book on the go.
Tourism application with CNN-Based Classification specialized for cultural information

Over-tourism has become an important difficulty in Japan because the number of visiting international tourists has increased in recent years. This intensive tourism leads to sightseeing problems because opportunities to inform tourists about culture and rules in tourist areas are few. Some system is needed to convey correct cultural aspects of tourist areas. This paper proposes a system to present a user with useful information such as area- specific culture from photographs taken with a convolutional neural network (CNN). Tourists can gain information by associating the contents with the real world by browsing useful information while viewing photographs. After we constructed the prototype system to present 30 types of useful information in English, we evaluated our system quantitatively. We also administered a questionnaire survey for Japanese and foreign residents. The results demonstrate that our system is effective to facilitate foreign tourists' understanding Japanese culture and norms.
Improved Object-Tracking Algorithm for an Underground Mining Environment

The continuously adaptive mean shift (CAMshift) algorithm, an adaptive tracking model based on colour probability distribution, is effective for coping with simple cases. However, it tends to fail in the environment of a coal mine, in which complex backgrounds and low contrasts occur. This paper proposes an improved CAMshift model which generating a linear combination of the hue and saturation component to obtain state-of-the-art tracking results regardless of non-uniform illumination, similar color interference and low-level distinctiveness. In addition, speeded up robust features (SURF) is incorporated to relocate the search window when tracking is lost. The proposed object-tracking algorithm using SURF and improved CAMshift model is named as SI-CAMshift. Rigorous experiments are conducted to demonstrate the effectiveness of the object-tracking algorithm both in stability and repeatability. Moreover, real-time tracking is a further advantage of the model.
Multi-interactive Intelligent Teaching Model of College English in the Information Technology Environment

The multi-interactive teaching mode based on the information technology is a new teaching mode, which promotes students’ autonomous learning and research-oriented learning through the online and offline effective interaction in and out of class, fully embodying the principle of the learning-orientation. Based on this, this paper focuses on the connotation and the theoretical basis of the multi-interactive teaching model, as well as its implementation practices and characteristics, with a view of providing help to improve the quality of the college English teaching.
Open Source Implementation of HTIP for Embedded Devices

The Home-network Topology Identifying Protocol (HTIP) aims to provide network topology information for the purposes of device and connectivity troubleshooting, with its most prominent use case being that of remote assistance. Although there exist multiple HTIP solutions for general purpose computers, no such solution exists for embedded devices. To address this gap and further stimulate the adoption rate of HTIP, we introduce an Open Source implementation of HTIP as a library which specifically targets embedded devices. We proceed to explain typical use cases and the functionality provided by the library. Finally we demonstrate interoperability with other solutions and conclude that this implementation is sound and suitable for embedded devices.
Modeling Open Data Usage: Decision Tree Approach

Predicting the quality of datasets is important for individuals, private or public organizations regardless of their intent, especially in today’s expeditious and competitive environment. If data are considered as valuable, then data need to have a stronghold in their quality or rather in indicators from which the quality consists of. Although there are several data mining methods that can be used for this issue, the regression tree method is used because of its advantages in comparison with other methods. Some of the pros are that tree is very easy to understand or explain, it is a collection of if-then rules, it can be used for investigating relationship between predictors and target attribute without understanding the form of the relationships. In this paper, open data usage predicting task was performed on the data collected from open data portal Data.gov.uk. Results show that it is possible to build a model of high accuracy.
Progressive Scale Expansion Network with Octave Convolution for Arbitrary Shape Scene Text Detection

Scene text detection is a challenging problem due to the image cluttering and high variability of text shape. Many methods have been proposed for multi-oriented and arbitrary shape text detection, in which the storage and computation costs of deep neural networks are still concerns. In this paper, we first introduce Octave Convolution into scene text detection for enlarging the receptive fields and reducing the spatial redundancy of networks. Then we combine Octave Convolution with a state-of-the-art arbitrary shape text detector PSENet, which predicts different scale of kernels for each text instance. Experimental results on several benchmarks show that the proposed method can improve both detection performance and speed in detecting multi-oriented and arbitrary shape texts. Furthermore, our method achieves state-of-the-art performances on these benchmarks.
GPS/SINS Speed Integral Matching Alignment Based on Improved Adaptive UKF

To solve the problem of system error and environmental interference of MEMS inertial sensor, which make SINS cannot quickly and accurately alignment, a GPS/SINS speed "integral" matching alignment method is designed. Firstly, the GPS velocity measurement values are used to assits the SINS for coarse alignment in this paper. Then, speed integral matching fine alignment is performed. Establishing the alignment model, the solution speed of the SINS is corrected by the least squares curve fitting method and the arm effect is compensated for the GPS speed signal before filtering. Using the unscented Kalman filter (UKF) and the improved adaptive UKF to achieve the fine alignment respectively. Outdoor experiments and simulation's results show that the improved adaptive UKF can achieve higher alignment accuracy and speed. Compared with the common UKF, the accuracy of the east, north and up misalignment angles is inhanced by 0.12°, 0.03° and 0.19°, respectively. The alignment time is shortened by 20s, 10s and 25s, respectively. The above work can provide a reference for AUV accurate navigation.