Miracle-dz/UniFAR-FTUs huggingface dataset:
Requirements engineering for User-Centered school food ordering system

Understanding user requirement for a technology design is central to user experience. A user-centered technology design should consider user needs and constraints as well as abide to the principles of HCI design heuristics. This paper describes the requirements gathering for Kantin Online System, a school food ordering system where we considered the needs and constraints of different stakeholders involved and elicit a compromise where the needs of all parties can be met. We then applied usability heuristics into the system design to facilitate perceived ease of use and perceived usefulness. We conclude this paper with experts’ evaluations towards the heuristic elements of the system design concept and possible future work.
Heterogeneous Parallel Architecture for Inverted Index Generation

The amount of data generated on the Web has increased dramatically, as well as the need for computational power to prepare this information. In particular, indexers process these data to extract terms and their occurrences, storing them in an inverted file, a compact data structure that provides quick search. However, this task involves processing of a large amount of data, requiring high computational power. In this article, we present a heterogeneous parallel architecture that uses CPU and GPU in a cluster to accelerate inverted index generation. Experimental results show that the proposed architecture provides faster execution times, up to 60 times in classification and 23 times in the compression of 1 million elements.
An innovative approach to scalable semantic embedding

Embedding words, entities and documents in compact, semantically meaningful vector spaces allows for computable semantic similarity/relatedness which could make search more intelligent and benefit other tasks conducted in libraries, such as entity disambiguation, de-duplication, clustering, recommendation, subject prediction, etc. Deep learning models are powerful but require high computing power and careful tuning hyperparameters for optimal performance. In our quest for practical solutions to support libraries in this field, we revisit the global co-occurrence based embedding methods and propose a conceptually simple and computationally lightweight approach. Our experiments show highly competitive results with a few state-of-the-art embedding methods on different tasks, including the standard STS benchmark and a subject prediction task, at a fraction of the computational cost. We will show the potentials of this scalable semantic embedding method for other applications such as entity disambiguation, citation recommendation, clustering and collection exploration.
Analysis of Emoticon and Sarcasm Effect on Sentiment Analysis of Indonesian Language on Twitter

Background: Indonesia is an active Twitter user that is the largest ranked in the world. Tweets written by Twitter users vary, from tweets containing positive to negative responses. This agreement will be utilized by the parties concerned for evaluation.Objective: On public comments there are emoticons and sarcasm which have an influence on the process of sentiment analysis. Emoticons are considered to make it easier for someone to express their feelings but not a few are also other opinion researchers, namely by ignoring emoticons, the reason being that it can interfere with the sentiment analysis process, while sarcasm is considered to be produced from the results of the sarcasm sentiment analysis in it.Methods: The emoticon and no emoticon categories will be tested with the same testing data using classification method are Naïve Bayes Classifier and Support Vector Machine. Sarcasm data will be proposed using the Random Forest Classifier, Naïve Bayes Classifier and Support Vector Machine method.Results: The use of emoticon with sarcasm detection can increase the accuracy value in the sentiment analysis process using Naïve Bayes Classifier method.Conclusion: Based on the results, the amount of data greatly affects the value of accuracy. The use of emoticons is excellent in the sentiment analysis process. The detection of superior sarcasm only by using the Naïve Bayes Classifier method due to differences in the amount of sarcasm data and not sarcasm in the research process.Keywords: Emoticon, Naïve Bayes Classifier, Random Forest Classifier, Sarcasm, Support Vector Machine
Pixel and region-based image processing algorithms for detection and classification of pavement cracks

Cracks are considered one of the most critical problems of pavement systems. Fully automatic distress evaluation systems are designed to assess pavement conditions. They are developed by combining image processing and pattern recognition techniques for detecting and classifying roadway surface distresses. Currently, there is a considerable need for developing fully automatic distress evaluation methods that are robust, fast and cost-effective and apply to any surface regardless of the pavement texture or type. In this thesis, two automatic crack assessment methods are proposed for the detection and classification of cracks from acquired 2-D pavement images. In the first method, a region-based image processing algorithm was developed. First, local thresholding was applied, and then the existence of cracks inside each region was detected based on spatial distribution of crack pixels. By fitting curves in the center of regions, they were connected and their type and length were determined by measuring the slopes and lengths of the curves. In the second method, a pixel-based image processing method was developed that uses an improved version of the weighted neighborhood pixels segmentation algorithm for crack detection purposes. This method uses the Gaussian cumulative density function as the adaptive threshold to obviate the problem of using fixed thresholds in noisy environments. The developed methods were tested on 100 and 300 images of Asphalt Concrete (AC) and Portland Cement Concrete (PCC) surfaces respectively; the validation results were measured for the first method as: Precision = 0.85, Accuracy = 0.87, Recall = 0.89 and F1 score =0.87 and for the second method as Precision = 0.79, Accuracy = 0.99, Recall = 0.95, and F1 score = 0.86. The validation
The Effect of the Phases of the Moon on Blood Pressure in Non-Hypertensive Individuals

Results: Ambulatory blood pressure monitoring (ABPM) was performed during four phases of the moon in non-hypertensive individuals. (New moon ”38 patients,” first quarter “41 patients,” full moon “42 patients,” last quarter “37 patients,” p=nonsignificant (NS)). However, there were no significant differences in average systolic and diastolic blood pressures between groups. Conclusion: We could not find any relationship between the phases of the moon and blood pressure.
A Fast-Transient and High-Accuracy, Adaptive-Sampling Digital LDO Using a Single-VCO-Based Edge-Racing Time Quantizer

A digital low-dropout (LDO) voltage regulator using a single-VCO-based edge-racing time quantizer (SVER TQ) was designed to achieve a fast-transient response and a high-accuracy of the output voltage. As the sampling frequency generated from the SVER TQ is scaled dynamically according to the magnitude of errors in the output voltage, its transient response can be improved without the increase in the power consumption in the steady state. For the SVER TQ, since two injected edges equally pass through all delay cells in a single VCO, the accuracy of regulation is not degraded by mismatches between the delay cells. The proposed digital LDO was fabricated in a 65-nm CMOS process, and it occupied a silicon area of 0.0488 mm2. In measurements, the digital LDO in this letter achieved a 0.29-ps-transient FOM and a sub-2-mV accuracy under a 0.5-V supply.
Reliable BIER With Peer Caching

BIER (Bit-Indexed Explicit Replication) alleviates the operational complexities of multicast protocols (associated to the multicast tree and the incurred state in intermediate routers), by allowing for source-driven, per-packet destination selection, efficient encoding thereof in packet headers, and stateless forwarding along shortest-path multicast trees. BIER per-packet destination selection enables efficient reliable multicast delivery: packets not received by a subset of intended destinations can be efficiently BIER-retransmitted to only that subset. While BIER-based reliable multicast exhibits attractive performance attributes, relying on source retransmissions for packet recovery may be costly – even unnecessary, if topologically close peers are able to provide a copy of the packet. Thus, this paper extends the use of reliable BIER multicast to allow recovery also from peers, using Segment Routing (SR) to steer retransmission requests through a set of potential (local) candidates, before requesting retransmissions from the source as a last resort only. A general framework is introduced, which can accommodate different policies for the selection of candidate peers for retransmissions. Simple (both static and adaptive) policies are introduced and analyzed, both (i) theoretically and (ii) by way of simulations in data-center-like and real-world topologies. Results indicate that local peer recovery is able to substantially reduce the overall retransmission traffic, and that this can be achieved through simple policies, where no signalling is required to build a set of candidate peers.
Understanding Illicit UI in iOS Apps Through Hidden UI Analysis

In <italic>Chameleon</italic> apps, benign UIs are displayed during Apple App vetting while their hidden potentially-harmful illicit UIs (<italic>PHI-UI</italic>) are revealed once they reached App Store. In this article, we report the <italic>first</italic> systematic study on iOS <italic>Chameleon</italic> apps, which sheds light on a largely overlooked threat that the illicit activities are launched solely based on UI. Our research employed <sc>Chameleon</sc>-<sc>Hunter</sc>, a new static analysis approach that determines the suspiciousness of a <italic>PHI-UI</italic> leveraging the <italic>semantic features</italic> generated from iOS app UI and metadata. The approach is based on the observation that <italic>PHI-UI</italic> not only is structurally hidden but also has notable semantic inconsistency with the benign UI. Our evaluation shows that <sc>Chameleon</sc>-<sc>Hunter</sc> is highly effective, achieving 92.6 percent precision and 94.7 percent recall. From 28K Apple App Store apps, we found 142 new <italic>Chameleon</italic> apps, which were confirmed and promptly removed by Apple. Our work reveals that Chameleon apps can easily bypass the App store vetting and conduct a set of suspicious activities including collecting users’ private information, swindling money with fake monetary services, and leading the user to a pirated app store.
Imputation-Based Ensemble Techniques for Class Imbalance Learning

Correct classification of rare samples is a vital data mining task and of paramount importance in many research domains. This article mainly focuses on the development of the novel class-imbalance learning techniques, which make use of oversampling methods integrated with bagging and boosting ensembles. Two novel oversampling strategies based on the single and the multiple imputation methods are proposed. The proposed techniques aim to create useful synthetic minority class samples, similar to the original minority class samples, by estimation of missing values that are already induced in the minority class samples. The re-balanced datasets are then used to train base-learners of the ensemble algorithms. In addition, the proposed techniques are compared with the commonly used class imbalance learning methods in terms of three performance metrics including AUC, F-measure, and G-mean over several synthetic binary class datasets. The empirical results show that the proposed multiple imputation-based oversampling combined with bagging significantly outperforms other competitors.
DEVELOPMENT OF AN EFFICIENT SMART GRID WITH ERROR CONTROL ALGORITHM

The in capabilities of the current power grid in distributing monitoring and managing due to the rising needs in the energy consumption and the aging infrastructure of the prevailing power grid, has led to the emergence of the grid that is smart in managing distributing and monitoring . The smart grid incorporating controls, computer, automation along innovative technologies and tools are able to provide an enhanced performance by being extra proficient, secure, reliable and greener. The smart grid also provides with the two way communication between the users and the utilities on the terms of electricity and information with the usage being completely monitored using the wireless sensor networks. The communication between the user and the utilities are often affected due to the noise, fading and path loss etc. experienced by the communication path. So the paper proffers an error control method that is adaptive to the changing behavior of the communication channels to make have an error less communication making the smart grid efficient. The result obtained based on the bit error rate, delay and the throughput evince the capability of the proposed scheme.
Crowdclustering

Is it possible to crowdsource categorization? Amongst the challenges: (a) each worker has only a partial view of the data, (b) different workers may have different clustering criteria and may produce different numbers of categories, (c) the underlying category structure may be hierarchical. We propose a Bayesian model of how workers may approach clustering and show how one may infer clusters / categories, as well as worker parameters, using this model. Our experiments, carried out on large collections of images, suggest that Bayesian crowdclustering works well and may be superior to single-expert annotations.
Twin‐engined diagnosis of discrete‐event systems

Diagnosis of discrete‐event systems (DESs) is computationally complex. This is why a variety of knowledge compilation techniques have been proposed, the most notable of them rely on a diagnoser. However, the construction of a diagnoser requires the generation of the whole system space, thereby making the approach impractical even for DESs of moderate size. To avoid total knowledge compilation while preserving efficiency, a twin‐engined diagnosis technique is proposed in this paper, which is inspired by the two operational modes of the human mind. If the symptom of the DES is part of the knowledge or experience of the diagnosis engine, then Engine 1 allows for efficient diagnosis. If, instead, the symptom is unknown, then Engine 2 comes into play, which is far less efficient than Engine 1. Still, the experience acquired by Engine 2 is then integrated into the symptom dictionary of the DES. This way, if the same diagnosis problem arises anew, then it will be solved by Engine 1 in linear time. The symptom dictionary can also be extended by specialized knowledge coming from scenarios, which are the most critical/probable behavioral patterns of the DES, which need to be diagnosed quickly.
State of Health Estimation for Lithium-Ion Batteries Based on Elman Neural Network

This paper proposes a state of health (SOH) estimation method with integration of grey relational analysis (GRA) with Elman neural network (NN). First, the experimental data of lithium-ion battery life attenuation are analyzed and the health factors (HFs) are extracted. Then, the correlation degree between HFs and SOH are analyzed by the GRA. Finally, the extracted HFs are considered as the model input, and the SOH as taken as a model target output for SOH prediction. The prediction results show that the proposed method has high prediction accuracy that it can be applied to the online SOH estimation.
Caracterización de los estudiantes de una institución de educación superior mediante big data

espanolEl articulo presenta resultados de un proyecto de investigacion cuyo objetivo principal consistio en realizar la caracterizacion de los estudiantes de una institucion de educacion superior mediante el uso de tecnicas y herramientas de big data. Esto con el proposito de dotar a la institucion de una herramienta para apoyar la toma de decisiones relacionadas con la comunidad estudiantil. El proyecto se desarrollo en cinco fases: definicion de la estrategia; captura y medicion de los datos; analisis de los datos; generacion de un informe de resultados; y, transformacion del negocio. Como tecnicas de analisis de datos se utilizaron el coeficiente de correlacion de Pearson y el agrupamiento mediante k-means. Como resultado, se obtuvo un inventario y caracterizacion de las fuentes de datos, y un modelo de analisis y procesamiento de la informacion, que al ser aplicado genera una caracterizacion de una comunidad estudiantil. EnglishThis article presents results of a research project whose main objective was to characterize students of a higher education institution through the use of big data techniques and tools. This with the purpose of providing the institution with a tool to support decision-making related to the student community. The project was developed in five phases: strategy definition; data capture and measurement, data analysis, results report, and, business transformation. As data analysis techniques, the Pearson correlation coefficient and the k-means grouping were used. As a result, an inventory and characterization of the data sources was obtained, as well as a model of analysis and information processing, when it is applied, generates a characterization of a student community.
Optimal Altitude Selection of Aerial Base Stations to Maximize Coverage and Energy Harvesting Probabilities: A Stochastic Geometry Analysis

This paper provides radio frequency (RF) energy harvesting and coverage probability analysis using stochastic geometry in a two-hop communication network. In the first hop, terrestrial base stations (TBSs) transmit RF energy and data to the aerial base stations (ABSs) and in the second hop, ABSs forward the data to the user. Numerical results show that the energy harvesting and coverage probabilities can be maximized at almost the same altitude of the ABSs with respect to the TBSs height for low TBSs densities. However, as the density of the backhaul nodes and the energy harvesting sub-timeslot increases, energy harvesting probability improves while coverage probability monotonically decreases by increasing the ABSs height.
An efficient emotion classification system using EEG

Emotion classification via Electroencephalography (EEG) is used to find the relationships between EEG signals and human emotions. There are many available channels, which consist of electrodes capturing brainwave activity. Some applications may require a reduced number of channels and frequency bands to shorten the computation time, facilitate human comprehensibility, and develop a practical wearable. In prior research, different sets of channels and frequency bands have been used. In this study, a systematic way of selecting the set of channels and frequency bands has been investigated, and results shown that by using the reduced number of channels and frequency bands, it can achieve similar accuracies. The study also proposed a method used to select the appropriate features using the Relief F method. The experimental results of this study showed that the method could reduce and select appropriate features confidently and efficiently. Moreover, the Fuzzy Support Vector Machine (FSVM) is used to improve emotion classification accuracy, as it was found from this research that it performed better than the Support Vector Machine (SVM) in handling the outliers, which are typically presented in the EEG signals. Furthermore, the FSVM is treated as a black-box model, but some applications may need to provide comprehensible human rules. Therefore, the rules are extracted using the Classification and Regression Trees (CART) approach to provide human comprehensibility to the system. The FSVM and rule extraction experiments showed that The FSVM performed better than the SVM in classifying the emotion of interest used in the experiments, and rule extraction from the FSVM utilizing the CART (FSVM-CART) had a good trade-off between classification accuracy and human comprehensibility.
Detection Technology of Intrusion Objects in Power Transmission Corridors Based on Convolutional Neural Networks

Intrusion objects for power transmission corridors always present a big threat to the successful operation of the power grid. Due to the complexity and total length of the transmission corridors, the conventional field inspection methods for detecting these objects is a significant workload with low efficiency. This paper proposes the method for detecting transmission line intrusion objects, including engineering vehicles such as exactors and cranes. Object detection technology is used to locate and identify the engineering vehicles near power transmission lines. Experimental results show that, compared with traditional identification methods, the proposed method can provide improved accuracy with respect to the specific classification information of engineering vehicles and serve as an information basis for transmission line fault warning.
A Mesh Sensor Network based on Bluetooth: Comparing Topologies to Crop Monitoring.

— The growth of the world population increases the demand for food and other products from agri-culture. Therefore, it is increasingly needed to enhance and optimize the productivity of farms. Through using a Wireless Sensor Network (WSN) and the application of the internet of things (IoT) technologies, we can better monitor the condition of farm crops. In this work, we designed a Bluetooth Low Energy mesh network joined ﬂuorescence sensors for agricultural crop monitoring, and also we evaluated the suitability of this solution in a crop ﬁeld scenario.
Why Software-Defined Radio (SDR) Matters in Healthcare?

Background: Wireless Body Area Networks (WBANs) have been drawing noteworthy academic and industrial attention. A WBAN states a network dedicated to acquire personal biomedical data via cutting-edge sensors and to transmit healthcare-related commands to particular types of actuators intended for health purposes. Still, different proprietary designs exist, which may lead to biased assessments. This paper studies the role of Software-Defined Radio (SDR) in a WBAN system for inpatient and outpatient monitoring and explains to health professionals the importance of the SDR within WBANs. Methods: A concern related to all wireless networks is their dependence on hardware, which limits reprogramming or reconfiguration alternatives. If an error happens in the equipment, firmware, or software, then, typically, there will be no way to fix system vulnerabilities. SDR solves many fixed-hardware problems with other benefits. Results: SDR entails more healthcare domain dynamics with more network convergence in agreement with the stakeholders involved. Then the SDR perspective can bring in innovation to the healthcare subsystems’ interoperability with recombination/reprogramming of their parts, updating, and malleability. Conclusion: SDR technology has many utilizations in radio environments and is becoming progressively more widespread among all kinds of users. Nowadays, there are many frameworks to manipulate radio signals only with a computer and an inexpensive SDR arrangement. Moreover, providing a very cheap radio receiver/transmitter equipment, SDR devices can be merged with free software to simplify the spectrum analyses, provide interferences detection, deliver efficient frequency distribution assignments, test repeaters' operation while measuring their parameters, identify spectrum intruders and characterize noise according to frequency bands.