Reviewerly/exHarmony huggingface dataset:
Prediction of the Next Sensor Event and Its Time of Occurrence in Smart Homes

We present work on sequential sensor events in smart homes with results on the prediction of the next sensor event and its time of occurrence in the same model using Recurrent Neural Network with Long Short-Term Memory. We implement four configurations for converting binary sensor events and elapsed time between events into different input sequences. Our dataset has been collected from a real home with one resident over a period of 40 weeks and contains data from a set of fifteen sensors including motion, magnetic, and power sensors. When including the time information in the input data, the accuracy of predicting the next sensor event was 84%. In our best implementation, the model is able to predict both the next sensor event and the mean elapsed time to the next event with a peak average accuracy of 80%.
Approximate Dynamic Programming with Probabilistic Temporal Logic Constraints

In this paper, we develop approximate dynamic programming methods for stochastic systems modeled as Markov Decision Processes, given both soft performance criteria and hard constraints in a class of probabilistic temporal logic called Probabilistic Computation Tree Logic (PCTL). Our approach consists of two steps: First, we show how to transform a class of PCTL formulas into chance constraints that can be enforced during planning in stochastic systems. Second, by integrating randomized optimization and dynamic programming with softmax Bellman operator, we devise a novel trajectory sampling-based approximate value iteration method to iteratively solve for an upper bound on the value function while ensuring the constraints that PCTL specifications are satisfied. Particularly, we show that by the on-policy sampling of the trajectories, a tight bound can be achieved between the upper bound given by the approximation and the true value function. The correctness and efficiency of the method are demonstrated using robotic motion planning examples.
Trust access authentication in vehicular network based on blockchain

Data sharing and privacy securing present extensive opportunities and challenges in vehicular network. This paper introduces ‘trust access authentication scheme’ as a mechanism to achieve real-time monitoring and promote collaborative sharing for vehicles. Blockchain, which can provide secure authentication and protected privacy, is a crucial technology. However, traditional cloud computing performs poorly in supplying low-latency and fast-response services for moving vehicles. In this situation, edge computing enabled Blockchain network appeals to be a promising method, where moving vehicles can access storage or computing resource and get authenticated from Blockchain edge nodes directly. In this paper, a hierarchical architecture is proposed consist of vehicular network layer, Blockchain edge layer and Block-chain network layer. Through a authentication mechanism adopting digital signature algorithm, it achieves trusted authentication and ensures valid verification. Moreover, a caching scheme based on many-to-many matching is proposed to minimize average delivery delay of vehicles. Simulation results prove that the proposed caching scheme has a better performance than existing schemes based on centralized model or edge caching strategy in terms of hit ratio and average delay.
Diagnosis of Dyslexia Students Using Classification Mining Techniques

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Auto-Encoding Nearest Neighbor i-Vectors for Speaker Verification

In the last years, i-vectors followed by cosine or PLDA scoring techniques were the state-of-the-art approach in speaker verification.PLDA requires labeled background data, and there exists a significant performance gap between the two scoring techniques.In this work, we propose to reduce this gap by using an autoencoder to transform i-vector into a new speaker vector representation, which will be referred to as ae-vector.The autoencoder will be trained to reconstruct neighbor i-vectors instead of the same training i-vectors, as usual.These neighbor i-vectors will be selected in an unsupervised manner according to the highest cosine scores to the training i-vectors.The evaluation is performed on the speaker verification trials of VoxCeleb-1 database.The experiments show that our proposed ae-vectors gain a relative improvement of 42% in terms of EER compared to the conventional i-vectors using cosine scoring, which fills the performance gap between cosine and PLDA scoring techniques by 92%, but without using speaker labels.
Widely Linear Complex-Valued Autoencoder: Dealing with Noncircularity in Generative-Discriminative Models

We propose a new structure for the complex-valued autoencoder by introducing additional degrees of freedom into its design through a widely linear (WL) transform. The corresponding widely linear backpropagation algorithm is also developed using the $$\mathbb {CR}$$ calculus, to unify the gradient calculation of the cost function and the underlying WL model. More specifically, all the existing complex-valued autoencoders employ the strictly linear transform, which is optimal only when the complex-valued outputs of each network layer are independent of the conjugate of the inputs. In addition, the widely linear model which underpins our work allows us to consider all the second-order statistics of inputs. This provides more freedom in the design and enhanced optimization opportunities, as compared to the state-of-the-art. Furthermore, we show that the most widely adopted cost function, i.e., the mean squared error, is not best suited for the complex domain, as it is a real quantity with a single degree of freedom, while both the phase and the amplitude information need to be optimized. To resolve this issue, we design a new cost function, which is capable of controlling the balance between the phase and the amplitude contribution to the solution. The experimental results verify the superior performance of the proposed autoencoder together with the new cost function, especially for the imaging scenarios where the phase preserves extensive information on edges and shapes.
Rise of Fluid Computing A Collective Effort Of Mist, Fog and Cloud

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Data Mining Techniques and Multivariate Analysis to Discover Patterns in University Final Researches

The aim of this study is to extract knowledge from the final researches of the Mumbai University Science Faculty. Five classification models were applied: Vector Support Machines, Neural Networks, Decision Tree, Random Forest and Powering; considering the Experiment Design and Multivariate Analysis Lines. Results showed that for the Experiment Design line, the most accurate model was Random Forest with 71.48% predictions that are correct respecting to the total. Regarding the Multivariate Analysis line, there was no significant difference in overall accuracy, fluctuating by 97%.
Adverse Drug Effect and Personalized Health Mentions, CLaC at SMM4H 2019, Tasks 1 and 4

CLaC labs participated in Task 1 and 4 of SMM4H 2019.We pursed two main objectives in our submission.First we tried to use some textual features in a deep net framework, and second, the potential use of more than one word embedding was tested.The results seem positively affected by the proposed architectures.
One-to-X Analogical Reasoning on Word Embeddings: a Case for Diachronic Armed Conflict Prediction from News Texts

We extend the well-known word analogy task to a one-to-X formulation, including one-tonone cases, when no correct answer exists.The task is cast as a relation discovery problem and applied to historical armed conflicts datasets, attempting to predict new relations of type 'location:armed-group' based on data about past events.As the source of semantic information, we use diachronic word embedding models trained on English news texts.A simple technique to improve diachronic performance in such task is demonstrated, using a threshold based on a function of cosine distance to decrease the number of false positives; this approach is shown to be beneficial on two different corpora.Finally, we publish a readyto-use test set for one-to-X analogy evaluation on historical armed conflicts data.
Sockets in Python

A Socket is an end point in a communication link between separate processes. In Python sockets are objects which provide a way of exchanging information between two processes in a straight forward and platform independent manner. In this chapter we will introduce the basic idea of socket communications and then presents a simple socket server and client application.
Publicación de los valores de las bibliotecas universitarias españolas a través de sus sedes web

The article aims to shed light on the values that guide professional practice in Spanish university libraries and the strategies deployed to publicise them on library websites. Identifying the statements that inform each library staff’s behaviour called for analysing the corporate documents published on such sites. Value visibility and accessibility were assessed on the grounds of the length of the route from the home page. The prominence of the corporate document containing the information was also studied. The analysis initially revealed values relating to the administration of public assets, such as engagement with the institution, its services and users. That was followed by professional values such as defence of access to information, data and science. The values most frequently espoused were innovation and professionalism. Libraries are encouraged to define their values which, along with their mission and vision, are a significant component of their internal and external strategy and communication policy. Their publication, in turn, redounds to transparency and enhances the relationship with their target audience.
Introduction to Computer Graphics

Computer Graphics are everywhere; they are on your TV, in cinema adverts, the core of many films, on your tablet or mobile phone and certainly on your PC or Mac as well as on the dashboard of your car, on your smart watch and in childrens electronic toys. This chapter introduces this topic.
Wearables Security and Privacy

Wearable devices equipped with various embedded sensors are finding many applications in health care and other sectors. As a relatively new class of mobile computing, there is little experience with security and privacy problems. This chapter aims to bring attention to these important but somewhat overlooked issues. We describe the components in wearables (sensors, processors, software, and communications) and highlight the security issues related to wireless protocols, vulnerabilities, and privacy.
An Introductory Study on an Architecture-Based Software Product Line Test Generation Method

Architecture-based testing allows test engineers to focus on the structure of complicated software and the interactions between software components that constitute the architecture of a software product. By observing and controlling the connections and interactions between components of complex or large systems during software testing, architecture-based testing can detect and localize such faults at those locations. The complexity of software product line testing is high because an implementation under test contains variability given the different binding times and is used by multiple products. This paper introduces how architecture-based testing is applied to test generation for a software product line and examines the strengths of the proposed method against existing software product line testing methods. The paper also illustrates the use of product line architecture and architectural artifacts to generate product line interaction tests. It was found that architecture-based testing can be applied to software product line test generation by tailoring it to deal with variability and product-line specific processes. The results of a comparison with existing methods show that architecture-based software product line test generation provides better capabilities in terms of variability in the testing stage, the explicit formation and application of binding, test coverage, and architectural awareness.
Towards a Reference Architecture for ADM-based Modernization Tools

Context: The modernization of a legacy software system is a reality of companies that need to improve their systems. Architecture-Driven Modernization (ADM) is a model-driven blueprint that support modernization engineers in the modernization processes. ADM considers the usage of reengineering and model driven architecture (MDA) concepts and advocates the employment of standard metamodels, such as Knowledge Discovery Metamodel (KDM). One of the main challenges while considering ADM is on how modernization tools (MTs) should be developed in order to allow the success of modernization projects that follows the ADM blueprint. Objective: In this paper we address the main requirements and guidelines on how modernization tools should be designed by means of a Reference Architecture (RA) for ADM-based Modernization Tools named RADMoTo. Methodology: We followed a four-steps methodological process to build RADMoTo and to evaluate it we submitted the RA technical report and a survey to ADM experts and software architects in order to get a feedback of the RA acceptance level. Results: As a result, we found out that, in general, the participants believe that our RA provides an acceptable level while considering the architectural views. Conclusion: The feedback from the participants helped in the improvement process of the RA. Thus, we claim that this paper provides concrete guidelines on how modernization tools should be designed and on how architectural instances derived from our RA could benefit from reusability and interoperability.
FPGA-based Design and Implementation of Real-time Robot Motion Planning

Collision detection in motion planning is time-consuming for robotic arm applications, especially real-time motion planning in a dynamic environment. It is necessary to design a collision detection accelerator for real-time robot motion planning. In this paper, an optimized FPGA-based design is proposed to reduce the hardware cost. Static random access memory (SRAM) is used to store encoded collision detection data for saving the FPGA look up tables (LUTs). The collision detection accelerator implementations based on SRAM and LUT are analyzed and compared in terms of hardware cost and time consumption. Experimental results are shown to prove the efficiency of the proposed design and implementation.
Scale-invariant MFCCs for speech/speaker recognition

The feature extraction process is a fundamental part of speech processing.Mel frequency cepstral coefficients (MFCCs) are the most commonly used feature types in the speech/speaker recognition literature.However, the MFCC framework may face numerical issues or dynamic range problems, which decreases their performance.A practical solution to these problems is adding a constant to filter-bank magnitudes before log compression, thus violating the scale-invariant property.In this work, a magnitude normalization and a multiplication constant are introduced to make the MFCCs scale-invariant and to avoid dynamic range expansion of nonspeech frames.Speaker verification experiments are conducted to show the effectiveness of the proposed scheme.
Comparison of Phonemic and Graphemic Word to Sub-Word Unit Mappings for Lithuanian Phone-Level Speech Transcription

Conventional large vocabulary automatic speech recognition (ASR) systems require a mapping from words into sub-word units to generalize over the words that were absent in the training data and to enable the robust estimation of acoustic model parameters.This paper surveys the research done during the last 15 years on the topic of word to sub-word mappings for Lithuanian ASR systems.It also compares various phoneme and grapheme based mappings across a broad range of acoustic modelling techniques including monophone and triphone based Hidden Markov models (HMM), speaker adaptively trained HMMs, subspace gaussian mixture models (SGMM), feed-forward time delay neural network (TDNN), and state-of-the-art low frame rate bidirectional long short term memory (LFR BLSTM) recurrent deep neural network.Experimental comparisons are based on a 50-hour speech corpus.This paper shows that the best phone-based mapping significantly outperforms a grapheme-based mapping.It also shows that the lowest phone error rate of an ASR system is achieved by the phoneme-based lexicon that explicitly models syllable stress and represents diphthongs as single phonetic units.
Signals Deinterleaving for ES systems using Improved CFSFDP Algorithm

The deinterleaving of radar signal is a significant function of the electronic support (ES) systems. This paper proposes an improved CFSFDP (fast search and find of density peaks) clustering algorithm for radar signal deinterleaving. First, we discussed the characteristics of the radar signal data set to ensure that the clustering method is suitable for the radar signal data set. Second, since most clustering algorithms require manual calibration of cluster centers and numbers, this paper defines a method for automatically selecting cluster centers, so that manual selection becomes automatic extraction. Finally, a simulation was performed to verify the effectiveness and accuracy of the algorithm.
GOOGLE CLASSROOM ON STUDENTS’ WRITING LEARNING PROCESS

This study is Mix-Method Research applying exploratory design which aim s to 1) know that the use of Google Classroom in Writing learning process can arouse students’ learning interest and motivation ; 2) know that Google Classroom can be used to improve student’s writing ability. The participants were the forth semester students of STKIP Muhammadiyah Pringsewu Lampung in academic year 2018/2019. Questionnaire, documentation, and test had been used to collect the data which was then analyzed using phased qualitative—quantitative analysis. The result showed that Google Classroom could encourage students to write, and help them to improve their writing abilit y. Keywords : Google Cla ssroom , Writing Learning Process .
DESIGN OF ORIENTATION ASSESSMENT FUNCTIONS FOR GESTALT-GROUPING UTILIZING LABELED SAMPLE-DATA

Abstract. Psychological evidence is given that perceptual grouping is an important help for various visual tasks. Object recognition and land use classification from remotely sensed imagery is an example. In machine vision, such a grouping process can be implemented by coding Gestalt laws such as proximity, symmetry, or good continuation. Since geometric relations are rarely fulfilled exactly, soft membership functions are utilized called Gestalt assessments. Hierarchical grouping is possible on increasing scales. Such an approach to hierarchical Gestalt grouping is modified in this paper. In its original form, the approach uses rather heuristic default assessment functions, which are a possible choice as long as no labeled example data are given. The assessment functions can be parameterized so as to improve the perceptual grouping, guiding it by the Gestalten salient to human perception. To this end, we use orientation statistics from the publicly available data set given for the ICCV symmetry recognition competition 2017. Also, with a particular recognition task at hand, labeled example data can serve as the desired foreground. Here we use the ground-truth layer for buildings of the Vaihingen benchmark of the ISPRS. A mixture distribution containing two von Mises-distributions and the uniform component for the clutter in the background is fitted using expectation maximization.
Identifying How the Brazilian Software Industry Specifies Legal Requirements

[Background] Software requirements are usually specified in Natural Language, bringing challenges for Requirements Engineering (RE) as these specifications are inherently ambiguous. These challenges become bigger when dealing with software requirements that must comply with regulations, the so-called legal requirements. The state of practice to tackle ambiguity of legal requirements and their compliance with regulations is missing. [Goal] This work investigates how ambiguity in legal requirements specification is addressed and how the software development industry performs legal compliance. [Method] We followed a qualitative approach based on semi-structured interviews involving nine professionals from different companies who presented their views on the RE process, including legal compliance and ambiguity resolution of legal requirements. Data was collected using audio-recorded and analyzed using Grounded Theory. [Results] Findings revealed that customer and legal expert support during the project could reduce the risk of misinterpreting the legislation. Verification and Validation of Legal Compliance are customer assignments. [Conclusions] Ambiguity resolution and legal compliance of requirements are based on the tacit knowledge of experienced team members or discussions between the team and the customer.
Evaluation of NoSQL DBMS in private cloud environment: An Approach Based on Stochastic Modeling

The amount of data generated from mobile gadgets, sensors and other computing devices has created the Big Data concept. In this context, NoSQL database management systems (DBMS) have become popular due to greater performance for manipulating big datasets with alternative data models. Many cloud computing services have adopted NoSQL DBMSs, but the assessment of quality of service (QoS) has additional challenges for these systems. This paper presents an approach based on generalized stochastic Petri nets (GSPN) for performability evaluation of private cloud computing environments that adopt NoSQL DBMS as the storage system. Models are presented to jointly estimate throughput and availability, which are prominent QoS indicators. Experiments are carried out to demonstrate the practical feasibility of the proposed technique.