Reviewerly/exHarmony huggingface dataset:
Why did you pick that? A study on smartwatch design qualities and people’s preferences

Several smartwatch vendors have over the past years entered the market with a large range of devices that differ in the design qualities they offer.In this paper, we focus on design qualities that are either perceived, descriptive or physical, and we investigate which of them may lead people to prefer particular smartwatches over others.To do so we conducted a laboratory study with 93 potential smartwatch users and a trained panel study with 8 participants.Results were analysed through a multivariate statistical technique called PreferenceMapping.The advantage of this technique is that it can relate a large number of design qualities to users' preferences.Our findings show that participants can be divided into four groups with homogenous preferences, each emphasizing a unique combination of design qualities.For example, some groups emphasized device shape, while others prioritized expensiveness for their preferred smartwatch.We conclude with implications of our work on practice and research.
The Impact of Negative Relevance Judgments on NDCG

NDCG is one of the most commonly used measures to quantify system performance in retrieval experiments. Though originally not considered, graded relevance judgments nowadays frequently include negative labels. Negative relevance labels cause NDCG to be unbounded. This is probably why widely used implementations of NDCG map negative relevance labels to zero, thus ensuring the resulting scores to originate from the [0,1] range. But zeroing negative labels discards valuable relevance information, e.g., by treating spam documents the same as unjudged ones, which are assigned the relevance label of zero by default. We show that, instead of zeroing negative labels, a min-max-normalization of NDCG retains its statistical power while improving its reliability and stability.
Speech spoofing detection using SVM and ELM technique with acoustic features

Now-a-days, the automatic speaker verification (ASV) systems are weak against attacks specially the voice conversion attacks and the speech synthesis attacks. To improve the robustness of the ASV systems, an anti-spoofing approach are developped to detect the spoofed speech from human speech. In this study, we focus on considering some acoustic features were proposed to differenciate spoofed speech from humain speech. We have used the proposed features with data from ASVspoof 2015 corpora. For the classification, we use Extreme learning machine (ELM) and Support Vector Machines (SVM) to obtain features and classified them to genuine or spoofed.
Driver Inattention Monitoring System Based on the Orientation of the Face Using Convolutional Neural Network

Driving inattentively is one of the prime reasons for vehicle accidents worldwide and has significant implications for road safety. A prompt alert to the inattentive driver can mitigate many accidents and save numerous lives, and reduce the cost of damages caused by accidents. To achieve this, a proposal of a nonintrusive and noninvasive driver inattention monitoring and alerting system in real time has been put forward. A mobile camera mounted on the windshield captures the video of the driver. Viola-Jones algorithm detects the face in each frame of the video and the Kanade-Lucas-Tomasi (KLT) algorithm tracks the detected face from one frame to another frame. The driver is classified as inattentive or attentive using Convolutional Neural Network (CNN). The transfer learning of the AlexNet Convolutional Neural Network architecture is adopted for the classification. The accuracy, precision, sensitivity, F1 score, and specificity of the system proposed in this paper are 98.24%, 100%, 96.47%, 98.21% and 100%, respectively.
A Fairness-Aware Fusion Framework for Multimodal Cyberbullying Detection

Recent reports of bias in multimedia algorithms (e.g., lesser accuracy of face detection for women and persons of color) have underscored the urgent need to devise approaches which work equally well for different demographic groups. Hence, we posit that ensuring fairness in multimodal cyber-bullying detectors (e.g., equal performance irrespective of the gender of the victim) is an important research challenge. We propose a fairness-aware fusion framework that ensures that both fairness and accuracy remain important considerations when combining data coming from multiple modalities. In this Bayesian fusion framework, the inputs coming from different modalities are combined in a way that is cognizant of the different confidence levels associated with each feature and the interdependencies between features. Specifically, this framework assigns weights to different modalities not just based on accuracy but also their fairness. Results of applying the framework on a multimodal (visual + text) cyberbullying detection problem demonstrate the value of the proposed framework in ensuring both accuracy and fairness.
Cyber Bullying Detection Based on Twitter Dataset

The acceleration of different social media platforms has alternated the way people communicate with each other it has also ensued in the rise of Cyberbullying cases on social media that has various adverse effects on an individual’s health. In this project, we aim to build a system that tackles Cyber bully by identifying the mean-spirited comments and also categorizing the comments into peculiar division. The target of developing such a system is to deal with Cyber bullying that has become a prevalent occurrence on various social media. The system uses two noticeable features—Convolutional Neural Network and Long Short-Term Memory which improves the efficiency of the system.
An Investigation on Different Underlying Quantization Schemes for Pre-trained Language Models

Recently, pre-trained language models like BERT have shown promising performance on multiple natural language processing tasks. However, the application of these models has been limited due to their huge size. To reduce its size, a popular and efficient way is quantization. Nevertheless, most of the works focusing on BERT quantization adapted primary linear clustering as the quantization scheme, and few works try to upgrade it. That limits the performance of quantization significantly. In this paper, we implement k-means quantization and compare its performance on the fix-precision quantization of BERT with linear quantization. Through the comparison, we verify that the effect of the underlying quantization scheme upgrading is underestimated and there is a huge development potential of k-means quantization. Besides, we also compare the two quantization schemes on ALBERT models to explore the robustness differences between different pre-trained models.
Behavioral Science of Software Engineering

Large-scale software development is a sociotechnical activity only bounded by human imagination, ingenuity, and creativity. It involves teams of developers progressing by coordinating their activities and communicating their bottlenecks, goals, and advancements toward the wider goal of creating large, high-quality software systems. The stakeholders they serve are diverse (for example, clients, infrastructure providers, open source communities, project managers, and regulatory authorities), and often they have many competing, implicit requirements. But, as the political and legal implications of algorithms and data (https://harvardmagazine.com/2000/01/code-is-law-html) increasingly affect society, it is imperative that the systems the developers build are high quality in terms of accurately embodying all of those requirements.
A framework to co-optimize task and social dialogue policies using Reinforcement Learning

One of the main challenges for conversational agents is to select the optimal dialogue policy based on the state of the interaction. This challenge becomes even harder when the conversational agent not only has to achieve a specific task, but also aims at building rapport. Although some work already tried to tackle this challenge using a Reinforcement Learning (RL) approach, they tend to consider one single optimal policy for all the users, regardless of their conversational goals. In this work, we describe a framework that allows us to build a RL-based agent able to adapt its dialogue policy depending on its user's conversational goals. After we build a rule-based agent and a user simulator communicating at the dialog-act level, we crowdsource the surface sentences authoring for both the simulated users and the agent, which allow us to generate a dataset of interactions in natural language. Then, we annotate each of these interactions with a single rapport score and analyze the links between simulated users' conversational goals, agent conversational policies, and rapport. Our results show that rapport was higher when both or none of the interlocutors tried to build rapport. We use this result to inform the design of a social reward function, and we rely on this social reward function to train a RL-based agent using an hybrid approach of supervised learning and reinforcement learning. We evaluate our approach by comparing two different versions of our RL-based agent: one that takes users' conversational goals into account and another that does not. The results show that an agent adapting its dialogue policy depending on users' conversational goals performs better.
An open architecture for complex event processing with machine learning

This paper proposes an advanced, open architecture to augment streaming data platforms with both complex event processing (CEP) and predictive machine learning models. We leverage the power of CEP to preprocess streams using sophisticated event pattern expressions then present these preprocessed streams for downstream training and predictive computations. We demonstrate this approach using specific technology components.
Quality-Aware Ranking of Arguments

Argument search engines identify, extract, and rank the most important arguments for and against a given controversial topic. A number of such systems have recently been developed, usually focusing on classic information retrieval ranking methods that are based on frequency information. An important aspect that has been ignored so far by search engines is the quality of arguments. We present a quality-aware ranking framework for arguments already extracted from texts and represented as argument graphs, considering multiple established quality measures. An extensive evaluation with a standard benchmark collection demonstrates that taking quality into account significantly helps to improve retrieval quality for argument search. We also publish a dataset in which arguments with respect to topics were tediously annotated by humans with three widely accepted argument quality dimensions.
Pilot-Based Physical-Layer Authentication with High Covertness

This article concerns the problem of generating an appropriate tag for the covert Physical-Layer Authentication (PLA) schemes. This problem is important because an inappropriate tag introduces the following limitations: low covertness, low robustness, high complexity, and security vulnerabilities to active attacks. In this article, we compare the performance of two ways of generating an appropriate tag in prior covert schemes: message-based and pilot-based. Then, we propose an enhanced Pilot-based Covert (PC) scheme that not only effectively defends against replay attacks but also overcomes the limitations of prior covert schemes. Our experimental results show that the enhanced PC scheme has better covertness and robustness than both the Message-based Covert (MC) and PC schemes for defending against an impersonation attack. Moreover, both the enhanced PC and MC schemes have much better robustness than the PC scheme for defending against a replay attack but the enhanced PC scheme has a lower store requirement than the MC scheme.
Benchmarking End-to-End Behavioural Cloning on Video Games

Behavioural cloning, where a computer is taught to perform a task based on demonstrations, has been successfully applied to various video games and robotics tasks, with and without reinforcement learning. This also includes end-to-end approaches, where a computer plays a video game like humans do: by looking at the image displayed on the screen, and sending keystrokes to the game. As a general approach to playing video games, this has many inviting properties: no need for specialized modifications to the game, no lengthy training sessions and the ability to re-use the same tools across different games. However, related work includes game-specific engineering to achieve the results. We take a step towards a general approach and study the general applicability of behavioural cloning on twelve video games, including six modern video games (published after 2010), by using human demonstrations as training data. Our results show that these agents cannot match humans in raw performance but do learn basic dynamics and rules. We also demonstrate how the quality of the data matters, and how recording data from humans is subject to a state-action mismatch, due to human reflexes.
Nonlinear Filtering With a Polynomial Series of Gaussian Random Variables

Filters relying on the Gaussian approximation typically incorporate the measurement linearly, i.e., the value of the measurement is premultiplied by a matrix-valued gain in the state update. Nonlinear filters that relax the Gaussian assumption, on the other hand, typically approximate the distribution of the state with a finite sum of point masses or Gaussian distributions. In this work, the distribution of the state is approximated by a polynomial transformation of a Gaussian distribution, allowing for all moments, central and raw, to be rapidly computed in a closed form. Knowledge of the higher order moments is then employed to perform a polynomial measurement update, i.e., the value of the measurement enters the update function as a polynomial of arbitrary order. A filter employing a Gaussian approximation with linear update is, therefore, a special case of the proposed algorithm when both the order of the series and the order of the update are set to one: it reduces to the extended Kalman filter. At the cost of more computations, the new methodology guarantees performance better than the linear/Gaussian approach for nonlinear systems. This work employs monomial basis functions and Taylor series, developed in the differential algebra framework, but it is readily extendable to an orthogonal polynomial basis.
A Survey of Solving SVP Algorithms and Recent Strategies for Solving the SVP Challenge

Abstract Recently, lattice-based cryptography has received attention as a candidate of post-quantum cryptography (PQC). The essential security of lattice-based cryptography is based on the hardness of classical lattice problems such as the shortest vector problem (SVP) and the closest vector problem (CVP). A number of algorithms have been proposed for solving SVP exactly or approximately, and most of them are useful also for solving CVP. In this paper, we give a survey of typical algorithms for solving SVP from a mathematical point of view. We also present recent strategies for solving the Darmstadt SVP challenge in dimensions higher than 150.
Fine-Grained Motion Estimation for Video Frame Interpolation

Recent advances in video frame interpolation have shown that convolutional neural networks combined with optical flow are capable of producing a high-quality intermediate frame between two consecutive input frames in most scenes. However, existing methods have difficulties dealing with large and non-uniform motions that widely exist in real-world scenes because they often adopt the same strategy to deal with different motions, which easily results in unsatisfactory results. In this article, we propose a novel fine-grained motion estimation approach (FGME) for video frame interpolation. It mainly contains two strategies: multi-scale coarse-to-fine optimization and multiple motion features estimation. The first strategy is to gradually refine optical flows and weight maps, both of which are used to synthesize the target frame. The second strategy aims to provide fine-grained motion features by generating multiple optical flows and weight maps. To demonstrate its effectiveness, we propose a fully convolutional neural network with three refinement scales and four motion features. Surprisingly, this simple network produces state-of-the-art results on three standard benchmark datasets and real-world examples, with advantages in terms of effectiveness, simplicity, and network size over other existing approaches. Furthermore, we demonstrate that the FGME approach has good generality and can significantly improve the synthesis quality of other methods.
Development of English Conversation Practice App with Artificial Intelligence &amp; Speech Recognition

In the progress of science and technology today, the need to master English becomes very important. English which is an international language has become a part of all aspects of people's lives. So that at an early age, learning has been applied to English. In learning English, there are competencies namely writing, listening, and reading. Currently how to learn reading is often implemented only by saying words or sentences from textbooks. This will cause students to be focused on the material provided and tend to decrease conversation practice with someone directly. In addition, English learning time generally has a smaller portion and costly. Therefore, it needs to be developed a learning medium that is able to provide a way of learning English that simulates conversation directly. It can be accessed anywhere and anytime and minimize the cost of expenditure. Website-based learning can be an alternative. Web-based English learning applications with speech recognition technology was integrated with aritificial intelligence to train students to become active speakers rather than silent readers.
Towards markerless computer-aided surgery combining deep segmentation and geometric pose estimation: application in total knee arthroplasty

Total knee arthroplasty (TKA) is a surgical procedure performed in patients suffering from knee arthritis. The correct positioning of the implants is strongly related to multiple surgical variables that have a tremendous impact on the success of the surgery. Computer-based navigation systems have been investigated and developed in order to assist the surgeon in accurately controlling those surgical variables. The existing technologies are very costly, require additional bone incisions for fixing markers to be tracked, and these markers are usually bulky, interfering with the standard surgical flow. This work presents a markerless navigation system that supports the surgeon in accurately performing the TKA procedure. The proposed system uses a mobile RGB-D camera for replacing the existing optical tracking systems and does not require markers to be tracked. We combine an effective deep learning-based approach for accurately segmenting the bone surface with a robust geometry-based algorithm for registering the bones with pre-operative models. The favourable performance of our pipeline is achieved by (1) employing a semi-supervised labelling approach for generating training data from real TKA surgery data, (2) using effective data augmentation techniques for improving the generalisation capability and (3) using appropriate depth data cleaning strategies. The construction of this complete markerless registration prototype that generalises for unseen intra-operative data is non-obvious, and relevant insights and future research directions can be derived. The experimental results show encouraging performance for video-based TKA.
Dissolved rare earth elements estimation of ion-absorption rare earth ores using reflectance spectroscopy in south Jiangxi province, China

Ion-absorption rare earth ores are an important mineral resource in China. Nowadays, the unauthorized mining has become a serious problem, resulting in severe water pollution and the wastage of rare earth elements (REEs). Being able to estimate the concentration of dissolved REEs in water bodies near mines is essential for tackling this environmental problem. Conventionally, quantitative analyses of the contents of dissolved REEs are performed using laboratory-based techniques, which can be time consuming and costly. Spectral reflectance is a rapid and cost-effective means of characterizing the chemical compositions of light-absorbing materials. In this study, reflectance spectroscopy was performed on dissolved REEs, and the correlation between their reflectance characteristics and REE content was determined. A total of 50 aqueous media samples collected in south Jiangxi Province and 25 laboratory-produced aqueous media samples were tested, and their reflectance spectra and REE contents were measured using reflectance spectroscopy and inductively coupled plasma mass spectrometry, respectively. Next, the reflectance, differential reflectance, and absorption depth were analysed based on the REE content. Six diagnostic absorption features related to REEs are recognised in the visible and near-infrared wavelength regions, along with several smaller peaks. It indicates that the results of the absorption depth analysis are in accordance with the absorption spectra characteristics of the REEs, with the R2 value being higher than 0.97. The intensity of each of the six absorption bands exhibits a linear correlation to the total REE content. Therefore, linear regression models can be derived for estimating the total concentration of REEs in aqueous media samples. What's more, the detection limit for REEs is determined to be about 30 μg/L. Thus, it can be concluded that reflectance spectroscopy is a suitable technique for estimating the concentration of dissolved REEs.
Embedding based Link Prediction for Knowledge Graph Completion

Knowledge Graphs (KGs) have recently gained attention for representing knowledge about a particular domain. Since its advent, the Linked Open Data (LOD) cloud has constantly been growing containing many KGs about many different domains such as government, scholarly data, biomedical domain, etc. Apart from facilitating the inter-connectivity of datasets in the LOD cloud, KGs have been used in a variety of machine learning and Natural Language Processing (NLP) based applications. However, the information present in the KGs are sparse and are often incomplete. Predicting the missing links between the entities is necessary to overcome this issue. Moreover, in the LOD cloud, information about the same entities is available in multiple KGs in different forms. But the information that these entities are the same across KGs is missing. The main focus of this thesis is to do Knowledge Graph Completion by tackling the link prediction tasks within a KG as well as across different KGs. To do so, the latent representation of KGs in a low dimensional vector space has been exploited to predict the missing information in order to complete the KGs.