Reviewerly/exHarmony huggingface dataset:
Ontology based conceptual models for predicting fundamental organic reactivity

Generic knowledge, related to fundamental organic reactivity is conceptualized and organized into model concept network resource. A concept of ‘terminal group’ is conceived to represent and encode reactivity of fundamental organic reactions such as substitution, elimination, addition, oxidation, reduction and condensation reactions. Chemical ontologies namely reactivity ontology, terminal group ontology, reagent ontology and skeletal carbon ontology are created to serve as domain knowledge repository. Graphical user interface is developed to compose various concepts of chemical ontologies to build generic reactivity axiom definitions in XML. Using the extendable and editable reactivity definitions, synthetic and retro-synthetic predictions are demonstrated with model applications.
Web Page Extraction and Classification Using JSOUP and Naïve Bayes

Abstract Classification of web pages manually requires quite a long time because most of the available web pages are not structured, so the classification method is needed quickly and accurately. Naïve Bayes algorithm with a good probabilistic approach in classifying web pages, seen from the advantages that are included in the classical category with a simple probability concept. However, this algorithm provides pretty good performance for many modern cases with large data. For the process of extracting information from web pages, it is proposed to use JSOUP which is a java library that provides a good API for extracting, manipulating data, and completing the initial data cleaning using the best methods from DOM, and CSS. The use of the JSOUP library makes it possible to be able to do web page analysis without having to save web documents to a computer store, so computer storage resources will be constant even though the amount of training data is increased. In this study, implementing JSOUP as a tool for extracting information from web pages and Naïve Bayes algorithm for classification of web pages.
Performance Comparison of an Automatic Smart Sun Tracking System Versus a Manual Sun Tracking

Sun position tracking methods are of great use to optimize energy of photovoltaic modules. As a matter of fact, its performance still remains low. In this paper, two tracking methods have been implemented in rapidly changing weather conditions: An automatic sun tracking and a manual sun tracking method. For the first method, an automatic 1-axis sun tracking prototype has been realized. For the second technics, sun position angle have been calculated from astronomical formula and from irradiance mathematical modeling, the solar panel have thus been positioned manually along the day accordingly. Measurements were recorded during sunny and cloudy days, in the University of Ngaoundere at the latitude 7.418° N and the longitude 13.542° E. Experimental data have been used for performance comparison purpose. This study shows that the automatic smart sun tracking offers better performance than the manual tracking. Nevertheless the manual tracking offer good performance for sunny skies in comparison to the energiy gained when the panel remains fixed.
Fine-Grained Query Authorization With Integrity Verification Over Encrypted Spatial Data in Cloud Storage

In this article, a fine-grained query authorization scheme with integrity verification is proposed over encrypted spatial data for location-based services (LBS). The fine-grained query authorization is enabled based on a distribution of the spatial data by employing a non-uniform partition in the spatial domain to generate a density-based space filling curve (DSC), which can be used to generate index values for querying and transformation keys. The transformation keys can be used to generate query tokens for a secure spatial query as well as construct a transformation key tree whose subtree can be distributed by the LBS provider to an authorized user as transformation key for query tokens generation. Furthermore, the proposed scheme constructs a Merkle quad tree (MQ-tree) to support integrity verification by aggregating a digest of the spatial data based on the DSC and employing the MQ-tree as a verification structure. The LBS provider can share a subtree of the MQ-tree to authorized user as his verification structure, which corresponds to the transformation key of the authorized user. In this way, the authorized user can only generate the valid query tokens and verify the query results in his authorized region. The security properties of the proposed scheme is discussed, and extensive experimental results demonstrate the high efficiency of verification structure generation and verification operations.
An Improved AES Key Generation in Audio Steganography

Abstract The higher Internet use also affects the level of information security in the digital world. Nowadays, many users send private messages or even secret messages through the Internet. This has an impact on the increasing number of illegal people who try to see and even change the information for personal gain. There is a solution to this problem by inserting a secret message in a file such as images, audio, or video. To increase the level of security, secret messages are encrypted first using cryptographic techniques. In this paper we propose a method for developing key generation in audio steganography techniques using the AES (Advanced Encryption Standard) algorithm. We use additional parameters to create the AES key, i.e. the user’s identity. The user’s identity is entered into the key table which is then randomly selected by the system when the user wants to get the key. There is a combination process and hash function to add complexity to the key. The performance of the proposed method is tested based on computing time and audio signal strength. The test results show that the message inserted in the audio file does not affect the file size, but rather affects the audio signal strength. Audio signals that experience additional data are at frequencies of 15 to 20 kHz and for other frequencies only experience additional noise.
Statistical inference for natural language processing algorithms with a demonstration using type 2 diabetes prediction from electronic health record notes

The pointwise mutual information statistic (PMI), which measures how often two words occur together in a document corpus, is a cornerstone of recently proposed popular natural language processing algorithms such as word2vec. PMI and word2vec reveal semantic relationships between words and can be helpful in a range of applications such as document indexing, topic analysis, or document categorization. We use probability theory to demonstrate the relationship between PMI and word2vec. We use the theoretical results to demonstrate how the PMI can be modeled and estimated in a simple and straight forward manner. We further describe how one can obtain standard error estimates that account for within-patient clustering that arises from patterns of repeated words within a patient's health record due to a unique health history. We then demonstrate the usefulness of PMI on the problem of predictive identification of disease from free text notes of electronic health records. Specifically, we use our methods to distinguish those with and without type 2 diabetes mellitus in electronic health record free text data using over 400 000 clinical notes from an academic medical center.
Face Recognition System Using a Hybrid Scale Invariant Feature Transform Based on Local Binary Pattern

In this paper, the face recognition system using a hybrid scale-invariant feature transform (SIFT) based on local binary pattern (LBP) has been implemented. The face recognition system is preprocessed to extract the features based on SIFT and LBP. The modified local binary pattern is extracted the local features on the binary pattern on each subblock of the image. Histogram is implemented on each subblock of the binary number of the neighbor values. Modified scale-invariant feature transform is preprocessed to extract the features based on the scaling factors and various orientations of the images. This proposed method is classified by the support vector machine. The experimental results have been executed on the ORL, YALE databases, and based on the performance measures in terms of precision, recall, and F-Score. This paper gives better as compared to existing models.
Group law on affine conics and applications to cryptography

In this paper, we highlight that the point group structure of elliptic curves, over finite or infinite fields, may be also observed on reducible cubics with an irreducible quadratic component. Starting from this, we introduce in a very general way a group’s structure over any kind of conic. In the case of conics over finite fields, we see that the point group is cyclic and lies on the quadratic component. Thanks to this, some applications to cryptography are described, considering convenient parametrizations of the conics. We perform an evaluation of the complexity of the operations involved in the parametric groups and consequently in the cryptographic applications. In the case of the hyperbolas, the Rédei rational functions can be used for performing the operations of encryption and decryption, and the More’s algorithm can be exploited for improving the time costs of computation. Finally, we provide also an improvement of the More’s algorithm.
Comparative Study Between the Integration of ITIL and ISO / IEC 27001 with the Integration of COBIT and ISO / IEC 27001

Abstract This paper tries to get a comparison between the integration of Information Technology Infrastructure Library (ITIL) and ISO / IEC 27001 with Integration of COBIT and ISO / IEC 27001. Factors to be considered are the increased level of information security services credibility, cost of the two standards integration of, the time of implementation, the multiple processes removal, and the increase in understanding between parts of the organization. A literature study is used as a research method. The results of the research can be a reference for organizations to consider two information security standards that will be integrated.
Augmented Reality: Telehealth Demonstration Application

Augmented Reality (AR) as a technology will improve the way we work and live in the future. The Microsoft HoloLens device allows for rendering of interactive virtual components into a real world space. The HoloLens is an augmented reality headset and can display these virtual components in front of the user's eyes, so the data needed to complete a real-world task will always be available. The nature of a HoloLens device lends itself useful for applications in a healthcare setting. Potential benefits come from transitioning to a more hands-free environment such as allowing the logging of data while in sterile environments without needing to sterilize repeatedly from touching paper or tablet. This project developed an augmented reality (AR) application to include a care plan tracker established by a patient's doctor to allow the patient to do daily tasks without a health care worker's supervision. The application displays the medications that the patient needs to ingest, daily tasks to complete, and health data to record. The application allows the physician to retrieve useful patient information regularly without scheduled physicals. This project sets a baseline that will provide future developers with documentation, research, and this sample application to assist in the design and construction of more complex applications in the future at the University of New Hampshire.
An Efficient ADMM-Aided Deep Learning-Based Signal Detector for Uplink Massive MIMO

This paper provides an efficient Alternating Direction Method of Multipliers (ADMM)-aided deep neural network (DNN) framework for signal detection based on uplink Massive Multiple Input Multiple Output (Massive MIMO) systems. Since the ADMM is a good candidate for distributed computing and solving complicated problems, it is integrated into a DNN to enhance the learning performance of the DNN. Not only can the developed DNN emulate the ADMM operations with fast speed and good performance, but also it fully exploits the characteristics of the samples for signal detection. Specifically, the DNN comprises two parts: the data-processing module is adopted for making the samples sparse and iteration-mapping module is for reconstructing transmit signals with the aid of the ADMM. Then, a learning policy is proposed, which is composed of an offline learning method and a lifelong learning-based online method. Simulation results show that the proposed scheme is capable of reducing signal-detection error compared with previous work. Also, it is corroborated that the proposed scheme only requires low computational complexity.
Reformulation of symptom descriptions in dialogue systems for fault diagnosis: How to ask for clarification?

Psycholinguistic research can inform the design of dialogue systems for fault diagnosis. When users provide ambiguous symptom descriptions, dialogue systems can reformulate these descriptions to check the correctness of their interpretation. The present study investigated whether such reformulations should be performed by users or dialogue systems, and how they should be phrased. In a Wizard-of-Oz study, subjects described faults symptoms to a chatbot, which subsequently asked for clarification. Experiment 1 compared the effects of requests for subjects to self-correct their descriptions and reformulations provided by the dialogue system in either common or technical terms. Experiment 2 combined reformulations in common and technical terms with each other and with pictures of fault symptoms. The results revealed that requests for self-correction increased solution times and verbal effort, that common terms decreased solution times but led to errors when seemingly easy reformulations were incorrect, and that technical terms did not mislead subjects into accepting them uncritically. Enrichments with pictures reduced the risk of accepting incorrect reformulations and were considered particularly helpful when combined with common terms. Lexical alignment with dialogue system reformulations was low, but subjects adopted its technical terms most readily when no common terms were provided. Taken together, the results suggest that combining reformulations in everyday language with visual information is most suitable to support grounding.
Refactoring a statistical package for demanding memory loads

An R library application employing distinct resampling and multithreaded overlap routines demanded memory beyond the limits of a desktop workstation. Initial efforts revealed that the necessary memory allocations for the dataset output from the resampling exceeded that available on a high memory node, making code refactoring necessary. Subsequent in-depth analysis of the employed package revealed that by combining and multithreading bootstrap sampling and overlap calculations, the memory footprint was reduced to a manageable size. However, the computation time was substantially increased. Further development, decomposing the resampling step into two routines, minimized the impact of a rate-limiting step, restoring shorter runtimes while maintaining the smaller memory footprint.
Integration at the Application Interface Level between SIAMIK and SIPERPUS (a Case Study in XX)

Abstract Many business organizations have information systems, but most still operate separately as a silo. The problem that arises is that there is ineffectiveness in the process that is in the application. So the need for an integrated information system that can access two or more systems in the organization is needed. The system integration process is carried out using Enterprise Application Integration (EAI) method at the application interface level. This method is used to integrate academic information systems (SIAMIK) and library information systems (SIPERPUS). The results of this study are that both applications are connected without changing source codes from both applications. The integrated system design can facilitate the admin in the process of registering new users from the SIPERPUS application.
Scientific Data Annotation and Dissemination: Using the ‘Ike Wai Gateway to Manage Research Data

Granting agencies invest millions of dollars on the generation and analysis of data, making these products extremely valuable. However, without sufficient annotation of the methods used to collect and analyze the data, the ability to reproduce and reuse those products suffers. This lack of assurance of the quality and credibility of the data at the different stages in the research process essentially wastes much of the investment of time and funding and fails to drive research forward to the level of potential possible if everything was effectively annotated and disseminated to the wider research community. In order to address this issue for the Hawai’i Established Program to Stimulate Competitive Research (EPSCoR) project, a water science gateway was developed at the University of Hawai‘i (UH), called the ‘Ike Wai Gateway. In Hawaiian, ‘Ike means knowledge and Wai means water. The gateway supports research in hydrology and water management by providing tools to address questions of water sustainability in Hawai‘i. The gateway provides a framework for data acquisition, analysis, model integration, and display of data products. The gateway is intended to complement and integrate with the capabilities of the Consortium of Universities for the Advancement of Hydrologic Science’s (CUAHSI) Hydroshare by providing sound data and metadata management capabilities for multi-domain field observations, analytical lab actions, and modeling outputs. Functionality provided by the gateway is supported by a subset of the CUAHSI’s Observations Data Model (ODM) delivered as centralized web based user interfaces and APIs supporting multi-domain data management, computation, analysis, and visualization tools to support reproducible science, modeling, data discovery, and decision support for the Hawai’i EPSCoR ‘Ike Wai research team and wider Hawai‘i hydrology community. By leveraging the Tapis platform, UH has constructed a gateway that ties data and advanced computing resources together to support diverse research domains including microbiology, geochemistry, geophysics, economics, and humanities, coupled with computational and modeling workflows delivered in a user friendly web interface with workflows for effectively annotating the project data and products. Disseminating results for the ‘Ike Wai project through the ‘Ike Wai data gateway and Hydroshare makes the research products accessible and reusable.
Noninteractive Lightweight Privacy-Preserving Auditing on Images in Mobile Crowdsourcing Networks

To determine whether images on the crowdsourcing server meet the mobile user’s requirement, an auditing protocol is desired to check these images. However, before paying for images, the mobile user typically cannot download them for checking. Moreover, since mobiles are usually low-power devices and the crowdsourcing server has to handle a large number of mobile users, the auditing protocol should be lightweight. To address the above security and efficiency issues, we propose a novel noninteractive lightweight privacy-preserving auditing protocol on images in mobile crowdsourcing networks, called NLPAS. Since NLPAS allows the mobile user to check images on the crowdsourcing server without downloading them, the newly designed protocol can provide privacy protection for these images. At the same time, NLPAS uses the binary convolutional neural network for extracting features from images and designs a novel privacy-preserving Hamming distance computation algorithm for determining whether these images on the crowdsourcing server meet the mobile user’s requirement. Since these two techniques are both lightweight, NLPAS can audit images on the crowdsourcing server in a privacy-preserving manner while still enjoying high efficiency. Experimental results show that NLPAS is feasible for real-world applications.
Incremental Spatio Temporal Clustering Application on Hotspot Data

Abstract Hotspot is one of indications for forest and land fires. Analysis of hotspot data needs to be done as an early warning activity to prevent the occurrence of forest and land fires. The previous study analyzed hotspot clusters using the incremental spatio-temporal density-based clustering (ST-DBSCAN) algorithm. Hotspots in a cluster are considered as strong indicator for forest and land fires. However, clustering of hotspots is implemented on the command line interface. Through this interface, users are required to execute manually the commands to perform the clustering task on the dataset. The disadvantage of command line interface is that the commands have to be typed precisely and mistypes will cause errors. Therefore, this study aims to build a web-based application for clustering hotspot data using the incremental ST-DBSCAN module. This study uses hotspot dataset in the period 2014 to 2017. The application was developed using R programming language and Shiny framework. The method of Adaptive Software Development (ASD) was adopted in this study. The main functions of the application are incremental clustering on hotspot data and visualization of clustering result. The testing using the black box approach show that all features of the application work properly. Based on the result of usability assessment, the user satisfaction level reach 78.45% meaning that the application is quite easy to be used.
A Construction of Self-dual Skew Cyclic and Negacyclic Codes of Length n over "Equation missing"

The aim of this note is to give a construction and an enumeration of self-dual \(\theta \)-cyclic and \(\theta \)-negacyclic codes of length n over Open image in new window where p is a prime number and \(\theta \) is the Frobenius automorphism over Open image in new window . We use the notion of isodual codes to achieve this construction.
Establishing a criminal justice cyber lab to develop and enhance professional and educational opportunities

Abstract Law enforcement agencies face increasing backlogs in their digital evidence processing. Most agencies utilize federal or private companies to process this evidence, however, recently, universities have begun to establish their own cyber forensics labs to aid law enforcement in reducing the time it takes for them to obtain digital evidence. Specifically, examiners in these labs use digital investigation and analysis techniques to determine what could be legally permissible evidence on devices ranging from computers to cell phones to cameras. This paper is the first to provide a general overview of one university's experience in building a cyber‐forensics lab from the ground up for the purpose of providing services to community partners as well as educational and training opportunities. We utilize data obtained from 59 Texas law enforcement agencies to explore the needs and abilities of law enforcement in processing digital evidence as a supporting argument for the development of a university cyber‐lab.