Reviewerly/exHarmony huggingface dataset:
Adaptive time series prediction and recommendation

The ubiquity of user-item interactions makes it essential and challenging to utilize the rich variety of hidden structural and temporal information for effective and efficient recommendation. In this work, our goal is to address the limitations of existing research: (i) inadequacy of popularity trend prediction and temporal recommendation (ii) failure to clarify the influence and mechanism of structural characteristics and the temporal evolution on recommendation. To this end, we first construct time sequences of grown popularity of items. Then we propose a family of time-series predictive models to predict the growing trend of popularity. Furthermore, we exploit the Broyden-Fletcher-Goldfarb-Shanno quasi-Newton optimization algorithm (BFGS) to adjust the predictive parameters adaptively. Moreover, to investigate the influence and interaction mechanism of structural and temporal information on recommendation, we propose a novel Hybrid Network Adaptive Time Series recommendation framework (HNATS), which improves synchronously the recommendation performance. Finally, we conduct comprehensive experiments on four real-world datasets of different sizes and time spans. The experimental results demonstrate that our proposed predictive models can capture the hidden temporal patterns and the HNATS method surpasses those compared state-of-the-art temporal ones, including the popularity-based, the time decay-based, and the Markov-based baselines.
Protecting Privacy and Transforming COVID-19 Case Surveillance Datasets for Public Use

Objectives Federal open-data initiatives that promote increased sharing of federally collected data are important for transparency, data quality, trust, and relationships with the public and state, tribal, local, and territorial partners. These initiatives advance understanding of health conditions and diseases by providing data to researchers, scientists, and policymakers for analysis, collaboration, and use outside the Centers for Disease Control and Prevention (CDC), particularly for emerging conditions such as COVID-19, for which data needs are constantly evolving. Since the beginning of the pandemic, CDC has collected person-level, de-identified data from jurisdictions and currently has more than 8 million records. We describe how CDC designed and produces 2 de-identified public datasets from these collected data. Methods We included data elements based on usefulness, public request, and privacy implications; we suppressed some field values to reduce the risk of re-identification and exposure of confidential information. We created datasets and verified them for privacy and confidentiality by using data management platform analytic tools and R scripts. Results Unrestricted data are available to the public through Data.CDC.gov, and restricted data, with additional fields, are available with a data-use agreement through a private repository on GitHub.com. Practice Implications Enriched understanding of the available public data, the methods used to create these data, and the algorithms used to protect the privacy of de-identified people allow for improved data use. Automating data-generation procedures improves the volume and timeliness of sharing data.
The visual impact of augmented reality during an assembly task

Many studies have shown the benefits of augmented reality (AR) to improve manufacturing and control processes in industry. However, the presentation of AR content through optical see-through head-mounted displays may induce unnatural viewing conditions, which consequences on the user’s visual system have not been investigated yet. This study aimed at assessing whether using AR instructions to guide a manual task, i.e., conditions where the user is forced to repeatedly look at and accommodate in different planes to extract information from both real and virtual environments, could potentially impact the visual system of operators. A before/after design study was carried out, asking 26 participants to perform Lego assemblies for 30 min with either paper or AR instructions. The effects of using AR compared to paper instructions were evaluated both on binocular vision, with classical optometric measurements, and on visual fatigue, with the Virtual Reality Symptoms Questionnaire. No clinically significant differences in optometric measurements (far/near visual acuity, stereoacuity, phoria, convergence, fusional amplitude, accommodation amplitude, accommodative convergence) have been found between AR and paper instructions, and only negligible fatigue symptoms have been seen specifically for AR. Results from both objective and subjective measurements suggest that there is no impact of AR on the oculomotor system and that, in this specific case of use, AR can be safely used for production operators.
Is Technology Enhancing or Hindering Interpersonal Communication? A Framework and Preliminary Results to Examine the Relationship Between Technology Use and Nonverbal Decoding Skill

Digital technology has facilitated additional means for human communication, allowing social connections across communities, cultures, and continents. However, little is known about the effect these communication technologies have on the ability to accurately recognize and utilize nonverbal behavior cues. We present two competing theories, which suggest (1) the potential for technology use to enhance nonverbal decoding skill or, (2) the potential for technology use to hinder nonverbal decoding skill. We present preliminary results from two studies to test these hypotheses. Study 1 ( N = 410) found that global screen time was unrelated to nonverbal decoding skill. However, how participants spent their time using technology mattered. Participants who reported more active technology use (i.e., posting content) self-reported that their nonverbal decoding skill (as measured by the Emotional Sensitivity subscale of the Social Skills Inventory) was superior but performed worse on objective measures of decoding skill (using standardized tests including the Diagnostic Analysis of Nonverbal Accuracy-Adult Faces and the Workplace Interpersonal Perception Skill). By contrast, passive users performed significantly better on objective measures of nonverbal decoding skill; although they did not self-report any difference in their skill compared to less passive users. Study 2 ( N = 190), and a mini-meta analysis of both studies, replicated this pattern. These effects suggest a roadmap for understanding the theoretical relationship between technology use and nonverbal communication skills. We also provide recommendations for future research, including the use of experimental designs to determine causal pathways and to advance our conceptual understanding of the relationship between technology use and nonverbal decoding skill.
P2PEdge: A Decentralised, Scalable P2P Architecture for Energy Trading in Real-Time

Current Peer-to-Peer (P2P) energy market models raise serious concerns regarding the confidentiality and integrity of energy consumption, trading and billing data. While Distributed Ledger Technology (DLT) systems (e.g., blockchain) have been proposed to enhance security, an attacker could damage other parts of the model, such as its infrastructure: an adversarial attacker could target the communication between entities by, e.g., eavesdropping or modifying data. The main goal of this paper is to propose a model for a decentralised P2P marketplace for trading energy, which addresses the problem of developing security and privacy-aware environments. Additionally, a Multi-Agent System (MAS) architecture is presented with a focus on security and sustainability. In order to propose a solution to DLT’s scalability issues (i.e., through transaction confirmation delays), off-chain state channels are considered for the energy negotiation and resolution processes. Additionally, a STRIDE (spoofing, tampering, repudiation, information disclosure, denial of service, elevation of privilege) security analysis is conducted within the context of the proposed model to identify potential vulnerabilities.
Motion Planning Algorithm for a Mobile Robot with a Smart Machine Vision System

This study is devoted to the challenges of motion planning for mobile robots with smart machine vision systems. Motion planning for mobile robots in the environment with obstacles is a problem to deal with when creating robots suitable for operation in real-world conditions. The solutions found today are predominantly private, and are highly specialized, which prevents judging of how successful they are in solving the problem of effective motion planning. Solutions with a narrow application field already exist and are being already developed for a long time, however, no major breakthrough has been observed yet. Only a systematic improvement in the characteristics of such systems can be noted. The purpose of this study: develop and investigate a motion planning algorithm for a mobile robot with a smart machine vision system. The research subject for this article is a motion planning algorithm for a mobile robot with a smart machine vision system. This study provides a review of domestic and foreign mobile robots that solve the motion planning problem in a known environment with unknown obstacles. The following navigation methods are considered for mobile robots: local, global, individual. In the course of work and research, a mobile robot prototype has been built, capable of recognizing obstacles of regular geometric shapes, as well as plan and correct the movement path. Environment objects are identified and classified as obstacles by means of digital image processing methods and algorithms. Distance to the obstacle and relative angle are calculated by photogrammetry methods, image quality is improved by linear contrast enhancement and optimal linear filtering using the Wiener-Hopf equation. Virtual tools, related to mobile robot motion algorithm testing, have been reviewed, which led us to selecting Webots software package for prototype testing. Testing results allowed us to make the following conclusions. The mobile robot has successfully identified the obstacle, planned a path in accordance with the obstacle avoidance algorithm, and continued moving to the destination. Conclusions have been drawn regarding the concluded research.
A case study comparing the completeness and expressiveness of two industry recognized ontologies

Enabling buildings with Smart Building applications will help to achieve the ongoing efficient commissioning of buildings, ultimately attaining peak performance in energy use and improved occupant health and comfort, at minimum cost. For these technologies to be scalable data ontology must be adopted to semantically represent data generated by building mechanical systems, acting as conduit for connection to Smart Building applications. The viability of Brick and Project Haystack ontologies, as found by industry and academia, prompted a quantitative comparison of completeness and expressiveness using a case study with an industry ontology as the baseline. Additionally, a qualitative comparison was completed using key ontology qualities outlined in literature. A recommendation of Brick is made based on results. Brick achieved higher assessment values in completeness and expressiveness achieving 59% and 100% respectively, as compared to Haystacks 43% and 96%. Additionally, Brick exhibited five of six desirable qualities, where Haystack exhibited only three. The recommendation of the appropriate ontology forms the basis for longer-term Smart Building application development, which will support innovative approaches to sustainability in building operations across scale, as well as next-generation building controls and automation strategies.
A Bioinspired Neural Network-Based Approach for Cooperative Coverage Planning of UAVs

This paper describes a bioinspired neural-network-based approach to solve a coverage planning problem for a fleet of unmanned aerial vehicles exploring critical areas. The main goal is to fully cover the map, maintaining a uniform distribution of the fleet on the map, and avoiding collisions between vehicles and other obstacles. This specific task is suitable for surveillance applications, where the uniform distribution of the fleet in the map permits them to reach any position on the map as fast as possible in emergency scenarios. To solve this problem, a bioinspired neural network structure is adopted. Specifically, the neural network consists of a grid of neurons, where each neuron has a local cost and has a local connection only with neighbor neurons. The cost of each neuron influences the cost of its neighbors, generating an attractive contribution to unvisited neurons. We introduce several controls and precautions to minimize the risk of collisions and optimize coverage planning. Then, preliminary simulations are performed in different scenarios by testing the algorithm in four maps and with fleets consisting of 3 to 10 vehicles. Results confirm the ability of the proposed approach to manage and coordinate the fleet providing the full coverage of the map in every tested scenario, avoiding collisions between vehicles, and uniformly distributing the fleet on the map.
GLAGC: Adaptive Dual-Gamma Function for Image Illumination Perception and Correction in the Wavelet Domain

Low-contrast or uneven illumination in real-world images will cause a loss of details and increase the difficulty of pattern recognition. An automatic image illumination perception and adaptive correction algorithm, termed as GLAGC, is proposed in this paper. Based on Retinex theory, the illumination of an image is extracted through the discrete wavelet transform. Two features that characterize the image illuminance are creatively designed. The first feature is the spatial luminance distribution feature, which is applied to the adaptive gamma correction of local uneven lighting. The other feature is the global statistical luminance feature. Through a training set containing images with various illuminance conditions, the relationship between the image exposure level and the feature is estimated under the maximum entropy criterion. It is used to perform adaptive gamma correction on global low illumination. Moreover, smoothness preservation is performed in the high-frequency subband to preserve edge smoothness. To eliminate low-illumination noise after wavelet reconstruction, the adaptive stabilization factor is derived. Experimental results demonstrate the effectiveness of the proposed algorithm. By comparison, the proposed method yields comparable or better results than the state-of-art methods in terms of efficiency and quality.
KeyNet: An Asymmetric Key-Style Framework for Watermarking Deep Learning Models

Many organizations devote significant resources to building high-fidelity deep learning (DL) models. Therefore, they have a great interest in making sure the models they have trained are not appropriated by others. Embedding watermarks (WMs) in DL models is a useful means to protect the intellectual property (IP) of their owners. In this paper, we propose KeyNet, a novel watermarking framework that satisfies the main requirements for an effective and robust watermarking. In KeyNet, any sample in a WM carrier set can take more than one label based on where the owner signs it. The signature is the hashed value of the owner’s information and her model. We leverage multi-task learning (MTL) to learn the original classification task and the watermarking task together. Another model (called the private model) is added to the original one, so that it acts as a private key. The two models are trained together to embed the WM while preserving the accuracy of the original task. To extract a WM from a marked model, we pass the predictions of the marked model on a signed sample to the private model. Then, the private model can provide the position of the signature. We perform an extensive evaluation of KeyNet’s performance on the CIFAR10 and FMNIST5 data sets and prove its effectiveness and robustness. Empirical results show that KeyNet preserves the utility of the original task and embeds a robust WM.
Direct Fidelity Estimation of Quantum States Using Machine Learning

In almost all quantum applications, one of the key steps is to verify that the fidelity of the prepared quantum state meets expectations. In this Letter, we propose a new approach solving this problem using machine-learning techniques. Compared to other fidelity estimation methods, our method is applicable to arbitrary quantum states, the number of required measurement settings is small, and this number does not increase with the size of the system. For example, for a general five-qubit quantum state, only four measurement settings are required to predict its fidelity with $\ifmmode\pm\else\textpm\fi{}1%$ precision in a nonadversarial scenario. This machine-learning-based approach for estimating quantum state fidelity has the potential to be widely used in the field of quantum information.
Optimising Hardware Accelerated Neural Networks with Quantisation and a Knowledge Distillation Evolutionary Algorithm

This paper compares the latency, accuracy, training time and hardware costs of neural networks compressed with our new multi-objective evolutionary algorithm called NEMOKD, and with quantisation. We evaluate NEMOKD on Intel’s Movidius Myriad X VPU processor, and quantisation on Xilinx’s programmable Z7020 FPGA hardware. Evolving models with NEMOKD increases inference accuracy by up to 82% at the cost of 38% increased latency, with throughput performance of 100–590 image frames-per-second (FPS). Quantisation identifies a sweet spot of 3 bit precision in the trade-off between latency, hardware requirements, training time and accuracy. Parallelising FPGA implementations of 2 and 3 bit quantised neural networks increases throughput from 6 k FPS to 373 k FPS, a 62× speedup.
Phishing for Long Tails: Examining Organizational Repeat Clickers and Protective Stewards

Organizational cybersecurity efforts depend largely on the employees who reside within organizational walls. These individuals are central to the effectiveness of organizational actions to protect sensitive assets, and research has shown that they can be detrimental (e.g., sabotage and computer abuse) as well as beneficial (e.g., protective motivated behaviors) to their organizations. One major context where employees affect their organizations is phishing via email systems, which is a common attack vector used by external actors to penetrate organizational networks, steal employee credentials, and create other forms of harm. In analyzing the behavior of more than 6,000 employees at a large university in the Southeast United States during 20 mock phishing campaigns over a 19-month period, this research effort makes several contributions. First, employees’ negative behaviors like clicking links and then entering data are evaluated alongside the positive behaviors of reporting the suspected phishing attempts to the proper organizational representatives. The analysis displays evidence of both repeat clicker and repeat reporter phenomena and their frequency and Pareto distributions across the study time frame. Second, we find that employees can be categorized according to one of the four unique clusters with respect to their behavioral responses to phishing attacks—“Gaffes,” “Beacons,” “Spectators,” and “Gushers.” While each of the clusters exhibits some level of phishing failures and reports, significant variation exists among the employee classifications. Our findings are helpful in driving a new and more holistic stream of research in the realm of all forms of employee responses to phishing attacks, and we provide avenues for such future research.
A Multi-Strategy Marine Predator Algorithm and Its Application in Joint Regularization Semi-Supervised ELM

A novel semi-supervised learning method is proposed to better utilize labeled and unlabeled samples to improve classification performance. However, there is exists the limitation that Laplace regularization in a semi-supervised extreme learning machine (SSELM) tends to lead to poor generalization ability and it ignores the role of labeled information. To solve the above problems, a Joint Regularized Semi-Supervised Extreme Learning Machine (JRSSELM) is proposed, which uses Hessian regularization instead of Laplace regularization and adds supervised information regularization. In order to solve the problem of slow convergence speed and the easy to fall into local optimum of marine predator algorithm (MPA), a multi-strategy marine predator algorithm (MSMPA) is proposed, which first uses a chaotic opposition learning strategy to generate high-quality initial population, then uses adaptive inertia weights and adaptive step control factor to improve the exploration, utilization, and convergence speed, and then uses neighborhood dimensional learning strategy to maintain population diversity. The parameters in JRSSELM are then optimized using MSMPA. The MSMPA-JRSSELM is applied to logging oil formation identification. The experimental results show that MSMPA shows obvious superiority and strong competitiveness in terms of convergence accuracy and convergence speed. Also, the classification performance of MSMPA-JRSSELM is better than other classification methods, and the practical application is remarkable.
Mars Image Content Classification: Three Years of NASA Deployment and Recent Advances

The NASA Planetary Data System hosts millions of images acquired from the planet Mars. To help users quickly find images of interest, we have developed and deployed content-based classification and search capabilities for Mars orbital and surface images. The deployed systems are publicly accessible using the PDS Image Atlas. We describe the process of training, evaluating, calibrating, and deploying updates to two CNN classifiers for images collected by Mars missions. We also report on three years of deployment including usage statistics, lessons learned, and plans for the future.
Typometrics: From Implicational to Quantitative Universals in Word Order Typology

This paper develops the concept of word order universals based on a data analysis of the Universal Dependencies project, which proposes treebanks of more than 90 languages encoded with the same annotation scheme. The nature of the data we work on allows us to extract rich details for testing well-known typological implicational universals and, further, explore new kinds of universals that we call quantitative universals. We show how such quantitative universals are in essence different from implicational universals, including statistical universals, by the fact that they no longer lay down any claims on categorical statements, but rather on continuous parameters, opening a new field of research we propose to call typometrics.
Commonsense Knowledge Aware Concept Selection For Diverse and Informative Visual Storytelling

Visual storytelling is a task of generating relevant and interesting stories for given image sequences. In this work we aim at increasing the diversity of the generated stories while preserving the informative content from the images. We propose to foster the diversity and informativeness of a generated story by using a concept selection module that suggests a set of concept candidates. Then, we utilize a large scale pre-trained model to convert concepts and images into full stories. To enrich the candidate concepts, a commonsense knowledge graph is created for each image sequence from which the concept candidates are proposed. To obtain appropriate concepts from the graph, we propose two novel modules that consider the correlation among candidate concepts and the image-concept correlation. Extensive automatic and human evaluation results demonstrate that our model can produce reasonable concepts. This enables our model to outperform the previous models by a large margin on the diversity and informativeness of the story, while retaining the relevance of the story to the image sequence.