Reviewerly/exHarmony huggingface dataset:
When Natural Language Processing Jumps into Collaborative Software Engineering

Software engineering is an intrinsically collaborative activity, especially in the era of Agile Software Development. Many actors are partaking in development activities, such that a common understanding should be reached at numerous stages during the overall development life-cycle. For a few years now, Natural Language Processing techniques have been employed either to extract key information from free-form text or to generate models from the analysis of text in order to ease the sharing of knowledge across all parties. A significant part of these approaches focuses on retrieving lost domain and architectural knowledge through the analysis of documents, issue management systems or other forms of knowledge management systems. However, these post-processing methods are time-consuming by nature since they require to invest significant resources into the validation of the extracted knowledge. In this paper, inspired by collaborative tools, bots and Natural Language extraction approaches, we envision new ways to collaboratively record and document design decisions as they are discussed. These decisions will be documented as they are taken and, for some of them, static or behavioural models may be generated on-the-fly. Such an interactive process will ensure everyone agrees on critical design aspects of the software. We believe development teams will benefit from this approach because manual encoding of design knowledge will be reduced and will not be pushed to a later stage, when not forgotten.
A newly proposed object detection method using Faster R-CNN inception with ResNet based on Tensorflow

The purpose of the project is to study the previous methods of Object Detection using Deep Learning and propose a new method. The new model consists of three different techniques or processes: Regional Convolutional Neural Network (RCNN), Inception and ResNet. In object detection, the prime target of each method is to make sure maximum accuracy, high FPS, greater resolution and faster speed. But, due to the limitation of computational power it is not always possible to maintain a balance between these four. R-CNN in general is capable of handling high resolution images with a decent number of frames per second. In our method we introduced the concept of inception by dividing each large convolutional layer into smaller convolutional layers. And, by adding ResNet, we were able to get rid any extra layer that was not helping us in the gaining higher accuracy. Though we could achieve a low FPS, but the input image size was high resolution and the mean average precision was also high (almost close to SSD). We retrained the COCO and OpenImage datasets and results were decent enough. The model we build was based on the TensorFlow library.
Pixel-Level View Synthesis Distortion Estimation for 3-D Video Coding

Recently, region-based 3D video coding has been proposed. However, existing view synthesis distortion estimation (VSDE) methods are performed at the frame level. To guide the rate-distortion optimization process of region-based 3D video coding schemes, this paper proposes the first pixel-level VSDE (PL-VSDE) method. We first give the definition of the pixel-level view synthesis distortion. To estimate it, a backward prediction method is then developed, which starts from the pixels of interest (POIs) in the virtual view and finds their corresponding pixels in the reference view via a coarse-to-fine approach, denoted as coarse-to-fine backward prediction (CFBP) method. Additionally, the CFBP fully considers the details of 3D warping, the rounding operation and the warping competition in view synthesis, leading to improve accuracy of the prediction. Besides, a table-lookup method and a warping property are introduced to speed up the CFBP. After integrating the CFBP into the PL-VSDE, we can estimate the view synthesis distortion at the pixel level. Our method is carried out pixel-by-pixel independently, which is friendly for parallel processing. The experimental results demonstrate that our proposed method has significant advantages in both accuracy and efficiency compared with the state-of-the-art frame-level VSDE methods.
Distinguishing activities of daily living in a multi-occupancy environment

Ambient sensor systems in an intelligent environment are often used to monitor and infer Activities of Daily Living (ADL). Existing research on the recognition of ADL from ambient sensors assumes that the intelligent environment is inhabited by a single-occupant (older adults). However, in the real environment there may be situations in which there is more than one occupant for a time. The focus of this study is to distinguish the number of people in the home environment by employing only PIR sensors in order to identify whether the environment is used by one person or more. In this paper, two different techniques, Fuzzy Entropy (FuzzyEn) and Indoor Mobility (IM), are investigated to distinguish the activities within a multi-occupancy environment. The model is tested and evaluated based on a set of data representing a multi-occupancy environment. The experiment results show that FuzzyEn and IM can detect and identify the existence of a visitor in a home environment with an accuracy of 100% and 98.7%, respectively.
Augmented rotations in virtual reality for users with a reduced range of head movement

A large body of research in the field of virtual reality is focused on making user interfaces more natural and intuitive by leveraging natural body movements to explore a virtual environment. For example, head-tracked user interfaces allow users to naturally look around a virtual space by moving their head. However, such approaches may not be appropriate for users with temporary or permanent limitations of their head movement.In this paper, we present techniques that allow these users to get virtual benefits from a reduced range of physical movements. Specifically, we describe two techniques that augment virtual rotations relative to physical movement thresholds.We describe how each of the two techniques can be implemented with either a head tracker or an eye tracker, e.g. in cases when no physical head rotations are possible.We discuss their differences and limitations and we provide guidelines for the practical use of such augmented user interfaces.
A Brief Analysis of Blockchain Algorithms and Its Challenges

Blockchain is the world's most trusted service. It serves as a ledger that allows transaction to take place in a decentralized manner. There are so many applications based on blockchain technology, including those covering numerous fields like financial services, non-financial services, internet of things (IoT), and so on. Blockchain combines a distributed database and decentralized ledger without the need of verification by central authority. This chapter surveys the different consensus algorithms, blockchain challenges, and their scope. There are still many challenges of this technology, such as scalability and security problems, waiting to be overcome. The consensus algorithms of blockchain are proof of work (POW), proof of stake (POS), ripple protocol consensus algorithm (RPCA), delegated proof of stake (dPOS), stellar consensus protocol (SCP), and proof of importance (POI). This chapter discusses the core concept of blockchain and some mining techniques, consensus problems, and consensus algorithms and comparison algorithms on the basis of performance.
Activity Recognition through Feature Learning and Annotations using LSTM

The tremendous growth in ubiquitous sensor networks and IoT has led to Human Activity Recognition (HAR) emerging to be an exciting challenge. Machine learning approaches proposed in this direction have proved to be competent solutions. HAR data treatment in a recurrent form, and further analysis using deep networks such as RNNs or LSTM is largely unexplored. In this work, we treat data generated from a smart home setup towards HRA, as a series of events and perform feature extraction using a the popular LSTM variant of LSTMs. We use the Aruba dataset from the CASAS project [2]. We apply LSTM to extract features and perform annotations. Additionally we apply standard classification techniques to recognize and evaluate the different activities in the newly annotated data. From experimentations using our method we can achieve annotation accuracies of up to 79.5% which is 13.6% better than other state-of-the-art solutions.
DArL: Dynamic Parameter Adjustment for LWE-based Secure Inference

Packed additive homomorphic encryption (PAHE) based secure neural network inference is attracting increasing attention in the field of applied cryptography. In this work, we seek to improve the practicality of LWE-based secure inference by dynamically changing the cryptographic parameters depending on the underlying architecture of the neural network. First, we develop and apply theoretical methods to closely examine the error behavior of secure inference, and propose parameters that can reduce as much as 67% of ciphertext size when smaller networks are used. Second, we use rare-event simulation techniques based on the sigma-scale sampling method to provide tight bounds on the size of cumulative errors drawn from (somewhat) arbitrary distributions. Finally, in the experiment, we instantiate an example PAHE scheme and show that we can further reduce the ciphertext size by 3.3x if we adopt a binarized neural network architecture, along with a computation speedup of 2x-3x.
The Rise of Enforceable Business Processes from the Hashes of Blockchain-Based Smart Contracts

Over the past few decades, the automation of inter-organizational processes has been the focus of several research efforts that have produced a broad spectrum of design methods and technologies. Recently, some experiments have shown how in principle a Decentralized Autonomous Process-Aware Information System (DAIS) can be implemented by means of Blockchain-based Smart Contracts (BSCs). In this paper, we cast a shadow on this novel approach by arguing that such kind of contracts cannot be considered an optimal abstraction to specify inter-organizational process models. We base our analysis on contractual incompleteness, a pivotal concept in widely accepted economic theories. We identify the main weakness in the conflict between the immutability-by-default of the BSCs and the nature of inter-organizational processes. As a result of this analysis, we introduce the concept of enforceable business process that is more in line with the original idea of smart contract and extends it to better match the essential requirements of a DAIS.
MAPVI

In recent years, the inclusion of persons with visual impairments (PVI) is taking tremendous steps, especially with regards to group meetings. However, a significant part of communication is conveyed through non-verbal communication which is commonly inaccessible, such as deictic pointing gestures or the mimics and body language of participants. In this vision paper, we present an overview of our project MAPVI. MAPVI proposes new technologies on making meetings more accessible for PVIs. Therefore, we explore which relevant information has to be tracked and how those can be sensed for the users. Finally, those captured information get translated into a multitude of haptic feedback to make them accessible.
A new semantic similarity approach for improving the results of an Arabic search engine

Determining semantic similarity between documents is crucial to many tasks such as plagiarism detection, automatic technical survey and semantic search. In this paper, we have mainly focused on detecting the semantic similarity between documents in large documents collection and queries based on an Arabic search engine, we investigated MapReduce as a specific framework for managing distributed processing in dataset pattern and semantic similarity measures of documents. Then we study the state of the art of different approaches for computing the similarity of documents. We propose an approach based on parallel algorithm of semantic similarity measures using MapReduce and WordNet after translation phase to detect the relevant documents in the face of the Arabic query. The numerical results obtained and presented showed the efficiency and the performance of the technique adopted.
Multi-touch 3D positioning with the pantograph technique

One advantage of touch interaction is the sense of direct manipulation; there is perhaps no more-intuitive interface than just reaching out and touching virtual entities. However, direct manipulation is generally limited to objects located on the 2D display surface. For 3D spaces extending behind or in front of a touchscreen, the direct manipulation metaphor quickly falls apart. In these cases, gestures are needed to convert 2D finger positions into 3D cursor positions. This paper presents the pantograph technique, a simple two-finger interaction method for positioning a 3D cursor within mono and stereoscopic applications. The pantograph's pseudomechanical linkage between fingers and cursor provides helpful depth cues and maintains the sense of direct manipulation. Extensions to the technique, which integrate selection and other advanced actions, are explored within the context of real-world visual analysis applications. A series of human factors experiments showed that, while the pantograph technique outperformed other similar multitouch 3D positioning techniques, multi-touch was still inferior to other traditional, non-touch-based interfaces for sustained 3D positioning tasks.
Analysis of hand segmentation on challenging hand over face scenario

One of the challenging problems in computer vision is hand segmentation, especially when the hands overlap with the face. There are many applications that require this type of segmentation, such as sign language recognition, action recognition and recognition of objects that hands interact with. Hand over face is a challenging scenario where faces are occluded by hands, that can be used to test the performance of hand segmentation methods. Not much work has been done on this topic. After analysis of related datasets for hand segmentation that include hands in front of or near to the face, we introduce our challenging public dataset for the hand-over-face segmentation problem. The new dataset contains 4384 annotated frames and includes color, depth, infrared streams recorded by Kinect. Additionally, hand(s) locations and shapes data using Leap Motion sensor, which is an infrared hand shape sensor, are included. We compare two leading semantic segmentation methods: SegNet [1] and RefineNet [12], to analyze the new dataset. Two experiments were executed: the first one for hand-background segmentation and the other one for right hand- left hand- background segmentation. RefineNet shows significantly better accuracy, 14% better than that of SegNet, on our new dataset. Nonetheless, the highest accuracy archived was 62.2%, demonstrating that VLM-HandOverFace1 is a challenging dataset for the current state of the art.
An Anatomy of a Lie:

Automated detection of text with misrepresentations such as fake reviews is an important task for online reputation management. The dataset of customer complaints - emotionally charged texts which are very similar to reviews and include descriptions of problems customers experienced with certain businesses – is presented. It contains 2746 complaints about banks and provides clear ground truth, based on available factual knowledge about the financial domain. Among them, 400 texts were manually tagged. Initial experiments were performed in order to explore the links between implicit cues of the rhetoric structure of texts and the validity of arguments, and also how truthful/deceptive are these texts.
Semantic Web Annotation using Deep Learning with Arabic Morphology

In order to realize the vision of Semantic Web, which is a Web of things instead of Web of documents, there is a need to convert existing Web of documents into Semantic content that could be processed by machines. Semantic annotation tool could be used to perform this task through using common and public ontologies. Due to exponential growth and the huge size of Web sources, there is a need to have a fast and automatic Semantic annotation of Web documents. The aim of this paper is to investigate the use of word embeddings from deep learning algorithms to semantically annotate the Arabic Web documents. To enhance the performance of the Semantic annotation, we utilized the complex morphological structure of Arabic words. Moreover, evaluating the performance of the proposed framework requires selecting a set of domain ontologies with relevant and annotated related documents. The proposed framework produces Semantic annotations for these documents by using different standard output formats. The initial results show a promising performance that will support the research in the Semantic Web with respect to Arabic language.
Comprehensive Feature Enhancement Module for Single-Shot Object Detector

Recent one-stage CNN based detectors attract lots of research interests due to the high detection efficiency. However, their detection accuracy usually is not good enough. The major reason is that with only one regression step, one-stage detectors like SSD build features which are not representative enough for both classification and localization. In this paper, we propose a novel module, Comprehensive Feature Enhancement (CFE) module, for largely enhancing the features of one-stage detectors. The effective yet lightweight module could improve the detection accuracy with only increasing little inference time. Moreover, we propose two new one-stage detectors by assembling CFEs into the original SSD: CFE-SSDv1 and CFE-SSDv2. The CFE-SSDv1 is of simple structure with high efficiency while CFE-SSDv2 is more accurate and improves dramatically on detecting small objects especially. We evaluate the proposed CFE-SSDv1 and CFE-SSDv2 on two benchmarks for general object detection: PASCAL VOC07 and MS COCO. Experimental results show that CFE-SSDv2 outperforms state-of-the-art one-stage methods such as DSSD and RefineDet on these two benchmarks. Moreover, additional ablation study demonstrates the effectiveness of the proposed CFE module. We further test the proposed CFE-SSDv2 on UA-DETRAC dataset for vehicle detection and BDD dataset for road object detection, and both get accurate detection results compared with other state-of-the-art methods.
An End-to-End Model for Vietnamese Speech Recognition

This paper presents an approach of End-to-End model based on Long Short-Term Memory (LSTM) and Time Delay Deep Neural Network (TDNN) models for Vietnamese speech recognition. Two Vietnamese End-to-End architectures using Connectionist Temporal Classification (CTC) as the loss function are proposed. The paper also presents the method to construct phonesets based on Vietnamese characters or tonemes to produce the label sequence for any given transcription when applying CTC model. The experimental results showed that CTC based End-To-End models are competitive to traditional models with only 5% of WER worse for Vietnamese speech recognition (SR), but the advantage is no requirement of forced alignment for training acoustic models. In addition, it is similar to proposed studies using traditional models, the tone information and the toneme set are solutions to optimize the performance for Vietnamese SR.
Color Watermarking based on DCT and YCbCr Color Space for Privacy Preservation in Smart Cities

In a smart city environment, a huge amount of data is monitored in real time every day by the interconnection between Information and Communication Technologies (ICT's) and Internet of the Things (IoT). However, this increases the vulnerabilities and risks of the acquired data. Most of these data is private information and in order to preserve its ownership protection, authentication, and secret communication we propose our Color Watermarking based on DCT and YCbCr Color Space for Privacy Preservation. The results of experiments show that our method is robust against JPEG and JPEG2000 compression, as well as it has high imperceptibility. Due to its characteristics is ideal to be used in smart cities environments.
Scaling techniques for parallel ant colony optimization on large problem instances

Ant Colony Optimization (ACO) is a nature-inspired optimization metaheuristic which has been successfully applied to a wide range of different problems. However, a significant limiting factor in terms of its scalability is memory complexity; in many problems, the pheromone matrix which encodes trails left by ants grows quadratically with the instance size. For very large instances, this memory requirement is a limiting factor, making ACO an impractical technique. In this paper we propose a restricted variant of the pheromone matrix with linear memory complexity, which stores pheromone values only for members of a candidate set of next moves. We also evaluate two selection methods for moves outside the candidate set. Using a combination of these techniques we achieve, in a reasonable time, the best solution qualities recorded by ACO on the Art TSP Traveling Salesman Problem instances, and the first evaluation of a parallel implementation of MAX-MIN Ant System on instances of this scale (≥ 105 vertices). We find that, although ACO cannot yet achieve the solutions found by state-of-the-art genetic algorithms, we rapidly find approximate solutions within 1 -- 2% of the best known.
YOLO-Based Valve Type Recognition and Localization

In this paper, we present a YOLO model to recognize valve types and localize their positions on an experimental test bench unit in real-time frames taken from a continuous stream of video data, which are captured by a camera in a hydrogen sulfide (H2S) laboratory. The model required input frames after a particular period. In additional, the model was able to determine the type of valve labels based on a single frame. We showed that the pre-processing of training images plays an important role in the performance of YOLO model. We also included the concept of anchor and intersection over union (IOU) for good accuracy. Valve type results were demonstrated over a specific time of the video stream. The achieved average loss error value of the model was less than 6 percentage and the IOU between the ground-truth bounding box and predicted bounding box for all regions in the final iterations was almost 95 percentage.
Instance Ranking and Numerosity Reduction Using Matrix Decomposition and Subspace Learning

One way to deal with the ever increasing amount of available data for processing is to rank data instances by usefulness and reduce the dataset size. In this work, we introduce a framework to achieve this using matrix decomposition and subspace learning. Our central contribution is a novel similarity measure for data instances that uses the basis obtained from matrix decomposition of the dataset. Using this similarity measure, we propose several related algorithms for ranking data instances and performing numerosity reduction. We then validate the effectiveness of these algorithms for data reduction on several datasets for classification, regression, and clustering tasks.