Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Privacy-Preserving Networking Strategy for Dynamic Wireless Charging of EVs

Dynamic wireless charging of electric vehicles (EVs) enables the exchange of power between a mobile EV and the electricity grid via a set of charging pads (CPs) deployed along the road. Accordingly, dynamic charging coordination can be introduced for a group of mobile EVs to specify where each EV can charge (i.e., from which CPs). This coordination mechanism maximizes the satisfied charging requests given the limited available energy supply. Upon specifying the optimal set of pads for a given EV, a fast authentication mechanism is required between the EV and the CPs to start the charging process. However, both the coordination and authentication mechanisms require exchanging private information, e.g., EV identities and locations. Hence, there is a need for a strategy that enables privacy-preservation in dynamic charging via supporting: (i) user anonymity and (ii) data unlinkability. In this paper, we propose a decentralized and scalable networking strategy based on a specially designed private blockchain that can support the privacy requirements of dynamic charging coordination, authentication, and billing. The proposed networking strategy relies on group signature and distributed random number generators to support the desirable features. Simulation results demonstrate the efficiency and low complexity of the proposed blockchain-based networking strategy.
That's interesting: An examination of interest theory and self‐determination in organisational cybersecurity training

Abstract With government and industry experiencing a critical shortage of trained cybersecurity professionals, organisations are spearheading various training programs to cultivate cybersecurity skills. With more people working from home and the existing cybersecurity staff shortages, cybercriminals are increasingly exploiting new and existing vulnerabilities by launching ubiquitous cyberattacks. This study focuses on how to close the gap in cybersecurity skills through interest cultivation and self‐determined motivation. Our study shows that situational interest (SI) in cybersecurity along with situational motivational determinants (i.e., perceived learning autonomy and perceived relatedness) engendered self‐determined motivation toward cybersecurity training. Consequently, self‐determined motivation facilitated actual learning behaviour. Meanwhile, individual interest in cybersecurity created positive moderating effects in the relationships between self‐determination and its key antecedents (i.e., perceived relatedness and situational interest). Based on these findings, we provide research implications accordingly.
QDiff: Differential Testing of Quantum Software Stacks

Over the past few years, several quantum software stacks (QSS) have been developed in response to rapid hardware advances in quantum computing. A QSS includes a quantum programming language, an optimizing compiler that translates a quantum algorithm written in a high-level language into quantum gate instructions, a quantum simulator that emulates these instructions on a classical device, and a software controller that sends analog signals to a very expensive quantum hardware based on quantum circuits. In comparison to traditional compilers and architecture simulators, QSSes are difficult to tests due to the probabilistic nature of results, the lack of clear hardware specifications, and quantum programming complexity.This work devises a novel differential testing approach for QSSes, named QDiff with three major innovations: (1) We generate input programs to be tested via semantics-preserving, source to source transformation to explore program variants. (2) We speed up differential testing by filtering out quantum circuits that are not worthwhile to execute on quantum hardware by analyzing static characteristics such as a circuit depth, 2-gate operations, gate error rates, and T1 relaxation time. (3) We design an extensible equivalence checking mechanism via distribution comparison functions such as Kolmogorov&#x2013;Smirnov test and cross entropy.We evaluate QDiff with three widely-used open source QSSes: Qiskit from IBM, Cirq from Google, and Pyquil from Rigetti. By running QDiff on both real hardware and quantum simulators, we found several critical bugs revealing potential instabilities in these platforms. QDiff&#x2019;s source transformation is effective in producing semantically equivalent yet not-identical circuits (i.e., 34% of trials), and its filtering mechanism can speed up differential testing by 66%.
A2SPPNet: Attentive Atrous Spatial Pyramid Pooling Network for Salient Object Detection

Recent progress in salient object detection (SOD) mainly depends on the Atrous Spatial Pyramid Pooling (ASPP) module for multi-scale learning. Intuitively, different input images, different pixels, and different network layers may have different preferences for various feature scales. However, ASPP treats all feature scales as equally important by a simple sum operation. To this end, we propose Attentive Atrous Spatial Pyramid Pooling (A2SPP) by adding a new Cubic Information-Embedding Attention (CIEA) module at each branch of ASPP. In this way, each position in the 3D feature map can automatically learn the feature scales it prefers. Specifically, CIEA consists of Spatial-Embedding Channel Attention (SECA) and Channel-Embedding Spatial Attention (CESA). Instead of the previous direct squeeze and ignoring of one dimension when computing the attention for the other dimension, SECA/CESA attempts to embed spatial/channel information into channel/spatial attention, respectively. In addition, CIEA learns SECA and CESA for each 3D position simultaneously rather than previous separate computation of channel and spatial attention for each 2D position. Incorporating A2SPP and CIEA, the proposed A2SPPNet performs favorably against previous state-of-the-art SOD methods.
Subtype discrimination of acute myeloid leukemia based on plasma SERS technique

Acute myeloid leukemia (AML) is a common hematologic malignancy. To this day, diagnose of AML and its genetic mutation still rely on invasive and time-consuming methods. In this study, 222 plasma samples were collected to discuss the performance of surface-enhanced Raman spectroscopy (SERS) to discriminate AML subtype acute promyelocytic leukemia and acute monocytic leukemia based on plasma. The Ag nanoparticles-based SERS technique was used to explore the biochemical differences among different AML subtypes. With the help of powerful supervised and unsupervised algorithms, the performance using the whole spectra and band intensities was confirmed to identify different subtypes of AML. The results demonstrated the intensities of several bands and band-intensity ratios were significantly different between groups, thus related to the discrimination of several AML subtypes and control. Combining indexes of band-intensity ratios, the result of multi-indexes ROC has excellent performance in differentiating AML patient with healthy control. Our work demonstrated the great potential of SERS technique as a rapid and micro detection method in clinical laboratory field, it's a new and powerful tool for analyzing human blood plasma.
Canonical derivation of the fermionic influence superoperator

We present a canonical derivation of an influence superoperator which generates the reduced dynamics of a Fermionic quantum system linearly coupled to a Fermionic environment initially at thermal equilibrium. We use this formalism to derive a generalized-Lindblad master equation (in the Markovian limit) and a generalized version of the hierarchical equations of motion valid in arbitrary parity-symmetry conditions, important for the correct evaluation of system correlation functions and spectra.
Designing Persuasive Food Conversational Recommender Systems With Nudging and Socially-Aware Conversational Strategies

Unhealthy eating behavior is a major public health issue with serious repercussions on an individual's health. One potential solution to overcome this problem, and help people change their eating behavior, is to develop conversational systems able to recommend healthy recipes. One challenge for such systems is to deliver personalized recommendations matching users' needs and preferences. Beyond the intrinsic quality of the recommendation itself, various factors might also influence users' perception of a recommendation. In this paper, we present Cora, a conversational system that recommends recipes aligned with its users' eating habits and current preferences. Users can interact with Cora in two different ways. They can select pre-defined answers by clicking on buttons to talk to Cora or write text in natural language. Additionally, Cora can engage users through a social dialogue, or go straight to the point. Cora is also able to propose different alternatives and to justify its recipes recommendation by explaining the trade-off between them. We conduct two experiments. In the first one, we evaluate the impact of Cora's conversational skills and users' interaction mode on users' perception and intention to cook the recommended recipes. Our results show that a conversational recommendation system that engages its users through a rapport-building dialogue improves users' perception of the interaction as well as their perception of the system. In the second evaluation, we evaluate the influence of Cora's explanations and recommendation comparisons on users' perception. Our results show that explanations positively influence users' perception of a recommender system. However, comparing healthy recipes with a decoy is a double-edged sword. Although such comparison is perceived as significantly more useful compared to one single healthy recommendation, explaining the difference between the decoy and the healthy recipe would actually make people less likely to use the system.
Secret-Key Exchange Through Synchronization of Randomized Chaotic Oscillators Aided by Logistic Hash Function

We have developed a method of secret-key exchange assisted by a secure hash algorithm for a stream cipher based on the augmented Lorenz map as a high-dimensional chaotic map. Two legitimate users are assumed to possess chaotic oscillators subject to the original Lorenz equations to exchange randomized chaotic signals. The oscillators achieve perfect synchronization and generate a sequence of binary numbers to be shared as the secret key. The users, as well as an eavesdropper, cannot estimate the synchronization error because of the randomization of chaotic signals. Nevertheless, only the legitimate users have high confidence in sharing the secret key because of the dynamical stability of the synchronization process. The users can confirm the sharing of the secret key by exchanging the hash values of the keys that are generated by a secure hash algorithm based on the logistic map. We discuss the performance of our method with reference to the results of numerical experiments.
Geochemical anomaly definition using stream sediments landscape modeling

Geochemical anomaly mapping is a crucial part of environmental and mineral exploration projects. Stream sediments are the most frequently sampling medium for geochemical mapping in regional scales, resulting from various geological processes. To enhance geochemical anomaly in stream sediments, the geochemical landscape, including topographic factors and drainage catchment basin extracted from a digital elevation model (DEM), and average precipitations, were applied. These factors were taken into consideration in this research to investigate the influence of the geochemical landscape constraints on the Mo polymetallic mineralization anomaly of stream sediments in a typical hilly area of Jining, Inner Mongolia, China, using Bayesian Maximum Entropy (BME) approach. Geographically weighted regression was applied to construct probability density function of soft data based on slope, aspect, drainage catchment basin and precipitation. Stream sediment data, at a scale of 1:200,000 (hard data), was integrated with the soft data to map stream sediment anomalies associated with molybdenum-polymetallic mineralization. Considering the influences of geochemical landscape during stream sediment anomaly identification can make up the shortage of missing and incomplete observations in regional geochemical data. By integrating the geochemical landscape factors during the stream sediment anomaly identification process, the impacts of the geochemical landscape on sediment dispersion halos were addressed, and the anomaly identification accuracy was improved. The anomaly areas defined by the BME model demonstrated that 31.41% of the study area contained 75% of the known mineral deposits and consequently the uncertainty of geochemical anomalies was reduced.
Threat Modeling Tools: A Taxonomy

Threat modeling tools allow for identifying weaknesses in a system’s design. Yet, understanding conceptual differences between the tools is not trivial. We propose a taxonomy of threat modeling tools, using it to compare several popular ones.
The influence of cultural tightness-looseness on cross-border acquisition performance

A growing body of research in economics focuses on whether cultural differences in social norms affect economic outcomes. Here we examine how differences in the strength of social norms—or tightness-looseness (TL)—across countries can explain the financial performance of cross-border acquisitions (CBAs). We hypothesize that differences in TL hamper CBA performance, and further propose that the direction and absolute level of TL, industry relatedness, and membership in high-tech industries moderate the TL-CBA performance relationship in important ways. Using data for 4,717 CBAs in 30 countries between 1989 and 2013, we find that a one standard deviation increase in TL difference is associated with an average decrease in acquirer's return on assets equivalent to 245 million US dollars in its net income. We further find that this effect is particularly pronounced when the acquirer is tighter than the target, at greater levels of tightness, and in high-tech industries. Theoretical and practical implications of the strength of social norms for CBAs as well as the broader field of economics are discussed.
A probabilistic topic model based on short distance Co-occurrences

A limitation of many probabilistic topic models such as Latent Dirichlet Allocation (LDA) is their inflexibility to use local contexts. As a result, these models cannot directly benefit from short-distance co-occurrences, which are more likely to be indicators of meaningful word relationships. Some models such as the Bigram Topic Model (BTM) consider local context by integrating language and topic models. However, due to taking the exact word order into account, such models suffer severely from sparseness. Some other models like Latent Dirichlet Co-Clustering (LDCC) try to solve the problem by adding another level of granularity assuming a document as a bag of segments, while ignoring the word order. In this paper, we introduce a new topic model which uses overlapping windows to encode local word relationships. In the proposed model, we assume a document is comprised of fixed-size overlapping windows, and formulate a new generative process accordingly. In the inference procedure, each word is sampled once in only a single window, while influencing the sampling of its other fellow co-occurring words in other windows. Word relationships are discovered in the document level, but the topic of each word is derived considering only its neighbor words in a window, to emphasize local word relationships. By using overlapping windows, without assuming an explicit dependency between adjacent words, we avoid ignoring the word order completely. The proposed model is straightforward, not severely prone to sparseness and as the experimental results show, produces more meaningful and more coherent topics compared to the three mentioned established models.
Improving Remote Sensing Image Captioning by Combining Grid Features and Transformer

Remote sensing image captioning (RSIC) has great significance in image understanding, which describes the image content in natural language. Existing methods are mainly based on deep learning and rely on the encoder–decoder model to generate sentences. In the decoding process, recurrent neural network (RNN) and long short-term memory (LSTM) are normally applied to sequentially generate image captions. In this letter, the transformer encoder–decoder is combined with grid features to improve the RSIC performance. First, the pretrained convolutional neural network (CNN) is used to extract grid-based visual features, which are encoded as vectorial representations. Then, the transformer outputs semantic descriptions to bridge visual features and natural language. Besides, the self-critical sequence training (SCST) strategy is applied to further optimize the image captioning model and improve the quality of generated sentences. Extensive experiments are organized on three public datasets of RSCID, UCM-Captions, and Sydney-Captions. Experimental results demonstrate the effectiveness of SCST strategy and the proposed method achieves superior performance compared with the state-of-the-art image captioning approaches on the RSCID dataset.
Incoherent optical image encryption based on coded aperture correlation holography

We propose an incoherent encoding system for image encryption utilizing the coded aperture correlation holography (COACH) technique. Incoherent imaging is the core component of this proposal, in which the point spread hologram (PSH) of the imaging system serves as the main key to encode the input intensity distribution. The proposal makes full use of PSH which consists of four point spread functions (PSFs) respectively generated by four coded phase masks (CPMs), so that a multidimensional key space is generated to resist illegal attacks. Incoherent illumination of the imaging system ensures that only the intensity information is manipulated. The encoded information is just an intensity distribution which is advantageous for data storage, simplifying the system structure and processing, and suppressing the information expansion involved in general conventional encryption methods. Both numerical and experimental results illustrate the feasibility and effectiveness of the proposed method. This work represents an effective idea for incoherent optical encryption techniques.
Saliency map-guided hierarchical dense feature aggregation framework for breast lesion classification using ultrasound image

Deep learning methods, especially convolutional neural networks, have advanced the breast lesion classification task using breast ultrasound (BUS) images. However, constructing a highly-accurate classification model still remains challenging due to complex pattern, relatively-low contrast and fuzzy boundary existing between lesion regions (i.e., foreground) and the surrounding tissues (i.e., background). Few studies have separated foreground and background for learning domain-specific representations, and then fused them for improving performance of models. In this paper, we propose a saliency map-guided hierarchical dense feature aggregation framework for breast lesion classification using BUS images. Specifically, we first generate saliency maps for foreground and background via super-pixel clustering and multi-scale region grouping. Then, a triple-branch network, including two feature extraction branches and a feature aggregation branch, is constructed to learn and fuse discriminative representations under the guidance of priors provided by saliency maps. In particular, two feature extraction branches take the original image and corresponding saliency map as input for extracting foreground- and background-specific representations. Subsequently, a hierarchical feature aggregation branch receives and fuses the features from different stages of two feature extraction branches, for lesion classification in a task-oriented manner. The proposed model was evaluated on three datasets using 5-fold cross validation, and experimental results have demonstrated that it outperforms several state-of-the-art deep learning methods on breast lesion diagnosis using BUS images.
Alert Optimization of the PLUM Earthquake Early Warning Algorithm for the Western United States

ABSTRACT We determine an optimal alerting configuration for the propagation of local undamped motion (PLUM) earthquake early warning (EEW) algorithm for use by the U.S. ShakeAlert system covering California, Oregon, and Washington. All EEW systems should balance the primary goal of providing timely alerts for impactful or potentially damaging shaking while limiting alerts for shaking that is too low to be of concern (precautionary alerts). The PLUM EEW algorithm forward predicts observed ground motions to nearby sites within a defined radius without accounting for attenuation, avoiding the earthquake source parameter estimation step of most EEW algorithms. PLUM was originally developed in Japan where the alert regions and ground motions for which alerts are issued differ from those implemented by ShakeAlert. We compare predicted ground motions from PLUM to ShakeMap-reported ground motions for a set of 22 U.S. West Coast earthquakes of magnitude 4.4–7.2 and evaluate available warning times. We examine a range of prediction radii (20–100 km), thresholds used to issue an alert (alert threshold), and levels of impactful or potentially damaging shaking (target threshold). We find optimal performance when the alert threshold is close to the target threshold, although higher target ground motions benefit from somewhat lower alert thresholds to ensure timely alerts. We also find that performance, measured as the cost reduction that a user can achieve, depends on the user’s tolerance for precautionary alerts. Users with a low target threshold and high tolerance for precautionary alerts achieve optimal performance when larger prediction radii (60–100 km) are used. In contrast, users with high target thresholds and low tolerance for precautionary alerts achieve better performance for smaller prediction radii (30–60 km). Therefore, setting the PLUM prediction radius to 60 km balances the needs of many users and provides warning times of up to ∼20 s.
Exploring Televend, an innovative combination of cryptomarket and messaging app technologies for trading prohibited drugs

Digital technologies continue to facilitate drug trading. Televend was an innovative combination of multiple digital technologies, with its backend hosted on the darknet, while purchases were made through the messaging app Telegram. Here, we provide an initial characterisation of this nascent market.Televend and White House Market (WHM) were scraped (Jun-Jul 2021) and a global cross-sectional web survey of 15,513 drug buyers (Global Drug Survey; GDS) was conducted (Dec 2020-Mar 2021).Televend was 10% of the size of WHM, the largest drug cryptomarket (4515/44,830 listings per week). Both markets predominantly contained drug-related listings covering similar drug categories, with similar country of origin and destination. Very few GDS drug buyers reported use of Televend (0.73%). Most Televend buyers (68/114) reported buying cannabis, then cocaine (20), MDMA (17), and LSD (12). The Televend and darknet groups had similar demographic and drug use characteristics; whereas compared with app purchasers, older age increased the odds of Televend use (aRRR = 1.06, p < .001), identifying as a cisgender woman decreased the odds (aRRR=0.43, p = .004), while last-year use of a greater number of drug types (aRRR = 1.20, p < .001) and less frequent drug use (aRRR=0.998, p = .032) increased the odds of Televend purchase.While smaller, Televend was not noticeably different in its drug offerings to its largest cryptomarket competitor, and it attracted a cohort more similar to darknet than to app drug buyers. Future Televend-like markets may be attractive to people with less specialised technical knowledge who already routinely scroll through social media feeds.