Reviewerly/exHarmony huggingface dataset:
Dynamic Bayesian Metric Learning for Personalized Product Search

In this paper, we study the problem of personalized product search under streaming scenarios. We address the problem by proposing a Dynamic Bayesian Metric Learning model, abbreviated as DBML, which can collaboratively track the evolutions of latent semantic representations of different categories of entities (i.e., users, products and words) over time in a joint metric space. In particular, unlike previous work using inner-product metric to model the affinities between entities, our DBML is a novel probabilistic metric learning approach that is able to avoid the contradicts, keep the triangle inequality in the latent space, and correctly utilize implicit feedbacks. For inferring dynamic embeddings of the entities, we propose a scalable online inference algorithm, which can jointly learn the latent representations of entities and smooth their changes across time, based on amortized inference. The inferred dynamic semantic representations of entities collaboratively inferred in a unified form by our DBML can benefit not only for improving personalized product search, but also for capturing the affinities between users, products and words. Experimental results on large datasets over a number of applications demonstrate that our DBML outperforms the state-of-the-art algorithms, and can effectively capture the evolutions of semantic representations of different categories of entities over time.
Ambient Intelligence in the Living Room

The emergence of the Ambient Intelligence (AmI) paradigm and the proliferation of Internet of Things (IoT) devices and services unveiled new potentials for the domain of domestic living, where the line between "the computer" and the (intelligent) environment becomes altogether invisible. Particularly, the residents of a house can use the living room not only as a traditional social and individual space where many activities take place, but also as a smart ecosystem that (a) enhances leisure activities by providing a rich suite of entertainment applications, (b) implements a home control middleware, (c) acts as an intervention host that is able to display appropriate content when the users need help or support, (d) behaves as an intelligent agent that communicates with the users in a natural manner and assists them throughout their daily activities, (e) presents a notification hub that provides personalized alerts according to contextual information, and (f) becomes an intermediary communication center for the family. This paper (i) describes how the "Intelligent Living Room" realizes these newly emerged roles, (ii) presents the process that was followed in order to design the living room environment, (iii) introduces the hardware and software facilities that were developed in order to improve quality of life, and (iv) reports the findings of various evaluation experiments conducted to assess the overall User Experience (UX).
Simple Specifications Grading

Specifications grading is a version of mastery grading distinguished by giving students clear specifications that their work must meet, and grading most things pass/fail based on those specifications. Mastery grading systems can get quite elaborate, with hierarchies of objectives and various systems for rewriting and retesting. In this article I present and discuss the results of two relatively simple implementations of specs grading, one in a differential equations course, and one in a discrete math course that includes an introduction to proof writing. I highlight several novel aspects of my systems: a buy-in activity, a “catch-up” quiz, and an oral quiz option.
Financial Event Extraction Using Wikipedia-Based Weak Supervision

Extraction of financial and economic events from text has previously been done mostly using rule-based methods, with more recent works employing machine learning techniques.This work is in line with this latter approach, leveraging relevant Wikipedia sections to extract weak labels for sentences describing economic events.Whereas previous weakly supervised approaches required a knowledgebase of such events, or corresponding financial figures, our approach requires no such additional data, and can be employed to extract economic events related to companies which are not even mentioned in the training data.
A Parallel Denoising Model for Dual-Mass MEMS Gyroscope Based on PE-ITD and SA-ELM

In a bid to solve the gyroscope temperature drift problem, a parallel denoising model based on PE-ITD and SA-ELM has been proposed in this paper, wherein, Intrinsic time-scale decomposition (ITD) is an effective signal decomposition algorithm, Permutation entropy (PE) is a entropy to accurately determine the complexity of the signal, Extreme Learning machine (ELM) is a machine learning algorithm for predicting, and Simulated Annealing(SA) for finding the optimal parameter set. First, ITD is employed to decompose the output signal of the gyroscope and the PE can distinguish the decomposition results into noise-only component, mixed component and trend component. Secondly, the mixed component is filtered by Forward liner prediction (FLP), the denoised signal can be obtained after processing. Finally, the trend component is compensated by SA-ELM, through reconstruction, the compensation result can be obtained. As is shown in experimental results, the parallel model proposed in this paper is able to effectively eliminate the temperature error compared with the traditional serial model. Compared with the EMD analysis method, the ITD is superior to the EMD analysis method in terms of computational efficiency and instantaneous information. The proposed SA-ELM algorithm takes simulated annealing algorithm to find hidden layer neurons' optimal number adaptively, which can improve the model's accuracy and generalization. This paper demonstrates the superiority of this method.
Hitting the Wall: Mid-Air Interaction for Eye-Hand Coordination

Reaction time training systems are used to improve user performance. Until now, such setups use physical 2D flat surfaces, e.g., a 2D touch screen or buttons mounted on a wall. We designed and investigated a mid-air reaction time training system with an immersive virtual reality (VR) headset. 12 participants performed an eye-hand coordination reaction test in three conditions: both in mid-air with or without VR controller as well as with passive haptic feedback through hitting a soft-surface wall. We also altered target and cursor sizes and used a Fitts’ law task to analyze user performance. According to the results, subjects were slower and their throughput was lower when they hit a solid surface to interact with virtual targets. Our results show that Fitts’s model can be applied to these systems to measure and assess participant training.
Measuring techniques suitable for verification and repairing of industrial components: A comparison among optical systems

On-machine verification is increasingly demanded in modern industry, due to the simultaneous necessity of high quality product and reduced time for production and verification. At the time being, contact probe systems are considered the most reliable ones, but they have many drawbacks, especially when they are used to measure free form surfaces in scanning mode. Optical techniques have the great advantage to acquire large amounts of points in very short time, and they can be used for several applications: from dimensional verification of manufactured products to the inspection of very expensive damaged parts for repairing processes in the aerospace industry. Although, they are affected by many kinds of error and they are very sensitive to the reflectivity or translucency of the sample. In this paper, a comparison between optical techniques potentially suitable to be implemented in on-machine dimensional verification is reported. The comparison involved two laser scanners, one structured light scanner and a photogrammetry-based scanner. The comparison was conducted through a free form reference object realized by the NPL Institute, with 150 × 150 × 40 mm3 of total volume.
Smart3DGuides: Making Unconstrained Immersive 3D Drawing More Accurate

Most current commercial Virtual Reality (VR) drawing applications for creativity rely on freehand 3D drawing as their main interaction paradigm. However, the presence of the additional third dimension makes accurate freehand drawing challenging. Some systems address this problem by constraining or beautifying user strokes, which can be intrusive and can limit the expressivity of freehand drawing. In this paper, we evaluate the effectiveness of relying solely on visual guidance to increase overall drawing shape-likeness. We identified a set of common mistakes that users make while creating freehand strokes in VR and then designed a set of visual guides, the Smart3DGuides, which help users avoid these mistakes. We evaluated Smart3DGuides in two user studies, and our results show that non-constraining visual guides help users draw more accurately.
Unsupervised fisheye image correction through bidirectional loss with geometric prior

Neural network based methods for fisheye distortion correction are effective and increasingly popular, although training network require a high amount of labeled data. In this paper, we propose an unsupervised fisheye correction network to address the aforementioned issue. During the training process, the predicted parameters are employed to correct strong distortion that exists in the fisheye image and synthesize the corresponding distortion using the original distortion-free image. Thus, the network is constrained with bidirectional loss to obtain more accurate distortion parameters. We calculate the two losses at the image level as opposed to directly minimizing the difference between the predicted and ground truth of distortion parameters. Additionally, we leverage the geometric prior that the distortion distribution depends on the geometric regions of fisheye images and the straight line should be straight in the corrected images. The network focuses more on the geometric prior regions as opposed to equally perceiving the whole image without any attention mechanisms. To generate more appealing corrected results in visual appearance, we introduce a coarse-to-fine inpainting network to fill the hole regions caused by the irreversible mapping function using distortion parameters. Each module of the proposed network is differentiable, and thus the entire framework is completely end-to-end. When compared with the previous supervised methods, our method is more flexible and shows better practical applications for distortion rectification. The experiment results demonstrate that our proposed method outperforms state-of-the-art methods on the correction performance without any labeled distortion parameters.
Enhancing Healthy Behaviors Through Virtual Self: A Systematic Review of Health Interventions Using Avatars

Objective: A systematic review of health interventions using avatars (N = 18) was conducted to provide comprehensive knowledge of the effectiveness of using avatars to promote healthy behaviors, specifically in relationship to healthy eating and exercising. Materials and Methods: Two researchers identified field or laboratory studies that had quantified study results, which were published in peer-reviewed journals in English from January 2000 to March 2019. Databases (PsychInfo, PubMed, and Web of Science), forward reference, and manual searches were used to identify the studies. Search terms included avatar, the Proteus effect, exercise, and diet, among others. Two field interventions and 16 laboratory studies were identified. Information on sample characteristics, technologies used, study design and conditions pertaining to avatars, outcome measures, results, and conclusion were extracted. Results: Six different avatar characteristics used to elicit health outcomes were identified, which are the similarity with the user, avatar body size, self-domain (e.g., ideal-self), customizability, body transformation, and avatar's behaviors. Only a few studies had a no-avatar control group; thus, it was not possible to conclude whether employing avatars in health interventions increases the effectiveness of the interventions in comparison to not using an avatar. Conclusion: The results indicate that using an avatar that is physically active, fit, and similar-looking (to the user) is effective in eliciting healthy behaviors.
Towards a Cyber Secure Shipboard Radar

This paper presents a comparative cyber security resilience estimation of shipboard radars that are implemented on two oil/chemical tankers certified as SOLAS ships. The estimated radars were chosen from the same manufacturer, but belonged to different generations. The estimation was conducted by means of ships' crew interviews and computational testing of the radars using a widely deployed vulnerability scanning software tool. The identified cyber threats were analysed qualitatively in order to gain a holistic understanding of cyber risks threatening shipboard radar systems. The results obtained experimentally indicate that potential cyber threats mainly relate to maintenance of the radars' underlying operating system, suggesting the need for regulatory standardisation of periodic cyber security testing of radar systems.
Automatic Gun Detection Approach for Video Surveillance

The immense crime rates resulting from using pistols have led governments to seek solutions to deal with such terrorist incidents. These incidents have a negative impact on public security and cause panic among citizens. From this point, facing a pandemic of weapon violence has become an important research topic. One way to reduce this kind of violence is to prevent it via remote detection and to give an appropriate response in a short time. Video surveillance is the process of monitoring the behavior of people and objects. Surveillance systems can be employed in security applications as legal evidence. Moreover, it is used widely in suspicious activity detection applications. Intelligent video surveillance systems (IVSSs) are the use of automatic video analytics to enhance the effectiveness of traditional surveillance systems. With the rapid development in Deep Learning (DL), it is now widely used to address the problems existing in traditional detection techniques. In this article, an approach to detect pistols and guns in video surveillance systems is proposed. The presented approach does not need any invasive tools in the weapon detection process. It uses DL in the classification and the detection processes. The proposed approach enhances the obtained results by applying Transfer Learning (TL). It employs two different DL techniques: AlexNet and GoogLeNet. Experimental results verify the adaptability of detecting different types of pistols and guns. The experiments were conducted on a benchmark gun database called Internet Movie Firearms Database (IMFDB). The results obtained suggest that the proposed approach is promising and outperforms its counterparts.
Multi-Modal Deep Guided Filtering for Comprehensible Medical Image Processing

Deep learning-based image processing is capable of creating highly appealing results. However, it is still widely considered as a "blackbox" transformation. In medical imaging, this lack of comprehensibility of the results is a sensitive issue. The integration of known operators into the deep learning environment has proven to be advantageous for the comprehensibility and reliability of the computations. Consequently, we propose the use of the locally linear guided filter in combination with a learned guidance map for general purpose medical image processing. The output images are only processed by the guided filter while the guidance map can be trained to be task-optimal in an end-to-end fashion. We investigate the performance based on two popular tasks: image super resolution and denoising. The evaluation is conducted based on pairs of multi-modal magnetic resonance imaging and cross-modal computed tomography and magnetic resonance imaging datasets. For both tasks, the proposed approach is on par with state-of-the-art approaches. Additionally, we can show that the input image's content is almost unchanged after the processing which is not the case for conventional deep learning approaches. On top, the proposed pipeline offers increased robustness against degraded input as well as adversarial attacks.
Adversarial Factorization Autoencoder for Look-alike Modeling

Digital advertising is performed in multiple ways, for e.g., contextual, display-based and search-based advertising. Across these avenues, the primary goal of the advertiser is to maximize the return on investment. To realize this, the advertiser often aims to target the advertisements towards a targeted set of audience as this set has a high likelihood to respond positively towards the advertisements. One such form of tailored and personalized, targeted advertising is known as look-alike modeling, where the advertiser provides a set of seed users and expects the machine learning model to identify a new set of users such that the newly identified set is similar to the seed-set with respect to the online purchasing activity. Existing look-alike modeling techniques (i.e., similarity-based and regression-based) suffer from serious limitations due to the implicit constraints induced during modeling. In addition, the high-dimensional and sparse nature of the advertising data increases the complexity. To overcome these limitations, in this paper, we propose a novel Adversarial Factorization Autoencoder that can efficiently learn a binary mapping from sparse, high-dimensional data to a binary address space through the use of an adversarial training procedure. We demonstrate the effectiveness of our proposed approach on a dataset obtained from a real-world setting and also systematically compare the performance of our proposed approach with existing look-alike modeling baselines.
Poster

Sharding-based consensus protocols were introduced to enable the parallelization of the consensus work and storage for blockchain systems. However, existing sharding-based consensus algorithms are not sufficiently designed for distributing miners and transactions to shards in a fair and secure manner, which would make the blockchain systems vulnerable to several attacks. To overcome such limitations of the existing sharding-based consensus protocols, we present a new sharding-based Proof-of-Stake (PoS) blockchain protocol using fair and dynamic sharding management. To show the security of the proposed consensus protocol, we numerically analyze attack probabilities and found that the proposed protocol is secure when the number of shards is less than or equal to 6. Moreover, the proposed protocol is approximately 186 times more efficient than Ethereum with the real parameter settings obtained by the Ethereum network.
Empowering knowledge: Political leaders, education, and economic liberalization

Recent literature reports significant effects of political leaders in driving economic policies but does not provide an exhaustive answer as to why. This paper argues that educational background plays an important role in shaping leaders' ideas and beliefs, which in turn matter for policy making in the long run. Using a cross-country data covering 137 countries and regions over 1960–2005, the paper documents a robustly positive relationship between leaders educational attainment and faster liberal reforms, regardless of regime type. The effects are more salient for leaders who majored in economics, social science, and natural science. Moreover, the effects of education seem to be orthogonal to alternative channels such as partisan politics, geopolitical factors, and public opinions.
Preliminary assessment on the application of biochar and spectral‐induced polarization for wastewater treatment

ABSTRACT In this work, we explore the use of biochar as a remediation agent, and the sensitivity of the spectral‐induced polarization method as a remediation monitoring aid. Biochar amended columns were fully saturated with industrial wastewater (olive oil mill waste) with very high concentration of phenols (∼2485 mg/L) and other substances. The biochar‐amended columns achieved very high removal rates of phenols compared to the control (sand only). Geophysical monitoring over the duration of the experiment (10 days) showed changes in the spectral‐induced polarization signal (imaginary conductivity) consistent with phenol removal as confirmed by geochemical monitoring. This experiment confirmed the utility of biochar as a remediation agent. Furthermore, spectral‐induced polarization can serve as long‐term, high resolution, monitoring aid in organic contaminant degradation processes.
Adaptive Sample-Level Graph Combination for Partial Multiview Clustering

Multiview clustering explores complementary information among distinct views to enhance clustering performance under the assumption that all samples have complete information in all available views. However, this assumption does not hold in many real applications, where the information of some samples in one or more views may be missing, leading to partial multiview clustering problems. In this case, significant performance degeneration is usually observed. A collection of partial multiview clustering algorithms has been proposed to address this issue and most treat all different views equally during clustering. In fact, because different views provide features collected from different angles/feature spaces, they might play different roles in the clustering process. With the diversity of different views considered, in this study, a novel adaptive method is proposed for partial multiview clustering by automatically adjusting the contributions of different views. The samples are divided into complete and incomplete sets, while a joint learning mechanism is established to facilitate the connection between them and thereby improve clustering performance. More specifically, the method is characterized by a joint optimization model comprising two terms. The first term mines the underlying cluster structure from both complete and incomplete samples by adaptively updating their importance in all available views. The second term is designed to group all data with the aid of the cluster structure modeled in the first term. These two terms seamlessly integrate the complementary information among multiple views and enhance the performance of partial multiview clustering. Experimental results on real-world datasets illustrate the effectiveness and efficiency of our proposed method.