Reviewerly/exHarmony huggingface dataset:
Optimizing Discount and Reputation Trade-Offs in E-Commerce Systems: Characterization and Online Learning

Feedback-based reputation systems are widely deployed in E-commerce systems. Evidences showed that earning a reputable label (for sellers of such systems) may take a substantial amount of time and this implies a reduction of profit. We propose to enhance sellers’ reputation via price discounts. However, the challenges are: (1) The demands from buyers depend on both the discount and reputation; (2) The demands are unknown to the seller. To address these challenges, we first formulate a profit maximization problem via a semiMarkov decision process (SMDP) to explore the optimal trade-offs in selecting price discounts. We prove the monotonicity of the optimal profit and optimal discount. Based on the monotonicity, we design a QLFP (Q-learning with forward projection) algorithm, which infers the optimal discount from historical transaction data. We conduct experiments on a dataset from to show that our QLFP algorithm improves the profit by as high as 50% over both the classical Q-learning and speedy Q-learning algorithm. Our QLFP algorithm also improves the profit by as high as four times over the case of not providing any price discount.
Chat-crowd: A Dialog-based Platform for Visual Layout Composition

Paola Cascante-Bonilla, Xuwang Yin, Vicente Ordonez, Song Feng. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations). 2019.
Performance of On-Site Earthquake Early Warning System Using Strong-Motion Records from Recent Earthquakes

The method of on-site earthquake early warning was proposed in the past decade to enhance seismic preparedness and safety measures for areas close to the earthquake epicenter. The method leverages critical information contained in the initial 3 s of a P wave that arrives early to predict possibly intense ground shaking at a site. In this study, two types of empirical relationships are developed: (1) Pd3-PGV relationship for predicting the horizontal peak ground velocity (PGV) based on the peak displacement in the first 3 s of the vertical motion (Pd3); and (2) τc−Mw relationship for estimating the moment magnitude (Mw) of an earthquake based on a ground-motion period parameter (τc). Performance of these empirical relationships in on-site earthquake early warning framework is examined using global earthquake records from the most updated Next Generation Attenuation (NGA-West2) strong-motion database. Region-specific empirical predictive equations for California and Japan are proposed based on the state of the art mixed-effect regression to separate ground-motion variability between different earthquakes and that within an earthquake. Applicability and inapplicability of the empirical models in the near-fault condition are investigated using ground motions with strong velocity pulses. The overarching goal of study is to leverage the comprehensive database and state of the art regression techniques to facilitate understanding and engineering practice of on-site earthquake early warning in a variety of regions and in the near-fault condition.
An Attribute-Based High-Level Image Representation for Scene Classification

Scene classification is increasingly popular due to its extensive usage in many real-world applications such as object detection, image retrieval, and so on. Traditionally, the low-level hand-crafted image representations are adopted to describe the scene images. However, they usually fail to detect semantic features of visual concepts, especially in handling complex scenes. In this paper, we propose a novel high-level image representation which utilizes image attributes as features for scene classification. More specifically, the attributes of each image are firstly extracted by a deep convolution neural network (CNN), which is trained to be a multi-label classifier by minimizing an element-wise logistic loss function. The process of generating attributes can reduce the “semantic gap” between the low-level feature representation and the high level scene meaning. Based on the attributes, we then build a system to discover semantically meaningful descriptions of the scene classes. Extensive experiments on four large-scale scene classification datasets show that our proposed algorithm considerably outperforms other state-of-the-art methods.
RSA Cryptography based Multi-Modal Biometric Identification System for High-Security Application

In recent days the requirements of Biometric Identification System (BIS) increased enormously.BIS Unimodal Biometric systems (UM-BS) have different kinds of problems like non-universality, noisy data, unacceptable error rate and spoof attacks.These limitations are solved by using multi-modal Biometric systems (MM-BS).MM-BS uses two or more individual modalities, like face, Palm, iris, retina, fingerprint, etc.This paper has introduced featurelevel fusion and Rivest Shamir Adleman (RSA) encryption based FEP-RSA-MM biometrics system.This FEP-RSA-MM system has taken combination of Face, iris and Palm biological characters for individual Identification.FEP-RSA-MM was implemented by using MATLAB and the performance were calculated and assessed in terms of Recall, Sensitivity, Specificity, Accuracy, F-Score, Precision, Mean Square Error, Root Mean Square (RMS) Error, etc.The performance of this FEP-RSA-MM system mainly depends on the accuracy.The accuracy of FEP-RSA-MM system is 93.33 % and it improved compared to two existing methods GF-FLF-MM, SIFT-KNN-MM, FLF-GSO-MM and SLF-PSO-MM.
Service Traceroute: Tracing Paths of Application Flows

Traceroute is often used to help diagnose when users experience issues with Internet applications or services. Unfortunately, probes issued by classic traceroute tools differ from application traffic and hence can be treated differently by routers that perform load balancing and middleboxes within the network. This paper proposes a new traceroute tool, called Service traceroute. Service traceroute leverages the idea from paratrace, which passively listens to application traffic to then issue traceroute probes that pretend to be part of the application flow. We extend this idea to work for modern Internet services with support for identifying the flows to probe automatically, for tracing of multiple concurrent flows, and for UDP flows. We implement command-line and library versions of Service traceroute, which we release as open source. This paper also presents an evaluation of Service traceroute when tracing paths traversed by Web downloads from the top-1000 Alexa websites and by video sessions from Twitch and Youtube. Our evaluation shows that Service traceroute has no negative effect on application flows. Our comparison with Paris traceroute shows that a typical traceroute tool that launches a new flow to the same destination discovers different paths than when embedding probes in the application flow in a significant fraction of experiments (from 40% to 50% of our experiments in PlanetLab Europe).
Online Tibetan Handwriting Recognition for Large Character Set on New Databases

The online handwriting recognition of Tibetan characters is still in its infancy. For further research, an online handwriting database of large Tibetan character set was developed, and a recognition research was carried out on this database as a baseline result. The Northwest Minzu University Online Tibetan Handwriting Database (NMU-OLTHWDB) contains 7240 different types of characters, and the sample number in each type is 5000. The total number of samples is [Formula: see text]. The database covers Tibetan Character Collection, Information Technology Tibetan Coded Character set (Extension Set A), and Information Technology Tibetan Coded Character set (Extension Set B). The characters in the database are composed of 170 types of different components. We studied the online handwritten Tibetan recognition software also, and the character feature extraction, classifier training, and the statistics and analysis of the recognition results on the test set were mainly introduced. The character features included the direction attribute coefficients and spatial combination, and the feature matrix was compressed by Linear Discriminate Analysis (LDA). A quick classifier was designed by a modified quadratic discriminate function (QMQDF), and was trained with 4500 sets of samples. In the large character set, the recognition rates of top 1, top 3, top 5, and top 10 were 75.2%, 89.56%, 93.02%, and 95.96%, respectively. Moreover, an online handwriting recognition system for Tibetan large character set was designed with good performance.
Large-Scale Answerer in Questioner's Mind for Visual Dialog Question Generation.

Answerer in Questioner's Mind (AQM) is an information-theoretic framework that has been recently proposed for task-oriented dialog systems. AQM benefits from asking a question that would maximize the information gain when it is asked. However, due to its intrinsic nature of explicitly calculating the information gain, AQM has a limitation when the solution space is very large. To address this, we propose AQM+ that can deal with a large-scale problem and ask a question that is more coherent to the current context of the dialog. We evaluate our method on GuessWhich, a challenging task-oriented visual dialog problem, where the number of candidate classes is near 10K. Our experimental results and ablation studies show that AQM+ outperforms the state-of-the-art models by a remarkable margin with a reasonable approximation. In particular, the proposed AQM+ reduces more than 60% of error as the dialog proceeds, while the comparative algorithms diminish the error by less than 6%. Based on our results, we argue that AQM+ is a general task-oriented dialog algorithm that can be applied for non-yes-or-no responses.
Tracking Algorithms Aided by the Pose of Target

The traditional target tracking algorithms have utilized the information on the target position. With the development of radar high-resolution technology, it is possible to obtain the pose of target. In this paper, two target tracking algorithms aided by the pose of target are proposed. First, the pose of the target is estimated in the real time by the high-resolution range profile, and then, the pose is added to the target measurement equation. Because the relationship between the pose and the motion parameters of the targets is nonlinear, the extended Kalman filter algorithm aided by the pose of target (Pose-EKF) and the unscented Kalman filter algorithm aided by the pose of target (Pose-UKF) are proposed. The results of simulation demonstrate that compared with the traditional extended Kalman filter algorithm (EKF) and the traditional Unscented Kalman filter algorithm (UKF), the proposed algorithm can greatly improve the target tracking accuracy (position precision and velocity precision) and the convergence speed. The pose measurement error has a little effect on the tracking performance. The difference in the tracking accuracy between Pose-EKF and Pose-UKF is very little. But the Pose-EKF is better than Pose-UKF in terms of computation time, but Pose-EKF fails and Pose-UKF is effective when the pose is critical.
Distractor-Aware Deep Regression for Visual Tracking

In recent years, regression trackers have drawn increasing attention in the visual-object tracking community due to their favorable performance and easy implementation. The tracker algorithms directly learn mapping from dense samples around the target object to Gaussian-like soft labels. However, in many real applications, when applied to test data, the extreme imbalanced distribution of training samples usually hinders the robustness and accuracy of regression trackers. In this paper, we propose a novel effective distractor-aware loss function to balance this issue by highlighting the significant domain and by severely penalizing the pure background. In addition, we introduce a full differentiable hierarchy-normalized concatenation connection to exploit abstractions across multiple convolutional layers. Extensive experiments were conducted on five challenging benchmark-tracking datasets, that is, OTB-13, OTB-15, TC-128, UAV-123, and VOT17. The experimental results are promising and show that the proposed tracker performs much better than nearly all the compared state-of-the-art approaches.
Machine Learning, Music and Creativity: An Interview with Rebecca Fiebrink

Rebecca Fiebrink is a Senior Lecturer at Goldsmiths, University of London, where she designs new ways for humans to interact with computers in creative practice. As a computer scientist and musician, much of her work focuses on applications of machine learning to music, addressing research questions such as: ‘How can machine learning algorithms help people to create new musical instruments and interactions?’ and ‘How does machine learning change the type of musical systems that can be created, the creative relationships between people and technology, and the set of people who can create new technologies?’ Much of Fiebrink’s work is also driven by a belief in the importance of inclusion, participation, and accessibility. She frequently uses participatory design processes, and she is currently involved in creating new accessible technologies with people with disabilities, designing inclusive machine learning curricula and tools, and applying participatory design methodologies in the digital humanities. Fiebrink is the developer of the Wekinator: open-source software for real-time interactive machine learning, whose current version has been downloaded over 10,000 times. She is the creator of a MOOC titled “Machine Learning for Artists and Musicians.” She was previously an Assistant Professor at Princeton University, where she co-directed the Princeton Laptop Orchestra. She has worked with companies including Microsoft Research, Sun Microsystems Research Labs, Imagine Research, and Smule. She has performed with a variety of musical ensembles playing flute, keyboard, and laptop. She holds a Ph.D. in Computer Science from Princeton University.
Technical Contributions to the Quality of Telerehabilitation Platforms: Case Study—ePHoRt Project

This chapter proposes three main technical contributions for the development of a telerehabilitation platform, named ePHoRT, for patients recovering from hip surgery.The first contribution is the application of a diffuse 3D model for the detection of rehabilitation exercises after hip surgery.The model applies fuzzy logic, which allows identifying in real time if a patient is performing a right or wrong movement, assisted by an avatar in 3D.The avatar copies the movements of the patient through a Kinect camera.The second contribution involves the proposal of an iterative method to improve the usability of telerehabilitation platforms along the development life cycle.The proposed method involves the use of an inspection method and includes protocols and instruments.This method has been validated in the ePHoRT project.Finally, the chapter describes accessibility guidelines for educational resources.It proposes accessibility standards for the content of educational resources in video and PDF formats in the telerehabilitation platform according to the Web Content Accessibility Guidelines (WCAG).
Introduction to PyTorch, Tensors, and Tensor Operations

PyTorch has been evolving as a larger framework for writing dynamic models. Because of that, it is very popular among data scientists and data engineers deploying large-scale deep learning frameworks. This book provides a structure for the experts in terms of handling activities while working on a practical data science problem. As evident from applications that we use in our day-to-day lives, there are layers of intelligence embedded with the product features. Those features are enabled to provide a better experience and better services to the user.
Threat Modeling the Cloud: An Ontology Based Approach

Critical Infrastructures (CIs) such as e-commerce, energy, transportation, defense, monitoring etc., form the basis of the modern ICT society, and these CI’s increasingly utilize ICT services such as the Cloud to provide for scalable, robust and cost-efficient services. Consequently, the resilience of the CI is directly connected with the resilience of the underlying Cloud infrastructure. However, performing a Cloud threat analysis (TA) is a challenging task given the complex interconnection of underlying computing and communication services. Thus, the need is of a comprehensive TA approach that can holistically analyze the relation across system level requirements and Cloud vulnerabilities. We target achieving such a requirement based threat analysis by developing an ontology depicting the relations among actors involved in the Cloud ecosystem. The ontology comprehensively covers requirement specifications, interaction among the Cloud services and vulnerabilities violating the requirements. By mapping the ontology to a design structure matrix, our approach obtains security assessments from varied actor perspectives. We demonstrate the effectiveness of our approach by assessing the security of OpenStack, an open source Cloud platform, covering user requirements and services involved in Cloud operations.
Formative Assessment of Inquiry Skills for Responsible Research and Innovation Using 3D Virtual Reality Glasses and Face Recognition

This exploratory study examines the experience and views of students about 3D Virtual Reality Glasses (3DVRG) and e-authentication systems. The authors developed the “Virtual Reality Classroom” App, which is an Open Educational Resource based on 360 photos of the renowned “Bletchley Park”. Participants were 2 groups of students from the UK and Brazil who explored in pairs this App using a 3DVRG in the classroom and also completed a formative assessment activity using the TeSLA face recognition system. Our research question focuses on whether the students’ interactions through the 3DVRG enhance learning and assessment of inquiry skills for Responsible Research and Innovation (RRI). Findings suggested that the combination of authentic scenario, interactive tasks and assessment-in-context helped students acquire new information and connect with their existing knowledge and practice the inquiry skills together. These interactions enhanced their immersion, particularly for those who found the activities fun as they did not experience motion sickness. Three types of interactions were identified between students with: the virtual space (1), their peer (2) and the topic (3). These three interactions propitiated, respectively, students virtual, social and cognitive presence, which supported their experiential learning.
Unconditionally Secure Distributed Oblivious Polynomial Evaluation

Oblivious polynomial evaluation (OPE) was first introduced by Naor and Pinkas in 1999. An OPE protocol involves a receiver, R who holds a value, $$\alpha $$ and a sender, S with a private polynomial, f(x). OPE allows R to compute $$f(\alpha )$$ without revealing either $$\alpha $$ or f(x). Since its inception, OPE has been established as an important building block in many distributed applications. In this article we investigate a method of achieving unconditionally secure distributed OPE (DOPE) in which the function of the sender is distributed amongst a set of n servers. Specifically, we introduce a model for DOPE based on the model for distributed oblivious transfer (DOT) described by Blundo et al. in 2002. We then describe a protocol that achieves the security defined by our model. Our DOPE protocol is efficient and achieves a high level of security. Furthermore, our proposed protocol can also be used as a DOT protocol with little to no modification.
Blockchain and sustainable growth

We are at a unique moment in history: our society is in transition from an industrial economy to one defined by a new set of technologies, ranging from digitalization to nanotechnology. Among the latest waves of digitalization is blockchain—a technology that many say promises to redefine trust, transparency and inclusion across the world. Blockchain, however, is a relatively immature technology and can create as many problems as it solves. What it has offered so far is a series of key insights into emerging technologies and how we can approach them in a rapidly changing world.
Pupil segmentation in the light-field camera and its relation to 3D object positions and the reconstructed depth of field

A ray-trace simulation of the light-field camera is used to calculate point source responses as a function of 3D source positions. Each point source location yields a unique and well-determined segmented-pupil pattern in the lenslet array’s focal plane, with lateral object offsets changing the pattern’s location and symmetry, and defocus distances altering the pattern’s diameter. Segmented-pupil images can thus be used to infer point sources’ 3D locations. Numerical simulations show that the centroids and widths of segmented pupil images can be used to deduce lateral image positions to the size of a detector pixel, and image defocus to the accuracy of the lenslet focal length. In sparse-source cases, such as, e.g., fluorescence microscopy or particle tracking, 3D point-source locations can thus be accurately determined from the observed point source response patterns. The degree of pupil segmentation also directly constrains the ability to refocus light-field images—for image defocus distances large enough that the number of pupil segments exceeds the number of pixels within a “whole” pupil behind a single lenslet, the image can no longer be brought to focus numerically, thus defining the light-field camera’s depth of field. This constraint implies a depth of field larger than the usual imaging depth of focus by a factor of the number of detector pixels per lenslet, consistent with the general expectation.
Parallelism-flexible Convolution Core for Sparse Convolutional Neural Networks on FPGA

The performance of recent CNN accelerators falls behind their peak performance because they fail to maximize parallel computation in every convolutional layer from the parallelism that varies throughout the CNN. Furthermore, the exploitation of multiple parallelisms may reduce calculation-skip ability. This paper proposes a convolution core for sparse CNN that leverages multiple types of parallelism and weight sparsity efficiently to achieve high performance. It alternates dataflow and scheduling of parallel computation according to the available parallelism of each convolutional layer by exploiting both intra- and inter-output parallelism to maximize multiplier utilization. In addition, it eliminates redundant multiply-accumulate (MACC) operations due to weight sparsity. The proposed convolution core enables both abilities with ease of dataflow control by using a parallelism controller for scheduling parallel MACCs on the processing elements (PEs) and a weight broadcaster for broadcasting non-zero weights to the PEs according to the scheduling. The proposed convolution core was evaluated on 13 convolutional layers in a sparse VGG-16 benchmark. It outperforms the baseline architecture for dense CNN that exploits intra-output parallelism by 4x speedup. It achieves 3x effective GMACS over prior arts of CNN accelerator in total performance.