Reviewerly/exHarmony huggingface dataset:
Swiping Angles Differentially Influence Young and Old Users’ Performance and Experience on Swiping Gestures

The efficiency and convenience of gesture shortcuts have an important influence on user experience. However, it is unknown how the number of permitted swiping angles and their allowable range affect users' performance and experience. In the present study, young and old users executed swiping in multiple directions on smartphones. Results showed that multiple allowable angles resulted in slower swiping speed and poorer user experience than the single allowable angle condition. However, as the number of allowable angles increased, only old users showed a significant decrease in swiping accuracy. Vertical-up and upper-right swiping were faster than swiping in the horizontal directions. Furthermore, narrower operable range of swiping only reduced swiping accuracy in the tilted direction. Though old users performed worse on swiping than younger users, their subjective ratings were more positive than younger users'. Suggestions on how to design swiping gestures on the human-mobile interface were discussed.
Role of Universities in Sustainable Rural Development

In this study, we examine the ways of assessing the contribution of higher education institutions to the socioeconomic development of regional districts. We put forward the following working hypothesis—areas with access to higher education usually grow spatially, while areas that lack higher education institutions contract. We attempted to empirically prove the working hypothesis and analyzed the potential of dual and online education in providing affordable, high-quality education for the residents of remote areas.
Concept Design Moves

Great designs are rarely inventions without precedent; more often they are skillful adaptations of earlier designs. Designers work by recognizing structures they have previously seen, and taking steps they have taken before. By making such patterns and design moves explicit, we can educate designers more effectively and promote good design. This paper explains concepts, a modular structure for describing software behavior that allows patterns to be recognized, and proposes three pairs of design moves for software design, illustrating their application in some widely used products.
The Effect of Activated Sludge Treatment and Catalytic Ozonation on High Concentration of Ammonia Nitrogen Removal from Landfill Leachate

This study adopted the combination of activated sludge treatment and catalytic ozonation technology to efficiently remove the high concentration of ammonia nitrogen from landfill leachate. Through the continuous optimization of parameters, the NH4+-N concentration descended from 1180.72 ± 13.55 mg/L to 9.77 ± 0 mg/L with the removal rate of 99.17 ± 0.01% within 14 days. Target genes of AOB-amoA, nxrA, napA, nirS and nosZ analysis indicated that ammonia-oxidizing bacteria, nitrated bacteria, and denitrifying bacteria, played a key role on nitrogen removal, aerobic denitrifying bacteria was dominated especially. The nitrogen removal process was as follows: catalytic ozonation converted nitrogen-containing organic matter into NH4+-N, then NH4+-N was converted into NO2--N and NO3--N with the action of ammonia oxidation, nitrification and catalytic ozonation. Finally, the denitrification microorganisms transformed NO3--N or NO2--N to N2. Therefore, this coupled process could realize the nitrogen removal effectively from landfill leachate.
Local Dimming for Video Based on an Improved Surrogate Model Assisted Evolutionary Algorithm

Compared with the traditional LCD systems, the local dimming systems can obtain higher display quality with lower power consumption. Considering local dimming of the static image as an optimization problem and solving it based on an evolutionary algorithm (EA), the optimal backlight matrix can be obtained. However, the local dimming algorithm based on EA is no longer applicable for the video sequences composed of multiple images, because the calculation is very time-consuming. This paper proposes a local dimming algorithm based on improved surrogate model assisted EA (ISAEA-LD). In this algorithm, the surrogate model assisted EA is applied to solve the local dimming problem of the video sequences. Firstly, the surrogate model is used to reduce the complexity of individual fitness evaluation of EA. And a surrogate model based on a convolutional neural network is adopted to improve the accuracy of individual fitness evaluation. Secondly, the algorithm introduces the backlight update strategy based on the content correlation of the video sequences' adjacent frames and the model transfer strategy based on transfer learning to improve the efficiency of the algorithm further. Experimental results show that compared with the existing algorithms, the proposed ISAEA-LD algorithm can obtain better visual quality and higher algorithm efficiency.
Finding Optimal Paths of All Lengths in Directed Grid Graphs

We study a problem of finding optimal paths of all lengths on a directed edge-weighted grid graph. Variations of this problem appear in different contexts using similar frame-works. We propose sequential and parallel algorithms for it. Our algorithms have applications for k-vertex problem in one dimension, and edit distance problem in two dimensions. They offer fast solutions for special cases of these problems.
Learning Shared and Discriminative Information from Multiview Data

With the advent of the Internet-of-Things (IoTs) and Big Data movements, the rate of gathering as well as accumulating information nowadays is growing dramatically. Huge amounts of data that abound with heterogeneous features representing distinct perspectives of the same underlying patterns arise in various scientific fields. For instance, LIDAR signals, radar signals, and camera videos can be seen as three different views of a particular self-driving car. In signal processing, machine learningMachine learning, statistics, and data science, multiview learning (a.k.a., data fusionData fusion/integration) is a popular field with well-documented analytical tools and wide-range application domains. In this chapter, we will introduce two algorithms which seek to find the shared and discriminative information from multiview data. First, graph multiview canonical correlation analysisCanonical correlation analysis (GMCCA) models will be introduced to unravel such shared information, which aims at searching for the low-dimensional representations from multiview data, while incorporating some graph priorGraph priors information of shared latent components of the multiview data. Capitalizing on kernel methods, a generalization of GMCCA to capture the latent components of nonlinear data data is further established. A theoretical analysis of the GMCCA is provided to estimate the generalization error bound. Second, we will introduce discriminative principal component analysis (dPCA) to learn the unique subspace of one dataset (a.k.a., target data) relative to the other dataset (a.k.a., background data). Under some mild assumptions, dPCA can be shown to be optimal in terms of finding the discriminative subspace of the target data with respect to the background data. Moreover, some representative applications are provided to validate the effectiveness of the aforementioned models in discovering shared or discriminative knowledge of multiview data.
Graphology Based Human Behavior and Personality Identification Using Artificial Neural Networks

Handwriting is often called mind writing. For each individual, handwriting may be unique and the behavior, nature and certain psychological features of a person can be determined from it. Graphology or handwriting analysis is the investigation of the patterns and physical attributes of handwriting with an endeavor to distinguish the author, demonstrate the psychological state while writing, or assess personality characteristics. The manual method of handwriting analysis is extremely expensive and tedious. Therefore, the proposed approach centers around building up software for personality identification that can predict the behavioral features directly with the help of a computer without any human involvement. Our proposed system can predict personality by analyzing English as well as Devanagari samples using artificial neural networks. In a country like ours, it is most probable that people prefer Hindi over English. Hence, we have proposed an approach for developing a device that eliminates the language barrier to predict the writer's personality or behavior.
Introduction of Metrics for Blockchain

This article presents an introduction to the definition of a set of metrics to address the performance, scalability and workload analysis of blockchain technologies, mainly with tools applied to the Argentine Federal Blockchain and Hyperledger Fabric. Issues to consider in the application of this technology for the protection of digital evidence are also raised. Although there are known methods to measure performance, there is still no common framework that facilitates the task of achieving a comparative measurement between the different blockchain solutions, which, considering the sustained use of this technology in a wide field of application, it is shown as a vacant area for which we consider that it is necessary to advance in order to evaluate the performance in different use cases and scenarios.
Combining Causal Machine Learning and Theory Driven Specification for Interpretable and Justifiable Resource Allocation

Efficient allocation of limited resources relies on accurate estimates of potential incremental benefits from each candidate. These heterogenous treatment effects (HTE) can be estimated with properly specified theory driven models and observational data that contains all confounders. Causal machine learning offers higher benefits by identifying additional heterogeneity dimensions, fitting arbitrary functional forms and interactions to estimate HTE from big data, but decisions based on blackbox models are not justifiable. We introduce a novel methodology to increase resource allocation efficiency, enhance understanding of the treatment effects, and increase acceptance of the resulting decisions with rationale that is in line with existing theory. The proposed generalized linear model with qualitative constraints (GLM_QC) method is used to modify theory driven models based on causal machine learning findings, extracted with explainable AI tools. GLM_QC efficiently fits an additive model, representing nonlinearity with discretized variables, subject to sign and monotonicity constraints on main and two-way effects. Beyond increasing interpretability, this regularization ensures stable estimates consistent with domain knowledge. Targeting efficiency metric facilitates trading off performance vs interpretability.The case study estimates HTE of increasing physical activity on diabetes and heart disease probability to maximize population health benefit by identifying the right individuals to incentivize. The results show that adding new heterogeneity dimensions based on causal machine learning and imposing qualitative constraints both increase benefits with limited resources. But leaving out qualitative constraints results in counter-intuitive effects and substantially lower benefits.
Transforming the Criteria of Quality and Efficiency of Higher Education in the Context of Digital Modernization

As both a donor and a recipient of digital technologies in the context of global digital modernization of the economy, a higher educational institution falls into the difficult conditions of the need for competent broadcasting to society of the quality and effectiveness of educational services. The purpose of the work was to determine the compliance of quality criteria and the effectiveness of the activities of universities in Russia with digital modernization processes. The research method was an analysis of these criteria for the activities of Russian universities, which are part of the first and last ten rankings of TOP-100 universities in Russia for 2019 according to Forbes, contained on their official websites, and reports on the results of self-examination. As a result of the study, it was revealed that even the leading universities of the country are far from fully integrated into the implementation of breakthrough areas of digitalization of the educational environment, which, in particular, necessitates a meaningful change in the existing criteria for quality and effectiveness of activities. It is determined that in the conditions of the need to broadcast the results of activities in three key areas (the Ministry of Science and Higher Education of the Russian Federation, consumers of educational services, the business community), the university should respond flexibly to requests from the external environment in terms of the formation of a system of quantitative criteria for quality and efficiency.
LRVP: Lightweight Real-Time Verification of Intradomain Forwarding Paths

The correctness of user traffic forwarding paths is an important goal of trusted transmission. Many network security issues are related to it, i.e., denial-of-service attacks, route hijacking, etc. The current path-aware network architecture can effectively overcome this issue through path verification. At present, the main problems of path verification are high communication and high computation overhead. To this aim, this article proposes a lightweight real-time verification mechanism of intradomain forwarding paths in autonomous systems to achieve a path verification architecture with no communication overhead and low computing overhead. The problem situation is that a packet finally reaches the destination, but its forwarding path is inconsistent with the expected path. The expected path refers to the packet forwarding path determined by the interior gateway protocols. If the actual forwarding path is different from the expected one, it is regarded as an incorrect forwarding path. This article focuses on the most typical intradomain routing environment. A few routers are set as the verification routers to block the traffic with incorrect forwarding paths and raise alerts. Experiments prove that this article effectively solves the problem of path verification and the problem of high communication and computing overhead.
Double-Channel Multi-layer Information Fusion for Text Matching

Text matching is one of the fundamental tasks in natural language processing. Most of the existing models focus only on encoding the text itself but ignore other semantic information which may further improve matching accuracy. In this paper, we propose a novel model for text matching with double-channel multi-layer information fusion. It treats text and part-of-speech information of words in a sentence as double-channel information which is fused by multi-layer interactions. Meanwhile, our model uses a Siamese network structure to learn common and unique features of two sentences, which can improve its ability to learn the relationship between two sentences while reducing the parameter size and complexity. Experimental results on SNLI dataset show that our model can achieve better performance than baseline methods.
Content-Attribute Disentanglement for Generalized Zero-Shot Learning

Humans can recognize or infer unseen classes of objects using descriptions explaining the characteristics (semantic information) of the classes. However, conventional deep learning models trained in a supervised manner cannot classify classes that were unseen during training. Hence, many studies have been conducted into generalized zero-shot learning (GZSL), which aims to produce system which can recognize both seen and unseen classes, by transferring learned knowledge from seen to unseen classes. Since seen and unseen classes share a common semantic space, extracting appropriate semantic information from images is essential for GZSL. In addition to semantic-related information (attributes), images also contain semantic-unrelated information (contents), which can degrade the classification performance of the model. Therefore, we propose a content-attribute disentanglement architecture which separates the content and attribute information of images. The proposed method is comprised of three major components: 1) a feature generation module for synthesizing unseen visual features; 2) a content-attribute disentanglement module for discriminating content and attribute codes from images; and 3) an attribute comparator module for measuring the compatibility between the attribute codes and the class prototypes which act as the ground truth.With extensive experiments, we show that our method achieves state-of-the-art and competitive results on four benchmark datasets in GZSL. Our method also outperforms the existing zero-shot learning methods in all of the datasets. Moreover, our method has the best accuracy as well in a zero-shot retrieval task.
CryptKSP: A Kernel Stack Protection Model Based on AES-NI Hardware Feature

The kernel stack is an important data structure in the kernel. It stores the return address, local variables and a large amount of state information of the kernel and application. Therefore, the kernel stack is often a valuable target for the attackers. More seriously, some types of attacks, which could bypass existing protection mechanisms, such as “return-to-schedule” rootkit, have posed a serious security threat to the security of the kernel stack. Therefore, some defensive approaches have been proposed to protect the integrity of the kernel stack by setting it read-only, however, it does not protect the confidentiality of the key data in the kernel stack. In this paper, we propose a kernel stack protection model for both the confidentiality and integrity without relying on another higher privilege layer. It takes advantage of AES-NI to encrypt the kernel stacks of the threads that are not running, and decrypt the kernel stack of the thread which will be running. In order to improve the security and adaptability, we implement two AES algorithms with different key lengths. The experimental results show that the kernel stack protection model could provide effective protection for the integrity and confidentiality of the kernel stack and do not impose high performance overhead.
Nef-Ggcn: Node-Edge Fusion Gated Graph Convolutional Networks for Skeleton-Based Medical Action Recognition

Owing to the global outbreak of new corona-viruses, it is imperative for medical personnel follow medical action standards to avoid infection. Although existing graph convolution methods have achieved great success in the field of skeleton-based action recognition, they have limitations in complex actions. There are a large number of hand-to-hand, hand-to-head, or hand-to-foot interactions in medical actions, such as donning gloves, doffing safety goggles, etc. We define such kind of interactions as near-end actions. To efficiently identify near-end actions by GCN-Based methods, we propose a novel network model: Node-Edge Fusion Gated Graph Convolutional Networks (NEF-GGCN). It adequately mining the skeleton information, we extract the nodes of the skeleton graph and all the edges connecting the nodes as features inputs to the model. Meanwhile, we design the weak dependency matrix to enhance the correlation of the end nodes, which fused with the adjacency matrix through a gated graph convolutional block. Furthermore, we capture the motion information of joints and bones in the skeleton as a temporal attention mechanism to improve the feature representation. We verify our model on two large datasets and one custom medical dataset: Kinetics, NTU-RGBD and PPE-DD12, it achieves impressive performance.
Probability boxes

In this chapter we use interval analysis to study probability intervals, and see how it can be used in applications involving epistemic uncertainty, i.e., in situations in which more than one probability measure is compatible with the observations. When this is the case, if the probabilities associated with one or more events are described by intervals of values rather than by single numbers, epistemic uncertainty can be treated by using probability intervals, or probability boxes, which generalize the concept of interval from a pair of points to a pair of cumulative distribution functions. Probability boxes can handle several problems, like model uncertainties, poorly known or unknown dependencies of random variables, or imprecisely specified distributions. The use of a probability box in lieu of an individual cumulative distribution functions results into performance analyses not relying on unwarranted distribution assumptions. With this approach, instead of selecting a single cumulative distribution functions from a class of distributions matching a set of constraints, the entire class is used. In addition, instead of deriving upper and lower bounds on performance parameters only at the end of calculations, all relevant calculations keep track of the uncertainties implicit in the entities involved, possibly using interval analysis as described in Chapter 4.
Longitudinal Structure Analysis and Segmentation Algorithm of Dongba Document

With the rapid development of information technology, Dongba hieroglyphs and Dongba culture have also been rapidly spread through information technology and the Internet. However, most of the Dongba documents still exist in the form of images at present, and it is impossible to extract and retrieve the text. Therefore, combined with the structural characteristics of the Dongba document, first we normalize the size of the document image; then, combined with the vertical projection algorithm to perform preliminary segmentation of the document, we extract the structural characteristics of the vertical column from it. Finally, we use statistical methods to analyze the attributes and effectiveness of different segmentation columns, and by removing abnormal columns, we achieve effective vertical segmentation of documents and text column extraction. The algorithm is intuitive, vivid, simple to implement, and lays a foundation for detailed analysis and digitization of documents.
Using Non-Autonomous Chaotic Clocks to Drive CPA-Resistant AES Cryptographic Chips

In this brief, we provide further evidence on the robustness of the chaotic clocking technique for driving a Correlation Power Analysis (CPA) resistant cryptographic chip running the Advanced Encryption Standard (AES). In particular, we explore the use of non-autonomous chaotic oscillators to improve the chip’s timing and power performance. We show that using this type of oscillator significantly reduces the performance degradation associated with switching from a normal periodic clock to a chaotic clock while remaining robust against side channel attacks. Simulations and experimental results using a Field Programmable Gate Array (FPGA) prototype are reported as a proof of concept.