Reviewerly/exHarmony huggingface dataset:
MINER: Multi-Interest Matching Network for News Recommendation

Personalized news recommendation is an essential technique to help users find interested news.Accurately matching user's interests and candidate news is the key to news recommendation.Most existing methods learn a single user embedding from user's historical behaviors to represent the reading interest.However, user interest is usually diverse and may not be adequately modeled by a single user embedding.In this paper, we propose a poly attention scheme to learn multiple interest vectors for each user, which encodes the different aspects of user interest.We further propose a disagreement regularization to make the learned interests vectors more diverse.Moreover, we design a category-aware attention weighting strategy that incorporates the news category information as explicit interest signals into the attention mechanism.Extensive experiments on the MIND news recommendation benchmark demonstrate that our approach significantly outperforms existing state-of-the-art methods.1 https://news.google.com/
Capsule Network-Based Deep Transfer Learning Model for Face Recognition

Face recognition (FR) is a technique for recognizing individuals through the use of face photographs. The FR technology is widely applicable in a variety of fields, including security, biometrics, authentication, law enforcement, smart cards, and surveillance. Recent advances in deep learning (DL) models, particularly convolutional neural networks (CNNs), have demonstrated promising results in the field of FR. CNN models that have been pretrained can be utilized to extract characteristics for effective FR. In this regard, this research introduces the GWOECN-FR approach, a unique grey wolf optimization with an enhanced capsule network-based deep transfer learning model for real-time face recognition. The proposed GWOECN-FR approach is primarily concerned with reliably and rapidly recognizing faces in input photos. Additionally, the GWOECN-FR approach is preprocessed in two steps, namely, data augmentation and noise reduction by bilateral filtering (BF). Additionally, for feature vector extraction, an expanded capsule network (ECN) model can be used. Additionally, grey wolf optimization (GWO) combined with a stacked autoencoder (SAE) model is used to identify and classify faces in images. The GWO algorithm is used to optimize the SAE model’s weight and bias settings. The GWOECN-FR technique’s performance is validated using a benchmark dataset, and the results are analyzed in a variety of aspects. The GWOECN-FR approach achieved a TST of 0.03 s on the FEI dataset, whereas the AlexNet-SVM, ResNet-SVM, and AlexNet models achieved TSTs of 0.125 s, 0.0051 s, and 0.0062 s, respectively. The experimental results established that the GWOECN-FR technology outperformed more contemporary approaches.
A Kalman-Filter-Incorporated Latent Factor Analysis Model for Temporally Dynamic Sparse Data

With the rapid development of services computing in the past decade, Quality-of-Service (QoS)-aware selection of Web services has become a hot yet thorny issue. Conducting warming-up tests on a large set of candidate services for QoS evaluation is time consuming and expensive, making it vital to implement accurate QoS-estimators. Existing QoS-estimators barely consider the temporal patterns hidden in QoS data. However, such data are naturally time dependent. For addressing this critical issue, this study presents a Kalman-filter-incorporated latent factor analysis (KLFA)-based QoS-estimator for accurate representation to temporally dynamic QoS data. Its main idea is to make the user latent features (LFs) time dependent, while the service ones time consistent. A novel iterative training scheme is designed, where the user LFs are learned through a Kalman filter for precisely modeling the temporal patterns, and the service ones are alternatively trained via an alternating least squares algorithm for precisely representing the historical QoS data. Empirical studies on large-scale and real Web service QoS datasets demonstrate that the proposed KLFA model significantly outperforms state-of-the-art QoS-estimators in estimation accuracy for dynamic QoS data.
A novel feature selection for evolving compact dispatching rules using genetic programming for dynamic job shop scheduling

Because of advances in computational power and machine learning algorithms, the automated design of scheduling rules using Genetic Programming (GP) is successfully applied to solve dynamic job shop scheduling problems. Although GP-evolved rules usually outperform dispatching rules reported in the literature, intensive computational costs and rule interpretability persist as important limitations. Furthermore, the importance of features in the terminal set varies greatly among scenarios. The inclusion of irrelevant features broadens the search space. Therefore, proper selection of features is necessary to increase the convergence speed and to improve rule understandability using fewer features. In this paper, we propose a new representation of the GP rules that abstracts the importance of each terminal. Moreover, an adaptive feature selection mechanism is developed to estimate terminals’ weights from earlier generations in restricting the search space of the current generation. The proposed approach is compared with three GP algorithms from the literature and 30 human-made rules from the literature under different job shop configurations and scheduling objectives, including total weighted tardiness, mean tardiness, and mean flow time. Experimentally obtained results demonstrate that the proposed approach outperforms methods from the literature in generating more interpretable rules in a shorter computational time without sacrificing solution quality.
How robust are wearable eye trackers to slow and fast head and body movements?

Abstract How well can modern wearable eye trackers cope with head and body movement? To investigate this question, we asked four participants to stand still, walk, skip, and jump while fixating a static physical target in space. We did this for six different eye trackers. All the eye trackers were capable of recording gaze during the most dynamic episodes (skipping and jumping). The accuracy became worse as movement got wilder. During skipping and jumping, the biggest error was 5.8 ∘ . However, most errors were smaller than 3 ∘ . We discuss the implications of decreased accuracy in the context of different research scenarios.
Massively parallel universal linear transformations using a wavelength-multiplexed diffractive optical network

Large-scale linear operations are the cornerstone for performing complex computational tasks. Using optical computing to perform linear transformations offers potential advantages in terms of speed, parallelism, and scalability. Previously, the design of successive spatially engineered diffractive surfaces forming an optical network was demonstrated to perform statistical inference and compute an arbitrary complex-valued linear transformation using narrowband illumination. We report deep-learning-based design of a massively parallel broadband diffractive neural network for all-optically performing a large group of arbitrarily selected, complex-valued linear transformations between an input and output field of view, each with Ni and No pixels, respectively. This broadband diffractive processor is composed of Nw wavelength channels, each of which is uniquely assigned to a distinct target transformation; a large set of arbitrarily selected linear transformations can be individually performed through the same diffractive network at different illumination wavelengths, either simultaneously or sequentially (wavelength scanning). We demonstrate that such a broadband diffractive network, regardless of its material dispersion, can successfully approximate Nw unique complex-valued linear transforms with a negligible error when the number of diffractive neurons (N) in its design is ≥2NwNiNo. We further report that the spectral multiplexing capability can be increased by increasing N; our numerical analyses confirm these conclusions for Nw > 180 and indicate that it can further increase to Nw ∼ 2000, depending on the upper bound of the approximation error. Massively parallel, wavelength-multiplexed diffractive networks will be useful for designing high-throughput intelligent machine-vision systems and hyperspectral processors that can perform statistical inference and analyze objects/scenes with unique spectral properties.
Visualization in virtual reality: a systematic review

Rapidly growing virtual reality (VR) technologies and techniques have gained importance over the past few years, and academics and practitioners have been searching for efficient visualizations in VR. To date, the emphasis has been on the employment of game technologies. Despite the growing interest and potential, visualization studies have lacked a common baseline in the transition period of 2D visualizations to immersive ones. To this end, the presented study aims to provide a systematic literature review that explains the state-of-the-art research and future trends in visualization in virtual reality. The research framework is grounded in empirical and theoretical works of visualization. We characterize the reviewed literature based on three dimensions: (a) Connection with visualization background and theory, (b) Evaluation and design considerations for virtual reality visualization, and (c) Empirical studies. The results from this systematic review suggest that: (1) There are only a few studies that focus on creating standard guidelines for virtual reality, and each study individually provides a framework or employs previous studies on traditional 2D visualizations; (2) With the myriad of advantages provided for visualization and virtual reality, most of the studies prefer to use game engines; (3) Although game engines are extensively used, they are not convenient for critical scientific studies; and (4) 3D versions of traditional statistical visualization techniques, such as bar plots and scatter plots, are still commonly used in the data visualization context. This systematic review attempts to add a clear picture of the emerging contexts, different elements, and interdependencies to the literature.
An adaptive reinforcement learning-based multimodal data fusion framework for human–robot confrontation gaming

Playing games between humans and robots have become a widespread human-robot confrontation (HRC) application. Although many approaches were proposed to enhance the tracking accuracy by combining different information, the problems of the intelligence degree of the robot and the anti-interference ability of the motion capture system still need to be solved. In this paper, we present an adaptive reinforcement learning (RL) based multimodal data fusion (AdaRL-MDF) framework teaching the robot hand to play Rock-Paper-Scissors (RPS) game with humans. It includes an adaptive learning mechanism to update the ensemble classifier, an RL model providing intellectual wisdom to the robot, and a multimodal data fusion structure offering resistance to interference. The corresponding experiments prove the mentioned functions of the AdaRL-MDF model. The comparison accuracy and computational time show the high performance of the ensemble model by combining k-nearest neighbor (k-NN) and deep convolutional neural network (DCNN). In addition, the depth vision-based k-NN classifier obtains a 100% identification accuracy so that the predicted gestures can be regarded as the real value. The demonstration illustrates the real possibility of HRC application. The theory involved in this model provides the possibility of developing HRC intelligence.
Efficient big data model selection with applications to fraud detection

As the volume and complexity of data continues to grow, more attention is being focused on solving so-called big data problems. One field where this focus is pertinent is credit card fraud detection. Model selection approaches can identify key predictors for preventing fraud. Stagewise Selection is a classic model selection technique that has experienced a revitalized interest due to its computational simplicity and flexibility. Over a sequence of simple learning steps, stagewise techniques build a sequence of candidate models that is less greedy than the stepwise approach. This paper introduces a new stochastic stagewise technique that integrates a sub-sampling approach into the stagewise framework, yielding a simple tool for model selection when working with big data. Simulation studies demonstrate the proposed technique offers a reasonable trade off between computational cost and predictive performance. We apply the proposed approach to synthetic credit card fraud data to demonstrate the technique’s application.
Computerized adaptive test and decision trees: A unifying approach

In the last few years, several articles have proposed decision trees (DTs) as an alternative to computerized adapted tests (CATs). These works have focused on showing the differences between the two methods with the aim of identifying the advantages of each of them and thus determining when it is preferable to use one method or another. In this article, Tree-CAT, a new technique for building CATs is presented. Unlike the existing work, Tree-CAT exploits the similarities between CATs and DTs. This technique allows the creation of CATs that minimise the mean square error in the estimation of the examinee’s ability level, and controls the item’s exposure rate. The decision tree is sequentially built by means of an innovative algorithmic procedure that selects the items associated with each of the tree branches by solving a linear program. In addition, our work presents further advantages over alternative item selection techniques with exposure control, such as instant item selection or simultaneous administration of the test to an unlimited number of participants. These advantages allow accurate on-line CATs to be implemented even when the item selection method is computationally costly.
Enabling Cooperative Privacy-preserving Personalized search in cloud environments

With the booming of the information society, individuals and companies have been generating huge amounts of data every second. As a result, as a new storage tool, cloud computing has provided great convenience for data storage and application. However, it is worth noting that security and privacy issues in cloud computing have hindered its further development. To solve this problem, researchers proposed to encrypt the sensitive data. However, the encrypted data brought some follow-up issues such as an increased computational overhead and information retrieval inconvenience. In this paper, to enable users to obtain the most satisfactory results in searching outsourced encrypted data and reduce the computational overhead of cloud servers as well, the Cooperative Privacy-preserving Personalized Search (CPPS) scheme in cloud environments is proposed, which makes use of matrix encryption to ensure the privacy of the user. Combining the search of multiple users has greatly reduced the computing cost of the cloud server and guaranteed the accuracy of the user’s personalized search results at the same time. Experiments on the Yelp dataset indicate that the CPPS scheme has low index construction overhead, low computational overhead and communication overhead while providing users with personalized search results.
Overview of the ImPACT Tough Robotics Challenge and Strategy for Disruptive Innovation in Safety and Security

The ImPACT Tough Robotics Challenge (ImPACT-TRC) is a national project of the Japan Cabinet Office (2014–2018, 62 PIs and 300 researchers, 30 MUSD/5 years) that focuses on tough robotic technologies to provide solutions to disaster response, recovery, and preparedness. It consists of sub-projects of six types of robot platforms and several component technologies integrated with the robots. One of them is the Cyber Rescue Canine suits for monitoring dogs’ behavior and commanding their movement, which has shown high effectiveness in regular exercises of the Japan Rescue Dog Association. Another platform is a new serpentine robot, Active Scope Camera, which can crawl and levitate in gaps of a few cm to search in rubble piles. Structural assessment and radiation measurement were performed by this robot in Fukushima-Daiichi from December 2016 to February 2017. The other serpentine robots showed high mobility in ducts, in and out of pipes, on uneven terrain, and on vertical ladders, and climbed a 1-m-high step by a 1.7-m-long body. The Omni Gripper can grasp a wide variety of targets, even with sharp edges, without the need for precise control by using the jamming phenomenon. The robust flight of a new drone, PF-1 under difficult conditions contributed to the response operations in the Northern Kyushu Heavy Rain Disaster by gathering high-resolution images of inaccessible areas in July 2017. The WAREC-1 can move on four legs or on two legs, or crawl, and can climb vertical ladders as well. The Construction Robot has a double-swing dual-arm mechanism, operator assistance by bird’s-eye view images created by a drone and multiple cameras, and assistance by force and touch feedback. It can perform both high-power tasks and precise tasks remotely. All of these technologies have been demonstrated at the Field Evaluation Forums, which have been organized twice a year since the beginning of the project. These forums have promoted the communication between researchers, production companies, service providers, and users in order to achieve disruptive innovation not only in technology but also in industry and society.
A theory on individual characteristics of successful coding challenge solvers

Background Assessing a software engineer’s ability to solve algorithmic programming tasks has been an essential part of technical interviews at some of the most successful technology companies for several years now. We do not know to what extent individual characteristics, such as personality or programming experience, predict the performance in such tasks. Decision makers’ unawareness of possible predictor variables has the potential to bias hiring decisions which can result in expensive false negatives as well as in the unintended exclusion of software engineers with actually desirable characteristics. Methods We conducted an exploratory quantitative study with 32 software engineering students to develop an empirical theory on which individual characteristics predict the performance in solving coding challenges. We developed our theory based on an established taxonomy framework by Gregor (2006). Results Our findings show that the better coding challenge solvers also have better exam grades and more programming experience. Furthermore, conscientious as well as sad software engineers performed worse in our study. We make the theory available in this paper for empirical testing. Discussion The theory raises awareness to the influence of individual characteristics on the outcome of technical interviews. Should the theory find empirical support in future studies, hiring costs could be reduced by selecting appropriate criteria for preselecting candidates for on-site interviews and potential bias in hiring decisions could be reduced by taking suitable measures.
A multi-agent dynamic system for robust multi-face tracking

The paper presents a new architecture framework in the field of expert and intelligent systems which is based on four paradigms: a novel multi-agent dynamic system architecture (MADS), an extended Belief Desire Intention (EBDI) agent community, autonomy-oriented entities (AOEs), and deep learning concepts. The main impact of the proposed framework is a new approach, or even a new way of thinking, which enables integration of the concepts of deep learning, a conventional approach to solving the domain problem, cognitive agents with mental attitudes, and concepts of nature-inspired computing. All these allow the effective use of the framework in the field of intelligent and expert systems. The significance of the framework lies in its flexibility and adaptability based on the formal logical description of EBDI agents, the definition of the behaviour of AOEs, the use of classical modules for domain problem solving, and modules based on deep-learning concepts. We believe that the example of the adaptation of the proposed architecture framework to robust multi-face tracking illustrates the significance of the proposed framework. In this paper, MADS is adapted to the first two stages of a face de-identification pipeline: robust face detection and multi-face tracking. The proposed architecture of MADS has a two-level hierarchical organization. At the first level there is a manager designed as an Extended Belief Desire Intention (mEBDI) agent. The extension of a manager BDI agent consists of a convolutional neural network-based face detector, a set of autonomous-oriented entities for the elimination of false positive face detections, and a trajectory memory. At the second level, there are many tracking agents (trEBDIs) which consist of a basic BDI agent extended with a face tracker based on position and scale correlation filters, a visual appearance memory, and a trajectory memory. The mEBDI and trEBDI agents are defined by the modal logic and are described at the implementation level. The proposed architecture for a robust multi-face tracking system was tested on a subset of YouTube music videos. The qualitative results, as well as the preliminary quantitative results expressed by the standard testing metrics, demonstrate the effective adaptation of the proposed multi-agent dynamic architecture to a robust multi-face tracking system.
Let a Non-barking Watchdog Bite: Cliptographic Signatures with an Offline Watchdog

We study how to construct secure digital signature schemes in the presence of kleptographic attacks. Our work utilizes an offline watchdog to clip the power of subversions via only one-time black-box testing of the implementation. Previous results essentially rely on an online watchdog which requires the collection of all communicating transcripts (or active re-randomization of messages). We first give a simple but generic construction, without random oracles, in the partial-subversion model in which key generation and signing algorithms can be subverted. Then, we give the first digital signature scheme in the complete-subversion model in which all cryptographic algorithms can be subverted. This construction is based on the full-domain hash. Along the way, we enhance the recent result of Russell et al. (CRYPTO 2018) about correcting a subverted random oracle.