Reviewerly/exHarmony huggingface dataset:
Fast Hole Filling for View Synthesis in Free Viewpoint Video

The recent emergence of three-dimensional (3D) movies and 3D television (TV) indicates an increasing interest in 3D content. Stereoscopic displays have enabled visual experiences to be enhanced, allowing the world to be viewed in 3D. Virtual view synthesis is the key technology to present 3D content, and depth image-based rendering (DIBR) is a classic virtual view synthesis method. With a texture image and its corresponding depth map, a virtual view can be generated using the DIBR technique. The depth and camera parameters are used to project the entire pixel in the image to the 3D world coordinate system. The results in the world coordinates are then reprojected into the virtual view, based on 3D warping. However, these projections will result in cracks (holes). Hence, we herein propose a new method of DIBR for free viewpoint videos to solve the hole problem due to these projection processes. First, the depth map is preprocessed to reduce the number of holes, which does not produce large-scale geometric distortions; subsequently, improved 3D warping projection is performed collectively to create the virtual view. A median filter is used to filter the hole regions in the virtual view, followed by 3D inverse warping blending to remove the holes. Next, brightness adjustment and adaptive image blending are performed. Finally, the synthesized virtual view is obtained using the inpainting method. Experimental results verify that our proposed method can produce a pleasant visibility of the synthetized virtual view, maintain a high peak signal-to-noise ratio (PSNR) value, and efficiently decrease execution time compared with state-of-the-art methods.
Toward Global Earthquake Early Warning with the MyShake Smartphone Seismic Network, Part 2: Understanding MyShake Performance around the World

Abstract The MyShake project aims to build a global smartphone seismic network to facilitate large-scale earthquake early warning (EEW) and other applications by leveraging the power of crowdsourcing. The MyShake mobile application first detects earthquake shaking on a single phone. The earthquake is then confirmed on the MyShake servers using a “network detection” algorithm that is activated by multiple single-phone detections. In part two of this two-article series, we report the first-order performance of MyShake’s EEW capability in various selected locations around the world. Because of the present sparseness of the MyShake network in most parts of the world, we use our simulation platform to understand and evaluate the system’s performance in various tectonic settings. We assume that 0.1% of the population in each region has the MyShake mobile application installed on their smartphone and use historical earthquakes from the last 20 yr to simulate triggering scenarios with different network configurations in various regions. Then, we run the detection algorithm with these simulated triggers to understand the performance of the system. The system performs best in regions featuring high population densities and onshore, upper crustal earthquakes M&amp;lt;7.0. In these cases, alerts can be generated ∼4–6 s after the origin time, magnitude errors are within ∼0.5 magnitude units, and epicenters are typically within 10 km of true locations. When the events are offshore or in sparsely populated regions, the alerts are slower and the uncertainties in magnitude and location increase. Furthermore, even with 0.01% of the population as the MyShake users, in regions of high population density, the system still performs well for earthquakes larger than M 5.5. The details of the simulation platform and the network detection algorithm are available in part 1 of this two-article series.
+-Evidence of Porphyry Deposits in the Ntem Complex: A Case Study from Structural and Hydrothermal Alteration Zones Mapping through Landsat-8 OLI, Aeromagnetic and Geological Data Integration in the Yaounde-Sangmelima Region (Southern Cameroon)

A semi-regional study was carried out in the Yaounde-Sangmelima area, a densely vegetated tropical region of southern Cameroon located in the Central Africa Fold Belt (CAFB)/Congo Craton (CC) transition zone.Towards structural lineaments and predictive hydrothermal porphyry deposits mapping, an integrated analysis of Landsat-8 OLI data, aeromagnetic, geological and mineral indices maps was performed.The Remote sensing using False colour composite images involving bands combinations and Crosta method (features oriented principal components analysis) enabled the mapping of the gneisses and schists domains without a clear differentiation between the Yaounde and Mbalmayo schists; despite the reflectance anomalies evidenced NW of Akonolinga, hydrothermal alterations in the study area failed to be detected.Besides, aeromagnetics depicted a moderately fractured northern zone (the CAFB) contrasting with a high densely fractured zone (the CC,
Cooperative Coupled Generative Networks for Generalized Zero-Shot Learning

Compared with zero-shot learning (ZSL), the generalized zero-shot learning (GZSL) is more challenging since its test samples are taken from both seen and unseen classes. Most previous mapping-based methods perform well on ZSL, while their performance degrades on GZSL. To solve this problem, inspired by the ensemble learning, this paper proposes a model with cooperative coupled generative networks (CCGN). Firstly, to alleviate the hubness problem, the reverse visual feature space is taken as the embedding space, with the mapping achieved by a visual feature center generation network. To learn a proper visual representation of each class, we propose a coupled of generative networks, which cooperate with each other to synthesize a visual feature center template of the class. Secondly, to improve the generative ability of the coupled networks, we further employ a deeper network to generate. Meanwhile, to alleviate loss semantic information problem caused by multiple network layers, a residual module is employed. Thirdly, to mitigate overfitting and to increase scalability, an adversarial network is introduced to discriminate the generation of visual feature centers. Finally, a reconstruction network, which reverses the generation process, is employed to restrict the structural correlation between the generated visual feature center and the original semantic representation of each class. Extensive experiments on five benchmark datasets (AWA1, AWA2, CUB, SUN, APY) demonstrate that the proposed algorithm yields satisfactory results, as compared with the state-of-the-art methods.
SAN-M: Memory Equipped Self-Attention for End-to-End Speech Recognition

End-to-end speech recognition has become popular in recent years, since it can integrate the acoustic, pronunciation and language models into a single neural network. Among end-to-end approaches, attention-based methods have emerged as being superior. For example, Transformer, which adopts an encoder-decoder architecture. The key improvement introduced by Transformer is the utilization of self-attention instead of recurrent mechanisms, enabling both encoder and decoder to capture long-range dependencies with lower computational complexity.In this work, we propose boosting the self-attention ability with a DFSMN memory block, forming the proposed memory equipped self-attention (SAN-M) mechanism. Theoretical and empirical comparisons have been made to demonstrate the relevancy and complementarity between self-attention and the DFSMN memory block. Furthermore, the proposed SAN-M provides an efficient mechanism to integrate these two modules. We have evaluated our approach on the public AISHELL-1 benchmark and an industrial-level 20,000-hour Mandarin speech recognition task. On both tasks, SAN-M systems achieved much better performance than the self-attention based Transformer baseline system. Specially, it can achieve a CER of 6.46% on the AISHELL-1 task even without using any external LM, comfortably outperforming other state-of-the-art systems.
On the Maximum Order Complexity of the Thue-Morse and Rudin-Shapiro Sequence

Abstract Expansion complexity and maximum order complexity are both finer measures of pseudorandomness than the linear complexity which is the most prominent quality measure for cryptographic sequences. The expected value of the N th maximum order complexity is of order of magnitude log N whereas it is easy to find families of sequences with N th expansion complexity exponential in log N . This might lead to the conjecture that the maximum order complexity is a finer measure than the expansion complexity. However, in this paper we provide two examples, the Thue-Morse sequence and the Rudin-Shapiro sequence with very small expansion complexity but very large maximum order complexity. More precisely, we prove explicit formulas for their N th maximum order complexity which are both of the largest possible order of magnitude N . We present the result on the Rudin-Shapiro sequence in a more general form as a formula for the maximum order complexity of certain pattern sequences.
A Blockchain-Based OTP-Authentication Scheme for Constrainded IoT Devices Using MQTT

The importance of the Internet of Things is constantly growing, together with the proliferation of IoT devices which are changing our daily life and empowering industrial processes. However, the most IoT devices and protocols were not designed with security in mind, and economic and energyconsumption constraints make the implementation of security measures a non-trivial problem. One of the most used messaging protocol in IoT, which is MQTT (Message Queuing Telemetry Transport), leaves to developers the task to implement security, as native security services provided by the protocol are very weak. This paper focuses on MQTT authentication, which is definitely insecure in the protocol, even though the implementations can combine MQTT with other mechanisms to obtain a suitable level of security. The aim of the present work is to propose an innovative OTP-authentication scheme for MQTT which uses Ethereum to implement an independent logic channel for the second-factor authentication. The implementation of the proposed scheme relies on the trusted behavior of smart contracts and adopts suitable strategies to preserve the privacy of users.
Machine learning through cryptographic glasses: combating adversarial attacks by key-based diversified aggregation

In recent years, classification techniques based on deep neural networks (DNN) were widely used in many fields such as computer vision, natural language processing, and self-driving cars. However, the vulnerability of the DNN-based classification systems to adversarial attacks questions their usage in many critical applications. Therefore, the development of robust DNN-based classifiers is a critical point for the future deployment of these methods. Not less important issue is understanding of the mechanisms behind this vulnerability. Additionally, it is not completely clear how to link machine learning with cryptography to create an information advantage of the defender over the attacker. In this paper, we propose a key-based diversified aggregation (KDA) mechanism as a defense strategy in a gray- and black-box scenario. KDA assumes that the attacker (i) knows the architecture of classifier and the used defense strategy, (ii) has an access to the training data set, but (iii) does not know a secret key and does not have access to the internal states of the system. The robustness of the system is achieved by a specially designed key-based randomization. The proposed randomization prevents the gradients' back propagation and restricts the attacker to create a "bypass" system. The randomization is performed simultaneously in several channels. Each channel introduces its own randomization in a special transform domain. The sharing of a secret key between the training and test stages creates an information advantage to the defender. Finally, the aggregation of soft outputs from each channel stabilizes the results and increases the reliability of the final score. The performed experimental evaluation demonstrates a high robustness and universality of the KDA against state-of-the-art gradient-based gray-box transferability attacks and the non-gradient-based black-box attacks (The results reported in this paper have been partially presented in CVPR 2019 (Taran et al., Defending against adversarial attacks by randomized diversification, 2019) & ICIP 2019 (Taran et al., Robustification of deep net classifiers by key-based diversified aggregation with pre-filtering, 2019)).
Development of an AI-based System for Automatic Detection and Recognition of Weapons in Surveillance Videos

Security cameras and video surveillance systems have become important infrastructures for ensuring safety and security of the general public. However, the detection of high-risk situations through these systems are still performed manually in many cities. The lack of manpower in the security sector and limited performance of human may result in undetected dangers or delay in detecting threats, posing risks for the public. In response, various parties have developed real-time and automated solutions for identifying risks based on surveillance videos. The aim of this work is to develop a low-cost, efficient, and artificial intelligence-based solution for the real-time detection and recognition of weapons in surveillance videos under different scenarios. The system was developed based on Tensorflow and preliminarily tested with a 294-second video which showed 7 weapons within 5 categories, including handgun, shotgun, automatic rifle, sniper rifle, and submachine gun. At the intersection over union (IoU) value of 0.50 and 0.75, the system achieved a precision of 0.8524 and 0.7006, respectively.
Towards end-2-end learning for predicting behavior codes from spoken utterances in psychotherapy conversations

Spoken language understanding tasks usually rely on pipelines involving complex processing blocks such as voice activity detection, speaker diarization and Automatic speech recognition (ASR). We propose a novel framework for predicting utterance level labels directly from speech features, thus removing the dependency on first generating transcripts, and transcription free behavioral coding. Our classifier uses a pretrained Speech-2-Vector encoder as bottleneck to generate word-level representations from speech features. This pre-trained encoder learns to encode speech features for a word using an objective similar to Word2Vec. Our proposed approach just uses speech features and word segmentation information for predicting spoken utterance-level target labels. We show that our model achieves competitive results to other state-of-the-art approaches which use transcribed text for the task of predicting psychotherapy-relevant behavior codes.
Classification-Based Self-Learning for Weakly Supervised Bilingual Lexicon Induction

Effective projection-based cross-lingual word embedding (CLWE) induction critically relies on the iterative self-learning procedure.It gradually expands the initial small seed dictionary to learn improved cross-lingual mappings.In this work, we present CLASSYMAP, a classification-based approach to self-learning, yielding a more robust and a more effective induction of projection-based CLWEs.Unlike prior self-learning methods, our approach allows for integration of diverse features into the iterative process.We show the benefits of CLASSYMAP for bilingual lexicon induction: we report consistent improvements in a weakly supervised setup (500 seed translation pairs) on a benchmark with 28 language pairs.
ANALISIS KUALITAS LAYANAN SISTEM INFORMASI AKADEMIK UNIVERSITAS ABDURRAB TERHADAP KEPUASAN PENGGUNA MENGGUNAKAN METODE SEVQUAL (Studi Kasus : Mahasiswa Universitas Abdurrab Pekanbaru)

Academic Information System is the name of an information system used for academic activities at Abdurrab University and also known as SIMAK. Abdurrab University has been implementing Academic Information Systems since 2016, but in the application of this system there are still some obstacles so that it will affect what is to be achieved, one of which is user satisfaction. In this study the method used is the Service Quality method with measurements based on dimensions of service quality which include Tangible, Reliability, Responsibility, Assurance and Empathy. The purpose of this study was to determine how much influence the quality of Academic Information Systems on user satisfaction using the Servqual method with the IBM SPSS Statistics 24.0 tool. This research is a quantitative research using survey method. The technique in sampling in this study is to use non-probability techniques or using the Quota Sampling Technique. In this case the formula used in sampling is the Slovin formula, so this study obtained a sample size of 96 respondents. Based on the evaluation results it can be concluded that the Empathy variable significantly influences user satisfaction by 44.8%, while simultaneously Tangible, Reliability, Responsiveness, Assurance and Empathy variables together have a significant influence on user satisfaction at the significance level α = 10% .
An efficient integration and indexing method based on feature patterns and semantic analysis for big data

Big Data has received much attention in the multi-domain industry. In the digital and computing world, information is generated and collected at a rate that quickly exceeds the boundaries. The traditional data integration system interconnects the limited number of resources and is built with relatively stable and generally complex and time-consuming design activities. However, the rapid growth of these large data sets creates difficulties in learning heterogeneous data structures for integration and indexing. It also creates difficulty in information retrieval for the various data analysis requirements. In this paper, a probabilistic feature Patterns (PFP) approach using feature transformation and selection method is proposed for efficient data integration and utilizing the features latent semantic analysis (F-LSA) method for indexing the unsupervised multiple heterogeneous integrated cluster data sources. The PFP approach takes the advantage of the features transformation and selection mechanism to map and cluster the data for the integration, and an analysis of the data features context relation using LSA to provide the appropriate index for fast and accurate data extraction. A huge volume of BibText dataset from different publication sources are processed to evaluated to understand the effectiveness of the proposal. The analytical study and the outcome results show the improvisation in integration and indexing of the work.
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games

This paper presents a recursive reasoning formalism of Bayesian optimization (BO) to model the reasoning process in the interactions between boundedly rational, self-interested agents with unknown, complex, and costly-to-evaluate payoff functions in repeated games, which we call Recursive Reasoning-Based BO (R2-B2). Our R2-B2 algorithm is general in that it does not constrain the relationship among the payoff functions of different agents and can thus be applied to various types of games such as constant-sum, general-sum, and common-payoff games. We prove that by reasoning at level 2 or more and at one level higher than the other agents, our R2-B2 agent can achieve faster asymptotic convergence to no regret than that without utilizing recursive reasoning. We also propose a computationally cheaper variant of R2-B2 called R2-B2-Lite at the expense of a weaker convergence guarantee. The performance and generality of our R2-B2 algorithm are empirically demonstrated using synthetic games, adversarial machine learning, and multi-agent reinforcement learning.
Forecasting of Solar Radiation using an Empirical Model

Global demand for energy is increasing rapidly and natural energy resources such as oil, gas and uranium are declining due to the widespread diffusion and development of the industry in recent years. To cover energy needs, research is being conducted on renewable energy. One of the renewable energies that can meet the world's demand so far is solar energy, which is free and inexhaustible in most parts of the world, and it has become an economic source. In this article we will make a forecast of the empirical Campbell model which will allow us to estimate the daily global irradiation on a horizontal plane and to compare it with the results measured at the Adrar site. The results show that the mean absolute percentage error (MAPE) less than 7%, the mean bias error does not exceed 3% in absolute value, relative RMSE does not exceed 7% and the correlation coefficient greater than 0.99 for the annual global radiation. It was concluded that this model could be used to predict the global solar radiation for Adrar site and for other sites with similar climatic conditions.
THE EFFECTIVENESS OF NUMBER HEAD TOGETHER STRATEGY ON IMPROVING STUDENTS’ ENGLISH ACHIEVEMENT AT XYZ SCHOOL

This is a study on the effectiveness of the Number Head Together (NHT) strategy on Improving Students' English achievement. It was done to find out whether the NHT strategy could improve the students' English ability. This study is a classroom action research that was done at the XYZ School. To find out the effectiveness of the implemented strategy, then the students were tested using the English test after they were treated teaching using NHT strategy by the teacher, besides, the students were also observed. To obtain these, so there were two instruments used in this study such as; a set of English tests and an observation sheet. After all of the data were analyzed, so the finding of this study is finally drawn as that the NHT strategy is very effective to be implemented on improving the students’ English achievement at XYZ School. Where 40 students were able to achieve the minimum score criteria, 31 of the students got the "good" score category and 9 of the students got a "very good" score category. Due to this result, then the English teacher is expected to apply the NHT strategy in learning English.
Towards Unified Dialogue System Evaluation: A Comprehensive Analysis of Current Evaluation Protocols.

As conversational AI-based dialogue management has increasingly become a trending topic, the need for a standardized and reliable evaluation procedure grows even more pressing. The current state of affairs suggests various evaluation protocols to assess chat-oriented dialogue management systems, rendering it difficult to conduct fair comparative studies across different approaches and gain an insightful understanding of their values. To foster this research, a more robust evaluation protocol must be set in place. This paper presents a comprehensive synthesis of both automated and human evaluation methods on dialogue systems, identifying their shortcomings while accumulating evidence towards the most effective evaluation dimensions. A total of 20 papers from the last two years are surveyed to analyze three types of evaluation protocols: automated, static, and interactive. Finally, the evaluation dimensions used in these papers are compared against our expert evaluation on the system-user dialogue data collected from the Alexa Prize 2020.