Reviewerly/exHarmony huggingface dataset:
End-To-End Capacities of Hybrid Quantum Networks

Future quantum networks will be hybrid structures, constructed from complex architectures of quantum repeaters interconnected by quantum channels that describe a variety of physical domains; predominantly optical-fiber and free-space links. In this hybrid setting, the interplay between the channel quality within network sub-structures must be carefully considered, and is pivotal for ensuring high-rate end-to-end quantum communication. In this work, we combine recent advances in the theory of point-to-point free-space channel capacities and end-to-end quantum network capacities in order to develop critical tools for the study of hybrid, free-space quantum networks. We present a general formalism for studying the capacities of arbitrary, hybrid quantum networks, before specifying to the regime of atmospheric and space-based quantum channels. We then introduce a class of modular quantum network architectures which offer a realistic and readily analysable framework for hybrid quantum networks. By considering a physically motivated, highly connected modular structure we are able to idealize network performance and derive channel conditions for which optimal performance is guaranteed. This allows us to reveal vital properties for which distance-independent rates are achieved, so that the end-to-end capacity has no dependence on the physical separation between users. Our analytical method elucidates key infrastructure demands for a future satellite-based global quantum internet, and for hybrid wired/wireless metropolitan quantum networks.
Are You Left Out?

Wearable computers engage in percutaneous interactions with human users and revolutionize the way of learning human activities. Due to rising privacy concerns, federated learning has been recently proposed to train wearable data with privacy preservation collaboratively. However, under the state-of-the-art (SOTA) schemes, user profiles on wearable devices of inferior networking conditions are regarded as 'left out'. Such schemes suffer from three fundamental limitations: (1) the widely adopted network-capacity-based client selection leads to biased training; (2) the aggregation has low communication efficiency; (3) users lack convenient channels for providing feedback on wearable devices. Therefore, this paper proposes a Fair and Communication-efficient Federated Learning scheme, namely FCFL. FCFL is a full-stack learning system specifically designed for wearable computers, improving the SOTA performance in terms of communication efficiency, fairness, personalization, and user experience. To this end, we design a technique named ThrowRightAway (TRA) to loose the network capacity constraints. Clients with poor networks are allowed to be selected as participators to improve the representation and guarantee the model's fairness. Remarkably, we propose Movement Aware Federated Learning (MAFL) to aggregate only the model updates with top contributions to the global model for the sake of communication efficiency. Accordingly, we implemented an FCFL-supported prototype as a sports application on smartwatches. Our comprehensive evaluation demonstrated that FCFL is a communication efficient scheme significantly reducing uploaded data by up to 29.77%, with a prominent feature of guaranteeing enhanced fairness up to 65.07%. Also, FCFL achieves robust personalization performance (i.e., 20% improvements of global model accuracy) in the face of packet loss below a certain fraction (10%-30%). A follow-up user survey shows that our FCFL-supported prototypical system on wearable devices significantly reduces users' workload.
Semi-quantum digital signature protocol based on Einstein–Podolsky–Rosen steering

Abstract We present a new semi-quantum signature protocol which is suitable for participant without quantum capabilities to communicate securely. In addition, the application of EPR steering is first extended to the field of quantum digital signature in our protocol. On the one hand, in order to avoid attacks by attackers, our protocol allows one participant to determine the identity of the other, if the steering conditions for EPR steering are satisfied. On the other hand, original message can be transmitted between participants via EPR steering. Moreover, our scheme is simple to implement and more efficient. The security analysis shows that our protocol can avoid the attack of deny, forgery and entangle-measure.
Advantages of manual and automatic computer-aided compared to traditional histopathological diagnosis of melanoma: A pilot study

Cutaneous malignant melanoma (CMM) accounts for the highest mortality rate among all skin cancers. Traditional histopathologic diagnosis may be limited by the pathologists' subjectivity. Second-opinion strategies and multidisciplinary consultations are usually performed to overcome this issue. An available solution in the future could be the use of automated solutions based on a computational algorithm that could help the pathologist in everyday practice. The aim of this pilot study was to investigate the potential diagnostic aid of a machine-based algorithm in the histopathologic diagnosis of CMM.We retrospectively examined excisional biopsies of 50 CMM and 20 benign congenital compound nevi. Hematoxylin and eosin (H&E) stained WSI were reviewed independently by two expert dermatopathologists. A fully automated pipeline for WSI processing to support the estimation and prioritization of the melanoma areas was developed.The spatial distribution of the nuclei in the sample provided a multi-scale overview of the tumor. A global overview of the lesion's silhouette was achieved and, by increasing the magnification, the topological distribution of the nuclei and the most informative areas of interest for the CMM diagnosis were identified and highlighted. These silhouettes allow the histopathologist to discriminate between nevus and CMM with an accuracy of 96% without any extra information.In this study we proposed an easy-to-use model that produces segmentations of CMM silhouettes at fine detail level.
Selecting the Fintech Strategy for Supply Chain Finance: A Hybrid Decision Approach for Banks

Many banks are eager to adopt technology solutions to enhance operational efficiency in managing supply chain finance, which involves various participants and complex financial activities. Previous research either focuses on the technology aspect or the optimization of a supply chain; there is little specific guidance on how banks can form a holistic model to evaluate their Fintech strategy for supply chain finance. By using an integrated approach, this study adopted the decision- making trial and evaluation laboratory (DEMATEL) and several analytical methods to construct a hybrid decision model for banks. We concluded four plausible Fintech strategies from previous research and highlighted the advantages of the blockchain-based strategy. We used a domestic bank in Taiwan as a case study during the evaluation phase and implemented crisp and confidence-based fuzzy assessments. The result indicates that the blockchain-based leading strategy would be ideal for this bank. The hybrid decision model also unveils the complicated relationships among those evaluation factors, which sheds light on banks pursuing their innovation in financial services. The findings contribute to banks developing their Fintech-based supply chain financing business, and the supply chain participants may also benefit from securing efficient loans to expedite their operations.
Autoencoder based Consensus Mechanism for Blockchain-enabled Industrial Internet of Things

Conventional blockchain technologies developed for cryptocurrency applications involve complex consensus algorithms which are not suitable for resource constrained Internet of Things (IoT) devices. Therefore, several lightweight consensus mechanisms that are suitable for IoT devices have been proposed in recent studies. However, these lightweight consensus mechanisms do not verify the originality of the data generated by the IoT devices, so false and anomalous data may pass through and be stored in the ledger for further analysis. In this work to address the data originality verification problem, we propose an autoencoder (AE)-integrated Chaincode (CC)-based consensus mechanism in which the AE differentiates normal data from anomalous data. The AE is invoked through the CC once a transaction is initiated; the result returned from the AE to the CC is stored in the ledger. We have conducted a case study to train and test the AE model on the IoTID20 dataset. Also, Minifabric (MF) is used to implement the CC and illustrate the CC operation that stores only original IoT data. Moreover, the performance has been shown for the CC in terms of latency and throughput.
An analysis model for detecting misbehaviors in anonymous cryptocurrency

In online shopping, consumers often rely on information such as sales, reviews or ratings to inform their decision making. Such preferences or user behaviors can be subjected to manipulation. For example, a merchant can artificially inflate product sales by paying a click farm. Specifically, the click farm will recruit a number of non-genuine buyers to purchase the products. After the purchases have been made, the buyers will either refund the product minus the commission or no product exchange actually takes place and these buyers are paid a commission for their role in the activity. Increasingly due to the popularity of cryptocurrency, such as bitcoin, such payment mechanisms are used in such activities. Hence, in this paper, we seek to detect click farm transactions using cryptocurrency. Specifically, we propose three models to capture click farm operations, and based on the models we design three algorithms to detect anonymous click farm transactions. Extensive analysis demonstrates that our model achieves a high accuracy rate in detecting anonymous click farm transactions, without incurring expensive computational costs.
A cooperative multi‐agent offline learning algorithm to scheduling IoT workflows in the cloud computing environment

Summary Regarding the problem of workflow scheduling in cloud environments, users want the workflow to be processed at a suitable time while cloud providers want to increase resource utilization. This article proposes a cooperative multi‐agent offline learning algorithm called CMOL for minimizing makespan and energy consumption. This algorithm schedules a workflow that is represented by a directed acyclic graph (DAG) and assigns them to virtual machines (VMs). Multiple parallel agents interact and cooperate based on an algorithm in three steps of research, improvement, and selection to meet the imposed constraints of deadline and energy. Depending on the number of DAG levels, there is the same number of specialist agents who use strategies to create a Pareto feasible solution and simultaneously gain experience in the first two steps. The parallel agents exploit the extracted knowledge to improve the solution obtained by ensembling their experience in the selection step. To compare the efficiency of CMOL, two algorithms based on multi‐agent systems and one algorithm based on single‐agent are developed. The performance of the four algorithms is investigated on different real‐world workflows and compared on various sizes. Computational results reveal the competitiveness of CMOL and its relative superiority compared with others.
Embedding Soft Thresholding Function into Deep Learning Models for Noisy Radar Emitter Signal Recognition

Radar emitter signal recognition under noisy background is one of the focus areas in research on radar signal processing. In this study, the soft thresholding function is embedded into deep learning network models as a novel nonlinear activation function, achieving advanced radar emitter signal recognition results. Specifically, an embedded sub-network is used to learn the threshold of soft thresholding function according to the input feature, which results in each input feature having its own independent nonlinear activation function. Compared with conventional activation functions, the soft thresholding function is characterized by flexible nonlinear conversion and the ability to obtain more discriminative features. By this way, the noise features can be flexibly filtered while retaining signal features, thus improving recognition accuracy. Under the condition of Gaussian and Laplacian noise with signal-to-noise ratio of −8 dB to −2 dB, experimental results show that the overall average accuracy of soft thresholding function reached 88.55%, which was 11.82%, 8.12%, 2.16%, and 1.46% higher than those of Sigmoid, PReLU, ReLU, ELU, and SELU, respectively.
The effect of intrinsic decoherence on quantum dynamics of two coupled double quantum dot systems

Double quantum dot (DQD) system is an ideal candidate for qubit in quantum information theory. Hence, we will investigate the two couple DQDs in the presence of intrinsic decoherence. We derive the energy spectrum and the corresponding eigenstates. Using the concurrence as a measure of entanglement for a bipartite pure state, the entanglement of the energy eigenstates is obtained and shows that the energy eigenstates reduce to the Bell states for Δ1=Δ2=0. The effects of the physical parameters such as tunneling rate between two QDs, Coulomb coupling, and intrinsic decoherence on the entanglement and the dynamics of DQD system will be examined. Furthermore, fidelity is used to measure how close the dynamically evolved state is to the initial state. It is found that for a given Γ and the smaller (larger) values of Δ2 (coulomb potential), the DQD system is more robust to the destructive effect of the intrinsic decoherence.
Alternative method for determining basis weight in papermaking by using an interactive soft sensor based on an artificial neural network model

Abstract Currently, there are two procedures to determine the basis weight in papermaking processes: the measurements made by the quality control laboratory or the measurements made by the quality control system. This research presents an alternative to estimating basis weight-based artificial neural network (ANN) modeling. The NN architecture was constructed by trial and error, obtaining the best results using two hidden layers with 48 and 12 neurons, respectively, in addition to the input and output layers. Mean absolute error and mean absolute percentage error was used for the loss and metric functions, respectively. Python was used in the training, validation, and testing process. The results indicate that the model can reasonably determine the basis weight given the independent variables analyzed here. The <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msup> <m:mrow> <m:mi mathvariant="italic">R</m:mi> </m:mrow> <m:mrow> <m:mn>2</m:mn> </m:mrow> </m:msup> </m:math> {R^{2}} reached by the model was 94 %, and MAE was 12.40 grams/m 2 . Using the same dataset, the fine tree regression model showed an <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msup> <m:mrow> <m:mi mathvariant="italic">R</m:mi> </m:mrow> <m:mrow> <m:mn>2</m:mn> </m:mrow> </m:msup> </m:math> {R^{2}} of 99 % and an MAE of 3.35 grams/m 2 . Additionally, a dataset not included in the building process was used to validate the method’s performance. The results showed that ANN-based modeling has a higher predictive capability than the regression tree model. Therefore, this model was embedded in a graphic user interface that was developed in Python.
Learning on the Rings

This paper presents ssLOTR (self-supervised learning on the rings), a system that shows the feasibility of designing self-supervised learning based techniques for 3D finger motion tracking using a custom-designed wearable inertial measurement unit (IMU) sensor with a minimal overhead of labeled training data. Ubiquitous finger motion tracking enables a number of applications in augmented and virtual reality, sign language recognition, rehabilitation healthcare, sports analytics, etc. However, unlike vision, there are no large-scale training datasets for developing robust machine learning (ML) models on wearable devices. ssLOTR designs ML models based on data augmentation and self-supervised learning to first extract efficient representations from raw IMU data without the need for any training labels. The extracted representations are further trained with small-scale labeled training data. In comparison to fully supervised learning, we show that only 15% of labeled training data is sufficient with self-supervised learning to achieve similar accuracy. Our sensor device is designed using a two-layer printed circuit board (PCB) to minimize the footprint and uses a combination of Polylactic acid (PLA) and Thermoplastic polyurethane (TPU) as housing materials for sturdiness and flexibility. It incorporates a system-on-chip (SoC) microcontroller with integrated WiFi/Bluetooth Low Energy (BLE) modules for real-time wireless communication, portability, and ubiquity. In contrast to gloves, our device is worn like rings on fingers, and therefore, does not impede dexterous finger motion. Extensive evaluation with 12 users depicts a 3D joint angle tracking accuracy of 9.07° (joint position accuracy of 6.55mm) with robustness to natural variation in sensor positions, wrist motion, etc, with low overhead in latency and power consumption on embedded platforms.
PyBNesian: An extensible python package for Bayesian networks

Bayesian networks are probabilistic graphical models that are commonly used to represent the uncertainty in data. The PyBNesian package provides an implementation for many different types of Bayesian network models and some variants, such as conditional Bayesian networks and dynamic Bayesian networks. In addition, the package can be easily extended with new components that can interoperate with those already implemented. Furthermore, the package also implements other related models such as kernel density estimation using OpenCL 1.2+ to enable GPU acceleration. PyBNesian is totally free and open-source under the MIT license.
Can I touch the clothes on the screen? The mental simulation for touch in online fashion shopping

Purpose This study aims to explore consumers' vicarious experience of touch, namely, mental simulation for touch, through product pictures as visual stimuli and the use of touch devices as motion stimuli in the context of online fashion shopping. Design/methodology/approach Participants were randomly exposed to one of the two texture conditions (weak vs strong tactile sensitivity). The responses from the participants who used a laptop as a non-touch device and a touch device were considered in the analysis. A total of 179 responses were analyzed with analysis of variance and the PROCESS procedure for path analysis using SPSS 20.0. Findings The interaction effects of tactile sensitivity and device types on mental simulation for touch were significant; seeing a less tactile-sensitive product facilitated a greater mental simulation for touch when using a touch device; however, seeing a tactile-sensitive product produced a similar mental simulation for touch, regardless of device types. Furthermore, browsing a less tactile-sensitive product using a touch device increased favorable product attitudes, fully mediated by mental simulation for touch. Originality/value This study contributes to the literature on online shopping behaviors of fashion consumers by showing the role of mental simulation for touch that is shaped by the tactile qualities of products and device types. Exploration of this topic can contribute significantly to online fashion retailers because studies on consumers' mental simulation for touch are limited.
Layered similarity detection for programming plagiarism and collusion on weekly assessments

Abstract When weekly programming assessments are used, it is often the case that some of them are either trivial or strongly directed. Common code similarity detectors are not particularly helpful with such assessments: some potential instances of misconduct are not selected for manual investigation as all submissions are expected to be similar and it is not feasible to check them all. Several dedicated similarity detectors have been developed to work with such assessments, but the experience is required to determine when to use them. This paper presents a similarity detector that works on many kinds of weekly assessments. It combines three‐layered types of similarity so that even within a set of highly similar submissions, program pairs are still sorted according to their levels of similarity. Our similarity detector is more effective than JPlag in distinguishing similar programs and helping to identify plagiarism and collusion. The similarity detector is slower than JPlag, but the longer execution time is partly offset by some optimization that has no negative impact on the effectiveness. As weekly assessments seldom entail large submissions, the execution time does not appear to be a barrier to use.
Game-Based Teaching of Basic Hardware Components With an Educational Virtual Reality at Different Levels of Immersion

Basic knowledge about the basic hardware components of a computer contributes to an understanding of our digital environment. Even though the best way to explore the components of a computer might be to build or take apart a real computer, this can be time consuming, dangerous, and expensive. In the virtual reality "Bill's Computer Workshop", students explore the inside of a computer while searching for hardware components. A study with 71 middle school students compared three different devices (laptop, Mobile VR, Head-Mounted-Display) regarding their induced levels of presence and resulting learning outcomes. The findings show 1) a strong effect of the virtual environment for the intended learning objectives, 2) a higher level of technological immersion does not automatically lead to higher learning outcomes, and 3) a positive correlation between presence and learning outcomes. We conclude that the use of immersive technologies for Computer Science Education can have merit when careful design considerations regarding vividness and interactivity of the virtual environments are taken into account.
Integrated Navigation Algorithm Based on Multiple Fading Factors Kalman Filter

An integrated navigation algorithm based on a multiple fading factors Kalman filter (MFKF) is proposed to solve the problems that the Kalman filtering (KF) algorithm easily brings about diffusion when the model becomes a mismatched or noisy, and the MFKF accuracy is reduced when the fading factor is overused. Based on the innovation covariance theory, the algorithm designs an improved basis for judging filtering anomalies and makes the timing of the introduction of the fading factor more reasonable by switching the filtering state. Different from the traditional basis of filter abnormality judgment, the improved judgment basis adopts a recursive way to continuously update the estimated value of the innovation covariance to improve the estimation accuracy of the innovation covariance, and an empirical reserve factor for the judgment basis is introduced to adapt to practical engineering applications. By establishing an inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation model, the results show that the average positioning accuracy of the proposed algorithm is improved by 26.52% and 7.48%, respectively, compared with the KF and MFKF, and shows better robustness and self-adaptability.