Reviewerly/exHarmony huggingface dataset:
Automatic Extraction of Heat Maps and Goal Instances of a Basketball Game Using Video Processing

The use of image processing techniques and computer vision to obtain teams statistics in different sports, currently represents a new source of information very useful for season preparedness. In this work, we propose different methods to show the players position for a specific time, to perform the point counting, and to extract clips of goal situations in the match. In order to accomplish this, we use a combination of transfer learning using a pre-trained deep neural network with a database of basketball game excerpts, and video processing techniques. As a proof of concept, the method was applied to a basketball game of local teams, showing the feasibility of the proposed approach.
The Special Conditions of the Post-War Economic Boom: A Note

It is a commonplace among analysts of economic growth that the record of the past half-century compares poorly with the mid-century period of boom--with those analysts offering an assortment of explanations, by and large claiming the existence of various factors present at mid-century and diminished or absent later that made the difference (if with significant differences reflecting the analysts' differing theoretical approaches to the subject). This working paper is intended to provide a convenient summation of the factors they have raised, and their various implications at the time and since, as well as a step toward a synthesis that may provide a more satisfactory understanding of the relevant developments.
Applying Source Classification Rules to Cloud Computing Transactions: Lease or Service?

As developers and companies around the world recognize the ineffeciency and costs associated with maintaining their own datacenters and computer hardware, the cloud providers beckon, offering worry-free maintenance, unlimited scalability, substantial cost savings, and automatic updating. Expensive IT departments thin as organizations opt for third-party systems managed by cloud providers. As cloud solutions continute to grow in popularity, cloud offering multiplying several times, many questions emerge on how such transactions fit into a classification system that never contemplated digital transactions of this nature, the consequences of which ripple around the globe. In response, the United States Treasury has issued proposed regulations attempting to resolve this question. The proposed regulations provide a nine-factor balancing test aimed at classifying cloud transactions as either a provision of services or a lease of property. This article analyzes each of those nine factors in detail, tailored to the unique characteristics of cloud computing. It ultimately concludes that the balancing test is unnecessary because true cloud computing is always a service. The classification issue is resolved definitively, with clear simplicity in application, by narrowing the definition used in the proposed regulations to a justification for classifying all cloud computing transactions as the provision of services.
Towed Streamer-Based Simultaneous Source Separation by Contourlet Transform

Simultaneous towed-streamer marine acquisition has the advantages of reducing the total time requirements and costs of surveys. However, the seismic records obtained are blended seismic data, so the successful deblending of such data is the key to this method. In this paper, we propose an effective deblending method with a new thresholding operator based on the shaping regularization framework in the contourlet domain. The new thresholding operator consists of an adaptive Bayesian threshold and a new thresholding function. Because of its multiresolution, locality, and directionality properties, the contourlet transform can effectively capture geometrical structures, which are the main features in natural images. To make the traditional Bayesian threshold adaptive in the contourlet domain, we propose a scale adjustment factor, a direction adjustment factor, and an attenuation factor to modify the threshold, and we also adopt local adaptive elliptic windows to estimate the standard deviations of useful signals; eventually, we obtain an adaptive Bayesian threshold. Furthermore, the new thresholding function can overcome the shortcomings of the existing soft and hard thresholding functions. Experimental results demonstrate that our method can effectively separate blended data.
Creating Geopositioned 3D Areas of Interest from Fleet Gaze Data

In order to observe driver’s attention levels, different approaches are followed. They include simple methods counting driver input changes [6], machine learning based approaches based on driver input [17], and methods considering additional inputs such as environmental data and eye tracking data [3–5, 7, 12, 16]. Recent studies have proposed geopositioned 3D AOIs as a tool for driver intention observation. Geopositioned 3D AOIs are three dimensional Areas (boxes), with fix geopositiones (e.g. GPS) which have to be observed for a safe completion of driving maneuvers. Examples are pedestrian waiting areas, crosswalks, and traffic light. Creating these AOIs by hand is a tedious task with ample room for potential errors, as the created AOIs might differ from the real AOIs drivers look at. We therefore propose a pipeline to generate real 3D AOIs from gaze clouds. To generate relevant gaze clouds we use the points of closest encounter in fleet gaze data collected in a driving simulator setup. The results show that the generation of 3D AOIs from fleet data is possible and the created AOIs are mostly consistent with the expected AOIs.
Hidden Markov Models

This chapter introduces hidden Markov models (HMMs), which can be viewed as an extension of mixture models, in which a unit of observation (e.g., a participant) can move between the states (mixture components) over time. While in mixture models, each observation is an independent draw from the mixture distribution, in hidden Markov models these draws are dependent. Leroux and Puterman (Biometrics 48:545–548, 1992) hence use the term dependent mixture model to describe a hidden Markov model, which is also where depmixS4 derives its name from. In particular, the sequence of components from which draws are taken in an HMM follow a Markov process. In other words, consecutive components are related through a Markov chain. As in mixture models, the states in a (hidden) Markov model are elements from a finite set. Models with an infinite set of states mapping onto a Euclidian space are called state-space models and are outside the scope of this book, see e.g. Durbin and Koopman, Time series analysis by state space methods, 2012. In Sect. 4.1, we first introduce (observed) Markov models, which are also called Markov chains, to discuss important properties of (hidden) Markov models, such as stationarity, homogeneity, and ergodicity. Section 4.2 then extends Markov models to hidden Markov models which are governed by unobserved (also called latent or hidden) states. Inferring the hidden states and computing the model likelihood is done efficiently through so-called filtering and smoothing recursions, which are the topic of Sect. 4.3. Parameter estimation of HMMs is much the same as for mixture models, and Sect. 4.4 is therefore relatively brief in discussing the relevant extensions to Sect. 2.3 . Assigning observations to hidden states is also called ‘decoding’ and this is the topic of Sect. 4.5. For global decoding, which takes into account the whole time series, the well-known Viterbi algorithm is introduced. Section 4.6 briefly discusses parameter inference, which again is mostly identical to inference in mixture models. In Sect. 4.7, we then discuss how to model the initial state and state transition probabilities with covariates, using multinomial logistic regression. Finally, we end the chapter with a discussion of missing data in the context of HMMs (Sect. 4.8).
VisIRML: Visualization with an Interactive Information Retrieval and Machine Learning Classifier

VisIRML is a visual analytic system to classify and display unstructured data. Subject matter experts define topics by iteratively training a machine learning (ML) classifier by labeling of sample articles facilitated via information retrieval (IR) query expansion—i.e. semi-supervised machine learning. The resulting classifier produces high quality labels better than comparable semi-supervised learning techniques. While multiple visualization approaches were considered to depict these articles, users exhibited a strong preference for a map-based representation.
Multi-channel Orthogonal Decomposition Attention Network for Sequential Recommendation

Sequential recommender systems aim to model users' evolving interests from historical behaviors and make customized recommendations. Except for items, the feature carried by the interaction also contains a wealth of information (e.g., item category and user rating). Therefore, many researches tried to leverage features, which directly fuse various types of features into the item vector. However, items and features are in different vector spaces, so the direct fusion destroys the consistency of the item vector space. Furthermore, the direct fusion of multiple features leads to mutual interference, making it hard to capture the transfer patterns of feature sequences. In this paper, we propose a novel Multi-channel Orthogonal Decomposition Attention Network (MODAN) for the sequential recommendation. Specifically, we apply two kinds of channels. One is the item channel, which only focuses on the pure dependency among items. The other is the feature channel, which captures the feature transfer patterns. In the feature channels, we adopt orthogonal decomposition and reverse orthogonal decomposition to maintain the consistency of both the item and feature vector space. Experimental results on three datasets demonstrate that MODAN achieves substantial improvement over state-of-the-art methods.
ANALYSIS OF INTERNATIONAL EXPERIENCE IN FINANCING EDUCATION

Everyone knows that education is the foundation of a nation. Countries that have implemented this motto and have long invested heavily in the sustainable development of the country's education sector are now leaders in innovation, global influence, employment, innovative thinking, world-class healthcare, technology, ideological influence and even in the military. They diversified investment in education, which spurred progress across all sectors. On the contrary, there are many countries that rely on some specific advanced countries for creativity, education and even making important decisions. These countries cannot support their own sustainable development because they rely on others. In addition, some countries are highly dependent on natural resources, which currently face constraints due to the sharp fall in the prices of these assets due to the global economic depression or the invention of alternatives. The world's best educated countries have more researchers, a diversified world-class education system, and the creativity to cope with changing global circumstances. Thus, funding for education opens up many employment opportunities, which leads to a decrease in the crime rate in the country and, as a result, leads to the sustainable economic development of the nation.
PushBox: Making Use of Every Bit of Time to Accelerate Completion of Data-Parallel Jobs

To minimize a job's completion time, we need to minimize the completion time of its final stage's last task. Scheduling of machine slots and networks largely dominates the variable part of each task's duration. Finding an optimal schedule is NP-hard even for offline and simplified scenarios. Previous work does lead to improved performance with various strategies. State-of-the-art task placement and network scheduling efforts are largely disjunctive. Without joint optimization, they are sub-optimal and myopic in many scenarios. Task placement usually treats the network as a black box. Thus, we use prioritized bandwidth allocation among tasks making the network both <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">predictable</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">efficient</i> to achieve joint scheduling. With this feature, joint scheduling can be transformed into a special <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bin-packing problem</i> . Over this minimal yet power-enough abstraction, we propose PushBox to schedule data-parallel jobs in multi-tenant clusters. When designing the joint scheduling algorithm, we not only embrace the wisdom of prior art but also respect administrators’ fairness intent, which is so far largely ignored. We implement PushBox on Hadoop 3. PushBox performs persistently well on both a small testbed and a trace-driven simulator.
Improving Moving Vehicles Detection and Vehicles Trajectory Plotting Using Optimized Background Subtraction Method

Moving vehicles detection, automatic counting and trajectory plotting of the passing vehicles on the highway or through an intersection during a particular time are one of the core components of Automated Vehicular Surveillance (AVS) systems for traffic control and monitoring in big and crowded cities. This paper presents a novel and simple method to improve moving vehicles detection and vehicles tracking using a background subtraction method, which background model and vehicles traffic areas are extracted by analyzing spectral values of pixels of frames, in order to reduce adverse effects of abrupt changes in ambient illumination, undesirable temporary movements within the off-road areas in the frame and permanent eliminating non-involved areas to prevent from recalculations. In addition, a dual-camera system was used to increase processing accuracy in detecting those vehicles which are hidden because of being tailgated or driving extremely close to the car in front or blended within surrounding colors from view-angle of one of the cameras. This algorithm analyses the procedure of a video-based traffic congestion system and divides into greying, binarization, de-nosing, and moving target detection and will be able to plot vehicles trajectory by interception the detected vehicles in successive frames. At last, experimental results show vehicles detection rate and trajectory plotting rate means by this method that are higher than 99.1% and 99.5%, respectively.
A Survey on Privacy Protection of Cross-Chain

Blockchain technology has gained widespread attention due to its features of decentralization, tamper resistance, and traceability. Various decentralized applications have formed numerous “value silos” while meeting the unique needs of different industries. Cross-chain technology, as an important technology to realize inter-chain interconnection and value transfer, has also been rapidly developed. However, cross-chain technology faces a number of privacy risks that hinder its practical application. Although numerous surveys have reviewed the privacy protection of blockchain, they fail to reveal its recent progress and fail to provide a comprehensive classification of uniform standards for privacy protection in cross-chain processes. Therefore, in this paper, we review the development of cross-chain technologies and summarize the potential privacy threats. According to different privacy protection objectives, we provide a detailed survey and classification of existing privacy protection technologies. Finally, we propose a few open research questions and future research directions. We hope this work will help future researchers to gain insight into cross-chain privacy protection technologies in terms of concepts, properties and applications.
A Quick Dynamic Attribute Subset Method for High Dimensional Data Using Correlation-Guided Cluster Analysis and Genetic Algorithm

In data mining and machine learning principles, dimensionality reduction is crucial. The word “dimensionality” means the reduction of the number of functions. Machine learning models are also finding it difficult to deal with high dimensional data and high computational costs in the current scenario. The model gets more dynamic as the number of features increases and over fitting becomes more likely. When a machine learning model is trained on a large number of features, it becomes overly dependent on the data it was trained on, resulting in poor results on real data and defeating the goal. This research suggests a new feature selection (FS) hybrid three-stage algorithm according to correlation-based cluster analysis and genetic algorithm to overcome the above issues. During the two stages, a filter FS approach and a function clustering-based method with low computing cost are built to make the third process’s quest area smaller. The third step is to identify an optimal function subset using a global readability evolutionary algorithm. The three phases are also designed to improve the effectiveness of a symmetrical insecurity based removal process; a rapid link-based clustering method and a genetic algorithm. Finally, on a variety of datasets from the physical world that are freely accessible, the proposed algorithm is contrasted to some FS algorithms. According to experimental findings, the suggested algorithm can obtain a successful function subset with the least price of computing.
Blockchain and Artificial Intelligence in Real Estate

Since their development, blockchain and artificial intelligence (AI) technologies have gained substantial momentum and immense adoption in different industries worldwide. The innovations of cryptocurrencies and machine learning algorithms have had significant implications for the growth and advancement of these technologies. The combination of the two presents incredible benefits to organizations in various sectors in terms of harnessing existing data for pattern recognition and insight identification. The technologies have impacted how industries do their businesses. This study includes a systematic review that explores how blockchain and AI, have changed the real estate industry, as well as the way the related businesses can take advantage of the technologies’ capabilities to stay afloat within this new technological development. This research adopts the Prisma methodology to explore how the application of blockchain and AI has impacted the real estate sector. The main finding is that in real estate, the combination of blockchain and AI has great potential, especially in modeling data and valuation, storing information in digital formats and securing transactions.
Implementation of the Spark technique in a matrix distributed computing algorithm

Abstract Two analyzes of Spark engine performance strategies to implement the Spark technique in a matrix distributed computational algorithm, the multiplication of a sparse multiplication operational test model. The dimensions of the two input sparse matrices have been fixed to 30,000 × 30,000, and the density of the input matrix have been changed. The experimental results show that when the density reaches about 0.3, the original dense matrix multiplication performance can outperform the sparse-sparse matrix multiplication, which is basically consistent with the relationship between the sparse matrix multiplication implementation in the single-machine sparse matrix test and the computational performance of the local native library. When the density of the fixed sparse matrix is 0.01, the distributed density-sparse matrix multiplication outperforms the same sparsity but uses the density matrix storage, and the acceleration ratio increases from 1.88× to 5.71× with the increase in dimension. The overall performance of distributed operations is improved.
Using Iso/Iec 25000 for Smart Contract Functional Suitability Assessment

Blockchain is a cross-cutting and emerging technology that allows interactions among untrusted entities in a distributed manner without the need for involving a (centralized) trusted third party. In this context, smart contracts (i.e., programs that run on the blockchain) enabled organizations to envision and implement solutions to numerous real-world problems in less cost and time. Given the immutability of blockchain and the lack of best practices for properly designing and developing smart contracts, it is crucial to assure smart contract quality before deployment. In this paper, with the help of a survey involving developers and researchers, we explore the practices and tools used to develop, implement, and evaluate smart contracts. The survey results show that inefficiencies may occur during the development cycle of a smart contract, especially in the requirements specification, design, and testing phases. In addition, considering the lack of a shared standard to evaluate the quality of implemented smart contracts, we propose to adopt and adapt the ISO/IEC 25000 standard used in software engineering, focusing on one of its features: functional suitability. Finally, through some examples, we show how to use the adapted standard and how to measure the functional completeness and correctness of smart contracts.
Smart Tutoring System for the Specially Challenged Children

Indian Sign Language (ISL) is used language among people with hearing loss and inability to speak in India. To interact with the deaf and dumb it is essential to learn this language and communicate efficiently through body movements. A knowledge of the sign language is required to teach children with these disabilities. The proposed system is built to assist users to converts speech to the sequence of sign language symbols. It performs speech to text, transforms it into a suitable format, and identifies the animations required for the phrases that will help convey the desired message. The model will help teachers to deliver the subjects to the students without explicit training in the sign language. Desktop version can comfortably get a good number of users. The sign language applications that are available are restricted to accepting text and are not able to handle combined phrases or Subject-object-verb patterns. All mobile applications that are available in the store do not work for audio inputs. Our work addresses these issues of handling combined phrases using the semantics of NLP that is used to reduce the text into smaller interpretable modules. A dictation recognizer takes in speech as input through microphone enabling the proposed model to accept audio. The animations for interpreting text are created using Unity 3D tool. In the front end, a dictation recognizer is included. Text pre-processing is done using NLTK in python. The pre-processing part includes elimination of the regularly used words, identification of base form of the words, handling of word meanings, and converting the given sentence into Subject-Object-Verb form. It is also necessary that the sentences of the Indian Sign Language have to be in this form. As the proposed model displays the conversations done in Indian Sign Language this can be used by teachers as well as any person who wants to communicate a given message without having any training for the same. This idea can be extended to multiple languages also, in which case languages other than English can also be used.