Reviewerly/exHarmony huggingface dataset:
Localization and Navigation Analysis of Mobile Robot Based on SLAM

Abstract In recent years, many advanced computer technologies have appeared on the market, for example, artificial intelligence. With the maturity of artificial intelligence technology, the transformation of intelligent robots are also more perfect. The difficulty that we have always wanted to break through is to install positioning navigation on mobile robots. Through continuous efforts, the navigation and positioning technology on the robot can now be used proficiently in a known environment, but we need further research when using it in an unknown environment. In the navigation and positioning technology, it introduced simultaneous positioning and map construction algorithms (SLAM, simultaneous localization and mapping). The algorithm can learn about the surrounding environment information through the sensors carried by the robot. If this algorithm can be skillfully applied to navigation and positioning technology, I believe it will be helpful for the application of positioning and navigation technology in unknown environments.
StyleLess layer: Improving robustness for real-world driving

Deep Neural Networks (DNNs) are a critical component for self-driving vehicles. They achieve impressive performance by reaping information from high amounts of labeled data. Yet, the full complexity of the real world cannot be encapsulated in the training data, no matter how big the dataset, and DNNs can hardly generalize to unseen conditions. Robustness to various image corruptions, caused by changing weather conditions or sensor degradation and aging, is crucial for safety when such vehicles are deployed in the real world. We address this problem through a novel type of layer, dubbed StyleLess, which enables DNNs to learn robust and informative features that can cope with varying external conditions. We propose multiple variations of this layer that can be integrated in most of the architectures and trained jointly with the main task. We validate our contribution on typical autonomous-driving tasks (detection, semantic segmentation), showing that in most cases, this approach improves predictive performance on unseen conditions (fog, rain), while preserving performance on seen conditions and objects.
Freedom of assembly under attack: General and indiscriminate surveillance and interference with internet communications

Abstract Every day across the world, as people assemble, demonstrate and protest, their pictures, their messages, tweets and other personal information are amassed without adequate justification. Arguing that they do so in order to protect assemblies, governments deploy a wide array of measures, including facial recognition, fake mobile towers and internet shutdowns. These measures are primarily analyzed as interferences with the right to privacy and freedom of expression, but it is argued here that protest and other assembly surveillance should also be understood as an infringement of freedom of assembly. This is necessary not only to preserve the distinct nature of freedom of assembly that protects collective action, but also to allow for better regulation of surveillance and interference with internet communications during assemblies.
Fairness for Whom? Understanding the Reader’s Perception of Fairness in Text Summarization

With the surge in user-generated textual information, there has been a recent increase in the use of summarization algorithms for providing an overview of the extensive content. Traditional metrics for evaluation of these algorithms (e.g. ROUGE scores) rely on matching algorithmic summaries to human-generated ones. However, it has been shown that when the textual contents are heterogeneous, e.g., when they come from different socially salient groups, most existing summarization algorithms represent the social groups very differently compared to their distribution in the original data. To mitigate such adverse impacts, some fairness-preserving summarization algorithms have also been proposed. All of these studies have considered normative notions of fairness from the perspective of writers of the contents, neglecting the readers' perceptions of the underlying fairness notions. To bridge this gap, in this work, we study the interplay between the fairness notions and how readers perceive them in textual summaries. Through our experiments, we show that reader's perception of fairness is often context-sensitive. Moreover, standard ROUGE evaluation metrics are unable to quantify the perceived (un)fairness of the summaries. To this end, we propose a human-in-the-loop metric and an automated graph-based methodology to quantify the perceived bias in textual summaries. We demonstrate their utility by quantifying the (un)fairness of several summaries of heterogeneous socio-political microblog datasets.
Counterintelligence Technologies: An Exploratory Case Study of Preliminary Credibility Assessment Screening System in the Afghan National Defense and Security Forces

The preliminary credibility assessment screening system (PCASS) is a US-based program, which is currently being implemented by intelligence units of the North Atlantic Treaty Organization (NATO) to make the initial screening of individuals suspected of infiltrating the Afghan National Defense and Security Forces (ANDSF). Sensors have been instrumental in the PCASS, leading to organizational change. The aim of this research is to describe how the ANDSF adapted to the implementation of PCASS, as well as implemented changes since the beginning of the program. To do so, we have conducted a qualitative, exploratory, and descriptive case study that allows one to understand, through the use of a series of data collection sources, a real-life phenomenon of which little is known. The results suggest that the sensors used in PCASS empower security forces with reliable technologies to identify and neutralize internal threats. It then becomes evident that the technological leadership that PCASS provides allows the developing of a relatively stable and consistent organizational change, fulfilling the objectives of the NATO and the ANDSF.
Cascaded Networks for Thyroid Nodule Diagnosis from Ultrasound Images

Computer-aided diagnostics (CAD) based on deep learning methods have grown to be the most concerned method in recent years due to its safety, efficiency and economy. CAD’s function varies from providing second opinion to doctors to establishing a baseline upon which further diagnostics can be conducted [3]. In this paper, we cross-compare different approaches to classify thyroid nodules and finally propose a method that can exploit interaction between segmentation and classification task. In our method, detection and segmentation results are combined to produce class-discriminative clues for boosting classification performance. Our method is applied to TN-SCUI 2020, a MICCAI 2020 challenge and achieved third place in classification task. In this paper, we provide exhaustive empirical evidence to demonstrate the applicability and efficacy of our method.
Spam Detection using ANN and ABC Algorithm

Social network becomes an effective method to engage with our friends. It enables the users to extract a number of in-formation and its usage are increasing day by day. Amidst all the social networking sites, Twitter is one of the interactive social networking services. To change the authorized user accounts, many spammers are utilized a vast amount of spam. Machine Learning (ML) technique is utilized for spam detection system in social sites and for the detection of spammer. Data collection is usually done from H-Spam 14 site with the help of pre-processing mechanism, the data is transforming into lowercase. After the first step, pre-processed data comes under feature extraction phase, which utilizes tokenization process to breakdown each sentence into word group in order to extract the best feature from the raw data. The optimization algorithm referred as Artificial Bee Colony (ABC) is used to pick the optimized value from extracted set of features. It is also utilized to get the optimal sets of features from spam and non-spam data. At the end, performance measure criterion and comparing the existing and proposed work in order to look over the progress of the proposed work. In this work, spam detection system is having higher accuracy, precision, recall, and F-measure as compares to classifiers used previously such as, Naïve Bayes and Support Vector Machine (SVM).
Phonetic Variation Modeling and a Language Model Adaptation for Korean English Code-Switching Speech Recognition

In this paper, we propose a new method for code-switching (CS) automatic speech recognition (ASR) in Korean. First, the phonetic variations in English pronunciation spoken by Korean speakers should be considered. Thus, we tried to find a unified pronunciation model based on phonetic knowledge and deep learning. Second, we extracted the CS sentences semantically similar to the target domain and then applied the language model (LM) adaptation to solve the biased modeling toward Korean due to the imbalanced training data. In this experiment, training data were AI Hub (1033 h) in Korean and Librispeech (960 h) in English. As a result, when compared to the baseline, the proposed method improved the error reduction rate (ERR) by up to 11.6% with phonetic variant modeling and by 17.3% when semantically similar sentences were applied to the LM adaptation. If we considered only English words, the word correction rate improved up to 24.2% compared to that of the baseline. The proposed method seems to be very effective in CS speech recognition.
Support Vector based Oversampling Technique for Handling Class Imbalance in Software Defect Prediction

The importance of software defect prediction has risen significantly over the past decade and is an inseparable part of software quality. Owing to the drawbacks of traditional techniques used for software quality assurance, machine learning algorithms are used to detect the defect in software modules. By the means of this study, we present a support vector based oversampling technique as a part of the software defect procedure and compare it with two other oversampling techniques namely Synthetic minority oversampling technique and Adaptive Synthetic oversampling technique. For the purpose of this study, we chose 5 datasets from the PROMISE and AEEEM repositories. After extracting a subset of attributes from the original dataset through Linear discriminant Analysis, we utilize the improved oversampled dataset to train a support vector machine classifier and a Naïve Bayesian classifier. The proposed Support Vector based oversampling technique along with Linear Discriminant Analysis performs better than the other techniques for the performance evaluation metric of F-measure score and the area under receiver operating characteristic curve and the consistency of result is maintained.
Anti-Forensics of Image Contrast Enhancement Based on Generative Adversarial Network

In the multimedia forensics community, anti-forensics of contrast enhancement (CE) in digital images is an important topic to understand the vulnerability of the corresponding CE forensic method. Some traditional CE anti-forensic methods have demonstrated their effective forging ability to erase forensic fingerprints of the contrast-enhanced image in histogram and even gray level cooccurrence matrix (GLCM), while they ignore the problem that their ways of pixel value changes can expose them in the pixel domain. In this paper, we focus on the study of CE anti-forensics based on Generative Adversarial Network (GAN) to handle the problem mentioned above. Firstly, we exploit GAN to process the contrast-enhanced image and make it indistinguishable from the unaltered one in the pixel domain. Secondly, we introduce a specially designed histogram-based loss to enhance the attack effectiveness in the histogram domain and the GLCM domain. Thirdly, we use a pixel-wise loss to keep the visual enhancement effect of the processed image. The experimental results show that our method achieves high anti-forensic attack performance against CE detectors in the pixel domain, the histogram domain, and the GLCM domain, respectively, and maintains the highest image quality compared with traditional CE anti-forensic methods.
Contextual Feature Constrained Semantic Face Completion With Paired Discriminator

Image semantic completion is to employ remaining image information to restore the damaged or missing areas. Face completion task is usually more challenging than other image inpainting problems as it requires stronger semantic consistency. We proposed a contextual feature constrained DCGAN with paired discriminator to inpaint damaged face images, which is capable of overcoming the DCGAN's shortages of insufficient feature learning capability and unstable training process. Our network is composed of an encoder-decoder generator and a local and global (paired) adversarial discriminator. Generator is used to produce missing parts, and the paired discriminators evaluate the authenticity of the local generated parts and the consistency of the global completed image respectively. In addition, the proposed network generates the image by optimizing the generator with three types of loss functions, i.e., image reconstruction loss, paired feature matching losses and paired feature reconstruction losses. The experiments on celebA and Stanford Cars dataset show that our model could generate reasonable repair results and that on some of the evaluation indicators were higher than baseline on most test datasets.
A Computational Model of Suspense for Non-Narrative Gameplay

In recent years, Various mechanisms have been proposed to optimize for players' emotional experience. In this paper, we focus on suspense, one of the key emotions in gameplay. Most previous research on suspense management in games focused on narratives. Instead, we propose a new computational model of Suspense for Non-Narrative Gameplay (SNNG). SNNG is built around a Player Suspense Model (PSM) with three key factors: hope, fear, and uncertainty. These three factors are modeled as three sensors that can be triggered by particular game objects (e.g., NPCs) and game mechanics (e.g., health). A player's feeling of suspense can be adjusted by altering the level of hope, fear, and uncertainty. Therefore, an SNNG-enhanced game engine could manage a player's level of suspense by adding or removing game objects, diverting NPCs, adjusting game mechanics, and giving or withholding information. We tested our model by integrating SNNG into a Pacman game. Our preliminary experiment with nine subjects was encouraging.
Twin Delayed Hierarchical Actor-Critic

Hierarchical Reinforcement Learning (HRL) addresses the common problem in sparse rewards environments of having to manually craft a reward function. We present a modified version of the Hierarchical Actor-Critic (HAC) architecture called Twin Delayed HAC (TDHAC), a method capable of sample-efficient learning on environments requiring object interaction. The vanilla algorithm fails to converge on this type of environment, while our method matches the best results so far reported in the literature. We carefully consider each feature added to the original architecture and demonstrate the abilities of TDHAC on the sparse-reward Pick-and-Place environment. To the best of our knowledge, this is the first HRL algorithm successfully applied on an environment requiring object interaction without external enhancements such as demonstrations.
Effects of Missing Members on Classifier Ensemble Accuracy

Classification uses a predictive model to predict labels for previously unseen data. Classifier ensembling techniques are used to combine the predictive outputs of multiple classifiers into one final predictive output in order to increase the accuracy of the predictive output. These techniques tend to assume every member classifier will always be available to make a prediction. This paper examines the effects missing members have on the prediction accuracy of a Stacking based ensemble and a Voting based ensemble. To determine the impact, 20 datasets were randomly selected from the UCI Machine Learning Repository; these datasets were used to create multiple Stacking and Voting models. Results indicate that the Stacking ensemble performs poorly in the face of even one missing member, while the Voting ensemble has very little issue with missing members.
Ultron-AutoML: an open-source, distributed, scalable framework for efficient hyper-parameter optimization

We present Ultron-AutoML, an open-source, distributed framework for efficient hyper-parameter optimization (HPO) of ML models. Considering that hyper-parameter optimization is compute intensive and time-consuming, the framework has been designed for reliability - the ability to successfully complete an HPO Job in a multi-tenant, failure prone environment, as well as efficiency - completing the job with minimum compute cost and wall-clock time. From a user's perspective, the framework emphasizes ease of use and customizability. The user can declaratively specify and execute an HPO Job, while ancillary tasks - containerizing and running the user's scripts, model checkpointing, monitoring progress, parallelization - are handled by the framework. At the same time, the user has complete flexibility in composing the code-base for specifying the ML model training algorithm as well as, optionally, any custom HPO algorithm. The framework supports the creation of data-pipelines to stream batches of shuffled and augmented data from a distributed file system. This comes in handy for training Deep Learning models based on self-supervised, semi-supervised or representation learning algorithms over large training datasets. We demonstrate the framework's reliability and efficiency by running a BERT pre-training job over a large training corpus using pre-emptible GPU compute targets. Despite the inherent unreliability of the underlying compute nodes, the framework is able to complete such long running jobs at 30% of the cost with a marginal increase in wall-clock time. The framework also comes with a service to monitor jobs and ensures reproducibility of any result.
SWQL: A new domain-specific language for mining the social Web

Social media generate a massive amount of information each day. This information is usually generated by people and may be used for many studies. Social media mining is a growing discipline inside data mining, and the results have proven to be quite revealing. Getting the data to work with might seem to be the easiest task in the process, but it can be very challenging for people without programming knowledge. Thus, there are numerous ways to extract information from social media, such as to use the network API, a Web scraper or specialized libraries. Using a straightforward and a cross social media query language we can hide the complexity of those mechanisms and gather information in a more efficient and easier way. This is because many social media share common elements, so we can create and unify queries to search, find and extract information from those platforms. In this paper, we propose a domain-specific query language, specially designed to allow developers or domain experts to extract data from different social media. With this language we unify and simplify the mechanisms of data extraction from social networks such as Twitter and Facebook.
Robust and Scalable Entity Alignment in Big Data

Entity alignment has always had significant uses within a multitude of diverse scientific fields. In particular, the concept of matching entities across networks has grown in significance in the world of social science as communicative networks such as social media have expanded in scale and popularity. With the advent of big data, there is a growing need to provide analysis on graphs of massive scale. However, with millions of nodes and billions of edges, the idea of alignment between a myriad of graphs of similar scale using features extracted from potentially sparse or incomplete datasets becomes daunting. In this paper we will propose a solution to the issue of large-scale alignments in the form of a multi-step pipeline. Within this pipeline we introduce scalable feature extraction for robust temporal attributes, accompanied by novel and efficient clustering algorithms in order to find groupings of similar nodes across graphs. The features and their clusters are fed into a versatile alignment stage that accurately identifies partner nodes among millions of possible matches. Our results show that the pipeline can process large data sets, achieving efficient runtimes within the memory constraints.
Usability Evaluation of Optimized Single-Pointer Arabic Keyboards Using Eye Tracking

This paper presents the design and usability evaluation of an Arabic keyboard for applications that predominantly use single-pointer input device. Such applications are particularly used in mobile devices like Portable Data Assistants (PDAs) and smartphones. They are also valuable in gaze-controlled interfaces that constitute a growing mode of communication and that particularly empower people with mobility impairments. A special focus is given to the optimization of the key arrangement based on the movement time and character transition frequencies. An optimization model as well as a Simulated Annealing algorithm are presented. Then, the performance of the optimized layout is assessed showing that it outperforms the commonly used Arabic keyboard in terms of the estimated typing speed. However, the main limitation that the new layout might face is that a new arrangement of keys may not be adopted by users, even if the currently used layouts are not optimum. Therefore, a usability evaluation of the optimized layouts was conducted using eye-tracking and task-based testing involving the end-users and considering both objective and subjective measures of usability. Implications for the design are also discussed.
Quadratic-exponential functionals of Gaussian quantum processes

This paper is concerned with exponential moments of integral-of-quadratic functions of quantum processes with canonical commutation relations of position-momentum type. Such quadratic-exponential functionals (QEFs) arise as robust performance criteria in control problems for open quantum harmonic oscillators (OQHOs) driven by bosonic fields. We develop a randomised representation for the QEF using a Karhunen–Loeve expansion of the quantum process on a bounded time interval over the eigenbasis of its two-point commutator kernel, with noncommuting position-momentum pairs as coefficients. This representation holds regardless of a particular quantum state and employs averaging over an auxiliary classical Gaussian random process whose covariance operator is specified by the commutator kernel. This allows the QEF to be related to the moment-generating functional of the quantum process and computed for multipoint Gaussian states. For stationary Gaussian quantum processes, we establish a frequency-domain formula for the QEF rate in terms of the Fourier transform of the quantum covariance kernel in composition with trigonometric functions. A differential equation is obtained for the QEF rate with respect to the risk sensitivity parameter for its approximation and numerical computation. The QEF is also applied to large deviations and worst-case mean square cost bounds for OQHOs in the presence of statistical uncertainty with a quantum relative entropy description.