Reviewerly/exHarmony huggingface dataset:
VISION: Evaluating Scenario Suitableness for DNN Models by Mirror Synthesis

Software systems assisted with deep neural networks (DNNs) are gaining increasing popularities. However, one outstanding problem is to judge whether a given application scenario suits a DNN model, whose answer highly affects its concerned system's performance. Existing work indirectly addressed this problem by seeking for higher test coverage or generating adversarial inputs. One pioneering work is SynEva, which exactly addressed this problem by synthesizing mirror programs for scenario suitableness evaluation of general machine learning programs, but fell short in supporting DNN models. In this paper, we propose VISION to eValuatIng Scenario suItableness fOr DNN models, specially catered for DNN characteristics. We conducted experiments on a real-world self-driving dataset Udacity, and the results show that VISION was effective in evaluating scenario suitableness for DNN models with an accuracy of 75.6-89.0% as compared to that of SynEva, 50.0-81.8%. We also explored different meta-models in VISION, and found out that the decision tree logic learner meta-model could be the best one for balancing VISION's effectiveness and efficiency.
A resistance to change perspective on nonusers of telecenters

Abstract We contend that nonuse is different from low use and explore reasons for nonuse of telecenters by small and marginal farmers in rural India. We take a resistance to change perspective to understand reasons for nonuse. Based on an exploratory empirical study of small and marginal farmers, we show that there are important differences between users and nonusers. In addition to demographic differences, nonusers were more satisfied with current sources of information, had lower awareness of the potential of telecenters, and were less concerned about the factors that influenced low use, such as quality of information, cost, and location. These differences have important implications for providers and policy makers who need to have different strategies toward nonusers and users.
An Approach Based on (Tasks-VMs) Classification and MCDA for Dynamic Load Balancing in the CloudIoT

Cloud IoT is a new paradigm that has emerged as a result of the integration of Cloud Computing and the Internet of Things. It provides a set of intelligent services and applications that can strongly influence on our daily lives. In the CloudIoT, physical sensors are responsible for detecting and transmitting data to the cloud in order to be treated and stored. In this context, the amount of data to be processed through the CloudIoT is in increasing usually and, in this situation, the load balancing mechanism is needed in order to distribute the captured data between the different CloudIoT resources. In this paper, we propose an approach that allows to balance the load between the different virtual machines of a CloudIoT. The proposed approach is composed of four essential components: The Classifier which assigns Tasks to its corresponding class and assigns the Virtual Machines (VM) to its corresponding class too, the Dispatcher which sends the tasks to the VM classes, the Local Balancer whose role is to balance the tasks between different VMs belonging to the same class used the spooling method for duplicate same VM, finally, the Manager component or general balancer that is responsible to balance the load between VMs classes with the test of the maximum cloud allowance bar to release VM. The results obtained through the proposed the case study show that our approach allows effectively the load balancing between VM.
Improving Nominalized Adjectives Tagging

Abstract Part of speech transitions represent an interesting issue in terms of Automatic Morphological Analysis (AMA). In these cases, two parts of speech have to be considered: initial and final. However, their automatic recognition is complicated by the same form. This article presents the results of a corpus study aimed at mapping nominalized adjectives tagging with a focus on detecting candidates for nominalization among frequent adjectives. Analysis of the data obtained from the ČNK SYN v5 corpus shows different reasons for incorrect tagging. Taking into account these reasons, we propose three solutions for the improvement nominalized adjectives tagging.
Privacy-Preserving of System Model with Perturbed State Trajectories using Differential Privacy: With application to a Supply Chain Network

Releasing state samples generated by a dynamical system model, for data aggregation purposes, can allow an adversary to perform reverse engineering and estimate sensitive model parameters. Upon identification of the system model, the adversary may even use it for predicting sensitive data in the future. Hence, preserving a confidential dynamical process model is crucial for the survival of many industries. Motivated by the need to protect the system model as a trade secret, we propose a mechanism based on differential privacy to render such model identification techniques ineffective while preserving the utility of the state samples for data aggregation purposes. We deploy differential privacy by generating noise according to the sensitivity of the query and adding it to the state vectors at each time instant. We derive analytical expressions to quantify the bound on the sensitivity function and estimate the minimum noise level required to guarantee differential privacy. Furthermore, we present numerical analysis and characterize the privacy-utility trade-off that arises when deploying differential privacy. Simulation results demonstrate that through differential privacy, we achieve acceptable privacy level sufficient to mislead the adversary while still managing to retain high utility level of the state samples for data aggregation.
Identifying and characterizing unmaintained projects in GitHub

Open source projects are key components of modern software development. Due to the appearance of novel platforms (e.g., GitHub and GitLab) for developing public code, developers has created thousands of open source projects. As a consequence, a significant number of open source projects is also unmaintained. To tackle this problem, in this thesis, we reported a set of quantitative and qualitative studies to help developers to maintain their open source projects. First, we surveyed the owners of open source projects that are no longer actively maintained, aiming to reveal the reasons for stop the maintenance of their projects. As result, we provide a set of nine reasons that motivated them to abandon their projects. Second, we conducted a survey with developers who recently became core contributors of popular GitHub projects. We reveal their motivations to contribute to these projects, the projects characteristics that mostly helped to contribute, and the barriers faced by them. Our key results show that the surveyed developers contributed to the projects because they are using them and need some improvements. The participants also answered that the lack of time of the project leaders was the principal barrier faced by them. Finally, the project characteristic which mostly helped them to contribute was the existence of a friendly community. Finally, in our third study, we propose a quantitative and datadriven model to identify GitHub projects that are not actively maintained. We train the model using a set of 13 features about project activity (e.g., commits, forks, and issues). The model achieved a precision of 80%, based on the feedback of 129 real open source developers and a recall of 96%. We also showed that the model can be used to identify unmaintained projects early, without having to wait for one year of inactivity, as commonly proposed in the literature. Finally, we defined a metric, called Level of Maintenance Activity (LMA), to assess the risks of projects become unmaintained. We provided evidence on the applicability of this metric, by investigating its usage in 2,927 active projects.
Speech Segmentation and Speaker Diarization using Time-Delay Neural Network

In recent years, important studies about speaker diarization, which is an important topic in the field of speech processing, have been carried out. Especially, significant improvements have been made in the problem of diarization with the i-vector method; and parallel to this, current deep learning methods have been used effectively in the field of speech processing. As a result of the improvements, the performance of speaker diarization systems have been increased. In this study, firstly, how various speech activity detection systems affect speaker diarization system is examined. Therefore, deep neural network, elevated deep neural network, adaptive context attention model and time-delayed deep neural network based methods are used. Then, the effect of i-vectors and x-vectors, on diarization error rate for speaker representation were examined.
Document images classification based on deep learning

In the financial business, there are cumbersome and error-prone manual procedures and long workflows. In this paper, MSCNN model is proposed to solve automatically identify and classify images such as documents. First, the image data is preprocessed to enhance the image features. Then, the image is extracted by the cross-connect structure and multi-channel convolution. Finally, the image is classified by softmax layer. The experimental results on the dataset show that the proposed model has a high classification accuracy in image classification.
Guided attention mechanism: Training network more efficiently

With the wide application of attention mechanism in multitudinous field of natural language processing (NLP), to date various deep neural networks based on this mechanism have been introduced and developed. However, a major problem with this kind of application is that a long time will be consumed due to the current networks still need to rely on their own ability to form attention values from scratch during the training. In this paper, we propose an auxiliary method called the Guided Attention Mechanism (GAM), which utilizes the prior knowledge to guide the network to form attention values in NLP field, thereby shortening the network training time and making the attention values more accurate. This work designed two sets of prior knowledge generation processes based on the regularization method and the deep learning method respectively. And the prior knowledge is used to guide the attention values of the original network in terms of values and angles. The experimental results show that compared with the original network, the classification accuracy of the network using GAM is improved by about 2%, and the training time is reduced by 5∼9%.
Motion Planning and Control with Randomized Payloads on Real Robot Using Deep Reinforcement Learning

In this study, a unified motion planner with low level controller for continuous control of a differential drive mobile robot under variable payload values is presented. The deep reinforcement agent takes 11-dimensional state vector as input and calculates each wheel’s torque value as a 2-dimensional output vector. These torque values are fed into the dynamic model of the robot, and lastly steering commands are gathered. In previous studies, intersection navigation solutions that uses deep-RL methods, have not been considered with variable payloads. This study is focused specifically on service robotic applications where payload is subject to change. In this study, deep-RL-based motion planning is performed by considering both kinematic and dynamic constraints. According to the simulations in a dynamic environment, the agent successfully navigates to target with 98.2% success rate in test time with unseen payload masses during training. Another agent is also trained without payload randomization for comparison. Results show that our agent outperforms the other agent, that is not aware of its own payload, with more than 40% gap. Our agent is also compared with the Time-to-Collision (TTC) algorithm. It is observed that our agent uses far less time than TTC to accomplish the mission while success rates of two methods are same. Lastly, the proposed method is applied on a real robot in order to show the real-time applicability of the approach.
Situation-Adaptive Object Grasping Recognition in VR Environment

In this paper, we propose a method for recognizing grasping of virtual objects in VR environment. The proposed method utilizes the fact that the position and shape of the virtual object to be grasped are known. A camera acquires an image of the user grasping a virtual object, and the posture of the hand is extracted from that image. The obtained hand posture is used to classify whether it is a grasping action or not. In order to evaluate the proposed method, we created a new dataset that was specialized for grasping virtual objects with a bare hand. There were three shapes and three positions of virtual objects in the dataset. The recognition rate of the classifier that was trained using the dataset with specific shapes of virtual objects was 93.18 %, and that with all the shapes of virtual objects was 87.71 %. This result shows that the recognition rate was improved by training the classifier using the shape-dependent dataset.
SoReady: An Extension of the Test and Defect Coverage-Based Analytics Model for Pull-Based Software Development

Pull-based software development is a distributed development model that offers an opportunity to review a pull request before it gets merged into the main repository. A pull request addresses new features, bug fixing, and maintenance issues submitted by both integrators or contributors. It appears that many empirical studies are conducted to discover how pull request evaluation is done, and to our knowledge, limited research exists for assessing release readiness of pull requests. Studies also reported that the failure rate of pull-requests rapidly increases when there are many forks created. It is therefore, questions worth exploring are whether the code review really contributing to the code quality, and how to determine the release readiness of pull requests? In our previous work, test and defect coverage-based analytics model (TDCAM) has been proven to be suitable to determine the readiness of releases for software that is rapidly evolving, in which this is also a characteristic of pull-based software development. In this paper, the TDCAM has been extended to include pull request coverage indicators. The proposed model, namely as SoReady and the visualization analysis presented herein has enabled five developers in a commercial setting to make informed and evidence-based decisions regarding the test status of each pull request and overall reliability of an open source software through a prototype dashboard.
Creating an Immersed Sheep and Wool VR/AI Experience

The development of a sheep and wool conservancy initiative, "Shave 'em to Save 'em," inspired the creation of a Virtual Reality/ Artificial Intelligence game "For Ewe," a proof-of-concept game. It aims to support agricultural and sustainable design initiatives. The game development process was guided by Activity Theory and was created using Unity 3D. "For Ewe" was tested on web, Samsung Gear VR, and Oculus Quest virtual reality platforms.
A method for the semantic analysis of documents in a business context in Spanish

Due to the ease of generating and storing written text documents, Natural Language Processing tools are increasingly integrated into software products on decision making in various fields. In this paper, we work with real requests from customers of a real estate company. The objective of the paper is to design a preliminary approximation of new intelligent algorithms that can carry out a semantic analysis of text documents in the business environment. For this purpose, different prototypes are developed, based on techniques and methods from different disciplines, such as Natural Language Processing, to process and structure text, Text Mining to find the most relevant terms and Knowledge Engineering, to create an ontology with the objective of measuring semantic similarity in business documents. The prototypes obtained are capable of determining the semantic similarity between simple phrases, structuring existing terms, and determining the subject on which they deal with requests from clients of a real estate company.
Product Spam Reviews Detection Based on Index Optimization

The quality of indicator selection plays a key role in the identification of spam reviews. In this paper, 9 indexes are extracted from product introduction and review content respectively. Support vector machine and naive bayes classifiers are used for recognition, and precision rate, recall rate and F value are used as evaluation indicators of spam review detection effect. Experimental results show that, the set of indexes selected in this paper can identify garbage comment more effectively and accurately.
Monte Carlo Tree Search Methods for Telescope Tasking

Space Domain Awareness demands continued tracking of an increasing catalog of satellites with limited resources. As this catalog of space objects (SOs) and debris grows over time, telescopes and directed RF receivers must be tasked to maximize information gain. Given a large set of observable objects over time, the problem of maintenance can be reduced to a sequential decision problem. Monte Carlo Tree Search (MCTS) methods are leveraged to approximately solve this problem over a finite time horizon. Continuous observation, state, and action spaces are considered. Double progressive widening methods are applied to avoid curses of dimensionality and history. A novel contribution is made using traditional filter update techniques enabling use of MCTS methods over a large state space. A variety of methodologies for sampling of the action space are designed and implemented. Complex measurement models are developed simulating clouds, atmospheric conditions, effects of saturation due to the moon, and occlusion by Earth's shadow. The resulting partially-observable Monte Carlo hybrid update planning (POMCHUP) algorithm incorporates each of these implemented techniques in a general, extensible manner with the goal of maximizing information gain over a receding horizon of observations. POMCHUP is considered for a variety of use cases, including observation of a set of geostationary objects (GEOs), a mixed observation campaign of GEOs and low-Earth orbit (LEO) objects, and a set of GEOs observed during a cloudy day. A greedy observation policy is developed as an initial comparison; POMCHUP methods outperform this implementation in all cases.
Optimal Control Approach to Terrain Following Trajectory Generation

This paper presents an optimal control formulation of the terrain following terrain avoidance path planning problem. The specific objective of this paper is to find the control which minimizes an aircraft’s height above terrain as the aircraft flies from one side of a mountain range to the other. The flight path must avoid terrain and keep out zones constraints while satisfying the aircraft dynamic constraints. In this problem formulation USGS terrain elevation data was used to provide accurate and realistic terrain elevation data. A simplified aircraft model was developed using load factor rate and roll acceleration as control inputs eliminating the need to compute aerodynamic forces and moments. This formulation resulted in potentially flyable minimum height above terrain paths which were computed using the optimal control software GPOPS-II and non-linear problem solver IPOPT. The resulting paths were flown in a non-linear flight simulator to qualitatively assess flyability. The results of the flyability study showed the paths were flyable by aircraft with performance capabilities greater than or equal to the performance constraints used in the optimization routine.
Intelligent Cost-Oriented Manufacturing Applications to Energy Buildings using Fuzzy Experts

The manufacturing industry faces currently and in the near future due to the information society, very important challenges. New data, network, automation, digital customer interfaces and many new advancements have brought us Industry 4.0. Cost-oriented manufacturing methods and tools play a major role in the smooth and effective operation of an industry. Industry 4.0 or Smart Manufacturing as has been called by some, describes the new era of the “intelligent environment”, in which formerly separate subsystems, components and processes link together through many industrial networking protocols to produce intelligent data and transforms our daily lives. Intelligent cost-oriented concepts and practices are very important for energy savings of a building. The human needs must be taken into consideration when automation is used for Building Energy Management Systems (BEMS).
Crowd Knowledge Enhanced Multimodal Conversational Assistant in Travel Domain

We present a new solution towards building a crowd knowledge enhanced multimodal conversational system for travel. It aims to assist users in completing various travel-related tasks, such as searching for restaurants or things to do, in a multimodal conversation manner involving both text and images. In order to achieve this goal, we ground this research on the combination of multimodal understanding and recommendation techniques which explores the possibility of a more convenient information seeking paradigm. Specifically, we build the system in a modular manner where each modular construction is enriched with crowd knowledge from social sites. To the best of our knowledge, this is the first work that attempts to build intelligent multimodal conversational systems for travel, and moves an important step towards developing human-like assistants for completion of daily life tasks. Several current challenges are also pointed out as our future directions.
A Novel Learning Method for Electric Circuit Course

In recent years, with the rapid development of mobile Internet technology and intelligent mobile terminal equipment, mobile learning has been widely used in various teaching modes and attained good teaching effectiveness. By introducing the mobile learning mode into the electric circuit teaching in universities and combining with the traditional teaching mode, this paper discussed the notion of the application of Wechat platform to create a learning environment. Then elaborated the designing of mobile learning environment mode and the evaluation mechanism of learning effectiveness, and finally presented and analyzed the data obtained from the new teaching mode. Through nearly three years of application of the mode into the teaching for students majoring in electric automation, the mobile learning effectiveness has become gradually outstanding, compared with the learning method applied in the traditional teaching mode. The number of the students involved in mobile learning in the past three years has increased from 60% to 96.75,%. And the passing rate has risen from 50% to 86.7%. In general, the new teaching methodology has become popular with the students.
Accurate Delivery Analysis of Distributed E-Commerce Based on Word2vector

The e-commerce market is fiercely competitive nowadays. How to achieve accurate customer delivery has become a key issue in e-commerce research. The purpose of this paper is to optimize the e-commerce advertising and push delivery based on the current customer search text big data. In this paper, the Word2vector algorithm is used to calculate the big data from Alibaba customers. The customers' search preferences, search histories and other related text content are used and analyzed. The research results show that the Word2vector algorithm is faster and more effective than the current mainstream CTR algorithm.