Miracle-dz/UniFAR-FTUs huggingface dataset:
Identity-Based Verifiable Aggregator Oblivious Encryption and Its Applications in Smart Grids

Verifiable aggregator oblivious encryption is a useful notion that not only allows an untrusted aggregator to compute aggregated data without learning any other information but also requires the aggregator to generate a proof of the aggregated data so that anyone can verify the accuracy. Since the aggregator does not learn data that may reveal users’ private information and also cannot forge the aggregated data, verifiable aggregator oblivious encryption is suitable for privacy-preserving smart metering. However, NIST has recommended not using public-key-based cryptography in the smart grid because public-key cryptography cannot provide sufficient scalability. Thus, we propose a notion of identity-based verifiable aggregator oblivious encryption and design concrete schemes in this paper. We prove the aggregator oblivious security and aggregator unforgeability of our schemes via the smooth projective hash function and computational Diffie-Hellman assumption. Furthermore, based on our identity-based verifiable aggregator oblivious encryption scheme, we propose an identity-based data aggregation protocol for the smart grid and provide a security analysis in the context of seven typical attacks on smart grids. The implementation of our protocol via the Intel Edison platform shows that it is sufficiently lightweight for resource-constrained smart meters.
Advantages and Necessities of Telehealth Care Service

Correspondence to Kwan Ik Lee, M.S. Bureau of Health Industry Policy, Korea Health Industry Development Institute, 187 Osongsaengmyeong 2-ro, Osong-eup, Heungdeok-gu, Cheongju 28159, Korea Tel: +82-43-713-8487, Fax: +82-43-713-8907, E-mail: fred@khidi.or. kr Copyrightc 2020 The Korean Association of Internal Medicine This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Perspectives
Hypertext Transfer Protocol Version 3 (HTTP/3)

The QUIC transport protocol has several features that are desirable in a transport for HTTP, such as stream multiplexing, per-stream flow control, and low-latency connection establishment. This document describes a mapping of HTTP semantics over QUIC. This document also identifies HTTP/2 features that are subsumed by QUIC, and describes how HTTP/2 extensions can be ported to HTTP/3.
Tunable Floating-Point Adder

In this work, we address the design of an adder in Tunable Floating-Point (TFP) precision. TFP is a variable precision format in which a given precision can be chosen for a single operation by selecting a specific number of bits for significand and exponent in the floating-point representation. By tuning the precision of an algorithm to the minimum precision achieving an acceptable target error, we can make the computation more power efficient. In previous work, we introduced a unit for TFP multiplication. Here, we focus on floating-point addition.
View synthesis in 3d video

In an example, a method of decoding video data includes determining whether a reference index for a current block corresponds to an inter-view reference picture, and when the reference index for the current block corresponds to the inter-view reference picture, obtaining, from an encoded bitstream, data indicating a view synthesis prediction (VSP) mode of the current block, where the VSP mode for the reference index indicates whether the current block is predicted with view synthesis prediction from the inter-view reference picture.
FACE RECOGNITION SYSTEM FOR MACHINE READABLE TRAVEL DOCUMENTS

This paper presents an upright frontal face recognition system, aimed to recognize faces on machine readable travel documents (MRTD). The system is able to handle large image databases with high processing speed and low detection and identification errors. In order to achieve high accuracy eyes are detected in the most probable regions, which narrows search area and therefore reduces computation time. Recognition is performed with the use of eigenface approach. The paper introduces eigenface basis ranking measure, which is helpful in challenging task of creating the basis for recognition purposes. To speed up identification process we split the database into males and females using high - performance AdaBoost classifier. At the end of the paper the results of the tests in speed and accuracy are given.
Structural Pattern Recognition of Fraudster Groups in P2P Transaction Websites

The advent of online platform economy has increased online fraudsters. To detect them, most research explored to recognize characteristics from behavioral data of online users. In those works, each user was regarded as an individual subject, while relationship between users was underestimated. This paper introduced methodology of social network analysis to mine characteristics of relationship among online fraudsters. Using dataset from a Bitcoin trading website, a weighted signed network is constructed. By removing normal user nodes from the network, relationships between fraudsters are clarified. Then, major structural patterns of the snipped network are uncovered by way of community partitioning method. Based on real data from the Bitcoin trading website, 3 typical structures of fraudster groups are found: star-shaped structure, double-core structure and reticular
Computer-supported reflective learning: how apps can foster reflection at work

ABSTRACT This paper discusses the potential of ICT to support reflective learning for professionals. We aggregated data collected in 20 field studies with 12 different applications, involving a total of 321 participants. The applications addressed individual reflection as well as collaborative reflection. Such a systematic analysis with different applications used across industry sectors and companies is unique in the current literature on computer-supported reflective learning in the workplace. Primarily, we assessed the reaction to reflection applications and their effectiveness regarding learning, behaviour change, and organisational impact. In addition, we investigated differences with respect to work experience. Results show that users had a positive reaction to the apps and perceived their use to be beneficial for their work by using them. In collaborative reflection an inexperienced employee can benefit from the experiences and perspectives of more experienced (co-)workers. In contrast, individual reflection was more profitable for more experienced workers. Notwithstanding the overall positive results, the actual implementation of reflection applications requires careful adaptation to the specific organisational and situational context, as well as introductory and accompanying measures to assure efficient and beneficial usage of the tools.
Power/Performance Modeling and Optimization: Using and Characterizing Machine Learning Applications

Energy and power are the main design constraints for modern high-performance computing systems. Indeed, energy eefficiency plays a critical role in performance improvement or energy saving for either state-of-the-art general purpose hardware platforms, such as FinFET-basedmulti-core systems, or widely-adopted applications such as deep learning applications and in particular, convolutional neural networks. To achieve higher energy eefficiency, power and performance models are key in enabling various predictive management algorithms or optimization techniques. To have accurate models, one needs to consider not only technology-related eff ects, including process variation, temperature eff ect inversion, and aging, but also application-related eff ects, such as the interaction between applications with software and hardware layers.In this thesis, we study these e ects and propose to combine machine learning techniques and domain knowledge to learn the performance, power, and energy models forhigh-performance computing systems. For technology-aware multi-core system design, we learn accurate performance and power models for FinFET-based multi-core systems considering various technology e ects. By applying these models, we propose efficient power-/performance-related management algorithms for multi-core systems to 1) increase performance under iso-power constraints; 2) reduce power while keeping the same performance; and 3) decrease aging eff ects with negligible power overhead for the same performance. Forapplication-aware computing system design, we propose a hierarchical framework based on sparse polynomial regression to predict the serving power, runtime, and energy consumption of deep learning applications, including convolutional neural networks. Extensive experimental results con rm the effectiveness of our proposed models, algorithms, and framework.
Design of Polar Codes for Rayleigh Fading Channel using Random Interleaver and DeInterleaver

Rayleigh fading channel in polar codes based on random interleaver and de-interleaver is presented in this paper. Multipath fading channels are obtained by inducing the sub channels based on the transformation of polarizations. This will track the noise and diversity in the channels. The performance of polar code is improved by constructing the dynamic frozen symbol and the Nakagami channel is introduced in estimating diversity system. Random inter leaver and deinterleaver will permute the signals in effective way. Code construction will allow the system to map the address of memory location. Here the polar code encoding system will analyze the characteristics of channels. The results show that effective output is obtained based on polar code fading channel
In-Network Data Aggregation Techniques for WSNs

In this paper we provide a comprehensive review of the existing literature on techniques, approaches and protocols for in-network aggregation in wireless sensor networks. We first define suitable criteria to classify existing solutions, and then describe them by separately addressing the different layers of the protocol stack, which is likely to be needed for optimal performance. To support these techniques and approaches, a discussion regarding efficient data aggregation and researches achievement in Wireless Sensor Networks (WSNs) are presented inclusively in this paper. Keywords— Aggregation; content; context; routing; WSN.
Translating Data from the Laboratory into Simulation: A Computational Framework for Subject-Specific Finite Element Musculoskeletal Simulation

Computational modeling is a powerful tool which has been used to inform decisions made by engineers, scientists, and clinicians for decades. Musculoskeletal modeling has emerged as a computational modeling technique used to understand the interaction between the body and its surroundings. There are several common approaches used for musculoskeletal modeling which take advantage of different model formulations to obtain information of interest. Unfortunately, models with different joint formulations inherit disparities in representations of ligament, muscle, and cartilage at joints of interest. These differences affect the way the joint functions and limit the insight it provides through computational analysis. Musculoskeletal models with high fidelity joint representations in a finite element framework have become increasingly viable in recent years, but three challenges limit progression: model personalization, modeling infrastructure, and computational efficiency. The goal of musculoskeletal modeling is almost entirely to understand the motion of the body, the mechanics of the joints, and the strain on the tissues in subjects performing various activities. These interests require models that act as the subject’s body would – a very complex task. Improving on methods in model personalization for calibrating joint strength, soft tissue response, and modeling geometry will continue to drive this work toward true subject specificity. Previously, software has
Knowledge Reused Outlier Detection

Tremendous efforts have been invested in the unsupervised outlier detection research, which is conducted on unlabeled data set with abnormality assumptions. With abundant related labeled data available as auxiliary information, we consider transferring the knowledge from the labeled source data to facilitate the unsupervised outlier detection on target data set. To fully make use of the source knowledge, the source data and target data are put together for joint clustering and outlier detection using the source data cluster structure as a constraint. To achieve this, the categorical utility function is employed to regularize the partitions of target data to be consistent with source data labels. With an augmented matrix, the problem is completely solved by a K-means-- a based method with the rigid mathematical formulation and theoretical convergence guarantee. We have used four real-world data sets and eight outlier detection methods of different kinds for extensive experiments and comparison. The results demonstrate the effectiveness and significant improvements of the proposed methods in terms of outlier detection and cluster validity metrics. Moreover, the parameter analysis is provided as a practical guide, and noisy source label analysis proves that the proposed method can handle real applications where source labels can be noisy.
Focus budget improvement using optimized wafer edge settings

To reduce defocus from leveling errors at the wafer edge, modern exposure tools offer a broad range of advanced leveling controls. These can be explored fully with minimum experimental effort by simulating alternative set-point curves (z; Rx; Ry) and resulting MA and MSD focus residuals from existing full wafer height maps. In this paper, optimizations are carried out to obtain the best focus edge clearance settings for several DRAM and NAND products, across different layers and exposure tools. The simulated die-fine focus errors are compared to the POR settings and verified with electrical results. Differences across products, layers, and exposure tools are discussed.
Method and device for autostereoscopy

A method for displaying an autostereoscopic image (12) a screen for viewing by the right eye (RA) and the left-eye (LA) is a partially interlaced image information displayed on the screen, and a barrier placed (18; 18 '), so that in each case, wherein in each section (28) in addition to the image information of this designated portion (L12-L15; L2-L5) than, for each eye is only image information associated therewith is visible; the image information (L11, L16; L1, L6) while copying a portion, which is assigned to the same eye for one or both of the abutting portion (32; 30 ") .
Mujeres y jóvenes: principales grupos afectados en México por la contracción económica durante la pandemia

El presente estudio documenta la evolución del mercado laboral mexicano en el año 2020, en el que la pandemia por COVID-19 generó una contracción significativa de la economía. Si bien el efecto de la crisis fue generalizado se encuentra que dos grupos específicos sufrieron impactos importantes. En primer lugar, las mujeres enfrentaron una mayor afectación en su nivel de empleo, lo cual parece estar cercanamente ligado a la dinámica sectorial. En segundo lugar, los jóvenes registraron las mayores pérdidas de empleo y de ingreso, con fuertes incrementos en el número de jóvenes que no estudian ni trabajan. Sin embargo, las dinámicas fueron más complejas de lo esperado, con una fase de cuasi parálisis de la economía, y luego una desigual reapertura, que tuvo repercusiones sobre las trayectorias del mercado de trabajo. Finalmente, en línea con lo esperado se encuentra que hubo un aumento en el porcentaje de la población en pobreza laboral y un incremento en la desigualdad medida por el ingreso per cápita del hogar.
Robot Design for Everyone: Computational Tools that Democratize the Design of Robots

A grand vision in robotics is that of a future wherein robots are integrated in daily human life just as smart phones and computers are today. Such pervasive integration of robots would require faster design and manufacturing of robots that cater to individual needs. For instance, people would be able to obtain customized smart assistant devices such as a home monitor personalized with their child’s favorite fantasy character, a helping hand robot for their specific art project, or a cleaning robot that can clean hard to access spaces of their homes. However, robots of today take years to be created by experts, and are often not customizable. To enable widespread integration of robots, we wish to democratize the robot design processin order to support rapid creation of custom robots for the people and by the people. In recent years, advances in digital fabrication technologies and the availabilityof affordable electronics such as Rasberry Pi, Arduino etc. is enabling rapid creation of smart devices. Unfortunately, currently these technologies are only accessibleto experts because of the skills and domain knowledge needed to build with them. To change this status quo, we present a suite of easy-to-use computational tools forenabling the design of a broad class of robotic devices that casual users might want to create. In particular, we develop tools for designing physical structure and task-specificbehavior of robotic devices with various form factors and functionalities. The key strengths of our tools include intuitive visual interfaces that can support user-in-theloopinteractive design, parameterized domain-specific models and physics-based simulation that can encode complex design aspects, and efficient algorithms that cansearch high-dimensional design spaces at interactive rates for user-preferred solutions. Our tools also automate tedious design steps allowing users to focus on the creative aspects of the design process. Finally, we show how simulation-based feedback and data-driven design can be used to lower the barriers to entry for casual users. We validate our tools by fabricating various prototypes and by conductinguser-studies with novices. In the past, design tools such as those offered by Adobe creative suite and Autodeskhave revolutionized the creation of diverse digital content ranging from images to animation. The tools presented in this thesis take a step towards enabling the same for the domain of robotics.
Target Recognition and Localization of Mobile Robot with Monocular PTZ Camera

The target recognition and location based on the vision sensor is a kind of more intuitive and effective method. The paper designs a human mobile robot and the monocular PTZ camera fixed on the differential driver of the mobile robot for the target recognition and localization. The camera parameters are calibrated using the Zhang Zhengyou calibration method; target recognition is based on the combination of the color and the edge detection. First, the objects with the same color are extracted by the color recognition method (by setting an appropriate threshold value for the HSV color space of the acquired image). Then the target is further extracted by the edge detection (by Hough circle transformation); target location is based on the Similar Triangle Principle because the PTZ camera has pitch, tilt, and zoom characteristics and the monocular vision sensor of the camera with different pitching angles is used to measure the distance between the robot and the target. According to the characteristics of the monocular PTZ camera the mobile robot realizes the target localization and tracking. The simulation and experimental results demonstrate that the mobile robot shows better target recognition and localization in mobile tracking target process and proves the effectiveness of the method.
COMPLEX SELECTION CRITERIA OF CLOUD GATEWAY

КОМПЛЕКСНИЙ КРИТЕРІЙ ВИБОРУ ХМАРНОГО ШЛЮЗУ У даній статті описуються дві реалізації хмарних шлюзів, що надаються компаніями Microsoft і Amazon. Клієнти хочуть набагато більше, ніж збір даних з Інтернету кінця в кінець речей платформи. Microsoft і AWS містять різні компоненти для збору, зберігання і аналізу даних.
Design of CIC based decimation filter structure using FPGA for WiMAX applications

This paper focuses on the e ﬃ cient design and FPGA realization of CIC based decimation ﬁ lter structure for WiMAX application. This structure reduces the sampling rate at each section and lower the power consumption in each section with improved magnitude response. The magnitude responses and device utilization of the ﬁ lter with di ﬀ erent combinations considering di ﬀ erent stages are estimated and compared with the existing structures. MATLAB Simulink environment is used for design and Xilinx Virtex-V XC5VLX110T-3 ﬀ 1136 FPGA is utilized for implementation. It is observed from the results that the passband droop and stopband characteristics of this ﬁ lter structure are improved when the decimation factor of ﬁ rst section is less compared to second section (M1 < M2). On the other hand power consumption of the ﬁ lter is less when the decimation factor of ﬁ rst section is high compared with second section (M1 > M2). The results show that the passband droop improvement is about 38% and stopband attenuation improvement is of 33% for the decimation factor M = 8.
Bridging Quantities in Tables and Text

There is a wealth of schema-free tables on the Web, holding valuable information about quantities on sales and costs, environmental footprint of cars, health data and more. Table content can only be properly interpreted in conjunction with the textual context that surrounds the tables. This paper introduces the quantity alignment problem: bidirectional linking between textual mentions of quantities and the corresponding table cells, in order to support advanced content summarization and faster navigation between explanations in text and details in tables. We present the BriQ system for computing such alignments. BriQ is designed to cope with the specific challenges of approximate quantities, aggregated quantities, and calculated quantities in text that are common but cannot be directly matched in table cells. We judiciously combine feature-based classification with joint inference by random walks over candidate alignment graphs. Experiments with a large collection of tables from the Common Crawl project demonstrate the viability of our methods.