Reviewerly/exHarmony huggingface dataset:
On the (In)security of Peer-to-Peer Decentralized Machine Learning

In this work, we carry out the first, in-depth, privacy analysis of Decentralized Learning—a collaborative machine learning framework aimed at addressing the main limitations of federated learning. We introduce a suite of novel attacks for both passive and active decentralized adversaries. We demonstrate that, contrary to what is claimed by decentralized learning proposers, decentralized learning does not offer any security advantage over federated learning. Rather, it increases the attack surface enabling any user in the system to perform privacy attacks such as gradient inversion, and even gain full control over honest users’ local model. We also show that, given the state of the art in protections, privacy-preserving configurations of decentralized learning require fully connected networks, losing any practical advantage over the federated setup and therefore completely defeating the objective of the decentralized approach.
Methods to Detect Breast Cancer Using Traditional Machine Learning Technique

In this research paper, a computer-aided diagnostic system based on image processing techniques and deep learning is proposed for the classification of breast cancer in mammograms. In the preprocessing sets for image processing pectoral muscle removal, An improved mammography is created by applying contrast-limited adaptive histogram equalization algorithms for image advancement.. Finally, the region of interest was generated using gradient field coverage features based thresholding technique. Next, the system uses a reformed ResNet-50 model for feature extraction. The gleaned features are then classified using three classifiers and their performance is compared. The classifiers belonging to different classification families namely AdaBoost, support vector machine (SVM), and random forest. The overall framework achieved high accuracy, sensitivity, and specificity, demonstrating its potential as a powerful tool for breast cancer diagnosis. Furthermore, the proposed image processing and feature extraction technique shows superior performance compared to individual feature sets, illustrating the significance of the suggested approach.
Emotion Classification of Social Media Posts using Artificial Intelligence and Machine Learning

Machines can nowadays understand almost any type of data and they can identify most of the patterns, but identification of emotions or emotion mining has always been a challenging task. Emotion analysis is one of the major research points which aims to classify the text according to the emotions detected. In this paper, we will be analyzing the categories of the emotions in the written text, audio, and videos/images, and the intensity of the emotions. By analyzing the emotions, we can understand the author's perspective on the text. Emotion mining has become a very important part of the online industry because it is one of the major points of customer satisfaction, helps in selecting online material to teach, recommends products based on user emotions, or can be used to detect if the user is undergoing through any mental stress or disorder. During this project, we will be studying and analyzing different approaches which were previously and currently being utilized for the detection of human emotions through data which is in text format. Also, different approaches along with different applications of emotion analysis are vast, we can use it in online shopping apps, to check for customer satisfaction, and it can be used in the field of medicine by doctors to understand the mental status and stress levels of the patient. Emotion analysis can also be used on social media platforms and marriage portals to check for genuineness and the intentions of a person.
Towards Personalized Job Recommendations: A Natural Language Processing Perspective

Online job portals have rapidly expanded, making it easier for job searchers to find employment. However, it can take much work for job searchers to find the ideal position that matches their skills and preferences due to the abundance of job postings. This paper presents a system for recommending relevant job listings to students using machine learning and natural language processing techniques to solve this issue. The system employs a hybrid strategy to generate precise suggestions, combining collaborative filtering and content-based filtering. First, the system examines the student's resume, specifications, and posting to provide the most pertinent job suggestions. Additionally, the system suggests the top jobs to the user by analyzing and gauging the similarity between the user choice and explicit job listing features. The Recommender System is then evaluated using precision, recall, and Fl score.
Customized Transformer for Confident Browse Node Classification on Imbalanced Product Data

Rapid digitization of the world has revitalized the potential of AI. As a result, this phenomenon awakened a huge demand for data-mining pipelines that can efficiently process massive volumes of data to generate valuable insights. For instance, the e-commerce industry is a hub for such high-capacity analytical pipelines as the big players in this industry deal with data pertaining to millions of products, transactions, and users. Consequently, the sheer amount of data used in such AI workflows gives rise to Scalability issues and resource-efficiency issues. As a result, there is a necessity for more computationally efficient approaches that optimize resource allocation and minimize infrastructure costs. Thus, this paper focuses on implementing an efficient product classification pipeline using a customized version of a state-of-the-art transformer-based model which is able to efficiently process data volumes that are comparable to the current voluminosity of industrial data. This customized workflow adapts a loss function that was originally developed for object detection and this adaptation has proved to help the model converge faster and combat class imbalance which is a serious issue in developing large-scale Natural Language Processing systems.
Efficient Machine Leaning Algorithms for Sentiment Analysis In Car Rental Service

Opportunities in both business and vacation travel spell good news for the future of the global vehicle rental sector. The global car rental market is projected to grow from its current value of $90 billion in 2020 to an estimated $120 billion in 2025, a CAGR of 6.1%. A growing international tourist sector, more international air passengers, and rising worldwide incomes are the primary forces propelling this market. Enhancing the user experience through digitization, increasing the number of environmentally friendly rental cars, and the idea of self-driving cars as an alternative to traditional chauffeur services are all examples of emerging trends that are having a significant impact on the industry as a whole. Companies may better cater to client needs by offering vehicles that are a good fit based on real-time data analysis. In this work, the proposed algorithm facilitates the renting of cars according to the requirements of individual clients, such as pick-up time, car type, location, and even more specialized need, such as infant car seats and various sports equipment carrying racks. Changing customer-service practices at car rental agencies are helping them attract and retain regulars. However, AI is the most important component in developing trust with customers through individualized service and suggestions.
Uncovering Spam in Twitter: A Machine Learning Approach

Presently, we are living in a digital world where we can interact and speak with anybody on the planet through social networking sites such as Twitter and Facebook. Twitter is the fastest-growing social networking service among many others. Because of its prominence, it deals with a slew of spammers that transmit falsified and misleading material. Researchers had a tough time obtaining un-spammed and legitimate information from Twitter due to this spamming. In this research, we provided a unique way to design/create a system that distinguishes between spammer and authentic account tweets in real-time. For the dataset, we used the Twitter API. Four machine learning classifiers Decision Tree (DT), Support Vector Machine (SVM), Naïve Bayesian (NB), and Logistic Regression (LR) were implemented. We compared the performance of these ML classifiers in terms of accuracy, stability, and scalability and found that the Nave Bayesian classifier provided the best results, i.e., 96.83 % accuracy among all.
RSFuzzer: Discovering Deep SMI Handler Vulnerabilities in UEFI Firmware with Hybrid Fuzzing

System Management Mode (SMM) is a secure operation mode for x86 processors supported by Unified Extensible Firmware Interface (UEFI) firmware. SMM is designed to provide a secure execution environment to access highly privileged data or control low-level hardware (such as power management). The programs running in SMM are called SMM drivers and System Management Interrupt (SMI) handlers are the most important components of SMM drivers since they are the only components to receive and handle data from outside the SMM execution environment. Although SMM can serve as an extra layer of protection when the operating system is compromised, vulnerabilities in SMM drivers, especially SMI handlers, can invalidate this protection and cause severe damages to the device. Thus, early detection of SMI handler vulnerabilities is important for UEFI firmware security.To this end, researchers have proposed to use hybrid fuzzing techniques for detecting SMI handler vulnerabilities. Particularly, Intel has developed a hybrid fuzzer called Excite and uses it to secure Intel products. Although existing hybrid fuzzing techniques can detect vulnerabilities in SMI handlers, their effectiveness is limited due to two major pitfalls: 1) They can only feed input through the most common input interface to SMI handlers, lacking the ability to utilize other input interfaces. 2) They have no awareness of variables shared by multiple SMI handlers, lacking the ability to explore code segments related to such variables. By addressing the challenges faced by existing works, we propose RSFuzzer, a hybrid greybox fuzzing technique which can learn input interface and format information and detect deeply hidden vulnerabilities which are triggered by invoking multiple SMI handlers. We implemented RSFuzzer and evaluated it on 16 UEFI firmware images provided by six vendors. The experiment results show that RSFuzzer can cover 617% more basic blocks and detect 828% more vulnerabilities on average than the state-of-the-art hybrid fuzzing technique. Moreover, we found and reported 65 0-day vulnerabilities in the evaluated UEFI firmware images and 14 CVE IDs were assigned. Noticeably, 6 of the 0-day vulnerabilities were found in commercial-off-the-shelf (COTS) products from Intel, which might have been tested by Excite before releasing.
Developing an English Vocabulary Material Using English Learning Application

This research focuses on developing English vocabulary material by using English Learning Application at 1st-grade MTs. Mambaus Sholihin Gresik. The material of this product was designed according to the needs and characteristics of the students. With this material, students can learn English vocabulary easily because the application is very practical to use for learning. In this research, the researcher used the approach of Research and development, Research and development is a method used to create some product and then try it to the objective of the study about the effectiveness of the product. The data collection techniques which were used in this research were distributing questionnaires to students and interviewing the teacher. The questionnaires were applied in the needs analysis stage. The students’ characteristics and learning resources were observed by doing a need analysis. The application was effective. Students were able to increase their vocabulary and able to reduce their boredom with learning English. They also believed that the product was useful to assist them in learning vocabulary. The role of the application as a medium for learning vocabulary is very influential, this is because before learning we must pay attention to the motivation and interest of students in learning. The application is proven to be able to overcome student motivation and interest. The improvement, revision, addition, and alteration had been done based on the feedback from the expert and the subjects of the product try-out.
Firm-Level Pay Disparity and Breach Risk

ABSTRACT The 2008 financial crisis highlighted the significant, vertical pay disparity between chief executive officers (CEOs) and all other employees. Following equity theory, prior research finds negative associations between vertical pay disparity and job satisfaction and performance (i.e., more errors). We build on this research to investigate if vertical pay disparity between the CEO and employees/other executives is a previously unidentified determinant of data security breaches (DSBs). Results suggest that firms with large, vertical pay disparities are more likely to be breached, are associated with more DSBs, and are more likely to be associated with internal DSBs. We also find some evidence of remediation through narrowing the pay disparity between CEOs and employees/other executives after the DSB. Our findings contribute to the growing accounting information systems (AIS) cybersecurity literature, extend equity theory to a new context, and should be of interest to AIS governance researchers and stakeholders.
Mining Recency–Frequency–Monetary enriched insights into resources’ collaboration behavior from event data

Organizations increasingly rely on teamwork to achieve their goals. Therefore they continuously strive to improve their teams as their performance is interwoven with that of the organization. To implement beneficial changes, accurate insights into the working of the team are necessary. However, team leaders tend to have an understanding of the team’s collaboration that is subjective and seldom completely accurate. Recently there has been an increase in the adoption of digital support systems for collaborative work that capture objective data on how the work took place in reality. This creates the opportunity for data-driven extraction of insights into the collaboration behavior of a team. This data however, does not explicitly record the collaboration relationships, which many existing techniques expect as input. Therefore, these relationships first have to be discovered. Existing techniques that apply discovery are not generally applicable because their notion of collaboration is tailored to the application domain. Moreover, the information that these techniques extract from the data about the nature of the relationships is often limited to the network level. Therefore, this research proposes a generic algorithm that can discover collaboration relationships between resources from event data on any collaborative project. The algorithm adopts an established framework to provide insights into collaboration on a fine-grained level. To this end, three properties are calculated for both the resources and their collaboration relationships: a recency, frequency, and monetary value. The technique’s ability to provide valuable insights into the team structure and characteristics is empirically validated on two use cases.
PL-GNet: Pixel Level Global Network for detection and localization of image forgeries

Unlike most Image Forgery Detection and Localization (IFDL) methods that classify the tampered regions by local patch, the features from the whole image in the spatial and frequency domains are leveraged in this paper to classify each pixel in the image. This paper proposes a high-confidence pixel level global network called PL-GNet to combat real-life image forgery that commonly involves different types and visually looks particularly realistic. There are three building blocks in our end-to-end PL-GNet framework: (1) An Encoding net allows us to extract the global features and generate the high-quality feature maps which indicate possible tampered regions. The newly designed first layer and backbone network architecture based on atrous convolution in Encoding net are adopted to capture the changes of pixel relationships and extract rich multi-scale spatial information. (2) A Long Short Term Memory (LSTM) network based on co-occurrence matrix is designed to capture the tampering traces and the discriminative features between manipulated and non-manipulated regions. (3) A Decoding net that incorporates the output of Encoding net and LSTM network learns the mapping from low-resolution feature maps to pixel-wise prediction masks. Furthermore, we conduct a series of ablation experiments to optimize the design of the Encoding network systematically. Extensive experiments on the six challenging datasets demonstrate that our PL-GNet outperforms each subnetwork and consistently achieves state-of-the-art performance compared to alternative methods over three evaluation metrics.
Does a Modern Manager Need Analytical Competencies (On the Example of Employees of the Emergency Response Department of the Main Directorate of the EMER COM of Russia in Moscow)

The article presents the results of the authors’ research on the state of analytical competencies among modern managers. The following research methods were comprehensively used in the study: traditional document analysis, expert survey (questionnaire), situational analysis, testing and participant observation. Employees working in the The Crisis Management Center of the Main Directorate of EMERCOM of Russia in Moscow were involved as respondents. The study showed that the experts who had been interviewed believed that they had poorly formed analytical competencies. It was also revealed that the interviewed experts understand the importance of analytical competencies and the need for their formation. Basing on the data from the study the authors conclude that the majority of modern officials need analytical competencies. This means that their managers should give them opportunity to improve their skills in this direction in a timely manner.
The limitations of irony detection in Dutch social media

Abstract In this paper, we explore the feasibility of irony detection in Dutch social media. To this end, we investigate both transformer models with embedding representations, as well as traditional machine learning classifiers with extensive feature sets. Our feature-based methodology implements a variety of information sources including lexical, semantic, syntactic, sentiment features, as well as two new data-driven features to model common sense. Based on patterns in the syntactic structure of tweets, we aim to model the presence of contrasting sentiments, a phenomenon that is known to be indicative of verbal irony and sarcasm. Feature selection, as well as voting ensemble techniques were implemented to enhance the classification performance. The final systems reach F1-scores up to 0.79, which are promising results for a task as difficult as irony detection. Besides a quantitative analysis, this paper also describes a thorough qualitative analysis of the system output. Although lexical cues appear to be very important to express irony, our analysis also revealed the need for more advanced modeling of common-sense knowledge to detect more subtle examples of irony.
Analysis of Machine Translation Performance on Translating Informative Text from English into Indonesian

This study aims to investigate the performance of 6 machine translation. The text translated was informative text from English into Indonesian. The document taken from 48 students paper in semester final test. The research design is descriptive qualitative and content analysis approach. The data obtained from the students translation result in final test, observation, and interview. In analyzing the result of translating, there were three categories: grammatical structure, cultural words, and mechanic writing (composition writing). The result shows the performance of 6 machine translations: Google translate (GT), DeepL, Yandex, Systran, Udictionary, Microsoft translator, and itranslate on grammatical structure analysis were understanable related to meaning because the language is news report in formal language and reporting facts. However, some roles of language were changes such as: tenses, word formation, active/passive, singular plural, article, and auxiliary verbs. There was no example in cultural words and mechanic writing because the form of language is news report. The result of observation indicated that the students already apply technology in translation by utilizing MT in translating text. Furtthermore, in the result of interview implied that the usage of MT can give an assistance in translating text from English into Indonesian especially for informative text.&#x0D;
A novel deep learning automatic modulation classifier with fusion of multichannel information using GRU

Abstract Automatic modulation classification (AMC) plays a vital role in modern communication systems, which can support wireless communication systems with limited spectrum resource. This paper proposes an AMC method, which integrates gated recurrent unit (GRU) and convolutional neural network (CNN) to utilize the complementary input features of received signals for spatiotemporal feature extraction and classification. Different from other state-of-the-art (SoA) frameworks, the proposed AMC classifier, named as fusion GRU deep learning neural network (FGDNN), aggregates firstly temporal features with GRUs and then extracts spatial features with CNNs. The GRUs can store temporal dynamic features, and facilitate to capture the characteristics of correlation and dependence among input features. The method is tested extensively with comparisons in order to verify its effectiveness. Experiment results show that the recognition rates of our method outperform other deep learning frameworks.
ANALYZING OF WEB-BLOG MEDIA IN LEARNING MATHEMATICS ON POST PANDEMIC COVID-19

Pandemic has brought everyone to the highest climax in the development of learning innovations, including digital learning. The popularity of web digital learning applications and pages, video conferencing, digital quizzes, to audio-visual-based learning tools has brought civilization to various forms of learning. This study aims to analyze the implementation of the use of web-blog-based digital learning media in mathematics learning in junior high schools. This research was conducted in the first week of March 2023. This research used a qualitative method, with data collection techniques in the form of observation, interviews, and documentation studies. The researcher in this case functions as the main tool for collecting data with the main data collection techniques through interviews, explanations and documentation. The results of the analysis in research on the use of web-blogs in learning mathematics show that there is ease in strengthening concept understanding, learning that is flexible and can be accessed repeatedly, the time for giving material and assignments is concise, and motivating the use of gadgets positively. Other results show obstacles in the form of resource capabilities that have not been maximized, minimal availability of devices, limited internet access based on region and economic capacity, to limited abilities of students and educators in using digital media. The analysis above provides a description to educators in terms of selecting learning media when analyzing material in the preparation of lesson implementation plans, so that the use of web-blog media can be adjusted and varied according to resources and learning needs.