Reviewerly/exHarmony huggingface dataset:
Effects of Gender and Prior Experience in Immersive User Experience with Virtual Reality

The purpose of this paper is to study the effect of gender and prior experience with virtual reality (VR) on Immersive User Experience. We propose to use IUX to describe user experience with immersive technologies. IUX includes traditional component of UX (i.e., pragmatic quality and hedonic qualities identification and stimulation) as well as components specific to VR (i.e., presence and cybersickness). We conducted an experiment with 52 students, asking them to perform an aeronautical assembly task in VR. Results show that gender has an effect on two subscales of presence – self-assessment of performance and ability to act – and on hedonic quality stimulation. Prior experience with VR has an effect on self-assessment of performance, pragmatic quality and hedonic quality stimulation. Gender and prior experience with VR have an insignificant effect on cybersickness.
Prostate Segmentation Using Z-Net

In this paper, we proposed a novel architecture of convolutional neural network (CNN), namely Z-net, for segmenting prostate from magnetic resonance images (MRIs). In the proposed Z-net, 5 pairs of Z-block and decoder Z-block with different sizes and numbers of feature maps were assembled in a way similar to that of U-net. The proposed architecture can capture more multi-level features by using concatenation and dense connection. A total of 45 training images were used to train the proposed Z-net and the evaluations were conducted qualitatively on 5 validation images and quantitatively on 30 testing images. In addition, three approaches including pad and cut, 2D resize, and 3D resize for uniforming the size of samples were evaluated and compared. The experimental results demonstrated that the 2D resize is the most suitable approach for the proposed Z-net. Compared to the other two classical CNN architectures, the proposed method was observed with superior performance for segmenting prostate.
Sentiment Analysis from Movie Reviews Using LSTMs

With the advent of social networking and internet, it is very common for the people to share their reviews or feedback on the products they purchase or on the services they make use of or sharing their opinions on an event.These reviews could be useful for the others if analyzed properly.But analyzing the enormous textual information manually is impossible and automation is required.The objective of sentiment analysis is to determine whether the reviews or opinions given by the people give a positive sentiment or a negative sentiment.This has to be predicted based on the given textual information in the form of reviews or ratings.Earlier linear regression and SVM based models are used for this task but the introduction of deep neural networks has displaced all the classical methods and achieved greater success for the problem of automatically generating sentiment analysis information from textual descriptions.Most recent progress in this problem has been achieved through employing recurrent neural networks (RNNs) for this task.Though RNNs are able to give state of the art performance for the tasks like machine translation, caption generation and language modeling, they suffer from the vanishing or exploding gradients problems when used with long sentences.In this paper we use LSTMs, a variant of RNNs to predict the sentiment analysis for the task of movie review analysis.LSTMs are good in modeling very long sequence data.The problem is posed as a binary classification task where the review can be either positive or negative.Sentence vectorization methods are used to deal with the variability of the sentence length.In this paper we try to investigate the impact of hyper parameters like dropout, number of layers, activation functions.We have analyzed the performance of the model with different neural network configurations and reported their performance with respect to each configuration.IMDB bench mark dataset is used for the experimental studies.
Deep Quadruplet Appearance Learning for Vehicle Re-Identification

Vehicle re-identification (Re-ID) plays an important role in intelligent transportation systems. It usually suffers from various challenges encountered on the real-life environments, such as viewpoint variations, illumination changes, object occlusions, and other complicated scenarios. To effectively improve the vehicle Re-ID performance, a new method, called the deep quadruplet appearance learning (DQAL), is proposed in this paper. The novelty of the proposed DQAL lies on the consideration of the special difficulty in vehicle Re-ID that the vehicles with the same model and color but different identities (IDs) are highly similar to each other. For that, the proposed DQAL designs the concept of quadruplet and forms the quadruplets as the input, where each quadruplet is composed of the anchor (or target), positive, negative, and the specially considered high-similar (i.e., the same model and color but different IDs with respect to the anchor) vehicle samples. Then, the quadruplet network with the incorporation of the proposed quadruplet loss and softmax loss is developed to learn a more discriminative feature for vehicle Re-ID, especially discerning those difficult high-similar cases. Extensive experiments conducted on two commonly used datasets VeRi-776 and VehicleID have demonstrated that the proposed DQAL approach outperforms multiple recently reported vehicle Re-ID methods.
MCFF-CNN: Multiscale comprehensive feature fusion convolutional neural network for vehicle color recognition based on residual learning

Automatic vehicle color recognition is very important for video surveillance, especially for intelligent transportation system. Currently, some approaches have been proposed. However, it is still very difficult to recognize the vehicle color correctly in the complex traffic scenes with constantly changing illuminations. To solve this problem, we propose a new network structure - Multiscale Comprehensive Feature Fusion Convolutional Neural Network (MCFF-CNN) based on residual learning for color feature extraction. First, we use MCFF-CNN network to extract the deep color features of the vehicles. Then, we employ support vector machine (SVM) classifier to obtain the final color recognition results. Based on the proposed approach, we have built a system for robust vehicle color recognition in practical traffic scenes. Extensive experimental results show our solution is effective.
Probabilistic Disassembly

Disassembling stripped binaries is a prominent challenge for binary analysis, due to the interleaving of code segments and data, and the difficulties of resolving control transfer targets of indirect calls and jumps. As a result, most existing disassemblers have both false positives (FP) and false negatives (FN). We observe that uncertainty is inevitable in disassembly due to the information loss during compilation and code generation. Therefore, we propose to model such uncertainty using probabilities and propose a novel disassembly technique, which computes a probability for each address in the code space, indicating its likelihood of being a true positive instruction. The probability is computed from a set of features that are reachable to an address, including control flow and data flow features. Our experiments with more than two thousands binaries show that our technique does not have any FN and has only 3.7% FP. In comparison, a state-of-the-art superset disassembly technique has 85% FP. A rewriter built on our disassembly can generate binaries that are only half of the size of those by superset disassembly and run 3% faster. While many widely-used disassemblers such as IDA and BAP suffer from missing function entries, our experiment also shows that even without any function entry information, our disassembler can still achieve 0 FN and 6.8% FP.
Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness

In this paper, we propose a novel Heterogeneous Gaussian Mechanism (HGM) to preserve differential privacy in deep neural networks, with provable robustness against adversarial examples. We first relax the constraint of the privacy budget in the traditional Gaussian Mechanism from (0, 1] to (0, infty), with a new bound of the noise scale to preserve differential privacy. The noise in our mechanism can be arbitrarily redistributed, offering a distinctive ability to address the trade-off between model utility and privacy loss. To derive provable robustness, our HGM is applied to inject Gaussian noise into the first hidden layer. Then, a tighter robustness bound is proposed. Theoretical analysis and thorough evaluations show that our mechanism notably improves the robustness of differentially private deep neural networks, compared with baseline approaches, under a variety of model attacks.
"Understanding the Elephant: The Discourse Approach to Boundary Identification and Corpus Construction for Theory Review Articles"

The goal of a review article is to present the current state of knowledge in a research area.Two important initial steps in writing a review article are boundary identification (identifying a body of potentially relevant past research) and corpus construction (selecting research manuscripts to include in the review).We present a theory-as-discourse approach, which (1) creates a theory ecosystem of potentially relevant prior research using a citation-network approach to boundary identification; and(2) identifies manuscripts for consideration using machine learning or random selection.We demonstrate an instantiation of the theory as discourse approach through a proof-of-concept, which we call the automated detection of implicit theory (ADIT) technique.ADIT improves performance over the conventional approach as practiced in past technology acceptance model reviews (i.e., keyword search, sometimes manual citation chaining); it identifies a set of research manuscripts that is more comprehensive and at least as precise.Our analysis shows that the conventional approach failed to identify a majority of past research.Like the three blind men examining the elephant, the conventional approach distorts the totality of the phenomenon.ADIT also enables researchers to statistically estimate the number of relevant manuscripts that were excluded from the resulting review article, thus enabling an assessment of the review article's representativeness.
Approximate data aggregation in sensor equipped IoT networks

As Internet-of-Things (IoT) networks provide efficient ways to transfer data, they are used widely in data sensing applications.These applications can further include wireless sensor networks.One of the critical problems in sensor-equipped IoT networks is to design energy efficient data aggregation algorithms that address the issues of maximum value and distinct set query.In this paper, we propose an algorithm based on uniform sampling and Bernoulli sampling to address these issues.We have provided logical proofs to show that the proposed algorithms return accurate results with a given probability.Simulation results show that these algorithms have high performance compared with a simple distributed algorithm in terms of energy consumption.
VOS: A new outlier detection model using virtual graph

Outlier detection has been well studied due to its wide applications in both academia and industry, among which graph-based methods have drawn extensive attention in recent years because of their robust expressiveness for various types of datasets. Combining the local information with the implicit connections in the graph representation of the original dataset, in this study, we propose a new outlier detection model named Virtual Outlier Score (VOS). The proposed model constructs a similarity graph by using the top-k similar neighbors of each object, and introduces a virtual node coupling with a collection of virtual edges to generate a k-virtual graph. A tailored Markov random walk process is then performed on the strongly connected virtual graph under the principle that a potential outlier should get more weight to be visited. After reaching equilibrium, the stationary distribution vector is utilized to deduce the virtual outlier score. Furthermore, we provide a theoretical analysis of the proposed VOS model. Experiments on both synthetic and real-world datasets showed that the proposed model obtains an improvement over eight state-of-the-art algorithms under the measures of ROC AUC as well as the outlier discovery curve.
NeuroX: A Toolkit for Analyzing Individual Neurons in Neural Networks

We present a toolkit to facilitate the interpretation and understanding of neural network models. The toolkit provides several methods to identify salient neurons with respect to the model itself or an external task. A user can visualize selected neurons, ablate them to measure their effect on the model accuracy, and manipulate them to control the behavior of the model at the test time. Such an analysis has a potential to serve as a springboard in various research directions, such as understanding the model, better architectural choices, model distillation and controlling data biases. The toolkit is available for download.1
Risk Assessment for Networked-guarantee Loans Using High-order Graph Attention Representation

Assessing and predicting the default risk of networked-guarantee loans is critical for the commercial banks and financial regulatory authorities. The guarantee relationships between the loan companies are usually modeled as directed networks. Learning the informative low-dimensional representation of the networks is important for the default risk prediction of loan companies, even for the assessment of systematic financial risk level. In this paper, we propose a high-order graph attention representation method (HGAR) to learn the embedding of guarantee networks. Because this financial network is different from other complex networks, such as social, language, or citation networks, we set the binary roles of vertices and define high-order adjacent measures based on financial domain characteristics. We design objective functions in addition to a graph attention layer to capture the importance of nodes. We implement a productive learning strategy and prove that the complexity is near-linear with the number of edges, which could scale to large datasets. Extensive experiments demonstrate the superiority of our model over state-of-the-art method. We also evaluate the model in a real-world loan risk control system, and the results validate the effectiveness of our proposed approaches.
Quasiprobability distributions for quantum-optical coherence and beyond

We study the quasiprobability representation of quantum light, as introduced by Glauber and Sudarshan, for the unified characterization of quantum phenomena. We begin with reviewing the past and current impact of this technique. Regularization and convolution methods are specifically considered since they are accessible in experiments. We further discuss more general quantum systems for which the concept of negative probabilities can be generalized, being highly relevant for quantum information science. For analyzing quantum superpositions, we apply recently developed approaches to visualize quantum coherence of states via negative quasiprobability representations, including regularized quasiprobabilities for light and more general quantum correlated systems.
Exploring Computational User Models for Agent Policy Summarization

AI agents support high stakes decision-making processes from driving cars to prescribing drugs, making it increasingly important for human users to understand their behavior. Policy summarization methods aim to convey strengths and weaknesses of such agents by demonstrating their behavior in a subset of informative states. Some policy summarization methods extract a summary that optimizes the ability to reconstruct the agent's policy under the assumption that users will deploy inverse reinforcement learning. In this paper, we explore the use of different models for extracting summaries. We introduce an imitation learning-based approach to policy summarization; we demonstrate through computational simulations that a mismatch between the model used to extract a summary and the model used to reconstruct the policy results in worse reconstruction quality; and we demonstrate through a human-subject study that people use different models to reconstruct policies in different contexts, and that matching the summary extraction model to these can improve performance. Together, our results suggest that it is important to carefully consider user models in policy summarization.
Optimum Efficiency of PV Panel Using Genetic Algorithms to Touch Proximate Zero Energy House (NZEH)

By optimizing the efficiency of a modular simulation model of the PV module structure by genetic algorithm, under several weather conditions, as a portion of recognizing the ideal plan of a Near Zero Energy Household (NZEH), an ideal life cycle cost can be performed. The optimum design from combinations of NZEH-variable designs, are construction positioning, window-to-wall proportion, and glazing categories, which will help maximize the energy created by photovoltaic panels. Comprehensive simulation technique and modeling are utilized in the solar module I-V and for P-V output power. Both of them are constructed on the famous five-parameter model. In addition, the efficiency of the PV panel is established by the genetic algorithm under the standard test conditions (STC) and a comparison between the theoretical and experimental results is done to achieve maximum performance ranging from 0.15 to 0.16, particularly with an error of about - 0.333 for an experimental power of 30 Watts compared with the theoretical power of 30.1 Watts. The results obtained by the genetic algorithm give the best value for efficiency at the range of 16% to 17% of solar radiation, from 500–600 W/m2. These values are almost identical to the efficiency obtained from the results of the operation, where the best value for efficiency in the experimental results was seen to be 15.7%.
The grammar of emoji? Constraints on communicative pictorial sequencing

Emoji have become a prominent part of interactive digital communication. Here, we ask the questions: does a grammatical system govern the way people use emoji; and how do emoji interact with the grammar of written text? We conducted two experiments that asked participants to have a digital conversation with each other using only emoji (Experiment 1) or to substitute at least one emoji for a word in the sentences (Experiment 2). First, we found that the emoji-only utterances of participants remained at simplistic levels of patterning, primarily appearing as one-unit utterances (as formulaic expressions or responsive emotions) or as linear sequencing (for example, repeating the same emoji or providing an unordered list of semantically related emoji). Emoji playing grammatical roles (i.e., ‘parts-of-speech’) were minimal, and showed little consistency in ‘word order’. Second, emoji were substituted more for nouns and adjectives than verbs, while also typically conveying nonredundant information to the sentences. These findings suggest that, while emoji may follow tendencies in their interactions with grammatical structure in multimodal text-emoji productions, they lack grammatical structure on their own.
Experimental analysis of multiple criteria for extractive multi-document text summarization

Automatic text summarization methods are increasingly needed in different fields of knowledge. In the scientific literature, generic extractive multi-document text summarization can be formulated as an optimization problem which involves several criteria. Only two criteria have been considered simultaneously, i.e., content coverage and redundancy reduction, whereas the other ones, relevance and coherence have been considered separately. Therefore, there is a lack of studies comparing the performance of different criteria. For this reason, a comparative study of the different criteria suitable for generic extractive multi-document text summarization is performed here. All possible combinations of two, three, and four criteria have been considered within a multi-objective optimization context. Experiments have been carried out based on datasets from Document Understanding Conferences (DUC), and the combinations of objective functions have been compared and evaluated with Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. Redundancy reduction has been demonstrated as an indispensable criterion, being the coherence the least significant and efficient criterion. The combination that includes content coverage, redundancy reduction, and relevance obtains the most balanced results in terms of average ROUGE and execution time.
Vision-Based Hand Gesture Recognition for Human-Robot Collaboration: A Survey

Recently, human-robot collaboration has attracted many research interests. Unlike traditional industrial robots, human-robot collaboration combines the advantages of robots with the flexibility and cognitive skills of human worker to work together in a shared manufacturing environment. However, to achieve an efficient human-robot collaboration, the communication channels between human workers and robots needs to be tackled. Vision-based hand gesture recognition has been effectively applied as the interface between humans and computer for long time. So, this paper presents a survey of the vision-based hand gesture recognition research in human-robot collaborative manufacturing. We first propose an overall model of vision-based hand gesture recognition for human-robot collaboration. Then, we review three essential technical components of this model: sensor technologies, hand gesture detection and segmentation and hand gesture classification. In addition, the relevant techniques for vision-based hand gesture recognition are compared. Towards the end of this paper, future research trends are discussed.
A Mixing Scheme Using a Decentralized Signature Protocol for Privacy Protection in Bitcoin Blockchain

Bitcoin transactions are not truly anonymous as an attacker can attempt to reveal a user's private information by tracing related transactions. Existing approaches to protect privacy (e.g., mixcoin, shuffle, and blinded token) suffer from a number of limitations. For example, some approaches assume the existence of a trusted third party, rely on exchanges among various currencies, or broadcast sensitive details before mixing. Therefore, there is a real risk of privacy breach or losing tokens. Thus in this paper, we design a mixing scheme with one decentralized signature protocol, which does not rely on a third party or require a transaction fee. Specifically, our scheme uses a negotiation process to guarantee transaction details, which is monitored by the participants. Furthermore, the scheme includes a signature protocol based on the ElGamal signature protocol and secret sharing. The proposed scheme is then proven secure.
GELP: GAN-Excited Linear Prediction for Speech Synthesis from Mel-Spectrogram

for agreeing to read the book in detail, as well as all the past discussions over the years and many to come.I do not understand how we have so far managed to avoid writing a paper together.Thanks to all the friends and colleagues at the various Aalto Speech groups, Aalto Acoustics laboratory, and the National Institute of Informatics in Tokyo.Working in such a diverse and nourishing environment has resulted in some very fruitful cross-pollination of ideas across our various research disciplines.