Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Generalizability of Two-dimensional Convolutional Neural Networks for Fake Speech Detection

The powerful capabilities of modern text-to-speech methods to produce synthetic computer generated voice, can pose a problem in terms of discerning real from fake audio. In the present work, different pipelines were tested and the best in terms of inference time and audio quality was selected to expand on the real audio of the TIMIT dataset. This led to the creation of a new fake audio detection dataset based on the TIMIT corpus. A range of different audio representations (magnitude spectrogram and energies representations) were studied in terms of performance on both datasets, with the two-dimensional convolutional neural networks trained only on the Fake or Real (FoR) dataset. While there was not a single best representation in terms of performance on both datasets, the Mel spectrogram and Mel energies representations were found to be more robust overall. No performance difference in recognition accuracy was evident during validation, while the two-dimensional convolutional neural network model showed a tendency to under-perform on the test set of the FoR dataset and the synthesized one based on the TIMIT corpus, regardless of the representation used. This fact was corroborated by the data distribution analysis that is presented in the present work.
Assessing User Privacy on Social Media: The Twitter Case Study

At the time of writing, nearly four billion people worldwide employ social media platforms such as Facebook, Instagram, WeChat, TikTok, etc. to share content of various kinds, which may also include personal data. In addition to this, users interact with members of the virtual community, leaving behind important behavioral traces. In most cases, people do not have a full understanding of who will be able to access and use such a body of information, and for what purposes. Although social platforms provide users with some tools to protect their privacy, the very nature of these technologies and the psychological characteristics of users often lead them to ignore such solutions. To address this issue, in this paper we aim to propose a model for assessing the privacy of users on social media by identifying the critical aspects associated with their content and interactions generated on such platforms. This model, in particular, considers distinct features, of different kinds, that capture the level of users’ exposure with respect to privacy. These features, dropped into a vector space, are used to derive a score that expresses, in a measurable way, the privacy risk of users compared to the information available on social media about them. The proposed model is instantiated and tested on data collected from the microblogging platform Twitter, on which the results of the experimental evaluation are analyzed. Specifically, the model is tested by considering both a binary scenario, i.e., where users’ privacy is evaluated as at risk or not, a multi-class scenario, i.e., where their privacy is evaluated against different risk ranges, and a ranking scenario, i.e., where the users are ranked according to their privacy assessment.
BlackLivesMatter 2020: An Analysis of Deleted and Suspended Users in Twitter

After George Floyd’s death in May 2020, the volume of discussion in social media increased dramatically. A series of protests followed this tragic event, called as the 2020 BlackLivesMatter movement. Eventually, many user accounts are deleted by their owners or suspended due to violating the rules of social media platforms. In this study, we analyze what happened in Twitter before and after the event triggers with respect to deleted and suspended users. We create a novel dataset that includes approximately 500k users sharing 20m tweets, half of whom actively participated in the 2020 BlackLivesMatter discussion, but some of them were deleted or suspended later. We particularly examine the factors for undesirable behavior in terms of spamming, negative language, hate speech, and misinformation spread. We find that the users who participated to the 2020 BlackLivesMatter discussion have more negative and undesirable tweets, compared to the users who did not. Furthermore, the number of new accounts in Twitter increased significantly after the trigger event occurred, yet new users are more oriented to have undesirable tweets, compared to old ones.
Analysis of Media Bias in Policy Discourse in India

Many citizens consume information on government policies from the mass media. Consequently, biases existing in the policy discourse in media sources may influence citizens’ understanding of the policies, about how they may affect diverse communities. These biases may also get amplified further through social media if it simply echoes the biases of mass media content. We build methods to quantify media bias in terms of preferred treatment given to certain issues corresponding to four economic policies, and alignment observed with the ideological stance of different political parties. We also examine how the social media community of followers of these media houses contribute to the policy discourse. Other than being one of the first large scale studies in the Indian context, our work contributes towards creating a standardized methodology to assess the ideological stance of a news-source, and its alignment with the social media discourse of its follower community. We find that the Indian mass media exhibits bias towards certain aspects or topics related to policy events. It also provides a significantly high coverage to aspects concerning the middle class and to political statements, neglecting the aspects directly relevant to the poor. Additionally, we find evidence of bias also in the representation provided to different political parties in the media. Social media seems to echo these biases rather than mitigate them. The tools and methods developed in this work can be useful for media watchdog institutions to call out biases in the media, and advocate for more complete coverage of issues across different news sources.
Note: Plant Leaf Disease Network (PLeaD-Net): Identifying Plant Leaf Diseases through Leveraging Limited-Resource Deep Convolutional Neural Network

Agriculture is the fundamental source of revenue and Gross Domestic Product (GDP) in many countries where economically developing countries; especially the Global South are no exception. Various types of plant-based diseases are strongly intertwined with the everyday lives of those who are connected with agriculture. Among the diseases, most of them can be diagnosed by leaves. However, due to the variety of illnesses, identifying and classifying any plant leaf disease is difficult and time-consuming. Besides, late identifications of diseases cause losses for the farmers on a large scale, which in turn affects their financial state. Therefore, to overcome this problem, we present a lightweight approach (called PLeaD-Net) to accurately recognize and categorize plant leaf diseases in this paper. Here, leveraging a limited-resource deep convolutional network (Deep CNN) model, we extract information from sick sections of a leaf to accurately identify locations of disease. In comparison to existing deep learning methods and other prior research, our proposed approach achieves a much higher performance using fewer parameters as per our experimental results. In our study and experimentation, we develop and implement an architecture based on Deep CNN. We test our architecture on a publicly available dataset that contains different types of plant leaves images and backgrounds.
Platform for 3D inline process control in additive manufacturing

3D - Inline - Process - Control is getting more attention in any fields of manufacturing processes to increase productivity and quality. Sensor systems are necessary to capture the currently process status and are basement for Inline-Process- Control. The presented work is a possibility to get inline information’s about the additive manufacturing process Fused Filament Fabrication. The requirement is the ability to manipulate the machine code to get free field of view to the topside of the object after every manufactured layer. The adaptable platform layout makes possible to create different approaches for inline process control. One approach is the single camera layout from bird view to get 2,5D information’s about the manufactured object and the other one is the active stereoscopic camera layout with pattern projection. Both approaches are showing a possibility to get information’s of the manufactured object in process. Additional this cases allow a view inside the manufactured object and defects can be located. Deviations in the manufacturing process can be corrected and relevant parameters can be adapted during slicing process to increase the manufacturing quality.
A Hybrid Multi-Objective Teaching Learning-Based Optimization Using Reference Points and R2 Indicator

Hybrid multi-objective evolutionary algorithms have recently become a hot topic in the domain of metaheuristics. Introducing new algorithms that inherit other algorithms’ operators and structures can improve the performance of the algorithm. Here, we proposed a hybrid multi-objective algorithm based on the operators of the genetic algorithm (GA) and teaching learning-based optimization (TLBO) and the structures of reference point-based (from NSGA-III) and R2 indicators methods. The new algorithm (R2-HMTLBO) improves diversity and convergence by using NSGA-III and R2-based TLBO, respectively. Also, to enhance the algorithm performance, an elite archive is proposed. The proposed multi-objective algorithm is evaluated on 19 benchmark test problems and compared to four state-of-the-art algorithms. IGD metric is applied for comparison, and the results reveal that the proposed R2-HMTLBO outperforms MOEA/D, MOMBI-II, and MOEA/IGD-NS significantly in 16/19 tests, 14/19 tests and 13/19 tests, respectively. Furthermore, R2-HMTLBO obtained considerably better results compared to all other algorithms in 4 test problems, although it does not outperform NSGA-III on a number of tests.
The Disinformation Dozen: An Exploratory Analysis of Covid-19 Disinformation Proliferation on Twitter

Shortly after the outbreak of the novel coronavirus disease (Covid-19), the United Nations declared an infodemic due to an unprecedented amount of false information spreading about Covid-19. A study made by the center for countering digital hate found out that twelve individuals, referred to as Disinformation Dozen (Disinfo12), were responsible for 65% of Covid-19 misinformation circulating on social media. Given the Disinfo12’s detrimental impact in spreading misinformation, in this work, we perform an exploratory analysis on Disinfo12’s activity on Twitter aiming at identifying their sharing strategies, favorite sources of information, and potential secondary actors contributing to the proliferation of questionable narratives. In our study, we uncovered the distinctive facets that allowed Disinfo12 to act as primary sources of information, and we recognized that YouTube represent one of the favorite information sources to spread questionable narratives and conspiracy theories. Finally, we recognized that right-leaning accounts are embedded in Disinfo12’s community and represent the main spreaders of content generated by the Disinformation Dozen.
Are Mutated Misinformation More Contagious? A Case Study of COVID-19 Misinformation on Twitter

The spread of online misinformation has become a major global risk. Understanding how misinformation propagates on social media is vital. While prior studies suggest that the content factors, such as emotion and topic in texts, are closely related to the dissemination of misinformation, the effect of users’ commentary on misinformation during its spreading on social media has been long overlooked. In this paper, we identify the patterns of “misinformation mutation” which captures ways misinformation is commented and shared by social media users. Our study focus on misinformation originated from digital news outlets and shared on Twitter. Through an analysis of over 240 thousand tweets capturing how users share COVID-19 pandemic-related misinformation news over a five-month period, we study the prevalence and factors of the misinformation mutation. We examine the different kinds of mutation in terms of how the article was cited from the news source, and how the content was edited, compared with its original text, and test the relationship between misinformation’s mutation and its spread on Twitter. Our results indicate a positive relationship between information mutation and spreading outcome – and such a relationship is stronger for news articles shared from non-credible outlets than those from credible ones. This study provides the first quantitative evidence of how misinformation propagation may be exacerbated by users’ commentary. Our study contributes to the understanding of misinformation spreading on social media and has implications for countering misinformation.
Resource Prediction of Virtual Network Function Based on Traffic Feature Extraction

∗ With the continuous innovation of the Internet, the development of Cloud Computing technology and standard server promotes the development of Network Function Virtualization (NFV). Although NFV solves the shortcomings of traditional network function equipment such as high cost and difficult operation, it also brings certain challenges. Resource management in NFV is a complex problem because the resource requirements of Virtual Network Function (VNF) vary with the dynamic traffic, so it is necessary to understand the resource requirements of VNF. Due to the limited physical network resources, it is very important to find an effective resource prediction method. Based on Heterogeneous Information Network (HIN) and Multilayer Perceptron (MLP), we propose VNF-RPHIN, a method of the VNF resource requirement prediction based on traffic feature extraction. Firstly, we construct the HIN by the correlation between traffic features. Secondly, we use the HIN2Vec model to obtain the feature representation of each traffic feature. Finally, the attention mechanism is used to measure the importance of each feature, and different weights are assigned to each feature, and then they are input into the MLP model. The hidden relationship between traffic features is mined by HIN to predict the resource requirement of the VNF. The experimental results show that the proposed method has good performance and is superior to the traditional machine learning model and common deep learning model
HAEM: Obtaining Higher-Quality Classification Task Results with AI Workers

Obtaining high-quality results for a fixed set of classification tasks with a limited budget is a critical issue in crowdsourcing. The introduction of AI models to complement the process should be explored. However, there are few existing approaches to directly address the problem, which have been proposed in the context of how to train AI models using noisy crowdsourced data. This paper presents a more direct approach for solving the problem of introducing AI to improve the task results of human workers for a fixed number of tasks with a limited budget; we deal with an AI model as a worker and aggregates the results of both human and AI workers in a symmetric manner. The proposed “Human-AI EM” (HAEM) algorithm, which extends the Dawid Skene model, treats the AI models as workers and explicitly computes their confusion matrices to derive higher-quality aggregation results. We conducted an extensive set of experiments and compared HAEM with two other methods (MBEM and Dawid Skene model). We found that AI-powered HAEM showed better performance than the two methods in most cases and that the HAEM often performed better than the Dawid Skene model with additional human workers. We also found that AI workers work well when they are good at identifying particular classes even if they do not have very good overall accuracy.
Note: Home Location Detection from Mobile Phone Data: Evidence from Togo

Algorithms for home location inference from mobile phone data are frequently used to make high-stakes policy decisions, particularly when traditional sources of location data are unreliable or out of date. This paper documents analysis we performed in support of the government of Togo during the COVID-19 pandemic, using location information from mobile phone data to direct emergency humanitarian aid to individuals in specific geographic regions. This analysis, based on mobile phone records from millions of Togolese subscribers, highlights three main results. First, we show that a simple algorithm based on call frequencies performs reasonably well in identifying home locations, and may be suitable in contexts where machine learning methods are not feasible. Second, when machine learning algorithms can be trained with reliable and representative data, we find that they generally out-perform simpler frequency-based approaches. Third, we document considerable heterogeneity in the accuracy of home location inference algorithms across population subgroups, and discuss strategies to ensure that vulnerable mobile phone subscribers are not disadvantaged by home location inference algorithms.
Sapling & the Travelling Forest: A table-top mobile robot platform for child-robot games

Mobile robots can provide unique and engaging experiences for child-robot gameplay, but are often overlooked due to challenges of setup, navigation, and space limitations. To combat these challenges, we designed “Sapling and the Travelling Forest,” a small mobile robot that uses a table-top game platform to play games with children. The system was designed as a low-cost and travel friendly alternative to larger mobile robot setups, and can be used in any location with a power outlet. The robot, “Sapling,” is an interactive agent that moves through the “Travelling Forest” platform which frames the system and contains the robot to the forest play area. The system currently offers three games focused on collaborative, spatial play. Radio frequency identification (RFID) enabled games allow Sapling to meaningfully interact with both the game elements and a child. In this paper, we present the technical design of the robot and its small, table-top platform, as well as the game design process for the three board games. The presented system is a convenient way to conduct single or multi-session child-robot interaction studies with mobile robots.
Note: Cheating and Morality Problems in the Tertiary Education Level: A COVID-19 Perspective in Bangladesh

COVID-19 pandemic has impacted every sphere of students’ life along with forcing the transition to online education which brought a significant change in the learning habits of students. Different options like using file-sharing websites, online solvers etc. for cheating were exploited by the students in the tertiary education level. We explored the driving factors of cheating by the university students (Female = 17, Male = 28). We found that the stress during the pandemic; easy availability of online materials; and competitive nature of students impelled them to cheat. The students asserted that adapting these ways of cheating has affected their various significant skills as students. The conversations show that appropriate measures to motivate students to stop cheating must be taken. Our study contributes to the research community by exploring the different factors of cheating in online exams in Bangladesh.
Practical implementation of 15 level inverter for THD reduction

Abstract Proposed designed of inverter incorporates three stage Topology with 15 levels resulting compact power control device. During this paper authors had made an attempt to propose a framework utilizing simply ten changes to provide fifteen levels for obtaining less mutilated and swish waveform, after declining costs and a diverse environment, implementing a few changes, encourages less exchange time and an improved Topology. After setting the stage and mixing at three Direct Current voltage sources (6Volts, 12Volts, and 24Volts), fifteen yield dimensions are achieved. The small leeway goal of put forth architecture is its comprehension to create yields voltage level without the H-circuit at the outlet converter. THDv% is 4% of the total test results satisfying (519) touchstones. Scheme put forth is useful for Voltage Restoring system, Voltage compensation system, Power Drives.
PHYTOCHEMICAL SCREENING AND FREE REDICAL SCAVENGING ACTIVITY OF METHANOL EXTRACT OF RHODODENDRON ARBOREUM (FLOWERS)

Cancer prevention agents expel free radicals from the body which can run wild and really harm cells, causing genuine sickness. Numerous wellbeing experts use them for medicines of stroke and neurodegenerative illnesses, for example, Alzheimer's and Parkinson's. They have additionally been useful in treating mind damage and may slow and even anticipate advancement of malignancies. 1,1-diphenyl-2-picryl hydrazyl (DPPH) was utilized to decide the free radical rummaging movement. IC 50 value of standard ascorbic acid corrosive for DPPH was 4. 46µg/ml and the IC 50 of the methanol concentration of Rhododendron arboreum was 12. 06µg/ml that was huge at all contrast and ascorbic acid corrosive. In the present examination, methanol concentration of Rhododendron arboreum was screened to assess its free radical rummaging impact.
A Tool for Knowledge Extraction from Source Code

Software maintenance tasks are the most expensive activities on legacy systems life cycle, and system understanding is the most important factor of this cost. This paper presents the LIFT: Legacy InFormation retrieval Tool, a tool for knowledge retrieval from legacy code, which requirements were defined from studies about current approaches and tools, in addition to a new requirement based on automatic suggestions to the user. In this context, companies have decided to migrate their legacy systems to new technologies, taking advantage of technological innovations, and have some alternatives: (i) to replace the applications by other software packages, losing the entire knowledge associated with the application and needing change in the business processes to adapt to new applications; (ii) to rebuild the applications from scratch, still losing the knowledge embedded in the application; or (iii) to perform application reengineering, reusing the knowledge embedded in the systems. Reengineering legacy systems is a choice that prioritizes knowledge reuse, instead of building everything from scratch again. However, despite of the existence of several methods, processes and tools to support reengineering, some activities are still difficult to replicate in industrial context, specially the adoption of effective tools in the first step, reverse engineering (Canfora 2007). Thus, we analyzed reengineering approaches and tools, identifying these strong and weak points, and present the requirements, architecture, implementation and usage of LIFT - a Legacy InFormation retrieval Tool, which is an effective tool knowledge extraction from source code. Currently, the tool is being used in an industrial project of reverse engineer NATURAL/ADABAS systems of a financial institution.