Reviewerly/exHarmony huggingface dataset:
Robust Ordinal Embedding from Contaminated Relative Comparisons

Existing ordinal embedding methods usually follow a twostage routine: outlier detection is first employed to pick out the inconsistent comparisons; then an embedding is learned from the clean data. However, learning in a multi-stage manner is well-known to suffer from sub-optimal solutions. In this paper, we propose a unified framework to jointly identify the contaminated comparisons and derive reliable embeddings. The merits of our method are three-fold: (1) By virtue of the proposed unified framework, the sub-optimality of traditional methods is largely alleviated; (2) The proposed method is aware of global inconsistency by minimizing a corresponding cost, while traditional methods only involve local inconsistency; (3) Instead of considering the nuclear norm heuristics, we adopt an exact solution for rank equality constraint. Our studies are supported by experiments with both simulated examples and real-world data. The proposed framework provides us a promising tool for robust ordinal embedding from the contaminated comparisons.
Control Design for an Aerial Manipulator for Pick-and-Place Tasks

This paper presents a control architecture for an aerial manipulator operating in indoor environments. The objective is to provide a viable solution to the growing need for indoor assistive technology. The study tries to address the problem of payload pick-and-place with unknown mass. The control structure consists of i) a baseline pitch angle tracking controller that provides satisfactory performance for the quadrotor without a payload; ii) an adaptive augmentation that compensates for the disturbance in the rotational dynamics due to the unknown payload; iii) a horizontal position tracking controller that generates the pitch angle command; iv) a baseline vertical position tracking controller; and v) another adaptive augmentation controller that compensates for the disturbance in the vertical motion from the pick-and-place of the unknown payload. Since the robotic manipulator operates in the vertical plane of symmetry of the quadrotor, the control design is considered for the motion only in this plane. The controller is verified in a simulation environment.
Digital Libraries for Open Science: Using a Socio-Technical Interaction Network Approach

This paper argues that using Socio-Technical Interaction Networks to build on extensively-used Digital Library infrastructures for supporting Open Science knowledge environments. Using a more social -technical approach could lead to an evolutionary reconceptualization of Digital Libraries. Digital Libraries being used as knowledge environments, built upon on the document repositories, will also emphasize the importance of user interaction and collaboration in carrying out those activities. That is to say, the primary goal of Digital Libraries is to help users convert information into knowledge; therefore, Digital Libraries examined in light of socio-technical interaction networks have the potential to shift Digital Libraries from individual, isolated collections to more interoperable, interconnected knowledge-creating repositories that support an evolving relationship between open science users and the Digital Library environment.
Symmetric Face Normalization

Image registration is an important process in image processing which is used to improve the performance of computer vision related tasks. In this paper, a novel self-registration method, namely symmetric face normalization (SFN) algorithm, is proposed. There are three contributions in this paper. Firstly, a self-normalization algorithm for face images is proposed, which normalizes a face image to be reflection symmetric horizontally. It has the advantage that no face model needs to be built, which is always severely time-consuming. Moreover, it can be considered as a pre-processing procedure which greatly decreases the parameters needed to be adjusted. Secondly, an iterative algorithm is designed to solve the self-normalization algorithm. Finally, SFN is applied to the between-image alignment problem, which results in the symmetric face alignment (SFA) algorithm. Experiments performed on face databases show that the accuracy of SFN is higher than 0.95 when the translation on the x-axis is lower than 15 pixels, or the rotation angle is lower than 18°. Moreover, the proposed SFA outperforms the state-of-the-art between-image alignment algorithm in efficiency (about four times) without loss of accuracy.
A flexible calibration method for the intrinsic and mechanical parameters of panoramic line-scan cameras

Panoramic line-scan cameras provide high definition images with a quasi uniform distribution of the sensing elements. Unfortunately, calibration of such devices with classic techniques is not possible making their use, in computer vision field, not straightforward. We propose a flexible and convenient calibration method for the computation of intrinsic and mechanism parameters of such systems by using images of a simple calibration pattern. Unlike existing approaches, the proposed method considers a full geometrical model and does not make any unattainable assumption such as concentricity of images, a priori knowledges about camera poses or coincidence of the rotation axis with the optical center. Using few images taken with free poses, the calibration is performed in two steps. First, the intrinsic parameters are computed using sets of 2D/3D point correspondences that are generated automatically by exploiting the structure of the calibration rig. Then, thanks to the parameterization of the projection of a 3D straight line, the mechanical parameters are computed using points at infinity. For each step, closed form solutions and iterative refinement techniques are presented. The proposed approach is validated with both synthetic and real data collected by our prototype.
Down the deep rabbit hole: Untangling deep learning from machine learning and artificial intelligence

Interest in deep learning, machine learning, and artificial intelligence from industry and the general public has reached a fever pitch recently. However, these terms are frequently misused, confused, and conflated. This paper serves as a non-technical guide for those interested in a high-level understanding of these increasingly influential notions by exploring briefly the historical context of deep learning, its public presence, and growing concerns over the limitations of these techniques. As a first step, artificial intelligence and machine learning are defined. Next, an overview of the historical background of deep learning reveals its wide scope and deep roots. A case study of a major deep learning implementation is presented in order to analyze public perceptions shaped by companies focused on technology. Finally, a review of deep learning limitations illustrates systemic vulnerabilities and a growing sense of concern over these systems.
The Body is Not a Given: Joint Agent Policy Learning and Morphology Evolution

Reinforcement learning (RL) has proven to be a powerful paradigm for deriving complex behaviors from simple reward signals in a wide range of environments. When applying RL to continuous control agents in simulated physics environments, the body is usually considered to be part of the environment. However, during evolution the physical body of biological organisms and their controlling brains are co-evolved, thus exploring a much larger space of actuator/controller configurations. Put differently, the intelligence does not reside only in the agent's mind, but also in the design of their body. We propose a method for uncovering strong agents, consisting of a good combination of a body and policy, based on combining RL with an evolutionary procedure. Given the resulting agent, we also propose an approach for identifying the body changes that contributed the most to the agent performance. We use the Shapley value from cooperative game theory to find the fair contribution of individual components, taking into account synergies between components. We evaluate our methods in an environment similar to the the recently proposed Robo-Sumo task, where agents in a software physics simulator compete in tipping over their opponent or pushing them out of the arena. Our results show that the proposed methods are indeed capable of generating strong agents, significantly outperforming baselines that focus on optimizing the agent policy alone. A video is available at: https://youtu.be/CHlecRim9PI
Efficient chosen-ciphertext secure hybrid encryption scheme tolerating continuous leakage attacks

For any PPT adversary, most traditional leakage-resilient cryptographic primitives cannot ensure that all elements in ciphertext are random. What’s worse, in a continuous leakage setting, there are few leakage-resilient cryptographic primitives available. Currently, not many researchers are studying leakage-resilient hybrid encryption (LR-HE) scheme. Thus, two continuous leakage-resilient hybrid encryption (CLR-HE) schemes are constructed in this paper, and the schemes’ security are proven based on the standard static assumption. Our constructions not only can maintain their claimed security in a continuous leakage setting, but also enjoy better performances. Because of these good performances, our constructions can provide continuous leakage-resilient security in the Internet of Things.
Negotiating on Our Terms: Harnessing the Collective Power of the Consortium to Transform the Journal Subscription Model

Libraries have been dealing with chronic, unsustainable journal pricing for many years. In response to this issue, the Virtual Library of Virginia (VIVA), the academic library consortium of Virginia, formed a Sustainable Journal Pricing Task Force in 2018 charged with designing a model built on VIVA’s mission and collection development priorities, with special attention given to Open Access (OA). Traditionally, libraries start from publishers’ pricing offers and negotiate from that point, but in the envisioned approach, negotiations start with a model proposed by the consortium. VIVA’s approach is designed to be a bridge between the past subscription model and an envisioned future of broadened OA options.
Hierarchical Active Learning with Proportion Feedback on Regions

Learning of classification models in practice often relies on human annotation effort in which humans assign class labels to data instances. As this process can be very time-consuming and costly, finding effective ways to reduce the annotation cost becomes critical for building such models. To solve this problem, instead of soliciting instance-based annotation we explore region-based annotation as the feedback. A region is defined as a hyper-cubic subspace of the input feature space and it covers a subpopulation of data instances that fall into this region. Each region is labeled with a number in [0, 1] (in binary classification setting), representing a human estimate of the positive (or negative) class proportion in the subpopulation. To learn a classifier from region-based feedback we develop an active learning framework that hierarchically divides the input space into smaller and smaller regions. In each iteration we split the region with the highest potential to improve the classification models. This iterative process allows us to gradually learn more refined classification models from more specific regions with more accurate proportions. Through experiments on numerous datasets we demonstrate that our approach offers a new and promising active learning direction that can outperform existing active learning approaches especially in situations when labeling budget is limited and small. Code related to this paper is available at: https://github.com/patrick-luo/hierarchical-active-learning.git .
Design of Feedback Shift Register of Against Power Analysis Attack

For simple hardware structure, high speed encryption and lower power consuming, stream ciphers well suited for constrained environments, such as satellite communications, RFID tag, sensor networks and IoT. Linear feedback shift register (LFSR) is widely used in the design of stream cipher to improve the periodic property of key stream. Since the switching of the status bits is regular, the power consumption of the LFSR is highly correlated in a linear way. The power consumption characteris-tics of stream cipher based on LFSR are vulnerable to leaking initialization vectors (IV) under the power attacks. Basing on the power model of LFSR, a new design of LFSR against power attacks is proposed in this paper. In this countermeasure, the power consumption characteristics of LFSR can be masked by using an additional LFSR and a new filter Boolean function. Compared with the others designs, this de-sign can confused the power consumption trace and can be more easily implemented by circuits.
Device-Free Localization for Human Activity Monitoring

Over the past few decades, human activity monitoring has grabbed considerable research attentions due to greater demand for human-centric applications in healthcare and assisted living. For instance, human activity monitoring can be adopted in smart building system to improve the building management as well as the quality of life, especially for the elderly people who are facing health deterioration due to aging factor, without neglecting the important aspects such as safety and energy consumption. The existing human monitoring technology requires additional sensors, such as GPS, PIR sensors, video camera, etc., which incur cost and have several drawbacks. There exist various solutions of using other technologies for human activity monitoring in a smartly controlled environment, either device-assisted or device-free. A radio frequency (RF)-based device-free indoor localization, known as device-free localization (DFL), has attracted a lot of research effort in recent years due its simplicity, low cost, and compatibility with the existing hardware equipped with RF interface. This chapter introduces the potential of RF signals, commonly adopted for wireless communications, as sensing tools for DFL system in human activity monitoring. DFL is based on the concept of radio irregularity where human existence in wireless communication field may interfere and change the wireless characteristics.
A Framework of Topology-Transparent Scheduling Based on Polynomial Ring

Topology transparent scheduling (TTS) has been widely studied over the last two decades, which provides guaranteed throughput for each node in the network with only knowledge of global parameters. Leveraging different mathematical approaches, the existing TTSs briefly fall into three board categories: 1) the Galois field (GF)-based; 2) the combinatory-based; and 3) the number theory-based schemes. In this letter, a new scheme via the polynomial ring is proposed as a generalization of Chinese remainder theorem (CRT)-based TTS presented recently. It brings significant improvement over the theoretic bounds of both maximum nodal degree supported and frame length required. In addition, we prove that the proposed scheme is also a generic framework of both GF and CRT-based TTS.
Ambiguity-Free Radiometric Calibration for Internet Photo Collections

Radiometrically calibrating nonlinear images from Internet photo collections makes photometric analysis applicable not only to lab data but also to big image data in the wild. However, conventional calibration methods cannot be directly applied to such photo collections. This paper presents a method to jointly perform radiometric calibration for a set of nonlinear images in Internet photo collections. By incorporating the consistency of scene reflectance of corresponding pixels across nonlinear images, the proposed method first estimates radiometric response functions of all the nonlinear images up to a unique exponential ambiguity using a rank minimization framework. The ambiguity is then resolved using the linear edge color blending constraint. Quantitative evaluation using both synthetic and real-world data shows the effectiveness of the proposed method.
Defining the Minimum Security Baseline in a Multiple Security Standards Environment by Graph Theory Techniques

One of the best ways to protect an organization’s assets is to implement security requirements defined by different standards or best practices. However, such an approach is complicated and requires specific skills and knowledge. In case an organization applies multiple security standards, several problems can arise related to overlapping or conflicting security requirements, increased expenses on security requirement implementation, and convenience of security requirement monitoring. To solve these issues, we propose using graph theory techniques. Graphs allow the presentation of security requirements of a standard as graph vertexes and edges between vertexes, and would show the relations between different requirements. A vertex cover algorithm is proposed for minimum security requirement identification, while graph isomorphism is proposed for comparing existing organization controls against a set of minimum requirements identified in the previous step.
A 2-week weather forecast may be as good as it gets

Experiments show how chaos defeats computer models.
Quality-Aware Service Selection for Multi-Tenant Service Oriented Systems Based on Combinatorial Auction

Multi-tenant service-oriented systems (SOSs) have become a major software engineering paradigm in the cloud environment. Instead of serving a single end-user, a multi-tenant SOS provides multiple tenants with similar and yet customized functionalities and potentially different quality-of-service (QoS) values. Multiple tenants' differentiated multi-dimensional quality constraints for the SOS further complicates the NP-hard problem of quality-aware service selection. Existing quality aware service selection approaches suffer from poor success rates of finding a solution, especially in scenarios where tenants' quality constraints are stringent, due to the lack of systematic consideration of three critical issues: 1) the need to fulfil multiple tenants' differentiated quality constraints; 2) the competition among service providers; and 3) the complementarity between services. This paper proposes a novel approach called combinatorial auction-based service selection for multi-tenant SOSs (CASSMT) to support effective quality-aware service selection for multi-tenant SOSs. CASSMT allows service providers to bid for the services of an SOS expressively. Based on received bids (i.e., QoS offers), CASSMT attempts to find a solution that achieves the system developer's optimization goal while fulfilling all tenants' quality constraints for the SOS. When no solution can be found based on the current bids, service providers can improve their bids to increase their chances of winning, which in the meantime, increases the chances of finding a solution. The experimental results show that CASSMT outperforms representative approaches in the success rate of finding a solution and system optimality. Meanwhile, its efficiency, measured by the number of auction rounds and computation time, is demonstrated to be satisfactory in scenarios on different scales.
Healthcare Informatics Using Modern Image Processing Approaches

Medical image segmentation is the first venture for abnormal state image analysis, significantly lessening the multifaceted nature of substance investigation of pictures. The local region-based active contour may have a few burdens. Segmentation comes about to intensely rely on the underlying shape choice which is an exceptionally capable errand. In a few circumstances, manual collaborations are infeasible. To defeat these deficiencies, the proposed method for unsupervised segmentation of viewer's consideration object of medical images given the technique with the help of the shading boosting Harris finder and the center saliency map. Investigated distinctive techniques to consider the image data and present a formerly utilized energy-based active contour method dependent on the choice of high certainty forecasts to allocate pseudo-names consequently with the point of diminishing the manual explanations.
Top Feasible Arm Identification

We propose a new variant of the top arm identification problem, \emph{top feasible arm identification}, where there are $K$ arms associated with $D$-dimensional distributions and the goal is to find $m$ arms that maximize some known linear function of their means subject to the constraint that their means belong to a given set $P \subset R^D$. This problem has many applications since in many settings, feedback is multi-dimensional and it is of interest to perform \emph{constrained maximization}. We present problem-dependent lower bounds for top feasible arm identification and upper bounds for several algorithms. Our most broadly applicable algorithm, TF-LUCB-B, has an upper bound that is loose by a factor of $O(D \log(K))$. Many problems of practical interest are two-dimensional and, for these, it is loose by a factor of $O(\log(K))$. Finally, we conduct experiments on synthetic and real-world datasets that demonstrate the effectiveness of our algorithms. Our algorithms are superior both in theory and in practice to a naive two-stage algorithm that first identifies the feasible arms and then applies a best arm identification algorithm to the feasible arms.
2D Line Draw Hardware Accelerator for Tiny Embedded Processor in Consumer Electronics

In this paper, we present a 2D line draw hardware accelerator for embedded devices. Embedded devices require the display to print out the image for consumers convenience. Therefore, the graphical computational overheads are required to embedded processor. By exploiting the 2D line draw accelerator in embedded devices, the device enhances the performance by decreasing the workload of the image processing. The line drawing accelerator is verified by using FPGA and demonstrated the system validation successfully.
An Asymmetric Controlled Bidirectional Quantum State Transmission Protocol

In this paper, we propose an asymmetric controlled bidirectional transmission protocol. In the protocol, by using the thirteen-qubit entangled state as the quantum channel, Alice can realize the transmission of a two-qubit eq... | Find, read and cite all the research you need on Tech Science Press
Digital language resources and NLP tools

The availability of digital language resources is the essential pre-requisite for both computational and corpus linguistics. In addition, as human communication becomes increasingly dependent upon digital media, digital language resources also become an integral and essential source for the study of language use; in turn computational applications developed based on language resources serve our daily activities. Lastly, as empirical quantitative approaches become the main stream of language sciences, language resources and language processing tools have become part of the basic infrastructure for scientific study of language. In this chapter, we survey the history of Chinese language resource construction as well as the available resources and tools including their applications.
A Bayesian Framework for Intent Prediction in Object Tracking

Engineering Department, University of Cambridge, Trumpington Street, Cambridge, UK, CB2 1PZ In this paper, we introduce a generic Bayesian framework for inferring the intent of a tracked object, as early as possible, based on the available partial sensory observations. It treats the prediction problem, i.e. not estimating the object state such as position, within an object tracking formulation. This leads to a low-complexity implementation of the inference routine with minimal training requirements. The proposed approach utilises suitable stochastic, namely linear Gaussian, models to capture long term dependencies in the object trajectory as dictated by intent. Numerical examples are shown to demonstrate the efficacy of this framework.