Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Symptoms of Post-traumatic Stress Disorders and Perceived Social Support among Patients with Burn Injury

Introduction Burns are a serious public health problem globally, causing an estimated 265,000 deaths per year. Although the association of burn injuries with mortality and morbidity rates has been well established, data on their psychological consequences are scarce. The present study explores the frequency of post-traumatic stress disorder (PTSD) and perceived social support among patients with burn injuries in Pakistan. Methods This cross-sectional study was conducted at two teaching hospitals in Lahore, Pakistan from May 2015 to July 2015. Eighty patients with burn injuries were included by convenience sampling and interviewed with a specifically designed questionnaire with items on demographics, and the Impact of Events Scale-Revised (IES-R) and Multidimensional Scale of Perceived Social Support (MSPSS) instruments. Results Data were analyzed for a total of 80 participants: 56 women (70.0%) and 24 men (30.0%). Mean age was 35.74 (11.15) years. A high proportion of participants perceived highest social support from friends, reported high ego resiliency levels, had more severe symptoms of avoidance and intrusion, and had high overall PTSD scores. There were no differences between groups in the proportions of respondents who reported high perceived social support from significant others or family, overall social support or symptoms of hyperarousal. Conclusion The findings reflect a high frequency of PTSD symptomatology and poor social support among Pakistani patients with burn injuries in our sample. These factors can exacerbate the patient’s physical injury, delaying both their physical and mental rehabilitation.
Will Distributed Computing Revolutionize Peace? The Emergence of Battlefield IoT

An upcoming frontier for distributed computing might literally save lives in future military operations. In civilian scenarios, significant efficiencies were gained from interconnecting devices into networked services and applications that automate much of everyday life from smart homes to intelligent transportation. The ecosystem of such applications and services is collectively called the Internet of Things (IoT). Can similar benefits be gained in a military context by developing an IoT for the battlefield? This paper describes unique challenges in such a context as well as potential risks, mitigation strategies, and benefits.
Mu-Transaction Sagas in Derecho

Cloud computing infrastructures weaken consistency to improve responsiveness [1], but there are applications for which weak consistency is inadequate. This has stimulated interest in consistency models that scale and perform well. Our work extends the Derecho system [4] to support a constrained but powerful form of ACID transaction sagas. Unusually, the transactional infrastructure requires no locking or 2-phase commits, and yet guarantees serializability. All inter-node actions map to RDMA, allowing the saga infrastructure to achieve exceptional performance at very low overheads.
Evaluating Machine Learning Techniques for Improved Adaptive Pedagogy

Literature has shown that learning gains may be improved significantly if students are offered individual attention. The traditional offering of such individualised attention is however not always possible due to limited resources that lead to high student-to-teacher ratios. One solution to addressing this gap in education has been the proposal of Intelligent Tutoring Systems which promised to deliver adaptive learning experience to improve student learning outcomes. Unfortunately, results have been less than satisfactory - one factor attributing to this poor performance is that such systems fail to adapt to varying student learning needs. To investigate how this poor performance can be improved upon, this paper examines several incremental machine learning techniques by incorporating them in intelligent tutor systems to evaluate their performance for adaptive pedagogy to determine the most appropriate incremental machine learning technique for implementation in intelligent tutor systems. Support Vector Machines (SVMs), kNearest Neighbour (k-NN) and the Naïve Bayes algorithm where investigated and put into practical tests. Results obtained from training the three methods using the same datasets revealed that the naïve Bayes outperformed the k-NN and the SVMs. The results led to the conclusion that Naïve Bayes would perform well when implemented for pedagogical decision-making in ITSs. It was therefore recommended that developers use effective incremental machine learning algorithms such as the naïve Bayes, if intending to build adaptive ITS that can be used by African Students.
Toward IoT-Friendly Learning Models

In IoT environments, data are collected by many distinct devices, at the periphery, so that their feature-sets can be naturally endowed with a faceted structure. In this work, we argue that the IoT requires specialized ML models, able to exploit this faceted structure in the learning strategy. We demonstrate the application of this principle, by a multiple kernel learning approach, based on the exploration of the partition lattice driven by the natural partitioning of the feature set. Furthermore, we consider that the whole data management, acquisition, pre-processing and analytics pipeline results from the composition of processes pursuing different and non-perfectly aligned goals (most often, enacted by distinct agents with different constraints, requirements competencies and with non-aligned interests). We propose the adoption of an adversarial modeling paradigm across the overall pipeline. We argue that knowledge of the composite nature of the learning process, as well as of the adversarial character of the relationship among phases, can help in developing heuristics for improving the learning algorithms efficiency and accuracy. We develop our argument with reference to few exemplary use cases.
Intra Block Copy for Screen Content in the Emerging AV1 Video Codec

Screen content coding plays an important role in many applications. To meet the growing demands of screen content coding, the emerging AV1 video codec incorporates several coding tools, which are specially designed for screen content utilizing its distinctive characteristics. Among these tools, the intra block copy utilizes the characteristic that repeating patterns frequently occur in screen content. This paper presents the technology of intra block copy in AV1. In particular, to efficiently search the predictor in the reconstructed regions of the current picture, AV1 uses the hash matching method at the encoder side. For the generation of hash table, a bottom-to-up manner is adopted to reduce the redundant computation and then decrease the encoding time. In addition, several constraints are involved to facilitate hardware design. Experimental results demonstrate that the intra block copy in AV1 can bring 27.1% bitrate saving for screen content. When compared with the non hash-based intra block copy, the hash-based method achieves 12.2% bitrate saving.
Real-Time Face Detection Using a Moving Camera

This paper presents a real-time face detection system using a moving camera. The proposed system consists of three modules, including (1) detection of face candidates: Face candidates are generated using the information of skin color, edges, and face area, (2) verification of face candidates: HOG (Histogram of Oriented Gradient) features are generated from face candidates and a two-class C-SVM (Support Vector Machine) classifier with pretrained face samples is employed to determine whether face candidates are real faces or not, (3) face tracking: Overlapping area of two face targets in current and previous frames is estimated to determine whether the tracking will be continuous or not. By use of estimation of face size, the proposed method can avoid a huge amount of computation time that is required by a point-by-point scanning way in conventional methods. Moreover, the accuracy of the face detection can be improved greatly. The proposed system can successfully detect most faces of the crowds in open space, which is beneficial for quickly searching the specified persons to prevent the occurrence of possible criminal events.
Pareto-Optimal Power- and Cache-Aware Task Mapping for Many-Cores with Distributed Shared Last-Level Cache

Two factors primarily affect performance of multi-threaded tasks on many-core processors with both shared and physically distributed Last-Level Cache (LLC): the power budget associated with a certain task mapping that aims to guarantee thermally safe operation and the non-uniform LLC access latency of threads running on different cores. Spatially distributing threads across the many-core increases the power budget, but unfortunately also increases the associated LLC latency. On the other side, mapping more threads to cores near the center of the many-core decreases the LLC latency, but unfortunately also decreases the power budget. Consequently, both metrics (LLC latency and power budget) cannot be simultaneously optimal, which leads to a Pareto-optimization that has formerly not been exploited. We are the first to present a run-time task mapping algorithm called PCMap that exploits this trade-off. Our approach results in up to 8.6% reduction in the average task response time accompanied by a reduction of up to 8.5% in the energy consumption compared to the state-of-the-art.
AnFlo: Detecting Anomalous Sensitive Information Flows in Android Apps

Smartphone apps usually have access to sensitive user data such as contacts, geo-location, and account credentials and they might share such data to external entities through the Internet or with other apps. Confidentiality of user data could be breached if there are anomalies in the way sensitive data is handled by an app which is vulnerable or malicious. Existing approaches that detect anomalous sensitive data flows have limitations in terms of accuracy because the definition of anomalous flows may differ for different apps with different functionalities; it is normal for "Health" apps to share heart rate information through the Internet but is anomalous for "Travel" apps. In this paper, we propose a novel approach to detect anomalous sensitive data flows in Android apps, with improved accuracy. To achieve this objective, we first group trusted apps according to the topics inferred from their functional descriptions. We then learn sensitive information flows with respect to each group of trusted apps. For a given app under analysis, anomalies are identified by comparing sensitive information flows in the app against those flows learned from trusted apps grouped under the same topic. In the evaluation, information flow is learned from 11,796 trusted apps. We then checked for anomalies in 596 new (benign) apps and identified 2 previously-unknown vulnerable apps related to anomalous flows. We also analyzed 18 malware apps and found anomalies in 6 of them.
Variation-Aware Pipelined Cores through Path Shaping and Dynamic Cycle Adjustment: Case Study on a Floating-Point Unit

In this paper, we propose a framework for minimizing variation-induced timing failures in pipelined designs, while limiting any overhead incurred by conventional guardband based schemes. Our approach initially limits the long latency paths (LLPs) and isolates them in as few pipeline stages as possible by shaping the path distribution. Such a strategy, facilitates the adoption of a special unit that predicts the excitation of the isolated LLPs and dynamically allows an extra cycle for the completion of only these error-prone paths. Moreover, our framework performs post-layout dynamic timing analysis based on real operands that we extract from a variety of applications. This allows us to estimate the bit error rates under potential delay variations, while considering the dynamic data dependent path excitation. When applied to the implementation of an IEEE-754 compatible double precision floating-point unit (FPU) in a 45nm process technology, the path shaping helps to reduce the bit error rates on average by 2.71 x compared to the reference design under 8% delay variations. The integrated LLPs prediction unit and the dynamic cycle adjustment avoid such failures and any quality loss at a cost of up-to 0.61% throughput and 0.3% area overheads, while saving 37.95% power on average compared to an FPU with pessimistic margins.
Proposal of Static Body Object Detection Methods with the DTN Routing for Life Safety Information Systems

Although the life safety information systems on the smartphones are widely spread for the sake of the emergent situations in disasters, there have been some problems to use them by people who are not good to operate or are injured. Therefore, this paper proposes the static body object detection methods with the Delay Tolerant Networking for life safety information system. In the system, the sensors on smartphones automatically detect the abnormal static situations by the time and position differences based with the Markov Chain Monte Carlo methods, and the emergent messages are automatically transmitted with the message priorities. Moreover, those messages are transmitted to the servers with the Delay Tolerant Networking. Then, the prototype system is introduced, and the experiments are discussed for the effectiveness of the proposed systems.
REST: A Reference-based Framework for Spatio-temporal Trajectory Compression

The pervasiveness of GPS-enabled devices and wireless communication technologies results in massive trajectory data, incurring expensive cost for storage, transmission, and query processing. To relieve this problem, in this paper we propose a novel framework for compressing trajectory data, REST (Reference-based Spatio-temporal trajectory compression), by which a raw trajectory is represented by concatenation of a series of historical (sub-)trajectories (called reference trajectories) that form the compressed trajectory within a given spatio-temporal deviation threshold. In order to construct a reference trajectory set that can most benefit the subsequent compression, we propose three kinds of techniques to select reference trajectories wisely from a large dataset such that the resulting reference set is more compact yet covering most footprints of trajectories in the area of interest. To address the computational issue caused by the large number of combinations of reference trajectories that may exist for resembling a given trajectory, we propose efficient greedy algorithms that run in the blink of an eye and dynamic programming algorithms that can achieve the optimal compression ratio. Compared to existing work on trajectory compression, our framework has few assumptions about data such as moving within a road network or moving with constant direction and speed, and better compression performance with fairly small spatio-temporal loss. Extensive experiments on a real taxi trajectory dataset demonstrate the superiority of our framework over existing representative approaches in terms of both compression ratio and efficiency.
A New Broadband Dielectric Model for Simultaneous Determination of Water Saturation and Porosity

In this paper, we propose a novel approach to broadband model relative effective complex permittivity of soils (augmented broadband complex dielectric mixture model). The approach involves a combination of mixture equation and relaxation model. The mixture equation is an extension in frequency domain developed from a volume average approach. The relaxation model aims at integrating polarization effects that would occur over the frequency range under interest. In this framework, the relative effective complex permittivity is computed from a set of parameters including water saturation, porosity, cementation exponent, and relaxation parameters. The proposed model was tested and validated on a new set of data measured over the 1-MHz–3-GHz frequency range on compacted kaolin. The model was fit to the measured spectra with satisfactory agreement in terms of root-square-mean error. Furthermore, the comparison between estimated and experimentally measured water saturation and porosity has shown a good correlation. This indicates the great potential of this new model for monitoring state parameters for applications in geophysics and geotechnics.
Increased Ratio of Non-mercaptalbumin-1 Among Total Plasma Albumin Demonstrates Potential Protein Undernutrition in Adult Rats

The redox state of plasma albumin shifts in response to dietary protein intake in growing rats, and the shift is more sensitive than that of plasma albumin level, a classical marker of protein nutritional status. While it has been suggested that plasma albumin redox state could be useful as a novel marker of protein nutritional status, the above animal model is highly sensitive to dietary protein intake and the observation may not be extrapolated widely to humans. This study aimed to investigate whether albumin redox state also reflects protein nutritional status in adult rats, which have a lower dietary protein requirement and are less responsive to protein intake. Male adult rats were placed on AIN-93M diet (14% casein), or AIN-93M-based low protein diets (10 or 5% casein) ad libitum for 24 weeks. Whereas there was no significant difference in body weight between the groups at the end of the experimental period, the 5% casein diet group had the smallest gastrocnemius muscle weight among the groups, which was significantly lower than that of the 10% casein diet group. Plasma albumin level was also lower in the 5% casein diet group compared with the other groups, but the differences were limited and inconsistent during the experimental period. Among the albumin redox isoforms such as mercaptalbumin, non-mercaptalbumin-1, and non-mercaptalbumin-2, the ratio of non-mercaptalbumin-1 among total albumin was significantly higher in the 5% casein diet group, and the increase remained constant throughout the experimental period. Increased non-mercaptalbumin-1 ratio would thus demonstrate the presence of potential protein undernutrition in adult rats, as manifested only by a decreased gain in a specific type of skeletal muscle; non-mercaptalbumin-1 among total albumin ratio could be useful as a robust marker of protein nutritional status, contributing to prevention of protein undernutrition-related diseases such as frailty and sarcopenia.
Model, Data and Reward Repair: Trusted Machine Learning for Markov Decision Processes

When machine learning (ML) models are used in safety-critical or mission-critical applications (e.g., self driving cars, cyber security, surgical robotics), it is important to ensure that they provide some high-level guarantees (e.g., safety, liveness). We introduce a paradigm called Trusted Machine Learning (TML) for making ML models more trustworthy. We use Markov Decision Processes (MDPs) as the underlying dynamical model and outline three TML approaches: (1) Model Repair, wherein we modify the learned model directly; (2) Data Repair, wherein we modify the data so that re-learning from the modified data results in a trusted model; and (3) Reward Repair, wherein we modify the reward function of the MDP to satisfy the specified logical constraint. We show how these repairs can be done efficiently for probabilistic models (e.g., MDP) when the desired properties are expressed in some appropriate fragment of logic such as temporal logic (for example PCTL, i.e., Probabilistic Computation Tree Logic), first order logic or propositional logic. We illustrate our approaches on case studies from multiple domains, e.g., car controller for obstacle avoidance, and a query routing controller in a wireless sensor network.
Tracking Food Insecurity from Tweets Using Data Mining Techniques

Data mining algorithms can be applied to extract useful patterns from social media conversations to monitor disasters such as tsunami, earth quakes and nuclear power accidents. While food insecurity has persistently remained a world concern, its monitoring with this strategy has received limited attention. In attempt to address this concern, UN Global Pulse demonstrated that tweets reporting food prices from Indonesians can aid in predicting actual food price increase. For regions like Kenya and Uganda where use of tweets is considered low, this option can be problematic. Using Uganda as a case study, this study takes an alternative of using tweets from all over the world with mentions of; (1) uganda +food, (2) uganda + hunger, and (3) uganda + famine for years 2014, 2015 and 2016. The study however utilized tweets on food insecurity instead of tweets on food prices. In the first step, five data mining algorithms (D-tree, SVM, KNN, Neural Networks and N-Bayes) were trained to identify tweets conversations on food insecurity. Algorithmic performance were found comparable with human labeled tweet on the same subject. In step two, tweets reporting food insecurity were generated into trends. Comparing with trends from Uganda Bureau of Statistics, promising findings have been obtained with correlation coefficients of 0.56 and 0.37 for years 2015 and 2016 respectively. The study provides a strategy to generate information about food insecurity for stakeholders such asWorld Food Program in Uganda for mitigation action or further investigation depending on the situation. To improve performance, future work can; (1) aggregate tweets with other datasets, (2) ensemble algorithms, and (3) apply unexplored algorithms.
Simultaneous Range and Cross-Range Variant Phase Error Estimation and Compensation for Highly Squinted SAR Imaging

This paper addresses an autofocusing technique for highly squinted airborne synthetic aperture radar (SAR) imaging. In highly squinted mode, the phase errors resulting by trajectory deviations usually exhibit both range and cross-range variance. To circumvent this problem, a 2-D space-variant phase error model is proposed. Then an autofocusing approach is presented to realize precise SAR reconstruction. This approach mainly consists of three processing steps. First, multiple local images are automatically selected based on sliding window operation in both range and cross-range dimensions. For each local image, weighted squint phase gradient autofocus kernel is applied to estimate the local phase error function. Second, the derived multiple local phase error functions are combined to resolve the 2-D space-variant phase error model using a weighted total least square method. Third, the fast factorized back-projection algorithm with pixelwise phase error correction is utilized to obtain focused image eventually. Experiments on both simulated and real-measured SAR data sets validate the focusing performance of the proposed approach.