Reviewerly/exHarmony huggingface dataset:
Interactive, Multiscale Urban-Traffic Pattern Exploration Leveraging Massive GPS Trajectories

Urban traffic pattern reflects how people move and how goods are transported, which is crucial for traffic management and urban planning. With the development of sensing techniques, accumulated sensor data are captured for monitoring vehicles, which also present the opportunities of big transportation data, especially for real-time interactive traffic pattern analysis. We propose a three-layer framework for the recognition and visualization of multiscale traffic patterns. The first layer computes the middle-tier synopses at fine spatial and temporal scales, which are indexed and stored in a geodatabase. The second layer uses synopses to efficiently extract multiscale traffic patterns. The third layer supports real-time interactive visual analytics for intuitive explorations by end users. An experiment in Shenzhen on taxi GPS trajectories that were collected over one month was conducted. Multiple traffic patterns are recognized and visualized in real-time. The results show the satisfactory performance of proposed framework in traffic analysis, which will facilitate traffic management and operation.
Formal specification and verification of reusable communication models for distributed systems architecture

The development of distributed computing systems involves complex concerns related to integrating numerous communication styles, technologies (Internet of Things, cloud, big data, etc.), stakeholders (architects, developers, integrators, etc.) and addressing a multitude of application domains (smart cities, health, mobility, etc.). Existing architectural description languages fail to rigorously bridge the gap between the abstract representation of communication styles and those supported by existing execution infrastructures. In this paper, we propose an approach combining semi-formal and formal languages which considers modeling and formalization from both the structural and communication behavior perspectives. As a prerequisite, we build reusable model libraries to specify and verify communication styles for modeling software architectures of distributed systems. First, we propose a metamodel to describe high-level concepts of architecture in a component–port–connector fashion focusing on different communication styles. Then, we formalize those concepts and their semantics following some properties (specifications) to check architectural conformance. To validate our work, we provide a set of reusable connector libraries within a set of properties to define architectures for systems with explicit communication models that are common to most distributed systems including message passing, remote procedure calls, and distributed shared memory. Through reuse of these specified and verified connectors, we demonstrate how concrete software architectures for a given application domain can be developed.
Unsupervised Anomaly Detection for Network Data Streams in Industrial Control Systems

The development and integration of information technology and industrial control networks have expanded the magnitude of new data; detecting anomalies or discovering other valid information from them is of vital importance to the stable operation of industrial control systems. This paper proposes an incremental unsupervised anomaly detection method that can quickly analyze and process large-scale real-time data. Our evaluation on the Secure Water Treatment dataset shows that the method is converging to its offline counterpart for infinitely growing data streams.
Cosine Similarity for Title and Abstract of Economic Journal Classification

The main factor in article publications is the accuracy of their content for journals. However, a lot of authors are still confused about the right topic. Therefore, there is a need for text mining classification system to simplify the classification process. This system used the Vector Space Model Approach to represent terms in spatial dimensions, with its advantage of working efficiently. The Cosine Similarity Method was used to calculate the similarities between the two documents. The advantage of this method is that, it is not affected by the length of the document, but rather by the value of each document's terms and its low error rate. Based on the performance testing of this method application using the K-Fold Cross Validation technique, with testing of 6 fold for each data randomization set 6 times, against 126 instances of economic article journals, the average accuracy results were 57,79%, precision was 57,79%, and recall was 62,96%. It can be concluded that cosine similarity can classify articles based on titles and abstracts.
When your wearables become your fitness mate

Acknowledging the powerful sensors on wearables and smartphones enabling various applications to improve users' life styles and qualities (e.g., sleep monitoring and running rhythm tracking), this paper takes one step forward developing FitCoach, a virtual fitness coach leveraging users' wearable mobile devices (including wrist-worn wearables and arm-mounted smartphones) to assess dynamic postures (movement patterns & positions) in workouts. FitCoach aims to help the user to achieve effective workout and prevent injury by dynamically depicting the short-term and long-term picture of a user's workout based on various sensors in wearable mobile devices. In particular, FitCoach recognizes different types of exercises and interprets fine-grained fitness data (i.e., motion strength and speed) to an easy-to-understand exercise review score, which provides a comprehensive workout performance evaluation and recommendation. Our system further enables contactless device control during workouts (e.g., gesture to pick up an incoming call) through distinguishing customized gestures from regular exercise movement. In addition, FitCoach has the ability to align the sensor readings from wearable devices to the human coordinate system, ensuring the accuracy and robustness of the system. Extensive experiments with over 5000 repetitions of 12 types of exercises involve 12 participants doing both anaerobic and aerobic exercises in indoors as well as outdoors. Our results demonstrate that FitCoach can provide meaningful review and recommendations to users by accurately measure their workout performance and achieve 93% and 90% accuracy for workout analysis and customized control gesture recognition, respectively.
HealthAid: Extracting domain targeted high precision procedural knowledge from on-line communities

Recent advances in semantic web have shown how entity related searches have benefited from entity-based knowledge graphs. However, much of the commonsense knowledge about the real world is in the form of procedures or sequences of actions. Also, search log analysis shows that 'how-to queries' make up a significant amount of users' queries. Unfortunately, these kinds of knowledge are missing from most knowledge graphs and commonsense knowledge bases in use. To empower semantic search, and other intelligent applications, computers need a much broader understanding of the world properties of everyday objects, human activities, and more. Luckily, such knowledge is abundantly available on-line and can be accessed from how-to communities. One domain of interest by on-line communities is the health domain, whereby users usually seek home remedies to common health-related issues. An example of such queries might be 'how to stop nausea using acupressure' or 'how to aid digestion naturally'. To answer such questions, we need systems that understand natural language and knowledge bases with task frames of solutions in a holistic approach, including the tools required, the agents involved, and the temporal order of the actions. Our goal is to construct a machine-readable domain targeted high precision procedural knowledge base containing task frames. We developed a pipeline of methods leveraging open information extraction tool to extract procedural knowledge by tapping into on-line communities. Also, we devised a mechanism to canonicalize the task frames into clusters based on the similarity of the problems they intend to solve. The resulting know-how knowledge base, HealthAidKB, consists of more than 71 K task frames which are structured hierarchically and categorically; and can be used in many applications such as semantic search, digital personal assistants, human-computer dialog and computer vision. A comprehensive evaluation of our knowledge base shows high accuracy.
An accessible, open‐source mobile application for macromolecular visualization using augmented reality

Abstract Understanding macromolecular structures is essential for biology education. Augmented reality (AR) applications have shown promise in science, technology, engineering, and mathematics (STEM) education, but are not widely used for protein visualization. While there are some tools for AR protein visualization, none of them are accessible to the layperson who possesses neither specialized AR hardware nor the technical skill to comfortably navigate three‐dimensional (3D) rendering and file conversions. Here, we describe Palantir , an open source mobile Android application easily installable on compatible devices from the Google Play Store. Palantir does not require specialized hardware, printed image, manual 3D rendering, or file format conversion. Palantir makes AR macromolecular visualization accessible to anyone with a compatible mobile device, and we hope it finds widespread application in STEM education.
Heterogenous output regression network for direct face alignment

Face alignment has gained great popularity in computer vision due to its wide-spread applications. In this paper, we propose a novel learning architecture, i.e., heterogenous output regression network (HORNet), for face alignment, which directly predicts facial landmarks from images. HORNet is based on kernel approximations and establishes a new compact multi-layer architecture. A nonlinear layer with cosine activations disentangles nonlinear relationships between representations of images and shapes of facial landmarks. A linear layer with identity activations explicitly encodes landmark correlations by low-rank learning via matrix elastic nets. HORNet is highly flexible and can work either with pre-built feature representations or with convolutional architectures for end-to-end learning. HORNet leverages the strengths of both kernel methods in modeling nonlinearities and of neural networks in structural prediction. This combination renders it effective and efficient for direct face alignment. Extensive experiments on five in-the-wild datasets show that HORNet delivers high performance and consistently exceeds state-of-the-art methods.
Optimizing predictive precision in imbalanced datasets for actionable revenue change prediction

In business environments where an organization offers contract-based periodic services to its clients, one crucial task is to predict changes in revenues generated through different clients or specific service offerings from one time epoch to another. This is commonly known as the revenue change prediction problem. In practical real-world environments, the importance of having adequate revenue change prediction capability primarily stems from scarcity of resources (in particular, sales team personnel or technical consultants) that are needed to respond to different revenue change scenarios including predicted revenue growth or shrinkage. It becomes important to make actionable decisions; that is, decisions related to prioritizing clients or service offerings to which these scarce resources are to be allocated. The contribution of the current work is twofold. First, we propose a framework for conducting revenue change prediction through casting it as a classification problem. Second, since datasets associated with revenue change prediction are typically imbalanced, we develop a new methodology for solving the classification problem such that we achieve maximum prediction precision while minimizing sacrifice in prediction accuracy. We validate our proposed framework through real-world datasets acquired from a major global provider of cloud computing services, and benchmark its performance against standard classifiers from previous works in the literature.
Towards high performance low bitwidth training for deep neural networks

Abstract The high performance of the state-of-the-art deep neural networks (DNNs) is acquired at the cost of huge consumption of computing resources. Quantization of networks is recently recognized as a promising solution to solve the problem and significantly reduce the resource usage. However, the previous quantization works have mostly focused on the DNN inference, and there were very few works to address on the challenges of DNN training. In this paper, we leverage dynamic fixed-point (DFP) quantization algorithm and stochastic rounding (SR) strategy to develop a fully quantized 8-bit neural networks targeting low bitwidth training. The experiments show that, in comparison to the full-precision networks, the accuracy drop of our quantized convolutional neural networks (CNNs) can be less than 2%, even when applied to deep models evaluated on ImageNet dataset. Additionally, our 8-bit GNMT translation network can achieve almost identical BLEU to full-precision network. We further implement a prototype on FPGA and the synthesis shows that the low bitwidth training scheme can reduce the resource usage significantly.
Interpretable policies for reinforcement learning by empirical fuzzy sets

This paper proposes a method and an algorithm to implement interpretable fuzzy reinforcement learning (IFRL). It provides alternative solutions to common problems in RL, like function approximation and continuous action space. The learning process resembles that of human beings by clustering the encountered states, developing experiences for each of the typical cases, and making decisions fuzzily. The learned policy can be expressed as human-intelligible IF-THEN rules, which facilitates further investigation and improvement. It adopts the actor–critic architecture whereas being different from mainstream policy gradient methods. The value function is approximated through the fuzzy system AnYa. The state–action space is discretized into a static grid with nodes. Each node is treated as one prototype and corresponds to one fuzzy rule, with the value of the node being the consequent. Values of consequents are updated using the Sarsa(λ) algorithm. Probability distribution of optimal actions regarding different states is estimated through Empirical Data Analytics (EDA), Autonomous Learning Multi-Model Systems (ALMMo), and Empirical Fuzzy Sets (εFS). The fuzzy kernel of IFRL avoids the lack of interpretability in other methods based on neural networks. Simulation results with four problems, namely Mountain Car, Continuous Gridworld, Pendulum Position, and Tank Level Control, are presented as a proof of the proposed concept.
Wide-Area Electric Grid Visualization Using Pseudo-Geographic Mosaic Displays

This paper introduces a new technique for wide-area visualization of information about electric power grids known as a pseudo-geographic mosaic displays (PGMDs). The PGMD approach uses dynamically created geographic data view (GDV) objects to show information about the attributes of different electric grid objects, and then arranges them on the screen to maximize the used display space. The paper presents the PGMD algorithm and then discusses design approaches to maximize their usability. Results are presented for several large-scale electric grids.
An Examination System Automation Using Natural Language Processing

This world has seen a lot many examination portals that are deployed over several servers which are used to conduct online examination for various purposes among which some may include conducting a test for entrance examinations, or olympiads at national and international level and while some portals are designed to conduct a test for placement purposes. But what we have seen is that mostly all the portals are designed to conduct tests that contain multiple choice questions. Here our aim is not to work on the technology that is already existing, rather some technology that is very rare. Here we talk of the descriptive online examination system. Multiple choice questions are easy to deal as they have a question, a few options and a field in the same question that stores the correct option in the database. While in the case of descriptive questions it is not so. It brings in or uses the concepts of Natural Language Processing or NLP to assign marks to answers. Answers are nothing but strings and the job of the model is to do some operations on the answer string such that it can assign the correct marks to answers written by the examinee. The data is basically collected from a descriptive online examination system. Further, it is analyzed and the designed model assigns accurate marks to the answers for the question. The back-end is written in Python where the web framework used is Django, the library used for Natural Language Processing includes NLTK and for database purpose, SQLite version 3 is used, while for the front-end HTML version-5, CSS version-3, Bootstrap and Javascript is used.
Humanistic infrastructure studies: hyper-functionality and the experience of the absurd

Bridging third wave HCI with infrastructure studies, this paper examines the relationship between infrastructural visibility, breakdown, and experience through an existentialist lens. We present and theorize a state of infrastructural functionality – which we term ‘hyper-functionality’ – that renders infrastructure visible because of its experiential effects on end-users, not necessarily because of malfunction. We introduce this term through the presentation of a story from the life of one of the authors in which an infrastructural assemblage behaved unexpectedly, giving rise to the experience of the absurd – a feeling of alienation from oneself and the technological assemblages that constitute one’s daily world. We explore the applicability of hyper-functionality for the interpretation and theorization of larger-scale scenarios by using it to interpret reactions to the role that social media – Facebook in particular – played in the troubled United States presidential election in 2016. We contend that the existentialist-tinted lens of hyper-functionality constitutes a novel and meaningful way of analyzing the human experience of the mundane in relation to infrastructures, thus forming the basis for a humanistic infrastructure studies.
Efficient binocular stereo correspondence matching with 1-D Max-Trees

Extraction of depth from images is of great importance for various computer vision applications. Methods based on convolutional neural networks are very accurate but have high computation requirements, which can be achieved with GPUs. However, GPUs are difficult to use on devices with low power requirements like robots and embedded systems. In this light, we propose a stereo matching method appropriate for applications in which limited computational and energy resources are available. The algorithm is based on a hierarchical representation of image pairs which is used to restrict disparity search range. We propose a cost function that takes into account region contextual information and a cost aggregation method that preserves disparity borders. We tested the proposed method on the Middlebury and KITTI benchmark data sets and on the TrimBot2020 synthetic data. We achieved accuracy and time efficiency results that show that the method is suitable to be deployed on embedded and robotics systems.
A Spatiotemporal Dilated Convolutional Generative Network for Point-Of-Interest Recommendation

With the growing popularity of location-based social media applications, point-of-interest (POI) recommendation has become important in recent years. Several techniques, especially the collaborative filtering (CF), Markov chain (MC), and recurrent neural network (RNN) based methods, have been recently proposed for the POI recommendation service. However, CF-based methods and MC-based methods are ineffective to represent complicated interaction relations in the historical check-in sequences. Although recurrent neural networks (RNNs) and its variants have been successfully employed in POI recommendation, they depend on a hidden state of the entire past that cannot fully utilize parallel computation within a check-in sequence. To address these above limitations, we propose a spatiotemporal dilated convolutional generative network (ST-DCGN) for POI recommendation in this study. Firstly, inspired by the Google DeepMind’ WaveNet model, we introduce a simple but very effective dilated convolutional generative network as a solution to POI recommendation, which can efficiently model the user’s complicated short- and long-range check-in sequence by using a stack of dilated causal convolution layers and residual block structure. Then, we propose to acquire user’s spatial preference by modeling continuous geographical distances, and to capture user’s temporal preference by considering two types of time periodic patterns (i.e., hours in a day and days in a week). Moreover, we conducted an extensive performance evaluation using two large-scale real-world datasets, namely Foursquare and Instagram. Experimental results show that the proposed ST-DCGN model is well-suited for POI recommendation problems and can effectively learn dependencies in and between the check-in sequences. The proposed model attains state-of-the-art accuracy with less training time in the POI recommendation task.
Factors in Information Assurance Professionals' Intentions to Adhere to Information Security Policies

Information security policies (ISPs) serve to clarify and formalize organizational information security practices and reduce data risks, but research shows that ISP noncompliance remains a prominent concern for both scholars and practitioners. This study utilized the unified theory of acceptance and use of technology 2 (UTAUT2) to explore factors that predict information assurance professionals' behavioral intentions to comply with ISPs. The research question addressed: To what extent do performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit predict information assurance professionals' behavioral intention to comply with information security policies in organizations? A nonexperimental, cross-sectional research design using structural equation modeling (PLS-SEM) addressed the research question with information assurance professionals in government agencies where habit emerged as the important component of ISP compliance with hedonic factors having a negative impact.
A Comprehensive Horizon‐Picking Method on Subbottom Profiles by Combining Envelope, Phase Attributes, and Texture Analysis

Abstract We propose a new method that considers the envelope, phase attributes, and texture analysis of the subbottom profile to automatically obtain continuous and accurate horizon picking. This method overcomes the shortcomings of the traditional methods in envelope and phase automatic horizon picking in terms of accuracy and continuity, and in efficiency when it comes to manual picking. Under the constraint of the envelope threshold from the envelope image and the texture segmentation from the phase image, the accurate envelope horizons are picked from the envelope image. Through mean filtering and template enhancement, the fine phase horizons are picked from the phase image. Finally, by combining the two kinds of picked horizons, more continuous, accurate, and finer horizons are obtained. The proposed method was tested on a real data set.