Reviewerly/exHarmony huggingface dataset:
Use of Triplet-Loss Function to Improve Driving Anomaly Detection Using Conditional Generative Adversarial Network

Driving anomaly detection is an important problem in advanced driver assistance systems (ADAS). The ability to immediately detect potentially hazardous scenarios will prevent accidents by allowing enough time to react. Toward this goal, our previous work proposed an unsupervised driving anomaly detection system using conditional generative adversarial network (GAN), which was built with physiological data and features extracted from the controller area network-Bus (CAN-Bus). The approach generates predictions for the upcoming driving recordings, constrained by the previously observed signals. These predictions were contrasted with actual physiological and CAN-Bus signals by subtracting the corresponding activation outputs from the discriminator. Instead, this study proposes to use a triplet-loss function to contrast the predicted and actual signals. The triplet-loss function creates an unsupervised framework that rewards predictions closer to the actual signals, and penalizes predictions deviating from the expected signals. This approach maximizes the discriminative power of feature embeddings to detect anomalies, leading to measurable improvements over the results observed by our previous approach. The study is implemented and evaluated with recordings from the driving anomaly dataset (DAD), which includes 250 hours of naturalistic data manually annotated with driving events. Objective and subjective metrics validate the benefits of using the proposed triplet-loss function for driving anomaly detection.
Few-Shot Image Recognition with Manifolds

In this paper, we extend the traditional few-shot learning (FSL) problem to the situation when the source-domain data is not accessible but only high-level information in the form of class prototypes is available. This limited information setup for the FSL problem deserves much attention due to its implication of privacy-preserving inaccessibility to the source-domain data but it has rarely been addressed before. Because of limited training data, we propose a non-parametric approach to this FSL problem by assuming that all the class prototypes are structurally arranged on a manifold. Accordingly, we estimate the novel-class prototype locations by projecting the few-shot samples onto the average of the subspaces on which the surrounding classes lie. During classification, we again exploit the structural arrangement of the categories by inducing a Markov chain on the graph constructed with the class prototypes. This manifold distance obtained using the Markov chain is expected to produce better results compared to a traditional nearest-neighbor-based Euclidean distance. To evaluate our proposed framework, we have tested it on two image datasets – the large-scale ImageNet and the small-scale but fine-grained CUB-200. We have also studied parameter sensitivity to better understand our framework.
Subjective Test Dataset and Meta-data-based Models for 360° Streaming Video Quality

During the last years, the number of 360° videos available for streaming has rapidly increased, leading to the need for 360° streaming video quality assessment. In this paper, we report and publish results of three subjective 360° video quality tests, with conditions used to reflect real-world bitrates and resolutions including 4K, 6K and 8K, resulting in 64 stimuli each for the first two tests and 63 for the third. As playout device we used the HTC Vive for the first and HTC Vive Pro for the remaining two tests. Video-quality ratings were collected using the 5-point Absolute Category Rating scale. The 360° dataset provided with the paper contains the links of the used source videos, the raw subjective scores, video-related meta-data, head rotation data and Simulator Sickness Questionnaire results per stimulus and per subject to enable reproducibility of the provided results. Moreover, we use our dataset to compare the performance of state-of-the-art full-reference quality metrics such as VMAF, PSNR, SSIM, ADM2, WS-PSNR and WS-SSIM. Out of all metrics, VMAF was found to show the highest correlation with the subjective scores. Further, we evaluated a center-cropped version of VMAF ("VMAF-cc") that showed to provide a similar performance as the full VMAF. In addition to the dataset and the objective metric evaluation, we propose two new video-quality prediction models, a bitstream meta-data-based model and a hybrid no-reference model using bitrate, resolution and pixel information of the video as input. The new lightweight models provide similar performance as the full-reference models while enabling fast calculations.
Persian Emoji Prediction Using Deep Learning and Emoji Embedding

The appearance of social networks and the increasing expansion of these networks has created many challenges, especially in the field of natural language processing. One of these social networks that has been welcomed by many researchers is Twitter. Twitter's users have the opportunity to consider one or more emojis for a tweet depending on the feeling and meaning of the tweet. Emojis contain information and concepts that the author of each tweet has in mind, the semantic and emotional range of each emoji is very wide and each emoji can be used in many different types of sentences. Therefore, by analyzing the content and emotion of each tweet, we can achieve the appropriate emoji of that tweet. For such reasons, predicting an emoji for a textual data is one of the challenges that has attracted the attention of researchers. In this article, using deep neural networks an attempt for the first time has been made to predict the emoji for Persian text data extracted from Twitter. And we were able to achieve F-score of 33% in 10 most frequent emojis which is 5% higher than the result of the SVM model and also 11% better than the result of the Naïve Bayes model, and F- score of 46% in 5 most frequent emojis which is 5% higher than the result of the SVM model and also 5% better than the result of the Naïve Bayes model.
Enhanced Natural Language Interface for Web-Based Information Retrieval

Database application is at the core of most web application systems such as web-based email, source codes repository management, public scientific data repository management, news portals, and publication repository of various fields. However, the usage of these database systems for data and information retrieval is severely limited because of lacking support for processing search queries expressed in a natural language (NL). Most web interfaces for databases today only take search queries entered in some form of logical combination of keywords or text strings, which restrict the scope and depth of what a web user really wants to search for, even though natural language based data or information retrieval has made significant advances in recent years. To overcome or at least to alleviate such limitation in web information services, we propose in this article an improved neural model based on an existing framework IRNet for NL query of databases, in which a representation of Gated Graph Neural Network (GGNN) is introduced to encode the database entities and relations. We also represent and use the database values in the prediction model to identify and match table and column names for automatic synthesize a correct SQL statement from a query expressed in a NL sentence. Experiments with a public dataset demonstrates the promising potential of our approach.
Algebraic Distinguishers: From Discrete Logarithms to Decisional Uber Assumptions

The algebraic group model, introduced by Fuchsbauer, Kiltz and Loss (CRYPTO '18), is a substantial relaxation of the generic group model capturing algorithms that may exploit the representation of the underlying group. This idealized yet realistic model was shown useful for reasoning about cryptographic assumptions and security properties defined via computational problems. However, it does not generally capture assumptions and properties defined via decisional problems. As such problems play a key role in the foundations and applications of cryptography, this leaves a significant gap between the restrictive generic group model and the standard model. We put forward the notion of algebraic distinguishers, strengthening the algebraic group model by enabling it to capture decisional problems. Within our framework we then reveal new insights on the algebraic interplay between a wide variety of decisional assumptions. These include the decisional Diffie-Hellman assumption, the family of Linear assumptions in multilinear groups, and the family of Uber assumptions in bilinear groups. Our main technical results establish that, from an algebraic perspective, these decisional assumptions are in fact all polynomially equivalent to either the most basic discrete logarithm assumption or to its higher-order variant, the q-discrete logarithm assumption. On the one hand, these results increase the confidence in these strong decisional assumptions, while on the other hand, they enable to direct cryptanalytic efforts towards either extracting discrete logarithms or significantly deviating from standard algebraic techniques.
Typography: the constant vector of dynamic logos

Visual identities can be constructed from a number of elements which together can be described as the Visual Identity System (VIS). Typography is one of the VIS’s central elements. Typically, the VIS elements have been considered as static and associated with prescribable visual mandates; however, the hypermodernity paradigm boosted the notion of mobility in everything – and brands are no exception. Brand logos now change in shape, colour, wear different textures and sit on top of a variety of backgrounds. All this incredible flexibility has implications for their typographical elements too. In the empirical part of this research, 50 dynamic logos were selected, grouped according to Van Nes’ categories in Dynamic Identities: How to Create a Living Brand (2012) and the changes in their typographic components were analysed under the Multilingual Typeface Anatomy Terminology framework (Amado, 2012), firstly by the researchers, and then by a group of independent coders. It was verified that dynamic logos present a consistent pattern regarding typography since they preserve consistency through type’s structural axes. This result led to a set of recommendations for both designers working with type in the context of the (re)design of dynamic logos, and academics preparing the next generation of brand designers. This research aimed at identifying the typographical inroads in brands with dynamic logos and is a relevant contribution to the perception of how the anatomy of type can define visual consistency.
Anomaly detection in a fleet of industrial assets with hierarchical statistical modeling

Abstract Anomaly detection in asset condition data is critical for reliable industrial asset operations. But statistical anomaly classifiers require certain amount of normal operations training data before acceptable accuracy can be achieved. The necessary training data are often not available in the early periods of assets operations. This problem is addressed in this paper using a hierarchical model for the asset fleet that systematically identifies similar assets, and enables collaborative learning within the clusters of similar assets. The general behavior of the similar assets are represented using higher level models, from which the parameters are sampled describing the individual asset operations. Hierarchical models enable the individuals from a population, comprising of statistically coherent subpopulations, to collaboratively learn from one another. Results obtained with the hierarchical model show a marked improvement in anomaly detection for assets having low amount of data, compared to independent modeling or having a model common to the entire fleet.
Global-Similarity Local-Salience Network for Traffic Weather Recognition

Recognizing the current weather conditions from a single image is of great theoretical significance. It also has potential practical value for daily life and traffic scheduling. To achieve that, typical weather recognition methods focus on learning a general weather description, e.g., sunny, cloudy, foggy, rainy and snowy etc, for the overall weather condition. However, it is far away from being sufficient for many tasks especially traffic management and control. To solve this key problem, this paper proposes a Global-Similarity Local-Salience Network (abbreviated as GSLSNet) for traffic weather recognition. Specifically, a simple but effective Global-Similarity Module (GSM) is proposed to recognize the overall weather condition and a Local-Salience Module (LSM) is presented to restrict the network to focus on road weather details. Besides, this paper also provides a new traffic weather dataset, named TWData, which is the first fine categorized dataset especially for highway weather recognition. Experimental results compared with state-of-the-art methods on both public datasets and TWData demonstrate the superiority of the proposed GSLSNet.
Sentiment Analysis of Indonesian Movie Trailer on YouTube Using Delta TF-IDF and SVM

YouTube is one of the most effective social media sites for promoting products, one of which is movies. The film industry usually publishes video trailers on YouTube to promote their upcoming film. The comments that appear on YouTube could help movie producers to estimate how the public will react to their movie once it is released. In this study, we conducted a sentiment analysis on the comments of Indonesian movie trailers on YouTube. We split movie comments into four popular movie genres: action, romance, comedy, and horror. Then, we use the Delta TF-IDF word weighting method and combine it with several classification methods to compare the model performance. Finally, we evaluated the model using Stratified K-Fold cross-validation with K = 10. Results showed that Logistic Regression and Naïve Bayes are better when classifying sentiment for a specific genre. Simultaneously, the SVM model gives good performance on sentiment analysis for a more general genre.
Accurate and dense point cloud generation for industrial Measurement via target-free photogrammetry

Industrial photogrammetry systems commonly require multiple coded targets to establish a global coordinate frame for relatively large objects. However, there are many industrial products that do not allow coded targets to be placed on their surfaces. We propose an accurate and dense point cloud generation approach for measuring large-sized, untextured objects. Unlike most existing industrial measurement methods, our photogrammetry approach is free of coded targets. We have three core contributions. First, a Rotation-Free Digital Image Correlation (RFDIC) method is proposed to improve the multi-view stereopsis matching precision. Second, based on the technique of structure from motion (SfM), a coarse-to-fine strategy is utilized to construct the multi-view geometry and accurately optimize camera poses. Third, the devices used are consumer-friendly and commonly available, i.e., only a digital projector and a camera are needed to obtain the dense points on the measured object surface. The experimental results show that the error of reconstructed length for a scale bar is less than 0.01 mm/m. Compared to the state-of-the-art commercial measurement system, the average error of point cloud reconstruction is about 0.055 mm, which meets the accuracy demand for industrial applications.
Adaptive Stress Testing without Domain Heuristics using Go-Explore

Recently, reinforcement learning (RL) has been used as a tool for finding failures in autonomous systems. During execution, the RL agents often rely on some domains-pecific heuristic reward to guide them towards finding failures, but constructing such a heuristic may be difficult or infeasible. Without a heuristic, the agent may only receive rewards at the time of failure, or even rewards that guide it away from failures. For example, some approaches give rewards for taking more likely actions, in order to to find more likely failures. However, the agent may then learn to only take likely actions, and may not be able to find a failure at all. Consequently, the problem becomes a hard-exploration problem, where rewards do not aid exploration. A new algorithm, go-explore (GE), has recently set new records on benchmarks from the hard-exploration field. We apply GE to adaptive stress testing (AST), one example of an RL-based falsification approach that provides a way to search for the most likely failure scenario. We simulate a scenario where an autonomous vehicle drives while a pedestrian is crossing the road. We demonstrate that GE is able to find failures without domain-specific heuristics, such as the distance between the car and the pedestrian, on scenarios that other RL techniques are unable to solve. Furthermore, inspired by the robustification phase of GE, we demonstrate that the backwards algorithm (BA) improves the failures found by other RL techniques.
A Threat Analysis Methodology for Security Requirements Elicitation in Machine Learning Based Systems

Machine learning (ML) models are now a key component for many applications. However, machine learning based systems (MLBSs), those systems that incorporate them, have proven vulnerable to various new attacks as a result. Currently, there exists no systematic process for eliciting security requirements for MLBSs that incorporates the identification of adversarial machine learning (AML) threats with those of a traditional non-MLBS. In this research study, we explore the applicability of traditional threat modeling and existing attack libraries in addressing MLBS security in the requirements phase. Using an example MLBS, we examined the applicability of 1) DFD and STRIDE in enumerating AML threats; 2) Microsoft SDL AI/ML Bug Bar in ranking the impact of the identified threats; and 3) the Microsoft AML attack library in eliciting threat mitigations to MLBSs. Such a method has the potential to assist team members, even with only domain specific knowledge, to collaboratively mitigate MLBS threats.
Multi-Layer Combinatorial Fusion Using Cognitive Diversity

Multiple scoring systems (including rank and score functions; MSS) have been widely used in multiple regression, intelligent biometric systems, multiple artificial neural nets, combining pattern classifiers, ensemble methods, machine learning and artificial intelligence (AI), data and information fusion, preference ranking, and deep learning.Combining MSS has achieved numerous successful results in a variety of domain applications.However, the reasons why this happens remains an active area of investigation.Combinatorial fusion analysis (CFA) combines MSS using the rank-score characteristic (RSC) function and cognitive diversity (CD).The RSC function was proposed to characterise the predictive behaviour of a scoring system.It was subsequently used to define the notion of ''cognitive diversity'', which measures the dissimilarity in the representation of information between two scoring systems.In this article, we first examine characterizations of and diversity between scoring systems.Then, we review combinatorial fusion analysis with a variety of domain applications, including biometric systems in cognitive neuroscience, and joint decision making with visual cognitive systems.Finally, we demonstrate that multi-layer combinatorial fusion (MCF) on the Kemeny rank space is a viable machine learning and AI framework for preference ranking and reinforcement learning.This work provides a scientific foundation and technological insights for the use of Combinatorial Fusion in ensemble methods, data and information fusion, preference ranking, and deep reinforcement learning with applications to a variety of domains in data science and informatics for secure and sustainable societies.
IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding

Although Indonesian is known to be the fourth most frequently used language over the internet, the research progress on this language in natural language processing (NLP) is slow-moving due to a lack of available resources. In response, we introduce the first-ever vast resource for training, evaluation, and benchmarking on Indonesian natural language understanding (IndoNLU) tasks. IndoNLU includes twelve tasks, ranging from single sentence classification to pair-sentences sequence labeling with different levels of complexity. The datasets for the tasks lie in different domains and styles to ensure task diversity. We also provide a set of Indonesian pre-trained models (IndoBERT) trained from a large and clean Indonesian dataset (Indo4B) collected from publicly available sources such as social media texts, blogs, news, and websites. We release baseline models for all twelve tasks, as well as the framework for benchmark evaluation, thus enabling everyone to benchmark their system performances.
Model-Based Transfer Learning and Sparse Coding for Partial Face Recognition

With the growing needs of practical applications such as security monitoring, partial face recognition is a challenging but important issue, because the captured faces in real-world surveillance videos may be occluded or with variations. Though current face recognition methods perform well in relatively constrained scenes, they may suffer from degradation for partial faces. In this paper, we propose a framework of model-based transfer learning and sparse coding (MTLSC) for partial face recognition. First, due to less information in partial face image, we exploit the mirrored image of an original probe sample as sample augment to provide further information. Considering the inadequacy of training face samples, we obtain face features based on model-based transfer learning VGGNet that is pre-trained on VGGFace dataset. Then we reconstruct face features by sliding window in view of different sizes of partial face hard to extract the same feature dimension. Finally we carry out sparse coding with rectification and calculate the minimum score of the probe and mirrored samples among all classes to get the results. Thus, by model-based transfer learning, sliding window for feature reconstruction and sparse coding with rectification, the proposed framework improves partial face recognition performance. Experimental results on three face databases (LFW, AR and NIR), and two person re-identification databases (iLIDS-VID and PKU-Reid) demonstrate our method is effective for partial face recognition.
Training Efficiency of Federated Learning: A Wireless Communication Perspective

In this paper, we optimize federated learning at network edge via radio resource allocation and scheduling, including user selection, bandwidth allocation and batch-size allocation. We define a new performance metric, namely training efficiency, to not only accelerate the convergence but also increase the accuracy of the training process. The studied problem is non-convex and we develop an efficient algorithm to solve it. The proposed policy adapts wireless channels, computing capacities, and local datasets of the users. The system performance improvement contributed by the proposed scheme is demonstrated by experiments.
QCEC: A JKQ tool for quantum circuit equivalence checking

<h2>Abstract</h2> Quantum computing is gaining serious momentum in these days. With increasing capabilities of corresponding devices also comes the need for efficient and automated tools to design them. Verification, i.e., ensuring that the originally intended functionality of a quantum algorithm/circuit is preserved throughout all layers of abstraction during the design process, is a vital part of the quantum software stack. In this work, we present <i>QCEC</i>, a tool for quantum circuit equivalence checking which is part of the JKQ toolset for quantum computing. By exploiting characteristics unique to quantum computing, the tool allows users to efficiently verify the equivalence of two quantum circuits using a variety of methods and strategies.