Reviewerly/exHarmony huggingface dataset:
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Mapping skills is important for software development organizations. Knowing the skills of the company practitioners helps the managers in making decisions to team building and to know what skills are necessary to development of projects. Identifying the adequate practitioner can contribute to good quality and minimize the time spent on the project. To optimize the process of team formation and management of competences, a software company carried out a process of mapping the skills of professionals in the area of software development. The process of identifying and organizing the competencies was carried out between the members of the development teams and the managers. From the mapping, we developed a tool that helped managers to have an overview of the company's competencies and facilitate the decisions making about which practitioners to allocate in each project, identify training needed to acquire competencies, and if was necessary contracting other practitioners. In addition, practitioners could gain insight into skills they could improve or acquire to fulfill certain roles within the company.
MapReduce Join Across Geo-Distributed Data Centers

MapReduce is with no doubt the parallel computation paradigm which has managed to interpret and serve at best the need, expressed in any field, of running fast and accurate analyses on Big Data. The strength of MapReduce is its capability of exploiting the computing power of a cluster of resources, by distributing the load on multiple computing units, and of scaling with the number of computing units. Today many data analysis algorithms are available in the MapReduce form: Data Sorting, Data Indexing, Word Counting, Relations Joining to name just a few. These algorithms have been observed to work fine in computing context where the computing units (nodes) connect by way of high performing network links (in the order of Gigabits per second). Unfortunately, when it comes to run MapReduce on nodes that are geographically distant to each other the performance dramatically degrades. Basically, in such scenarios the cost for moving data among nodes connected via geographic links counterbalances the benefit of parallelization. In this paper the issues of running MapReduce Joins in a geo-distributed computing context are discussed. Furthermore, we propose to boost the performance of the Join algorithm by leveraging a hierarchical computing approach.
Disparity Maps Based Path Planning Algorithm for Autonomous Robot Navigation

This paper discusses a method of using disparity maps, generated from a stereo matching process, to be used for obstacle detection. The disparity map is a direct indicator of the distances the various objects have with respect to the cameras. This information can be further segmented to highlight the obstacle regions. Once the obstacle regions are highlighted, this information can be used for a path planning algorithm to generate the required control signals to direct the robot from a source position to destination position while avoiding the detected obstacles.
Implementing hybrid data storage with hybrid search

Databases serves an essential role while implementing any application. Take the data out, and serves no purpose. Until and unless a real data is involved in an application, it is only a program. Over the time data has shown how crucial is its existence for an industry and hence management of data in an efficient way is of absolute importance. This paper intent to propose such an efficient way to storing data for a web application, which could actually solves some problems that generally a database faces. Further, a hybrid search approach is also proposed which in turn intent to enhance the performance of the searches in the model. Primarily the approach merges two database, one of RDBMS and another of NoSQL, using its advantages for segregating the read and write operations along with enabling dynamic scalability.
MAIN ISSUE OF PHILOSOPHY: CORRELATION OF REALITY AND SIMULATION

Technical progress in the field of virtual reality actualizes the ontological problem of relation between reality and simulation creating the need for philosophical understanding of boundary between the physical world and the virtual one. The authors, relying on the tradition of the statement of the so-called “main issue of philosophy”, offer an updated version of it. What comes first, simulation or reality? The findings of modern science suggest that sociogenic, psychogenic and man-made simulations create models of reality in which a person’s life flows. Simulation is a necessary component of consciousness and social life.
Motion-Augmented Inference and Joint Kernels in Structured Learning for Object Tracking

This paper proposes an object tracking method, where we first predict target dynamics by harmonic means and particle filter in which we exploit kernel machines to derive a new entropy-based observation likelihood distribution. We then employ online Structured SVMs (Structured SVMs) to model object appearance, where we analyze responses of several kernel functions for various feature descriptors and study how such kernels can be optimally combined to formulate a single joint kernel function. We gain efficiency improvements by (1) exploiting particle filter for sampling the search space instead of commonly adopted dense sampling strategies, and (2) introducing a motion-augmented regularization term during inference to constrain the output search space. We objectively demonstrate that our method strongly competes against state-of-the-art structured object tracking methods.
Reproducibility and Validity in CLEF

In this paper, we investigate CLEF’s contribution to the reproducibility of IR experiments. After discussing the concepts of reproducibility and validity, we show that CLEF has not only produced test collections that can be re-used by other researchers, but also undertaken various efforts in enabling reproducibility.
Comparison of Statistical Table- and Non-Statistical Table-based XCS in Noisy Environments

Accuracy based Learning Classifier System (XCS) acquires generalized classifiers that can guess the appropriate output for all inputs with a small number of the classifiers in ideal environments where there is no uncertainty in inputs, outputs, and rewards. However, if uncertainty is included in any of inputs, outputs, and rewards, XCS cannot be properly learned and cannot stably acquire generalized classifiers. We proposed Learning Classifier Systems that can properly learn in environments to which specific noise is added. These methods are divided into two types: (i) statistical table based XCS that record the mean of rewards acquired in all input-output pairs, and (ii) non-statistical table based XCS that do not record their values. This paper applies these methods to multiple noise environments and clarifies the features of each method.
A Software Product Line Approach to Design Secure Connectors in Component-Based Software Architectures

This paper describes a software product line approach to design secure connectors in distributed component-based software architectures. The variability of secure connectors is modelled by means of a feature model, which consists of security pattern and communication pattern features. Applying separation of concerns, each secure connector is designed as a composite component that encapsulates both security pattern and communication pattern components. Integration of these components within a secure connector is enabled by a security coordinator, the high-level template of which is customized based on the selected security pattern features.
A preliminary study on topical model for multi-domain speech recognition via word embedding vector

In this paper, we suggest a basic topical model(TM) framework to adapt speech recognition system to multi-domain and prevent topical errors. This paper employs the cosine similarities between target and context words at a spoken utterance as the topical model parameters. The TM is applied to frames having a large number of candidate words at lattice network, and it adjusts the ranking of candidate words by adding it to total cost estimated from acoustic model(AM) and language model(LM). To cover multidomain, the word embedding was trained with 5.5 billion text corpus from multi-domain. As an acoustic model and a language model, DNN-HMM and N - gram were selected. 501 sentences (10,054 words) includes 35 topics were used as an evaluation data set. As a result, the best performances were obtained by our approach, and the performance of WERR was increased up to about 4% compared with N-gram based model. The WERR increased above 10% when the word errors were correctly detected. The results show this suggestion has a possibility of adapting a model to multi-domains without sub-topic models.
An Approach to Compress English Posts from Social Media Texts

Compression of sentences in FacebookRudrapal, Dwijen posts and Twitter is one of the important tasksDatta, Manaswita for automatic summarization of social media text. The task can be formally definedDatta, Ankita as transformation of sentence into precise form by preservingDas, Puja the original meaning of the sentence. In this paper, we propose an approach for compressing sentences from Facebook English posts byDas, Subhankar dropping those words who contribute very less importance to the overall meaning of sentences. We develop one parallel corpus of Facebook English posts and corresponding compressed sentences for our research task. We also report evaluation result of our approach through experiments on develop dataset.
Nature-Inspired Techniques for Data Security in Big Data

Inspired computing is based on biomimcry of natural occurrences. It is a discipline in which problems are solved using computer models which derive their abstractions from real-world living organisms and their social behavior. It is a branch of machine learning that is very closely related to artificial intelligence. This form of computing can be effectively used for data security, feature extraction, etc. It can easily be integrated with different areas such as big data, IoT, cloud computing, edge computing, and fog computing for data security. The chapter discusses some of the most popular biologically-inspired computation algorithms which can be used to create secured framework for data security in big data like ant colony optimization, artificial bee colony, bacterial foraging optimization to name a few. Explanation of these algorithms and scope of its application are given. Furthermore, case studies are presented to help the reader understand the application of these techniques for security in big data.
A New Bi-level PSO Algorithm based on Dynamic Constraint Processing and Approximate Navigation

Bi-level optimization problems with constraints are well known for wide applications and difficulties. A new particle swarm optimization algorithm (PSO) with dynamic constraint processing is proposed. First, we divide constraints into three cases and design a new PSO based crossover operator to dynamically deal with constraints; second, a normal distribution is added into PSO to work as mutation operator, which will enhance the diversity of the swarm and navigate the search direction. Finally, we give the convergence proof of the proposed algorithm.
A holographic image robust watermarking algorithm based on DWT-SIFT and neural network model

In order to improve the robustness of holographic watermarking, we propose a watermarking algorithm combining wavelet transform (DWT) and scale-invariant feature transform (SIFT). And neural network model is applied to digital watermarking technology. Firstly, the watermark image is transformed by Fresnel diffraction to generate hologram, which is divided into 16 × 16 blocks. And wavelet transform is carried out on each block to obtain the low-frequency information. Then, the feature points of the carrier image are extracted by SIFT, and the 16 × 16 blocks around the feature points are selected as the embedding areas to realize the embedding of the watermark.Finally, the random coordinates are taken as the input data of the neural network model, and the embedded position coordinates are taken as the output data of the neural network model. The relationship model between coordinates is constructed to achieve the confidentiality of the embedded position. Experimental results show that the watermarking algorithm has good robustness and visibility under the premise of ensuring image quality.
Device-Aware Adaptive Video Streaming

The rising demand for high-resolution videos and high video frame rates are leading to increasing pressure on the memory available in mobile devices. In this work, we analyze how and when memory usage on a device impacts video performance. We propose and evaluate DAVS, a video client module that adapts the playback buffer size and the video bitrate based on device memory pressure.
2D Color Image Enhancement Based on Conditional Generative Adversarial Network and Interpolation

In the rapid development of autonomous driving technology. Precise detection of objects might assist self-driving cars to drive as safely as human. The object detection is frequently uses point clouds and produces high quality environment color images to match. However, at night or when the light is dim, it affects the quality of color images. In order to overcome this, the existing image enhancement is focused on the histogram equalization method [1] and Retinex algorithm [2]. This paper proposes to use the Conditional Generative Adversarial Network (cGAN) [3] to train the intrinsic images for quickly decomposed shadow layer, and then use the interpolation method to achieve the image contrast enhancement.
A Fault Isolation Method Based on S1000D Fault Data Module

Fault diagnosis isolation is one of the important function in IETM application. Aiming at the problems existing in the integrated use of third-party fault diagnosis system and IETM and the low application level of fault reporting information in IETM, the fault reporting information structure in S1000D is analysed in detail. A new method which integrates the fault reporting data within IETM platform is presented.
Extracting Business Compliant Finite State Models from I $$^*$$ Models

Goal models are primarily used to represent and analyze requirements at an early stage of software development. However, goal models are sequence agnostic and fall short for analyzing temporal properties. Limited works are found in the existing literature that aims to bridge this gap. There are tools that transform a goal model to a finite state model (FSM). The existing works and their implementations can onlyDeb, Novarun check whether a given temporal property is satisfied by a goal model or not. However, it does not provide us with a compliant FSM which satisfies the complianceChaki, Nabendu rules. This paper aims to generate a business compliant FSM for a given goal model specification that complies with the business rules (specified in some temporal logic). We have chosenRoy, Mandira to work with tGRL (textual modeling language for goal-oriented requirement language) as the goal model specification language for representing i $$^*$$ models. The frameworkPal, Surochita extends the current i $$^*$$ ToNuSMV ver2.02 tool by allowing the user to give a CTL property as input along with a goal model. The proposed framework generatesBhaumick, Ankita a compliant FSM that satisfies the CTL constraint.
Importance of Signal Processing Cues in Transcription Correction for Low-Resource Indian Languages

Accurate phonetic transcriptions are crucial for building robust acoustic models for speech recognition as well as speech synthesis applications. Phonetic transcriptions are not usually provided with speech corpora. A lexicon is used to generate phone-level transcriptions of speech corpora with sentence-level transcriptions. When lexical entries are not available, letter-to-sound (LTS) rules are used. Whether it is a lexicon or LTS, the rules for pronunciation are generic and may not match the spoken utterance. This can lead to transcription errors. The objective of this study is to address the issue of mismatch between the transcription and its acoustic realisation. In particular, the issue of vowel deletions is studied. Group-delay-based segmentation is used to determine insertion/deletion of vowels in the speech utterance. The transcriptions are corrected in the training data based on this. The corrected data are used in automatic speech recognition (ASR) and text to speech synthesis (TTS) systems. ASR and TTS systems built with the corrected transcriptions show improvements in the performance.
Is a Virtual Ferrari as Good as the Real One? Children’s Initial Reactions to Virtual Reality Experiences

The Virtual Reality (VR) world is very suggestive as it intensely affects the senses of vision, hearing and—to a limited extent—touch. It can be expected that in the near future VR will be widely disseminated and used by people of all ages, including children. We decided to conduct a qualitative research project to assess children’s (aged 7–12) first reactions to the use of VR. Children’s opinions and reactions gathered during the interviews indicate that children highly appreciated the attractiveness of the virtual experiences, which were often assessed at a similar or higher level than their real-world counterparts. Our findings clearly suggest that children very easily adopt VR without any prior experience with that technology. We recommend that studies on children’s behavior in VR are continued.
Conclusion: Spatial Information Technology for Sustainable Development Goals

Remote sensing, GPS and GIS play an important role in achieving SDGs through mapping, monitoring, measurement and modelling of the Earth’s resources. Global communities are widely applying this technology for improving our understanding. There were global initiatives toward mapping technology launched in 1996 to prepare eight basic layers of information: boundaries, drainage, transportation, population centres, elevation, land use, land cover and vegetation. The World Geodetic System 84 (WGS 84) was developed for the uniform datum of global mapping. The United Nations Geospatial Information Section has also developed global multi-scale data sets named ‘UNmap’. Similarly, India has initiated advanced GIS and satellite-based technologies for the application of natural resources. Various organisations such as Rastriya Krishi Vikas Yojana (RKVY), Niti Ayog, Department of Sciences and Technology, National Platform for Disaster Risk Reduction (NPDRR0) and many NGOs are utilising spatial information technology for development planning and management.
Graph grammar induction

We propose a novel approach to the graph grammar induction problem, the goal of which is to find a concise graph grammar whose language (the set of graphs which can be generated using the grammar's productions, i.e., graph substitution rules) contains all of the given “positive” graphs and none of the given “negative” graphs. Most of the existing induction methods learn graph grammars from positive examples alone, performing an iterative specific-to-general search procedure in which candidate productions for the grammar being induced are created from repeated subgraphs in the input graph set. Our approach, by contrast, couples the induction process with a graph grammar parser, thus inducing grammars from both positive and negative graphs. In addition to a graph grammar generalization operator akin to those found in the majority of existing approaches, we employ a novel generalization technique based on merging similar productions, which makes it possible to further reduce the size of the induced grammar. We applied our approach to several nontrivial graph sets, including a set of chemical structural formulas with both positive and negative examples, and obtained graph grammars which can be regarded as a concise and meaningful generalization of the input graphs.
SAR Image Denoising Via Fast Weighted Nuclear Norm Minimization

A new synthetic aperture radar (SAR) image denoising method based on fast weighted nuclear norm minimization (FWNNM) is proposed. SAR image is firstly modelled by a logarithmic additive model for modelling of the speckle. Then, the non-local similarity is used for image block matching. Next, according to the framework of the low-rank model, randomized singular value decomposition (RSVD) is introduced to replace the singular value decomposition (SVD) in weighted nuclear norm minimization (WNNM) for approximating the low-rank matrix. Finally, the gradient histogram preservation (GHP) method is employed to enhance the texture of the image. Experiments on MSTAR database show that the proposed approach is effective in SAR image denoising and the edge preserving in comparison with some traditional algorithms. Moreover, it is three times faster than WNNM method.
A semi-supervised clustering algorithm for real network traffic with concept drift

Abstract Traffic classification has been widely applied for networking. Previous works paid much attention to static network traffic. In this paper, we propose a new strategy for the semi-supervised clustering algorithm to deal the concept drift in a dynamic network, as well as updating the model incrementally. Moreover, our algorithm can find new clusters and reduce the impact of noises. The results of simulation demonstrate the effectiveness of semi-supervised clustering algorithm.
A Moving Target Tracking Method Based on Particle Filter and Mean-shift

Due to the limitation of using a single algorithm Mean-shift or particle filter, the deterministic tracker Mean-shift is introduced into the framework of particle filter, and a new hybrid tracking method based on particle filter and Mean-shift is proposed. The tracking method first uses Dynamic K-Means Soft Clustering algorithm to cluster multiple hypotheses, and then carries out Mean-shift deterministic search for the centers of clusters. The tracking experiments show that the proposed method can perform well when the target changes, rotates and scales, and achieves a satisfactory tracking speed.
Retrieving and ranking short medical questions with two stages neural matching model

Internet hospital is a rising business thanks to recent advances in mobile web technology and high demand of health care services. Online medical services become increasingly popular and active. According to US data in 2018, 80 percent of internet users have asked health-related questions online. Numerous data is generated in unprecedented speed and scale. Those representative questions and answers in medical fields are valuable raw data sources for medical data mining. Automated machine interpretation on those sheer amount of data gives an opportunity to assist doctors to answer frequently asked medical-related questions from the perspective of information retrieval and machine learning approaches. In this work, we propose a novel two-stage framework for the semantic matching of query-level medical questions, which takes advantages of sentence similarity-based search engine techniques and Siamese inspired recent recurrent neural network. The two-stage hierarchical design optimises the performance of automatic information retrieval of user queries. Compared against the classical TFIDF search technique as a single-stage, our novel soft search technique performs significantly better. Incorporating an advanced deep learning model as the second stage can improve the results further, which we believe is the new state-of-the-art in the current problem setting with the unique medical corpus from one of the largest online healthcare provider in market.