Reviewerly/exHarmony huggingface dataset:
Extracting Construction Knowledge from Project Schedules Using Natural Language Processing

A sound and good quality schedule is critical to the success of a construction project. However, the little time available for proper project scheduling in the planning and design stage often impairs the quality of a schedule. Few efforts have been made to evaluate and maintain the schedule quality in the construction stage. Usually project teams need to put intensive manual efforts to conduct schedule quality diagnosis which is time-consuming and subjective to a large extent. One major challenge of diagnosing schedule quality is understanding the activity characteristics and construction logic. The multi-partite nature of construction projects (i.e. schedulers and project teams) further exacerbates the difficulty of diagnosis. This paper thus proposes a novel semantic-based logic reasoning and representation methodology to extract construction methods from the schedule to ensure a consistent project schedule. The intellectual contributions of this paper are twofold. First, this paper develops an ontology of tasks with hierarchies from the schedule to automatically extract the construction methods and activities. Second, this paper presents a novel dependency-based information representation schema for representing the logics between tasks and key constraints to facilitate the complete automation in construction logic reasoning from the schedule. To test the proposed system, this paper evaluates the average rate of recall and precision achieved by the system for extracting construction activities and logics in the schedule within one month and compared the results with the rate achieved by manual check. The developed system provides both academics and practitioners a method to detect the deficiencies of project schedules and assists project planners to produce and maintain good quality schedules starting from project initiation until its completion.
Finding Characteristics of Users in Sensory Information: From Activities to Personality Traits

The main objective of this work was to use information provided by a sensor-based activity recognition system to create a profile comprising user habits and link this information to the personality traits of users. User habits are represented by the sequence and duration of often observed daily life activities. Based on this information, we represented the user trials (sequence of activities) following a numerical method using Fourier series. The duration and sequence of the activities changed the phase and amplitude of the harmonics present in the Fourier representation. Each trial represented in this manner is called a behavioral spectrum. These data and the scores obtained from personality questionnaires were clustered separately and then an association was created between the clusters. The objective was to associate the activity-related features (sensor-based) and personality traits. The experimental results showed that for both young and elderly subjects, there is an association between the user personality traits and the manner in which they perform their activities. Moreover, the results obtained in this work show a promising method of assessing the personality traits of users based on their activities.
Automatic Extraction of Blur Regions on a Single Image Based on Semantic Segmentation

Blur region detection from a single image with spatially-varying blur is a challenging task. Although many methods are proposed in the past decades, most of them are based on hand-crafted features. These features are not robust to image context, image size, blur type and other factors, which cannot obtain sound performance. In addition, the craft of these features requires a lot of domain knowledge. To address these problems, in this paper, a blur region detection method based on semantic segmentation is proposed to extract blur regions, which well integrates global image-level context and cross-layer context information making the auto-learned features more robust. Specifically, we design a blur detection net (BDNet) for blur detection by combining ResNets and FCNs. A binary mask can be produced in an end-to-end way. By our method, the mean region intersection over union (Mean IoU) increased by nearly 20% compared with most other blur detection methods. We make the code publicly available at <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com/SEU-DongHan/BDNet</uri>.</i>
Data Storage Security Issues in Cloud Computing

Cloud computing allow users to store information in data centers, it also enables on-demand computation of resources. Remote servers can access the information inside the cloud. Cloud computing serves as an internet-accessible resource allocation business model and offers pay-per-use computing infrastructure. This paper primarily discusses about the main data storage security issues- in cloud computing and certain solutions which could be adopted as a remedy to such issues. It also includes an analysis review which helps in picking out the best fitting solution.
A Smart-Decision System for Realtime Mobile AR Applications

With the development of the hardware and software platforms, we can implement the deep learning model on the mobile device for mobile augmented reality (AR) applications. However, not all mobile AR tasks can be finished on mobile devices. Meanwhile, the limited computation resources on mobile devices are still the main obstacle to achieve realtime mobile AR applications. In this paper, we proposed a smart-decision framework which combines the advantages of the on-device mobile AR system and the edge-based mobile AR system to achieve real-time object recognition. High computation complexity tasks will be offloaded to the edge servers. Low complexity tasks will be executed on mobile devices or the edge server depending on the network latency. To overcome the dynamic changes of network condition and the limitations of the on-device deep learning models, we design a cache and matching algorithm on the mobile devices to enhance the performance of the recognition tasks. With our proposed system, the quality of the mobile AR application is improved. The performance of the smart-decision framework is validated through experiments with a testbed.
P-Cube: A New Two-Layer Topology for Data Center Networks Exploiting Dual-Port Servers

Connecting a large number of servers with high bandwidth links is one of the most crucial and challenging tasks that the Data Center Network (DCN) must fulfill. DCN faces a lot of difficulties like the effective exploitation of DC components that, if highlighted, can aid in constructing high performance, scalable, reliable, and cost-effective DCN. In this paper, we investigate the server-centric structure. We observe that current DCs use servers that mostly come with dual ports. Effective exploitation of the ports of interest for building the topology structure can help in realizing the potentialities of reducing expensive topology. Our new network topology, named “Parallel Cubes” (PCube), is a duplicate defined structure that utilizes the ports in the servers and mini-switches to form a highly effective, scalable, and efficient network structure. P-Cube provides high performance in network latency and throughput and fault tolerance. Additionally, P-Cube is highly scalable to encompass hundreds of thousands of servers with a low stable diameter and high bisection width. We design a routing algorithm for P-Cube network that utilizes the P-Cube structure to strike a balance among the numerous links in the network. Finally, numerical results are provided to show that our proposed topology is a promising structure as it outperforms other topologies and it is superior to Fat-tree, BCube and DCell by approximately 24%, 16%, 8% respectively in terms of network throughput and latency. Moreover, P-Cube extremely outperforms Fat-tree, and BCube structures in terms of total cost, complexity of cabling and power consumption.
Dendritic Branches of DNM Help to Improve Approximation accuracy

This paper rigorously establishes that, by increasing the number of dendritic branches, we can achieve an infinite approximation to any continuous function with any accuracy. In other words, the accuracy can be improved by using one neuron with more dendritic branches under the premise of using the backpropagation neural network theory.
UXJs: Tracking and Analyzing Web Usage Information With a Javascript Oriented Approach

Knowing what the user does inside your web and how he does it is crucial nowadays to understand the strengths and inconveniences of your web's design and architectural structure as well as about the usability of the site.Currently, there are several solutions that allow the tracking of the user behavior but these have some limitations due to the information they are able to capture and how they can present that information in a useful way for the web developer.Many of these platforms don't capture information about the user activity in the websites, clicks, mouse movements, etc.Some solutions do capture some of this user activity, but they only process the information visually showing heatmaps.In this paper we present UXJs, a novel research approach for collecting automatically all possible information about the user activity in websites, showing this information quantitatively and allowing its automatic statistical analysis and the rapid understanding by web developers.
Parallel Processing of Genetic Algorithms in Python Language

Modern genetic algorithms are derived from natural laws and phenomenons and belong to evolutionary algorithms. Genetic algorithms are, by their very nature, suitable for parallel processing that leads to increased speed and to optimization. The paper deals with selected ways of parallelization of genetic algorithms with subsequent implementation. Parallelization brings an increase in algorithm speed and load distribution, which is compared to a serial model. Python language is used for demonstration. Four Python modules have been selected to provide parallel processing. They are the Global One - Population Master-Slave Model, the One-Population Fine-Grained Model, the Multi-Population Coarse-Grained Model, and the Hierarchical Model.
LUT‐based high‐speed point multiplier for Goldilocks‐Curve448

Recent studies have shown that existing elliptic curve-based cryptographic standards provide backdoors for manipulation and hence compromise the security. In this regard, two new elliptic curves known as Curve448 and Curve25519 are recently recommended by IETF for transport layer security future generations. Hence, cryptosystems built over these elliptic curves are expected to play a vital role in the near future for secure communications. A high-speed elliptic curve cryptographic processor (ECCP) for the Curve448 is proposed in this study. The area of the ECCP is optimised by performing different modular operations required for the elliptic curve Diffie–Hellman protocol through a unified architecture. The critical path delay of the proposed ECCP is optimised by adopting the redundant-signed-digit technique for arithmetic operations. The segmentation approach is introduced to reduce the required number of clock cycles for the ECCP. The proposed ECCP is developed using look-up-tables (LUTs) only, and hence it can be ported to any field-programmable gate array family or standard ASIC libraries. The authors' ECCP design offers higher speed without any significant area overhead to recent designs reported in the literature.
Converting Handwritten Text to Editable Format via Gesture Recognition for Education

In this project, we present a real-time Internet of Things (loT)-based system to convert handwritten text into editable format by implementing Hand Gesture Recognition (HGR) with Raspberry Pi for classroom education. HGR is implemented using edge detection algorithm and it is used to reduce computational complexity and improve the efficiency of the system. Raspberry Pi is used to retrieve and perceive HGR and to build a smart classroom via loT technologies. Handwritten images are converted into editable format by using OpenCV and machine learning algorithms. In the text conversion, recognition of uppercase and lowercase alphabets, numbers, special characters, mathematical symbols, equations, and figures are included with recognition of word, lines, blocks, and paragraphs. With the help of Raspberry Pi and IoT technologies, students can access the editable format of lecture notes via a desktop application which helps students to edit and share notes and images according to their necessity. Implementation details and comprehensive evaluations of the system are summarized in the paper.
Cross Refinement Techniques for Markerless Human&lt;?brk?&gt; Motion Capture

This article presents a global 3D human pose estimation method for markerless motion capture. Given two calibrated images of a person, it first obtains the 2D joint locations in the images using a pre-trained 2D Pose CNN, then constructs the 3D pose based on stereo triangulation. To improve the accuracy and the stability of the system, we propose two efficient optimization techniques for the joints. The first one, called cross-view refinement, optimizes the joints based on epipolar geometry. The second one, called cross-joint refinement, optimizes the joints using bone-length constraints. Our method automatically detects and corrects the unreliable joint, and consequently is robust against heavy occlusion, symmetry ambiguity, motion blur, and highly distorted poses. We evaluate our method on a number of benchmark datasets covering indoors and outdoors, which showed that our method is better than or on par with the state-of-the-art methods. As an application, we create a 3D human pose dataset using the proposed motion capture system, which contains about 480K images of both indoor and outdoor scenes, and demonstrate the usefulness of the dataset for human pose estimation.
High Accuracy Face Geometry Capture using a Smartphone Video

What's the most accurate 3D model of your face you can obtain while sitting at your desk? We attempt to answer this question in our work. High fidelity face reconstructions have so far been limited to either studio settings or through expensive 3D scanners. On the other hand, unconstrained reconstruction methods are typically limited by low-capacity models. Our method reconstructs accurate face geometry of a subject using a video shot from a smartphone in an unconstrained environment. Our approach takes advantage of recent advances in visual SLAM, keypoint detection, and object detection to improve accuracy and robustness. By not being constrained to a model subspace, our reconstructed meshes capture important details while being robust to noise and being topologically consistent. Our evaluations show that our method outperforms current single and multi-view baselines by a significant margin, both in terms of geometric accuracy and in capturing person-specific details important for making realistic looking models.
Introducing External Knowledge to Answer Questions with Implicit Temporal Constraints over Knowledge Base

Knowledge base question answering (KBQA) aims to analyze the semantics of natural language questions and return accurate answers from the knowledge base (KB). More and more studies have applied knowledge bases to question answering systems, and when using a KB to answer a natural language question, there are some words that imply the tense (e.g., original and previous) and play a limiting role in questions. However, most existing methods for KBQA cannot model a question with implicit temporal constraints. In this work, we propose a model based on a bidirectional attentive memory network, which obtains the temporal information in the question through attention mechanisms and external knowledge. Specifically, we encode the external knowledge as vectors, and use additive attention between the question and external knowledge to obtain the temporal information, then further enhance the question vector to increase the accuracy. On the WebQuestions benchmark, our method not only performs better with the overall data, but also has excellent performance regarding questions with implicit temporal constraints, which are separate from the overall data. As we use attention mechanisms, our method also offers better interpretability.
A List-Decoding Approach to Low-Complexity Soft Maximum-Likelihood Decoding of Cyclic Codes

This paper provides a reduced-complexity approach to maximum likelihood (ML) decoding of cyclic codes. A cyclic code with generator polynomial g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cyclic</sub> (x) may be considered a terminated convolutional code with a nominal rate of 1. The trellis termination redundancy lowers the rate from 1 to the actual rate of the cyclic code. The proposed decoder represents g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cyclic</sub> (x) as the product of two polynomials, a convolutional code (CC) polynomial g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cc</sub> (x) and a cyclic redundancy check (CRC) polynomial g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">crc</sub> (x), i.e., g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cyclic</sub> (x) = g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cc</sub> (x)g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">crc</sub> (x). This representation facilitates serial list Viterbi algorithm (S-LVA) decoding. Viterbi decoding is performed on the natural trellis for g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cc</sub> (x), and g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">crc</sub> (x) is used as a CRC to determine when the S-LVA should conclude. At typical target frame error rates, the expected list size of S-LVA is small, and the average decoding complexity is dominated by the trellis complexity of g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cc</sub> (x) rather than g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cyclic</sub> (x). Some high-rate binary Bose-Chaudhuri- Hocquenghem (BCH) examples show that the proposed use of S-LVA via factorization significantly lowers complexity as compared to using the minimum-complexity trellis representation of g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cyclic</sub> (x) for soft ML decoding.
Projection Recurrent Neural Network Model: A New Strategy to Solve Maximum Flow Problem

We study the maximum flow problem (MFP) employing the concepts of Recurrent Neural Networks (RNN)s. The aim of the present attempt is to find a solution for MFP utilizing projection RNN models based on mixed linear complementarity problem (MLCP). The Karush-Kuhn-Tucker (KKT) optimality conditions of the original problem are applied to develop the projection RNN model based on MLCP. Besides, the Lyapunov stability and the global convergence of the projection RNN model are proved. Finally, several illustrative examples are given to demonstrate the performance of this approach. The obtained results are compared with previous approaches to solving MFP.
A Novel AR Whiteboard System and Usability Study

In remote AR conference or classroom, attendees are not being able to write freely on the whiteboard as they do in a real conference or classroom. In this paper, we solved the problem by generating an AR whiteboard in the AR space, using AR controller to write on AR whiteboard and outputting the character drawn there in a text file as a memo. The pathfinding experiment results show that the AR whiteboard can improve communication efficiency by 72% compared with the traditional real space method.
CryptoCurrency

The study looks at the benefits and usage of cryptocurrency as it seeks to compete with and also replace traditional currency. Despite many nations classifying cryptocurrency as a threat and confirming it to be an illegal tender, mostly due to the disruption providing little or no advantage to the government bodies, many private organisations are now taking up cryptocurrency as a legally acceptable method of payment in various sectors from education, travel, wealth management and even traditional practices like targeting of customers and advertising. Cryptocurrency may be temporary, as long as the process of obtaining, safeguarding, and storing it in the Blockchain remains tedious and an extremely confusing process and the governments of nations are not going to accept the currency as a legal tender, as long as they get no benefit with it and as long as usage of the Bitcoin is linked heavily with payments for drugs and weapons. However, the usage in the legal markets may also show a marked increase.
Adaptive Aggregation of Arbitrary Online Trackers with a Regret Bound

We propose an online visual-object tracking method that is robust even in an adversarial environment, where various disturbances may occur on the target appearance, etc. The proposed method is based on a delayed-Hedge algorithm for aggregating multiple arbitrary online trackers with adaptive weights. The robustness in the tracking performance is guaranteed theoretically in term of "regret" by the property of the delayed-Hedge algorithm. Roughly speaking, the proposed method can achieve a similar tracking performance as the best one among all the trackers to be aggregated in an adversarial environment. The experimental study on various tracking tasks shows that the proposed method could achieve state-of-the-art performance by aggregating various online trackers.
Factors Affecting Sprint Effort Estimation

Scrum projects consist of multiple iterations, known as sprints during which user stories are implemented. Estimating user stories of an iteration accurately is important in order to provide clarity and help management to control the project successfully. This leads to the necessity of identifying factors that impact the accuracy of a sprint effort estimation. This paper therefore aims to identify factors and reasons for inaccurate effort estimation of a sprint. For this purpose, a survey is conducted in a scrum environment with professionals with proven agile expertise. The survey is based on 15 completed small-scaled agile projects in a well-known Mauritian company. Results of the survey are summarized in the paper, and recommendations are made.