Reviewerly/exHarmony huggingface dataset:
Counterfactual Trojan horse attack

Trojan horse attack is a common eavesdropping strategy which can attack various quantum secure communication systems. Its basic idea is to send auxiliary photons into a legitimate communicator's apparatuses and steal information by analyzing the reflected photons. In this paper, we consider a different kind of Trojan horse attack, the so-called counterfactual Trojan horse attack, which has not been studied in detail so far. In such an attack, the eavesdropper may steal the secret information by ``ghost'' photons, which can spontaneously avoid being detected, even if the detector is an ideal one. We present the details and requirements of such an attack. We also illustrate our results by considering two protocols, the ping-pong protocol and the counterfactual quantum cryptography. Furthermore, we discuss the nature of the counterfactual Trojan horse attack and the strategy to successfully deal with it. Our results indicate that additional resources may be required for the protection against such an attack.
Performance Analysis of BlockChain-based Medical Records Management System

Medical diagnostics such as medical tests, x-rays etc. are stored in two ways- Physical records and Online records, which cannot be accessed by patients without authorized permission. Even doctors/medical professionals do not get this data during crucial times. This could be avoided by sharing medical data of patients to the doctors and patient directly. Present storage method is also at a security threat of being tampered and accessed by unauthorized. This paper proposes blockchain network to address the issues related to medical records system which is easily accessed by the patient or doctor to take better and effective decisions ensuring immutability. Doctors view/update past visits for a medical record, if given the permission to by the respective patients. The proposed model uses Hyperledger Composer, Composer REST server, Hyperledger Fabric and Angular 4. Hyperledger Fabric is a permissioned blockchain framework thus allowing the model to have permission control. The UI allows doctors/patients to easily access the patients records as and when required. The patients grant permission to a particular doctor when required and revoke access once the doctor as completed a visit. Similarly every doctor is allowed to view a patients data only when granted permission and is then allowed to update the history. Only a doctor can update the patient's history to ensure data integrity.
XANES spectroscopy of sulfides stable under reducing conditions

Abstract X-ray absorption near-edge structure (XANES) spectroscopy is a powerful technique to quantitatively investigate sulfur speciation in geologically complex materials such as minerals, glasses, soils, organic compounds, industrial slags, and extraterrestrial materials. This technique allows non-destructive investigation of the coordination chemistry and oxidation state of sulfur species ranging from sulfide (2–oxidation state) to sulfate (6+ oxidation state). Each sulfur species has a unique spectral shape with a characteristic K-edge representing the s → p and d hybridization photoelectron transitions. As such, sulfur speciation is used to measure the oxidation state of samples by comparing the overall XANES spectra to that of reference compounds. Although many S XANES spectral standards exist for terrestrial applications under oxidized conditions, new sulfide standards are needed to investigate reduced (oxygen fugacity, fO2, below IW) silicate systems relevant for studies of extraterrestrial materials and systems. Sulfides found in certain meteorites (e.g., enstatite chondrites and aubrites) and predicted to exist on Mercury, such as CaS (oldhamite), MgS (niningerite), and FeCr2S4 (daubréelite), are stable at fO2 below IW-3 but rapidly oxidize to sulfate and/or produce sulfurous gases under terrestrial surface conditions. XANES spectra of these compounds collected to date have been of variable quality, possibly due to the unstable nature of certain sulfides under typical (e.g., oxidizing) laboratory conditions. A new set of compounds was prepared for this study and their XANES spectra are analyzed for comparison with potential extraterrestrial analogs. S K-edge XANES spectra were collected at Argonne National Lab for FeS (troilite), MnS (alabandite), CaS (oldhamite), MgS (niningerite), Ni1–xS, NiS2, CaSO4 (anhydrite), MgSO4, FeSO4, Fe2(SO4)3, FeCr2S4 (daubréelite), Na2S, Al2S3, Ni7S6, and Ni3S2; the latter five were analyzed for the first time using XANES. These standards expand upon the existing S XANES end-member libraries at a higher spectral resolution (0.25 eV steps) near the S K-edge. Processed spectra, those that have been normalized and “flattened,” are compared to quantify uncertainties due to data processing methods. Future investigations that require well-characterized sulfide standards, such as the ones presented here, may have important implications for understanding sulfur speciation in reduced silicate glasses and minerals with applications for the early Earth, Moon, Mercury, and enstatite chondrites.
Open science practices in higher education: Discussion of survey results from research and teaching staff in Germany

Aspects of open science and scholarly practices are often discussed with a focus on research and research dissemination processes. There is currently less discussion on open science and its influence on learning and teaching in higher education, and reversely. This paper discusses open science in relation to educational practices and resources and reports on a study to investigate current educational practices from the perspective of open science. We argue that offering students opportunities via open educational practices raises their awareness of future open science goals and teaches them the skills needed to reach those goals. We present online survey results from 210 participants with teaching responsibility at higher education institutions in Germany. While some of them try to establish more open learning and teaching settings, most respondents apply rather traditional ways of learning and teaching. 60% do not use open educational resources – many have not even heard of them – nor do they make their courses open for an online audience. Participants’ priority lies in resource accuracy and quality and we still see a gap between the benefit of open practices and their practicability and applicability. The paper contributes to the general discussion of open practices in higher education by looking at open science practices and their adaptation to the learning and teaching environment. It formulates recommendations for improvements of open practice support and infrastructure.
Reinforcement learning with convolutional reservoir computing

Recently, reinforcement learning models have achieved great success, mastering complex tasks such as Go and other games with higher scores than human players. Many of these models store considerable data on the tasks and achieve high performance by extracting visual and time-series features using convolutional neural networks (CNNs) and recurrent neural networks respectively. However, these networks have very high computational costs because they need to be trained by repeatedly using the stored data. In this study, we propose a novel practical approach called reinforcement learning with a convolutional reservoir computing (RCRC) model. The RCRC model uses a fixed random-weight CNN and a reservoir computing model to extract visual and time-series features. Using these extracted features, it decides actions with an evolution strategy method. Thereby, the RCRC model has several desirable features: (1) there is no need to train the feature extractor, (2) there is no need to store training data, (3) it can take a wide range of actions, and (4) there is only a single task-dependent weight matrix to be trained. Furthermore, we show the RCRC model can solve multiple reinforcement learning tasks with a completely identical feature extractor.
Swallow: A Versatile Accelerator for Sparse Neural Networks

Sparse neural networks (SNNs) are emerging as a promising technique for resource-limited intelligent embedded systems because of the compact model size and the un compromised accuracy. Recently, most of the dedicated neural network accelerators are beginning to exploit the sparsity of neural network models for performance boost and energy saving. However, existing sparsity-aware accelerators fail to support both sparse weights and activations in neural networks or support them at the same time for both convolutional (Conv) layers and fully connected (FC) layers, which dominate the computational time of neural networks. In this article, we propose a novel sparsity-aware accelerator architecture, called Swallow, to sufficiently improve the inference performance by eliminating ineffectual weights and activations of neural networks. Swallow comprises: 1) a 2-D systolic architecture that fully utilizes the sparsity of both weights and activations in both Conv and FC layers and 2) a sparsity-aware dataflow which is optimized to reuse both weights and activations and to achieve high processing element (PE) utilization by sparse matrix multiplication tiling. Comprehensive evaluations based on a place-and-route process show that Swallow, with 614 GOP/s peak performance and 1.26-W power, outperforms a state-of-the-art sparsity-aware accelerator Cambricon-X by 1.32× in term of energy efficiency.
With respect to what?

Direct manipulation interactions on projections are often incorporated in visual analytics applications. These interactions enable analysts to provide incremental feedback to the system in a semi-supervised manner, demonstrating relationships that the analyst wishes to find within the data. However, determining the precise intent of the analyst is a challenge. When an analyst interacts with a projection, the inherent ambiguity of some interactions leads to a variety of possible interpretations that the system could infer. Previous work has demonstrated the utility of clusters as an interaction target to address this "With Respect to What" problem in dimension-reduced projections. However, the introduction of clusters introduces interaction inference challenges as well. In this work, we discuss the interaction space for the simultaneous use of semi-supervised dimension reduction and clustering algorithms. Within this exploration, we highlight existing interaction challenges of such interactive analytical systems, describe the benefits and drawbacks of introducing clustering, and demonstrate a set of interactions from this space.
Automated Assessment of the Visual Design of Android Apps Developed with App Inventor

One way to teach computing in K-12 is through the development of mobile applications with App Inventor. Although already broadly used worldwide, there is still a need for support for the assessment of the applications created by the students. Existing rubrics focusing mostly on programming concepts do not cover more comprehensively the performance-based assessment of user interface design concepts, important for the usability and aesthetics of the applications. Thus, in order to support the assessment of the visual design of apps based on its compliance with design theory and guidelines, we developed the CodeMaster UI Design - App Inventor rubric in the context of computing education. In order to facilitate its application in practice, we automated the assessment of applications created with App Inventor through a static code analysis by an online tool. We evaluated the reliability and validity of the rubric based on the automated assessment of 1,775 projects from the App Inventor Gallery. The results indicate that the rubric can be considered reliable (Cronbach's alpha = 0.84). In terms of construct validity, there is also evidence of convergent validity. The results presented in this article can be used to support the assessment of computing education in practice as well as to point out further research opportunities.
Knowledge-driven decision analytics for commercial banking

Although the corporate relationship manager seems to be the key enabler in commercial banking, the personal relationship sales model is not a sustainable model for the paradigm shift in digital financial markets. In this research, we propose a knowledge-driven decision analytics approach to improve the decision process. However, most of the corporate client documents processed in banks are not well-structured and the traditional analysis approach does not consider the document structure, which carries rich semantic information. We propose a document structure-based text representation approach with incorporating auxiliary information in the predictive analytics of unstructured data to improve the performance in the document classification task. The proposed approach significantly outperforms the traditional whole document approach which does not take into considerations of the document structure. With the proposed approach, knowledge can be effectively and efficiently used for business decisions and planning to improve the competitive advantage and substantiality of banks.
Bounds on the Length of Functional PIR and Batch Codes

A functional k-Private Information Retrieval (k-PIR) code of dimension s consists of n servers storing linear combinations of s linearly independent information symbols. Any linear combination of the s information symbols can be recovered by k disjoint subsets of servers. The goal is to find the minimum number of servers for given k and s. We provide lower bounds on the minimum number of servers and constructions which yield upper bounds on this number. For k ≤ 4, exact bounds on this number are proved. Furthermore, we provide some asymptotic bounds. The problem coincides with the well known PIR problem based on a coded database to reduce the storage overhead, when each linear combination contains exactly one information symbol. If any multiset of size k of linear combinations from the linearly independent information symbols can be recovered by k disjoint subset of servers, then the servers form a functionalk-batch code. A functional k-batch code is a functional k-PIR code, where all the k linear combinations in the multiset are equal. We provide some bounds on the minimum number of servers for functional k-batch codes. In particular we present a random construction and a construction based on simplex codes, Write-Once Memory (WOM) codes, and Random I/O (RIO) codes.
Health and wellness monitoring using ambient sensor networks

Smart homes equipped with ambient wireless sensor networks provide new opportunities to help older adults age-in-place, improve their quality of life and help better manage their health and wellness. In this paper, we present a methodology that estimates occupants’ status as active, sedentary, in-b ed, out-of-home and unobservable, their location in the house, and their daily activities related to overall health and wellness. The methodology is used to visualize and examine the daily patterns and activities of older adults living in their own homes and participating in a smart home research project. The proposed location and status estimation algorithm is highly accurate as validated by a mobile app that prompts participants with questions about the estimated time of their daily activities. A case study involving a significant health-related life event is presented where the participant’s account of changes in her patterns and activities through bi-weekly interviews are shown to confirm inferences based on the results of the proposed methodology.
Architectural Optimization of Parallel Authenticated Encryption Algorithm for Satellite Application

High-speed data communication is becoming essential for many applications, including satellite communication. The security algorithms associated with the communication of information are also required to have high-speed for coping up with the communication speed. Moreover, the Authenticated Encryption (AE) algorithms provide high-speed communication and security services include data encryption, authentication, and integrity. The AE algorithms are available with serial and parallel architectures; among them, the Galois Counter Mode (GCM) algorithm has a parallel architecture. The Synthetic Initialization Vector (SIV) mode in the AES-GCM-SIV algorithm provides the nonce misuse protection using the GCM algorithm. Besides, reduced data throughput is provided using the AES-GCM-SIV algorithm as compared to the AES-GCM algorithm. This work introduced a parallel algorithm with re-keying and randomization of the initialization vector for high data throughput, nonce misuse protection, and side-channel attack protection. The implementation of the proposed algorithm is performed on Field Programmable Gate Array (FPGA) and it's compared with the FPGA implementations of AES-GCM, AES-GCM-SIV, and recently introduced algorithms. The optimization of the proposed algorithm and security analysis is presented for space application using different optimizations and a combination of optimizations.
EduBloud: A Blockchain-based Education Cloud

Cloud service for education purpose is the critical infrastructure for the smart campus. The state-of-the-art education clouds are usually developed and maintained by individual schools. The isolation nature makes these data being tampered easily, which leads to malicious tampering and information fragmentation. The blockchain is a perfect technology to address these issues. By leveraging the advantages of the public blockchain, consortium blockchain, and private blockchain, we propose EduBloud, a heterogeneous blockchain system empowered education cloud. The system showed higher reliability, lower latency, higher data throughput, and better economic efficiency than homogeneous blockchain implementations.
Similar Face Recognition Using the IE-CNN Model

In the field of face recognition, similar face recognition is difficult due to the high degree of similarity of the face structure. The following two factors are needed to make progress in this field: (i) the availability of large scale similar face training datasets, and (ii) a fine-grained face recognition method. With the above factors fulfilled, we make two contributions. First, we show how a large scale similar face dataset (SFD) can be assembled by a combination of automation and human in the loop, and divide the dataset into five grades according to different degrees of similarity. Second, a new fine-grained face feature extraction method is proposed to solve this problem using the attention mechanism which combines the Internal Features and External Features. The Labeled Faces in the Wild (LFW) database, CASIA-WebFace and similar face dataset (SFD) were selected for experiments. It turns out that the true positive rate is improved by 1.94 - 5.66% and the recognition accuracy rate improved by 2.08 - 5.8% for the LFW and CASIA-WebFace database, respectively. Meanwhile for SFD, the recognition accuracy rate improved by 18.80 - 35.84%.
Efficient Hierarchical Data Access Control for Resource-Limited Users in Cloud-Based e-Health

Recently, the rapid development of Internet of things (IoT) and cloud computing technologies has greatly facilitated the electronic healthcare (eHealth) application. The widely deployed eHealth will significantly benefit for and bring convenience to people. However, there exist a large number of problems about data security and user privacy in eHealth. Although as one of the most promising technique, ciphertext-policy attribute-based encryption (CP-ABE) can provide fine-grained access control for data security in eHealth, there are still many drawbacks which impede the direct adoption of conventional CP-ABE. On the one hand, the personal health records (PHRs) in eHealth usually have hierarchical structures which are not compatible with access policies of conventional CP-ABE. On the other hand, the access policies in encrypted PHRs usually consist of much sensitive health-aware privacy information. Moreover, the high computation overhead also extremely hinders resource-limited users in eHealth. To solve these problems, we propose HPEH, a hierarchical policy-hiding eHealth access control system which is suitable for resource-limited users and privacy-aware access policies with hierarchical PHRs in eHealth. Specifically, in HPEH, we preserve the privacy of access policies by hiding only attribute values rather than attribute names. HPEH realizes hierarchical PHR encryption using integrated access structure and efficient PHR decryption with decryption test and outsourced decryption. In addition, HPEH introduces time-limit technique to provide more fine-grained access control for various requests. The security analysis and performance complexity evaluation indicate that HPEH is secure and efficient and is suitable for eHealth.
SBNN: Slimming binarized neural network

With the rapid developments of deep neural networks related applications, approaches for accelerating computationally intensive convolutional neural networks, such as network quantization, pruning, knowledge distillation, have attracted ever-increasing attention. Network binarization is an extreme form of network quantization technique, which binarizes the network weights and/or activation values to save computational resources. However, it often introduces noises into the network, and requires larger model size (more parameters) to compensate for the loss of representation capacity. To address the model complexity reduction challenges and further improve the network performance, this paper proposes an approach: slimming binarized neural networks (SBNN), which reduces complexity of binarized networks with acceptable accuracy loss. SBNN prunes the convolutional layers and fully-connected layer in a binarized network. Then it is refined by the proposed SoftSign function, knowledge distillation and full-precision computation to enhance the network accuracy. The proposed SBNN can be also conveniently applied to a pre-trained binarized network. We demonstrate the effectiveness of our approach through several state-of-the-art binarized models. For AlexNet and ResNet-18 on ILSVRC-2012 dataset, SBNN obtains negligible accuracy loss but even a better accuracy than the pre-pruning model while using only 75% of original filters.