175 research outputs found
Multi-party Poisoning through Generalized -Tampering
In a poisoning attack against a learning algorithm, an adversary tampers with
a fraction of the training data with the goal of increasing the
classification error of the constructed hypothesis/model over the final test
distribution. In the distributed setting, might be gathered gradually from
data providers who generate and submit their shares of
in an online way.
In this work, we initiate a formal study of -poisoning attacks in
which an adversary controls of the parties, and even for each
corrupted party , the adversary submits some poisoned data on
behalf of that is still "-close" to the correct data (e.g.,
fraction of is still honestly generated). For , this model
becomes the traditional notion of poisoning, and for it coincides with
the standard notion of corruption in multi-party computation.
We prove that if there is an initial constant error for the generated
hypothesis , there is always a -poisoning attacker who can decrease
the confidence of (to have a small error), or alternatively increase the
error of , by . Our attacks can be implemented in
polynomial time given samples from the correct data, and they use no wrong
labels if the original distributions are not noisy.
At a technical level, we prove a general lemma about biasing bounded
functions through an attack model in which each
block might be controlled by an adversary with marginal probability
in an online way. When the probabilities are independent, this coincides with
the model of -tampering attacks, thus we call our model generalized
-tampering. We prove the power of such attacks by incorporating ideas from
the context of coin-flipping attacks into the -tampering model and
generalize the results in both of these areas
Measurement of (IL-11 ) Levels in Patients with Rheumatoid Arthritis
التهاب المفاصل الروماتويدي (RA) هو مرض مناعي ذاتي التهابي مزمن يصيب المفاصل بشكل أساس ، ويعد التهاب الغشاء المفصلي والغضاريف وتآكل العظام من أبرز الأعراض للمرض . مراحل تطور هذا المرض معقدة للغاية ، يتم خلالها تكاثر الخلايا الزليليّة التي تتلف وتشكل التهاب الغشاء الزليلي ، يليه الغضاريف وتدهور العظام. السيتوكينات هي بروتينات تلعب دورًا حاسمًا في ظهور المرض وتطوره. كان الهدف من هذا البحث معرفة المزيد عن الإنترلوكين -11 ، وهو سايتوكين ينتجه الجهاز المناعي ، والانترليوكين -11 ينتمي إلى عائلة إلانترلوكين -6 ، والتي لها إمكانات مؤيدة ومضادة للالتهابات وعلاقتها بالمرض عن طريق القياس لمستوى تركيز (IL-11).
طرق العمل:
اشتملت الدراسة الحالية على 45 مريضًا مصابًا بـ (RA) ، بالإضافة إلى 45 (يبدو أنهم أصحاء) كمجموعة ضابطة للدراسة. جمعت هذه العينات من مستشفى المرجان بمحافظة بابل (وحدة المفاصل) خلال الفترة من 1/11/2021 إلى 15/3/2022. تم فحص امصال جميع المرضى والمجموعة الضابطة لتحديد مستوى تركيز IL-11 باستخدام مقياس الممتز المناعي المرتبط بالإنزيم (ELISA).
الاستنتاجات:
، كشفت نتائج الدراسة عن انخفاض كبير في مستوى IL-11 عند مستوى احتمالية (P≤0.05) للمرضى الذين يعانون من (الروماتيزم الرثوي) عند مقارنة معدلات نفس المعايير لمجموعة السيطرة.Background:
Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease that affects the joints principally. Synovitis , cartilage and bone erosion are the most prominent symptoms of (RA). Stages of development of this disease are very complex, Synovial cells that rupture and form inflammation in the synovium, followed by cartilage and bone deterioration. Cytokines are proteins that play a crucial role in the onset arise causing and progression of the disease. This research aims to learn more about interleukin-11, which is a protein produced by the immune system. A cytokine belongs to the interleukin-6 family. It has pro- and anti-inflammatory potentials and its relationship to the disease can be determined by measuring the level of (IL-11) concentration.
Materials and Methods:
The current study included 45 patients with (RA), in addition to 45 (apparently healthy) subjects as a control group of the study. These samples were collected from Marjan Hospital in Babylon Governorate (joints unit) during the period from 1/11/2021 to 3/15/2022. The sera of all patients and control group were examined for determination of IL-11 concentration level, using enzyme-linked immunosorbent assays, which are a type of immunoassay in which the enzyme is coupled to (ELISA).
Results:
At the likelihood level, the study's findings have revealed a considerable decrease in IL-11 level (P≤0.05) for patients with (RA), when comparing the rates of the same criteria for the control group.
Conclusion:
The concentration of interleukin 11 in the sera of rheumatoid arthritis patients was found to be less than in the control group in the current investigation
Burden of Right Ventricular Infarction in Patients with Inferior Myocardial Infarction in Babylon
هدف الدراسة: هده الدراسة تهدف الى المقارنة في نسبة حصول الصدمة القلبية والوفاة للمرضى الدين يحصل لديهم احتشاء للعضلة القلبية السفلى والمرضى الدين بحصل لديهم احتشاء للعضلة القلبية السفلى مع احتشاء البطين الايمن .
طريقة الدراسة : هده دراسة مقارنة اجريت في مدينة مرجان الطبية في وحدة الانعاش للفترة من 1-2-2014 الى 30-9-2014
للمرضى الدين الدين أصيبوا باحتشاء العضلة القلبية السفلى .تم تقسيم المرضى الى مجموعتين ,المجموعة الاولى هم المرضى المصابين باحتشاء العضلة القلبية السفلى والمجموعة الثانية هم المرضى المصابين باحتشاء العضلة القلبية السفلى مع احتشاء البطين الايمن للقلب .تم متابعة المرضى طول فترة بقائهم بالمستشفى لأي تعقيدات ممكن ان تحدث.
النتائج: مجموع 80 مريض ادخلوا في الدراسة وقسموا الى مجموعتين : المجموعة الاولى26 مريض أصيبوا باحتشاء العضلة القلبية السفلى مع احتشاء البطين الايمن ,والمجموعة الثانية 54 اصيبوا باحتشاء العضلة القلبية السفلى ,المجموعتين لديهم نفس الخصائص .معدل العمر للمرضى هو 59.30 للمجموعة الاولى , 55.03 للمجموعة الثانية .نسبة الذكور للمجموعة الاولى هو 17 (65.4%) و33(61.1%) للمجموعة الثانية .نسبة الوفيات للمجموعة الاولى هو 4(15.4) و1 (1.9) للمجموعة الثانية . قيمو البي 0.036 وتعتبر نسبة مهمة .عدد المرضى الدين اصيبوا بالصدمة القلبية للمجموعة الاولى هو 7(26.9) وللمجموعة الثانية هو 5(5.6) ,قيمة البي هي 0.011 وتعتبر مهمة .
الاستنتاجات:
1-المرضى المصابين باحتشاء العضلة القلبية السفلى والبطين الايمن لديهم نسبة عالية لحدوث الصدمة القلبية اكثر من المرضى المصابين باحتشاء العضلة القلبية السفلى في فترة بقائهم بالمستشفى .
2- المرضى المصابين باحتشاء العضلة القلبية السفلى والبطين الايمن لديهم نسبة عالية للوفاة اكثر من المرضى المصابين باحتشاء العضلة القلبية السفلى في فترة بقائهم بالمستشفىAim of study:the study aim to comparing the rate of cardiogenic shock and mortality rate between patient with RV infarction and inferior MI and the patient with inferior MI alone.
Patient and method: Across sectional study conducted in coronary care unit in merjan medical city at time from the first of March 2014 to the 30th of September 2014 for patient with inferior MI. patients were divided to two group, the first group for patients with inferior MI and RV infarction and the second group for patients with inferior MI alone. The both groups were monitor in the hospital for any complications that can take place in the hospital stay.
Result: A total of 80 patient were enrolled in the study and divided to two groups, the first group (26 patients) for patients with inferior MI and RV infarction and the second group (54 patients) for patients with inferior MI alone, both group had the same baseline characteristic. The mean age was 59.30 ± 7.56 for first group and it was 55.03 ± 5.18 for second group, male patients were 17 (65.4%) and 33 (61.1%) in the first and second group respectively. The in hospital mortality was 4 (15.4%) and 1(1.9%) in the first and second group respectively and the P value was 0.036 (significant).The risk of cardiogenic shock was 7 (26.9%) and 5 (5.6%) for first and second group respectively with P value 0.011(significant).
Conclusion:
1- The patients with RV infarction and inferior MI expose to higher rate of cardiogenic shock than the patients with inferior MI alone during the time of his hospital stay.
2- The patient with RV infarction and inferior MI had higher in hospital mortality rate when compared with patient with inferior MI alone.
 
Computationally efficient algorithms and implementations of adaptive deep brain stimulation systems for Parkinson's disease
Clinical deep brain stimulation (DBS) is a tool used to mitigate pharmacologically intractable neurodegenerative diseases such as Parkinson's disease (PD), tremor and dystonia. Present implementations of DBS use continuous, high frequency voltage or current pulses so as to mitigate PD. This results in some limitations, among which there is stimulation induced side effects and shortening of pacemaker battery life. Adaptive DBS (aDBS) can be used to overcome a number of these limitations. Adaptive DBS is intended to deliver stimulation precisely only when needed. This thesis presents work undertaken to investigate, propose and develop novel algorithms and implementations of systems for adapting DBS. This thesis proposes four system implementations that could facilitate DBS adaptation either in the form of closed-loop DBS or spatial adaptation. The first method involved the use of dynamic detection to track changes in local field potentials (LFP) which can be indicative of PD symptoms. The work on dynamic detection included the synthesis of validation dataset using mainly autoregressive moving average (ARMA) models to enable the evaluation of a subset of PD detection algorithms for accuracy and complexity trade-offs. The subset of algorithms consisted of feature extraction (FE), dimensionality reduction (DR) and dynamic pattern classification stages. The combination with the best trade-off in terms of accuracy and complexity consisted of discrete wavelet transform (DWT) for FE, maximum ratio method (MRM) for DR and k-nearest neighbours (k-NN) for classification. The MRM is a novel DR method inspired by Fisher's separability criterion. The best combination achieved accuracy measures: F1-score of 97.9%, choice probability of 99.86% and classification accuracy of 99.29%. Regarding complexity, it had an estimated microchip area of 0.84 mm² for estimates in 90 nm CMOS process. The second implementation developed the first known PD detection and monitoring processor. This was achieved using complementary detection, which presents a hardware-efficient method of implementing a PD detection processor for monitoring PD progression in Parkinsonian patients. Complementary detection is achieved by using a combination of weak classifiers to produce a classifier with a higher consistency and confidence level than the individual classifiers in the configuration. The PD detection processor using the same processing stages as the first implementation was validated on an FPGA platform. By mapping the implemented design on a 45 nm CMOS process, the most optimal implementation achieved a dynamic power per channel of 2.26 μW and an area per channel of 0.2384 mm². It also achieved mean accuracy measures: Mathews correlation coefficient (MCC) of 0.6162, an F1-score of 91.38%, and mean classification accuracy of 91.91%. The third implementation proposed a framework for adapting DBS based on a critic-actor control approach. This models the relationship between a trained clinician (critic) and a neuro-modulation system (actor) for modulating DBS. The critic was implemented and validated using machine learning models, and the actor was implemented using a fuzzy controller. Therapy is modulated based on state estimates obtained through the machine learning models. PD suppression was achieved in seven out of nine test cases. The final implementation introduces spatial adaptation for aDBS. Spatial adaptation adjusts to variation in lead position and/or stimulation focus, as poor stimulation focus has been reported to affect therapeutic benefits of DBS. The implementation proposes dynamic current steering systems as a power-efficient implementation for multi-polar multisite current steering, with a particular focus on the output stage of the dynamic current steering system. The output stage uses dynamic current sources in implementing push-pull current sources that are interfaced to 16 electrodes so as to enable current steering. The performance of the output stage was demonstrated using a supply of 3.3 V to drive biphasic current pulses of up to 0.5 mA through its electrodes. The preliminary design of the circuit was implemented in 0.18 μm CMOS technology
A framework for adapting deep brain stimulation using Parkinsonian state estimates
The mechanisms underlying the beneficial effects of deep brain stimulation (DBS) for Parkinson's disease (PD) remain poorly understood and are still under debate. This has hindered the development of adaptive DBS (aDBS). For further progress in aDBS, more insight into the dynamics of PD is needed, which can be obtained using machine learning models. This study presents an approach that uses generative and discriminative machine learning models to more accurately estimate the symptom severity of patients and adjust therapy accordingly. A support vector machine is used as the representative algorithm for discriminative machine learning models, and the Gaussian mixture model is used for the generative models. Therapy is effected using the state estimates obtained from the machine learning models together with a fuzzy controller in a critic-actor control approach. Both machine learning model configurations achieve PD suppression to desired state in 7 out of 9 cases; most of which settle in under 2 s
The Effect of Gender and Status on the Apology Strategies Used by American Native Speakers of English and Iraqi EFL University Students
In fact, apology varies from one language to another. Thus, teachers and EFL learners must discern the similarities and the differences between their native language and the target one because what is deemed right in one language may not be considered right in the other. No study is conducted to compare apology strategies of Iraqi EFL university students along with that of the American native speakers of English in terms of gender and status. Therefore, the present study will explore these issues in order to clarify them. As such, a discourse completion test has been designed and applied to Iraqi EFL university students and Americans native speakers of English. The results show that Iraqi EFL male leaners use more strategies with people of higher level, while the American males use more strategies with people of lower position. Moreover, unlike the Americans, Iraqi females use more apology strategies than Iraqi males. Key words: speech acts, apology strategies, Americans, Iraqi EFL learners, gender, statu
Toward on-demand deep brain stimulation using online Parkinson’s disease prediction driven by dynamic detection
In Parkinson’s disease (PD), on-demand deep brain stimulation (DBS) is required so that stimulation is regulated to reduce side effects resulting from continuous stimulation and PD exacerbation due to untimely stimulation. Also, the progressive nature of PD necessitates the use of dynamic detection schemes that can track the nonlinearities in PD. This paper proposes the use of dynamic feature extraction feature extraction and dynamic pattern classification to achieve dynamic PD detection taking into account the demand for high accuracy, low computation and real-time detection. The dynamic feature extraction and dynamic pattern classification are selected by evaluating a subset of feature extraction, dimensionality reduction and classification algorithms that have been used in brain machine interfaces. A novel dimensionality reduction technique, the maximum ratio method (MRM) is proposed, which provides the most efficient performance. In terms of accuracy and complexity for hardware implementation, a combination having discrete wavelet transform for feature extraction, MRM for dimensionality reduction and dynamic k-nearest neighbor for classification was chosen as the most efficient. It achieves mean accuracy measures of classification accuracy 99.29%, F1-score of 97.90% and a choice probability of 99.86%
OPTIMIZATION OF RESOURCE ALLOCATION AND LEVELING USING GENETIC ALGORITHMS
Resource allocation and leveling are of the top challenges in project management, due to the
complexity of projects. This research aims to develop an optimization model for resource
smoothing, so that.
The proposed model is formulated using C++ program for resource smoothing. The project
management software MS-Projects is adopted hereto perform resource leveling to facilitate
achieving the optimal solution.
The proposed model utilizes a system that depends on Genetic Algorithms (GAs) procedure built
in C++ program to find the optimum solution.
This research reach concludes that it is possible to smooth resources using Genetic Algorithms
program and compares then with MS-Project when the GA results are better than MS-Project.
Three case studies have been applied in this research and the application results come identical
with research objectives, to form the conclusion.
Then comes the recommendations regarding adopting and using the research results in
construction planning and project management. Further suggestions related to the research subject
are proposed for future works.
Purchasing Power Parity Hypothesis: Empirical Validity of Purchasing Power Parity in the Long Run among the Developing and Developed Countries Using Co-integration and OLS Techniques.
This study finds the empirical validity of exchange rate and price relationship implied by purchasing power parity among the seven countries .i.e. (Australia, Canada, Pakistan, India, Japan, Spain and Korea) using the Augmented Dickey Fuller, Engel Granger, Johansen and ordinary least squares econometrics techniques based on quarterly data instead of annually data .We have used both developing and developed countries in the PPP testing and compared the econometric results of developing and developed countries with each other. Using the quarterly data over the time period of 1961 to 2010, We have found the long run validity of purchasing power parity theory among the three developing and four developed countries. We have applied different econometric methodologies, PPP results differ among different econometric techniques. So, it can be implied that choice of price level and appropriate econometric methodology is very important in the PPP testing. Keywords: Purchasing power parity; Exchange rate; Developed and Developing countries
3-DOF Parallel robotics System for Foot Drop therapy using Arduino
This paper discusses a robotic system used for physical therapy for foot drop case, caused by brain stroke. This device provides most exercises practiced by patient for treatment at any time or any place without going to the rehabilitation center located in hospitals. The robotics system designed according to the mechanism of parallel robot and controlled by computer or microcontroller (Arduino). This robot allows the patient to do the exercise without any need for any knowledge about computer or programing. The developed robot system show a good potential to be developed and distributed for large number of physical therapy clinics with low cost and good reliability. Keywords: 3DOF Robot, Parallel Robot, Foot Drop Physical Therapy, Arduino
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