Reviewerly/exHarmony huggingface dataset:
Proof-of-principle demonstration of quantum key distribution with seawater channel: towards space-to-underwater quantum communication

Quantum communication via seawater channels has attracted much attention. Here, we present the design and implementation of a prototype for space-to-underwater quantum key distribution (QKD). The influence of the fluctuating atmosphere–seawater interface is theoretically analyzed. The setup consists of a specially designed optical source working in the blue–green band and a receiving optical antenna with large field-of-view (FOV). To achieve a larger FOV, photomultipliers with 25 mm active area are adopted to measure the single photon signals. The proof-of-principle experiment of QKD was implemented in the laboratory with near-coast seawater to verify the performance of the devices. This study provides a first step towards space-to-underwater quantum communication.
Unsupervised low-dimensional vector representations for words, phrases and text that are transparent, scalable, and produce similarity metrics that are not redundant with neural embeddings

Neural embeddings are a popular set of methods for representing words, phrases or text as a low dimensional vector (typically 50–500 dimensions). However, it is difficult to interpret these dimensions in a meaningful manner, and creating neural embeddings requires extensive training and tuning of multiple parameters and hyperparameters. We present here a simple unsupervised method for representing words, phrases or text as a low dimensional vector, in which the meaning and relative importance of dimensions is transparent to inspection. We have created a near-comprehensive vector representation of words, and selected bigrams, trigrams and abbreviations, using the set of titles and abstracts in PubMed as a corpus. This vector is used to create several novel implicit word-word and text-text similarity metrics. The implicit word-word similarity metrics correlate well with human judgement of word pair similarity and relatedness, and outperform or equal all other reported methods on a variety of biomedical benchmarks, including several implementations of neural embeddings trained on PubMed corpora. Our implicit word-word metrics capture different aspects of word-word relatedness than word2vec-based metrics and are only partially correlated (rho = 0.5–0.8 depending on task and corpus). The vector representations of words, bigrams, trigrams, abbreviations, and PubMed title + abstracts are all publicly available from http://arrowsmith.psych.uic.edu/arrowsmith_uic/word_similarity_metrics.html for release under CC-BY-NC license. Several public web query interfaces are also available at the same site, including one which allows the user to specify a given word and view its most closely related terms according to direct co-occurrence as well as different implicit similarity metrics.
Safe Navigation With Human Instructions in Complex Scenes

In this letter, we present a robotic navigation algorithm with natural language interfaces that enables a robot to safely walk through a changing environment with moving persons by following human instructions such as “go to the restaurant and keep away from people.” We first classify human instructions into three types: goal, constraints, and uninformative phrases. Next, we provide grounding in a dynamic manner for the extracted goal and constraint items along with the navigation process to deal with target objects that are too far away for sensor observation and the appearance of moving obstacles such as humans. In particular, for a goal phrase (e.g., “go to the restaurant”), we ground it to a location in a predefined semantic map and treat it as a goal for a global motion planner, which plans a collision-free path in the workspace for the robot to follow. For a constraint phrase (e.g., “keep away from people”), we dynamically add the corresponding constraint into a local planner by adjusting the values of a local costmap according to the results returned by the object detection module. The updated costmap is then used to compute a local collision avoidance control for the safe navigation of the robot. By combining natural language processing, motion planning, and computer vision, our developed system can successfully follow natural language navigation instructions to achieve navigation tasks in both simulated and real-world scenarios. Videos are available at https://sites.google.com/view/snhi.
Better beware: comparing metacognition for phishing and legitimate emails

Every electronic message poses some threat of being a phishing attack. If recipients underestimate that threat, they expose themselves, and those connected to them, to identity theft, ransom, malware, or worse. If recipients overestimate that threat, then they incur needless costs, perhaps reducing their willingness and ability to respond over time. In two experiments, we examined the appropriateness of individuals' confidence in their judgments of whether email messages were legitimate or phishing, using calibration and resolution as metacognition metrics. Both experiments found that participants had reasonable calibration but poor resolution, reflecting a weak correlation between their confidence and knowledge. These patterns differed for legitimate and phishing emails, with participants being better calibrated for legitimate emails, except when expressing complete confidence in their judgments, but consistently overconfident for phishing emails. The second experiment compared performance on the laboratory task with individuals' actual vulnerability, and found that participants with better resolution were less likely to have malicious files on their home computers. That comparison raised general questions about the design of anti-phishing training and of providing feedback essential to self-regulated learning.
DASS-CARE: A Decentralized, Accessible, Scalable, and Secure Healthcare Framework using Blockchain

The healthcare industry is a complex system of interconnected entities. Each of these entities has disjoint information systems to manage patient data and records. The current IT solutions in healthcare systems have several challenges such as sharing and accessing medical records across several stakeholders while still maintaining security and privacy of these records. This global problem on how to create, maintain, and share sensitive medical records and clinical data among various stakeholders without sacrificing data privacy and integrity is still unresolved. In fact, existing healthcare records are decentralized, disjointed, non-uniform, and fragmented in nature. In this paper, we present a Blockchain-based framework, called DASS-CARE, that supports decentralized, accessible, scalable, and secure access to healthcare services including medical records. Such framework will greatly facilitate the process of real time access and updates without compromising security, integrity and confidentiality of patient data. Our objective is multifold. First, improve the quality of healthcare and lower the cost of delivery. Second, enhance medical records management including electronic health records unification. Finally, provide users with the ability to view their medical records regardless of their history, a task that is difficult to accomplish under the current fragmented systems.
Linear Multistep Discretization Methods With Variable Step-Size in Nonlinear Wave Digital Structures for Virtual Analog Modeling

There is a growing interest in Virtual Analog modeling algorithms for musical audio processing designed in the Wave Digital (WD) domain. Such algorithms typically employ a discretization strategy based on the trapezoidal rule with fixed sampling step, though this is not the only option. In fact, alternative discretization strategies (possibly with an adaptive sampling step) can be quite advantageous, particularly when dealing with nonlinear systems characterized by stiff equations. In this paper, we propose a unified approach for modeling capacitors and inductors in the WD domain using generic linear multi-step discretization methods with variable time-step size, and provide generalized adaptation conditions. We also show that the proposed approach for implementing dynamic (energy-storing) elements in the WD domain is particularly suitable to be combined with a recently developed technique for efficiently solving a class of circuits with multiple one-port nonlinearities, called Scattering Iterative Method. Finally, as examples of application, we develop WD models for a Van Der Pol oscillator and a dynamic diode-based ring modulator, which use different discretization methods.
LightChain: On the Lightweight Blockchain for the Internet-of-Things

The Internet of Things (IoT) and the blockchain are justly regarded as the technology for the future. The blockchain is a Distributed Ledger Technology (DLT) solution which has enormous potential as can be seen in the numerous avenues it has been deployed. Simplistically, it is a decentralized database which can revolutionize the current centralized world we live. IoT is the interconnection of devices with mostly bounded resources. IoT applications are also distributed in nature thereby making it inevitable that the paths of the blockchain and IoT will cross in the future. Blockchain's DLT will eventually play a crucial role in how IoT devices will communicate. The Proof of Work (PoW) mechanism was the original consensus technique introduced in the first blockchain based application (Bitcoin). PoW guaranteed consensus in the network by verifying transactions. However, the PoW had deficiencies. The solving of the PoW puzzle is computationally expensive which has become an impediment in the potential marriage of IoT and blockchain. This predicament arises as IoT devices are plagued with limited resources. In this work, we address this issue by presenting an approach where IoT devices can combine their resources to solve PoW puzzles that they might not have been able to solve on their own. This would ensure a successful merger between the blockchain and the IoT.
A Feature Extraction Method for Credit Card Fraud Detection

As credit card fraud has caused huge economic losses and harm cardholders seriously, credit card fraud detection is important and has been paid much attention. An effective feature engineering is the key to build an effective model of detecting fraud. However, we find that the current feature engineering that is based on the frequency of transactions is not perfect. Although frequency-based feature engineering depicts the temporal features of user transactions, it does not enough consider the fraud characteristics and the distinction of transaction behaviors. Starting from the two points, we propose a rule-based feature engineering that considers both individual behavior and group behavior, and portrays the individual behavior as group features, and thus can more effectively distinguish legitimate and fraudulent transactions. Our experiments illustrate the advantages of our method.
Faster SeaSign Signatures Through Improved Rejection Sampling

We speed up the isogeny-based “SeaSign” signature scheme recently proposed by De Feo and Galbraith. The core idea in SeaSign is to apply the “Fiat–Shamir with aborts” transform to the parallel repeated execution of an identification scheme based on CSIDH. We optimize this general transform by allowing the prover to not answer a limited number of said parallel executions, thereby lowering the overall probability of rejection. The performance improvement ranges between factors of approximately 4.4 and 65.7 for various instantiations of the scheme, at the expense of roughly doubling the signature sizes.
Unifying Task-Oriented Knowledge Graph Learning and Recommendation

Incorporating knowledge graphs (KGs) into recommender systems (knowledge-aware recommendation) to improve the recommendation accuracy and explainability has attracted considerable research efforts. However, existing methods largely assume that KGs are complete when transferring knowledge from them, which may lead to suboptimal performance for those KGs, can be hardly complete in real-life scenarios. In this paper, we present a robustly co-learning model (RCoLM) that takes the incompleteness nature of KGs into consideration when incorporating them into recommendation. The RCoLM aims at transferring knowledge between recommendation task and knowledge graph completion (KG completion) task by utilizing a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">transfer learning</i> model. An earlier version of this paper appeared in KDD 2019. This version is an extension of the previous submission and two major innovations are presented here. At first, distinct from previous knowledge-aware recommendation methods, which mainly focus on transferring knowledge from KGs to item recommendations, the RCoLM attempts to exploit user-item interactions from recommendations for KG completion, and unifies the two tasks in a joint model for mutual enhancements. Second, the RCoLM provides a general task-oriented negative sampling strategy on KG completion task, which further improves the adaptive ability of the proposed algorithm and plays an essential role for obtaining superior performance in various sub-tasks of the KG completion. The extensive experiments on two real-world public datasets demonstrate that RCoLM outperforms not only state-of-the-art knowledge-aware recommendation methods but also existing KG completion methods.
A Proposed Approach Integrating DLT, BIM, IoT and Smart Contracts: Demonstration Using a Simulated Installation Task

The complementarity among Building Information Modelling (BIM), distributed ledger technology (DLT), smart contracts, and the Internet of Things (IoT) is increasingly acknowledged in industry reports and major national digital transformation initiatives.However, theoretical foundation and empirical evidence to ascertain such a prerogative are still very limited.This paper analyses the interactions between these technologies and proposes an approach that capitalises on their complementarity by linking the physical environment; the digital environment; agreements representing the contract; and the DLT environment.A simulated installation activity is used to verify the conceptual interrelations included in the proposed framework as a proof-of-concept.The simulation reveals how a mini smart contract -for a limited scope such as an installation activity work -can be executed within the proposed approach and how payments can be automated when project delivery is coupled with machine-readable BIM requirements and contract clauses.The paper also discusses the key limitations and challenges facing the adoption of the proposed approach and in particular the diffusion of smart contracts.
Leveraging Blockchain Technology in a BIM Workflow: A Literature Review

Building Information Modelling (BIM) involves the exchange of models and information between stakeholders and within collaborating teams.This information is prone to contractual, legal, security and system issues amongst others.The existing practices aim to address a digital concept such as BIM with solutions from the paper worldcontracts and other documents, which do not solve the problem completely.A recent advancement in database management -Blockchain Technology (BCT) aims to provide a new stream of solutions to industries across various sectors.BCT is a system of recording a database that stores information chronologically and distributes a copy of it over a network of computers that maintain its authenticity and security collectively.This paper first reviews the literature on the issues of information exchange in a BIM workflow and next explores the concept of BCT and its connection with BIM.The literature indicates that BCT shows high potential for solving challenges during the design phase of the project by clarifying liabilities, increasing the reliability of information and enhancing the security of information flow.Its ability to incorporate self-executing contracts enable many more applications around ownership and payments.Finally, the paper discusses a few of its challenges with scalability, user acceptance amongst others.
Detection of Digital Image Forgery using Fast Fourier Transform and Local Features

Multimedia security is one of the key challenges in today's world, as dependency on multimedia information is increasing day by day. Easily available image editing software have enabled every common user of a smart phone and computer, to hack into the information of the images and video and alter it to some extent. To authenticate the genuineness of images, detection of image tempering is need of the time. Various techniques have been proposed to use image features for detection of image forgery. The techniques of forgery detection work in two domains of image forgery; copy-move forgery detection (CMFD) and image splicing detection (ISD). This paper presents a comprehensive comparative analysis for the use of local texture descriptors i.e. local binary pattern (LBP) and local ternary pattern (LTP) for forgery detection in an image. The paper also presents a technique to integrate fast fourier transform (FFT) with local texture descriptors for image forgery detection using existing block-based methodology. Performance of the technique(s) and descriptor(s) is tested for benchmarked dataset CASIA v1.0. Results are evaluated by using standard detection metrics detection accuracy and recall. The paper also suggests a relatively better texture descriptor.
Fast GPU-Enabled Color Normalization for Digital Pathology

Normalizing unwanted color variations dne to differences in staining processes and scanner responses has been shown to aid machine learning in computational pathology. Of the several popular techniques for color normalization, structure preserving color normalization (SPCN) is well-motivated, convincingly tested, and published with its code base. However, SPCN makes occasional errors in color basis estimation leading to artifacts such as swapping the color basis vectors between stains or giving a colored tinge to the background with no tissue. We made several algorithmic improvements to remove these artifacts. Additionally, the original SPCN code is not readily usable on gigapixel whole slide images (WSIs) due to long run times, the use of proprietary software platform and libraries, and its inability to automatically handle WSIs. We completely rewrote the software such that it can automatically handle images of any size in popular WSI formats. Our software utilizes GPU-acceleration and open-source libraries that are becoming ubiquitous with the advent of deep learning. We also made several other small improvements and achieved a multifold overall speedup on gigapixel images, processing 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9</sup> pixels in 3 minutes. Our algorithm and software is usable right out-of-the-box by the computational pathology community.
On the Feasibility of Attribute-Based Encryption on Constrained IoT Devices for Smart Systems

The Internet of Things (IoT) is enabling a new generation of innovative services based on the seamless integration of smart objects into information systems. Such IoT devices generate an uninterrupted flow of information that can be transmitted through an untrusted network and stored on an untrusted infrastructure. The latter raises new security and privacy challenges that require novel cryptographic methods. Attribute-Based Encryption (ABE) is a new type of public-key encryption that enforces a fine-grained access control on encrypted data based on flexible access policies. The feasibility of ABE adoption in fully-fledged computing systems, i.e. smartphones or embedded systems, has been demonstrated in recent works. In this paper we assess the feasibility of the adoption of ABE in typical IoT constrained devices, characterized by limited capabilities in terms of computing, storage and power. Specifically, an implementation of three ABE schemes for ESP32, a low-cost popular platform to deploy IoT devices, is developed and evaluated in terms of encryption/decryption time and energy consumption. The performance evaluation shows that the adoption of ABE on constrained devices is feasible, although it has a cost that increases with the number of attributes. The analysis in particular highlights how ABE has a significant impact in the lifetime of battery-powered devices, which is impaired significantly when a high number of attributes is adopted.
Secure D2D Group Authentication Employing Smartphone Sensor Behavior Analysis

Nowadays, with rapid advancement of both the upcoming 5G architecture construction and emerging Internet of Things (IoT) scenarios, Device-to-Device (D2D) communication provides a novel paradigm for mobile networking. By facilitating continuous and high data rate services between physically proximate devices without interconnection with access points (AP) or service network (SN), spectral efficiency of the 5G network can be drastically increased. However, due to its inherent open wireless communicating features, security issues and privacy risks in D2D communication remain unsolved in spite of its benefits and prosperous future. Hence, proper D2D authentication mechanisms among the D2D entities are of great significance. Moreover, the increasing proliferation of smartphones enables seamlessly biometric sensor data collecting and processing, which highly correspond to the user’s unique behavioral characteristics. For the above consideration, we present a secure certificateless D2D authenticating mechanism intended for extreme scenarios in this paper. In the assumption, the key updating mechanism only requires a small modification in the SN side, while the decryption information of user equipment (UEs) remains constant as soon as the UEs are validated. Note that a symmetric key mechanism is adopted for the further data transmission. Additionally, the user activities data from smartphone sensors are analyzed for continuous authentication, which is periodically conducted after the initial validation. Note that in the assumed scenario, most of the UEs are out of the effective range of cellular networks. In this case, the UEs are capable of conducting data exchange without cellular connection. Security analysis demonstrates that the proposed scheme can provide adequate security properties as well as resistance to various attacks. Furthermore, performance analysis proves that the proposed scheme is efficient compared with state-of-the-art D2D authentication schemes.