Reviewerly/exHarmony huggingface dataset:
One-Class Collaborative Filtering with the Queryable Variational Autoencoder

Variational Autoencoder (VAE) based methods for Collaborative Filtering (CF) demonstrate remarkable performance for one-class (implicit negative) recommendation tasks by extending autoencoders with relaxed but tractable latent distributions. Explicitly modeling a latent distribution over user preferences allows VAEs to learn user and item representations that not only reproduce observed interactions, but also generalize them by leveraging learning from similar users and items. Unfortunately, VAE-CF can exhibit suboptimal learning properties; e.g., VAE-CFs will increase their prediction confidence as they receive more preferences per user, even when those preferences may vary widely and create ambiguity in the user representation. To address this issue, we propose a novel Queryable Variational Autoencoder (Q-VAE) variant of the VAE that explicitly models arbitrary conditional relationships between observations. The proposed model appropriately increases uncertainty (rather than reduces it) in cases where a large number of user preferences may lead to an ambiguous user representation. Our experiments on two benchmark datasets show that the Q-VAE generally performs comparably or outperforms VAE-based recommenders as well as other state-of-the-art approaches and is generally competitive across the user preference density spectrum, where other methods peak for certain preference density levels.
Gray Box Modeling Methodology for Runtime Prediction of Apache Spark Jobs

Nowadays, many data centers facilitate data processing and acquisition by developing multiple Apache Spark jobs which can be executed in private clouds with various parameters. Each job might take various application parameters which influence its execution time. Some examples of application parameters can be a selected area of interest in spatiotemporal data processing application or a time range of events in a complex event stream processing application. To predict its runtime accurately, these application parameters shall be considered during constructing its runtime model. Runtime prediction of Spark jobs allows us to schedule them efficiently in order to utilize cloud resources, increase system throughput, reduce job latency and meet customers requirements, e.g. deadlines and QoS. Also, the prediction is considered as important advantage when using a pay-as-you-go pricing model. In this paper, we present a gray box modeling methodology for runtime prediction of each individual Apache Spark job in two steps. The first one is building a white box model for predicting the input RDD size of each stage relying on prior knowledge about its behaviour and taking the application parameters into consideration. The second one is extracting a black box runtime model of each task by observing its runtime metrics according to various allocated resources and variant input RDD sizes. The modeling methodology is validated with experimental evaluation on a real-world application, and the results show a high matching accuracy which reached 83-94% of the actual runtime of the tested application.
How publishers can work with Crossref on data citation

It aims to explain why data citation is important, how publishers and data repositories can do this and what use will be made of the information they provide.There are large benefits to be accrued from sharing research data such as guarantee of reproducibility and transparency.Consistent citation practice around data is essential to helping these benefits to be realized.Data citation metadata is being disseminated and used through its application programming interfaces and the Event Data application programming interface.Event Data extracts this information into a separate service, so data citations are pre-filtered from the Crossref metadata.There are two methods by which publishers can register data citation information with Crossref.The first method is to deposit data citations in the citation section of the metadata, i.e., the part containing the reference list of the article.The second method publishers can use to register data citations with Crossref is to use the relationships section of the metadata.There are a number of services already using Event Data to show information on data citation.To achieve the benefits of data citation, publishers or editors should have a data sharing and citation policy so that they share with their authors and readers.
Video intra prediction using convolutional encoder decoder network

Intra prediction is an effective method for video coding to remove the spatial redundancy of content. Classical intra prediction method usually creates a prediction block by extrapolating the encoded pixels surrounding the target block. However, existing methods cannot guarantee the prediction efficiency for rich textural structure, especially when weak spatial correlation exists between the target block and reference pixels. To remedy this issue, this paper proposes a novel intra prediction method via convolutional encoder-decoder network, which we term IPCED. IPCED can learn and extract the internal representation of reference blocks, and progressively generate a prediction block from this representation. IPCED is a data-driven method, which represents an improvement over hand-crafted methods, and is capable of improving the accuracy of intra prediction. Extensive experimental results demonstrate that IPCED can generate higher-quality intra prediction results, achieves 3.41%, 3.07% and 3.44% bitrate saving for the Y/Cb/Cr channel compared with HEVC baseline, which is significantly beyond existing methods.
Incorporating Data into EFSM Inference

Models are an important way of understanding software systems. If they do not already exist, then we need to infer them from system behaviour. Most current approaches infer classical FSM models that do not consider data, thus limiting applicability. EFSMs provide a way to concisely model systems with an internal state but existing inference techniques either do not infer models which allow outputs to be computed from inputs, or rely heavily on comprehensive white-box traces to reveal the internal program state, which are often unavailable. In this paper, we present an approach for inferring EFSM models, including functions that modify the internal state. Our technique uses black-box traces which only contain information visible to an external observer of the system. We implemented our approach as a prototype.
A Tensor-Based Low-Rank Model for Single-Image Rain Streaks Removal

Images were taken in rainy weather always contain unexpected rain streaks, which severely affect the subsequent image processing procedures in outdoor vision systems. Removing rain streaks from a single image is a challenging task and recently has been investigated extensively. In this paper, we propose a novel tensor-based low-rank model for removing rain streaks from a single rainy image. Our method fully exploits the similar repeated patterns and directional smoothness of rain streaks. Different from the existing matrix-based methods, we stack the rain patches to construct a three-order tensor and characterize the similarity and repeated patterns by considering the low-rankness in tensor form. We further regularize the low-rankness by the efficient tensor nuclear norm (TNN) so that the intrinsic spatial structures of rain streaks can be preserved. Moreover, two unidirectional total variation terms are employed to depict the directional smoothness of rain streaks and the rain-free image. The sparsity of rain streaks is also enhanced by an ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm. We develop an efficient alternating direction method of multipliers (ADMM) to tackle the proposed model. The experimental results on synthetic and real-world rain images show that our method outperforms the state-of-the-art methods quantitatively and visually.
Selective Engineering for Preparing Entangled Steady States in Cavity QED Setup

We propose a dissipative scheme to prepare maximally entangled steady states in cavity QED setup, consisting of two two-level atoms interacting with the two counter-propagating whispering-gallery modes (WGMs) of a microtoroidal resonator. Using spontaneous emission and cavity decay as the dissipative quantum dynamical source, we show that the steady state of this system can be steered into a two-atom single state as well as into a two-mode single state. We probed the compound system with weak field coupled to the system via a tapered fiber waveguide, finding it is possible to determine whether the two atoms or two modes are driven to a maximally entangled state. Through the transmission and reflection measurements, without disturbing the atomic state, when the cavity modes are being driven, or without disturbing the cavity field state, when a single atom being driven, one can get the information about the maximal entanglement. We also investigated for both subsystem, two-atom and two-mode states, the entanglement generation and under what conditions one can transfer entanglement from one subsystem to the other. Our scheme can be selectively used to prepare both maximally entangled atomic state as well as maximally entangled cavity-modes state, providing an efficient method for quantum information processing.
Fluid geochemistry of the Serra Pelada Au-Pd-Pt deposit, Carajás, Brazil: Exceptional metal enrichment caused by deep reaching hydrothermal oxidation

Serra Pelada is a high-grade hydrothermal Au-Pd-Pt deposit within the Carajás mineral province, eastern Amazon craton, which shows an exceptional metal enrichment at carbon-enriched hosted ores (>1000’s ppm Au + Pt + Pd). The main orebody is structurally hosted at the hinge of a recumbent syncline, consisting of dolomitic metasandstones at the outer part of the fold, a layer of carbonaceous metasiltstone in which most orebodies are located, and red siltstone in the core of the fold. Distal hydrothermal alteration comprises spatially separated chlorite – carbon (reduced) and hematite (oxidized) alteration zones. The mineralized zones include silicification and argillic (carbon- kaolinite-, and hematite-rich) alteration zones. Fluid inclusions hosted by quartz and dolomite crystals from distal alteration zones and silicified portions within the mineralized zone record a wide range of salinity (5–22 wt% NaCleqv.) but relatively constant homogenization temperatures (170 ± 25 °C). Pressure and temperature estimations based in the fluid inclusion microthermometry and selenide thermometry constrain the formation of the deposit at 250–270 °C and 1.2–1.7 kbar. Element concentrations of alkali metals, Pb, Cs and to a lesser extent Ba correlate with salinity, whereas redox-sensitive elements including U and Ce vary widely. Gold concentrations from 50 to 500 ppb detected in some inclusions are consistent with the highly oxidizing conditions defined by selenide mineralogy. The presence of uranium and gold within fluid inclusions of the distal hematite alteration zone confirms the oxidizing, metal carrier fluids. The occurrence of hydrothermally precipitated carbon implies that a reducing CH4-bearing fluid was also present, and the structural relationships suggest that mixing between this reducing fluid (and interaction with carbon-upgraded rocks) with an acidic, oxidizing and metal-carrying fluids was the main process driving ore metal precipitation. The genesis of the Serra Pelada Au–Pd-Pt deposit shows similarities with sediment-hosted copper deposits and unconformity-related U ± Au ± PGE deposits, having in common the downflow of highly oxidizing fluids into the hydrothermal domain of the Earth's upper crust. Major differences in metal proportions of these broad classes of ore deposits and other sediment-hosted systems in different ore provinces are primarily caused by differences in source rocks available for oxidative leaching and variations in the geometry of the hydrothermal system.
Deformed landmark fitting for sequential faces

Fitting facial landmarks on unconstrained videos is a challenging task with broad applications. At present, many methods of one-shot landmark fitting have been proposed with varying degrees of success. However, most of them are heavily sensitive to initializations and usually rely on offline-trained static models, which limit their performance on sequential images with extensive variations. Therefore, they usually can’t align the deformed face very well. To address these limitations, we propose a method of deformed landmark fitting (DLF) for sequential faces, which is designed based on active shape model (ASM) and deformation tracking/correction. This method overcomes the loss of consecutive information between frames, and makes full use of the motion variation information of video sequences in time and space dimensions. Firstly, the optical flow values of several possible deformation points on the face are calculated by the large displacement optical flow (LDOF) model, and the tracking of these points in the current frame are performed through the optical flow motion vector. Secondly, the initial shape of face in each frame is established by the locations of these deformation points and the global shape model in ASM algorithm. Finally, on the basis of initial shape, according to the guidance of local texture model in ASM algorithm, different correction strategies are applied to different landmarks for local search, and then each landmark is reasonably suppressed to obtain the ultimate results. Our DLF observably improves the fitting accuracy for deformed faces, and takes full advantage of the continuity among video sequences. Compared with some state-of-the-art landmarkers, extensive experiments on landmark fitting for sequential faces show that our DLF performs outstandingly in terms of accuracy and robustness.
DeepVisual: A Visual Programming Tool for Deep Learning Systems

As deep learning (DL) opens the way to many technological innovations in a wild range of fields, more and more researchers and developers from diverse domains start to take advantage of DLs. In many circumstances, a developer leverages a DL framework and programs the training software in the form of source code (e.g., Python, Java). However, not all of the developers across domains are skilled at programming. It is highly desirable to provide a way so that a developer could focus on how to design and optimize their DL systems instead of spending too much time on programming. To simplify the programming process towards saving time and effort especially for beginners, we propose and implement DeepVisual, a visual programming tool for the design and development of DL systems. DeepVisual represents each layer of a neural network as a component. A user can drag-and-drop components to design and build a DL model, after which the training code is automatically generated. Moreover, DeepVisual supports to extract the neural network architecture on the given source code as input. We implement DeepVisual as a PyCharm plugin and demonstrate its usefulness on two typical use cases.
Towards the Evaluation of the Effects of Ambient Illumination and Noise on Quality of Experience

The physical context, i.e., the characteristics of location and space where the multimedia service is consumed, may strongly influence the overall perceived Quality of Experience (QoE). In this paper, we investigate the effects of ambient illumination and noise on two multimedia consumption scenarios: watching a video on TV and reading a comic strip on tablet. To this aim, we organized an experiment considering different combinations of ambient illumination and introducing a disturbing noise. Then, we conducted a subjective quality assessment involving 20 people, who were asked to rate the perceived QoE using the 5-level Absolute Category Rating (ACR) quality scale and to express their emotions completing the Self-Assessment Manikin (SAM) questionnaire. The impact of illumination and noise on ACR ratings and SAM scores is evaluated computing the Multivariate Analysis of Variance (MANOVA). Finally, a QoE prediction model based on illumination and noise context factors is presented.
Offline Witness Encryption from Witness PRF and Randomized Encoding in CRS Model

Witness pseudorandom functions, in short witness PRFs, (Zhandry, TCC 2016) and witness encryption (Garg et al., ACM 2013) are two powerful cryptographic primitives where the former produce a pseudorandom value corresponding to an instance of an NP language and the latter possesses the ability to encrypt a message with an NP problem. Mostly, these primitives are constructed using computationally expensive tools like obfuscation or multilinear maps. In this work, we build (single relation) witness PRFs using a puncturable pseudorandom function and a randomized encoding in common reference string (CRS) model. Next, we propose construction of an offline witness encryption having short ciphertexts from a public-key encryption scheme, an extractable witness PRF and a randomized encoding in CRS model. Furthermore, we show how to convert our single relation witness PRF into a multi-relation witness PRF and the offline witness encryption into an offline functional witness encryption scheme.
Can Virtual Reality Satisfy Entertainment Needs of the Elderly? The Application of a VR Headset in Elderly Care

Aging has become one of the most significant population trends, and eldercare, particularly regarding emotional and entertainment needs, has become a challenge and an opportunity. Virtual reality (VR) can offer more direct and effective entertainment methods for the elderly. We interviewed ten elderly people in aging community to further know about their social cycles and entertainment activities and concluded their requirements. Then we conducted an experimental study where fourteen elderly people played three selected VR applications while their emotional state and attitude toward the applications were recorded. Results indicate that participants had more positive valence and higher arousal of emotion when they were playing the VR application, and applications related to tourism and easy-to-play games are favored by the elderly. Based on the insights from the user interviews and experimental study, we propose some guidelines of VR applications for the elderly. For example, the contents should be closer to actual life.
Challenges and Mitigation Strategies in Reusing Requirements in Large-Scale Distributed Agile Software Development: A Survey Result

Requirements re-usability in a distributed software development project is applied to increase system productivity, reliability, quality, decreasing system development sprint and maintaining consistency between two identical systems, which later help to reduce both project time and cost. Nowadays, most of the projects are driven by market so that the intention of this research is to identify the challenges faced by practitioners in requirements re-usability in distributed large-scale agile projects and to find out how practitioners apply the concept of re-usability to mitigating those challenges in distributed large-scale agile software development from requirement engineering or re-usability perspective. In this study, a survey is used to identify requirement re-usability challenges and mitigation approach from practitioners. From a series of semi-structured interview, we have identified 14 challenges and 10 mitigation approaches into three categories, such as communication, coordination, and control from the global software engineering perspective. The findings from this research will help industry people to make a decision in their industry oriented activities.
User Experience Study: The Service Expectation of Hotel Guests to the Utilization of AI-Based Service Robot in Full-Service Hotels

With the dramatic development of AI technology, the concept of robotic hotel is entering the public's awareness. Although AI application brings in high efficiency, low labor cost and novelty, practical operation of robotic hotels still faces with challenges. This quantitative research aims at understanding the current user expectation level of AI robotic hotel and robot appliance. Based on that, it tries to make the user classification by demographic, behavioral and attitude factors. By using the refined SERVQUAL model, it gathers the expectation from five dimensions involving tangibles, reliability, responsiveness, assurance and empathy. These research objectives were realized by using survey-designed questionnaires and distributed by a snowball sampling method conducted in Beijing. After validity and reliability test, data collected from the field were analyzed by a variety of inspections. It is found that education, attitude and income level have a significant effect on the expectation to stay in the robotic hotel, which provided the basis of market position for robotic hotel operators. Through regression analysis, the model was established to identify what factors played an important part and how they worked. It is found that tangibles and responsiveness expectation significantly and positively contributed to increases in general user expectation to robotic hotels. This thesis drew up several conclusions, which would help industry players including hoteliers, AI robot suppliers better understand details of the user group in their decision-making process, as well as academic side to formulate a tailored model to evaluate the interaction between AI robots and hotel guests.
A User-Centered Framework for the Design of Usable ATM Interfaces

Nowadays, the design of usable interfaces is a challenge for every software developer. Little or no consideration of the user-centered design guidelines make systems difficult to use, and the embedded systems in the Automated Teller Machines (ATM) are not an exception to this problem. In order to improve and ensure the usability of the user interfaces of software in this domain, several user-centered design methods and techniques were analyzed and applied to formulate a framework for guiding a design process. The paper provides an analysis of a variety of methods used in the self-service technologies domain and by formal design methodologies; for instance, the standard ISO 13047. In addition, the article describes the process followed to select the most appropriate user-centered design methods that fit the specific ATM domain. This analysis includes surveys and expert judgment, conducted with ATM and usability experts, which show a correct selection of the methods.
Enabling Immunology Learning in Virtual Reality Through Storytelling and Interactivity

Immunology concepts typically taught at the college level involve both factual and process-based knowledge and present learning barriers to college students. Immunology knowledge can be difficult for students to visualize and relate to. To help students better understand specific immunology concepts and increase their learning motivation and engagement, we designed the Immunology virtual reality (VR) application. Immunology VR leverages the rich interactivity and immersion offered by virtual reality systems to create a highly interactive and narrative-driven immersive VR experience that takes students on an exciting journey inside the human body. In this paper, we describe the design of the Immunology VR experience, focusing on our use of an interactive digital storytelling approach to enable learning.