Miracle-dz/UniFAR-FTUs huggingface dataset:
Current Practices of Sustainable Procurement in the Sri Lankan Construction Industry

Construction project procurement is a fundamental process in construction project management. The current conventional procurement practices have been widely criticised due to associated issues including disregarding sustainability. Even if, Sustainable Procurement (SP) has been emerged as one of the best solutions for such issues, current practice level of SP in Sri Lanka (SL) is unclear. Therefore, the aim of this study is to review SP practices in Sri Lankan construction industry. Concurrent triangulation mixed method was used in this study by combining both qualitative and quantitative research approaches. Data was collected through questionnaire surveys and semi-structured interviews and analysed using statistical analysis and content analysis respectively. Data triangulation was used to combine the results of all three research tools. The study proved that SP principles have been used in Sri Lankan construction industry at a moderate level. Limited sustainability initiatives were used in the project procurement, while economical sustainability criteria were given the prominence. Furthermore, SL is at a lower level of practicing SP when compared with most of developed countries like United Kingdom (UK) and Canada. Non-availability of policies, procedures and legislations in regional or national levels is the main factor that limit the SP practices. Further, the study identified drivers which influence the practice level of SP in SL.Thus, the study recommended that strengthening drivers and mitigating constrains are the appropriate strategies to increase the level of practice of SP in Sri Lankan construction industry.
A compressed graph representation for services composition

Service-oriented computing enhances business scalability and flexibility. Providers who expect to benefit from it may bring the explosive growth of web services. As a result, the huge storage required in searching a composition solution may be too much to afford. In this paper, we apply a compressed data structure to tackle this challenging issue. We propose a system model to solves web service composition (WSC) problem. In this model, we use a compressed tree to represent the search graph. This work innovatively solve the composition problem with compressed graph representation. The proposed model is verified through theoretical analysis and experimental study. The obtained experimental results show that this system may handle a large number of web services with small space requirement.
A decoupled access-execute architecture for reconfigurable accelerators

Mapping computational intensive applications on reconfigurable technology for acceleration requires two main implementation parts: (a) the data plane, i.e., efficient interconnected units that accelerate processing, and (b) the access-plane, i.e., efficient ways to access data and transfer them to/from the accelerator. Data plane construction is well understood and mature tools -such as High Level Synthesis (HLS)- that produce efficient reconfigurable architectures exist. The access plane, however, is more challenging: data fetching for big-data and high-performance computing applications is even more complex and time consuming than processing. Towards this end, we present DAER, a Decoupled Access-Execute architecture and framework for Reconfigurable accelerators. Our approach maps the code to be accelerated in two separate parts: (a) the fetch unit, responsible for fetching data to the accelerator and storing results back in memory, and (b) the processing unit, which processes the fetched data in a streaming way. This approach offers the user a structured and well-defined way of mapping applications on an FPGA. Additionally, it bodes well with other hardware-based optimization techniques, e.g. pipelining, custom processing and data prefetching, which hide the memory data access latency. We use the DAER framework and HLS mapping tools on five applications and show the proposed DAER framework achieves an order of magnitude performance speed-up compared to unmodified applications, and as much as 2x performance improvement compared to their optimized HLS versions. We, also, map the DAER-based architectures on HPC platforms showing the performance advantages of our approach on real world platforms.
A Dynamical System for Prioritizing and Coordinating Motivations

We develop a dynamical systems approach to prioritizing and selecting multiple recurring tasks with the aim of conferring a degree of deliberative goal selection to a mobile robot confronted with competing objectives. We take navigation as our prototypical task, and use reactive (i.e., vector field) planners derived from navigation functions to encode control policies that achieve each individual task. We associate a scalar "value" with each task representing its current urgency and let that quantity evolve in time as the robot evaluates the importance of its assigned task relative to competing tasks. The robot's motion control input is generated as a convex combination of the individual task vector fields. Their weights, in turn, evolve dynamically according to a decision model adapted from the literature on bioinspired swarm decision making, driven by the values. In this paper we study a simple case with two recurring, competing navigation tasks and derive conditions under which it can be guaranteed that the robot will repeatedly serve each in turn. Specifically, we provide conditions sufficient for the emergence of a stable limit cycle along which the robot repeatedly and alternately navigates to the two goal locations. Numerical study suggests that the basin of attraction is quite large so that significant perturbations are recovered with a reliable return to the desired task coordination pattern.
Prevalence and Clinical Correlates of and Cognitive Function at the Time of Suicide Attempts in First-Episode and Drug-Naive Patients With Schizophrenia.

BACKGROUND It is well established that patients with chronic schizophrenia have a substantially higher rate of attempted and completed suicide than the general population. However, the actual prevalence of suicide attempts at first-episode psychosis is relatively unknown. Previous studies showed that suicidal schizophrenia patients demonstrate higher cognitive function than nonsuicidal patients, though with inconsistent results. The aims of the study were to examine the prevalence of suicide attempts and the association of this prevalence with demographic and clinical variables and cognitive function in Chinese first-episode, drug-naive (FEDN) schizophrenia patients using a cross-sectional and case-control design. METHOD A total of 357 FEDN inpatients meeting DSM-IV criteria for schizophrenia and 380 healthy controls were enrolled and completed a detailed in-house questionnaire. The suicide attempt data were collected from medical records and interviews with the patients and their family members. The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) was administered to measure cognition in the 28 patients with and 95 patients without a history of suicide attempt and 151 healthy controls. Also, patients were rated on the Positive and Negative Syndrome Scale (PANSS). This study was conducted from June 2013 to December 2015. RESULTS A suicide attempt rate of 12.0% was found in inpatients with first-episode schizophrenia. The attempters were more likely to smoke (34.4% vs 17.9%; χ² = 5.49, P = .019) and had lower severity of negative symptoms (F₁,₃₅₄ = 4.12, P = .043) as compared to FEDN patients without a suicide attempt. All 5 RBANS subscales (all P < .001) except for the Visuospatial/Constructional index (P > .05) showed significantly lower cognitive performance for FEDN patients than for healthy controls. Among the FEDN patients, the suicide attempters performed better than nonattempters on attention (F₁,₁₂₁ = 5.12, P = .025), with an effect size of 0.49. The following variables were independently associated with suicide attempt as shown by multivariate regression analysis: PANSS negative symptom subscale score (Wald χ²₁ = 7.90 P = .005; adjusted OR = 0.807, 95% CI, 0.696-0.936) and Attention (Wald χ²₁ = 4.69, P = .03; adjusted OR = 0.957, 95% CI, 0.918-0.997). CONCLUSIONS FEDN patients with schizophrenia attempt suicide more often than the general population. The suicidal patients were more likely to smoke, had lower severity of negative symptoms, and showed better attention than nonsuicidal patients.
Relative Efficiency of Memristive and Digital Neuromorphic Crossbars

Machine learning is becoming an increasingly large consumer of compute resources. This demand has led to interest in neuromorphic processors (NMPs) that can quickly and efficiently perform learning and inference tasks. Both analog and digital NMPs have been proposed in literature, with many papers indicating that memristor based analog designs provide significant efficiency gains over digital approaches. In this work, we use extensive SPICE simulations to compare the energy efficiency and throughput per area of basic analog and digital neuromorphic processors after architectural differences have been factored out. We find that for our test system, memristor-based analog designs offer about four times better throughput per watt for computation than digital systems and that they provide nearly ten times the throughput per area. If these designs were used in a complete system, including memory and I/O, the gains from using memristors would be small.
Constructing Full-Coverage 3D UAV Ad-Hoc Networks through Collaborative Exploration in Unknown Urban Environments

We consider a 3D network construction problem in the post-disaster scenario, where large urban areas are communication-wise isolated from the outside environment due to the severely damaged network infrastructure. Our main goal is to reconnect the isolated regions with the outside environment using unmanned aerial vehicles (UAVs) by building 3D aerial ad-hoc networks. Prior to network construction, we aim to capture the global map information over region of interests (RoI) by exploring all obstacles in the unknown region. We propose an efficient technique for collaborative 3D terrestrial exploration using multiple UAVs based on our distributed path planning algorithm, which finds collision-free exploration paths. Then, we present an optimal full-coverage 3D aerial ad-hoc network construction by deploying the minimum number of UAVs to indispensable spots while obtaining maximum network coverage. Simulation results demonstrate that our proposed exploration scheme outperforms several counterpart algorithms in terms of traversal time and redundant visit rate. Also, our network construction algorithm guarantees almost full coverage toward terrestrial space with only minimal UAV usage.
Non-invasive imaging of CSF-mediated brain clearance pathways via assessment of perivascular fluid movement with diffusion tensor MRI

The glymphatics system describes a CSF-mediated clearance pathway for the removal of potentially harmful molecules, such as amyloid beta, from the brain. As such, its components may represent new therapeutic targets to alleviate aberrant protein accumulation that defines the most prevalent neurodegenerative conditions. Currently, however, the absence of any non-invasive measurement technique prohibits detailed understanding of glymphatic function in the human brain and in turn, it’s role in pathology. Here, we present the first non-invasive technique for the assessment of glymphatic inflow by using an ultra-long echo time, low b-value, multi-direction diffusion weighted MRI sequence to assess perivascular fluid movement (which represents a critical component of the glymphatic pathway) in the rat brain. This novel, quantitative and non-invasive approach may represent a valuable biomarker of CSF-mediated brain clearance, working towards the clinical need for reliable and early diagnostic indicators of neurodegenerative conditions such as Alzheimer’s disease.
Dietary acrylamide intake and the risk of endometrial or ovarian cancers in Japanese women

A meta‐analysis published in 2015 noted a marginally increased risk of endometrial and ovarian cancers in non‐smoking women with dietary acrylamide intake, but only a few studies were included, and they were limited to Western countries. The aim of this study was to investigate the association between dietary acrylamide intake and endometrial or ovarian cancer risk in the Japan Public Health Center‐based Prospective Study (JPHC Study). In this prospective cohort study, 47 185 participants aged 45‐74 years at the follow‐up starting point in the JPHC Study were enrolled. Dietary acrylamide intake was assessed using a validated food frequency questionnaire. Cox proportional hazards regression models were used to estimate hazard ratios (HR) and 95% confidence intervals (95%CI). In participants with endometrial and ovarian cancer, the average follow‐up periods were 15.5 and 15.6 years, respectively, and 161 and 122 cases of endometrial and ovarian cancer were diagnosed, respectively. Energy‐adjusted dietary acrylamide intake was negatively associated with endometrial cancer, but the association disappeared after adjusting for coffee consumption with an adjusted HR for the highest vs lowest tertile of 0.85 (95%CI: 0.54‐1.33). No association was observed, however, for ovarian cancer (adjusted HR, 0.77; 95%CI: 0.49‐1.23). Furthermore, after stratifying by smoking status, coffee consumption, alcohol consumption, body mass index, and menopause status, no association was observed. Dietary acrylamide intake was not associated with the risk of endometrial or ovarian cancer in Japanese women with a relatively lower dietary intake of acrylamide compared with Western populations.
Clinical presentation and outcome of tricuspid regurgitation in patients with systolic dysfunction

Aims The impact of tricuspid regurgitation (TR) in patients with left ventricular systolic dysfunction on presentation and clinical outcome is uncertain due to confounding comorbidities and mediocre regurgitation ascertainment. Methods and results In a cohort of patients with left ventricular systolic dysfunction (ejection fraction, EF < 50%) and functional TR (assessed quantitatively), we matched TR grade-groups for age, sex, EF, and TR velocity. Association of quantified TR (effective regurgitant orifice, ERO, severe if ≥0.4 cm2) to clinical presentation and outcome was analysed. In the 291 cohort patients (age 70 ± 12 years) with left ventricular dysfunction (EF 31 ± 10%), functional TR ERO was 0.26 ± 0.3 cm2. Presentation with right heart failure was strongly related to TR quantified severity [adjusted odds ratios were 4.15 (1.95-8.84), P = 0.0002 for moderate TR and 6.86 (3.34-14.1), P < 0.0001 for severe TR]. Effective regurgitant orifice ≥0.4 cm2 was associated with increased mortality [hazard ratio 1.6 (1.17-2.2), P = 0.003] unadjusted and after comprehensive adjustment [hazard ratio 1.8 (1.16-2.8), P = 0.009]. Furthermore, ERO ≥0.4 cm2 was associated with increased cardiac events (mortality, new atrial fibrillation or heart failure) unadjusted [hazard ratio 1.9 (1.3-2.7), P = 0.002] and after comprehensive adjustment [hazard ratio 2.2 (1.1-4.6), P = 0.02]. Conclusion Tricuspid regurgitation, even moderate, is associated at diagnosis with more severe heart failure presentation. While moderate TR is associated with heart failure at presentation, our quantitative data show that the threshold associated with reduced survival and more cardiac events is ERO ≥0.4 cm2. These data emphasize the clinical impact of functional TR and warrant large cohort-analysis and clinical trials of treatment of TR associated with left ventricular dysfunction.
Evaluation and Improvement of Activity Detection Systems with Recurrent Neural Network

Channel State Information of WiFi signal has attracted tremendous interests in recent years for activity identification. Although existing work can achieve desirable performance using different algorithms, similar system modules are often shared. In this paper, we first summarize and compare various techniques employed in different modules such as preprocessing, activity extraction, feature dimension reduction, and classification. Specifically, different feature reduction methods are applied in order to address the challenge of classifying various length signals and extracting representative abstractions, including manually selecting features and Dynamic Time Warping based classification with Principal Component Analysis. By targeting at multiple human activities, we then compare the performance of two common system structures from difference aspects. Experimental results show that it can be subjective and environment dependent by manually selecting particular features, while DTW based classification can be time consuming especially with larger dataset. In order to address these challenges, we propose a novel framework based on Deep Learning Network. Long Short Term Memory model, a type of Recurrent Neutral Network, is employed for time-series sequence classification. Extensive results show that it can achieve higher efficiency and accuracy.
Adaptive Knowledge Sharing in Multi-Task Learning: Improving Low-Resource Neural Machine Translation

Neural Machine Translation (NMT) is notorious for its need for large amounts of bilingual data. An effective approach to compensate for this requirement is Multi-Task Learning (MTL) to leverage different linguistic resources as a source of inductive bias. Current MTL architectures are based on the Seq2Seq transduction, and (partially) share different components of the models among the tasks. However, this MTL approach often suffers from task interference and is not able to fully capture commonalities among subsets of tasks. We address this issue by extending the recurrent units with multiple “blocks” along with a trainable “routing network”. The routing network enables adaptive collaboration by dynamic sharing of blocks conditioned on the task at hand, input, and model state. Empirical evaluation of two low-resource translation tasks, English to Vietnamese and Farsi, show +1 BLEU score improvements compared to strong baselines.
Policy Optimization with Demonstrations

Exploration remains a signiﬁcant challenge to re-inforcement learning methods, especially in environments where reward signals are sparse. Recent methods of learning from demonstrations have shown to be promising in overcoming exploration difﬁculties but typically require considerable high-quality demonstrations that are difﬁcult to collect. We propose to effectively leverage available demonstrations to guide exploration through enforcing occupancy measure matching between the learned policy and current demonstrations, and develop a novel Policy Optimization from Demonstration (POfD) method. We show that POfD induces implicit dynamic reward shaping and brings provable beneﬁts for policy improvement. Furthermore, it can be combined with policy gradient methods to produce state-of-the-art results, as demonstrated experimentally on a range of popular benchmark sparse-reward tasks, even when the demonstrations are few and imperfect.
High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach

This paper considers the generation of prediction intervals (PIs) by neural networks for quantifying uncertainty in regression tasks. It is axiomatic that high-quality PIs should be as narrow as possible, whilst capturing a specified portion of data. We derive a loss function directly from this axiom that requires no distributional assumption. We show how its form derives from a likelihood principle, that it can be used with gradient descent, and that model uncertainty is accounted for in ensembled form. Benchmark experiments show the method outperforms current state-of-the-art uncertainty quantification methods, reducing average PI width by over 10%.
eyeDNS: Monitoring a University Campus Network

The Domain Name System (DNS) is responsible for mapping human readable domain names to internet protocol (IP) addresses. DNS is a ubiquitous part of internet and intranet communication, making it a convenient and comprehensive source for data to infer network health, performance, and security. A victim of its own success, monitoring real-time DNS traffic is a challenge due to sheer volume: huge amounts of DNS packets flow through a typical enterprise in a single day. In this paper, we describe eyeDNS, a scalable and extensible system for near real-time aggregation, storage, analysis, and visualization of DNS traffic collected by a hardware back-end. We report on eyeDNS's deployment and data collection on a large public university's network over a timeframe of 15 months. Moreover, we leveraged data from the following 6 months to validate findings made during the initial timeframe. With fast query response, aggregation, and visualization of DNS data, eyeDNS helped identify instances of anomalous network use, malware-specific behaviors, and scamming activities. eyeDNS is currently being used by the university's security personnel and has demonstrated its effectiveness in extracting trends and outliers from large volumes of DNS data collected from a diverse environment, where even commercial tools struggle to provide timely and actionable analysis.
Performance Analysis of Millimeter Wave NOMA Networks with Beam Misalignment

Non-orthogonal multiple access (NOMA) and millimeter wave (mmWave) are two key enabling technologies for the fifth generation (5G) wireless networks. In this paper, we develop a general performance analysis framework for mmWave-NOMA networks with spatially random users taking into account link blockage and directional beamforming. To facilitate NOMA transmission in mmWave networks, we propose an angle-based user pairing strategy. Specifically, the base station first randomly selects one user and then pairs it with the line-of-sight user that has the minimum relative angle difference. NOMA is enabled when the beamwidth of the main lobe created by directional beamforming is not smaller than the angle difference between the paired NOMA users. Tools from stochastic geometry are utilized to derive the coverage probability and the sum rate of the proposed NOMA scheme, where beam misalignment at both the base station and the users is taken into account. Simulations validate the performance analysis and show that the proposed NOMA scheme achieves a larger coverage probability and a higher sum rate than conventional NOMA with distance-based user pairing and orthogonal multiple access.