Reviewerly/exHarmony huggingface dataset:
Multimodale Produktbewertungen in Videos von Influencerinnen auf YouTube: Zur parainteraktiven Konstruktion von Warenwelten

Abstract The presented paper is about the evaluation of products in digital styling formats, such as tutorials, hauls and tops&amp;flops. Therein, we provide an approach to analyze specific para-interactive processes specifically addressing a teenage audience. Semiotic as well as interactional approaches are considered to demonstrate, based on video material, how YouTube influencers use multimodal potentials of various text types para-interactively to integrate their (female) recipients into the video itself. This phenomenon leads to the fact, that the audience may not only share the perception of presented products, but also, the influencers’ evaluation.
Programming-by-example for data transformation to improve machine learning performance

In this study, we propose a programming-by-example (PBE)-based data transformation method for feature engineering in machine learning. Data transformation by PBE is not new. However, we utilized the one proposed herein to improve the performance of machine learning in synthesizing a transformation rule from examples. Herein, the system first generates candidate rules, and then chooses the rule that achieves the highest performance in a target machine learning task. We tested this system with the Titanic dataset, and the result shows that the proposed method can avoid worst-case performance compared to the original PBE method.
CloudCFI: Context-Sensitive and Incremental CFI in the Cloud Environment

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Control-Flow Integrity</i> (CFI) is one of the most promising techniques against control-flow hijacking attacks. For <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Commercial Off-the-Shelf</i> (COTS) binaries, a number of solutions provide coarse-grained CFI and thus are context-insensitive, while having the benefit of introducing a low runtime overhead. However, they can hardly defend against elaborately designed attacks due to the inaccuracy of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Control-Flow Graphs</i> (CFGs). This paper presents <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CloudCFI</i> , a context-sensitive and incremental CFI, which specifically makes full use of the characteristic of the cloud environment, where multiple instances of a software run on multiple virtual machines, and the control flow checking result from one software instance could be utilized to handle the control-hijacking occurred on other sibling instances. In <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CloudCFI</i> , the accuracy of the control flow checking can be continuously increased to offer the incremental CFI, and a context-sensitive CFI policy is enforced to determine the validity of the control flow of the execution path through checking the entire execution path instead of the single edge or partial edges in the execution path. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CloudCFI</i> includes the static phase and the runtime phase respectively. Control-flow information and basic-block information is collected through emulation execution in the static phase, and the execution paths are tracked in runtime phase to collect process-tracking information. Next, it recovers the execution path by using basic-block information and process-tracking information, and checks the validity of the control flow by using the control-flow information. A prototype system is implemented and evaluated from several aspects using RIPE and SPEC benchmarks, as well as real-world cloud applications, Memcached and Redis. The evaluation results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CloudCFI</i> can defend against most common control-flow hijacking attacks. Meanwhile, it only introduces a low runtime performance overhead.
A Web-Based Telepathology Framework for Collaborative Work of Pathologists to Support Teaching and Research in Latin America

Early diagnosis in cancer is very important for an appropriate treatment and effective recovery in some cases. The biggest problem in developing countries, such as Latin America, is the delay of diagnosis because poor availability of health services in remote regions and lack of pathologists who are concentrated in main cities. In this context, this paper presents a web-based telepathology framework for collaborative work of pathologists. The evaluation was addressed to analyze the computational performance in terms of time response when several and concurrent simulated user's navigations of whole-slide images are performed. The preliminary results show that the framework is able to support concurrent users with an average response time of 10 s when navigations is performed at the same magnification, which increases until 35 s in average when navigation includes changes of magnification.
GPU-based acceleration of the Linear Complexity Test for random number generator testing

The Linear Complexity Test is a statistical test for verifying the randomness of a binary sequence produced by a random number generator (RNG). It is the most time-consuming test in the widely used randomness testing suite that was published by the National Institute of Standards and Technology (NIST). The slow performance of the original Linear Complexity Test implementation is one of the major hurdles in the RNG testing process. In this work, we present a parallelized implementation of the Linear Complexity Test for GPU computation. We incorporate two levels of parallelism and various design optimization approaches to accelerate the test execution on modern GPU architectures. To further enhance the performance, we also create a hybrid computation approach that uses both CPU and GPU simultaneously. We achieve a speedup of more than 4000 times over the original Linear Complexity Test implementation from NIST (27 times over the previous best implementation of the test).
Deep blind quality evaluator for multiply distorted images based on monogenic binary coding

Perceptual quality evaluation of multiply distorted images has become a very challenging research topic. In this paper, we present a novel and efficient deep blind quality evaluator for multiply distorted images based on monogenic binary coding (MBC). Local complementary structural information and a deep learning method are employed to blindly evaluate the quality of multiply distorted images. First, a monogenic signal representation is utilized to decompose a multiply distorted image into three complementary components: orientation, phase, and magnitude. The quality-predictive features are then determined from the complementary components. Finally, the features are mapped to the human quality score of the multiply distorted image based on the deep neural network. The results on two newly established multiply distorted image subjective databases confirm that our metric has a better prediction performance than existing state-of-the-art full-reference and classical blind metrics.
Three-dimensional modeling of alteration information with hyperspectral core imaging and application to uranium exploration in the Heyuanbei uranium deposit, Xiangshan, Jiangxi, China

Hyperspectral technology is particularly good at identifying hydrothermally altered minerals and can offer support for three-dimensional (3-D) alteration modeling of hydrothermal deposit. We propose a 3-D alteration modeling method with hyperspectral core imaging to deepen hydrothermal alteration research for uranium exploration of the Heyuanbei uranium deposit in the western Xiangshan ore field, Jiangxi, South China. We use the following three steps: (1) identify and map the altered minerals of drill cores, (2) quantify all types of altered minerals using a pixel classification-based statistical algorithm, and (3) establish 3-D alteration model using a parallel section and a triangulated irregular network. Hyperspectral core imaging data from 14 drill cores from the Heyuanbei uranium deposit are used to establish a 3-D alteration model of the superimposed stratigraphy and structural information by this method. The modeling results indicate that large-scale illite and kaolinite–dickite alterations have developed in the Heyuanbei deposit. The illite alteration has the following obvious zoning characteristics: (1) the spatial correlation of short-wavelength illite and kaolinite–dickite, which developed in two volcanic formations, is high, and the alteration is stronger in the upper part of the drill cores than in the lower part of the drill cores; (2) the long-wavelength illite is mainly distributed in the lower volcanic formations. The zoning characteristics reveal that the hydrothermal fluid is acidic in the upper cores, whereas in the lower cores, alkaline fluid is present, and the fluid environment has the characteristics of a transition from early alkaline to late acidic conditions. Moreover, the short-wavelength illite is more closely related to fluorite-hydromica-type uranium mineralization than the long-wavelength illite, and the conversion action from long-wavelength illite to short-wavelength illite is favorable for uranium enrichment. Thus, short-wavelength illite, kaolinite, dickite, and fluorite can be used as prospective indicators for uranium exploration in the Heyuanbei mining area and nearby areas.
Robust global and local color matching in stereoscopic omnidirectional content

Shooting a live-action immersive 360-degree experience, i.e. omnidirectional content (ODC) is a technological challenge as there are many technical limitations which need to be overcome, especially for capturing and post-processing in stereoscopic 3D (S3D). In this paper, we introduce a novel approach and entire system for stitching and color mismatch correction and detection in S3D omnidirectional content, which consists of three main modules: pre-processing, spherical color correction and color mismatch evaluation. The system and its individual modules are evaluated on two datasets, including a new dataset which will be publicly available with this paper. We show that our system outperforms the state of the art in color correction of S3D ODC and demonstrate that our spherical color correction module even further improves the results of the state of the art approaches.
Information reconciliation (IR) for continuous variable quantum key distribution (QKD) over free space optics (FSO) channel

Quantum Key Distribution (QKD) is a communication method which exchanges secret keys using cryptographic protocols involving elements from quantum science. Continuous Variable (CV) QKD is a method to implement key exchange using sampling of Gaussian signals. Reconciliation in CV-QKD is fundamentally realized via coding of the Alice or Bob binary labels of the Gaussian samples using either one-way or interactive communications between the parties, Alice and Bob, over a public authenticated channel. We assume that the communication is performed through an Optical Wireless (OW) or Free Space Optics (FSO) channel. In that case the received signal suffers from stochastic fading due to pointing jitter or atmospheric turbulence. As a result of the channel fading and noise Alice and Bob Gaussian samples will not match. Information Reconciliation (IR) is the phase of the CV-QKD protocol that makes sure that Alice and Bob agree on a common and identical labeling of their samples, i.e. agree on a common stream of bits that we denote as “reconciled key.” The information reconciliation in CV-QKD could be done by one-way or two-way channel coding to correct the actual labels so that they do match. To do so Bob generates a sequence of parity bits using a systematic code. These bits are sent to Alice over an authenticated public channel. Alice then uses her own sequence of labels she obtains after quantization with the redundancy provided by Bob to recover Bob’s binary sequence. In this work we analyze the problem of information reconciliation for continuous variable quantum key distribution over a free space optics channel.
A 3D Touchless Hand Navigation Sensor for Small-Size and Low-Power Applications

Modern electronic devices have been designed to allow many types of human-machine interfaces with flexibility. One of the user-friendly navigations is interfacing a human hand, but the implementation using conventional devices, such as additional controllers or image sensors, requires a larger size or a higher power. In this paper, a small-size and low-power 3D touchless hand navigation sensor (HNS) is presented. The proposed HNS based on a proximity sensor consists of an LED and integrated photosensors. The x- and y-directions can be controlled by tilting a hand in the x-axis and the y-axis without the need of swiping. The distance between the hand and HNS in the z-axis is calculated by the photosensor dedicated to the z-axis. The experimental results show that the proposed HNS is superior to other types of navigation sensors in terms of size and power.
Escrowed decryption protocols for lawful interception of encrypted data

Escrowed decryption schemes (EDSs) are public-key encryption schemes with an escrowed decryption functionality that allows authorities to decrypt encrypted messages under investigation, following a protocol that involves a set of trusted entities called ‘custodians’; only if custodians collaborate, the requesting authority is capable of decrypting encrypted data. This type of cryptosystem represents an interesting trade-off to privacy versus surveillance dichotomy. In this study, the authors propose two EDSs where they use proxy re-encryption to build the escrowed decryption capability, so that custodians re-encrypt ciphertexts, in a distributed way, upon request from an escrow authority, and the re-encrypted ciphertexts can be opened only by the escrow authority. Their first scheme, called EDS, follows an all-or-nothing approach, which means that escrow decryption only works when all custodians collaborate. Their second scheme, called threshold EDS, supports a threshold number of custodians for the escrow decryption operation. They propose definitions of semantic security with respect to the authorities, custodians and external entities, and prove the security of their schemes, under standard pairing-based hardness assumptions. Finally, they present a theoretical and experimental analysis of the performance of both schemes, which show that they are applicable to real-world scenarios.
A comparative analysis of virtual-reality art-making software for the production of VR digital holograms

Virtual reality holography (VRH) is a new art form that synthesizes the qualities of traditional hand-drawing with the unique features of holography and virtual reality (VR) art. In this paper, we compare two VR art-making applications and describe the process of transferring VR imagery to digital holography. This will show how using the specificity of various VR art-making platforms and transferring the resulting images to holography can produce original pieces of art emphasizing the qualities of these new media. Such art productions are likely to enhance the value perception of holography and VR imagery within the sphere of the art establishment.
Explainable recommender system that maximizes exploration

In this work, we illustrate the design of an explainable recommendation framework that seeks to maximize exploration while maintaining recommendation relevance. We use Gated Recurrent Unit (GRU) based Recurrent Neural Networks (RNNs) to model the temporal evolution of users' preference for items. We define an objective metric for exploration and frame the overall optimization loss function to incorporate exploration as a regularizer. We report evaluations on state of the art Netflix and IMDb datasets, and highlight future research agenda for developing an interactive framework based on the proposed algorithm.
FPGA-based approximate calculation system of General Vector Machine

In this paper, a General Vector Machine (GVM) approximate calculation system based on FPGA is introduced. The author designed a parallel computing architecture of GVM on FPGA, discussed a matrix parallel computing method, developed approximation calculation methods of sigmoid function and softmax layer on FPGA. As an example, the paper implemented GVM on FPGA to identify MNIST data sets and tested the recognition rate with 14 different data accuracy of parameters, then gave some suggestions for data accuracy selection for parameters. Finally, the approximate calculation system is implemented and tested on XCKU3P, XC7Z020, XC7VX690 and XCUV190 FPGA chips. The results demonstrate that the computation speed is 112 times higher than CPU (Intel Core(TM) i9-7900X), and the performance can be equivalent to the GPU (NVIDIA GTX 1080 Ti). The approximate calculation system can effectively accelerate the calculation of GVM. It has the characteristics of good acceleration, and is suitable for embedded intelligent devices.
Evaluating the efficacy of traditional fitness tracker recommendations

Wearable devices make self-monitoring easier by the users, who usually tend to increase physical activity and weight loss maintenance over time. But in terms of behavior adaptation to these goals, these devices do not provide specific features beyond monitoring the achievement of daily goals, such as a number of steps or miles walked, and caloric outtake. The purpose of this study is to evaluate the efficacy of the recommendations provided by traditional fitness tracker apps with respect to weight loss scenarios.
Multi‐resource workload mapping with minimum cost in cloud environment

Summary Workload mapping in cloud environment refers to map multiple workloads provided by the cloud users/tenants to the substrate network provided by the cloud providers, which is a NP‐hard problem. The workload is a service demand made to the cloud, which is modeled as a logical network consists of virtual nodes and virtual links. Substrate network is a physical network consists of physical nodes that are inter‐connected via communication links. Devising heuristic methods has become the mainstream of workload mapping problem, which can obtain a feasible solution, but the quality of the solution is not guaranteed. Pointing to this issue, this paper takes the mapping cost of the workloads as the solving objective, and models the workload mapping as a constraint optimization problem. Based on the constraint optimization model, we devise two algorithms to solve the problem. These algorithms can not only find the feasible solution, but also ensure the solution is optimal. Lastly, we have demonstrated the optimality of the proposed algorithms through theoretical proof and evaluated the performance of them through simulation experiment.
Single-channel Hadamard gate by exploiting frequency conversion of single-photon Raman scattering in chiral quantum nanophotonics

To enhance the spatial utility of typical two-port Hadamard gate, we propose a novel single-channel scheme through frequency conversion in &Lambda;-atom-mediated single-photon Raman scattering process in chiral quantum nanophotonics. We demonstrate faithful and efficient gate operations, and quantitatively analyze gate performance. Moreover, by manipulating photon-emitter coupling and frequency detuning, it is confirmed that an arbitrary unitary single-qubit operations are achieved in the presented configuration, including most representative single-qubit Hadamard, X, Z, S, and T gates. Due to the advent of directional emission techniques, the chiral condition is readily experimentally feasible. In addition, we further propose an alternative N-type atom architecture to perform Hadamard operations that are enabled by hyperfine structure in the strong-coupling regime.
Real-Time Tracking Based on Keypoints and Discriminative Correlation Filters

Recently, tracking methods based on discriminative correlation filters (DCF) and CNN features have seen a great improvement in accuracy and performance. However, increasingly complex models and heavy computation burdens can reduce their speed and real-time capability. In this paper, we analyze the key factors that increased computation in the state-of-the-art DCF trackers and provide some solutions for solving these problems. We propose a novel method for real-time tracking based on keypoint consensus clustering and improved DCFs. First, we use the consensus-based keypoint clustering scheme to coarsely locate the bounding box, which employs the geometric compatibility of keypoints and separates correct correspondences from erroneous ones by voting. Next, we propose regions around the estimated location and exploit an improved DCF to track the object. In the new DCF, we modify the core formulation and reduce the number of parameters. In our tracking scheme, we also propose a strategy to collect a training sample set based on keypoints, which contributes to a clear acceleration in training. The experiments are carried out based on two well-known benchmarks: VOT2016 and OTB2015. Compared to the state-of-the-art tracking systems, our method can provide a competitive accuracy with a significant improvement in tracking speed.
Security-Aware Department Matching and Doctor Searching for Online Appointment Registration System

In a modern hospital information system, an online appointment registration system has become a mainstream trend. It brings convenience and reduces waiting time for patients in the hospital. However, it also causes patients' personal privacy disclosure and security vulnerability problems. In order to address these issues, we propose a novel secure-aware online appointment registration system, which can achieve department matching and doctor searching with privacy preservation. First, a patient can describe his/her symptoms with searchable encryption and sends the ciphertext to the cloud server. Then, the electronic health record (EHR) cloud server matches information of the department associated with similar symptoms and sends the department in the ciphertext to the patient. Furthermore, the patient sends his/her requirements with attribute-based keyword search encryption to doctors' profile system (DPS) server, which can search for the appropriate doctors corresponding to the encrypted requirement without decrypting it. The doctors' profiles are sent to the searcher in the ciphertext. At last, the patient can make an online appointment with the expected doctor. The security analysis demonstrates that the system can achieve data confidentiality and integrity, mutual authentication, secure search, anonymity, and trapdoor unlinkability.