Miracle-dz/UniFAR-FTUs huggingface dataset:
FLaaS - enabling practical federated learning on mobile environments

Federated Learning (FL) [2] has emerged as a popular solution of Confidential Computing [3] to distributedly train a model on user devices, improving privacy and system scalability. Such privacy-preserving models can be used in wide range of applications, and especially in Telco networks [4]. However, there are no practical systems to easily enable FL training on mobile apps, and especially in an as-a-service fashion. In this demo, we implement and test FLaaS, our recently proposed end-to-end FL service [1]. FLaaS includes a client-side framework with app library and service, and a back-end server, to enable secure and easy to deploy intra- and inter-app FL model training on mobile environments.
Floo: automatic, lightweight memoization for faster mobile apps

Owing to growing feature sets and sluggish improvements to smartphone CPUs (relative to mobile networks), mobile app response times have increasingly become bottlenecked on client-side computations. In designing a solution to this emerging issue, our primary insight is that app computations exhibit substantial stability over time in that they are entirely performed in rarely-updated codebases within app binaries and the OS. Building on this, we present Floo, a system that aims to automatically reuse (or memoize) computation results during app operation in an effort to reduce the amount of compute needed to handle user interactions. To ensure practicality - the struggle with any memoization effort - in the face of limited mobile device resources and the short-lived nature of each app computation, Floo embeds several new techniques that collectively enable it to mask cache lookup overheads and ensure high cache hit rates, all the while guaranteeing correctness for any reused computations. Across a wide range of apps, live networks, phones, and interaction traces, Floo reduces median and 95th percentile interaction response times by 32.7% and 72.3%.
Shack-Hartmann based wavefront and intensity sensing via U-Net

Neural networks present a new approach for solving nonlinear problems and is widely applied in optical science. In this research, we integrate neural network with Shack-Hartmann wavefront sensor (SHWS), not only reconstruct the wavefront but also the intensity of beam profile. This network has the capability to obtain the beam wavefront information without calculating the slop of wavefront, which cost most of the time in traditional algorithm, and also grab the features of beam intensity distribution simultaneously. We also compare the result of reconstruction using single focal spot. The experimental results show that though SHWS pattern training result has slightly better root mean square (RMS), both the reconstructions have high accuracy in wavefront and beam profile.
SnuQS: scaling quantum circuit simulation using storage devices

Since the state-of-the-art quantum computers are still noisy and error-prone, classical simulation of quantum circuits is essential in verifying/calibrating quantum computers and prototyping/debugging complex quantum algorithms. Classical simulation of large quantum systems is challenging due to its exponential increase in space and computation requirements. In this paper, we propose a full-state simulation framework, SnuQS. It exploits storage devices, such as HDDs and NVMe SSDs, to enlarge the available main memory capacity at a small cost. To achieve maximum I/O bandwidth, we propose an overlay-based memory management technique and optimization techniques. We also propose an I/O subsystem architecture that guarantees the maximum bandwidth of each storage device. We evaluate SnuQS on a 64-core CPU and 4-GPU system with 80 2TB HDDs and 10 4TB NVMe SSDs using quantum supremacy and quantum Fourier transform circuits. The experimental result indicates that SnuQS and the proposed I/O subsystem together is an effective and practical solution to scale the full-state simulation of large quantum circuits at about 300X lower cost than the DDR4 DRAM main-memory-only system.
Augmenting mmWave localization accuracy through sub-6 GHz on off-the-shelf devices

Millimeter-wave (mmWave) technology is an important element to increase the throughput and reduce latency of future wireless networks. At the same time, its high bandwidth and highly directional antennas allow for unprecedented accuracy in wireless sensing and localization applications. In this paper, we thoroughly analyze mmWave localization and find that it is either extremely accurate or has a very high error, since there is significant mmWave coverage via reflections and even through walls. As a consequence, sub-6 GHz technology can not only provide (coarse) localization where mmWave is not available, but is also critical to decide among multiple candidate antennas and APs for accurate mmWave localization. Based on these insights, we design a high-accuracy joint mmWave and sub-6 GHz location system. We enable CSI-based angle estimation and FTM-based ranging on off-the-shelf mmWave devices to implement our mechanism and carry out an extensive measurement campaign. Our system is the first to achieve 18 cm median location error with off-the-shelf devices under their normal mode of operation. We further release the location system (and in particular the CSI and FTM functionality) as well as the trace data from the measurement campaign to the research community.
Speech privacy attack via vibrations from room objects leveraging a phased-MIMO radar

Speech privacy leakage has long been a public concern. Through speech eavesdropping, an adversary may steal a user's private information or an enterprise's financial/intellectual properties, leading to catastrophic consequences. Existing non-microphone-based eavesdropping attacks rely on physical contact or line-of-sight between the sensor (e.g., a motion sensor or a radar) and the victim sound source. In this poster, we discover a new form of speech eavesdropping attack that senses minor speech-induced vibrations upon common room objects using mmWave. By integrating phasedarray and multiple-input and multiple-output (MIMO) on a single mmWave transceiver, our attack can capture and fuse micrometerlevel vibrations upon the surfaces of multiple objects to reveal speech content in a remote and non-line-of-sight fashion. We successfully demonstrate such an attack by developing a deep speech recognition scheme grounded on unsupervised domain adaptation. Without prior training on the victim's data, our attack can achieve a high success rate of over 90% in recognizing simple speech content.
Towards low-latency I/O services for mixed workloads using ultra-low latency SSDs

Low-latency I/O services are essential for latency-sensitive workloads when they co-run with throughput-oriented workloads in cloud data centers. Although advanced SSDs such as Intel Optane SSDs can offer ultra-low latency at the device layer, I/O interference among various workloads through the I/O stack can still significantly enlarge I/O latency. It is still an open problem to best utilize ultra-low latency SSDs in cloud computing environments. In this paper, we analyze the entire I/O stack and reveal that I/O interference is mainly attributed to resource contention in the SSD device, transactions commit in the file system, and costly process scheduling. To address these problems, we propose FastResponse, a holistic approach to use ultra-low latency SSDs for latency-sensitive workloads. First, we propose a new I/O scheduler at the block layer to throttle I/O requests of throughput-oriented workloads, and thus reduce the resource contention in the SSD device. Second, we develop a fine-grained journaling scheme to reduce the latency of transaction at the file system layer. Third, we redesign Completely Fair Scheduler (CFS) to promote the priority of latency-sensitive processes. We implement FastResponse in Linux kernel and evaluate it with several mixed workloads. Compared with the vanilla Linux and the state-of-the-art SelectISR, FastResponse can reduce the average response time of latency-sensitive workloads by 18--70% and 10--67%, respectively, and reduce the 99.9th percentile response time by 58--80% and 52--78%, respectively. Meanwhile, the performance degradation for throughput-oriented workloads is less than 6%.
Argosleep: Monitoring Sleep Posture from Commodity Millimeter-Wave Devices

We propose Argosleep, a millimeter-wave (mmWave) wireless sensors based sleep posture monitoring system that predicts the 3D location of body joints of a person during sleep. Argosleep leverages deep learning models and knowledge of human anatomical features to solve challenges with low-resolution, specularity, and aliasing in existing mmWave devices. Argosleep builds the model by learning the relationship between mmWave reflected signals and body postures from thousands of existing samples. Since practical sleep also involves sudden toss-turns, which could introduce errors in posture prediction, Argosleep designs a state machine based on the reflected signals to classify the sleeping states into rest or toss-turn, and predict the posture only during the rest states. We evaluate Argosleep with real data collected from COTS mmWave devices for 8 volunteers of diverse ages, gender, and height performing different sleep postures. We observe that Argosleep identifies the toss-turn events accurately and predicts 3D location of body joints with accuracy on par with the existing vision-based system, unlocking the potential of mmWave systems for privacy-noninvasive at-home healthcare applications.
EarHealth: an earphone-based acoustic otoscope for detection of multiple ear diseases in daily life

With the aging of the population and the long-time wearing of earphones, hearing health has gradually emerged as a worldwide health issue. Early detection of hearing health conditions would greatly reduce potential risks with timely medical intervention. This study proposes an earphone-based ear condition monitoring system, named EarHealth, which is low-cost, non-invasive, and easily usable in daily life. It can detect three major hearing health conditions: ruptured eardrum, earwax buildup and blockage, and otitis media. By analyzing the recorded echoes evoked by a chirp sound stimulus, EarHealth recognizes the distinguishable characteristics from ear canal structure and eardrum mobility. EarHealth achieves an accuracy of 82.6% in 92 human subjects, including 27 normal subjects, 22 patients with ruptured eardrum, 25 patients with otitis media, and 18 patients with earwax blockage. EarHealth is the first earphone-based system capable of monitoring hearing health conditions by utilizing the ear canal geometry and eardrum mobility. It is anticipated that EarHealth would provide pervasive and proactive protection for hearing health.
Realtime intelligent control for NextG cellular radio access networks

RAN Intelligent Control (RIC) has developed in parallel with Open Radio Access Networks (O-RAN) as a means of utilizing newly available interfaces. Focus has been largely on non-realtime (non-RT: > 1 sec) dealing with RAN management and offline training, and near-realtime (near-RT: 10 ms to 1 sec) dealing with UE load balancing and RAN configuration. We contend that the true power of RIC can be unleashed only with realtime (RT: < 100 μs) measurement, optimization, and control of RAN resources, corresponding to the cellular transmission time interval (TTI: 125 μs to 1 ms).
Producers’ approaches about good agricultural practices in Manisa andİzmir

This study includes the agricultural enterprises which apply and do not apply good agricultural practices in Manisa and Izmir provinces. The basic purposes of this research are; to make a comparison between the enterprises apply and do not apply good agricultural practices, to determine the awareness of the producers to good agricultural practices, to specify the tendency of the farmers towards good agricultural practices and to present the factors affecting to good agriculture. In the scope of the study, 25 grape producers in Manisa and 20 mandarin producers in Izmir that apply good agricultural practices have been participated in a survey. Besides, the same survey was also conducted with the same number of producers which do not apply good agricultural practices. To analyze the data, simple calculations such as average, percentage, frequency and through the use of the crosstabs were used and the social-economic status of the producers and some technical and economical specifications of the enterprises were determined. The tendency of the producers towards performing good agricultural practices and the factors affecting to these tendencies have been observed. The tendency of the producers were determined by using Logit Regression Analysis.
Estimation of serum uric acid levels in Parkinson’s disease patients along with their comorbidities and treatment pattern

To find association of uric acid levels in patients with Parkinson’s disease and its severity. To study the affect of different Comorbid conditions and treatment patterns in Parkinson’s disease patients. A Prospective observational study among 84 patients with Parkinson’s disease was carried out. Serum uric acid levels were determined and association among uric acid levels with disease severity in Parkinson’s disease patients was evaluated using Hoehn and Yahr staging scale. Out of 84 patients, Serum uric acid test was done in 60 patients. Among them 65% were found with uric acid levels less than median value 5.5 mg/dL and 56.6% were found in early stage, 11% were found in advanced stage Parkinson’s disease. In our study, we found inverse association of uric acid levels and its significant correlation with disease severity in early and advanced stage Parkinson’s disease patients. Comorbid condition such as hypertension may be associated with a moderate to severe decline in motor functions. Majority of the patients were treated with Trihexyphenidyl, Carbidopa & Levodopa, Rasagiline. Clinical pharmacist should identify whether the dyskinesia occurred in the patient is drug induced or disease related. This could help physician to provide fractionised dose of Levodopa/Carbidopa for patients and prevent adverse drug reactions.
In£uenceof xylem£uid chemistryon planktonicgrowth, bio¢lmformationandaggregationofXylella fastidiosa

Xylella fastidiosa is the causal agent of Pierce’s disease in grapevines. The mechanisms of pathogenicity are largely due to occlusion of xylem vessels by aggregation of X. fastidiosa and biofilm formation. Xylella fastidiosa was subjected to xylem fluids with varying chemistries to examine the effects of nutritional components on bacterial growth in vitro. The exposure of X. fastidiosa to xylem fluids collected from different Vitis genotypes resulted in highly significant differences in both planktonic growth and biofilm formation. Planktonic growth of X. fastidiosa in Vitis xylem fluid was correlated to the concentration of citric acid, amino acids (glutamic acid, glutamine, histidine, valine, methionine, isoleucine and phenylalanine) and inorganic ions (copper, magnesium, phosphorus and zinc). Biofilm formation was correlated to many amino acids at 1 h of incubation. Xylem fluid from Vitis rotundifolia cv. Noble (fluid that supported low planktonic growth) was supplemented with the compounds that were correlated above to levels found in Vitis champinii cv. Ramsey (fluid that supported high planktonic growth) to determine the direct impact of xylem constituents on the growth characteristics of X. fastidiosa. Augmentation of fluid from Noble with the amino acids listed above, citric acid, calcium and magnesium resulted in increased planktonic growth and aggregation.
Examining supply chain quality management in the Chinese automobile industry

The aim of this thesis is to understand what supply chain quality management (SCQM) is and to examine SCQM practices in the Chinese automobile industry by comparing the different SCQM implementation between Chinese Self-owned Brands (CSBs) and Joint Ventures (JVs). This thesis attempts to reveal and understand SCQM practices in order to provide new insights in dealing with quality issues along the supply chain. Hence, in this thesis three research questions (RQs) are explored and answered: RQ1) What are the main factors impacting on SCQM in the Chinese automobile industry? RQ2) What are the differences between CSBs and JVs in the field of SCQM? RQ3) Why do such differences occur? A mixed research methodology was implemented to answer these three RQs. First, a case study in seven Chinese automobile companies was conducted. Resulting from this qualitative method, this research proposes a robust SCQM framework and generates SCQM measurements. SCQM is conceptualised as a multidimensional construct and the framework is composed of companywide quality management, supplier-side SCQM, customer-side SCQM, and performance. The measurement model evaluation, structural model evaluation, and multigroup analysis were conducted in sequence to investigate multi-dimensionality of SCQM, test the relationships among these dimensions, and clarify the different SCQM between CSBs and JVs. Variance-based structural equation modelling of Partial Least Square (PLS) on the platform of Smart-PLS 3.0 was used to analyse the 196 quantitative data that were collected from CSBs and JVs. Further, six follow-up interviews were then conducted to identify the root causes of the findings of the survey study. This research finds that companywide quality management cannot directly influence operational performance due to the complexity of China’s automobile supply chain. It illustrates that customer-side SCQM practices have the most significant influence on operational performance. This study also clarifies that ownership bears a critical impact on the relationship between customer-side SCQM practices and operational performance. It extends the SCQM literature by studying a large number of automobile companies in China and comparing the different supply chain level quality practices between CSBs and JVs. It provides good breadth to the literature by answering the calls for ownership and emerging market research. This thesis also enhances the understanding of managers about the best SCQM practices to assist companies in moving from their current practices to their preferred one. It also illustrates significant directions for supply chain level quality system designs for automobile companies.
An Adaptive Fuzzy Framework based on Optimized Fuzzy Contexts for Detecting Network Intrusions

AIDS (Anomaly based Intrusion Detection System) is one of the key component of a reliable security infra-structure. Working at second line of defense, detection accuracy is the key objective that largely depends upon the precision of its normal profile. Due to existence of vague boundaries between normal and anomalous classes and dynamic network behavior, building accurate and generalize normal profile is very difficult. Based on the assumption that intruder's behavior can be grouped into different phases active at different times, this article proposes to evolve and use 'short-term fuzzy profiles/contexts' for each such individual intrusion phase resulting in enhanced detection accuracy for low-level attacks. The result is a context-driven, adaptable implementation framework based on a double layer hierarchy of fuzzy sensors. The framework adapts to network conditions by switching between different contexts, according to network traffic patterns, anomaly conditions and organization's security policies. These contexts are evolved in incremental fashion with GA (Genetic Algorithm) using real-time network traces. The framework is tested using DARPA 98/99 dataset showing accurate detection of low-level DoS attack.
The global return on investment from preventing and treating adolescent mental disorders and suicide: a modelling study

Introduction Despite the high burden of mental disorders among adolescents and the potentially lifelong consequences of these conditions, access to mental health services remains insufficient for adolescents in low-income and middle-income countries. We conducted an economic modelling study to quantify the potential costs and benefits of mental health interventions to prevent or treat anxiety, depression, bipolar disorder, and suicide among adolescents. Methods We developed a Markov model that followed cohorts of adolescents (ages 10–19) from 36 countries to assess the impact of addressing anxiety, depression, bipolar disorder, and suicide during adolescence on health and non-health outcomes through their lives. We estimated the costs of interventions using an ingredients-based approach and modelled impacts on education and employment and the resulting economic, morbidity, and mortality benefits. Results Implementing the selected interventions offers a return on investment of 23.6 and a cost of $102.9 per disability adjusted life year (DALY) averted over 80 years. The high return on investment and low cost per DALY averted is observed across regions and country income levels, with the highest return on investment arising from treating mild depression with group-based cognitive behavioural therapy, prevention of suicide attempts among high-risk adolescents, and universal prevention of combined anxiety and depression in low-income and lower-middle income countries. Conclusions The high return on investment and low cost per DALY averted suggests the importance and value of addressing mental disorders among adolescents worldwide. Intervening to prevent and treat these mental disorders even only during adolescence can have lifelong health and economic benefits.
Blind Image Deconvolution using Frequency Spectrum in Motion Blur Estimation

: Image deconvolution refers to the act of minimizing the blur quantity from a blurred image and discovers the sharp and clear original image [7]. The blind image deconvolution (BID), refers to the task of separating two convolved signals.We have taken motion blur estimation method which will extract the features from the frequency domain of the image. This method has low complexity as compare to other estimation methods like radon, steerable filter methods.
EFFECT OF MINERAL AND BIO-FERTILIZATION ON NPK AVAILABILITY, UPTAKE, AND MAIZE YIELD

Two field trails were conducted during the two successive seasons 20072008 on maize plants at the experimental farm of Sakha Agric. Res. Station, Kafr ElSheikh Governorate, Egypt. The aim of this study was to investigate the influence of maize grain treated with Azotobacter chroococcum and Bacillus megatherium and their combinations with NPK under three N-levels; 80, 100, and 120 kg N/fad for N1, N2, and N3 to increase yield, NPK uptake by maize plants and the availability of NPK in the soil. The experiments were conducted in split plot design, with three replicates. The observed results can be summarized as follows:  The yield and components of maize, NPK uptake and the availability of NPK in the studied soil were affected significantly by biofertilizer treatments and N-levels.  The maximum values of available N were obtained by the application of Azotobacter chroococcum and Bacillus magetherium with NPK under N3 treatment. The maximum values of available P were recoded by the application of Bacillus megatherium with NPK under N3 treatment.  Combination of A.chroococcum, B.megatherium, and NPK fertilizers under N3 significantly increased grain yield (20.9 and 17.9%), straw yield (16.8 and 20.6%), 100-grain weight (9.9 and 13.3%), ear weight (21.0 and 18.0) and N, P, and K uptake by maize grain [(35.1 and 31.3%), (21.2 and 26.9%) and (18.1 and 31.1%)] over the control for N, P, and K, respectively, in 2007 and 2008 seasons. While shelling percentage did not significantly affected by different treatments. The application of these results should help in reducing environmental pollution.