Reviewerly/exHarmony huggingface dataset:
Challenges in Future Intra-Data-Center Networks and Possible Solutions

This chapter briefly introduces the data center network and reviews the challenges for future intra-data-center networks in terms of scalability, cost effectiveness, power efficiency, upgrade cost, and bandwidth utilization. Current data center network architecture is discussed in detail and the drawbacks are pointed out in terms of the above-mentioned parameters. A detailed background is provided that how the technology moved from opaque to transparent optical networks. Additionally, it includes different data center network architectures proposed so far by different researchers/team/companies in order to address the current problems and meet the demands of future intra-data-center networks.
Threats in the Internet of Things Pertaining to Digital Data

With the advent of Internet of Things (IoT) and the rapid advancements in this area, it is predicted that there will be over ten billions wireless accessories linked to the world of internet. Such devices will serve as the foundations in the field of computer science, impacting the everyday course of life. These wireless devices will be capable of absorbing a huge number of personal and sensitive information to enhance the results. The IoT technology puts the collective data to good use by integrating different data sets together that generates amazing outcomes. This technology, however, poses significant challenges to certain agencies aiming to apply this system in their routine businesses. This paper further highlights various threats and challenges that the IoT presents in digital forensics and assists in the identification of important areas which should be modified in future. Finally, the paper highlights the lack of digital forensic education in Pakistan.
Nature-Inspired Optimization of Type-2 Fuzzy Logic Controllers

In this paper we perform a comparison of the use of type-2 fuzzy logic in two nature-inspired methods: Ant Colony Optimization (ACO) and Gravitational Search Algorithm (GSA).
Improving Visual Quality for Probabilistic and Random Grid Schemes

This chapter focuses on improving visual quality for grayscale secret images. First, we review available designs of VC for grayscale secret image. Then we provide a new design for (n, n)-threshold VC based on an Analysis-by-Synthesis (AbS) framework. The AbS-based probabilistic VC and AbS-based random grid VC will be elaborated in full detail.
A Novel Attention-based Neural Network for Video Scene Classification in Complex Background

As the video surveillance develops, research on video scene classification has increased accordingly. In order to further improve the accuracy of scene classification for intelligent surveillance video, a novel attention-based neural network was proposed. Firstly, given a video sequence, we used CNN (Convolutional Neural Network) to extract features of each its frames and later forward it to an LSTM (Long-Short Term Memory Network) to further obtain the temporal information. The LSTM outputs were then connected as the feature vector of video sequences. Finally, the feature vectors were forward to the attention part for weight calculation. The experimental results showed that our algorithm improved the video scene classification accuracy effectively.
Enforcing Social Semantic in FIPA-ACL Using SPIN

Agent technology is an emerging software paradigm for developing open, distributed and heterogeneous complex system. There is a need for different software to communicate with one another in order to achieve its task. Therefore, a standard protocol is required. In agent technology, this common protocol is referred to as agent communication language. Agent communication language is a high-level protocol/language that allows agent applications to exchange, parse and understand the meaning of the exchanged content. One of the widely adopted agent communications is FIPA-ACL. An ontology for FIPA-ACL has been developed in previous work. However, the developed ontology does not incorporate any semantic model. Hence, in this paper, SPIN notation is used to model the social semantic of FIPA-ACL as it is able to link the class definition with SPARQL queries to capture rules and constraints to formalize the expected behaviour of classes.
Towards Configurable Cloud Application Security

Security solutions for cloud applications usually exploit security tools as is by utilising their default configuration. On one hand, this can lead to a waste of resources. On the other hand, it can also lead to not properly protecting the different application components based on their diverse security requirements. To this end, this paper proposes a security solution for cross-cloud applications which is configurable according to the flexible configuration specification given by the devops. Such a specification conforms to a certain UML-based meta-model and is independent of the underlying security tools exploited. In this way, devops can enable to produce a varied security level per each application component that better suits its security requirements. We demonstrate the suitability of our solution through an evaluation showcasing that it can lead to reduced resource consumption without compromising the security of the components that it protects.
Reducing the Number of Multiplications in Convolutional Recurrent Neural Networks (ConvRNNs)

This is an extension from a selected paper from JSAI2019. Convolutional variants of recurrent neural networks, ConvRNNs, are widely used for spatio-temporal modeling, since they are well suited to model sequences of two-dimensional inputs. Similar to conventional RNNs, the introduction of gating architecture, such as ConvLSTM, brings additional parameters and increases the computational complexity. The computation load can be an obstacle in training efficient models and putting ConvRNNs in operation in real-world applications. However, the correspondence between ConvRNN unit complexity and its performance is not well investigated. We propose to reduce the number of parameters and multiplications by substituting some convolutional operations with the Hadamard product. We evaluate our proposal using the task of next video frame prediction and the Moving MNIST dataset. The proposed method requires 38% less multiplications and 21% less parameters compared to the fully convolutional counterpart. In price of the reduced computational complexity, the performance measured by structural similarity index measure (SSIM) decreased about 1.5%. ConvRNNs with reduced computations can be used in a wider range of situations like in web applications or embedded systems. This paper is an extension of a selected paper from JSAI2019 [10].
Using Conformal Prediction for Multi-label Document Classification in e-Mail Support Systems

For any corporation the interaction with its customers is an important business process. This is especially the case for resolving various business-related issues that customers encounter. Classifying the type of such customer service e-mails to provide improved customer service is thus important. The classification of e-mails makes it possible to direct them to the most suitable handler within customer service. We have investigated the following two aspects of customer e-mail classification within a large Swedish corporation. First, whether a multi-label classifier can be introduced that performs similarly to an already existing multi-class classifier. Second, whether conformal prediction can be used to quantify the certainty of the predictions without loss in classification performance. Experiments were used to investigate these aspects using several evaluation metrics. The results show that for most evaluation metrics, there is no significant difference between multi-class and multi-label classifiers, except for Hamming loss where the multi-label approach performed with a lower loss. Further, the use of conformal prediction did not introduce any significant difference in classification performance for neither the multi-class nor the multi-label approach. As such, the results indicate that conformal prediction is a useful addition that quantifies the certainty of predictions without negative effects on the classification performance, which in turn allows detection of statistically significant predictions.
A Students’ Perspective of Native and Cross-Platform Approaches for Mobile Application Development

One of the software industries with the most significant growth nowadays is the mobile application industry. In this paper, we study the differences between two mobile development approaches: (i) native (the development that's specific for each platform) and (ii) cross-platform (when a standard code is shared among different platforms). Inexperienced teams, students, and all newcomers in mobile development might find difficulties in choosing among approaches and frameworks. Our goal is to provide a comparison of these approaches from 3 different surveys with 166 participants, from students to experts perspectives, and study the factors that might influence the choice between them. We focus not only on quantitative aspects but also on qualitative and practical considerations that may also affect this decision (e.g., project requirements, final product category, and platform support). The results can aid students and inexperienced teams to obtain a more unobstructed view of the most recommended mobile development approach in a specific context and guide them to systematize their choice. Through our research, we observed that even when the quantitative aspects point towards a hybrid approach, it may not be the best decision due to other qualitative and practical factors. We found two nontechnical characteristics that may impact the selection of the best approach: previous team experiences with the chosen technology; and how well established and consolidated the technology is. With that in mind, we propose a mapping between project requirements and the benefits/drawbacks of each approach.
A brief survey on probability distribution approximation

Probability distributions are extensively utilized in many areas of Computer Science such as Machine Learning, Information Retrieval and Databases. The computation of a probability distribution can be a difficult task because of the exponential size of the event space. As a consequence, researchers investigated methods for approximating probability distributions. This article provides a brief survey of the key research contributions to the design and the implementation of probability approximation methods. The article allows the reader to rapidly obtain relevant information which can effectively be utilized in research work.
Parrot

Open source softwares play an important role in information retrieval research. Most of the existing open source information retrieval systems are implemented in Java or C++ programming language. In this paper, we propose Parrot1, a Python-based interactive platform for information retrieval research. The proposed platform has mainly three advantages in comparison with the existing retrieval systems: (1) It is integrated with Jupyter Notebook, an interactive programming platform which has proved to be effective for data scientists to tackle big data and AI problems. As a result, users can interactively visualize and diagnose a retrieval model; (2) As an application written in Python, it can be easily used in combination with the popular deep learning frameworks such as Tersorflow and Pytorch; (3) It is designed especially for researchers. Less code is needed to create a new retrieval model or to modify an existing one. Our efforts have focused on three functionalists: good usability, interactive programming, and good interoperability with the popular deep learning frameworks. To confirm the performance of the proposed system, we conduct comparative experiments on a number of standard test collections. The experimental results show that the proposed system is both efficient and effective, providing a practical framework for researchers in information retrieval.
Directives of Communicability: Teaching Students How to Improve Communication Through Software Modeling

In the development process, software models are used to support communication among development team members. Failures of communication that is mediated by software models may generate inconsistencies in software behavior, generating rework to correct them. This may occur because producers usually focus only on model content, although they should also reflect on how other members of the development team will interpret their models. We should therefore teach software engineering students how to communicate better through software models. In an attempt to improve communication through software models, we have developed the Directives of Communicability (DCs), based on theories that investigate different kinds of communication. In this poster, we present the highlights of a study to find out whether DCs improve software engineering students' communication through software models. Our results confirm that DCs have improved the students' reflections on how their models' content will be interpreted during the software development process.
Speeding up Scalar Multiplication on Koblitz Curves Using $$\mu _4$$ Coordinates

Koblitz curves are a special family of binary elliptic curves satisfying equation $$y^2+xy=x^3+ax^2+1$$ , $$a\in \{0,1\}$$ . Scalar multiplication on Koblitz curves can be achieved with point addition and fast Frobenius endomorphism. We show a new point representation system $$\mu _4$$ coordinates for Koblitz curves. When $$a=0$$ , $$\mu _4$$ coordinates derive basic group operations—point addition and mixed-addition with complexities $$7\mathbf{M}+2\mathbf{S}$$ and $$6\mathbf{M}+2\mathbf{S}$$ , respectively. Moreover, Frobenius endomorphism on $$\mu _4$$ coordinates requires $$4\mathbf{S}$$ . Compared with the state-of-the-art $$\lambda $$ representation system, the timings obtained using $$\mu _4$$ coordinates show speed-ups of $$28.6\%$$ to $$32.2\%$$ for NAF algorithms, of $$13.7\%$$ to $$20.1\%$$ for $$\tau $$ NAF and of $$18.4\%$$ to $$23.1\%$$ for regular $$\tau $$ NAF on four NIST-recommended Koblitz curves K-233, K-283, K-409 and K-571.
A multi-metric approach for block-level video quality assessment

Developing an objective video quality metric that accurately estimates perceived video quality is challenging. Developing a metric that can additionally be embedded in the rate distortion optimization process of a video codec can be even harder given that decisions have to be made locally. In this paper, we present a method for combining a number of existing state of the art objective video quality metrics at the coding block level by employing a fusion of local content features for deciding how to best utilize the chosen metrics. Our results indicate promising performance in terms of the correlation of the developed locally-acting quality metric with the overall perceived quality of the video.
Shared Memory Implementation and Scalability Analysis of Recursive Positional Substitution Based on Prime-Non Prime Encryption Technique

The paper reports shared memory implementation of Recursive Positional Substitution Based on Prime-Nonprime (RPSP) algorithm using OpenMP application programming interface. Recursive Positional Substitution Based on Prime-Nonprime (RPSP) is a secret-key cryptosystem that helps to protect electronic data, while transmitting over internet. Cryptography provides techniques for information authenticity, confidentiality and integrity, but at the same time implementing cryptographic algorithm involves challenges such as speed of execution, processor and memory requirements. Shared memory implementation of cryptographic algorithms exploiting the immense computational power provided by modern multicore architecture improves the performance of these algorithms. This paper proposes a parallel programming model of RPSP algorithm using OpenMP directives, runtime library routines and environment variables. Scalability analysis of the proposed parallel algorithm has been presented for encryption/decryption of different sizes of standard image eso1705a taken from the database provided online by European Southern Observatory(ESO). Results obtained confirm approximately linear scalability for both encryption/decryption phases of the parallelized algorithms.
Design and development of lemmatizer for Sindhi language in devanagri script

AbstractAbstractThe Study of word formation is called as morphology. Morphological analysis is one of the important tasks of Natural Language Processing. IR and Machine Translation are two important applications of Morphological Analyzer. In order to search or retrieve any information from web we need, normalize form of a word .This task is accomplish by two morphological analyzer Stemmer and Lemmatizer . Stemming is a simple process of strip off the word endings to get the normalized form of a word, called stem. The process of stemming suffers from two types of errors i.e. over stemming and under stemming. We can handle Under stemming by making a database of exceptional words whereas Over stemming is handle by lemmatizer .Lemma is correct root word or dictionary word generated by applying some rules for affix removal and some additional rules for making a correct dictionary word. This paper presents design and development of lemmatizer for Sindhi language in Devnagri script. This is our first attempt to develop a lemmatizer for Sindhi language in devanagri script.Keywords: StemmingLemmatizerUnder-stemmingOver-stemminglemmaStemIR
QuickCareRecord: Efficient Care Recording Application with Location-based Automatic View Transition and Information Complement

In Japan, the demand for nursing home is increasing due to the increase in the number of elderly people. However, the work burden for nursing staffs is very large. Specially, they spend much time in making care reports which reduces the time for doing the essential care for residents in a nursing home. Therefore, to reduce the load for staffs, the system on which staffs can create reports easily and instantly is needed. In this paper, we propose a mobile memo system that switches the view according to the location automatically and assists inputting the necessary information. Our proposed system enables care staffs to prepare the care report about caring for the elderly person quickly. Through the evaluation in the emulated environment, we show that our system succeed to reduce the time for recording by 37.9% and 58.9%, compared with the conventional system and traditional handwriting, respectively.
Non-Negative Matrix Factorization of Clustered Data with Missing Values

We propose the approximation-theoretic technique of optimal recovery for imputing missing values in clustered data, specifically for non-negative matrix factorization (NMF), and develop an algorithm for implementation. Under certain geometric conditions, we prove tight upper bounds on NMF relative error, which is the first bound of this type for missing values. Experiments on image data and biological data show that this technique performs as well as or better than other imputation techniques that account for local structure.
Analysis of geochemical basalts database as a simulant for uranium ore concentrates data: the Galaxy Serpent 3 Exercise

The Nuclear Forensics International Technical Working Group (NF-ITWG) conducted an exercise, Galaxy Serpent 3 (GS3), by the National Nuclear Forensics Libraries Task Group, during 2017. The main goal of the GS3 exercise was to illustrate the usefulness of a National Nuclear Forensic Library for assessing the potential consistency of unknown material with national holdings. Moreover, it offers the possibility to produce a realistic database resembling uranium ore concentrate (UOC) that can be used for algorithm development activities. A database was built based on basalt compositions from different tectonic settings: MORB, IAB, OIB and CRFB. Classes (Affinities) with subtly different chemical compositions. All data are open source and sample names can be traced to original data for error checking. Participants (29 teams) had to use existing techniques, or develop their own algorithms, to allow for dealing with the sparse database and managing incomplete data points. The exercise’s scenario introduced 3 simulated origin-unknown UOC samples, interdicted by authorities. Each team was asked to answer two main questions—“Are the 3 unknown samples consistent with each other?” and “are the 3 unknown samples consistent with the Mines in the database given?”. For the first time, the Israeli National Nuclear Forensic Laboratory (INNFL) participated in the GS exercise. A dedicated team was assigned the task of building capabilities needed and applying them to answer the raised questions. Methodology and tools developed and applied by the Israeli team are described. The results are presented and discussed, including new signatures allowing differentiation between unknown samples and mines.
Coverage Path Planning for Large-Scale Aerial Mapping

Aerial coverage path planning is a type of path planning where the sensor footprint covers all accessible parts of the area of interest. This type of path planning finds application in precision agriculture, precision forestry and service robots. Limited endurance of micro aerial vehicles has limited their operations to small areas coverable in a single flight. New application domains like geological survey cover vast areas exceeding endurances of most modern aerial platforms and the available path planners do not address coverage of such areas. This paper presents an approach for generating coverage paths for large-scale aerial mapping. The planner applies voronoi partitioning to decompose large areas into manageable cells. Then generates boustrophedon paths to cover each cell. The proposed planner is incorporated into Mission Planner. Software in the loop simulation results have ascertained the feasibility and completeness of the generated paths, even with multiple micro aerial platforms.
From an Annotated BPMN Model to a Use Case Diagram: DESTINY Methodology

The necessity of aligning an enterprise's Information System (IS) model to its Business Process (BP) model is incontestable. However, the main difficulty of establishing/maintaining BP-IS models alignment stems from the dissimilarities in the knowledge of the information system developers and the business process experts. To face these predicaments, we propose a Model Driven Architecture (MDA) compliant methodology that aims to automate the generation of UML models from BPMN models. It allows mastering transformation from Computation Independent Model (CIM) to Platform Independent Model (PIM). The CIM level expresses the BP, which is modelled through the standard BPMN (Business Process Modelling Notation) and, at the PIM level represents the aligned, IS model is generated as use case diagram. Its originality resides in the CIM to PIM transformation which accounts for the BP structural and semantic perspectives in order to generate an aligned IS model that respects the best-practice granularity level of a use case.
xLineMapper: A Product Line Feature-Architecture-Implementation Mapping Toolset

This paper presents an Eclipse-based toolset named xLineMapper to automatically manage the relationships (e.g., traceability, conformance) among product line features, architecture, and source code. The xLineMapper toolset can (1) trace a product line feature to its related architectural elements and code fragments, (2) automatically update both product line architecture and code when a feature is changed, and (3) automatically customize product line architecture and code for a particular product based on its feature configuration. This is an improvement from existing product line approaches which only support either feature-architecture or feature-code mapping. We conducted a case study of xLineMapper with a large open-source software system, Apache Solr. A video demo of xLineMapper is available at https://youtu.be/pdO-vnE8U6w.