Miracle-dz/UniFAR-FTUs huggingface dataset:
SoPa: A Social Media for a Participatory Society

The recent political, economic, and social situation has given rise to question on the part of citizens regarding their own space for political participation. Social movements in the form of protests are an example of this growing need for society’s participation. One thing that can expand these possibilities is technology. It is correct to affirm that the advancement of smart city and popularization of technology — more specifically, of social media — has established new opportunities to expand these virtual spaces. In line with this scenario, there are several initiatives whose objective is to develop tools that facilitate or promote citizen participation. However, there are still many issues that need to be investigated in order for the use of these tools to be effective. The objective of this work was to propose a tool that facilitates the discussion of societal problems by citizens. In the analysis of the results — obtained through an experiment with users to evaluate the effectiveness of the tool — it could be seen that, although the majority of users agreed that the discussion model and the proposed problem register can contribute to the real solution of the problem, it could also be seen that citizens still lack motivation and engagement — something that needs to be studied and improved.
State of Art of the Effectiveness in Adopting Blockchain Technology - UAE Survey Study

The Government of the United Arab Emirates (UAE) is moving towards the Smart City and integrating Blockchain (BC) technology among its services to the public. In April 2018, the UAE government launched a Blockchain Strategy of 2021. Under this initiative, 50 percent of government transactions will be processed over a BC platform. Eleven billion dirhams are expected to be saved in transactions and documents prepared in the government sector. The UAE 2021 strategy includes four pillars, focusing on residence happiness, government efficiency, advanced legislation, and global entrepreneurship. In October 2016, the emirate of Dubai, launched a local BC strategy to become the first energy-powered city that leads the future economy by 2020. This motivation placed the Government of UAE as a unique and pioneered country in innovation and enabling the ecosystem to be addressed in both the public and private sectors. This strategy is based on three domains: government effectiveness, industry innovation, and international, domestic leadership. BC projects will be experimenting throughout the country in many sectors such as energy, transportation, logistics, tourism, health, education and employment, economic development, safety and justice, social services, municipal and land works. This paper study the state of art of the UAE strategy towards adopting BC technology among all its entities. A survey has been conducted to evaluate the strategic effectiveness of this new technology. The study involves a combination of 71 public and private companies that operate in the UAE. The outcome shows that there is a need to integrate this technology. At the same time, the survey concluded that 92.0% of the companies are aware of the technology. It also highlighted that 55% of companies suffer from the lack of experienced consultants and outsources their services. The survey confirmed that BC technology had been used in different industrial use cases such as supply chain (35%), Internet of things (27%), and financial transactions (25%).
Visual and Textual Jointly Enhanced Interpretable Fashion Recommendation

With the rapid development of online shopping, interpretable personalized fashion recommendation using image has attracted increasing attention in recent years. The current work has been able to capture the user’s preferences for visible features and provide visual explanations. However, they ignored the invisible features, such as the material and quality of the clothes, and failed to offer textual explanations. To this end, we propose a Visual and Textual Jointly Enhanced Interpretable (VTJEI) model for fashion recommendations based on the product image and historical review. The VTJEI can provide more accurate recommendations and visual and textual explanations through the joint enhancement of textual information and visual information. Specifically, we design a bidirectional two-layer adaptive attention review model to capture the user’s visible and invisible preferences to the target product and provide textual explanations by highlighting some words. Moreover, we propose a review-driven visual attention model to get a more personalized image representation driven by the user’s preference obtained from the historical review. In this way, we not only realize the joint enhancement of visual information and textual information but also provide a visual explanation by highlighting some regions. Finally, we performed extensive experiments on real datasets to confirm the superiority of our model on Top-N recommendations. We also built a labeled dataset for evaluating our provided visible and invisible explanations quantitatively. The result shows that we can not only provide more accurate recommendations but also can provide both visual and textual explanations.
Indirect Field Oriented Control of Pole Phase Modulated Nine-Phase Induction Motor with the evaluation of PI Controllers' Parameters

Electronic modulation of poles and phases of the induction motor extends the operating range of torque and speed. Hence it is useful in the electric vehicle application. The nine-phase pole phase modulated induction motor (PPMIM) operates in two pole-phase combinations: 3-phase, 12-pole combination that is related to high torque and low speed operation, and 9-phase, 4-pole combination that is related to low torque and high speed operation. The three-phase mode is useful during the starting of the motor, and the nine-phase mode is related to steady-state operation. The indirect field oriented control (IFOC) strategy controls the speed of the motor. Sine pulse width modulation (SPWM) controls the input power of the nine-phase PPMIM that is fed by voltage source inverter. IFOC requires PI controllers that have to be tuned. State variable approach is used to find out the transfer function of the plant and the PI controller of each control loop. The modeling of IFOC of nine-phase PPMIM is done using MATLAB-SIMULINK software.
A Review on Requirements Engineering for Internet of Things (loT) Applications

Organizations and customers are adopting Internet of things (loT) due to many advantages that have impacted human life in various ways - smart homes, smart transportation, smart parking, and many smart applications in the medical, defense and other sectors. As these loT applications are emerging rapidly, there is a need to focus on software development life cycle (SDLC) of these systems to ensure necessary levels of quality. Requirements engineering for loT based systems did not get that much attention in its early years and thus has not been well explored by the Software Requirements Engineering (SRE) research community. Hence, it required to identify and recognize the challenges in loT development especially from the SRE perspectives. This article presents a comprehensive and inclusive overview of the issues pertaining to requirements engineering in the context of loT-based smart applications. Each issue pertaining to RE of loT based system has been evaluated using parameters based on the existing literature.
A Hybrid Learning approach for Sentiment Classification in Telugu Language

Sentiment or Opinion Analysis is a subset of Natural Language Processing which is employed in wide range of business verticals to disentangle, analyze, distinguish and comprehend the general opinion of user reviews, comments, feedback, news, and so on. Humans generate close to 2.5 quintillion bytes of data each day on internet and business leaders are tasked to derive hidden patterns and meaningful insights from this data to understand human behavior and take shrewd decisions. Since last decade, enormous amounts of text data on internet is generated for Indian languages. Researchers have shown inquisitiveness in deriving and analyzing this data to extract relevant information. To the best of our knowledge, there has been no requisite amount of research in classifying sentiment in Telugu language because of inadequate language resources and also being a regional language. This research paper illustrates a methodical approach which leverages lexicon based approach and machine learning in the field of sentiment analysis to classify the opinions in Telugu language. Firstly, by employing Lexicon based approach - Telugu SentiWordNet we identified the subjective sentences from the Telugu corpus. Secondly, by utilizing machine learning algorithms – SVM, Naïve Bayes and Random Forest we categorized the sentiment in the corpus. Our proposed methodology achieved highest accuracy of 85%.
Language of Power: Frequency Dynamics and Semantic Transformations of the Title "Emperor" in Russian

In this paper we study the relationship between the frequency of the title Emperor usage and the development of its semantics against the background of sociocultural changes in society using the material of Russian. The research includes the semantic and axiological evaluation of the Google Books Ngram corpus digrams. The diachronic quantitative study of the linguistic-cultural concept was carried out according to the method developed by the authors of the article also in comparative purposes in English, Chinese, German, French and Spanish.
User Power Consumption Cluster Analysis Based on Cloud Computing and Improved K-means Algorithm

Power companies store a huge amount of user historical electricity consumption data, which is in a complex, independent, and scattered state. If the historical electricity consumption data can be reasonably excavated, we can cluster users according to different users’ electricity consumption, electricity usage habits and characteristics, etc. These are beneficial to both data management and power forecasting. Based on this, the electricity consumption data of some users in a certain city is used as the data source. Based on cloud computing and the usage of improved K-means algorithm, we could analyze its historical electricity consumption, monthly electricity consumption change, peak-to-valley power consumption per day and other data, which is useful to cluster the power consumption of each user. After clustering, we obtain the information about electricity consumption of users of each category, which presents distinctive features, guides users to passive and lagging in energy-saving renovation, and supports intelligent business analysis and decision-making.
Motion Planning and Control for a Class of Partially Differentially Flat Systems

This paper deals with motion planning and control problems for a class of partially differentially flat systems. A Betafunction based motion planning algorithm is first constructed by selecting the base variable to be the 2-nd order derivative of Beta-function weighted by a constant parameter. The proposed algorithm has lower computational cost than the optimal control while it provides similar system performance. Then, a function approximation technique based control algorithm is proposed by restructuring a partially differentially flat system in the form of the combination of an auxiliary linear system and its variation from the original system. The variation term was replaced by its approximation as a chosen basis function weighted by constant parameters to be determined. These unknown plant parameters are estimated at each instant, denoted by the adjustable control parameters using a defined update law.
Flexible BaTiO3-PDMS Composite Thin Films for Sensing Applications

In this paper, a simple fabrication technique for preparing flexible and robust thin films of piezoelectric composite has been reported. The fabrication process involves dispersing Barium titanate (BaTiO$_{3}\,)$ nanoparticles (NPs) in cross-linked (Polydimethylsiloxane) PDMS matrix. Corona poling of films is performed to increase the piezoelectric charge coefficient. The effect of BaTiO3 concentration on piezoelectric and mechanical properties of the film has been investigated. A 40 wt% BaTiO3 composite poled film demonstrates a piezoelectric coefficient of 72 pm/V with good flexibility. Good piezoelectric voltage response, and low stiffness of the prepared composite films show that they have potential to be used in flexible sensors such as tactile sensor, acoustic sensor. Also, these films offer low cost, bio compatibility, flexibility along with good piezoelectric properties.
Keyword based Searching in a P2P Network

The growth of network contents in today's world is exponential and it is difficult to create and maintain a centralized document index for efficient searching. Peer to Peer(P2P)systems are highly prominent and complicated nowadays which are used by millions of people for distributing documents or other internet contents. Although P2P network can be a scalable replacement to traditional client-server model, searching files efficiently in a large and dynamic network is a challenging problem. Recent research on keyword-based searching shows different problems in load balancing, storage redundancy and fault tolerance. In this paper, we assess various Keyword based search strategies for searching files in a P2P network.
Automatic Stopword Detection Using Term Ranking between Written and Machine Speech Recognition Transcribed Reviews

Video feedback and machine speech recognition are fast-becoming a popular choice for companies to gain insight into their products. In conjunction with this, text analytics can be used to extract insight from these video translations. Currently, there is little work in the area to analyse and compare techniques for natural language processing, information retrieval and information extraction. A commonly practiced technique in text analytics is the extraction of stop words; words whose presence do not contribute context or information to a document. In this paper, we explore statistical techniques for the automated extraction of stop words, comparing 4 datasets from written and translated reviews. Using statistical variations of the successful technique ‘term ranking’, we evaluate their performance using a common list of stop words. Results suggest that variation, TFnormIDFnorm, was the most successful with a best performing precision rate of 46.7% and a recall rate of 86.6%. The best results were seen in the largest dataset using written reviews, however comparison of the remaining 3 datasets revealed that spoken text performed 0.4% better in precision than the next best dataset and 2.6% better in recall. Initial results show marginally better performance in machine speech recognition transcribed texts from videos in comparison to comparably size datasets of written reviews.
DC to DC converter based Asymmetrical multilevel inverter with reduced number of components

In this paper, the variable voltage method has been presented to minimize the components of an asymmetrical multilevel inverter (AMI). AMI consists of one main bridge and many auxiliary bridges to increment the number of levels (NOLs) in the output voltage which minimizes the total harmonic distortion (THD). However, the number of auxiliary bridges increases the space for installation, cost and components. To overcome these drawbacks a buck converter (BC), having low rating compared to the rated value of AMI is attached to the auxiliary bridge. This method gives possibility to obtain the higher NOLs with a reduced number of components of AMI. A comparative study is done with existing AMIs topologies to show the effectiveness of the variable voltage method on the basis of the number of semiconductor switches, a variety of DC sources, number of DC sources, and total standing voltage. In this paper, simulation is performed in the MATLAB/SIMULINK environment. The output voltage and THD have been calculated by simulation of 9-level and 15-level multilevel inverter. The hardware implementation for the 9-level multilevel inverter has been done using a low-cost microcontroller. The simulation results are verified by comparing them with those obtained experimentally
Open Government Data Use by Malaysian Researchers. Some empirical evidence

Sharing data by the government with citizens will lead to a more transparent and trustworthy government. Open Government Data (OGD) has been initiated by many governments worldwide aiming to publicly and freely share government data via online. In a similar effort, the government of Malaysia has launched an OGD portal in 2014 and currently more than 12,000 data sets are available for citizens and businesses to access. However, there is a concern on the level of usage of these datasets. Additionally, there is lack of empirical studies done to understand this issue. The purpose of this study is to fill this gap. Based on a questionnaire survey method, data was collected from Malaysian researchers and academicians to understand their use of OGD. Despite a rather small sample of 62 responses, the findings shed light on the access and purposes of using OGD in the Malaysia context.
An Ensemble Learning Approach for Automatic Brain Hemorrhage Detection from MRIs

Brain hemorrhage is one of the conditions that could affect people for several reasons such as high blood pressure, drug abuse, aneurysm, and trauma. Neurologists ordinarily use Magnetic Resonance Imaging (MRI) scan to examine patients for brain hemorrhage. In this study, we have developed an intelligent automatic model to identify MRIs of patients with hemorrhage from intact ones using an ensemble learning approach. Moreover, our proposed model can annotate the affected area of the brain in an axial view of MRI, which helps trainee doctors to improve their reasoning and decision making. In our experimental settings, we have applied a segmentation-based feature texture analysis to prepare MRIs for classification using an adaptive boosting algorithm. Our proposed method has achieved a classification accuracy of 89.2%, with 100% sensitivity in detecting the affected area of the brain.
Cancelable Multi-Biometric Approach Using Fuzzy Extractor and Novel Bit-Wise Encryption

The widespread deployment of multi-biometrics to authenticate users prompts the need for biometric systems with high recognition performance. Further, the biometric data, once leaked or stolen, remains compromised forever. Hence biometric security is of utmost importance. Existing biometric template protection schemes either degrade the recognition performance or they have issues with security and speed. We propose a cancelable multi-biometric authentication approach where a novel bit-wise encryption scheme transforms a biometric template to a protected template using a secret key generated from another biometric template. It fully preserves the number of bit-errors in the original and the protected template to ensure recognition performance equivalent to the performance of the unprotected systems. We introduce Algorithm I and Algorithm II for bit-wise encryption; both are defined over cryptographic-primitives- block cipher based encryption and keyed-hash function. We profile these algorithms on various hardware architectures to calculate the efficiency in terms of the time taken during enrolment and authentication phase. For Algorithm II, we observe that a 3.3 GHz desktop architecture takes about 18 milliseconds on an average of over 200 runs to authenticate a user. Additionally, we provide mathematical proof to show that the proposed scheme guarantees secrecy and irreversibility. The results of comparisons with the existing biometric template protection schemes on the various face and iris databases show that the proposed work provides significantly good recognition performance and efficiency, while it achieves high security. Finally, the bit-wise encryption scheme can be built over the commercial-off-the-shelf systems to achieve security with equivalent high performance.
Analysis of meiotic recombination initiation in Saccharomyces cerevisiae

Meiosis is the unique process in which diploid cells undergo two consecutive divisions to produce haploid daughter cells. It is indispensable for sexual reproduction in all eukaryotic organisms and maintains proper chromosome number through generations. An integral step in the meiotic program is genetic recombination; recombination is required for a successful reductional division. In the yeast Saccharomyces cerevisiae, recombination is initiated by DNA double strand breaks (DSBs) that are created by ten recombination initiation proteins. Similar phenotypes are observed when any of these genes is mutated. This has made the mechanism by which these proteins function to initiate recombination difficult to unravel. One hypothesis is that these proteins form a functional complex for activity, in which all (or most) of them physically interact. The work described in Chapter 2 contributes to understanding the putative DSB-producing recombination initiation complex, suggesting there is substantial flexibility among initiation protein interactions. The results are also consistent with the view that the proteins assemble on the DNA. Studies in Chapter 3 examined the recombination initiation protein interactions during DSB formation in more detail using a novel experimental approach. While the initial experiments using this approach produced unexpected results, the assay is a promising tool for the future. In addition to creating DSBs, a subset of the initiation proteins perform a second function during early meiosis; they create a recombination initiation signal (RIS) to delay the onset of the reductional division in wild-type cells. Although the signal and the downstream target are well-defined, less is known about how the RIS is transduced to the downstream target. The work in Chapter 4 contributes to defining this transduction, and therefore enhances our understanding of the relationship between the recombination initiation proteins and the reductional division.
Supplementary data

Supplementary data Text S1. Toxicity testing methods (1) Acute toxicity The acute toxicities of water samples were detected with a DeltaTox II luminometer (SDIX, USA), a commercial bioassay according to the ISO standard based on the inhibition of bioluminescence emitted by the luminescent bacteria V. fischeri. The specific steps are as follows: Firstly, 310 μL of Micro Tox dilution was added to the luminescent bacteria freezedried test tube, which was evenly distributed into 3 test tubes after being cultured for 15 min at room temperature and the initial luminosity (E0) was recorded using the physiological fluid as a blank control. Then, 100 μL of Micro Tox osmotic adjustment solution (OAS) was taken into 1000 μL of water sample to be tested and shaken until uniform to adjust the salinity of the water sample to 3%. Finally, 900 μL of the above water sample was added into the corresponding sequence of luminescent bacteria tubes. The luminosity (E) of the water sample was recorded after 5 min of exposure. The pH of the water sample was adjusted to neutral before the test and the test temperature was controlled at 20±5 °C. The 15 min EC50 of the quality control reference poison HgCl2 to the luminescent bacteria was 0.08~0.12 mg/L. The acute toxicity of the water sample was expressed by the relative luminosity inhibition rate (T%). The calculation formula was as shown in the following formula: