Reviewerly/exHarmony huggingface dataset:
A comparison of different machine learning algorithms, types and placements of activity monitors for physical activity classification

This study classified physical activities using supervised machine learning (SML) algorithms based on accelerometer measures. The influences of different types, placements, and monitor modalities of the GT3X+ and GT9X have been further analysed. Specifically, 9 healthy participants were recruited to perform 14 activities by wearing GT3X+ and GT9X together at the hip and the thigh, respectively. Four different SML algorithms were utilized and evaluated in the classification of physical activities. The experimental results showed that the performance of the SML algorithms would not be affected by different placements and monitor modalities. Support vector machine performed satisfactorily across all monitor modalities (around 89% accuracy rate). Meanwhile, in both placements of the hip and the thigh, the overall accuracy of the GT9X was not better than that of the GT3X+, and the overall accuracy of the combined mode (two monitors together) was not better than that of the single mode (one monitor).
Pattern Recognition-Recognition of Handwritten Document Using Convolutional Neural Networks

Handwritten documents recognition is a challenging task in the field of pattern recognition. It has an array of applications wherein recognition of words, alphabets, digits and other characters are the mandate. This review article mainly focuses review on Convolutional Neural Network (CNN) based handwritten documents recognition system. Basically, the handwritten recognition is divided into two different types: online and offline recognition. The difficulty of this system is dealing with huge variety of handwritten styles written by different writers. The system wants to recognize and identify such characters in effective manner. The scope of this review paper is to represent the merits and limitations of different techniques used in the development of recognition system. This paper definitely helps the researcher to get new idea to develop a new technique with good environment and architecture to propose more accuracy and less error rate. The major bottlenecks in this system are the issues of recognizing unconstrained handwritings like cursive, block, and tilt that cause huge variation in writing styles, the overlapping and the interconnections between characters. These systems are designed to assure high accuracy and reliability. This article summarizes different strategies in handwritten recognition system, which may help the researchers interested in this area to spot the research gap. The Future work of this paper is to implement a robust technique providing more accuracy and less error rate.
Self-Checking Residue Number System for Low-Power Reliable Neural Network

Neural Network suffers four major issues including acceleration, power consumption, area overhead and fault tolerance. In this paper we develop a systematic approach to design a low-power, compact, fast and reliable neural network based on a redundant residue number system. Residue number systems have been applied in designing neural network except the CORDIC-based activation functions including hypertangent, logistic and softmax functions. This issue results in that the entire neural network cannot be totally self-checked and extra operations make the time, power and area reductions wasted. In our systematic approach we propose some design rules for ensuring the checking rate without loss of reductions in time, area and power consumption. From experiments on three neu-ral network with 24-bit fixed-point operations for the MNIST handwritten digit data set, 3, 4, and 5 moduli are separately employed for achieving all balanced improvements in power-saving, area-reduction, speed-acceleration and reliability pro-motion. Experimental results show that all the power, time and area can be reduced to only about one third, and the entire network in any combination of software and hardware can be self-checked in an aliasing rate of only 0.39% and TMR-correctable under the single-residue fault model.
Overview of the seventh Dialog System Technology Challenge: DSTC7

This paper provides detailed information about the seventh Dialog System Technology Challenge (DSTC7) and its three tracks aimed to explore the problem of building robust and accurate end-to-end dialog systems. In more detail, DSTC7 focuses on developing and exploring end-to-end technologies for the following three pragmatic challenges: (1) sentence selection for multiple domains, (2) generation of informational responses grounded in external knowledge, and (3) audio visual scene-aware dialog to allow conversations with users about objects and events around them. This paper summarizes the overall setup and results of DSTC7, including detailed descriptions of the different tracks, provided datasets and annotations, overview of the submitted systems and their final results. For Track 1, LSTM-based models performed best across both datasets, allowing teams to effectively handle task variants where no correct answer was present or when multiple paraphrases were included. For Track 2, RNN-based architectures augmented to incorporate facts by using two types of encoders: a dialog encoder and a fact encoder plus using attention mechanisms and a pointer-generator approach provided the best results. Finally, for Track 3, the best model used Hierarchical Attention mechanisms to combine the text and vision information obtaining a 22% better result than the baseline LSTM system for the human rating score. More than 220 participants were registered and about 40 teams participated in the final challenge. 32 scientific papers reporting the systems submitted to DSTC7, and 3 general technical papers for dialog technologies, were presented during the one-day wrap-up workshop at AAAI-19. During the workshop, we reviewed the state-of-the-art systems, shared novel approaches to the DSTC7 tasks, and discussed the future directions for the challenge (DSTC8).
Deep gaze pooling: Inferring and visually decoding search intents from human gaze fixations

Predicting the target of visual search from human eye fixations (gaze) is a difficult problem with many applications, e.g. in human-computer interaction. While previous work has focused on predicting specific search target instances, we propose the first approach to predict categories and attributes of search intents from gaze data and to visually reconstruct plausible targets. However, state-of-the-art models for categorical recognition, in general, require large amounts of training data, which is prohibitive for gaze data. To address this challenge, we further propose a novel Gaze Pooling Layer that combines gaze information with visual representations from Deep Learning approaches. Our scheme incorporates both spatial and temporal aspects of human gaze behavior as well as the appearance of the fixated locations. We propose an experimental setup and novel dataset and demonstrate the effectiveness of our method for gaze-based search target prediction and reconstruction. We highlight several practical advantages of our approach, such as compatibility with existing architectures, no need for gaze training data, and robustness to noise from common gaze sources.
Optimized Path Planning for Three-Wheeled Autonomous Robot Using Teaching–Learning-Based Optimization Technique

Path planning is a leading topic in the field of the wheeled robot (WR). Three basic characteristic path planning should have when the WR is traveling toward the goal: (1) obtain information about the given working space conditions, (2) location of itself, and (3) optimize the decision to reach the target. The current research paper focuses on obtaining an efficient and robust technique to guide the WR. Teaching–learning-based optimization technique is the centerpiece of the present research work. Fitness function has been presented to optimize the path planning and reaching target. Parameters selected for the proposed technique are (1) distance between robot, start point, goal, and obstacles and (2) turning angle while avoiding obstacles. The technique is examined in various environments with the different level of difficulties. The WR efficiently reaches the target by avoiding collision with the obstacles. In addition, the proposed technique is compared with the previously used technique. The obtained simulated results justified that the teaching–learning-based optimization technique selects better travel path and have shorter travel length.
Adaptive Convolution Local and Global Learning for Class-Level Joint Representation of Facial Recognition With a Single Sample Per Data Subject

Due to the absence of training samples and intraclass variation, the extraction of discriminative facial features and construction of powerful classifiers have bottlenecks in improving the performance of facial recognition (FR) with a single sample per data subject (SSPDS). In this paper, we propose to learn regional adaptive convolution features that are locally and globally discriminative to facial identity and robust to facial variation. Then, a novel class-level joint representation framework is presented to exploit the distinctiveness and class-level commonality of different facial features. In the proposed class-level joint representation with regional adaptive convolution features (CJR-RACF), both discriminative facial features that are robust to facial variations and powerful representations for classification with generic facial variations have been fully exploited. Furthermore, the gallery discrimination is extracted by our proposed weight-embedded supervision in the training phase (denoted by CJR-RACFw), which is conducive to more specific features for FR with SSPDS. CJR-RACF and CJR-RACFw have been evaluated on several popular databases, including the large-scale CMU Multi-PIE, LFW, Megaface, and VGGFace datasets. Experimental results demonstrate the much higher robustness and effectiveness of the proposed methods compared to the state-of-the-art methods.
V2: Fast Detection of Configuration Drift in Python

Code snippets are prevalent, but are hard to reuse because they often lack an accompanying environment configuration. Most are not actively maintained, allowing for drift between the most recent possible configuration and the code snippet as the snippet becomes out-of-date over time. Recent work has identified the problem of validating and detecting out-of-date code snippets as the most important consideration for code reuse. However, determining if a snippet is correct, but simply out-of-date, is a non-trivial task. In the best case, breaking changes are well documented, allowing developers to manually determine when a code snippet contains an out-of-date API usage. In the worst case, determining if and when a breaking change was made requires an exhaustive search through previous dependency versions. We present V2, a strategy for determining if a code snippet is out-of-date by detecting discrete instances of configuration drift, where the snippet uses an API which has since undergone a breaking change. Each instance of configuration drift is classified by a failure encountered during validation and a configuration patch, consisting of dependency version changes, which fixes the underlying fault. V2 uses feedback-directed search to explore the possible configuration space for a code snippet, reducing the number of potential environment configurations that need to be validated. When run on a corpus of public Python snippets from prior research, V2 identifies 248 instances of configuration drift.
Small Talk Conversations and the Long-Term Use of Chatbots in Educational Settings – Experiences from a Field Study

In this paper, we analyze the use of small talk conversations based on a dialogue analysis of a long-term field study in which university students regularly interacted with a chatbot during a 3-month period of time in an educational setting. In particular, we analyze (1) how often the students engage with small talk topics during the field study, and (2) whether a larger amount of small talk conversations correlates with the students’ engagement in learning activities within our chatbot-based learning system, i.e., if engaging in small talk conversations correlates to a more intensive use of the chatbot during our field test. Our results suggest that small talk conversations might play an important role in the design of our chatbot as students who chat about small talk topics also frequently chat about learning-related topics. Nevertheless, the overall impact of small talk capabilities of chatbots should not be overestimated.
Volcanogenic talc‐copper deposits of Darhib‐Abu Jurdi area, Egypt: Petrogenesis and remote sensing characterization

Economic talc deposits were developed as an integral part of a volcanogenic massive sulfide system in Darhib‐Abu Jurdi bimodal‐mafic type submarine volcanics in the South Eastern Desert of Egypt. These Pan‐African metavolcanics were erupted in a rift‐related environment and experienced low‐grade greenschist metamorphism. They host talc‐malachite mineralization, mainly within E–W trending alteration zones along the major latitudinal shear planes. These mineralized alteration zones contain economic talc deposits as well as averages of 1.7% Cu and 0.46% Zn; the maximum may exceed 11% Cu. Sentinel‐2, ASTER, and Landsat‐8 dataset in conjunction with a portable TerraSpec Halo mineral identifier device (ASD) were employed in detecting and mapping these mineralized alteration zones within the Darhib‐Abu Jurdi metavolcanics. Petrography, mineralogy, and geochemistry of these deposits and host volcanics enabled to put constraints on their petrogenesis and evolution as a submarine hydrothermal vent system. Mineralogical characteristics of pervasive chloritization stringer zone, hypogene primary sulfide ore zone, and supergene oxidized zone are addressed and discussed. Talc deposits were formed at Darhib‐Abu Jurdi area as the main gangue phase via (a) direct precipitation on seafloor by interaction of silica‐saturated hydrothermal fluids with Mg‐rich seawater, (b) precipitation with actinolite/tremolite at shallow depth beneath the sulfide ore lenses due to penetration of Mg‐rich seawater into hydrothermal conduit, and (c) replacement of pre‐existing tremolite, actinolite, chlorite, and septechlorite.
Distributed Robust Cubature Information Filtering for Measurement Outliers in Wireless Sensor Networks

In wireless sensor networks (WSN), measurements are always corrupted by outliers or impulsive noise. Cubature information filtering (CIF) is founded based on minimum mean square error (MMSE) criterion, which is not applicable to non-Gaussian noise. Hence, a novel robust CIF (RCIF) is derived based on maximum correntropy criterion (MCC) to enhance the robustness of state estimation in the local node. For the information fusion, weighted average consensus (WAC) based distributed RCIF (DRCIF) is founded to improve the stability of sensor networks and the accuracy of state estimation. The estimation error of DRCIF is proved to be bounded in mean square. Numerical simulations are provided to evaluate the effectiveness of proposed algorithms.
De-Anonymizing Social Networks With Overlapping Community Structure

The advent of social networks poses severe threats on user privacy as adversaries can de-anonymize users' identities by mapping them to correlated cross-domain networks. Without ground-truth mapping, prior literature proposes various cost functions in hope of measuring the quality of mappings. However, their cost functions, whose minimizers may remain algorithmically unknown, usually bring imponderable mapping errors when the true mapping cannot minimize these cost functions. We jointly tackle above concerns under a more practical social network model parameterized by overlapping communities, which, neglected by prior art, can serve as side information for de-anonymization. Regarding the unavailability of ground-truth mapping to adversaries, by virtue of the Minimum Mean Square Error (MMSE), our first contribution is a well-justified cost function minimizing the expected number of mismatched users over all possible true mappings. While proving the NP-hardness of minimizing MMSE, we validly transform it into the weighted-edge matching problem (WEMP), which, as disclosed theoretically, resolves the tension between optimality and complexity: 1) WEMP asymptotically returns a negligible mapping error in large network size under mild conditions facilitated by higher overlapping strength; 2) WEMP can be algorithmically characterized via the convex-concave based de-anonymization algorithm (CBDA), effectively finding the optimum of WEMP. Extensive experiments further confirm the effectiveness of CBDA under overlapping communities: 90% users are re-identified averagely in a series of networks when communities overlap densely, and the re-identification ratio is enhanced about 70% compared to non-overlapping cases.
Machine Learning for Web Vulnerability Detection: The Case of Cross-Site Request Forgery

We propose a methodology to leverage machine learning (ML) for the detection of web application vulnerabilities. We use it in the design of Mitch, the first ML solution for the black-box detection of cross-site request forgery vulnerabilities. Finally, we show the effectiveness of Mitch on real software.
Random Weighting-Based Nonlinear Gaussian Filtering

The Gaussian filtering is a commonly used method for nonlinear system state estimation.However, this method requires both system process noise and measurement noise to be white noise sequences with known statistical characteristics.However, it is difficult to satisfy this condition in engineering practice, making the Gaussian filtering solution deviated or diverged.This paper adopts the random weighting concept to address the limitation of the nonlinear Gaussian filtering.It establishes the random weighting estimations of system noise characteristics on the basis of the maximum a-posterior theory, and further develops a new Gaussian filtering method based on the random weighting estimations to restrain system noise influences on system state estimation by adaptively adjusting the random weights of system noise characteristics.Simulation, experimental and comparison analyses prove that the proposed method overcomes the limitation of the traditional Gaussian filtering in requirement of system noise characteristics, leading to improved estimation accuracy.
Coupling Rendering and Generative Adversarial Networks for Artificial SAS Image Generation

There is a growing demand for large-scale Synthetic Aperture Sonar (SAS) datasets. This demand stems from data-driven applications such as Automatic Target Recognition (ATR) [1]-[3], segmentation [4] and oceanographic research of the seafloor, simulation for sensor prototype development and calibration [5], and even potential higher level tasks such as motion estimation [6] and micronavigation [7]. Unfortunately, the acquisition of SAS data is bottlenecked by the costly deployment of SAS imaging systems, and even when data acquisition is possible, the data is often skewed towards containing barren seafloor rather than objects of interest. This skew introduces a data imbalance problem wherein a dataset can have as much as a 1000-to-1 ratio of seafloor background to object-of-interest SAS image chips.
Factorization Meets Neural Networks: A Scalable and Efficient Recommender for Solving the New User Problem

Predicting new user behavior has always been a challenging issue in intelligent recommender systems.This challenge is mainly due to the extreme asymmetry of information between new users and old users.Existing factorization models can efficiently process and map asymmetric information, but they are not good at mining deep relationships between contexts when compressing high-dimensional data.In contrast, neural network methods can deeply exploit the relationship between contexts; however, their training cost is much larger than factorization approaches.Therefore, this paper proposes a scalable and efficient recommender to solve the new user problem by filling the gap between factorization models and neural networks.The scalable part is a neural network that can jointly encode, compress, and fuse various types of contexts.The efficient part is a factorization model based on a correlation constraint mechanism and a projection strategy, which enables an asymmetric mapping of information between old and new users.The entire recommender fuses the two parts so that the factorization model and the neural network can complement each other.The experimental results show that our approach can achieve a good balance between performance and training efficiency compared to state-of-the-art methods.
Reversible Data Hiding in Encrypted Images With Dual Data Embedding

Typical reversible data hiding in encrypted image (RDH-EI) methods merely embed data in the encrypted domain, ignoring the requirement of the image owner for data embedding. To address this issue, this paper proposes a novel RDH-EI method with dual data embedding based on generalized integer transformation (GIT). The image owner first vacates embedding room, and performs data embedding before image encryption. After data encryption, the remote server utilizes the vacated room to further embed additional data into the encrypted image. The embedded data by the image owner and remote server can be extracted exactly, and the original image can be recovered losslessly. The experimental results prove the effectiveness of the proposed method.
Synthesizing Talking Faces from Text and Audio: An Autoencoder and Sequence-to-Sequence Convolutional Neural Network

Synthesizing talking face from text and audio is increasingly becoming a direction in human-machine and face-to-face interactions. Although progress has been made, several existing methods either have unsatisfactory co-articulation modeling effects or ignore relations between adjacent inputs. Moreover, some of these methods often train models on shaky head videos or utilize linear-based face parameterization strategies, which further decrease synthesized quality. To address the above issues, this study proposes a sequence-to-sequence convolutional neural network to automatically synthesize talking face video with accurate lip sync. First, an advanced landmark location pipeline is used to accurately locate the facial landmarks, which can effectively reduce landmark shake. Then, a part-based autoencoder is presented to encode face images into a low-dimensional space and obtain compact representations. A sequence-to-sequence network is also presented to encode the relation of neighboring frames with multiple loss functions, and talking faces are synthesized through a reconstruction strategy with a decoder. Experiments on two public audio-visual datasets and a new dataset called CCTV news demonstrate the effectiveness of the proposed method against other state-of-the-art methods.
Interval Type-2 Fuzzy Restricted Boltzmann Machine

Restricted Boltzmann machine (RBM) is an energy-based artificial neural network (ANN), applied in several applications like image processing, topic modeling, classification, regression, and pattern recognition. The fuzzy version of RBM is a new approach in this field, with parameters considered as fuzzy numbers. In this article, a fuzzy RBM is extended through interval type-2 membership functions, named the interval type-2 fuzzy RBM (IT2FRBM). The additional uncertainties in the structures of the membership functions are embedded in this model. This is formulated as a maximum likelihood problem which allows the parameters of the type-2 fuzzy numbers to be learned. The capabilities of this proposed approach as a discriminative or generative model are assessed. The robustness of this method against noise is analyzed. The results indicate that this IT2FRBM outperforms RBM and its different fuzzy versions.