Reviewerly/exHarmony huggingface dataset:
Efficient and Verifiable Proof of Replication with Fast Fault Localization

Proof of replication technique has been widely used to verify whether the cloud service providers (CSPs) store multiple replications of a file with dedicated and unique storage space, which effectively prevents CSPs from colluding and storing only one copy of the file. In this field, many representative schemes have been proposed and applied to various scenarios. However, most of the existing schemes are based on the timing assumption (i.e., the verifier rejects the proof of replication if the prover's response is timeout) and do not explicitly consider the problem of batch verification and fault localization. This will bring unnecessary computational overhead to the verifier and reduce the efficiency of batch auditing. To address the above problems, we propose a verifiable proof of replication scheme with fast fault localization and high efficiency. By integrating incompressible encoding and homomorphic linear authenticator, our scheme can effectively audit the integrity of file replications without timing assumptions. To support batch verification and fault localization, we propose a reversed signature aggregation tree (Rev-tree) by integrating the quick binary search and exponent testing. Compared with the traditional binary tree, Rev-tree can further reduce the overhead of batch verification and effectively locate a single fault replication. Moreover, benefit from the property of Rev-tree taking the existing error probability as an estimate of the rest of the tree, our scheme can adjust the verification strategy dynamically to meet with different situations. Finally, security analysis and experimental results show that our scheme is secure and efficient in proof of replication and fast fault localization.
Taxonomy of Threats to Ubiquitous Computing

Ubiquitous computing is used extensively to power devices and systems embedded intricately in the ecosystem we exist and have grown to depend on their flawless execution. Reliance on these ubiquitous computing systems has turned them into lucrative targets for adversaries to exploit and exposes them to a variety of attacks. In this chapter, we explore, enumerate, and catalog threats to these systems and later discuss the adversarial threat motivations.
Some Cases of Object Tracking Parametrs Optimization in Video Stream

Object tracking in video stream can be used in many ways such as face recognition in photos, creating deepfake videos, augmented reality. A significant part of the object tracking algorithms implemented work less accurately and more slowly than they could. The main reason for this problem lies in the insufficiently careful selection of tracker parameters. This article discusses methods for solving this problem based on the analysis of tracking algorithms both using ground truth and not. The article describes methods for evaluating various parameters of tracking algorithms, and deduces patterns that occur during the operation of tracking algorithms. As a result of the study, the performance and "efficiency" of various tracking algorithms depend on their parameters, and optimal sets of parameters were selected for working on different types of data under different performance conditions.
Emotional Literacy in the Education Context

Emotional literacy is about learning to understand and talk about how we feel, to reflect on our emotions and how they are affecting our thinking and behaviour and to manage our emotions and those of other people in a productive way. Emotional literacy is now actively promoted in many schools. This can take the form of a whole-school curriculum aimed at enabling staff and pupils to become more emotionally literate. As a responsible society, we have a duty to help children develop and reach their potential as well-rounded, emotionally healthy individuals who can play a full and active part in the society of their future. The development of emotional literacy is at the heart of how we can do this, in our homes, schools and all aspects of our lives.
Energy consumption of TAGS in a heterogeneous environment under unknown service demand

This paper models the task assignment based on guessing size (TAGS) job allocation algorithm using Markovian processing algebra; PEPA. It aims to analyse performance and energy consumption. The working environment is assumed to be heterogeneous, and the job size distribution is assumed to be a two phase hyper-exponential. Furthermore, the queues are bounded. A two nodes system is implemented with exponentially distributed incoming tasks. We analysed the performance metrics and energy consumption under different arrival rates. We found TAGS can perform well and improve performance, although it increases total energy consumption. Finally, we calculated the energy per job to evaluate TAGS in a heterogeneous environment, and demonstrated that TAGS reduces energy consumption per job when the system is under a heavy load.
Research on the application of 5G technology in the cultivation and construction of educational informatization innovation and entrepreneurship ability

With the increasing popularity of education informatization and the emergence of intelligent learning space, students’ demand for personalized, convenient and diversified learning content of innovation and entrepreneurship ability is becoming increasingly prominent. The fifth generation mobile communication (5G) technology is characterized by its large bandwidth, ultra-high reliability, ultra-low delay and large-scale Internet of things. College Students’ innovation and entrepreneurship education is the main theme of modern higher education and an important way to promote the transformation and development of local colleges. The quality of business plan and roadshows is one of the important means of financing for start-ups. Improving the ability of college students to write high- quality business plans and develop roadshows is not only conducive to the scientific evaluation and assessment of students’ ability, but also helps to stimulate the enthusiasm of college students for innovation and entrepreneurship, and cultivate their ability of innovation and entrepreneurship. Combined with the characteristics of contemporary college students’ innovation and entrepreneurship education, this project analyzes the problems existing in college students’ business plan writing and roadshow at this stage, and puts forward the establishment of an innovation and entrepreneurship ability training system based on the rapid development of business plan and roadshow, so as to promote the improvement of College Students’ innovation and entrepreneurship ability and provide a strong guarantee for deepening the reform of innovation and entrepreneurship education in Colleges and universities.
APPLICATION OF PICTURE AND PICTURE MODELS TO IMPROVE STUDENT LEARNING OUTCOMES IN LEARNING SOCIAL SCIENCES

The formulation of the problem in this classroom action research is whether the application of the picture and picture model can improve student learning outcomes in social studies subjects in grade IV SDN 19 Dungingi, Gorontalo City. This study aims to improve student learning outcomes in the fourth grade social studies subject at SDN 19 Dungingi, Gorontalo City. In achieving the above objectives, quantitative and qualitative research approaches are used with this type of classroom action research carried out in two research cycles. Data collection techniques used are observation, interviews, tests and documentation. Data were analyzed by reducing data, presenting data, and drawing conclusions. The results showed that with the picture and picture model there was a pleasant learning atmosphere so that student learning outcomes increased, namely from the results of cycle I to cycle II there was an increase. In the first cycle the number of students who got a complete score was 20 students or 62.50%, the incomplete score in the first cycle was 12 students or 37.50%. Whereas in the second cycle there was an increase in the completeness score, namely to 27 students or 84.40%, an increase of 22.10%. It is concluded that the picture and picture learning model can improve student learning outcomes.
Navigation and Source Localization based on Single Pseudo-Ranges with an Unknown Multiplicative Factor

This paper presents a novel estimation solution for the problems of navigation and source localization based on pseudo-range measurements to a single pinger. In particular, the distance measurements are assumed to be corrupted by an unknown multiplicative factor, which is explicitly taken into consideration in the design. First, the equivalence between the problems of navigation and source localization is established, as well as cooperative navigation of two vehicles in tandem. Then, an augmented system is derived and its observability is carefully studied. The analysis is constructive, in the sense that the means to design an observer for the new system dynamics with globally exponentially stable error dynamics are readily available, resorting to linear systems theory. Moreover, the new augmented system is shown to be equivalent to the original one. Finally, simulations results are presented and discussed to assess the performance of the proposed solution in the presence of sensor noise.
Medical decision support system using data mining semicircular-based angle-oriented facial recognition using neutrosophic logic

A well-known strategy for facial recognition is image-processing technique. This process is extracting the faces through perspective orientation using the semicircular approach. The semicircular approach is primarily based on angle-oriented photo popularity, which is evolved from the authors by considering the scale and pose. If the size of the image is not precisely identical, it fails to recognize. Similarly, if the direction of the pose varies, then theoretical dialogue leads to problem. Demonstrate the idea of neutrosophic logic for better judgment in recognizing the face and judging to overcome such situations. Here, the concept of evaluation based facial recognition uses the popularly evolved neutrosophic logic and it is far proven with existing databases. In addition, the primary intricacy of face recognition gadget is profound use of pose verification. In fact, semicircular model for angle orientation is an illustrious technique for face processing applications. Several authors discussed numerous strategies of Face Recognition for face identity. The procedure of knowing the face using any perspective-oriented photo needs optimization in two tiers, specifically microlevel optimization and macrodegree optimization. The Evolutionary Optimization method is used to gain microdegree optimization by means of providing parallel computation. Using the rotational and reduction procedures lead to a macrostage optimization. To eliminate the undesirable attitude-oriented images from input dataset and identify the similar face from specific angle-orientated images, microlevel optimization is essential. Owing to the extent, range, and speed individuality of entire angle-oriented images and tremendous volumes of photos already saved in the database, macrodegree optimization is needed. Experimental effects records for identification compared to perspective-based images of various large databases such as Yale, MIT, FERET, and College Academic. The exponential failure control version is used to estimate reliability by estimating the failure rate.
Future Predicting Intelligent Camera Security System

The need for continuous human supervision, i.e. these systems are unable to perform certain functions without any operator monitoring the cctv video, is one of the major disadvantages of any camera-based monitoring system. An individual can hold his/her focus on the screen feed for a limited number of hours and not be distracted. Such poor monitoring could lead to a reduction in the effectiveness of the human operator's immediate action against a potential threat if detected on the screen. Therefore, the unique features of future prediction through the live video analysis method would have a huge effect on the surveillance system-based industries in order to address the limitations of the human attention span. The proposed system would be able to process the live stream from the cctv camera and generate output that will warn the operator of any possible danger that appears to occur or is occurring. For this method, a deep learning architectural approach is used with Convolutional Neural Networks. In this way, the surveillance camera system can detect an individual and identify and recognize those items carried by him or her on the basis of the level of danger. Similarly, the system can also identify and classify those acts that occur in the video feed into three distinct categories: natural, suspicious, malicious (based on the threat level) and send an alert to the respective human operator. Thus, the system will be able to help companies overcome security surveillance challenges and protect themselves from theft or any act of violence taking place in the area surrounding cctv.
Automatic calibration with character recognition software

Abstract This work presents the development and implementation of a character recognition software of digital displays with serial communication for the automation of the readings, registry and transcription of measurement instrument’s indications during calibrations. The software proved to be flexible, allowing calibrations with automatic registry, reducing transcription errors, improving the work conditions and organizing the registers, besides allow remote calibrations.
Development of a Neural Net to Add Greenery to an Image

The article is devoted to research of methods for image-to-image translation using neural network technology, and development of a neural network for adding greenery to a photo. The article provides a description of the implementation of such neural networks using the GAN architecture and evaluation of the obtained results. Neural network implementation was divided into 3 main stages: Collection and preparation of a dataset, creation of the structure of a neural network, it's subsequent training.
Assisted Learning: Cooperative AI with Autonomy

The rapid development in data collecting devices and computation platforms produces an emerging number of agents, each equipped with a unique data modality over a particular population of subjects. While an agent’s predictive performance may be enhanced by transmitting others’ data to it, this is often unrealistic due to intractable transmission costs and security concerns. In this paper, we propose a method named ASCII for an agent to improve its classification performance through assistance from other agents, without sharing proprietary data and model information. The main idea is to iteratively interchange an ignorance value between 0 and 1 for each collated sample among agents, where the value represents the urgency of further assistance needed. The method is naturally suitable for privacy-aware, transmission-economical, and decentralized learning scenarios. The method is also general as it allows the agents to use arbitrary classifiers such as logistic regression, ensemble tree, and neural network, and they may be heterogeneous among agents. We demonstrate the proposed method with extensive experimental studies.
Challenges and Perspectives of Recommender Systems in Vending Machines

This paper describes the features of using recommendation algorithms for vending machines. A vending machine is an automated machine with a significantly limited range of products (snacks, beverages, cigarettes), and it also does not provide any additional information about the customer, which imposes some limitations on the predicting relevant products. In order to increase the demand for vending products and make a profit, this paper proposes to consider how modern recommendation algorithms can be applied in this area. One of the important stages in building recommendation system is preparing data for analysis, so this paper describes an approach for grouping data by sales intensity and also considers the set cover problem.
Automation of discussion board evaluation through keyword extraction techniques: a comparative study

Abstract With the current trend in lieu of the ongoing pandemic, online learning and technology-based pedagogy receive worldwide attention. There are several pedagogical practices one can use to evaluate students mindful and the method will vary based on the learning objective. In this paper, our goal is to dive deeper into identifying the best keyword extraction technique which can be closely mapped with human evaluation. We report the results of three popular keyword extraction techniques specifically RAKE, TF-IDF, and Semantic Fingerprinting on the dataset generated from the discussion board post of a Learning Management System. The results illustrate that the TF-IDF algorithm shows the highest correlation with a human evaluation with 0.76 correlations, RAKE with 0.36, and Semantic Fingerprinting with 0.58. It was also identified Semantic Fingerprinting had a lowest mean square error of 18.26.
Linking the Elements Together: Will NoSQL Databases Live up to Their Promise?

The field of computing is not as unified as we like to think. The commercial and academic cohorts can have different goals and pursue different kinds of priorities. Some of Computer’s most influential articles are those that bring these two groups together.
Road Detection for Driving Applications based on Histograms

A simple method to detect the boundaries of a road from images is proposed. General considerations for the calculation of a bird's-eye view transformation and the use of histograms are established. A high-level algorithm is developed and implemented at RTL level, which is further synthesized for an FPGA.
An Evaluation of Deep Learning Networks to Extract Emotions from Yelp Reviews

User reviews are increasing exponentially in the online world and they play a crucial role in the decision-making process of buying products or services. A lot of thoughts go by a customer mind when it comes to making online purchases. While reviews are important for customers, businesses can also derive various advantages from the data generated by its user base. They can leverage user opinions, sentiment and emotion to gain customer insights. While several successful studies have already been conducted in analysing sentiments expressed in documents, we find that latest trends in machine learning, specifically deep learning, are still not well studied when it comes to emotion detection. We therefore, present here a study to evaluate the application of two deep learning networks namely CNN and LSTM in identifying emotions in yelp reviews.
Extraction and Analysis of Facebook Public Data and Images

Social networks play vital role in human communication and to improve the business applications. The Facebook is one of the most popular social networking application. However, the Facebook generates huge amount of data in the form of text, advertisements, posts, images, and videos. By analyzing Facebook data, we can find the location where our business promising in the real world. While sharing personal data, users demand security. Trust becoming an essential parameter in social networking. In this paper, a new technique is presented to identify duplicate logo images and profile pictures to prevent the fraud in business by keeping secret information in the profile picture or logo without distraction along with theoretical description of Facebook.
Signal processing and information theory fundamentals

This chapter is not intended to be a complete course in discrete-time signal analysis or information theory – many excellent texts on this topic already exist covering both 1D [1] and 2D [2] signals. We assume here that the reader is familiar with the basics of sampling, linear systems analysis, digital filters, transforms, information theory, and statistical signal processing. Our purpose is to provide an overview of these important topics as they form the basis of many of the compression techniques described later in the book. We firstly review the sampling theorem and consider the cases of 2D (image) and 2D+time (video) sampling. This provides the discrete time samples that represent our image or video and that are processed during the various stages of compression and coding. In Section 3.2, we introduce the means of describing signals using statistical representations. We focus on second order statistics and we use these to characterize the redundancy contained in an image or video signal. It is this redundancy that is exploited during the compression process. Filters and transforms form the basic work horses of most compression systems and these are introduced in Section 3.3. The signal distortions introduced in most compression systems can be attributed to coefficient quantization after filtering or transformation; this important aspect is covered in Section 3.4. Prediction operators are also in common usage across many aspects of compression, from intra-prediction to motion estimation. The basic concepts of linear prediction are introduced in Section 3.5 and we pay particular attention to the mitigation of decoder drift. This architecture is important as it provides a framework for all current video compression standards. As a precursor to coverage of lossless compression and entropy coding in Chapter 7, we review the basics of information theory: self-information, entropy, symbols, and their statistics. This topic is key to all symbol encoding methods in use today. Finally, in Section 3.7 we provide an introduction to the important topic of machine learning (ML), which is having an increasing impact on both compression methods and quality assessment.
On the Migration to and Synthesis of (Micro-)services: The Use of Intelligent Techniques

This chapter investigates the use of Computational Intelligence (CI) to tackle two challenges in the area of services. The first is involved with providing efficient decision support for migrating from monolithic to service-oriented software, while the latter addresses automatic service composition, which is a special form of service migration. Migration to service-oriented architecture (SOA) is influenced by a number of different and intertwined factors. These factors are identified through literature review and expert consultation. Different CI models, such as Fuzzy Influence Diagrams and Fuzzy Cognitive Maps, are employed to organize the factors and study their behavior. Various simulations are conducted that enable decision makers to execute what-if scenarios and take informed decisions as to whether to migrate or not to SOA, as well as to study the decisive factors contributing in favor or against this migration. Service synthesis is a tedious task considering on one hand the plethora of available services and on the other their different, often conflicting characteristics. Automation of this task is therefore a critical issue which deserves attention. In this context, the challenge of automatic service synthesis is addressed through specific methods and techniques based on Evolutionary Computation to achieve such automation to the best possible extent.