Reviewerly/exHarmony huggingface dataset:
Low-Cost and Fast Failure Recovery Using In-VM Containers in Clouds

Recently, various services are provided using virtual machines (VMs) in clouds. Therefore, it is necessary to prepare for system failures of VMs, hosts running VMs, and even data centers, e.g., using active/standby clustering. However, a trade-off exists between the maintenance cost for additional VMs and the recovery time in traditional techniques. For example, hot standby can rapidly fail over to the secondary system on a system failure, but the secondary system has to always run the same number of VMs as the primary system. In contrast, cold standby does not need to run VMs until a system failure, but it has to boot VMs on failure recovery. In this paper, we propose VCRecovery, which is the system for achieving both low-cost and fast failure recovery. VCRecovery consolidates services using containers inside VMs (in-VM containers) in the secondary system. For hot standby, it can reduce the maintenance cost by using only a smaller number of VMs in the secondary system. For cold standby, it can reduce the recovery time by quickly booting in-VM containers. If a VM is overloaded after the recovery, VCRecovery can migrate several in-VM containers to other VMs. To synchronize storage between VMs in the primary system and in-VM containers in the secondary system, it efficiently performs minimum file-based synchronization based on software packages. We have implemented VCRecovery using LXD and Zabbix and examined the performance.
A Method for Segmentation of Vietnamese Identification Card Text Fields

The development of deep learning in computer vision has motivated researches in related fields, including Optical Character Recognition (OCR).Many proposed models and pre-trained models in the literature demonstrate their efficient in optical text recognition.In this context, image processing techniques has an essential role in improving the accuracy of recognition task.Because, depending on the practical application, image text often suffering several degradation from blur, uneven illumination, complex background, perspective distortion and so on.In this paper, we propose a method for pre-processing, text area extraction and segmentation of Vietnamese Identification Card, in order to improve the accuracy of Region of Interest detection.The proposed method was evaluated with a large data set with different practical qualities.Experiment results demonstrate the efficiency of our method.
A recursive algorithm to increase the speed of regression-based binary recommendation systems

In this paper, a novel method is proposed to solve the Regression-based Binary Recommendation problem (RBR). The RBR is a univariate minimization problem, which has been solved recently using the Trichotomy approach with a logarithmic time complexity. The Trichotomy can obtain the global optimal minimum of univariate quasi-convex function. In this paper, it has been asserted that the objective function of the RBR problem is not necessarily quasi-convex; therefore, the Trichotomy method cannot always find its optimal solution that can be ultimately achieved by using the full search. Nonetheless, the difficulty of the full search method is that for each possible value of the variable of the problem, the objective function of the problem must be computed which involves the user-item tables to be reviewed entirely for each possible value of the variable. Since these tables are usually large, the runtime of the full search method is very high. In this paper, to unravel this difficulty, we made use of a recursive method to compute the objective function. Our proposed algorithm requires only one review of the user-item tables. Hence, the runtime of our proposed method is far less than the two other methods.
Image Specific Similar Target Detection in Aerial Images Using Gaussian Mixture Model

Image specific similar target detection is an image analysis method, where the regions of an image similar to that of the template image or query image (mostly selected from the same image) are detected. The proposed method makes use of Gaussian Mixture Model (GMM) for modeling the template image. The quantized color features of the template image are used in building template's GMM. Based on GMM, local descriptors of the query image are extracted by ranking the Gaussian mixtures, analyzing the regional distributions, contour shapes, etc of the individual Gaussian mixtures. A novel method of conditional merging of two top ranked Gaussian mixtures is proposed to improve the similar target detection accuracy. The proposed algorithm is rotation invariant and the experimental results show that the proposed algorithm performs well for similar target detection applications.
Local Extreme Co-occurrence Edge Binary Pattern for Bio-medical Image Retrieval

A new approach to bio-medical image retrieval, local extreme co-occurrence edge binary pattern (LECoEBP) pattern proposed. Local extreme edge binary patterns (LEEBP) give the texture information and gray level co-occurrence matrix finds the co-occurrence of binary patterns. LEEBP extracts the four edges for each pixel and GLCM applied on each edge to find the joint occurrence of edge binary patterns. In LEEBP, each edge has directional relative information of all possible directions of a pixel. The directional relative information calculated based on the sign code magnitudes of local differences from center pixel to its directional pixels. The combination of LEEBP and GLCM showed significant results compared with the current algorithms on benchmark databases Emphysema-CT and OASIS-MRI.
Realistic versus Rational Secret Sharing

The study of Rational Secret Sharing initiated by Halpern and Teague regards the reconstruction of the secretin secret sharing as a game. It was shown that participants (parties) may refuse to reveal their shares and so the reconstruction may fail. Moreover, a refusal to reveal the share may be a dominant strategy of a party. In this paper we consider secret sharing as a sub-action or subgame of a larger action/game where the secret opens a possibility of consumption of a certain common good. We claim that utilities of participants will be dependent on the nature of this common good. In particular, Halpern and Teague scenario corresponds to a rivalrous and excludable common good. We consider the case when this common good is non-rivalrous and non-excludable and find many natural Nash equilibria. We list several applications of secret sharing to demonstrate our claim and give corresponding scenarios. In such circumstances the secret sharing scheme facilitates a power sharing agreement in the society. We also state that non-reconstruction may be beneficial for this society and give several examples.
Emerging Culture of Social Computing

Social computing is proposed to improve the quality of life of citizens and preserve the ecology of the planet through moral digital and human-free management of each citizen based on accurate monitoring of their preferences. The model of cyber social computing is described, which will save humanity from its vices and direct the efforts and minds of people to moral solving the existing problems of energy, materials, ecology, quality of life. We propose solutions to the problems of managing social groups from the perspective of deterministic metric computing instead of management based on statistical analysis that does not take into account the interests of each individual citizen. Democracy is an anti-scientific method of probabilistic management of social groups and making incompetent decisions. It should be replaced by moral cyber-computing of metric management of society based on exhaustive testing of the interests of every citizen. The strategy of uniting social groups is shown to effectively address economic and industrial problems based on the adoption of a constitution and laws, which integrate citizens' efforts through the implementation of the doctrine of the unity of diversity of languages, religions, histories, traditions and cultures.
What Rocks Are Made of: Towards an Ontological Pattern for Material Constitution in the Geological Domain

We propose an ontological pattern for dealing with the material constitution relation in Geology domain. This is important because geologists are often interested only in properties that are dependent to the matter (the rock, the minerals) or to the object (a geological unit, a grain). The scale of analysis is very important in Geology and may range from millimeters to kilometers. Differentiating the matter from the object that it constitutes allows one to represent properties from different scales separately. We first provide a short review of the state of the art for the constitution relation and how our vision fits within the existing theories.
Computational lighting for extracting optical features from RGB images

Optical measurements for capturing optical features that show the physical and chemical properties of target objects and scenes fall under the nondestructive measurement method. These measurements require a long period of time and expensive specialized equipment. This paper proposes a practical system composed of commercial LEDs and RGB cameras for extracting optical features and predicting the properties from RGB images. Besides the predictor optimization by supervised learning, the system also utilizes computational lighting techniques for optimizing the synthesized illuminants, which are composed of readily available LEDs. In addition to the low-cost implementation, our system provides fast measurement because the number of images that are photographed can be reduced through computational lighting. We demonstrate the effectiveness of our system in prediction problems where we analyze the fluorescence intensity of scenes drawn with markers and pearl quality.
AI, IoT hardware and Algorithmic Considerations for Hearing aid and Extreme Edge Applications

Artificial Intelligence (AI) has made many significant advances over recent years. Starting out as mainly university research endeavors, recent significant breakthroughs have pushed the practicality of AI to the forefront. It is making fast inroads to traditional industrial applications and it is clear that given sufficient computing resources, AI is applicable to almost anything. The breakthrough referenced is mostly the result of the diligent persistence of a number of AI researchers in combination with large increases in available computation power to reconsider much deeper neural networks than previously used which were consistently rejected because of their large complexity reasons. Deep Neural Nets have proven to be an adept framework and up to solving the difficult challenges proven previous machine learning approaches could not solve. Recently, driven by enhanced computational power and necessity, edge applications have arisen to the forefront. Generally, now that cloud computing is available, a choice may be made where to locate the recognition engine, local to the data source or on the cloud where considerable computing resources are available. Hearing aids, a product on the edge now connected via one or more wireless links and fully immersed in IoT are the subject and consideration of this paper. It is natural to consider whether, via these links, remote computation is possible and appropriate for hearing aid applications. Difficulties arise when remote computation is attempted simply because the local data to be inferenced must be transmitted. Summarizing, utilizing remote computing for local recognition creates, two immediate problems: 1) the transmission of possibly private data across an insecure channel, and 2) the channel may not exist in remote or adverse transmission environments. Two further difficulties emerge in an important subset of applications, including hearing aids and hearable products: 1) Delay from the transmission latency required to obtain Cloud computation - although fast and capable - once the information is obtained and 2) Transmission power.
Life Assistants for the Elderly Based on Mobile Devices

With the exacerbation of aging in China, the problem of nursing for the elderly has increasingly become one of the focuses of social concerns. Because of the popularization of mobile devices, using mobile devices to assist the care of the elderly has become a feasible solution. In this paper, we developed an elderly life assistant system (ELAS) based on mobile devices, which includes two apps (applications), i.e., the Supporter side and the Elderly side. The Supporter side helps the supporters to care for the elderly, remotely helps the elderly to input the drug information and record medication; the Elderly side assists the daily lives of the elderly. The main functions of the system are medication reminding, reading assistance, navigation route assistance and adaptive voice navigation (AVN), in which AVN can automatically adjust the volume of navigation according to external noise, providing better navigation effect for the elderly. Experiments show that ELAS is helpful to the elderly in daily life, and improves the ability of the elderly to take care of themselves.
ECG Arrhythmia Classification based on Fuzzy Cognitive Maps

Medical Diagnosis is the process of determining the disease or condition of a patient by seeing his symptoms. Diagnostic Methods are the procedures used to diagnose a particular disease usually following the report of symptoms, or based on other medical test results. Electrical signals produced by the heart can be assessed in the Electrocardiogram (ECG) signal for identifying heart problems and carry out a diagnosis. In this work, we propose a method for classifying cardiac arrhythmias by using a Fuzzy Cognitive Maps (FCMs) approach as a tool to assist the physician to diagnose more efficiently the patient condition. First, a QRS complex detection algorithm is used. From each QRS complex, two linear prediction coefficients (LPC) are calculated as well as the mean square value, which altogether generate the data set utilized for training the FCM classifier. The ECG signals used to generate the training data set were obtained from the MIT-BIH arrhythmia database. In this paper, the method has been focused on classifying premature ventricular contraction (PVC) and normal beats, but indeed the proposed procedure can be extended for detecting other cardiopathies as well.
Tchebichef Image Watermarking based on PN-Sequence

In the modern era, sharing and distribution of digital data over the internet have increased significantly. This makes it easier to duplicate data, even though not all data may be duplicated without permission because it has copyright. Image watermarking is the most popular solution for protecting copyright in digital images. The way to embed copyright is generally by transforming the image into the frequency domain, which in this way proves that copyright embedding is more resistant to various attacks. Discrete Tchebichef transform (DTT) is a domain transformation that has advantages in computational speed, is low in complexity and can reduce memory usage and has properties similar to discrete cosine (DCT) transformations. DCT is the most popular transformation to be developed, wherein one of the studies using the PN Sequence combined with DCT at the time of the embedding process, it was proven to improve imperceptibility quality. However, the increase in imperceptibility generally makes watermark resistance less. Based on this hypothesis the research aims to implement a watermark embedding scheme with PN Sequence on DTT to obtain a watermarking scheme that produces better imperceptibility and keeps the quality of watermark resilience against various manipulations. Based on the results of testing and measurement using PSNR, it is evident that the imperceptibility quality rises around 3dB. Watermark resistance testing for attacks also proves that using the proposed scheme but can maintain watermark resistance.
Providing Cooperative Data Analytics for Real Applications Using Machine Learning

This paper presents a data analytics system which determines optimal analytics algorithms by selectively testing a wide range of different algorithms and optimizing parameters using Transformer-Estimator Graphs. Our system is applicable to situations in which multiple clients need to perform calculations on the same data sets. Our system allows clients to cooperate in performing analytics calculations by sharing results and avoiding redundant calculations. Computations may be distributed across multiple nodes, including both client and server nodes. We provide multiple options for dealing with changes to data sets depending upon the data consistency requirements of applications. Another key contribution of our work is the Transformer-Estimator Graph, a system for specifying a wide variety of options to use for machine learning modeling and prediction. We show how Transformer-Estimator Graphs can be used for analyzing time series data. A key feature that we provide for making our system easy to use is solution templates which are customized to problems in specific domains.
Research Paper Recommender System with Serendipity Using Tweets vs. Diversification

So far, a lot of works have studied research paper recommender systems. However, most of them have focused only on the accuracy and ignored the serendipity, which is an important aspect for user satisfaction. The serendipity is concerned with the novelty of recommendations and to which extent recommendations positively surprise users. In this paper, we investigate a research paper recommender system focusing on serendipity. In particular, we examine (1) whether a user's tweets lead to a generation of serendipitous recommendations and (2) whether the use of diversification on a recommendation list improves serendipity. We have conducted an online experiment with 22 subjects in the domain of computer science. The result of our experiment shows that tweets do not improve the serendipity, despite their heterogeneous nature. However, diversification delivers serendipitous research papers that cannot be generated by a traditional strategy.
FPGA-Based Real-Time Embedded Vision System for Autonomous Mobile Robots

In this paper, we developed an embedded vision system for autonomous mobile robots. This system enable to help a robot to measure in real-time the distance of an obstacle that we have defined in advance and avoid it. The vision system uses a single front camera as sources of information on the surrounding environment and an FPGA as reconfigurable and flexible device for image processing. The whole system was implemented only in VHDL a parallel programming language on a Nexys 4 DDR development board with Artix FPGA device from Xilinx that operates at 100 MHz. This allowed obtaining a real-time image processing for a 640x480 @ 30 fps video stream.
Decoupled Hybrid 360° Panoramic Stereo Video

Although virtual reality (VR) and 360° panoramic videos amplify the viewer's immersion, they bring unique challenges from a content creator's perspective. As the audience gazes all around, the intended region of interest may not be seen at the right moment. In this paper, we present a novel hybrid 360° panoramic video camera rig that has both stereoscopic and monoscopic regions. Such non-uniform distribution of disparity serves as a new cinematographic tool. We also propose a custom camera rig capable of recording the hybrid format and is meant to be used similarly to a conventional camera: track the subject of interest and keep it inside the stereoscopic region. Camera tracking reduces motion blur of the subject. The rotational motion caused by tracking are decoupled from the stitched output video to reduce cybersickness. The stitched hybrid format was acceptable when tested on three different settings where the camera rig went through: no motion, yaw rotation, and any motion.
Technology of Smart Contracts

Chapter 3 introduces the reader to the most relevant technological aspects of blockchain and smart contracts. Specifically, Chapter 3 presents the technological foundations blockchain and smart contracts are built upon, by providing some basic examples, which could help to grasp how this technology works. In addition, it includes an overview of existing, soon to be developed, or potential applications of blockchain and smart contracts in different fields, in order to help the reader understand how they could affect existing processes or, more in general, society.
Google vs. Instagram Machine Translation: Multilingual Application Program Interface Errors in Translating Procedure Text Genre

The field of translation has benefited from the advance of technology and the Internet nowadays. Hence, it is common to find online machine translation tools in the cyber world. Google Translate and Instagram translation feature, a multilingual application program interface (API), are cases in point. This research focuses on the errors in how procedural texts in Instagram posts are translated into Indonesian language by using Google Translate and API. The translation error typology presented by the American Translators Association (ATA) was used as a framework of analysis. The result shows that Instagram higher errors were made by Instagram than Google Translate, with Terminology error (T), Syntax error (SYN), and Literalness (L) error being the most notable error categories in both translation machines. There is an interrelation among these three. Terminology errors were caused by the Literalness of the text elements, creating the translation of terms not suitable for the context. This research suggests that with improvements, Google Translate has a possibility to be one of the most reliable free MTs in the future. Also, the field of linguistics may become a fruitful contribution in the betterment of machine translation results.
Text Detection on Books Using CNN Trained with Another Domain Data

Text detection on products can allow us to manage them easily. In this paper, we focus on how to detect texts on books in bookshelves monitored by a camera. We investigate the application of a CNN method, which was trained with another domain data, onto the detection. Some refinement ways of results generated by the method are proposed without training the CNN with appropriate data. We evaluate them empirically and discuss the effect of the ways on the detection.
Applied Data Science in Financial Industry

In a time when the employment of Natural Language Processing techniques in domains such as biomedicine, national security, finance and law, is flourishing, this study takes a deep look in its application in policy documents. Besides providing an overview of the current state of the literature that treats these concepts, the study at hand implements a set of unprecedented Natural Language Processing techniques on internal bank policies. The implementation of these techniques, together with the results that derive from the experiment and the experts’ evaluation, introduce a Meta-Algorithmic Modelling framework for processing internal business policies. This framework relies on three Natural Language Processing techniques, namely information extraction, automatic summarization and automatic keyword extraction. For the reference extraction and keyword extraction tasks we calculated Precision, Recall and F-scores. For the former we obtained 0.99, 0.84, and 0.89; for the latter we obtained 0.79, 0.87 and 0.83, respectively. Finally, our summary extraction approach was positively evaluated using a qualitative assessment.