Reviewerly/exHarmony huggingface dataset:
A Study on Visual Representations for Active Plant Wall Data Analysis

The indoor climate is closely related to human health, well-being, and comfort. Thus, an understanding of the indoor climate is vital. One way to improve the indoor climates is to place an aesthetically pleasing active plant wall in the environment. By collecting data using sensors placed in and around the plant wall both the indoor climate and the status of the plant wall can be monitored and analyzed. This manuscript presents a user study with domain experts in this field with a focus on the representation of such data. The experts explored this data with a Line graph, a Horizon graph, and a Stacked area graph to better understand the status of the active plant wall and the indoor climate. Qualitative measures were collected with Think-aloud protocol and semi-structured interviews. The study resulted in four categories of analysis tasks: Overview, Detail, Perception, and Complexity. The Line graph was found to be preferred for use in providing an overview, and the Horizon graph for detailed analysis, revealing patterns and showing discernible trends, while the Stacked area graph was generally not preferred. Based on these findings, directions for future research are discussed and formulated. The results and future directions of this research can facilitate the analysis of multivariate temporal data, both for domain users and visualization researchers.
Towards a Business Value Framework for Linked Enterprise Data

In light of new industry trends and ICT-developments enterprises face an unprecedented amount of data, produced on a daily basis by countless in-house and external ICT-applications and stakeholders. Gaining valuable information and business-insights out of these masses of data has become a true challenge. At the same time, traditional data warehouse and content management systems, involving data silos, relational databases and data warehouses are reaching their limits. Linked Data and Semantic Web Technologies offer a new approach to Enterprise Content Management. Based on graph-based data models and semantic models to establish logical connections between data sources, enterprises can build Linked Enterprise Data Networks (LEDN), incorporating both internal and/or external data sources and allowing users to retrieve documents, information and datasets across entire organizations in a fast and easy manner. To outline the potentials of Linked Enterprise Data Networks this paper presents a framework of relevant business values and aspects to be considered by entrepreneurs, when implementing Linked Data technologies in their business.
Sensors and Civics: Toward a Community-centered Smart City

The ability to gather, store, and make meaning from large amounts of sensor data is becoming a technological and financial reality for cities. Many of these initiatives are happening through deals brokered between vendors, developers, and cities. They are made manifest in the environment as infrastructure – invisible to citizens and communities. We assert that in order to have community-centered smart cities, we need to transform sensor data collection and usage from invisible infrastructure into visible and legible interface. In this chapter, we compare two different urban sensing initiatives and examine the methods used for feedback between sensors and people. We question how value gets produced and communicated to citizens in urban sensing projects and what kind of oversight and ethical considerations are necessary. Finally, we make a case for “seamful” interfaces between communities, sensors, and cities that reveal their inner workings for the purposes of civic pedagogy and dialogue.
Automatic Keyword Extraction Using TextRank

Summarizing and extracting keywords from textual documents is a fundamental task involving in many applications in natural language processing and related fields. This work presents an automatic keyword extraction algorithm based primarily on a weighted TextRank model. In this model, word embedding vectors are used to compute a similarity measure as an edge weight. Incorporating sentence importance scores derived from the TextRank model at a sentence level enhances an overall performance. The proposed algorithm is experimented and compared with the traditional TextRank algorithm as well as the weighted TextRank algorithm with word embedding-based weights.
Modification Advanced Encryption Standard for Design Lightweight Algorithms

The acknowledgment of IoT requires a colossal measure of sensor hubs which have limited battery power, memory, computational latency and communication bandwidth to obtain contributions from the associated objects. In the current developments of the resource constraint environments, the trend is shifted towards lightweight cryptographic algorithm. Many lightweight cryptographic algorithms have been developed and also existing algorithms are modified in terms of resource constraint environment.
SeeSpeech

Speech recognition is a mature technology which specifically refers to translating spoken language into text. All modern OS platforms include speech to text tools. It is used for dictation and voice device control, as well as in AI assistants, such as Alexa, Google Assistant, Siri and Cortana. Such solutions do not work out-of-the-box for the hearing impaired. Special requirements have to be taken into account when offering a specialized real-time speech to text transcription device addressing the hearing impaired. The objective of this paper is to present an application specifically designed for deaf or hearing-impaired people, to help them understand what other people are saying to them. The SeeSpeech application implements an interface for a simple dialogue with a deaf or hearing-impaired person in different languages, integrating two APIs for transforming speech to text. A key feature of the application is the ability to register sound from an external Bluetooth microphone. This makes SeeSpeech a very competitive application in the arena of similar scope applications. Enhanced reliability is demonstrated through a detailed comparison with a similar scope application available in Google Play, called TextHear. SeeSpeech is also logging the conversations, allowing anytime the review of past messages in a stored conversation.
Segmentation of Microscopic Breast Cancer Images for Cancer Detection

Breast cancer is one of serious diseases that affect mainly woman and late diagnosis can lead to death. However early diagnosis increases survivability significantly, therefore making it very important. There are different diagnosis techniques for early detection of breast cancer. Breast tissue samples analyzed under microscope is considered reliable way to diagnose breast cancer. Automated classification techniques are so popular in many areas in order to reduce human dependency considering third world countries. Our purpose is to determine if sample is malignant or benign in automated manner. Many algorithms are studied so far in medical area along with other areas. However, algorithms are generally too complex even for simple tasks. We propose a simple algorithm that can differentiate cancerous and non-cancerous samples from breast tissue in automated manner. The images were taken from Near East University Hospital which is consisted of 50 cancerous and 100 healthy images. Total of 150 images were correctly differentiated through our algorithm.
SmartDBO: Smart Docker Benchmarking Orchestrator for Web-application

Containerized web-applications have gained popularity recently due to the advantages provided by the containers including light-weight, packaged, fast start up and shut down and easy scalability. As there are more than 267 cloud providers, finding a flexible deployment option for containerized web-applications is very difficult as each cloud offers numerous deployment infrastructure. Benchmarking is one of the eminent options to evaluate the provisioned resources before product-level deployment. However, benchmarking the massive infrastructure resources provisioned by various cloud providers is a time consuming, tedious and costly process and is not practical to accomplish manually.
Introduction to Meta-heuristic Optimization Algorithms

This chapter begins with a concise definition of the optimization problem and its parameters, along with a mathematical description of an optimization problem with continuous and discrete decision-making variables whose objective functions are employed in a standard form of an optimization problem along with equality and inequality constraints. Subsequently, the authors address the classifications of an optimization problem from different perspectives, which deserve attention and can achieve full knowledge regarding an optimization problem and its parameters. In addition, a succinct overview pertaining to the optimization algorithms with a focus on meta-heuristic optimization algorithms is reported.
Credibility versus fake news in digital newspapers on tablets in primary education / Credibilidad versus falsedad de las noticias de periódicos digitales sobre la tableta en la Educación Primaria

The press is often attributed with little credibility, and the recent phenomenon of fake news has put it under the spotlight once again. As a result, the objective of this study is to discover the margin of the veracity versus falsehood of the news reports on tablets in primary education published in digital newspapers. This qualitative study has been developed on the basis of an analysis of 120 news items in Spanish newspapers. The conclusions show that news items on the topic correspond with reality and can be considered credible since they are in line with results provided in the scientific literature. Nevertheless, and in accordance with this type of publication, the treatment of the topic in these news items is superficial, even though they usually dedicate a full page to it. When discussing benefits and controversial points, both newspaper articles and scientific studies highlight the immediacy offered by tablets and the motivation generated in students. At the same time, their use is also linked with harmful effects associated with technopathologies.
A Comparative Study on the Efficiency of POS Tagging Techniques on Amazigh Corpus

Part-of-speech (POS) tagging is a fundamental task of Natural Language Processing (NLP). It provides useful information for many other NLP tasks, including word sense disambiguation, text chunking, named entity recognition, syntactic parsing, semantic role labeling, and semantic parsing. In this paper, we have developed POS taggers for Amazigh language using Conditional Random Field (CRF), Support Vector Machine (SVM) and TreeTagger system. We have manually annotated approximately 85000 tokens, collected from the written texts with a POS tagset of 28 tags defined for the Amazigh language. The POS taggers make use of the different contextual and orthographic word features. These features are language independent and applicable to other languages also. POS taggers have been trained, and tested with the same Amazigh corpus. Evaluation results demonstrated the accuracies of 90.08%, 89.38% and 92.06% in the CRF, SVM and TreeTagger, respectively.
Model‐based clustering for noisy longitudinal circular data, with application to animal movement

Abstract In this work, we introduce a model for circular data analysis to robustly estimate parameters, under a longitudinal clustering setting. A hidden Markov model for longitudinal circular data combined with a uniform conditional density on the circle to capture noise observations is proposed. A set of exogenous covariates is available; they are assumed to affect the evolution of clustering over time. Parameter estimation is carried out through a hybrid expectation–maximization algorithm, using recursions widely adopted in the hidden Markov model literature. Examples of application of the proposal on real and simulated data are performed to show the effectiveness of the proposal.
New Publicly Verifiable Cloud Data Deletion Scheme with Efficient Tracking

Cloud storage service, one of the most important services in the cloud computing, can offer high-quality storage service for tenants. By employing the cloud storage service, the resource-constraint data owners can outsource their data to the remote cloud server to reduce the heavy storage burden. Due to the attractive advantages, there are an increasing number of data owners prefer to embrace the cloud storage service. However, the data owners will lose the direct control over their outsourced data, and they can not directly execute operations over their outsourced data, such as data deletion operation. That will make outsourced data deletion become a crucial security problem: the selfish cloud server may not honestly perform the data deletion operation for economic interests, and then returns error results to mislead the data owners. Although a series of solutions have been proposed to solve this problem, most of them can be described as “one-bit-return” protocol: the storage server removes the data and then returns a one-bit deletion reply, and the data owners have to trust the deletion reply because they can not verify it conveniently. In this paper, we put forward a new publicly verifiable data deletion scheme with efficient tracking for cloud storage. In our novel scheme, the remote cloud server executes the data deletion operation and returns a deletion proof. If the remote cloud server maliciously reserves the data backups, the data owner can easily detect the malicious data reservation by verifying the returned deletion proof. Besides, we utilize the primitive of Merkle Hash Tree to solve the problem of public verifiability in data deletion. Furthermore, our scheme can realize efficient data leakage source tracking when the data are leaked, which can prevent the data owner and the cloud server from exposing the data designedly to slander each other. Finally, we prove that our novel scheme can satisfy the desired security properties.
Application of human computing in image captioning under deep learning

Image captioning refers to a kind of machine learning technology that makes an input of an image automatically generate some descriptive texts, with this being widely applied in image search, unmanned driving, VR and AR. Deep learning performs well in some designated situations, but since it lacks reasoning ability compared with humans, it has difficulty solving problems like UUs. This paper introduces image captioning under the deep learning method based on human computing to solve UUs problems where the training set is not identical to the test set. We test this method on the data sets of Corel5K and PASCAL VOC 2012, using several popular image captioning models, and the result shows it to be efficient.
Asymptotically Bias-Corrected Regularized Linear Discriminant Analysis for Cost-Sensitive Binary Classification

In this letter, the theory of random matrices of increasing dimension is used to construct a form of regularized linear discriminant analysis (RLDA) that asymptotically yields the lowest overall risk with respect to the bias of the discriminant in cost-sensitive classification of two multivariate Gaussian distributions. Numerical experiments using both synthetic and real data show that even in finite-sample settings, the proposed classifier can uniformly outperform RLDA in terms of achieving a lower risk as a function of regularization parameter and misclassification costs.
Research on MOOC System Based on Bipartite Graph Context Collaborative Filtering Algorithm

The MOOC platform is a good tool to help people learn, but with the increase of resources and numbers on the platform, choosing a learning resource that suits you has become a big problem. Personalized recommendations can help learners alleviate the problem, and recommending algorithms is the core of the recommendation process. Based on the analysis of the existing algorithms in the existing MOOC platform, in order to improve the accuracy and effectiveness of the recommendation and solve the cold start problem, this paper proposes a bipartite graph context collaborative filtering algorithm based on the characteristics of the MOOC platform: first, combined with the context information, the collaborative user filtering algorithm is used to process the initial user-resource score data and obtain the nearest neighbor. second, the nearest neighbor was used to get new user-resource score data. Last, in order to get a lists of recommendations, the bipartite graph method was used to process the new data. The algorithm improves the recommendation quality of the algorithm by preprocessing the data set and constructing different contact quantized values. Finally, the effectiveness of the algorithm is verified by experiments.
Isolation of the interaction between CaCO3 filler and acrylic binder. Part II: Effect of the amount and type of functional monomer

The aim of this work is to better understand the effect that different functional monomers and amounts have on the dispersion of calcium carbonate (CaCO3) fillers in waterborne acrylic coatings. This will allow to improve the final performance of coatings at a certain pigment volume concentration (PVC) or to increase the PVC without significant losses in the properties. In this paper we report the preparation of CaCO3/poly(methyl methacrylate-co-butyl acrylate) P(MMA/BA) latex blends without the use of any additive and with applicable rheology. This has been possible thanks to the direct addition of CaCO3 powder to diluted latex. Paint-like blends have been prepared with latexes synthesized with acrylic acid (AA) and methacrylic acid (MAA) functional monomers at 1, 3, and 5 wt based on monomer percent (wbm%) and with itaconic acid (IA) at 1 wbm%, at different PVC values of 30, 40 and 50. Moreover, the effect of water soluble species on the final paint properties has been checked by dialyzing latexes synthesized with the minimum and the maximum amount of MAA, before the blending with CaCO3. The effect of all these variables has been investigated by analysing the final paint-like film properties (rheology, gloss, mechanical properties and scrub resistance). The interaction between the different latexes and CaCO3 filler was also studied by investigating the adsorption capacity of polymer particles on the surface of CaCO3 particles with NMR measurements. Higher gloss value, more elastic mechanical properties and higher scrub resistance have been observed for films prepared with the latex containing 1 wbm% of MAA functional monomer.
Performance Assessment of Prospective PV Systems in Queensland and New South Wales of Australia

Large-scale renewable energy, especially solar photovoltaic (PV), is increasing into the Australian power network (known as NEM) due to sustainability and environmental benefits. The analysis of energy yield and variability for solar PV at different locations is essential for transmission system operator(s) when making long-term planning for cross-border transmission system design. This work presents the large-scale PV regional models for various potential locations in Queensland (QLD) and New South Wales (NSW) according to the integrated development plan for Australia. The model gives an estimation of PV production with one-hour resolution. The correlation among the variability of PV production at different locations has been established which can be useful for the transmission system operator(s) for future planning of reserve margin for the system.
A Reverse Converter for Three-Moduli Set (2<sup>k</sup>, 2<sup>n</sup> - 1, 2<sup>n</sup> + 1), k &lt; n

This work proposes a new residue-representation to binary-representation arithmetic converter for the three-moduli set (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> , 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> - 1, 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> + 1), k <; n, where n and k are positive integers such that 1 <; k <; n. The proposed reverse conversion algorithm uses the Chinese-Reminder-Theorem. The paper also introduces the multiplicative inverses needed to implement the conversion process. The structure suggested in this paper is more efficient than other similar recently proposed structure. Based on VLSI layout simulation results, the proposed structure has a better area, time and power performance by 22.5%, 24.6%, and 20.5%, respectively, when compared with the competitive structure.
The Efficacy of ICT in Weather Forecast Information Dissemination: Evidence from Farming Communities in Mbale and Rakai Districts, Uganda

Information and communication technology (ICT) has pervaded all spheres of life from the upscale rich to the rural poor in developing countries. ICT is increasingly being harnessed universally to better the quality of life of communities. This chapter highlights the need to utilize ICT tools to improve livelihoods of farmers in Uganda, in the face of climate change and variations in seasonal weather. We investigated the use of ICT tools and services in enhancing farmers’ access to weather forecast information to improve agricultural productivity in Uganda. The ICT tools in question included mobile phones and computers/laptops, while the services included the use of emails, websites, and social network sites. We used focus group sessions with farmers in Mbale and Rakai districts to (1) capture their perception of the use of ICT tools and (2) establish the mode of ICT-supported dissemination that would be most effective and efficient for relevant weather forecast information dissemination. Extra information was sourced from key informant interviews with agricultural extension workers and personnel from Uganda National Meteorological Authority. We transcribed the information gathered into descriptive narratives, used thematic analysis and coding with spreadsheets for analysis. We found the mobile phone to be the ICT tool that most farmers have access to, and we found them open to solutions designed around the mobile phone. We establish and recommend using ICT tools to complement existing and conventional weather information dissemination strategies such as mass media. ICT tools allow for customized information to be sent to farmers in text, graphic, audio, or visual formats. We also found that ICT tools enhance user control and interaction, allow farmers to query and receive specific responses of weather forecast information they are interested in, provide a mechanism to collect crowdsourced feedback that can be used to improve the weather information services and products provided by UNMA, and promote timely and easy access to weather information. We noted that if fully exploited, ICT tools have the potential to contribute to effective dissemination of tailored weather information, which would help to improve the livelihoods of the farmers in Uganda.
FWLSA-score: French and Wolof Lexicon-based for Sentiment Analysis

With the advent of Internet, people actively express their opinions about products, services, events, political parties and other one in social media, blogs, and website comments. The amount of research work on sentiment analysis is growing explosively. However, the majority of research efforts are devoted to English language data, while a great share of information is available in other languages. It is a challenging task to identify sentiment polarity of reviews written in both Wolof and French languages because theirs spelling are usually incorrect or non-uniform. In this paper, we propose a novel framework that contains (i) an extended French lexicon [1] with a new words and expressions currently used in both languages; and (ii) a sentiment scoring algorithm that uses string (word) similarity algorithm to address the spelling problem. Our algorithm classifies reviews as positive or negative based on the polarity of the words or expressions. Our experimental results on a real corpus demonstrated the effectiveness of our proposal.