Miracle-dz/UniFAR-FTUs huggingface dataset:
Information-Theoretic Privacy for Smart Metering Systems with a Rechargeable Battery

Smart-metering systems report electricity usage of a user to the utility provider on almost real-time basis. This could leak private information about the user to the utility provider. In this paper, we investigate the use of a rechargeable battery in order to provide privacy to the user. We assume that the user load sequence is a first-order Markov process, the battery satisfies ideal charge conservation, and that privacy is measured using normalized mutual information (leakage rate) between the user load and the battery output. We study the optimal battery charging policy that minimizes the leakage rate among the class of battery policies that satisfy causality and charge conservation. We propose a series reduction on the original problem and ultimately recast it as a Markov Decision Process (MDP) that can be solved using a dynamic program. In the special case of i.i. d. demand, we explicitly characterize the optimal policy and show that the associated leakage rate can be expressed as a single-letter mutual information expression. In this case, we show that the optimal charging policy admits an intuitive interpretation of preserving a certain invariance property of the state. Interestingly an alternative proof of optimality can be provided that does not rely on the MDP approach, but is based on purely information theoretic reductions.
A Data-Driven Analysis of Workers' Earnings on Amazon Mechanical Turk

A growing number of people are working as part of on-line crowd work. Crowd work is often thought to be low wage work. However, we know little about the wage distribution in practice and what causes low/high earnings in this setting. We recorded 2,676 workers performing 3.8 million tasks on Amazon Mechanical Turk. Our task-level analysis revealed that workers earned a median hourly wage of only ~$2/h, and only 4% earned more than $7.25/h. While the average requester pays more than $11/h, lower-paying requesters post much more work. Our wage calculations are influenced by how unpaid work is accounted for, e.g., time spent searching for tasks, working on tasks that are rejected, and working on tasks that are ultimately not submitted. We further explore the characteristics of tasks and working patterns that yield higher hourly wages. Our analysis informs platform design and worker tools to create a more positive future for crowd work.
Online concurrent workload classification for multi-core energy management

Modern embedded multi-core processors are organized as clusters of cores, where all cores in each cluster operate at a common Voltage-frequency (V−f). Such processors often need to execute applications concurrently, exhibiting varying and mixed workloads (e.g. compute- and memory-intensive) depending on the instruction mix and resource sharing. Runtime adaptation is key to achieving energy savings without trading-off application performance with such workload variabilities. In this paper, we propose an online energy management technique that performs concurrent workload classification using the metric Memory Reads Per Instruction (MRPI) and pro-actively selects an appropriate V−f setting through workload prediction. Subsequently, it monitors the workload prediction error and performance loss, quantified by Instructions Per Second (IPS) at runtime and adjusts the chosen V−f to compensate. We validate the proposed technique on an Odroid-XU3 with various combinations of benchmark applications. Results show an improvement in energy efficiency of up to 69% compared to existing approaches.
Research on the course discrimination based on multi-classification method and feature selection

Exam distinction can only reflect a course of the distinction between students. In order to find the main differentiating courses in the university curriculum, the paper proposes the concept of the course differentiation, focusing on the value of course discrimination, the classification method and the proportion of professional courses in selected courses. In order to obtain the value of course differentiation, a method of combining multi-classification and feature selection is proposed. First of all, the data sources of students' achievement are classified by traditional five-level, N-score, M-classification and unsupervised four methods. Then, using the wrapper feature selection method, the classification accuracy rate and the feature subset of each dataset are calculated by different classifiers. Finally, we found the connotation and extension of the course discrimination. Experiments show that the proposed method can find the maximum value of the course distinction and the corresponding classification method, the proportion of professional courses is much larger than the proportion of public courses. It achieves the curriculum and assessment of the distinction requirements.
A performance analysis of user's intention classification from EEG signal by a computational intelligence in BCI

Knowing the user's intentions is very important and can be useful in our daily life. It would be a very useful way, especially if people with disabilities can use these functions as a means of self-expression. The user's intension classification is a kind of common time-series problem for detecting human cognitive state. In this paper, we classify user intention by analyzing EEG signal using machine learning in BCI. The performance of the classification accuracy can be achieved by using the proposed approach in terms of the number of neurons in the hidden layer, which also leads types of membership function in fuzzy rules. We prepared training and test data using the Emotive headset for the experiment. Our experimental results show that the proposed approach gives us a quite promising method with 5 fuzzy rules obtained through a fuzzy C-means clustering. It is a simple fuzzy system with neural network structure by tuning GA providing statistically superior solutions. Experimental results show that the best results were obtained using the electrode position {F7, F8, FC5, FC6} of EEG. Experimental results using training data showed an accuracy of 94.2%. However, the result of using the test data after learning shows a slightly lower accuracy of 92.3%. This experiment shows that using training data and test dares can result in more than 90% accuracy. Experimental results show that all 4--action behaviors have similar accuracy.
Comparison of Glomerular Transcriptome Profiles of Adult-Onset Steroid Sensitive Focal Segmental Glomerulosclerosis and Minimal Change Disease

Objective To search for biomarkers to differentiate primary focal segmental glomerulosclerosis (FSGS) and minimal change disease (MCD). Methods We isolated glomeruli from kidney biopsies of 6 patients with adult-onset steroid sensitiveFSGS and 5 patients with MCD, and compared the profiles of glomerular transcriptomes between the two groups of patients using microarray analysis. Results Analysis of differential expressed genes (DEGs) revealed that up-regulated DEGs in FSGS patients compared with MCD patients were primarily involved in spermatogenesis, gamete generation, regulation of muscle contraction, response to unfolded protein, cell proliferation and skeletal system development. The down-regulated DEGs were primarily related to metabolic process, intracellular transport, oxidation/reduction andestablishment of intracellular localization. We validated the expression of the top 6 up-regulated and top 6 down-regulated DEGs using real-time PCR. Membrane metallo-endopeptidase (MME) is a down-regulated gene that was previously identified as a key gene for kidney development. Immunostaining confirmed that the protein expression of MME decreased significantly in FSGS kidneys compared with MCD kidneys. Conclusions This report was the first study to examine transcriptomes in Chinese patients with various glomerular diseases. Expressions of MME both in RNA and protein level decreased significantly in glomeruli of FSGS kidneys compared with MCD kidneys. Our data suggested that MME might play a role in the normal physiological function of podocytes and a decrease in MME expression might be related to podocyte injury. We also identified genes and pathways specific for FSGS versus MCD, and our data could help identify potential new biomarkers for the differential diagnosis between these two diseases.
Streets for People: Engaging Children in Placemaking Through a Socio-technical Process

In this paper, we present a socio-technical process designed to engage children in an ongoing urban design project-Streets for People-in Newcastle, UK. We translated urban design proposals developed by residents and the local authority to enable children to contribute ideas to the project. Our process comprised three stages: situated explorations and evidence gathering through digitally supported neighbourhood walks; issue mapping and peer-to-peer discussions using an online engagement platform; and face-to-face dialogue between children, residents, and the local authority through a 'Town Hall' event. We report insights gained through our engagement and show how our activities facilitated issue advocacy and the development of children's capacities, but also surfaced tensions around the agency of children in political processes. We reflect on the challenges of working in this space, and discuss wider implications for technology design and ethical questions that 'scaling up' such work might pose.
Stochastic Geometry Modeling of Cellular V2X Communication on Shared Uplink Channels

To overcome the limitations of Dedicated Short Range Communications (DSRC) with short range, non-supportability of high density networks, unreliable broadcast services, signal congestion and connectivity disruptions, Vehicle-to-anything (V2X) communication networks, standardized in 3rd Generation Partnership Project (3GPP) Release 14, have been recently introduced to cover broader vehicular communication scenarios including vehicle-to-vehicle (V2V), vehicle-to-pedestrian (V2P) and vehicle-to-infrastructure/network (V2I/N). Motivated by the stringent connection reliability and coverage requirements in V2X , this paper presents the first comprehensive and tractable analytical framework for the uplink performance of cellular V2X networks, where the vehicles can deliver its information via vehicle-to-base station (V2B) communication or directly between vehicles in the sidelink, based on their distances and the bias factor. By practically modeling the vehicles on the roads using the doubly stochastic Cox process and the BSs, we derive new association probability of the V2B communication, new success probabilities of the V2B and V2V communications, and overall success probability of the V2X communication, which are validated by the simulations results. Our results reveal the benefits of V2X communication compared to V2V communication in terms of success probability.
The Potent Cdc7-Dbf4 (DDK) Kinase Inhibitor XL413 Has Limited Activity in Many Cancer Cell Lines and Discovery of Potential New DDK Inhibitor Scaffolds

Cdc7-Dbf4 kinase or DDK (Dbf4-dependent kinase) is required to initiate DNA replication by phosphorylating and activating the replicative Mcm2-7 DNA helicase. DDK is overexpressed in many tumor cells and is an emerging chemotherapeutic target since DDK inhibition causes apoptosis of diverse cancer cell types but not of normal cells. PHA-767491 and XL413 are among a number of potent DDK inhibitors with low nanomolar IC50 values against the purified kinase. Although XL413 is highly selective for DDK, its activity has not been extensively characterized on cell lines. We measured anti-proliferative and apoptotic effects of XL413 on a panel of tumor cell lines compared to PHA-767491, whose activity is well characterized. Both compounds were effective biochemical DDK inhibitors but surprisingly, their activities in cell lines were highly divergent. Unlike PHA-767491, XL413 had significant anti-proliferative activity against only one of the ten cell lines tested. Since XL413 did not effectively inhibit DDK in multiple cell lines, this compound likely has limited bioavailability. To identify potential leads for additional DDK inhibitors, we also tested the cross-reactivity of ∼400 known kinase inhibitors against DDK using a DDK thermal stability shift assay (TSA). We identified 11 compounds that significantly stabilized DDK. Several inhibited DDK with comparable potency to PHA-767491, including Chk1 and PKR kinase inhibitors, but had divergent chemical scaffolds from known DDK inhibitors. Taken together, these data show that several well-known kinase inhibitors cross-react with DDK and also highlight the opportunity to design additional specific, biologically active DDK inhibitors for use as chemotherapeutic agents.
Navigation Systems for Motorcyclists: Exploring Wearable Tactile Feedback for Route Guidance in the Real World

Current navigation systems for motor cyclists use visual or auditory cues for guidance. However, this poses a challenge to the motorcyclists since their visual and auditory channels are already occupied with controlling the motorbike, paying attention to other road users, and planing the next turn. In this work, we explore how tactile feedback can be used to guide motorcyclists. We present MOVING (MOtorbike VIbrational Navigation Guidance), a smart kidney belt that presents navigation cues through 12 vibration motors. In addition, we report on the design process of this wearable and on an evaluation with 16 participants in a real world riding setting. We show that MOVING outperforms off-the-shelf navigation systems in terms of turn errors and distraction.
Temperature-controlled electrospinning of EVOH nanofibre mats encapsulated with Ag, CuO, and ZnO particles for skin wound dressing

To treat skin burns, a new wound dressing, nanofibre mats with metal or metal oxide nanoparticles (Ag, CuO, and ZnO), was fabricated using the electrospinning technique. During a therapeutic process, the antibacterial ability and bio-compatibility of a new dressing material are of major concern. To expound the characteristics of ethylene vinyl alcohol (EVOH) nanofibre mats encapsulated with metal or metal oxide nanoparticles, denoted as Ag-EVOH, CuO-EVOH, and ZnO-EVOH, for use as new wound dressing materials, we investigated the suitable processing parameters to fabricate these materials, such as the voltage, tip-to-collector distance, concentration of the solution, and effect of environmental temperature. The antibacterial ability and bio-compatibility of Ag-EVOH, CuO-EVOH, and ZnO-EVOH were then tested and quantified. The outcomes show that the most suitable temperature for the fabrication of the materials is 40 °C (±3 °C). The antibacterial experiment results indicate that 0.08 g ml−1 of metal/metallic oxide shows the highest antibacterial ability toward Staphylococcus aureus. Furthermore, the largest diameters of the bacteriostatic loops of the three types of nanofibre mats, i.e. Ag-EVOH, CuO-EVOH, and ZnO-EVOH, are 5.89, 5.21, and 4.12 mm, respectively. Finally, the cell proliferations on the three nanofibre mats show a similar growth trend.
A soft-error resilient route computation unit for 3D Networks-on-Chips

Three-dimensional Networks-on-Chips (3D-NoCs) have emerged as an alternative to further enhance the performance, functionality, and packaging density of 2D-NoCs. However, the increasing complexity of NoC routers, the continuous miniaturization of silicon technology, the lower operating voltages, and the higher operating frequencies have made the NoC increasingly vulnerable to soft errors. In particular, transient faults occurring in the route computation unit (RCU) can provoke misrouting which may lead to severe effects such as deadlocks or packet loss, corrupting the operation of the entire chip. By combining a reliable fault detection circuit leveraging circuit-level double-sampling, with a cost-effective rerouting mechanism, we develop a full fault-tolerance solution that can efficiently detect and correct such fatal errors before the affected packets leave the router. To validate the proposed solution, we also introduce a novel method for simulation-based fault-injection based on the NoC's gate-level netlist. Experimental results obtained from a partially and vertically connected 3D-NoC indicate that our solution can provide a high level of reliability in the presence of errors, at the expense of an area and power overhead of 4.1% and 6.8% respectively.
Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer Interface Classification

Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern (CSP) as preprocessing step before classification. The CSP method is a supervised algorithm. Therefore a lot of time-consuming training data is needed to build the model. To address this issue, one promising approach is transfer learning, which generalizes a learning model can extract discriminative information from other subjects for target classification task. To this end, we propose a transfer kernel CSP (TKCSP) approach to learn a domain-invariant kernel by directly matching distributions of source subjects and target subjects. The dataset IVa of BCI Competition III is used to demonstrate the validity by our proposed methods. In the experiment, we compare the classification performance of the TKCSP against CSP, CSP for subject-to-subject transfer (CSP SJ-to-SJ), regularizing CSP (RCSP), stationary subspace CSP (ssCSP), multitask CSP (mtCSP), and the combined mtCSP and ssCSP (ss + mtCSP) method. The results indicate that the superior mean classification performance of TKCSP can achieve 81.14%, especially in case of source subjects with fewer number of training samples. Comprehensive experimental evidence on the dataset verifies the effectiveness and efficiency of the proposed TKCSP approach over several state-of-the-art methods.
Physical activity and weight loss: does prescribing higher physical activity goals improve outcome? 1–3

Background: Recommending increased physical activity facili-tates long-term weight loss, but the optimal level of physical activity to recommend is unknown. Objective: The objective of the study was to evaluate the efficacy for long-term weight loss of recommendations for much higher physical activity than those normally used in behavioral treatments. Design: Overweight men and women ( n = 202) were randomly assigned to either a standard behavior therapy (SBT) for obesity, incorporating an energy expenditure (EE) goal of 1000 kcal/wk, or to a high physical activity (HPA) treatment, in which the goal was an EE of 2500 kcal/wk. To help HPA treatment group participants achieve this high exercise goal, their treatment included encour-agement to recruit 1–3 exercise partners into the study, personal counseling from an exercise coach, and small monetary incentives. Results: The HPA treatment group reported achieving higher mean ( ± SD) physical activity levels than did the SBT group at 6 mo (EE of 2399 ± 1571 kcal/wk compared with 1837 ± 1431 kcal/wk), 12 mo (EE of 2249 ± 1751 kcal/wk compared with 1565 ± 1309 kcal/wk), and 18 mo (EE of 2317 ± 1854 kcal/wk compared with 1629 ± 1483 kcal/wk) (all P < 0.01). Mean ( ± SEM) cumu-lative weight losses at 6, 12, and 18 mo in the HPA treatment group were 9.0 ± 7.1, 8.5 ± 7.9, and 6.7 ± 8.1 kg, respectively. In the SBT group, the corresponding weight losses were 8.1 ± 7.4, 6.1 ± 8.8, and 4.1 ± 7.3 kg, respectively. Between-group differences in weight loss were significant at 12 and 18 mo. Conclusion: These results suggest that recommendations of higher levels of physical activity (EE of 2500 kcal/wk) promote long-term weight loss better than do conventional recommendations. 2003;78:684–9.
Comparative analysis of conventional 2-level DWT and color plane watermarking for telemedicine applications

In telemedicine applications, there is a huge demand for sharing of digital images among specialist and hospitals for better and more accurate diagnosis. Medical image watermarking is a powerful tool that can be used as a medium for sharing medical images among the specialists. Therefore, medical image watermarking cannot be compromised by the quality of image, as well as it is important that image is flawlessly retrieved without the risk of error after image decomposition. In this paper a comparative analysis of conventional 2-level DWT medical image watermarking technique and color plane watermarking technique is proposed. The performance of watermarked image is evaluated by Peak Signal to Noise Ratio (PSNR). The PSNR values of both the technique are compared with varying scaling factor alpha. Higher the PSNR, better is the quality of watermarked image. It is found that a maximum of 73.6239 dB PSNR is observed in color plane watermarking technique as compared to conventional 2-level DWT based watermarking, which is having a PSNR of 56.1993 dB at the same scaling factor. The security of the watermark is enhanced by Arnold's Scrambling method and the robustness is evaluated by Correlation coefficient parameter. The performance of the proposed watermarking system is evaluated against common attacks. It is further suggested that, especially for medical images, watermarking should be done in any of the color plane (R, G or B) instead of doing watermarking on the entire image so as to achieve high PSNR for better reconstruction of watermarked medical image.
How loyal is your Alexa?: Imagining a Respectful Smart Assistant

Smart assistants are the current must-have device in the home. Currently available products do little to respect the autonomy and privacy of end users, but it doesn't have to be this way. My research explores a speculative 'respectful' assistant which is more socially aware, and treats its users in a more nuanced way than occurs at present. Mixing computer science, philosophy, and art, the project uses a combination of user studies and technical comparison to discover a potential future for the smart digital assistant.
Are You Dreaming?: A Phenomenological Study on Understanding Lucid Dreams as a Tool for Introspection in Virtual Reality

Virtual reality (VR) is resurging in popularity with the advancement of low-cost hardware and more realistic graphics. How might this technology help others? That is, to increase mental well-being? The ultimate VR might look like lucid dreaming, the phenomenon of knowing one is dreaming while in the dream. Lucid dreaming can be used as an introspective tool and, ultimately, increase mental well-being. What these introspective experiences are like for lucid dreamers might be key in determining specific design guidelines for future creation of a technological tool used for helping people examine their own thoughts and emotions. This study describes nine active and proficient lucid dreamers' representations of their introspective experiences gained through phenomenological interviews. Four major themes emerged: sensations and feelings, actions and practices, influences on experience, and meaning making. This knowledge can help design a VR system that is grounded in genuine experience and preserving the human condition.