Reviewerly/exHarmony huggingface dataset:
Data-Flow Programming for Smart Homes and Other Smart Spaces

Visual programming languages are a common way for enabling end users to create simple, customized programs in their smart homes. Although visual programming aims at lowering the syntactic and cognitive barriers existing languages lack a simple method for accessing services of smart spaces surrounding the home and therefore restrain the innovative capabilities of users. For example, accessing information about energy consumption or smart parking is often cumbersome and limited to users with advanced technical skills. Based on expert interviews, we extended the existing visual programming language Node-RED with functionalities that enable users to easily access data and services within and beyond their smart homes. Using this prototype, we performed a first evaluation of our solution with some inhabitants of smart homes.
Using Design-science for the Representation of Non Functional Requirements

Context: In software development, non-functional requirements (NFRs) determine a large part of the quality of software products. Despite this, the Software Industry does not yet have a clear outlook of how to elicit this type of requirements from the early stages of development. Proposal: Given this problem, the MERliNN Framework was created to support this process of elicitation of NFRs based in knowledge management, which is evolving through the construction of a component for the iconographic representation of NFRs, called NFR-REP. Objective: To present the progress in the construction of the NFR-REP component to represent the NFRs. Method: The Design-science methodology is used to construct the representation component, and semantic analysis to determine the structure and constituent elements of the component. Results: Two elements of the component are defined: (1) the aspects of the NFRs identified from the literature, (2) the strategy to represent the NFRs supported by the theory of semiotics and signs. Conclusions: (1) the methods used for the construction of the component NFR-REP are adequate to guide such construction, (2) the theory of semiotics gives capacity to the component of NFRs representation of address the knowledge problem against NFRs.
Multi-scale Object Detection Model with Anchor Free Approach and Center of Gravity Prediction

Object detectors usually required manual design of sliding windows, but they usually faced the shortage of feature generalization capabilities. Even the development of modern mainstream anchor base detectors also required artificial design scale and ratio of the anchor. This is a complicated and tedious process, and the quality of final effect needs to be determined through multiple experiments. In this paper, we present a new structure of object detection that choosing anchor free detection method, treat the object as entirety with advanced semantic features, the convolution predictor scans all regions and activated by these advanced features. Compared to anchor base method, the detector's activated object is the high-level feature of the target, and therefore it is not vulnerable to complex and changeable background environments. In addition, we set the center of gravity of instance as the key point, which effectively avoids the shift of subject area of interest caused by the special shape pull; using the center of gravity combined with the height and width information provided by the anchor box, resulting asymmetric Gaussian mask can play better effect in crowded scenarios; add FPN structure to the detection network aim to enhanced the generalization ability of the detector for objects of different scales. By designing a simple structured object detection network MFGP, we achieved considerable performance comparable to complex structured object detector, which has a good performance on the object detection dataset COCO and the pedestrian detection dataset CityPersons. The best neutralization of accuracy and speed was achieved on the COCO dataset, reaching 41.2% AP when 18 FPS; on the reasonable subset of the CityPersons dataset, it reached 10.9% MR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> without significantly increasing the test time.
Selective Sensor Control via Cauchy Schwarz Divergence

Ishtiaq, Nida Panicker, Sabita Gostar, Amirali K. Bab-Hadiashar, Alireza Hoseinnezhad, RezaA novel information theoretic objective function is introduced for optimal control of sensors in multi-object systems (e.g., multi-target tracking solutions) in which the sensors need to be controlled toward acquiring the most accurate measurements relative to a selected subset of objects (not all of them). This is continuation of our previous works where we tackled the same problem by devising task-driven cost functions to be optimized within the sensor control solution. In this paper, a novel objective function is presented. It is formulated using an information divergence that presents the difference in information contents between prior density and posterior density. Numerical experiments are presented for a multi-target tracking application. The tracking results show the proposed method adequately works in principle, and the tracking MSE error for the targets of interest is similar or better than the state of art in most of the times.
The Impact of Mobile Augmented Reality Design Implementation on User Engagement

With the rapid advances in mobile technologies, one of the main goals of Colleges and universities is to reach national and international recognition. Recently many Universities used new strategies to stay competitive, trying to adopt new technologies like Augmented Reality (AR) To capture students’ attention. Augmented Reality (AR) had used as a useful tool in education, but besides teaching and learning, technology may play an important role in university and student engagement. The research aims to study the effect of using augmented reality (AR) on human interaction and engagement. The study focuses on designing an AR prototype framework and a pilot AR application to test its impact on the user’s interaction engagement with the university information platform. The researcher assumes that by applying AR technology, it will improve two way of communication between universities and students, employees and visitors. Research findings show that AR UI and UX collaborate to achieve students engagement. Universities may consider thinking of the efficacy of AR implementations within their business strategy to enrich its recognition in recent intense competition.
Underwater Bearing-Only Multitarget Tracking in Dense Clutter Environment Based on PMHT

Underwater bearing-only multitarget tracking in clutter environment is challenging because of the measurement nonlinearity, range unobservability, and data association uncertainty. In terms of the principle of expectation maximization, combining the extended Kalman filter (EKF) and unscented Kalman filter algorithm(UKF), a new bearing-only multi-sensor multitarget tracking via probabilistic multiple hypothesis tracking(PMHT) algorithm is proposed. The PMHT algorithm introduces an association variable to deal with the data association uncertainty problem between the measurements and the targets. Furthermore, the EKF-based PMHT for multi-sensor multitarget system is simplified, which obviate the need to "stack" the synthetic measurements and can reduce the computation cost. The estimation accuracy of the EKF based on PMHT approach and UKF based on PMHT approach in simulation experiments for underwater bearing-only cross-moving targets and closely spaced targets for the case of stationary multiple observations and maneuvering single observation under dense clutter environment is analyzed. The experimental results demonstrate that the present algorithm is very well in a highly clutter environment and its computational load is low, which confirms the effectiveness of the algorithm to the bearing-only multitarget tracking in dense clutter.
Improving Black Hole Algorithm using Gravitational Search, White Hole Operator, and Local Search

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Extracting Online Recruitment Information Based on BiLSTM-Dropout-CRF Model

To improve the feature learning based on different job requirements, BiLSTM-Dropout-CRF (BLDC) model was proposed. Firstly, the original sentence sequences are imported into the embedding layer to obtain the word vectors. Then, BiLSTM and the dropout layer are used to learn the contextual information and key features. Finally, the CRF layer is used to accomplish the optimal sequence labeling and complete the training. For evaluating the model performance, precision, recall rate and F1 score are used to assess the extraction accuracy. The result shows that compared with traditional models, the F1 score of BLDC model severally increases 4.4% and 1.5% averagely based on two datasets about different industries and positions. It adequately illustrates that the effectiveness of online recruitment information extraction has been promoted.
Systematic Literature study on estimation and prioritization of quality requirements in software development

Quality requirements (also known as non-functional requirements) are those that address the quality of the software. They involve diverse aspects such as availability, security, performance, scalability, portability and usability, among others. Continuous technological advances such as cloud computing or the Internet of Things present new challenges in guaranteeing a satisfactory level of quality in these aspects. Moreover, agile development methodologies, whose use is increasing, such as SCRUM, XP, Kanban, do not provide the necessary support for the management of these quality requirements. To support software engineers when making decisions about the level of quality needed in a project, it is essential to know in advance (1) what criteria will be considered to verify, prioritize, plan, and/or negotiate quality requirements. Likewise, it is necessary to specify (2) how these criteria will be evaluated and (3) what factors in the project context may affect that evaluation. To try to answer these 3 research questions, the authors of this article have designed and are carrying out a systematic literature review. This article presents for discussion the description of the methodology followed in that literature review, as well as some of the preliminary results obtained during its execution.
Fraud identification architecture using data mining and machine learning in a private transport company that operates by applications

With the digital transformation over the years and the recent expansion of the use of different applications, it is possible to notice a significant change in several businesses. The diversification of electronic payments has contributed to companies suffering more from fraud. The purpose of this article is to detail a fraud detection architecture based on the identification of patterns of behavior and was applied in the racing bases of the usage of an application transport company. The study considered the construction of an artifact capable of minimizing the problem using unsupervised and supervised algorithms and machine learning techniques. The research was carried out using the DSR - Design Science Research method and considered the stages of construction of a possible conceptual structure with the systematic review of the literature, studies of fraud practices and machine learning techniques used for the detection. The architecture was implemented and allowed to validate the model capable of identifying suspected fraud in a more accurate way.
Streaming graph processing and analytics

Graphs are now ubiquitous as many applications emerge where the relationships among entities are paramount and require being modeled as first-class objects. Graph database systems empower such applications by enabling querying and processing both the data stored on the graph and its topology, and they have gained significant attention both in industry and academia. The graphs used in many modern applications are not static and not fully available for analysis; rather the graph vertices and edges are streamed, and the graph "emerges" over time. These are called streaming graphs. Processing and analyzing streaming graphs are challenging, because the difficulties of streaming combine with the complexities of graph processing. In this talk, I have two objectives. First is the introduction and positioning of the problem and the environment. The second is to highlight some of our recent work in this area within the context of s-Graffito project.
Archival Authority Records as Linked Data thanks to Wikidata, schema.org and the Records in Context Ontology

The presentation showcases how an XML-dataset of authority records can be converted into Linked Data (as N-Triples) and into the archival standard XML-EAC (Encoded Archival Context).
Graphical Semantic Authentication

Authenticate on the system using only the authentication method based on username and password is not enough to ensure an acceptable level of information security for a critical system. It has been used in a multi factor authentication to increase the information security during the authentication process. However factors like what you have cause an inconvenience to the users, because the users during the authentication process always will need to have a device in their possession that complements the authentication process. By the other side of the biometric factor might change during the time, it needs an auxiliary device that will increase the costs and it also might be dependent from environmental conditions to work appropriately. To avoid some problems that exist in multi factor authentication, this work purposes authentication through semantic representation in OWL (web Ontology Language) tuples of recognized concepts in images as a form to increase the security in the authentication process. A proof of the concept was modeled and implemented, it has a demonstration that the robustness of this authentication system depends on the complexity of relationship in the semantic base (ontology) and in the simplicity of the relationship identified in the images.
Solving the Interdependency Problem: A Secure Virtual Machine Allocation Method Relying on the Attacker’s Efficiency and Coverage

Cloud computing dominates the information communication and technology landscape despite the presence of lingering security issues such as the interdependency problem. The latter is a co-residence conundrum where the attacker successfully compromises his target virtual machine by first exploiting the weakest (in terms of security) virtual machine that is hosted in the same server. To tackle this issue, we propose a novel virtual machine allocation policy that is based on the attacker's efficiency and coverage. By default, our allocation policy considers all legitimate users as attackers and then proceeds to host the users' virtual machines to the server where their efficiency and/or coverage are the smallest. Our simulation results show that our proposal performs better than the existing allocation policies that were proposed to tackle the same issue, by reducing the attacker's possibilities to zero and by using between 30 - 48% less hosts.
How Deep Learning Works for Information Retrieval

Information retrieval (IR) is the science of search, the search of user query relevant pieces of information from a collection of unstructured resources. Information in this context includes text, imagery, audio, video, xml, program, and metadata. The journey of an IR process begins with a user query sent to the IR system which encodes the query, compares the query with the available resources, and returns the most relevant pieces of information. Thus, the system is equipped with the ability to store, retrieve and maintain information.
Utilization of rainwater harvesting installation to fulfil water needs in educational buildings

Abstract The fulfillment of STT-PLN water needs to be supplied from ground water. This will be decreased soil and soil water discharge due to large quantities of consumption. Areas of Indonesia have a high rainfall, i.e. 2, 000-4, 000 mm/year, should not have to worry about the availability of clean water. The problem faced is that there is no adequate management of rainwater. Based on these problems, it is necessary to develop methods of managing rainwater, harvesting and utilizing rainwater to meet the needs of clean water and water conservation for the sustainability of the carrying capacity of the environment. By calculating using 15 years daily rainfall data and roofing area as a catchment area to obtain the volume of potential water. The aim of this research is to analyze the amount of rainwater volume that can be accommodated in the rainwater harvesting system and to be processed as a clean water consumption.
Sentiment Analysis of Food Reviews Using User Rating Score

This paper presents various techniques use in review of products to build a recommender system for a customer to help and identify which product is beneficial among all. The process of analyzing text form data and classify those customer reviews as negative, positive, and neutral is a process of sentiment analysis. Sentiment analysis is an increasing task nowadays because it includes many real-time applications. Rather than to identify just the opinion, system can extract the attributes of the expressions too. Binary classification on reviews based on their sentimental behavior is the most popular task of NLP nowadays. Which technique gives high accuracy and best prediction result by using different algorithm can be identified by this paper. This classification defines from the reviewer’s score present in the topic.
Correction to: Facial expression recognition sensing the complexity of testing samples

The original version of this article unfortunately contained a mistake. Graphs c, d and e are missing in Figure 4. The correct and complete graphs of Figure 4 is shown here.
3D Reconstruction using convolution smooth method

3D imagery is an image with depth data. The use of depth information in 3D images still has many drawbacks, especially in the image results. Raw data on the 3D camera even does not look smooth, and there is too much noise. Noise in the 3D image is in the form of imprecise data, which results in a rough image. This research will use the convolution smooth methods to improve the 3D image. Will smooth noise in the 3D image, so the resulting image will be better. This smoothing system is called the blurring effect. This research has been tested on flat objects and objects with a circle contour. The test results on the flat surface obtained a distance of 1.3177, the test in the object with a flat surface obtained a distance of 0.4937, and the test in circle contour obtained a distance of 0.3986. This research found that the 3D image will be better after applying the convolution smooth method.
On the Labeled Multi-Bernoulli Filter with Merged Measurements

In this work, we propose a Labeled Multi-Bernoulli (LMB) filter for multi-object tracking with a merged measurement model. The finite resolution capabilities of practical sensing systems can lead to scenarios where multiple objects interact and generate merged measurements. In this work, we rely on the tractable LMB model for multi-object tracking and derive the Merged-Measurement LMB (MM-LMB) filter. Subsequently, we achieve an efficient implementation of the MM-LMB filter by relying on the K-shortest paths algorithm to find likely object-set partitions given a particular measurement set. Numerical results of our proposed filter show improved performance with respect to the standard LMB filter.
Exploring the impact of Maker initiatives on cities and regions with a research through design approach

During the last decades, economic, social and technological phenomena have influenced the role, importance and perception of cities and regions. Cities and rural areas are increasingly divided because of manufacturing and its globalisation; digital technologies in manufacturing are introducing more automation in factories, reducing thus the workforce and aggravating these phenomena. But at the same time, the Maker Movement connects these two opposites by adopting such digital technologies with an open approach, enabling a distributed manufacturing ecosystem based on individuals and communities such as Fab Labs, Makerspaces and Hackerspaces that work locally but that are connected globally. How can we measure the impact of Maker initiatives over cities and regions? This article addresses this issue with a research through design strategy that connects both design research and practice focusing on a) a theoretical context that connects peer production, manufacturing and cities and regions, b) a model for measuring Maker initiatives and their impact on cities and regions and c) a tool for the visualization and exploration of such impact. In this way designers, makers and researchers can actively participate in intentionally building the future of the Maker Movement in cities and regions instead of only analysing its present and past.
Detection of Information Hiding at Physical Layer in Wireless Communications

This article concerns the problem of detecting the use of information hiding at the physical layer in wireless communications because they are harder to be detected than other layer-based information hiding schemes. Prior schemes for detecting physical layer based information hiding are either heuristic based or machine learning based. The key limitation of prior heuristics based information hiding detection schemes is that they do not answer the fundamental question of why the information hidden at the physical layer can be detected. The key limitation of prior machine learning based information hiding detection schemes is that they lack robustness because wireless signals at the physical layer are very much environmental dependent, and thus an information hiding detection scheme trained in one environment often does not work well in another environment. Our insight is that embedding information on wireless signals at the physical layer will inevitably have a negative impact on the decodability of the cover signals, such as the increase of the error probability at the receiver (as well as the monitor). Based on the above insight, in our approach, after the monitor demodulates and decodes the cover signals, it will re-encode and re-modulate the cover signals, and then compare the resulting recovered signals with the raw signals that it received from the sender. We further propose a new estimation scheme for calculating receiver noise variance and conducted theoretical analysis. Specifically, we propose two hidden information detection schemes, a noise grouped based detection scheme and a constellation distance based detection scheme, both taking estimation errors into consideration. In particular, our constellation distance based detection scheme is the first scheme that can pinpoint the exact location on the received signals that are embedded with hidden information. We implemented our schemes and conducted extensive performance comparison between our schemes and prior schemes. Our experimental results show that when the received SNR is more than 20 dB, our approach with the new estimation scheme has the probability of detection more than 0.95, and our constellation distance based detection scheme can correctly pinpoint all embedded locations.