Reviewerly/exHarmony huggingface dataset:
A Multi-View Stereo Evaluation for Fine Object Reconstruction

Current stereo matching methods based on end to end learning frameworks have shown strong results in the field of depth estimation, bringing significant improvements in robustness as well as flexibility in the accuracy and evaluation time trade-off. In this line of research we observe that the two sub-fields of binocular, and multi-view stereo have converged and are based on fundamentally the same architectures. In this work we aim to perform an objective comparison of these methods, controlling for architecture, and accounting for the rectification process typically used in binocular stereo. To our knowledge there is no prior work directly comparing the two. We aim to measure the performance of matching between rectified pairs, and plane-sweep based multi-view stereo. We test a range of camera configurations and studying the effectiveness of additional cameras in the context of a synthetic multi-view stereo dataset developed for evaluating 3D reconstruction in agriculture.
PRPOIR: Exploiting the Region-Level Interest for POI Recommendation

Point of interest (POI) recommendation has become an important means to help people discover attractive locations. Previous studies show that modeling the context information of users' check-ins is necessary for POI recommendation. However, most previous hybrid models ignore the fact that a user's interested region is likely to drive him to visit other POIs in it, which means that his region-level interest is indispensable in the recommendation process. In this paper, we take this challenge and propose a hybrid model called POI-Region POI Recommendation (PRPOIR). Specifically, PRPOIR contains two modules: Interest Module and Context Module. In Interest Module, inspired by the success of the Logistic Matrix Factorization (LMF) to model implicit feedback, we apply it to model users' POI-level interest and region-level interest respectively. To capture the difference in the users' emphasis on region-level interests, we innovatively introduce a strategy to establish the dynamic weight of users' region-level interest. Then in Context Module, by further integrating geographical influence and social correlations into PRPOIR, it can alleviate the sparsity of check-ins and bring further performance improvement. To verify the effectiveness of PRPOIR, we conduct experiments on two real-world datasets, experimental results show that PRPOIR outperforms the state-of-the-art models.
ENVIAR

Context-aware applications (CAAs) sense and react to changes from the environment when performing their tasks. Testing such applications is challenging. Firstly, due to the number of combinations of possible events and values they can handle. Secondly, due to the different execution scenarios that may be hard to reproduce manually. We present the ENVIAR tool whose objective is to support test case generation and execution of Android CAAs. Generation combines a set of events that may trigger failure in such applications filtered by pairwise testing. Execution simulates the environment in which the CAA runs by sending mock data. Current version focus on Android events, GPS, and Internet sensor events and values. Empirical results have shown that the tool can generate test cases that detect context defects in real Android applications, particularly covering scenarios that would be tricky to run in a real environment.
Reliability Evaluation Methods of Deep Learning Algorithm in Computer Vision

With the rapid development of deep learning technology, the application based on deep learning shows explosive growth, especially in the field of computer vision. However, some characteristics of the deep learning algorithm make it face many unexpected threats in actual use. It has become an urgent problem to ensure the reliability of the algorithm. In this paper, we propose a deep learning algorithm reliability evaluation system and corresponding evaluation indicators from the three aspects of data, model, and operating framework, then describe reliability evaluation methods for each specific indicator.
The Importance of Bounding Box in Motion Detection

In today's digital era, we are used to living with various technologies that help ensure safety and comfort in our daily activities. One of the technologies commonly used to help safety and comfort is the use of Closed-Circuit Television (CCTV). Many people or groups are helped by the presence of CCTV in their environment. Some CCTVs have been shown to help identify criminals, but this is done manually. In this research, CCTV is supported by computational techniques that can identify object activities. The computational technique used is a bounding box for moving objects. Thus, it is easier for stakeholders to find out suspicious things in a particular area or incident. The technique to determine the bounding box by calculating the body aspect ratio and object speed.
Visualization and Interaction System for Hologram-Like

Recently, various researches and developments have been made to create a new computer that exceeds the capacity of a classical computer by utilizing a quantum computer. Quantum computing controls with operations of quantum bits called Qubit and uses various quantum physics phenomena to perform computations faster than classical computing. The quantum physics phenomena such as quantum entanglement and quantum superposition are used to carry out the computation. Although conventional classical computing systems can easily understand the current state of the system with pre-defined logic gates, symbols and procedures, on the other hand, the quantum computer have a complicated structure and difficult to intuitively understand. For example, when the number of controllable quantum bits hugely increases, complex quantum operations by quantum computing algorithms is generated. Also, to visualize large-scale complex quantum computer circuits, we need better ways to visualize quantum circuits. In this paper, in the process of designing a large-scale quantum computer system, the complex quantum algorithm and quantum system generated by controlling a large number of quantum bits are hierarchically visualized so that users can more effectively recognize the quantum computer system. In order to effectively design a computing circuit, we propose a quantum computing circuit visualization system using a hologram-like system.
Collective Creative Activity in Foreign Language Classes at an Agrarian University

The relevance of this article is due to the consideration of the problem of organizing collective creative activity in classes in mixed groups in the foreign language, especially at the universities which are part of the consortium of supporting educational organizations-experts of Russian education
Train Throughput Analysis of Distributed Reinforcement Learning

Distributed deep reinforcement learning can increase the train throughput, which is defined as the timesteps per second used for training, easily by just adding computing nodes to a cluster, which makes it an essential technique for solving complex problems. The more complicated the virtual learning environment and the policy network become, the more the CPU computing power in the rollout phase and the GPU computing power in the policy update phase is required. Recall that the reinforcement learning iterates the phases of acquiring data through rollout in the virtual learning environment and updating the policy from that data over millions of iterations. In this paper, the train throughput analysis is performed with RLlib and IMPALA on two different problems: CartPole, a simple problem, and Pong, a relatively complex problem. The effects of various scalability metrics, clustering, and observation dimensions on train throughput are analyzed. Throughout the analysis, we show that 1) the train throughput varies significantly according to the scalability metrics, 2) it is vital to monitor the bottleneck in the train throughput and configure the cluster accordingly, and 3) when the GPU computing power is the bottleneck, reducing the observation dimensions can be a great option as the train throughput increases up to 3 times by reducing the dimension from 84 to 42.
Synchronization of Vertical Electrooculography Sensor (EOGV) Data on Eye Image Data as Blink Data Validator

This paper presents research on synchronizing the data of the Electrooculography (EOG) sensor with eye image data when someone blinks. In this study, we carry out the validation of the vertical blink data synchronization from the EOGV sensor to the blinking eye image. The research uses two modalities to get data validity, whether someone blinks or not. There is ambiguity in the two modalities that have different data rates, and both might also have different false data that should not be recognized as signals or blinking eyelid data the same. Data modality matching and data interpolation are implemented to get complete data following the data duration and to get the pair of both modalities. We use linear regression to obtain a formulation of the synchronous relationship between the EOG blinking data sequence and the number of blinking eyelid image frame data.
A Pupil Detection Algorithm Based on Contour Fourier Descriptors Analysis

A high detection rate pupil detection algorithm is presented. This algorithm includes a preprocessing phase of the dark pupil image consisting of blurring, binarization and morphological operations. Next, during the processing phase, for each pupil candidate contour the Fourier descriptors of the convex-hull enclosure are determined. Each Fourier descriptor is used to obtain a metric using the Total Harmonic Distortion (THD) measure. This metric describes the similarity degree of the considered contour to an ideal ellipse. The algorithm selects the contours that fulfill the THD based metric condition and chooses the largest one as the pupil. Finally, to accurately estimate the pupil center, the Least Square Fit to Ellipse (LSFE) procedure is used. The proposed algorithm achieves over 85% average five pixels detection rate for three representative databases. The main advantage of the method is that the proposed THD based metric can be used for both circular and elliptical shaped pupil detection.
Hofstede and Hobbitses: Generational Evolution of Power Distance and Masculinity in UK in Popular Literature

A knowledge of cultural values of users is implicit to developing personas for HCI research. Hofstede's dimensions have been a prominent model in tracing cross-cultural influences in HCI but there has been significant criticism for treating culture as monolithic and reducible to a few statistics. In this paper we investigate two of Hofstede's Cultural Dimensions, namely Power Distance and Masculinity. Is popular literature an accurate reflection of the cultural values of its intended audience? Through the viewpoint of the modern, English-educated Indian, we conducted a qualitative inquiry, with a focus on elements like characters, events and reflections that bring out the Power Distance and Masculinity in popular fiction from the UK, by authors Arthur Conan Doyle, J.R.R. Tolkien and J.K. Rowling. We conducted workshops with expert readers familiar with the work of these authors and analyzed the data to formulate an evolution of power disparities and social predilections in the UK society over the past century. We gathered insights into the evolution of societal values and how authors contrast the mainstream with the exceptional to create a diegetic universe that is cogent with the life and times of the audience they are written for. As the first probe in a larger study, we reflect on the methodological learnings from this inquiry, reflecting on the mechanisms of conducting post factum inter-cultural studies and biases. Our conclusions discuss the feasibility of using popular literature as a barometer of cultural values of society, and its potential role in developing user personas.
Android arcade game “City Protector” with Touch Gesture Recognizer

Abstract City Protector is an Arcade Tower Defense Game with endless element. This game implements touch gesture recognizer feature. This game is developed with Unity game engine and playable in Android-based smartphone. To kill zombies, players must draw the symbols contained in the explosives that brought by zombies. Testing is done by blackbox testing, alpha testing, and beta testing through a survey of 32 respondents. The results indicate that City Protector has interesting features and gameplay concepts. It is provides new experiences because the Touch Gesture Recognizer feature is rarely found in general.
5 Questions Public Leaders Should Ask About Cybersecurity

Elected officials, agency heads and other government leaders need to understand their organizations' challenges and issues. It's the key to establishing a shared culture of security in a public org...
The Green Light SONATA: Foundations for Musical Agents Controlling Traffic Signals

The goal of this research is to improve signal operation and reduce delay through an innovative transdisciplinary integration of traffic signal control and music theories. The main hypothesis in this research is that “the underlying sense that musicians exhibit during improvisation can be utilized to better synchronize signal control with internal traffic rhythm.” In this work, we developed and used a SONATA interface to control a simulated traffic signal. It was found that two of the three musicians in this study have outperformed the Webster's optimal control (by 22.3% and 17.6%). It was also found that all three musicians controlled the traffic signal better when they were not looking at the simulation (i.e., using only auditory sensing).
ECC processor over the Koblitz curves with τ-NAF Converter and Square-Square-Add Algorithm

This paper introduces the ECC (Elliptic Curve Cryptography) processor, in which the τ-NAF converter and Square-Square-Add Algorithm are utilized, for point multiplication on Koblitz curves. The proposed ECC processor operates over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">163</sup> ). The τ-NAF converter can turn the complicated point double to simple point square, and the Square-Square-Add Algorithm can decrease the number of point addition. With the proposed design, the execution time for executing point multiplication will decrease by 21%, and the AT value (Area-Time product) will be lowered by 15% than the state-of-the-art design, which achieves better efficiency on the execution of ECC over Koblitz curves.
Counterexample generation for program verification based on ownership refinement types

Type-based program verification, which reduces program verification to type inference, has been used as a lightweight approach to automated program verification. Whilst it is often effective and faster than other methods such as model checking, the type-based approach often fails to provide useful information upon a failure of the verification. We address this problem for a recent type-based verification tool called ConSORT for imperative programs. Producing a useful error message is particularly challenging for ConSORT, as the underlying type system combines the notions of ownership, refinement types, and context-sensitivity. This paper proposes a method to produce error messages for ConSORT and reports an implementation and experimental results. The proposed method is potentially useful also for other type-based tools for automated program verification.
Arbitrary Length Reducible and Irreducible Perfect Gaussian Integer Sequences with A Pre-Given Gaussian Integer

In this paper we will discuss two construction schemes on arbitrary length perfect Gaussian integer sequence (PGIS) with a pre-given constant. The first scheme uses geometric series, which brings reducible PGIS. We will also discuss the irreducible case and find an easy way to obtain PGIS for even length. Moreover, the same concept can be applied for some odd length N = 3p by using Ramanujan's Sum. Concrete examples are provided.
Compressing Facial Makeup Transfer Networks by Collaborative Distillation and Kernel Decomposition

Although the facial makeup transfer network has achieved high-quality performance in generating perceptually pleasing makeup images, its capability is still restricted by the massive computation and storage of the network architecture. We address this issue by compressing facial makeup transfer networks with collaborative distillation and kernel decomposition. The main idea of collaborative distillation is underpinned by a finding that the encoder-decoder pairs construct an exclusive collaborative relationship, which is regarded as a new kind of knowledge for low-level vision tasks. For kernel decomposition, we apply the depth-wise separation of convolutional kernels to build a light-weighted Convolutional Neural Network (CNN) from the original network. Extensive experiments show the effectiveness of the compression method when applied to the state-of-the-art facial makeup transfer network - BeautyGAN [1].
Map Reduce Based Association Rule Mining from Big Data

In today’s world, the shopping is the largest fashionable trend where the transaction processing is meticulous to fetch the items from the shopping transaction history by using traditional Apriori algorithm. An Apriori algorithm is the one which is used for finding frequent pattern from the given dataset. The problem of Apriori is to find useful itemsets for business purpose was time consuming. To overcome this problem, we have proposed Map Reduce based Apriori algorithm which generates frequent itemset and association rules by using parallel computations to reduce computations. The Spark distributed systems along with data bricks technology have been used. The experimental result shows that have been reduced the time taken fetch the data from the database.
Study of blockchains implementation prospects in manufacturing sector

This study project is focused on the study of Blockchain in various sectors primarily in manufacturing sector. Author made a detailed study related to Block chain and found out it various uses in different sectors like public sector, financial sector, etc. The main aim/objective of this white paper is to solve the problems faced by the manufacturing sector. The author uses secondary and primary sources to find out the problems and solutions for those problems by using Blockchain technology. In this study the author is able to link the various stakeholders with the manufacturing unit. This study is also helpful for the organisation to link various manufacturing processes to produce a final product which will reduce the time lag and delay in the manufacturing process. The author is able to solve the problem of transparency among shareholders, government and the manufacturing unit. This will help the organisation to build a confidence in the market especially in front of the investors.
Chebyshev Accelerated Spectral Clustering

Spectral clustering is widely used in modern data analysis. Spectral clustering methods speed up the computation and keep useful information by reducing dimensionality. Recently, graph signal filtering (GSF) has been introduced to further speed up the dimensionality reduction process by avoiding solving eigenvectors.
Power and Area Oriented Implementations of Lightweight Cryptographic Algorithms for Wireless Sensor Networks

Security is a concern in wireless sensor networks, which are inherently prone to third party attacks. As such, cryptography is used to make a secure mode of communication among nodes and/or between nodes and base stations. However, conventional algorithms are resource-hungry and therefore not fit for small devices, hence the creation of a new category of cryptography known as Lightweight Cryptographic Algorithms. These algorithms are still continuously being improved to fit in the decreasing sizes of small scale devices like FPGA-based wireless sensor networks. In this study, we quantify the effects of Data Width Reduction, and Round Unrolling on area, and power consumption. These are tested on three lightweight ciphers: LiCi, ANUII, and QTL. Data Width Reduction reduces area by using only a fraction of the original datapath. It was however found to be ineffective for the chosen ciphers, increasing area by 5.64%, 6%, and 9.2% in LiCi, ANUII, and QTL, respectively. Round Unrolling reduces latency which lessens the contribution of static power for each computation. Results show that round unrolling improves power efficiency by 25.97%, 3%, and 14% in LiCi, ANUII, and QTL, respectively. This comes at 299%, 414%, and 52% increase in area. The results allow newer and better lightweight ciphers to be further improved for small scale devices.
Analysis on the Dimensionality Reduction using Domain Ontology for Clustering Algorithms

To vanquish the issue of overly features faced to suffering from outfitting in Machine Learning, dimensionality reduction algorithms will be substantial. The dimensionality reduction is mostly depend on extraction of features. Therefore, we have to utilize the methodology to extract significant features for specific domain. In this paper, we propose a domain ontology that builds up with significant terms of the specific domain and their semantic relations, and other dominant words such as titles and names of persons, countries, and elements. Thereafter, analyze the influence of dimensionality reduction in two clustering algorithms: Particle Swarm Optimization Clustering (PSO) and K-means clustering.
Efficient Feature Recombining Network Based on Refining Multi-Level Feature Maps for Semantic Segmentation

Modern approaches for semantic segmentation usually concatenate the feature map of the last convolutional layer and multi-scale features to form the final feature representation, which can achieve the more accurate classification of target pixels for the input image. However, the feature information of the last layer is not complete and refined so that there is a performance bottleneck in the concatenation between the final feature map and multi-scale feature representations. To solve this problem, we propose the Feature Recombining Network to get more refined and precise features for Semantic Segmentation. Our network is composed of Feature Recombining Module and Modified Pyramid Pooling Module. The two modules can extract more detailed and representative features through the feature recombination and acquire richer context information than the previous module respectively. Experiments show that our modules are effective to improve the segmentation precision and the Modified Pyramid Pooling Module is also superior to the previous module. Based on our proposed network, we achieve the performance of 51.9% mIoU on Pascal Context dataset and 44.75% mIoU on ADE20K dataset.
Australian Copyright Law Impedes the Development of Artificial Intelligence: What Are the Options?

Artificial Intelligence (AI) is an emerging technology that has a huge potential in contributing to the Australian economy and addressing economic and social problems in society. However, Australian copyright laws are likely to impede the development of AI, and machine learning in particular, by requiring authorization every time when copyrighted content is used in machine learning processes. This puts Australian AI industries in competitive disadvantage, since other AI-focused jurisdictions, such as the US, EU, UK and Japan, allow such use under copyright exceptions. After analyzing the scope of copyright with relation to machine learning and licensing options, this paper examines different copyright exceptions (fair use, fair dealing and EU-style specific TDM exception) as potential solutions for Australia.