Reviewerly/exHarmony huggingface dataset:
Extending Linked USDL by Trust Assertion

Since a decade researchers of information systems try to generalize descriptions of services and service transactions to enable automatic discovery and matchmaking of services to empower the vision of the digitalization of the service industry. Industry 4.0. is an actual challenge that requires those generic descriptions of services to enable search, comparison and matchmaking of service requests and service advertisements by the use of semantics of services. The Unified Service Description Language (USDL) is the most cited and popular research strand were researchers try to unify the vocabulary and description of service transactions for the purposes of digitalizing services not only from a technical perspective, but also from a business view to facilitate the trade of services on the web. But the research in USDL is by history an informatics driven matter due to its' growth out of the SOA paradigm. This article proposes an extension of the business perspective of Linked USDL by linking it to the Trust Assertion Ontology (TAO), so that the trustworthiness evaluation of a service offer is incorporated into the USDL model. This research fills a gap of USDL, so that it can be better applied for describing service interactions in business environments.
Bayesian Filtering for States Uniformly Distributed on a Parallelotopic Support

This paper contributes to the literature on Bayesian filtering in the case where the processes driving the states and observations are uniformly distributed on finite intervals. We introduce the class of uniform distributions on parallelotopic supports (UPS). We derive optimal local distributional projections (i.e. approximations) within this UPS class-in the sense of minimum Kullback-Leibler divergence-of the outputs of the data and time updates of filtering. We demonstrate that the UPS class provides a tighter approximation (and therefore more precise inferences) than a previously reported approximation on orthotopic supports. It does this, while still achieving bounded complexity in the resulting recursive filtering algorithm. The comparative performance of the UPS-closed filtering algorithm is explored-via both Bayesian and frequentist performance measures-as a function of signal-to-noise ratio and state dimension in a position-velocity system.
The Effects of Static Analysis for Dynamic Software Updating: An Exploratory Study

Dynamic software updating (DSU) is the act of modifying software without stopping its execution. DSU is employed to preserve the high availability in the deployed software systems. Although significant investigations have been conducted on static analysis (SA) to determine DSU errors, no particular study exists that explores the effects of SA for dynamic software updating. The objective of the study presented in this paper is to explore the effects of static analysis for DSU. In this exploration, four evaluation metrics were declared including the number of update operations, the number of violations, the cyclomatic complexity, and the patch size. Also, a novel framework was introduced and 18 versions of baseline target programs were employed to explore the effects of static analysis for DSU. The results have explained that static analysis can detect violations of DSU. It may affect the complexity of the new versions of the target programs and having optimization can further reduce those violations. In addition, the results show that the SA may change the number of update operations and the patch size for DSU. Overall, the results have proved that SA for dynamic software updating could affect the complexity of the target programs, the number of update operations, and the patch size. Furthermore, this exploration has offered a novel framework and four evaluation metrics for measuring the effects of static analysis for DSU.
Optimizing Root Cause Analysis Time Using Smart Logging Framework for Unix and GNU/Linux Based Operating System

The computer activity records are used for statistical purposes, backup, recovery, and root cause analysis of failure on application. These records are referred as a log. The log files are written for recording incoming dialogs, debug, error, status of an application and certain transaction details, by the operating system or other control program. The logs generated by an application that can be referred by user that may be helpful in the event of failure. For example, in a file transfer, FTP program generates a log file consist of date, time, source and destination, etc. In this number of logs generated by the application uses too much disk space. If the logging is tuned down (e.g., by lowering the log level) then the disk space usage is less, but then not enough information is available for debugging issue. To address this problem we proposed a Smart Logging Framework. The Smart Logging Framework provides the feature such as In-memory logging, In-memory packet capturing and Zoom-in log viewer.
An Online Incremental Clustering Framework for Real-Time Stream Analytics

With the evolution of data acquisition methods, our ability to collect real time data has increased. This requires the development of real-time analytics, using the most recent data to generate valuable insights. One example is customer profiling, where we want to identify groups of similar clients who were active recently, and improve the quality of the suggestions. Traditional clustering algorithms perform well on finite datasets, but their execution is often not compatible with real-time requirements, especially for rapid changing trends. In this context, we propose a novel approach for the definition of incremental clustering algorithms to work within real-time constraints, in an online fashion, while preserving accuracy. We show the general applicability of the framework by employing this method to three different clustering algorithms. We compare the experimental results between traditional and online approaches evaluating accuracy and computational cost. The results show that algorithms executed in our framework are comparable to their offline implementation in terms of accuracy and with a high gain in execution time, up to three orders of magnitude on average.
CS-DE: Differential Evolution with Cooperative Strategy for numerical optimization

Differential Evolution (DE) is arguably a very simple and efficient global optimization algorithm, nevertheless, there are still some shortcomings existing within its latest powerful variants. For example, the mutation strategy in the state-of-the-art DE algorithm performing good on some benchmarks may perform worse on the others as each mutation strategy has its own disadvantages. Therefore, in this paper, we proposed a novel Differential Evolution variant with Cooperative Strategy (CS-DE) which can tackle the weakness of DE variant employing a single mutation strategy. The novel algorithm proposed in the paper can be considered as a further improvement of a former proposed LPALMDE algorithm as they share the similar evolution framework and employ the same strategy in classifying individuals. Moreover, by incorporating a novel mutation strategy with depth information, the novel CS-DE algorithm proposed in this paper can obtain an overall better performance in comparison with several well-known DE variants. CEC2013 test suite for real-parameter single-objective optimization is employed in the validation of our algorithm and the experiment results demonstrate its competitiveness.
Digital Avatars for Older People’s Care

The continuous increase in life expectancy poses a challenge for health systems in modern societies, especially with respect to older people living in rural low-populated areas, both in terms of isolation and difficulty to access and communicate with health services. In this paper, we address these issues by applying the Digital Avatars framework to Gerontechnology. Building on our previous work on mobile and social computing, in particular the People as a Service model, Digital Avatars make intensive use of the capabilities of current smartphones to collect information about their owners, and applies techniques of Complex Event Processing extended with uncertainty for inferring the habits and preferences of the user of the phone and building with them a virtual profile. These virtual profiles allow to monitor the well-being and quality of life of older adults, reminding pharmacological treatments and home health testings, and raising alerts when an anomalous situation is detected.
Sentiment Analysis of Product Reviews of Ecommerce Websites

While buying a product on an e-commerce website, a consumer has to face the inevitable question whether the product which he is buying is the best option or not. With the exponential growth in the number of e-commerce sites and the products that they are selling, the customer has to identify the most optimally useful product. He has to choose the product that is best fit and gives value for the money. The previous advances used for analyzing whether a product is good or bad uses traditional sentiment analysis methods performing operations on the ratings and reviews submitted by a customer after purchase, whose accuracy lies in the lower half of the graph. Apart from giving comparatively low accuracy percentage, these previous approaches used bipolar classification, which means that the products were classified as good or bad without acknowledging the fact that a product can be average or just okay.
Language Model Bootstrapping Using Neural Machine Translation for Conversational Speech Recognition

Building conversational speech recognition systems for new languages is constrained by the availability of utterances capturing user-device interactions. Data collection is expensive and limited by speed of manual transcription. In order to address this, we advocate the use of neural machine translation as a data augmentation technique for bootstrapping language models. Machine translation (MT) offers a systematic way of incorporating collections from mature, resource-rich conversational systems that may be available for a different language. However, ingesting raw translations from a general purpose MT system may not be effective owing to the presence of named entities, intra sentential code-switching and the domain mismatch between the conversational data being translated and the parallel text used for MT training. To circumvent this, we explore following domain adaptation techniques: (a) sentence embedding based data selection for MT training, (b) model finetuning, and (c) rescoring and filtering translated hypotheses. Using Hindi language as the experimental testbed, we supplement transcribed collections with translated US English utterances. We observe a relative word error rate reduction of 7.8-15.6%, depending on the bootstrapping phase. Fine grained analysis reveals that translation particularly aids the interaction scenarios underrepresented in the transcribed data.
Robust Belief State Space Representation for Statistical Dialogue Managers Using Deep Autoencoders

Statistical Dialogue Systems (SDS) have proved their humongous potential over the past few years. However, the lack of efficient and robust representations of the belief state (BS) space refrains them from revealing their full potential. There is a great need for automatic BS representations, which will replace the old hand-crafted, variable-length ones. To tackle those problems, we introduce a novel use of Autoencoders (AEs). Our goal is to obtain a low-dimensional, fixed-length, and compact, yet robust representation of the BS space. We investigate the use of dense AE, Denoising AE (DAE) and Variational Denoising AE (VDAE), which we combine with GP-SARSA to learn dialogue policies in the PyDial toolkit. In this framework, the BS is normally represented in a relatively compact, but still redundant summary space which is obtained through a heuristic mapping of the original master space. We show that all the proposed AE-based representations consistently outperform the summary BS representation. Especially, as the Semantic Error Rate (SER) increases, the DAE/VDAE-based representations obtain state-of-the-art and sample efficient performance.
Group Activity Recognition via Computing Human Pose Motion History and Collective Map from Video

In this paper, we propose a deep learning based approach that exploits multi-person pose estimation from an image sequence to predict individual actions as well as the collective activity for a group scene. We first apply multi-person pose estimation to extract pose information from the image sequence. Then we propose a novel representation called pose motion history (PMH), that aggregates spatio-temporal dynamics of multi-person human joints in the whole scene into a single stack of feature maps. Then, individual pose motion history stacks (Indi-PMH) are cropped from the whole scene stack and sent into a CNN model to obtain individual action predictions. Based on these individual predictions, we construct a collective map that encodes both the positions and actions of all individuals in the group scene into a feature map stack. The final group activity prediction is determined by fusing results of two classification CNNs. One takes the whole scene pose motion history stack as input, and the other takes the collective map stack as input. We evaluate the proposed approach on a challenging Volleyball dataset, and it provides very competitive performance compared to the state-of-the-art methods.
Toward Human-like Billiard AI Bot Based on Backward Induction and Machine Learning

A human-like billiard AI bot approach is proposed in this paper. We analyzed actual game records of human players to obtain feature vectors. The Backward Induction algorithm and machine learning are then proposed to imitate decisions by human players. A run-out sequence is searched backwardly with the assists from heuristics and predictions of neural network models. Through the planning process, a strike unit is found to help guide the physics simulator. With our AI suggestion of strategies, it avoids being over-dependent on the robust and precise physics simulation. Also, we defined an appropriate approach to gauge the human likeness of AI and evaluate our proposed methods. The experimental results show that our method overall is more similar to the way how human players play than that of original AI.
Study for Randomness of Diverse Pseudo-Random Bit Generators Oriented to Telecommunication Applications

The paper presents the results from the research for randomness of 8 pseudo random bit generators (PRBGs) for short sequences (256, 512 and 1024 bits) - Linear Feedback Serial Registers (LFSRs) with 8, 9, 16, 21 bits and different taps, Galois, Kasami, Gold and Mersenne-Twister (used in MATLAB). The goal is to determine the best PRBGs for telecommunications implementations for which randomness is critical as Analog-to-Information Converters (AICs) based on compressive sensing, where the recovery of the signal samples at under-Nyquist rate uses a random bit sequence. The PRBGs are tested with 10 tests from NIST suite for randomness which are applicable for short sequences. The PRBGs passing the tests are implemented in a laboratory set-up of AIC for obtaining the solution with the highest Signal-to-Noise and Distortion (SINAD).
Analysis of Forest Fire Data Using Neural Network Rule Extraction with Human Understandable Rules

Forest fires spread fast, uncontrollably, and may lead to massive destruction. This makes the prevention of them a safety critical issue. Neural networks are a sub-area of machine learning that can be used to analyze the complex behavior of neural systems and help to predict forest fires. To make the knowledge learned by a neural network more accessible, rules can be extracted from the neural network to demystify the system behavior and directly relate inputs to outputs. In this paper, we present a Dynamic Cell Structure (DCS) neural network used for forest fire data prediction, determining which environmental factors lead to fires. We apply an intuitive rule extraction algorithm to extract understandable rules for this prediction. The results are verified through direct comparison with the raw data.
Probabilistic open-destination teleportation based on SAM-Path-OAM hybrid entanglement W State

As a multi-particle entangled state, W state is more robust than GHZ state, because even if one of the entangled particles is traced out or projected to a specific state, the remaining particles of W state are still entangled. In this paper, we propose a novel scheme to generate hybrid W state entanglement where three photons entangled in spin angular momentum (SAM), path (line momentum), and orbital angular momentum (OAM). Based on the hybrid entanglement W state, we present a probabilistic open-destination teleportation scheme, in which Alice can remotely control the OAM generation of Bob's photon. Different from the original teleportation, with certain probability, the specified state can be transmit to any photon of the multi-particle entanglement, and its information can be obtained from any individual DOF of OAM or linear momentum, depending on postprocessing. At last, we evaluate the performance of this scheme using quantum state tomography, and we find Bob's OAM state not only electrically tuned by Alice, but also depend on the linear momentum (path) of the W state and the polarization of Carol's photon.
Exponential convergence of adaptive quarklet approximation

This paper is concerned with approximation properties of polynomially enriched wavelet systems, so-called quarklet frames. We show that certain model singularities that arise in elliptic boundary value problems on polygonal domains can be approximated from the span of such quarklet systems at inverse-exponential rates. In order to realize these, we combine spatial refinement in the vicinity of the singularities with suitable growth of the polynomial degrees in regions where the solution is smooth, similar to adaptive hp-finite element approximation.
Applying Software Design Metrics to Developer Story: A Supervised Machine Learning Analysis

Object-oriented analysis is a significant step that plays a vital role in the success of software development. The planning and management stages, in particular, profoundly rely on the deliverance of an accurate estimate that takes the software's complexity and size into consideration. Today, several software industries are transforming their development methodologies to Agile due to its ability to deliver value in a short time and its cost efficiency. However, Agile methods prevent heavyweight modeling analysis and depend on user stories to drive the estimation process. Because user stories are descriptive language, they may not provide a clear picture for the implementation. Also, they may not help Agile developers give an accurate estimation due to their difficulty in measuring the complexity and size of a feature. Thus, this paper presents a new Agile artifact called developer story that allows the Agile developer to not only analyze and design software products but also predict the size of each feature, including its complexity. In this paper, a case study is presented that shows how the utilization of developer story is a practical approach in predicting the source code size of a feature and its complexity.
Privacy-Preserving Fuzzy Multi-Keyword Search for Multiple Data Owners in Cloud Computing

With cloud computing’s development, more users are decide to store information on the cloud server. Owing to the cloud server’s insecurity, many documents should be encrypted to avoid information leakage before being sent to the cloud. Nevertheless, it leads to the problem that plaintext search techniques can not be directly applied to the ciphertext search. In this case, many searchable encryption schemes based on single data owner model have been proposed. But, the actual situation is that users want to do research with encrypted documents originating from various data owners. This paper puts forward a privacy-preserving scheme that is based on fuzzy multi-keyword search (PPFMKS) for multiple data owners. For the sake of espousing fuzzy multi-keyword and accurate search, secure indexes on the basis of Locality-Sensitive Hashing (LSH) and Bloom Filter (BF)are established. To guarantee the search privacy under multiple data owners model, a new encryption method allowing that different data owners have diverse keys to encrypt files is proposed. This method also solves the high cost caused by inconvenience of key management.
Heuristic Algorithm for Resolving Pronominal Anaphora in Hindi Dialects

Artificial intelligence is a necessity for today’s realistic world knowledge. The facts hidden by the anaphoric expressions can be revealed by anaphora resolution only. The relevance of anaphora resolution could not be avoided as its productivity affects the performance of text summarization, automatic question answering system, information extraction, etc. The paper comes up with implementations of algorithms for first, second, and third-person pronouns for Hindi language, with the ability to tackle intersentential anaphora within the scope of 3–5 sentences and subsequently the algorithms can be tailored-up for more sentences. Approximately, 698 sentences were experimented for feature selection of each type of personal pronoun. The proposed algorithms have been tested on the synthetic datasets of 1059 sentences which contain 712 pronominal anaphors out of 781 anaphoric pronouns. The F-measure evaluation for selected corpora gives promising results, indicating that the algorithms are effective in resolving Hindi pronominal anaphora.
Ad Blocking Whitelist Prediction for Online Publishers

The fast increase in ad blocker usage results in large revenue loss for online publishers and advertisers. Many publishers initialize counter-ad-blocking strategies, where a user has to choose either whitelisting the publisher's web site in their ad blocker or leaving the site without accessing the content. This paper aims to predict the user whitelisting behavior, which can help online publishers to better assess users' interests and design corresponding strategies. We present several techniques for personalized whitelist prediction for a target user and a target web page. Our prediction models are evaluated on real-world data provided by a large online publisher, Forbes Media. The best prediction performance was achieved using the gradient boosting regression tree model, which also demonstrated robustness and efficiency.