Reviewerly/exHarmony huggingface dataset:
Managing IT Projects and Evaluating their Cost and Complexity: State of the Problem

According to statistics, about a quarter of all started software projects are completed on time, a quarter are canceled, and about half of all projects are completed over budget or late. Existing methods and software products for estimating the time and cost of software development projects are often not suitable for evaluating the project at an early stage, where the wrong project deadlines and budget are most often set. Within the framework of this research, the task is to investigate the existing approaches to estimating the cost and labor intensity, as well as to propose an author's approach taking into account the shortcomings of existing methods. This will improve the quality of information systems development by reducing the number of delays in completing tasks, as well as by minimizing possible costs over the budget allocated for the project. This will also help to minimize costs in the decision-making process during the project.
Informed RRT* with Adjoining Obstacle Process for Robot Path Planning

Rapidly-exploring Random Tree (RRT) algorithm is efficient and popular in robot path planning, but cannot guarantee the optimality. RRT, an extension of RRT, claims to converge to asymptotic optimality by random optimized tree node in domain with a very slow rate. Informed RRT(IRRT*), samples random nodes by RRT* within a transformed ellipse which is the subset of original domain, outperforms RRT* in convergence rate and final solution quality. However, IRRT* depends on the current solution cost to build the ellipse. If the ellipse is not converged to optimal solution, the sampling time will be increased. This paper proposes Adjoined obstacle IRRT*(AIRRT*), a modified IRRT* that improves the velocity of ellipse sampling and generates the globally optimal path faster. AIRRT* uses an Adjoined process with linear interpolation to obtain valid obstacle vertices, reduce the ellipse area and converge to a shorter path to form a new ellipse and new solution. Simulations shown that AIRRT* can obtain the optimal path in effective time under different environments, and the performance is better than other algorithms.
BLAC: A Named Entity Recognition Model Incorporating Part-of-Speech Attention in Irregular Short Text

Irregular text refers to text with incomplete sequence information of the sentence or text that does not meet normal grammatical specifications, such as Weibo text. Existing named entity recognition algorithms recognize such text, the effect is poor due to the lack of context information. Because the attention mechanism has advantages in obtaining contextual information, we merge part-of-speech attention with the BI-LSTM-CRF model and propose a BLAC model. We tested on several public datasets and compared the results with the basic model BI-LSTM-CRF. The results show that the method we proposed has a certain improvement in the entity recognition of irregular short text.
Design and Implementation of Blockchain Based Single Sign-On Authentication System for Web Applications

Today, many services are provided through web applications and the number of these applications is increasing rapidly. Nowadays, most users use their username and password to login to web applications. Many of these users also use the same login information in different applications. This causes a major security vulnerability for applications and users. As a solution to these weaknesses in the field of authentication, there have been many developments in recent years. Some of these studies have been third party identity authentication systems like Google and Facebook. Since this method also contains potential risks, studies have been conducted on the Two-Factor Authentication (2FA) method for more security. In parallel with the innovations that emerge every day, methods should be used in the field of authentication. In these times, blockchain technology offers solutions that make life easier in many areas thanks to its distributed, transparent, secure and immutable structure. In this study, blockchain based single sign-on (SSO) authentication system was developed and implemented for web applications. In this system, a public address and a private key are defined on the private blockchain network for users and this information is used for the 2FA method through the developed mobile application. Detailed information was given about the proposed system and technologies used in the study.
Absolute 3D Human Pose Estimation via Weakly-supervised Learning

In this paper, we attempt to estimate absolute 3D human poses directly from monocular images. Not limited to estimating root-relative 3D human poses, our method can recover the absolute depth for each joint. Our method is trained with multi-view images in a weakly-supervised manner removing the need for 3D ground-truth annotations. We conduct extensive experiments on the Human3.6M benchmark to show the effectiveness of our method.
FIFE: an Infrastructure-as-Code Based Framework for Evaluating VM Instances from Multiple Clouds

To choose an optimal VM, Cloud users often need to step a process of evaluating the performance of VMs by benchmarking or running a black-box search technique such as Bayesian optimisation. To facilitate the process, we develop a generic and highly configurable Framework with Infrastructure-as-Code (IaC) support For VM Evaluation (FIFE). FIFE abstract the process as a searcher, selector, deployer and interpreter. It allows users to specify the target VM sets and evaluation objectives with JSON to automate the process. We demonstrate the use of the framework by setting up of a Bayesian optimization VM searching system. We evaluate the system with various experimental setups, i.e. different combinations of cloud provider numbers and parallel search. The results show that the search efficiency remains the same for the case when the search space is consist of VM from multiple cloud providers, and the parallel search can significantly reduce search time when the number of parallelisation is set properly.
Colloquy

APIs (application programming interfaces) play a key role in software development. Virtually, all programmers are potential users of third-party APIs. From the perspective of the theory of Semiotic Engineering, we may characterize an API as an artifact mediating the communication between two types of developers: the API designers and its users. During the construction of an API, the designers should establish proper dialogues with the users. These dialogues will enable the conversation of these actors at the interaction time. In this way, we define a conversational API as an API capable of offering effective dialogues to its users. In this paper, we introduce Colloquy, a method for supporting the design of conversational APIs. Colloquy results from the lessons learnt during an action research conducted for redesigning a real and complex API. The set of Colloquy resources allow API designers to go beyond conventional concerns with usability. We also report in this paper a case study in which Colloquy was used for redesigning a refactoring API. The study findings indicate the method helped the designer creating empathy with the API users, as well as better reflecting and depicting the requirements and the conversations that the API should attend to the different user profiles.
Effect of Data Augmentation on DNN-Based VAD for Automatic Speech Recognition in Noisy Environment

In recent years, DNN-based systems are extremely popular for performing automatic speech recognition (ASR) task. They have shown better performance than other methods. To perform ASR task efficiently, segmentation of the input data also has to be accurate. There are different kinds of methods of voice activity detection (VAD) including power based statistical methods. But to incorporate with the ASR system, it is better to perform the VAD task with the help of DNN-based method. In this paper, we investigate the superiority of DNN-based VAD over power based statistical methods. Not only that but also the effect of data augmentation on the performance of VAD in various conditions is also investigated. Performance of the VAD is evaluated using CENSREC-1-C data especially developed for evaluation of VAD task. The VAD trained with multi-condition data shows better performance than the baseline and other popular power based statistical VAD tools. We have also evaluated the performance for eval1 test dataset of CSJ corpus as well as its telephone variants.
Lecture Information Service based on Multiple Features Fusion

Information service is always a hot topic especially when web is accessible anywhere. In university, lecture information is very import for students and teachers who want to take part in academic meetings. Therefore, lecture news extraction is an important and imperative task. Although many open information extraction methods have been proposed, but due to the highly heterogeneity of website, this task is still a challenge. In this manuscript, we propose a method based on fusing multiple features to locate lecture news in university web site. These features including the organization structure of lecture news catalog webpage, the visual similarity and the semantic of webpage. Additionally, this paper provide an information service based on a main content extraction algorithm for extracting lecture information. The stable and invariant features enable the propose method could adaptive to many kinds of campus website. The experiments conducted on 50 websites show the effectiveness and efficiencies of provided service.
A Baseline Approach for Goalkeeper Strategy using Sarsa with Tile Coding on the Half Field Offense Environment

The following topics are dealt with: computer games; learning (artificial intelligence); virtual reality; mobile robots; genetic algorithms; data visualisation; multi-agent systems; neural nets; artificial intelligence; software agents.
Image re-sampling from a perspective of wave field propagation

This paper deals with the problem of 2D image resampling. Image re-sampling refers to the process of re-sizing an image at a higher or lower resolution one. This process has often been tackled in 2D spatial domain and seems to still remain there up to now. In this paper, we extend image resampling to a higher domain by considering it as a wave field propagation problem in 3D space. By doing so, we observe that the possibility is more increased for a re-scaled image to preserve spectral information well in it compared to previous interpolation techniques which tend to usually sacrifice high frequency information when the image is down-scaled or upscaled also. We present a novel 3D space based image resampling method by using a technique of wave field propagation. Experiments results on both natural and CG images are provided which validates our method. Comparison results with a few representative interpolation techniques clearly show desirable characteristics of our 3D wave field based image re-sampling over those in 2D domain.
Utilizing Multi-modal Emotion Information in Dialogue Strategy Classification

Dialogue models that automatically generate utterance sentences are being actively studied with the increase of data and the development of deep neural network technology. Research is also being conducted using information from multiple modalities to create dialogues that are more natural and reflect various aspects in the dialogue. In this paper, we focus dialogue strategy, which is important for understanding a natural dialogue flow. We implement a deep-learning based, dialogue strategy classifier based on the data labeled with emotion information from multiple modalities (i.e., text, sound, and image). Results of our experiment indicate that the use of emotion information from multi-modalities can help classify dialogue strategies with approximately 10% performance increase compared to the baseline. Results also show that the emotion information from the sound modality contributes to dialogue strategy classification more than that from the text and image modalities.
Identification of Food Quality Descriptors in Customer Chat Conversations using Named Entity Recognition

Chatbots are increasingly being used for providing customer support. One of the fundamental challenges for a bot, or for that matter any human agent, is to understand the context of a customer message. Chat conversations are typically associated with agrammatical structure, spelling mistake/variants, informal and slang words, and code-mixing, i.e., the use of words from more than one language. We focused on a use case related to the conversations between customers and agents regarding issues with the quality of food delivered by an online food delivery company. Accurate identification of words that describe the poor quality of food can immensely benefit prompt resolution of the issue and also provide vital feedback to the company and its partner restaurants. This feature can be used in a chatbot to effectively resolve customers' food quality related concerns. This paper presents a named entity recognition (NER) approach to identify the food quality descriptors in a given message. On an internal benchmark dataset, we achieved an F1 score of 0.93 while outperforming classical baseline approaches in NER.
Towards Trust-Augmented Visual Analytics for Data-Driven Energy Modeling

The promise of data-driven predictive modeling is being increasingly realized in various science and engineering disciplines, where experts are used to the more conventional, simulation-driven modeling practices. However, trust remains a bottleneck for greater adoption of machine learning-based models for domain experts, who might not be necessarily trained in data science. In this paper, we focus on the building energy domain, where physics-based simulations are being complemented or replaced by machine learning-based methods for forecasting energy supply and demand at various spatio-temporal scales. We study the trust problem in close collaboration with energy scientists and engineers and describe how visual analytics can be leveraged for alleviating this trust bottleneck for stakeholders with varying degrees of expertise and analytical goals in this domain.
Correction to “Gaussian Process Online Learning With a Sparse Data Stream” [Oct 20 5977-5984]

A letter entitled “Gaussian Process Online Learning With a Sparse Data Stream” suggests an approach for extending the infinite-horizon Gaussian processes (IHGPs, [1]) to deal with a sparse data stream. We point out that there is an error in differentiating the discrete algebraic Riccati equation (DARE), which significantly changes the results of the benchmarking study in a sense that the proposed approach using the solution of the Lyapunov equation does not show outperformance against the original IHGP. In this letter, we provide a correction with details and its consequential implication.
Uniform Recognition Activated Gate for Dress Code Implementation of Pamantasan ng Cabuyao

Wearing of improper uniform has been one of the problems being faced by Pamantasan ng Cabuyao due to a massive number of students entering the university. The security guards do not have the ability to monitor the student's attire all the time. There are also some students who do not wear Identification Cards (ID) upon entering the school premise which is also important for the student's or staff's identification as well as the school's security and integrity. This paper aims to plan and built a device whose main function is to monitor student's attire for most of the time. Uniform recognition-activated gate for dress code implementation of Pamantasan ng Cabuyao focused on improving the security system upon entering the gate of the university. This device used biometrics, barcode scanner of the Identification (ID) card and image recognition for uniform to open the gate. The mechanism to open the gate uses a servo motor which is connected to the gate structure. Based on the evaluation done by the professionals and preferred users, the device has been considered very good for each criteria provided of its scores. The device will be available for further improvement to develop more functions necessary to the workplace of its application.
Shadow-based Light Detection for HDR Environment Maps

High dynamic range (HDR) environment maps (EMs) are spherical textures containing HDR pixels used for illuminating virtual scenes with high realism. Detecting as few necessary pixels as possible within the EM is important for a variety of tasks, such as real-time rendering and EM database management. To address this, we propose a shadow-based algorithm for detecting the most dominant light sources within an EM. This algorithm takes into account the relative impact of all other light sources within the upper-hemisphere of the texture. This is achieved by decomposing an EM into superpixels, sorting the superpixels from brightest to least, and using ℓ0-norm minimisation to keep only the necessary superpixels that maintains the shadow quality of the EM with respect to the just noticeable difference (JND) principle. We show that our method improves upon prior methods in detecting as few lights as possible while still preserving the shadow-casting properties of EMs.
Prototype Development of Face and Speaker Recognitions based on Edge Computing

In recent years, cognitive services like face recognition and speech recognition has found wide applications in smart cities. The main approach of providing cognitive services depends on cloud computing, which however suffers high response latency and information security problems. In this paper, based on edge computing, prototypes on face recognition and speaker recognition are developed on a cloud radio access network platform. In the face recognition architecture, a traditional face recognition network is cascaded to a face detection network so as to mitigate the interference of irrelevant information. In the speaker recognition architecture, multi-task learning is exploited to improve the generalization of neural network, and a phoneme recognition task is employed as an auxiliary task to assist the speaker recognition. Experimental results corroborate the effectiveness of the proposed architectures in terms of recognition accuracy and response latency.
Design of Fault Diagnosis System for Steam Turbine Based on UML

Intelligent fault diagnostic systems have become a new hotspot of research with the wave of intelligent manufacturing, effective modeling method is an important means to build a reliable diagnosis system. Aiming at the current problems such as the various system functions, complex structure and expansibility, a new method to model the fault diagnosis system of steam turbine with UML is presented. In this paper, the use case diagram, class diagram, function decomposition diagram, activity diagram, sequence diagram, and deployment diagram of steam turbine fault diagnosis system are constructed by UML, and the diagnosis process is described completely, which provides a development framework for developers. It is proved that the modeling method is reliable and effective by the development of the research group.
Advanced Cloud based Task Scheduling Architecture to Optimize Performance in Datacenter

In cloud datacenter, job scheduling is used to manage real-time user data for improving response time of user query. Completion of tasks depends on datacenter availability, the capacity of virtual machines, network connection, and broker management policies. We propose a cloud-based architecture with a cloud service manager to select virtual machines from vmpool for optimizing tasks in the datacenter. The scheduling algorithm decides which virtual machine executes the task according to the nature and size of the task with minimal waiting time for execution.
A Hybrid-Pipelined Architecture for FPGA-based Binary Weight DenseNet with High Performance-Efficiency

The DenseNet achieves remarkable performance in various computer vision tasks with much fewer parameters and operations. However, there are few acceleration designs about the DenseNet, due to its dense-connectivity structure. In this paper, we apply the binary weight method on the DenseNet and then propose a hybrid-pipelined architecture for FPGA-based acceleration of the binary weight DenseNet, which can be stored entirely in a chip. To deal with the dense-connectivity, a reusable convolution unit is developed to support conv1×1 and conv3×3 efficiently. Moreover, a theoretical method of system parallelism is proposed to guide the top-level pipelined design for the maximum efficiency. To evaluate the proposed architecture, the binary weight DenseNet-100 model is trained on CIFAR10 dataset and then implemented on VX690T FPGA, at the cost of 4.18% accuracy loss. The experiment demonstrates that our architecture can achieve the throughput of 514 GOPS and 889 FPS at 200MHz, and the performance-efficiency is up to 62.4%, which outperforms the most related works.
Content Region Detection and Feature Adjustment for Securing Genuine Documents

Motivated by increasing the tampering of genuine documents during a transmission over digital channels, we focus on developing a data hiding framework for determining whether a received document is genuine or falsified. The input document is transformed into a standard form to minimize geometric distortions. Fully convolutional networks (FCN) is utilized to detect document's content regions. Next, we construct hiding patterns used for hiding a secret information. Modifying the pixel values of these patterns for carrying secret bits depends on the edge and comer features of document content, and the connectivity of their neighboring pixels. The hiding process is then conducted by changing the ratio between the number of edge features and the number of comer features of subregions within the content regions. The experiments are performed on various binary documents, and our approach gives competitive performance compared to state-of-the-art approaches.
The Temporal Logic of Coalitional Goal Assignments in Concurrent Multiplayer Games

We introduce and study a natural extension of the Alternating time temporal logic ATL , called Temporal Logic of Coalitional Goal Assignments (TLCGA). It features one new and quite expressive coalitional strategic operator, called the coalitional goal assignment operator ⦉ γ ⦊, where γ is a mapping assigning to each set of players in the game its coalitional goal , formalised by a path formula of the language of TLCGA, i.e., a formula prefixed with a temporal operator X , U , or G , representing a temporalised objective for the respective coalition, describing the property of the plays on which that objective is satisfied. Then, the formula ⦉ γ ⦊ intuitively says that there is a strategy profile Σ for the grand coalition Agt such that for each coalition C , the restriction Σ | C of Σ to C is a collective strategy of C that enforces the satisfaction of its objective γ (C) in all outcome plays enabled by Σ | C . We establish fixpoint characterizations of the temporal goal assignments in a μ-calculus extension of TLCGA, discuss its expressiveness and illustrate it with some examples, prove bisimulation invariance and Hennessy–Milner property for it with respect to a suitably defined notion of bisimulation, construct a sound and complete axiomatic system for TLCGA, and obtain its decidability via finite model property.