Reviewerly/exHarmony huggingface dataset:
Edge Computing: A Neural Network Implementation on an IoT Device

This demonstration showcases the use of reconfigurable logic to implement edge computing for IoT devices able to provide specific information from raw data produced from some sensor, e.g. a camera or microphone, instead of the raw data itself. In what concerns the embedded processing capabilities, the focus is image processing using convolutional neuronal networks (CNN). This approach is clearly distinct from the current trends in IoT devices of using cloud computing to process the collected data. We intend a twist on the established paradigm and pursue an edge computing approach. Since we are targeting small and simple devices, we need some low power solution for the CNN computation. The demonstration will be made on a Terasic DE1 (SoC) reconfigurable system, with a field programmable gate array (FPGA), and hardwired ARM processor to build the IoT device. The collected data from the CNN computation, is transmitted using an IoT protocol to a broker.
The internet of the orals

research-article Share on The internet of the orals Authors: Aditya Vashistha Cornell University, Ithaca, NY Cornell University, Ithaca, NYView Profile , Umar Saif ICTD, Lahore, Pakistan ICTD, Lahore, PakistanView Profile , Agha Ali Raza Information Technology University, Lahore, Pakistan Information Technology University, Lahore, PakistanView Profile Authors Info & Claims Communications of the ACMVolume 62Issue 11November 2019 pp 100–103https://doi.org/10.1145/3343452Published:24 October 2019Publication History 4citation3,203DownloadsMetricsTotal Citations4Total Downloads3,203Last 12 Months119Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Measuring sustainability of seed-funded earth science informatics projects

Short term funding is a common funding model for informatics projects. Funders are interested in maximizing the sustainability and accessibility of the outputs, but there are no commonly accepted practices to do so in the Earth sciences informatics field. We constructed and applied a framework for sustainability drawing from other disciplines that have more published work focusing on sustainability of projects. This framework had seven sustainability influences (outputs modified, code repository used, champion present, workforce stability, support from other organizations, collaboration/partnership, and integration with policy), and three ways of defining sustainability (at the individual-, organization-, and community-level). Using this framework, we evaluated outputs of projects funded by the U.S. Geological Survey's Community for Data Integration (CDI). We found that the various outputs are widely accessible, but not necessarily sustained or maintained. Projects with most sustainability influences often became institutionalized and met required needs of the community. Even if proposed outputs were not delivered or sustained, knowledge of lessons learned could be spread to build community capacity in a topic, which is another type of sustainability. We conclude by summarizing lessons for individuals applying for short-term funding, and for organizations managing programs that provide such funding, for maximizing sustainability of project outcomes.
Aberrance-aware Gradient-sensitive Attentions for Scene Recognition with RGB-D Videos

With the developments of deep learning, previous approaches have made successes in scene recognition with massive RGB data obtained from the ideal environments. However, scene recognition in real world may face various types of aberrant conditions caused by different unavoidable factors, such as the lighting variance of the environments and the limitations of cameras, which may damage the performance of previous models. In addition to ideal conditions, our motivation is to investigate researches on robust scene recognition models for unconstrained environments. In this paper, we propose an aberrance-aware framework for RGB-D scene recognition, where several types of attentions, such as temporal, spatial and modal attentions are integrated to spatio-temporal RGB-D CNN models to avoid the interference of RGB frame blurring, depth missing, and light variance. All the attentions are homogeneously obtained by projecting the gradient-sensitive maps of visual data into corresponding spaces. Particularly, the gradient maps are captured with the convolutional operations with the typically designed kernels, which can be seamlessly integrated into end-to-end CNN training. The experiments under different challenging conditions demonstrate the effectiveness of the proposed method.
Fiddling, Pointing, Hovering, and Sliding

In user studies with children, it is important to use age appropriate evaluation tools to better understand their preferences, opinions, and thoughts. Here, we studied two accepted evaluation tools: The Five Degrees of Happiness, and the Sticky Ladder rating scale; together with the Paper Ladder, a paper version of the latter. Thirty-six preschoolers rated two creative and play activities ("Painting" and "Construction Blocks") and a game ("Musical Chairs") in terms of difficulty, enjoyment, and preference. Drawing from theories of embodied and distributed cognition, we performed a video analysis of the children's interactions with these tools, focusing on how each tool supported the children's cognitive processes and communication with the researcher. Here, we first describe children's embodied behavior and discuss how these were supported by design features and affordances of the tools. Then, we discuss strengths and shortcomings of each evaluation method. Last, we provide recommendations for their design, appropriation, and usage by researchers developing and evaluating playful solutions and games for children.
Text-based indoor place recognition with deep neural network

Indoor place recognition is a challenging problem because of the hard representation to complicated intra-class variations and inter-class similarities.This paper presents a new indoor place recognition scheme using deep neural network. Traditional representations of indoor place almost utilize image feature to retain the spatial structure without considering the object's semantic characteristics. However, we argue that the attributes, state and relationships of objects are much more helpful in indoor place recognition. In particular, we improve the recognition framework by utilizing Place Descriptors (PDs) in text from to connect different types of place information with their categories. Meanwhile, we analyse the ability of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) for classification in natural language, for which we use them to process the indoor place descriptions. In addition, we improve the robustness of the designed deep neural network by combining a number of effective strategies, i.e. L2-regularization, data normalization, and proper calibration of key parameters. Compared with existing state of the art, the proposed approach achieves well performance of 70.73%, 70.08% and 70.16% of accuracy, precision and recall on Visual Genome database respectively. Meanwhile, the accuracy becomes 98.6% after adding voting mechanics.
Two constructions of zero correlation zone aperiodic complementary sequence sets

Two constructions of zero correlation zone (ZCZ) aperiodic complementary sequence (ZACS) sets are presented. Based on aperiodic complementary sequence (ACS) sets and orthogonal matrices, construction 1 proposed ZACS sets by utilising cascade operation. Also, the parameters of the resulting sets are optimal if the original ACS sets can achieve the theoretical bound. In construction 2, according to different setting methods of , two classes of ZACS sets are presented based on two orthogonal matrices, and the length of ZCZ can be chosen flexibly. Furthermore, compared with the existing references, the order of the two orthogonal matrices can be different. The ZACS sets presented in this study are of practical interest in multi-carriers code division multiple access systems.
N-Gram and TF-IDF for Feature Extraction on Opinion Mining of Tweets with SVM Classifier

This work evaluates the performance of the Support Vector Machine (SVM) classifier on tweets opinion mining in five datasets available on the literature. For feature extraction, the N-Gram and TF-IDF, k-folds cross-validation techniques were used in the classifier modeling step. Variations of N-Gram with L-gram, 2-gram, and 3-gram combined with k-folds cross-validation in 10-folds, 15-folds and 20-folds yielded 63.93% to 81.06% accuracy. Satisfactory results were obtained, which can be improved with the application of an optimization technique to adjust the classifier parameters.
Developing students’ cognitive and communicative skills when learning foreign languages

The article is devoted to the problem of forming and improving students’ cognitive skills in connection with development of communicative skills. The aim of the article is to study the effects of designing supporting schemes of different types on development of students’ cognitive and communicative skills in the process of learning a foreign language. The authors summarized research findings on regularities of foreign language learning by university students and revealed conditions of enhancing the effectiveness of this process. The authors have justified the necessity and appropriateness of supporting schemes application when teaching foreign languages that are analogues of inner speech universal subject codes. The authors came to the conclusion that one of the most efficient ways to form and develop students’ cognitive and communicative skills is to involve them in self-education, provide them with an appropriate algorithm of conscious and independent work with information, with each stage being constantly self-controlled.
GraphQL Schema Generation for Data-Intensive Web APIs

Sharing data as a (non-)commercial asset on the web is typically performed using an Application Programming Interface (API). Although Linked Data technologies such as RDF and SPARQL enable publishing and accessing data on the web, they do not focus on mediated and controlled web access that data providers are willing to allow. Thus, recent approaches aim at providing traditional REST API layer on top of semantic data sources. In this paper, we propose to take advantage of the new GraphQL framework that, in contrast to the dominant REST API approach, exposes an explicit data model, described in terms of the so-called GraphQL schema, to enable precise retrieving of only required data. We propose a semantic metamodel of the GraphQL Schema. The metamodel is used to enrich the schema of semantic data and enable automatic generation of GraphQL schema. In this context, we present a prototype implementation of our approach and a use case with a real-world dataset, showing how lightly augmenting its ontology to instantiate our metamodel enables automatic GraphQL schema generation.
Intra-data center interconnects, networking, and architectures

This chapter describes the interconnect technologies applied in a data center network (DCN) architecture along with those used in the modern network systems. We start with a discussion on the characteristics of a DCN and show how these characteristics drive the development of the network topology, architecture, and network cabling in data centers. Then we discuss different characteristics and applications of interconnect technologies used inside data centers. These technologies include direct attach cables, active optical cables, and optical transceivers. Pluggable optical technologies for 40, 100, and 400 G networks are presented in detail, including multimode and single-mode technologies. We show how optical interconnect technologies have evolved as the DCN bandwidth has increased from one generation to the next. This leads to a discussion on the advantages and limitations of different technologies in data centers. Finally, we present our perspectives on future development for intra-data center interconnects and networks, including coherent detection, mid-board optics, copackaging of electronic and optical circuits, and optical switching technologies.
FemTech: Broadening Participation to Digital Technology Development

In the digital age, the fields and professions related to computing are having an unprecedent impact on our lives, and on societies. As computing becomes integrated in fundamental ways in healthcare [10,11], labor markets [2,4], and political processes [3,6], questions about who participates and takes decisions in developing digital technologies are becoming increasingly crucial and unavoidable [7].
An Empirical Framework for Web Service Anti-pattern Prediction using Machine Learning Techniques

In todays software industries, the concepts of Web Services are applied to design and develop distributed software system. These distributed software system can be designed and developed by integrating different Web Services provided by different parties. Similar to other software systems, Web Services based system also suffers from bad or poor design i.e., bad design selection, anti-pattern, poor planning etc.. Early prediction of anti-patterns can help developer and tester in fixing design issue and also effectively utilize the resources. The work in this paper empirically investigates and evaluates six classification techniques, 8 feature selection techniques (7 feature ranking techniques and 1 feature subset evaluation technique), and 1 data sampling technique to handle imbalance data in predicting 5 different types of anti-patterns. These all techniques are validated on 226 real-world web-services across several domains. The performance of the developed models using these techniques are evaluated using AUC value. Our analysis reveals that the model developed using these techniques able to predict different anti-patterns using source code metrics. Our analysis also reveals that the best feature selection technique is OneR, data sample is better that without sampling and Random Forest is best classification algorithm for anti-pattern predictions.
Early Identification of Potential Distributed Ledger Technology Business Cases Using e3value Models

Many Distributed Ledger Technology (DLT) projects end prematurely without reaping benefits. Previous research has indicated a lack of sustainable business cases for many Blockchain projects. A successful project has a disruptive impact on the business ecosystem. The paper investigates how e3value modeling can contribute to identifying the potential success of DLT implementation. Using insights from a first DLT case-study, an abstract e3value model fragment is defined that indicates potential success. As a test, the e3value model fragment is subsequently applied to a second case-study that is currently being implemented as a DLT-based platform. The paper concludes by reflecting on how an e3value model can provide evidence of meeting the requirements for building a sustainable DLT business case.
Integrating Geographical and Functional Relevance to Implicit Data for Web Service Recommendation

Designing efficient and effective Web service recommendation, primarily based on usage feedback, has become an important task to support the prevalent consumption of services. In the mashup-API invocation scenario, the most available feedback is the implicit invocation data, i.e., the binary data indicating whether or not a mashup has invoked an API. Hence, various efforts are exploiting potential impact factors to augment the implicit invocation data with the aim to improve service recommendation performance. One significant factor affecting the context of Web service invocations is geographical location, however, it has been given less attention in the implicit-based service recommendation. In this paper, we propose a recommendation approach that derives a contextual preference score from geographical location information and functionality descriptions. The preference score complements the mashup-API invocation data for our implicit-tailored matrix factorization recommendation model. Evaluation results show that augmenting the implicit data with geographical location information and functionality description significantly increases the precision of API recommendation for mashup services.
Optimizing scheduling decisions of container management tool using many‐objective genetic algorithm

Summary Container virtualization is an emerging technology in cloud computing mainly due to its portability and lightweight features. Scheduling is a key task, performed by container management tool, which indirectly affects the characteristics of distributed software system in terms of availability, realizability, scalability, resources utilization, as well as power consumption. However, current schedulers only focus on some of the aforementioned aspects but not all. In this paper, a Many‐Objective Genetic Algorithm Scheduler (MOGAS) is proposed to handle all such objectives to realize solutions with better characteristics. The proposed scheduler is compared with the Ant Colony Optimization (ACO)–based scheduler. Based on the proposed objective functions, simulation results show that MOGAS is better than the ACO scheduler in equally distributing tasks by 50%, assigning unique set of tasks per node by 40%, and reducing power consumption by 7%, on average.
Investigating pre-service science teachers’ mobile augmented reality integration into worksheets

Research on Mobile Augmented Reality (Mobile AR) includes studies addressing Mobile AR integration into learning environments along with the quality of instructions by researchers about using mobile AR in classrooms. Findings revealed that even inexperienced teachers/pre-service science teachers who lacked any prescribed material could use mobile AR, for which teaching materials are scant. This study aimed to reveal which teaching strategy pre-service science teachers preferred and how they integrate the strategy in Anatomy 4D and Elements 4D applications to enrich worksheets. The survey method was used to probe pre-service science teachers’ integration of mobile AR technology into their worksheets. Participants, who were 81 senior pre-service science teachers (64 females, 17 males; ages 19 to 21), attended a 14-week elective course. Data were obtained from worksheets produced in the 3rd to 6th weeks during of the course. The sequences for inductive, inquiry, and expository strategy were adopted as the coding scheme. It was concluded that pre-service science teachers used inductive strategy instead of expository or inquiry teaching strategy in their worksheets. The current study suggests that pre-service science teachers should be advised to use inquiry strategy for integrating mobile AR into science worksheets.
Impact of the E-Journals of Academic Libraries Consortium on Research Productivity: An Iranian Consortium Experience

This article reports on a research project undertaken at a university in Iran to study the impact of academic libraries consortium on research productivity. The overall objective of study was to examine to what extent the subscribed resources meet the needs of users and to determine the quality of cited journals by researchers. It also examined the relationship between the access method to a cited journal, its quality and the average citing to it. The results of the present research can help decision makers for selecting and providing suitable resources and databases based on the users' needs.
Direct Feedback Alignment Based Convolutional Neural Network Training for Low-Power Online Learning Processor

There were many algorithms to substitute the backpropagation (BP) in the deep neural network (DNN) training. However, they could not become popular because their training accuracy and the computational efficiency were worse than BP. One of them was direct feedback alignment (DFA), but it showed low training performance especially for the convolutional neural network (CNN). In this paper, we overcome the limitation of the DFA algorithm by combining with the conventional BP during the CNN training. To improve the training stability, we also suggest the feedback weight initialization method by analyzing the patterns of the fixed random matrices in the DFA. Finally, we propose the new training algorithm, binary direct feedback alignment (BDFA) to minimize the computational cost while maintaining the training accuracy compared with the DFA. In our experiments, we use the CIFAR-10 and CIFAR-100 dataset to simulate the CNN learning from the scratch and apply the BDFA to the online learning based object tracking application to examine the training in the small dataset environment. Our proposed algorithms show better performance than conventional BP in both two different training tasks especially when the dataset is small. Furthermore, we examined the efficiency improvement by real chip implementation, and finally, DNN training accelerator with BDFA shows 35.3% lower power consumption compared with hardware which is optimized for BP.
Audit of Provincial Library Information System based on COBIT 4.1

Current technological developments cannot be damp again. Progress in every field cannot be separated from technology as its support, especially information technology. Likewise, the Bangka Belitung Islands Provincial Library and Archive has implemented an information system in managing book data, visitors, and the process of returning and borrowing books. However, a system is said to be good if it meets the needs and is able to solve problems. However, this must be balanced with an evaluation of the information system so that threats or losses can be avoided or prevented. This study purposes to determine the extent of the performance of the information system of the Provincial Bangka Belitung Islands Library and Archive based on the Control Objectives for Information and Related Technology Standards (COBIT 4.1). Since this system is already in the implementation stage, the audit process will use a subdomain of DS (Delivery and Support) domains, DS4 (Ensure Continuous Service), DS5 (Ensure System Security), DS10 (Manage Problems), DS11 (Manage Data) and DS12 (Manage the Physical Environment). The five subdomains were chosen because they had the greatest influence in the use of information systems in the Bangka Belitung Islands Provincial Library and Archive. From the results of audit analysis, it can be concluding that the quality of information systems services in the DPAD of Bangka Belitung Province is at maturity defined at level 3 which means the standard process in the development of a new product is documented. Besides, this process is based on the product development process that has been integrated.
Advanced Transfer Learning Approach for Improving Spanish Sentiment Analysis

In the last years, innovative techniques like Transfer Learning have impacted strongly in Natural Language Processing, increasing massively the state-of-the-art in several challenging tasks. In particular, the Universal Language Model Fine-Tuning (ULMFiT) algorithm has proven to have an impressive performance on several English text classification tasks. In this paper, we aim at developing an algorithm for Spanish Sentiment Analysis of short texts that is comparable to the state-of-the-art. In order to do so, we have adapted the ULMFiT algorithm to this setting. Experimental results on benchmark datasets (InterTASS 2017 and InterTASS 2018) show how this simple transfer learning approach performs well when compared to fancy deep learning techniques.