Reviewerly/exHarmony huggingface dataset:
Educational and Workforce Cybersecurity Frameworks: Comparing, Contrasting, and Mapping

Three current cybersecurity professional and academic program schemes target cyber education, training, and workforce roles. They have complementary elements that can serve as a pathway from learning cyber to working cyber if correctly navigated.
Variational Deep Collaborative Matrix Factorization for Social Recommendation

In this paper, we propose a Variational Deep Collaborative Matrix Factorization (VDCMF) algorithm for social recommendation that infers latent factors more effectively than existing methods by incorporating users' social trust information and items' content information into a unified generative framework. Unlike neural network-based algorithms, our model is not only effective in capturing the non-linearity among correlated variables but also powerful in predicting missing values under the robust collaborative inference. Specifically, we use variational auto-encoder to extract the latent representations of content and then incorporate them into traditional social trust factorization. We propose an efficient expectation-maximization inference algorithm to learn the model's parameters and approximate the posteriors of latent factors. Experiments on two sparse datasets show that our VDCMF significantly outperforms major state-of-the-art CF methods for recommendation accuracy on common metrics.
NIRS quantification of lake sediment composition by multiple regression using end-member spectra

Here we develop a novel method for quantifying sediment components, e.g. biogenic silica, organic or mineral matter, from near infrared (NIR) spectra based on fitting by multiple regression of measured spectra for end-member materials. We show that with suitable end-members our new open-source multiple regression routine gives excellent simultaneous quantification of the major components of a sediment, the concentrations comparing well with independent methods of quantification. Widely used partial least squares regression approaches rely on large environmental training data sets; our method produces comparable results, but with the advantages of negating the need for a training dataset and with greater simplicity and theoretical robustness. We demonstrate that component NIR spectra are additive, a prerequisite for use of multiple regression to un-mix the compound spectra, and show that a number of environmental materials make suitable end-members for this analysis. We show that spectral mixing is not conservative with respect to mass proportion, but rather to the relative chromatic intensity of contributing sediment components. Concentrations can be calculated using the measured spectra by correction using a chromatic intensity factor, the value of which can be measured independently. We have applied our approach to a postglacial sediment sequence from Loch Grannoch (SW Scotland) and reveal a down-core pattern of varying dominance by biogenic silica, organic and mineral content from the late glacial to present. With isolation and measurement of appropriate end-members this multivariate regression approach to interrogating NIR spectra has utility across a wide range of sedimentary environments and potentially for other spectral analytical methods.
Simplified Neural Networks with Smart Detection for Road Traffic Sign Recognition

Improving driver’s safety is the main goal of the advanced driver assistance system, which has been widely deployed for proactive driving security in recent years. For road driving, the advanced driver assistance system should visually recognize circular prohibition and triangular warning traffic signs to help drivers to grab complete traffic conditions. In this paper, we proposed a low-computation neural assistance system for traffic sign recognition. First, we proposed shaped-based detection algorithms to detect the regions, which are with circle and triangular traffic signs in designated regions of interest. For classification to those detected regions, we then suggest a convolutional neural network to achieve about 5% improvement of top 1 accuracy compared with LeNet model in German traffic sign recognition benchmarks dataset. For real applications, we also establish a Taiwanese traffic sign database to train the proposed neural network. The simulation results on self-collect driving videos demonstrate that the proposed traffic sign recognition system achieved above 97% recognition rate can be effectively adopted in ADAS applications.
Comparative Analysis of Usability of Data Entry Design Patterns for Mobile Applications

A comparative analysis of usability of data entry design patterns for mobile applications was presented in the paper. For this purpose three versions of a mobile application for a computer store were developed. Each version included the same functions which were designed and programmed using different data entry patterns. The applications were then utilized to conduct usability tests with three groups of users. The research was conducted based on the ISO model of usability. The usability metrics such as task completion time, number of actions, binary task completion rate and percentage of tasks accomplished without any of errors were considered. User satisfaction was measured using the Single Ease Question (SEQ) questionnaire. The results obtained were thoroughly analysed and recommendations for each data entry design patterns for mobile applications were formulated.
On the Multi-Language Construction

Modern software is no more developed in a single programming language. Instead, programmers tend to exploit cross-language interoperability mechanisms to combine code stemming from different languages, and thus yielding fully-fledged multi-language programs. Whilst this approach enables developers to benefit from the strengths of each single-language, on the other hand it complicates the semantics of such programs. Indeed, the resulting multi-language does not meet any of the semantics of the combined languages. In this paper, we broaden the boundary functions-based approach à la Matthews and Findler to propose an algebraic framework that provides a constructive mathematical notion of multi-language able to determine its semantics. The aim of this work is to overcome the lack of a formal method (resp., model) to design (resp., represent) a multi-language, regardless of the inherent nature of the underlying languages. We show that our construction ensures the uniqueness of the semantic function (i.e., the multi-language semantics induced by the combined languages) by proving the initiality of the term model (i.e., the abstract syntax of the multi-language) in its category.
A Continuous-Time Model-Based Approach for Activity Recognition in Pervasive Environments

We present a model-based approach to Activity Recognition (AR) in Ambient Assisted Living (AAL). The approach leverages an a priori stochastic model termed Continuous-Time Hidden Semi-Markov Model (CT-HSMM), capturing the continuous-time durations of activities and inter-event times. The model is enhanced according to the observed statistics, associating the events with an occurrence probability, and the sojourn time and the inter-event time in each activity with a continuous-time probability density function, allowing effective fitting of observed durations through non-Markovian distributions. The model is updated at run time according to a sequence of time-stamped observations, exploiting the method of stochastic state classes to perform transient analysis and derive a measure of likelihood that an activity is currently performed. The approach supports both online AR, predicting the activity performed at time t using only the events observed until that time, and offline AR, applying a forward- backward procedure that exploits all the events observed before and after time t. The approach is experimented on a real dataset of the literature, providing performance measures that can be compared with those of offline Hidden Markov Models (HMMs) and offline Hidden Semi-Markov Models (HSMMs).
Seeding Cybersecurity Workforce Pathways With Secondary Education

To remain competitive in a digital economy, the United States must broaden computing and cybersecurity education and career pathways. Our results provide insight into teachers' knowledge growth, their understanding of cyber principles, and their application of these principles in the classroom.
Reproducing and Generalizing Semantic Term Matching in Axiomatic Information Retrieval

In the framework of axiomatic information retrieval, the semantic term matching technique proposed by Fang and Zhai in SIGIR 2006 has been shown to be effective in addressing the vocabulary mismatch problem, with experimental evidence provided from newswire collections. This paper reproduces and generalizes these results in Anserini, an open-source IR toolkit built on Lucene. In addition to making an implementation of axiomatic semantic term matching available on a widely-used open-source platform, we describe a series of experiments that help researchers and practitioners better understand its behavior across a number of test collections spanning newswire, web, and microblogs. Results show that axiomatic semantic term matching can be applied on top of different base retrieval models, and that its effectiveness varies across different document genres, each requiring different parameter settings for optimal effectiveness.
Demonstrating a Framework for Rapid Development of Physically Situated Interactive Systems

We demonstrate an open, extensible framework for enabling faster development and study of physically situated interactive systems. The framework provides a programming model for parallel coordinated computation centered on temporal streams of data, a set of tools for data visualization and processing, and an open ecosystem of components. The demonstration showcases an interaction toolkit of components for systems that interact with people via natural language in the open world.
Optimal power allocation and load balancing for non-dedicated heterogeneous distributed embedded computing systems

This paper investigates on the optimal power allocation and load balancing problem encountered by heterogeneous and distributed embedded systems with mixed tasks. Given that each node has real and different urgent tasks in the majority of practical heterogeneous embedded systems, three priority disciplines are considered: dedicated jobs without priority, prioritized dedicated jobs without preemption, and prioritized dedicated jobs with preemption. A model is established for heterogeneous embedded processors with dedicated-task-dependent dynamic power and load balancing management; each processor is considered as an M/M/1 queueing sub-model with mixed generic and dedicated tasks. The processors have different levels of power consumption, and each one can employ any of the three disciplines. The objective of this study is to find an optimal load balancing (for generic tasks) and power allocation strategy for heterogeneous processors preloaded by different amounts of dedicated tasks such that the average response time of generic tasks is minimized. Considering that this problem is a multi-constrained, multi-variable optimization problem for which a closed-form solution is unlikely to be obtained, we propose an optimal power allocation and load balancing scheme by employing Lagrange method and binary search approach, which are completed by utilizing two new rules established by observing numerical variations of parameters. Several numerical examples are presented to demonstrate the effectiveness of our solution. To the best of our knowledge, this is the first work on analytical study that combines load balancing, energy efficiency, and priority of tasks in heterogeneous and distributed embedded systems.
A LSTM Approach for Sales Forecasting of Goods with Short-Term Demands in E-Commerce

This study proposed a model to forecast short-term goods demand in E-commerce context. The model integrated LSTM approach with sentiment analysis of consumers' comments. In the training stage, the sales figures and comments crawled from "taobao.com" were preprocessed, and the sentiment rating of comments were analyzed for "positive", "negative" and confidence. The LSTM model was trained to learn the prediction of future value according to the time-series sequence of sales and sentiment rating of comments. Due to the characteristics of short-term goods, there are not enough history data to evaluate cyclic and periodic variation, so the decision makers have to react to market conditions and take appropriate actions as soon as possible. It also suggested that to adjust the weight of sentiment rating appropriately could further improve the forecasting accuracy. The study fulfilled the goal for supporting them to make use of minimal trading data to achieve maximal predictive accuracy. The results demonstrated that the proposed LSTM approach performed high-level accuracy for sales forecasting of goods with short-term demands.
Deep learning review and discussion of its future development

This paper is a summary of the algorithms for deep learning and a brief discussion of its future development. In the first part, the concept of deep learning and the advantages and disadvantages of deep learning are introduced. The second part demonstrates several algorithms for deep learning. The third part introduces the application areas of deep learning. Then combines the above algorithms and applications to explore the subsequent development of deep learning. The last part makes a summary of the full paper.
Improving NeuroEvolution Efficiency by Surrogate Model-Based Optimization with Phenotypic Distance Kernels

In NeuroEvolution, the topologies of artificial neural networks are optimized with evolutionary algorithms to solve tasks in data regression, data classification, or reinforcement learning. One downside of NeuroEvolution is the large amount of necessary fitness evaluations, which might render it inefficient for tasks with expensive evaluations, such as real-time learning. For these expensive optimization tasks, surrogate model-based optimization is frequently applied as it features a good evaluation efficiency. While a combination of both procedures appears as a valuable solution, the definition of adequate distance measures for the surrogate modeling process is difficult. In this study, we will extend cartesian genetic programming of artificial neural networks by the use of surrogate model-based optimization. We propose different distance measures and test our algorithm on a replicable benchmark task. The results indicate that we can significantly increase the evaluation efficiency and that a phenotypic distance, which is based on the behavior of the associated neural networks, is most promising.
Towards Knowledge Formalization and Sharing in a Cognitive Vision Platform for Hazard Control (CVP-HC)

Hazards can be found in all work environments and may cause injuries, illnesses, or fatalities. In this context, controlling of risks and safety management has become indispensable to guarantee the laborers wellbeing in worksites. Aiming to achieve a systematic, explicit and comprehensive system for managing safety risks, a Cognitive Vision Platform for Hazard Control (CVP-HC) has been proposed. This platform is designed to automatically detect unsafe activities and improve the decision making process when they occur in different workplace scenarios, while attending specific safety requirements of organizations by adapting its behavior accordingly. To meet generality, the CVP-HC utilizes the Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) to administrate knowledge. To ensure scalability and adaptability, a loosely coupled communication model, the publishing/subscribe interaction scheme is used over the Robot Operating System (ROS) framework.
Parsing clinical text using the state-of-the-art deep learning based parsers: a systematic comparison

A shareable repository of clinical notes is critical for advancing natural language processing (NLP) research, and therefore a goal of many NLP researchers is to create a shareable repository of clinical notes, that has breadth (from multiple institutions) as well as depth (as much individual data as possible).We aimed to assess the degree to which individuals would be willing to contribute their health data to such a repository. A compact e-survey probed willingness to share demographic and clinical data categories. Participants were faculty, staff, and students in two geographically diverse major medical centers (Utah and New York). Such a sample could be expected to respond like a typical potential participant from the general public who is given complete and fully informed consent about the pros and cons of participating in a research study.2140 respondents completed the surveys. 56% of respondents were "somewhat/definitely willing" to share clinical data with identifiers, while 89% of respondents were "somewhat (17%) /definitely willing (72%)" to share without identifiers. Results were consistent across gender, age, and education, but there were some differences by geographical region. Individuals were most reluctant (50-74%) sharing mental health, substance abuse, and domestic violence data.We conclude that a substantial fraction of potential patient participants, once educated about risks and benefits, would be willing to donate de-identified clinical data to a shared research repository. A slight majority even would be willing to share absent de-identification, suggesting that perceptions about data misuse are not a major concern. Such a repository of clinical notes should be invaluable for clinical NLP research and advancement.
Adaptive Adjustment with Semantic Feature Space for Zero-shot Recognition

In most recent years, zero-shot recognition (ZSR) has gained increasing attention in machine learning and image processing fields. It aims at recognizing unseen class instances with knowledge transferred from seen classes. This is typically achieved by exploiting a pre-defined semantic feature space (FS), i.e., semantic attributes or word vectors, as a bridge to transfer knowledge between seen and unseen classes. However, due to the absence of unseen classes during training, the conventional ZSR easily suffers from domain shift and hubness problems. In this paper, we propose a novel ZSR learning framework that can handle these two issues well by adaptively adjusting semantic FS. To the best of our knowledge, our work is the first to consider the adaptive adjustment of semantic FS in ZSR. Moreover, our solution can be formulated to a more efficient framework that significantly boosts the training. Extensive experiments show the remarkable performance improvement of our model compared with other existing methods.
Live detection of text in the natural environment using Convolutional Neural Network

With the exponential growth in the quantity of born-digital images, the problem of comprehending text from natural scene images has acquired greater significance. This paper proposes a deep-learning based approach, to detect the presence of text in the natural scene images. The proposed approach is built with the capability to distinguish text and non-text images from the live-stream of the smart-phone camera, thereby eliminating the need for capturing the image, to locate the presence of text. A streamlined Convolutional Neural Network (CNN) MobileNet is harnessed for the process of distinguishing text images from non-text images. The proposed approach can be adopted as a filter, to decide whether to permit the image further down the processing pipeline for the text detection task, which in turn leads to the reduction in false-positives and false-negatives by not processing an image which does not have text. It was inferred from the experimental results that the width multiplier value of 0.75 and resolution multiplier of 224 yields the accuracy of 99.31% in classifying the text images from non-text images.
Deviance in Social Media

In this chapter, we explain what we mean by deviance in social media Deviance on social media . We give examples of four types of deviance as observed on social media, viz., deviant acts Deviant acts , deviant events Deviant events , deviant tactics Deviant tactics , and deviant groups Deviant groups . We provide historical information, definitions, and examples that will be studied/explained in more details throughout the book. This chapter would help the readers understand the scope of the problem of deviance in social media, familiarize with definitions and examples of deviant events, groups, acts, and tactics, and peek at the social science theories that can explain such emergent deviant behaviors on social media.
On cross‐linguistic variation and measures of linguistic complexity in learner texts: Italian, French and English

The paper investigates possible effects of cross‐linguistic variation on measures of syntactic complexity in 60 texts from Swedish L1 learners of English, French and Italian as foreign languages at CEFR level A and CEFR level B. A previous study on the same learners and texts, showed significant differences between proficiency levels for two length measures of complexity in English and French, but not in Italian. In this paper we hypothesize that due to the Null Subject property of Italian, the developmental prediction for some complexity measures might be different in Italian compared to French and English. In fact, previous research has suggested that beginner learners of Italian overuse overt subjects which might lead to higher scores, relatively speaking, of length measures in Italian at the lowest levels of proficiency. However, contrary to our hypothesis, we did not find more Null subjects at CEFR level B than at CEFR level A, but we did find clear restrictions on their distribution. We conclude that we are a long way from understanding how cross‐linguistic differences interact with other variables such as tasks and language combinations and what the effects might be on measures of syntactic complexity.
Stoic Sequent Logic and Proof Theory

This paper contends that Stoic logic (i.e. Stoic analysis) deserves more attention from contemporary logicians. It sets out how, compared with contemporary propositional calculi, Stoic analysis is closest to methods of backward proof search for Gentzen-inspired substructural sequent logics, as they have been developed in logic programming and structural proof theory, and produces its proof search calculus in tree form. It shows how multiple similarities to Gentzen sequent systems combine with intriguing dissimilarities that may enrich contemporary discussion. Much of Stoic logic appears surprisingly modern: a recursively formulated syntax with some truth-functional propositional operators; analogues to cut rules, axiom schemata and Gentzen’s negation-introduction rules; an implicit variable-sharing principle and deliberate rejection of Thinning and avoidance of paradoxes of implication. These latter features mark the system out as a relevance logic, where the absence of duals for its left and right introduction rules puts it in the vicinity of McCall’s connexive logic. Methodologically, the choice of meticulously formulated meta-logical rules in lieu of axiom and inference schemata absorbs some structural rules and results in an economical, precise and elegant system that values decidability over completeness.