Generative Adversarial Networks for Mitigating Biases in Machine Learning Systems

In this paper, we propose a new framework for mitigating biases in machine learning systems. The problem of the existing mitigation approaches is that they are model-oriented in the sense that they focus on tuning the training algorithms to produce fair results, while overlooking the fact that the training data can itself be the main reason for biased outcomes. Technically speaking, two essential limitations can be found in such model-based approaches: 1) the mitigation cannot be achieved without degrading the accuracy of the machine learning models, and 2) when the data used for training are largely biased, the training time automatically increases so as to find suitable learning parameters that help produce fair results. To address these shortcomings, we propose in this work a new framework that can largely mitigate the biases and discriminations in machine learning systems while at the same time enhancing the prediction accuracy of these systems. The proposed framework is based on conditional Generative Adversarial Networks (cGANs), which are used to generate new synthetic fair data with selective properties from the original data. We also propose a framework for analyzing data biases, which is important for understanding the amount and type of data that need to be synthetically sampled and labeled for each population group. Experimental results show that the proposed solution can efficiently mitigate different types of biases, while at the same time enhancing the prediction accuracy of the underlying machine learning model

Consumer Search And Switching Behavior: Evidence From The Credit Card Industry

The introduction of the credit card in the mid-twentieth century revolutionized and transformed how people live. Based on a set of new survey data, this dissertation empirically investigates and analyzes consumers\u27 behavior in the credit card market. Specifically, it investigates the underlying determinants of consumers\u27 choices regarding switching credit-card balances. To estimate the likelihood that consumers switch credit cards, two logit models are estimated. Using data from the Consumer Finance Monthly (CFM) of The Ohio State University, the author finds that at the conventional 5 percent level of significance, the following variables have significance: old interest rate, new interest rate, duration of the introductory rate, balances, number of credit cards, home ownership, and age. As expected, interest rates, balance, and the duration of new introductory offer rates have the greatest influence on why or why not people switch credit cards. The findings are consistent with the view that consumers make rational decisions in the credit card market, since balance-carrying consumers are sensitive to the terms of credit card contracts, such as the interest rate on existing balances, the new rate, and the duration of the new rate. It also implies that switching costs are important, challenging Ausubel\u27s (1991) argument of credit card consumer irrationality and Calem and Mester\u27s (1995) empirical finding that credit card rates are sticky because consumers are irresponsive and to rate cuts

Estimate of Some Hormones in Patient Suffeer from Impotence in Samarra City

Impotence is one of the commonness sexual healthy issues which influenced on more than one hundred fifty millions of males in worldwide in years of 1995 in addition to will be reported more than three hundred millions in years of 2025. Objective: The current study aimed to-measurement of some hormones from serum of patients that suffered from impotence and compared these results with a healthy control group to find out how these hormones affect the sexual state. Methods: The study presented was included ninety respondents (250 cases with Impotence and 50 control), from the period of starting of October 2022 to end of December 2022, attended to Samarra General Hospital and some of outpatients-clinics. Results: The-presented study appear the Testosterone hormone level was higher in the serum for control group (6.2515± 1.88953 ng/ml) in comparison with the patients-group with Impotence (2.5861± 0.84982 ng/ml). While there is height in Prolactin-hormone-concentration-level in the serum of patient suffered from impotence, and was (21.1883±7.89562ng/ml) but the concentration level of this hormone in control-group was (4.5590±1.35849ng/ml). on the other side, there is little difference between study groups in the serum concentration level of Estradiol hormone, in control group-the Mean ± SD was (97.6870± 31.38880 ng/ml) while in Impotence group was (93.6653± 30.86834 ng/ml). Conclusions: Level of Testosterone hormone was higher in the-serum of control group other than the group of patients with impotence. While there is height in Prolactin-hormone-concentration-level in the serum of patient suffered from impotence. there is little difference between study groups in the serum concentration level of Estradiol hormone

Text Messaging in English and Arabic with Reference to Translation

In the twenty-first century, many people now live their lives through text messaging. In fact, you can witness people using their cell phones to send character-based messages to their friends, classmates, family members, and coworkers in malls, schools, and pretty much anywhere else. The popularity of this method of communication has increased particularly among young people. One benefit is that technology enables people to speak with others virtually anywhere. Second, it enables individuals to speak softly, which is useful in noisy places like bars, where it would be challenging to have a productive conversation over the phone, or when extraneous communication needs to be done quietly, such as in a school. Thirdly, it combines some of the advantages of phone and email communication by allowing them to communicate both synchronously (i.e., two-way communication occurs concurrently) and asynchronously (i.e., two-way communication is delayed). The usage of acronyms, abbreviations, and other shorthand notations has become commonplace in this technology's creation of a new language form. The focus of this research is on these qualities specifically and how they are used. The aim of this study was to analyze not only how frequently but also how these symbolic expressions are used in relation to the linguistic functions that they signal, which was followed by a number of discoveries

Effects of industrial processing methods on skimmed camel milk properties

Camel milk (CM) has an integral role in the diet of the population in the arid and semiarid regions of Africa and Asia where scarce agricultural areas, high temperatures and small amount of precipitation. Recent studies have shown that it has potential therapeutic effects, including anti-cancer, hypo-allergic and anti-diabetic properties. Nowadays, CM has become increasingly commercialised and consumed in urban areas; which has led to an increased interest in the processing of CM to improve its microbial quality and extend its shelf-life. However, there is still a scarcity of available information regarding the effects of different processing methods (e.g. thermal and high-pressure treatments) on CM properties. Therefore, the aims of the current research were to characterise and quantify CM proteins and to evaluate the effect of high-temperature short-time pasteurisation (HTST), ultra-high-temperature (UHT) and high-pressure processing (HPP) on the physical, chemical and the organoleptic properties of skimmed CM in comparison to bovine skimmed milk. Capillary electrophoresis (CE) was successful in identifying and quantifying the major whey and casein proteins in CM (chapter 3). Major variations were found between camel and bovine milk in terms of both concentration and composition of whey and casein proteins. Unlike bovine whey, camel whey had no β-lactoglobulin (β-lg) and instead a high concentration of α-lactalbumin (α-la) followed by lactoferrin (LF) and serum albumin (SA) was observed. β-casein (β-CN) was the main camel casein followed by α-casein (α-CN) while ҡ-casein (ҡ-CN) represented only minor amount. These variations were found to have an impact on the technological properties of CM, and quality of dairy products made from CM. In general, HTST (72oC for 15s), UHT (140oC for 5s) and HP (200 to 800 MPa at 20oC for 30 min) treatments significantly affected components of skimmed CM and their functional properties (chapter 4). UHT treatment resulted in the highest levels of denaturation of whey proteins and greatest colour change of CM compared to the HTST and HP treatments. Casein micelles size of CM was significantly decreased after both heat and HP treatments. While, bovine micelles size increased after UHT treatment. Similar to bovine milk, the rennet coagulation time (RCT) of CM was significantly delayed and coagulum strength (G') decreased after HTST pasteurisation. UHT treatment hindered the coagulation of milk from both species. In contrast, HP treatment at 200 and 400 MPa increased the RCT of CM and G' value was the highest after treatment at 200 MPa. Unlike bovine milk, HP treatment at pressures higher than 400 MPa impaired the rennet coagulation properties of CM. The volatile profile of skimmed CM subjected to HTST, UHT, and HP treatments was found to differ from the volatile profile of raw CM (chapter 5). HTST pasteurisation and UHT treatment resulted in an increase of aldehydes, furans, and terpenes content in CM. Moreover, the increase of heat severity during the UHT treatment led to the formation of sulphur compounds in CM. On the other hand, HP treatments tended to enhance the formation of alcohol and ketones in CM. Both thermal and non-thermal treatments had limited effect on amino acids and lactose content of skimmed CM. The volatile profiles and sensory properties of HTST pasturised and UHT skimmed CM were different to pasteurised and UHT bovine skimmed milk. Heated CM samples were described as having attributes such as cardboard, musty, sulphur odours, as well as sour, savoury, aged, and whey taste/flavours. While, bovine milk samples were described as having cooked milk, creamy, and dairy aroma. Overall, conventional heat treatments resulted in the formation of volatile compounds which were responsible for off-flavours in processed CM

Arabic Text Categorization Using Support vector machine, Naïve Bayes and Neural Network

Text classification is a very important area ininformation retrieval. Text classificationtechniques used to classify documents into a setof predefined categories. There are severaltechniques and methods used to classify data andin fact there are many researches talks aboutEnglish text classification. Unfortunately, fewresearches talks about Arabic text classification.This paper talks about three well-knowntechniques used to classify data. These threewell-known techniques are applied on Arabicdata set. A comparative study is made betweenthese three techniques. Also this study used fixednumber of documents for all categories ofdocuments in training and testing phase. Theresult shows that the Support Vector machinegives the best results

MAG-PUFs:Authenticating IoT devices via electromagnetic physical unclonable functions and deep learning

The challenge of authenticating Internet of Things (IoT) devices, particularly in low-cost deployments with constrained nodes that struggle with dynamic re-keying solutions, renders these devices susceptible to various attacks. This paper introduces a robust alternative mitigation strategy based on Physical-Layer Authentication (PLA), which leverages the intrinsic physical layer characteristics of IoT devices. These unique imperfections, stemming from the manufacturing process of IoT electronic integrated circuits (ICs), are difficult to replicate or falsify and vary with each function executed by the IoT device. We propose a novel lightweight authentication scheme, MAG-PUFs, that uses the unintentional Electromagnetic (EM) emissions from IoT devices as Physical Unclonable Functions (PUFs). MAG-PUFs operate by collecting these unintentional EM emissions during the execution of pre-defined reference functions by the IoT devices. The authentication is achieved by matching these emissions with profiles recorded at the time of enrollment, using state-of-the-art Deep Learning (DL) approaches such as Neural Networks (NN) and Autoencoders. Notably, MAG-PUFs offer compelling advantages: (i) it preserves privacy, as it does not require direct access to the IoT devices; and, (ii) it provides unique flexibility, permitting the selection of numerous and varied reference functions. We rigorously evaluated MAG-PUFs using 25 Arduino devices and a diverse set of 325 reference function classes. Employing a DL framework, we achieved a minimum authentication F1-Score of 0.99. Furthermore, the scheme's efficacy in detecting impostor EM emissions was also affirmed, achieving a minimum F1-Score of 0.99. We also compared our solution to other solutions in the literature, showing its remarkable performance. Finally, we discussed code obfuscation techniques and the impact of Radio Frequency (RF) interference on the IoT authentication process.</p