Reviewerly/exHarmony huggingface dataset:
Influence of Pollution Degree and Humidity on Surface Discharge Characteristics of Insulators

Insulators are an essential part of overhead transmission and distribution lines, which have the functions of electrical insulation and mechanical connection. Insulator pollution flashover is a serious threat to the safe and stable operation of the power system, so it is of great significance to grasp the insulation status of polluted insulators in time. In order to explore the influence of pollution degree and humidity on insulator surface discharge characteristics, this paper built an artificial fog chamber to conduct insulator artificial pollution test. According to the test results, the fitting function expressions among the number of discharge photons, the gain of UV imager and the shooting distance was obtained. And, it was concluded that the higher the pollution degree of insulators and the higher the humidity, the more photons by the domestic zt301 UV imager could shoot the polluted insulators, which provided a reference for the subsequent onsite inspection of operating state of insulators on transmission lines using UV imager.
Augmented Reality for Wine Industry: Past, Present, and Future

In this paper, we study the concepts, materials, tools, and applications that constitute what we call augmented reality (AR) for the wine industry. A comprehensive review of what are the basic multimedia content for constructing successful AR applications for wine products is given. To this end, we provide a detailed analysis of how AR technology is used to create augmented “live” wine labels, and how digital storytelling has revolutionized wine products marketing. Also, we describe the use of AR technology to promote winemaking companies to influence consumer preferences. Finally, we report the characteristics of future research directions and some open issues and challenges on using AR for wine product promotion.
Driver activity recognition by learning spatiotemporal features of pose and human object interaction

Detecting hazardous activity during driving can be useful in curbing roadside accidents. Existing techniques utilizing image based features for encoding such activity can sometimes misclassify crucial scenarios. One particular work by Zhao et al. (2013 [1], 2013 [2], 2011 [3]) suggests an image based feature set that encodes the driver's pose, which is categorized into one of four activities. We bring more clarity in understanding the activity by proposing a richer, video based feature set that adeptly exploits spatiotemporal information of the driver. Our feature set encodes the driver's pose, crucial variations in pose and interactions with objects within the vehicle. The feature set is tested on our newly created dataset since the ones used in literature are not publicly available. Our proposed feature set captures a larger number of activities and using standard classifiers and benchmarks it has shown significant improvements over the existing ones.
Entangled state generation via quantum walks with multiple coins

Abstract Generation of entangled state is of paramount importance both from quantum theoretical foundation and technology applications. Entanglement swapping provides an efficient method to generate entanglement in quantum communication protocols. However, perfect Bell measurements for qudits, the key to entanglement swapping, have been proven impossible to achieve by using only linear elements and particle detectors. To avoid this bottleneck, we propose a scheme to generate entangled state including two-qubit entangled state, two-qudit entangled state, three-qubit GHZ state and three-qudit GHZ state between several designate parties via the model of quantum walks with multiple coins. Then we conduct experimental realization of Bell state and three-qubit GHZ state between several designate parties on IBM quantum platform and the result has high fidelity by performing quantum tomography. In the end, we give a practical application of our scheme in multiparty quantum secret sharing.
Older Adults’ Experiences and Perceptions of Immersive Virtual Reality: A Protocol for a Systematic Review and Thematic Synthesis

Background: In the current digital age, new opportunities arise to support healthy ageing in older adults as frailty and immobility become more prominent. Immersive virtual reality, which can be defined as a fully computer-generated environment that is displayed through a headmounted display, is one of these technologies. Recently, there has been an increase in the number of qualitative studies exploring the potential for immersive virtual reality in assisting older adults—providing opportunities in health promotion and tackling social isolation and loneliness. With this in mind, the current review aims to explore the following research questions: (1) What are older adults’ experiences and perceptions of immersive virtual reality? (2) What are the specific barriers and facilitators to older adults’ use of immersive virtual reality? (3) Do older adults find immersive virtual reality acceptable? The objective of the current study is to systematically review and synthesize qualitative data exploring older adults’ experiences and perceptions of immersive virtual reality. Methods: A systematic review and thematic synthesis will be conducted. Two reviewers will complete title and abstract screening, full-text screening and, data extraction and quality appraisal. A thematic synthesis will be conducted by the lead author, after which the research team and other key stakeholders will discuss the findings and make any necessary changes. Thematic synthesis is derived from the qualitative method, thematic analysis. It involves three key steps: initial coding and grouping of these codes, the formation of descriptive themes from these codes and finally, going beyond the data to form novel insights and theories known as analytical themes. The following protocol has been written following the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P) guidelines.
A two-party private string matching fuzzy vault scheme

A Fuzzy Vault is a cryptographic structure where a secret is being locked by a key and can be unlocked only by another key with significant overlap. In this paper, we introduce a two-party privacy-preserving approximate string matching methodology based on a novel Fuzzy Vault scheme, combining the approximation and security properties of Fuzzy Vaults for privacy-preserving record linkage purposes.
A Survey on Data Science Techniques for Predicting Software Defects

In recent years, data science has been used extensively to solve several problems and its application has been extended to several domains. This paper summarises the literature on the synergistic use of Software Engineering and Data Science techniques (e.g. descriptive statistics, inferential statistics, machine learning, and deep learning models) for predicting defects in software. It shows that there is a variation in the use of data science techniques and limited reasoning behind the choice of certain machine learning models but also, in the evaluation of the obtained results. The contribution of this paper has to be intended as a categorization of the literature according to the most used data science concepts and techniques from the perspectives of descriptive and inferential statistics, machine learning, and deep learning. Furthermore, challenges in software defect prediction and comments on future research are discussed and forwarded.
Concept drift detection and localization in process mining

Business processes are subject to changes over time due to the need for adaptation and flexibility to a complex environment. Detecting drift as soon as possible and identifying the process elements involved, lead to a much better understanding of the process behavior, which can be a competitive edge for businesses. However, most existing approaches focus on each of these two tasks separately. Isolated approaches do not always have interfaces between them that allow you to combine solutions effectively for each corresponding task. In such cases, using the two isolated solutions together is neither feasible nor even useful from the point of view of a business analyst. This paper proposes an integrated approach to detect and locate concept drifts based on an online setting for trace clustering. Experiments with synthetic event logs with different types of control-flow changes showed that concept drifts can be detected and located efficiently.
Child Sexual Exploitation Communities on the Darkweb: How Organized Are They?

Because of the growing incidence and increasing technical sophistication of Darkweb child sexual exploitation (CSE), some have begun to label it as organized crime. By itself however, this label adds little to our understanding of the phenomenon. To gain a more detailed insight into the workings of Darkweb CSE, we apply the conceptual framework suggested by Von Lampe (Organized crime: Analyzing illegal activities, criminal structures and extra-legal governance. Sage, Thousand Oaks, CA, 2016a) and instead ask: how organized is CSE on the Darkweb? Six police investigation case files were systematically analyzed using methods akin to the Dutch Organized Crime Monitor, complemented with interviews with police officers and public prosecutors. While the barter of CSE material in itself is a deviant exchange, it is embedded in the social network provided by the forum environment. Darkweb CSE requires organization to the extent that running a forum involves a set of interlocking tasks, a certain level of technical sophistication, and continued effort to protect the forum from (outside) threats. We conclude that both the CSE crime and the criminals perpetrating it show clear signs of organization. CSE Darkweb fora constitute both associational and entrepreneurial structures that serve the social and criminal needs of their members. In the trust-based hierarchy of these networks, key players are able to exert some internal governance. Monetary profit, violence, and the desire to monopolize the market however, are largely absent. Detailed insight in the dynamics of Darkweb CSE interactions will contribute more to reducing the harm caused by these crimes than the mere application of a label.
Cretaceous intermediate sulfidation ore system in the Dongkeng volcanic Basin, SE China: Constraints from the Dongji and Maluntou gold deposits

The Dongji (>12.5 t Au @ 4.27 g/t) and Maluntou (>5.0 t Au @ 3.70 g/t) gold deposits are the two largest ones in the Dongkeng Volcanic Basin (DVB), SE China, that are hosted by volcanic rocks. Mineralization is represented by three stages (i.e., stage 1, 2, and 3) that are characterized by four types of unzoned hydrothermal pyrite (i.e., pyrite1, 2a, 2b, and 3). Hydrothermal fluids responsible for pyrite1 deposition are moderate temperatures (308–377 °C) and low salinity (4.6–9.1 wt% NaCl equiv.). The deposition of pyrite2a and 2b is related to the stage 2 fluids featured by moderate-low temperatures (253–341 °C) and low salinity (3.2–9.1 wt% NaCl equiv.). Pyire3 is deposited from the stage 3 fluids with low temperatures (220–250 °C) and salinities (1.0–6.5 wt% NaCl equiv.). Hydrothermal fluids potentially have a magmatic origin and experience fluid boiling and mixing of meteoric water. Scanning electron microscopy and laser ablation inductively coupled plasma-mass spectrometry were used to investigate the occurrence of visible gold and the distribution of invisible gold in pyrite from different generations. Visible gold mainly exists as native gold and electrum within crystal interstices, fractures, and hollows of pyrite2b, and precipitates directly from the fluids during fluid boiling at ca. 329 °C with pressure of ca.120 bar. Invisible gold mainly presents in the form of solid solution in all pyrite types. Solid solution gold is also closely related to pyrite2b as the measured Au concentrations in pyrite2b (up to 27 ppm) exceed those in other pyrite types (<0.6 ppm) by one or two orders of magnitude. The elevated content of invisible Au (together with As, Ag, Zn, and Sb) in pyrite2b is potentially associated with lattice dislocations caused by rapid precipitation of Py2b during boiling process. The Re–Os isochron age of pyrite2b (99 ± 10 Ma) and the zircon U–Pb dating of the volcanic and subvolcanic rocks (95.1–104 Ma) indicate that the formation of gold mineralization and the igneous activity in the DVB were coeval during the Turonia–Albian. Geochronology, fluid characteristics, together with low Ni concentrations (<98 ppm) and high mean Co/Ni ratios (≥2.0) of pyrite from different generations, support that the Dongji and Maluntou gold deposits formed in a magmatic-hydrothermal ore system and have an intermediate-sulfidation epithermal origin.
Low-Complexity Parallel Cyclic Redundancy Check

Cyclic redundancy check (CRC) is adopted in many digital communication and storage systems to ensure data integrity. CRC en/decoding is carried out using linear feedback shift registers (LFSRs) and a parallel LFSR can be implemented by registers with a feedback matrix multiplication and an input pre-processing matrix multiplication. A large parallelism is needed to achieve the high throughput required by modern applications. In prior designs, the complexity of parallel LF- SRs has been reduced by applying state transformation and/or modifying the input tap. In this paper, we first show that the input tap modification can be actually described by a category of state transformation. Using this type of transformation, the pre-processing matrix in a highly-parallel LFSR can be simplified without changing the feedback matrix. Additionally, we show that the post-processing matrix multiplication in state-transformed designs can be eliminated without affecting the error detection capability of the CRC. Utilizing these two techniques, the area requirement of highly-parallel CRC can be reduced by 7-16% without increasing the critical path for various parallelisms and most generator polynomials compared to the best previous design.
AC2: An Efficient Protein Sequence Compression Tool Using Artificial Neural Networks and Cache-Hash Models

Recently, the scientific community has witnessed a substantial increase in the generation of protein sequence data, triggering emergent challenges of increasing importance, namely efficient storage and improved data analysis. For both applications, data compression is a straightforward solution. However, in the literature, the number of specific protein sequence compressors is relatively low. Moreover, these specialized compressors marginally improve the compression ratio over the best general-purpose compressors. In this paper, we present AC2, a new lossless data compressor for protein (or amino acid) sequences. AC2 uses a neural network to mix experts with a stacked generalization approach and individual cache-hash memory models to the highest-context orders. Compared to the previous compressor (AC), we show gains of 2–9% and 6–7% in reference-free and reference-based modes, respectively. These gains come at the cost of three times slower computations. AC2 also improves memory usage against AC, with requirements about seven times lower, without being affected by the sequences’ input size. As an analysis application, we use AC2 to measure the similarity between each SARS-CoV-2 protein sequence with each viral protein sequence from the whole UniProt database. The results consistently show higher similarity to the pangolin coronavirus, followed by the bat and human coronaviruses, contributing with critical results to a current controversial subject. AC2 is available for free download under GPLv3 license.
Leakage Resilient Value Comparison with Application to Message Authentication

Side-channel attacks are a threat to secrets stored on a device, especially if an adversary has physical access to the device. As an effect of this, countermeasures against such attacks for cryptographic algorithms are a well-researched topic. In this work, we deviate from the study of cryptographic algorithms and instead focus on the side-channel protection of a much more basic operation, the comparison of a known attacker-controlled value with a secret one. Comparisons sensitive to side-channel leakage occur in tag comparisons during the verification of message authentication codes (MACs) or authenticated encryption, but are typically omitted in security analyses. Besides, also comparisons performed as part of fault countermeasures might be sensitive to side-channel attacks. In this work, we present a formal analysis on comparing values in a leakage resilient manner by utilizing cryptographic building blocks that are typically part of an implementation anyway. Our results indicate that there is no need to invest additional resources into implementing a protected comparison operation itself if a sufficiently protected implementation of a public cryptographic permutation, or a (tweakable) block cipher, is already available. We complement our contribution by applying our findings to the SuKS message authentication code used by lightweight authenticated encryption scheme ISAP, and to the classical Hash-then-PRF construction.
Local Stereo Matching Using Adaptive Cross-Region-Based Guided Image Filtering with Orthogonal Weights

Adaptive cross-region-based guided image filtering (ACR-GIF) is a commonly used cost aggregation method. However, the weights of points in the adaptive cross-region (ACR) are generally not considered, which affects the accuracy of disparity results. In this study, we propose an improved cost aggregation method to address this issue. First, the orthogonal weight is proposed according to the structural feature of the ACR, and then the orthogonal weight of each point in the ACR is computed. Second, the matching cost volume is filtered using ACR-GIF with orthogonal weights (ACR-GIF-OW). In order to reduce the computing time of the proposed method, an efficient weighted aggregation computing method based on orthogonal weights is proposed. Additionally, by combining ACR-GIF-OW with our recently proposed matching cost computation method and disparity refinement method, a local stereo matching algorithm is proposed as well. The results of Middlebury evaluation platform show that, compared with ACR-GIF, the proposed cost aggregation method can significantly improve the disparity accuracy with less additional time overhead, and the performance of the proposed stereo matching algorithm outperforms other state-of-the-art local and nonlocal algorithms.
Visual Analytics of Smartphone-Sensed Human Behavior and Health

Smartphone health sensing tools, which analyze passively gathered human behavior data, can provide clinicians with a longitudinal view of their patients' ailments in natural settings. In this Visualization Viewpoints article, we postulate that interactive visual analytics (IVA) can assist data scientists during the development of such tools by facilitating the discovery and correction of wrong or missing user-provided ground-truth health annotations. IVA can also assist clinicians in making sense of their patients' behaviors by providing additional contextual and semantic information. We review the current state-of-the-art, outline unique challenges, and illustrate our viewpoints using our work as well as those of other researchers. Finally, we articulate open challenges in this exciting and emerging field of research.
Reducing BERT Computation by Padding Removal and Curriculum Learning

BERT is very computationally expensive, which is a hurdle for its training and deployment. This work focuses on removing the unnecessary computation due to input padding in BERT. The input of BERT consists of two concatenated sentences. If the length of the two concatenated sentences is shorter than the maximum sequence length, padding must be added to the end of the sentences to fill the empty slots in the input. Because the lengths of sentences vary greatly, there can be a large amount of padding in input. For the English Wikipedia & BooksCorpus dataset, the percentage of padding among all the input tokens is 17% and 48%, respectively, when the max sequence length is set to 128 and 512. For the Chinese Wikipedia dataset, this percentage is 35% and 79%, respectively, when the max sequence length is 128 and 512. For SQuAD-v1.1 [2], padding accounts for 54% of the total input tokens when the max sequence length is 384. Thus, there is a lot of wasted computation on padding, which significantly increases the training and inference time.
An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation

Approaches based on order-adaptive regularisation belong to the most accurate variational methods for computing the optical flow. By locally deciding between first- and second-order regularisation, they are applicable to scenes with both fronto-parallel and ego-motion. So far, however, existing order-adaptive methods have a decisive drawback. While the involved first- and second-order smoothness terms already make use of anisotropic concepts, the underlying selection process itself is still isotropic in that sense that it locally chooses the same regularisation order for all directions. In our paper, we address this shortcoming. We propose a generalised order-adaptive approach that allows to select the local regularisation order for each direction individually. To this end, we split the order-adaptive regularisation across and along the locally dominant direction and perform an energy competition for each direction separately. This in turn offers another advantage. Since the parameters can be chosen differently for both directions, the approach allows for a better adaption to the underlying scene. Experiments for MPI Sintel and KITTI 2015 demonstrate the usefulness of our approach. They not only show improvements compared to an isotropic selection scheme. They also make explicit that our approach is able to improve the results from state-of-the-art learning-based approaches, if applied as a final refinement step – thereby achieving top results in both benchmarks.
Non-invasive Low Cost Fever Detection Systems

With the current covid-19 pandemic ongoing, staying safe is o upmost importance. In being proactive, people everywhere are clothed in facial masks and gloves. Businesses and universities are conducting temperature checks in a multitude of methods such as thermal imaging, forehead, oral, and aural thermometer handguns. From our research, we discovered that the non-contact based devices available in the market today are quite expensive, ranging from $10k-$60k and would still require some close contact with the next individual. Our Artificial Intelligence and Visualizations (AiVi) team at North Carolina A&T State University aimed to create fever detection systems that would promote safer testing in regard to exposure to any viruses or illness one may carry, affordability, as well as equal or greater accuracy. For this research project, we designed, built, and programmed three devices, namely: (i) Aggie Arm, thermometer inspired device that reads temperature values from an individual's forehead when within range; (ii) Aggie Eye, a thermal imaging device that measures body temperature based on values gathered from the tear duct of the eye with higher accuracy; (iii) Aggie Talk, an intercom system coupled with the Aggie Eye to be integrated with existing k-12 school intercom systems.