Miracle-dz/UniFAR-FTUs huggingface dataset:
Framework for Rapid Performance Estimation of Embedded Soft Core Processors

The large number of embedded soft core processors available today make it tedious and time consuming to select the best processor for a given application. This task is even more challenging due to the numerous configuration options available for a single soft core processor while optimizing for contradicting design requirements such as performance and area. In this article, we propose a generic framework for rapid performance estimation of applications on soft core processors. The proposed technique is scalable to the large number of configuration options available in modern soft core processors by relying on rapid and accurate estimation models instead of time-consuming FPGA synthesis and execution-based techniques. Experimental results on two leading commercial soft core processors executing applications from the widely used CHStone benchmark suite show an average error of less than 6% while running in the order of minutes when compared to hours taken by synthesis-based techniques.
Mining Cross-Cultural Differences and Similarities in Social Media

Cross-cultural differences and similarities are common in cross-lingual natural language understanding, especially for research in social media. For instance, people of distinct cultures often hold different opinions on a single named entity. Also, understanding slang terms across languages requires knowledge of cross-cultural similarities. In this paper, we study the problem of computing such cross-cultural differences and similarities. We present a lightweight yet effective approach, and evaluate it on two novel tasks: 1) mining cross-cultural differences of named entities and 2) finding similar terms for slang across languages. Experimental results show that our framework substantially outperforms a number of baseline methods on both tasks. The framework could be useful for machine translation applications and research in computational social science.
Joint Access and Resource Management for Delay-Sensitive Transcoding in Ultra-Dense Networks with Mobile Edge Computing

Driven by the large-scale video traffic, mobile edge computing (MEC) has emerged as a promising technique that extends cloud-computing capabilities to the proximate small base stations (SBSs) in wireless networks, especially in ultra-dense networks (UDNs). With MEC, video transcoding, which processes the adaptive bitrates of a video and provides the adaptive video streaming to users, can significantly release the backhaul burden of networks. However, video transcoding is a time-consuming task, and how to guarantee quality-of- service (QoS) for large video data with MEC is still challenging. To address this issue, in this paper, we propose a joint SBSs selection, tasks scheduling, and resource allocation approach for achieving a delay- optimal transcoding under the constraints of network cost. Specifically, to reduce the delay, a set of SBSs are formed into a Virtual SBSs Group (VSG) to perform the video transcoding and delivering in parallel for a given user. Then, the joint tasks scheduling and feasible resource allocation are performed to minimizing total delay while maintaining a low network cost. The optimization problem is formulated as a mixed integer non- convex programming problem and a three-stage search solution is proposed to solve it. Simulation results show that our proposed approach can significantly improve the transcoding performance while satisfying the resource consumption constraint.
A joint model for recurrent events and a semi-competing risk in the presence of multi-level clustering

Clinical trial designs often include multiple levels of clustering in which patients are nested within clinical sites and recurrent outcomes are nested within patients who may also experience a semi-competing risk. Traditional survival methods that analyze these processes separately may lead to erroneous inferences as they ignore possible dependencies. To account for the association between recurrent events and a semi-competing risk in the presence of two levels of clustering, we developed a semi-parametric joint model. The Gaussian quadrature with a piecewise constant baseline hazard was used to estimate the unspecified baseline hazards and the likelihood. Simulations showed that the proposed joint model has good statistical properties (i.e. <5% bias and 95% coverage) compared to the shared frailty and joint frailty models when informative censoring and multiple levels of clustering were present. The proposed method was applied to data from an AIDS clinical trial to investigate the impact of antiretroviral treatment on recurrent AIDS-defining events in the presence of a semi-competing risk of death and multi-level clustering and showed a significant dependency between AIDS-defining events and death at the patient level but not at the clinic level.
Adaptive full-order and reduced-order observers for one-sided Lur'e systems with set-valued mappings

This article proposes an adaptive observer design method for one-sided Lipschitz Lur’e differential inclusion systems with unknown parameters. First, under some assumptions, we design an adaptive full-order observer for the system. Then, under the same assumptions, a reduced-order observer is proved to be valid. Finally, we simulate an example to show the effectiveness of the presented method under the background of rotor system.
EnLoc: Data Locality-Aware Energy-Efficient Scheduling Scheme for Cloud Data Centers

With the rapid proliferation of big data, real-time processing of huge datasets becomes a challenging task; primarily because of their heterogeneous nature. Due to this, one of the most serious concerns of the modern cloud data centers is massive energy consumption during job execution. Hence, energy-aware task scheduling with data placement are considered as two important parameters for enhanced energy efficiency of modern cloud data centers. Moreover, considering the ``pay-per-use" model of cloud computing infrastructure, it is important to maintain desirable service level agreement (SLA) while attaining improved data locality. Poor task scheduling decisions with limited focus of data locality are the prime reasons for escalated data communications and energy utilization levels. In order to deal with the aforementioned issues, data locality- aware energy-efficient (EnLoc) scheme for task scheduling and data placement has been proposed, particularly for MapReduce framework. The proposed EnLoc scheme is a multi-objective optimization problem (MOOP) and is solved using multi-objective evolutionary algorithm with ``Tchebycheff decomposition"; wherein the formulated MOOP is decomposed into theoretically finite number of subproblems to get optimal scheduling and placement decisions. The proposed scheme has been evaluated on real-time data traces acquired from OpenCloud Hadoop Cluster. The results obtained clearly demonstrate that the proposed EnLoc scheme outperforms the existing schemes in terms of energy efficiency, SLA assurance, and data locality.
Delay-Optimal Random Access for Large-Scale Energy Harvesting Networks

Energy harvesting technology enables the devices to collect the energy from the surrounding environment. In energy harvesting networks, besides the coupling among different devices, the data transmission depends on the available energy of the devices as well, which leads to a complicated coupling and brings new technical challenges for delay optimization. In this paper, we study delay-optimal random access for large-scale energy harvesting networks. To overcome the challenges, we model a two-dimensional Markov decision process (MDP) with reflections to address the coupling between data and energy, and adopt the mean field game (MFG) theory to address the mutual coupling between devices by utilizing the large-scale property. Specifically, we decompose the optimization problem into two parts. First, to obtain the optimal access policy of each device, we derive the Hamilton-Jacobi-Bellman (HJB) equation which needs the statistical information of other devices. Second, to model the evolution of the state distribution in the system, we derive the Fokker-Planck-Kolmogorov (FPK) equation which needs the access policy of the devices. By solving these two coupled equations iteratively, we obtain the delay-optimal access solution by adopting the Lax-Friedrichs scheme and Lagrange relaxation method. Finally, the numerical results show that the proposed algorithm achieves significant performance gain compared to conventional algorithms.
Convolutional Neural Network based Audio Event Classification

This paper proposes an audio event classification method based on convolutional neural networks (CNNs). CNN has great advantages of distinguishing complex shapes of image. Proposed system uses the features of audio sound as an input image of CNN. Mel scale filter bank features are extracted from each frame, then the features are concatenated over 40 consecutive frames and as a result, the concatenated frames are regarded as an input image. The output layer of CNN generates probabilities of audio event (e.g. dogs bark, siren, forest). The event probabilities for all images in an audio segment are accumulated, then the audio event having the highest accumulated probability is determined to be the classification result. This proposed method classified thirty audio events with the accuracy of 81.5% for the UrbanSound8K, BBC Sound FX, DCASE2016, and FREESOUND dataset
The Energy Efficiency of Modern Multicore Systems

Motivated by the proliferation of both homogeneous and heterogeneous shared-memory multicore systems with large core counts, and by the energy usage issue faced by computing today we extend Amdahl's and Gustafson's laws for speedup to estimate the energy savings of a multicore system compared to the system using a single core. To define energy savings, we introduce two key parameters, (i) the active power fraction (APF) of a core representing the ratio between core's average active power and the power of the idle system, and (ii) the inter-core speedup (ICS) depicting the difference in speed among different types of cores in heterogeneous multi-cores. We show that energy savings are achievable, but they rapidly plateau on large core counts and are affected by system's APF such that a lower APF value leads to higher energy savings. However, on low core counts, energy savings are affected by both the APF and the sequential fraction such that for workloads with large sequential fraction, energy savings are small, regardless of system's APF. We validate our analytical models for energy savings with seven applications covering a wide range of sequential fractions, on two homogeneous server systems with 48 cores representing both traditional brawny x86/64 and emerging wimpy ARM server nodes, and on one heterogeneous system representing the emerging ARM big.LITTLE architecture.
Machine Learning Based Prediction of Consumer Purchasing Decisions: The Evidence and Its Significance

Every day consumers make decisions on whether or not to buy a product. In some cases the decision is based solely on price but in many instances the purchasing decision is more complex, and many more factors might be considered before the ﬁnal commitment is made. In an effort to make purchasing more likely, in addition to considering the asking price, companies frequently introduce additional elements to the offer which are aimed at increasing the perceived value of the purchase. The goal of the present work is to examine using data driven machine learning, whether speciﬁc objective and readily measurable factors inﬂuence customers’ decisions. These factors inevitably vary to a degree from consumer to consumer so a combination of external factors, combined with the details processed at the time the price of a product is learnt, form a set of independent variables that contextualize purchasing behaviour. Using a large real world data set (which will be made public following the publication of this work), we present a series of experiments, analyse and compare the performances of different machine learning techniques, and discuss the signiﬁcance of the ﬁndings in the context of public policy and consumer education.
The Causality Research between Syndrome Elements by Attribute Topology

Background The traditional Chinese medicine (TCM) is an empirical medical system and has its own diagnosis and treatment method. The syndrome elements are atoms to modern TCM diagnosis proposed by Professor Zhu Wenfeng. Researching and analyzing the syndrome element system is one of the active issues for TCM research. At present, most related researches focus on the correlativity and hierarchical relationship of the diseases and symptoms, but the causality researches between syndrome elements themselves have not been reported so far. Methods To explore the causality between syndrome elements, a method named causality by attribute topology (CAT) is proposed. Based on the subordinate relations in attribute topology, the inference method analyzes and reasons the dependency relationship between the sets of objects which contain attributes. Through the removal of attributes in the attribute topology, the formal context is updated constantly. Thus, the causal relationship among the attributes is deduced. In this method, 500 records are mathematically transferred to a binary context for syndrome element analysis. Through the analysis and verification of the potential causal relationship between the syndrome elements, knowledge discovery of the diagnostic data of traditional Chinese medicine based on attribute topology structure diagram is conducted. Results This paper has verified the causal transformation between these syndrome elements. The experimental results between the female group data and the male group data show that different genders have different characteristics and relations of syndrome elements. The experimental results are basically consistent with the traditional Chinese medicine theory. Conclusion The experiment shows that causality by attribute topology (CAT) is feasible to describe the causality between TCM syndrome elements. Further research on possible knowledge discovery in TCM diagnostic data should be conducted through the analysis of the potential causal relationship between TCM diagnostic data and each syndrome element.
Complex Haplotypes of GSTM1 Gene Deletions Harbor Signatures of a Selective Sweep in East Asian Populations

The deletion of the metabolizing Glutathione S-transferase Mu 1 (GSTM1) gene has been associated with multiple cancers, metabolic and autoimmune disorders, as well as drug response. It is unusually common, with allele frequency reaching up to 75% in some human populations. Such high allele frequency of a derived allele with apparent impact on an otherwise conserved gene is a rare phenomenon. To investigate the evolutionary history of this locus, we analyzed 310 genomes using population genetics tools. Our analysis revealed a surprising lack of linkage disequilibrium between the deletion and the flanking single nucleotide variants in this locus. Tests that measure extended homozygosity and rapid change in allele frequency revealed signatures of an incomplete sweep in the locus. Using empirical approaches, we identified the Tanuki haplogroup, which carries the GSTM1 deletion and is found in approximately 70% of East Asian chromosomes. This haplogroup has rapidly increased in frequency in East Asian populations, contributing to a high population differentiation among continental human groups. We showed that extended homozygosity and population differentiation for this haplogroup is incompatible with simulated neutral expectations in East Asian populations. In parallel, we found that the Tanuki haplogroup is significantly associated with the expression levels of other GSTM genes. Collectively, our results suggest that standing variation in this locus has likely undergone an incomplete sweep in East Asia with regulatory impact on multiple GSTM genes. Our study provides the necessary framework for further studies to elucidate the evolutionary reasons that maintain disease-susceptibility variants in the GSTM1 locus.
Spatial Modeling and Latency Analysis for Mobile Edge Computing in Wireless Networks

Next-generation wireless networks will provide users ubiquitous low-latency computing services using devices at the network edge, called mobile edge computing (MEC). The key operation of MEC is to offload computation intensive tasks from users. Since each edge device comprises an access point (AP) and a computer server (CS), a MEC network can be decomposed as a radio access network (RAN) cascaded with a CS network (CSN). Based on the architecture, we investigate network constrained latency performance, namely communication latency (comm- latency) and computation latency (comp-latency) under the constraints of RAN coverage and CSN stability. To this end, a spatial random network is constructed featuring random node distribution, parallel computing, non-orthogonal multiple access, and random computation-task generation. Based on the model and the network constraints, we derive the scaling laws of comm-latency and comp-latency with respect to network-load parameters and network-resource parameters. Essentially, the analysis involves the interplay of stochastic geometry, queueing, and parallel computing. Combining the derived scaling laws quantifies the tradeoffs between the latency, network coverage and network stability.
An SDN Architecture for Improving Throughput of Large Flows Using Multipath TCP

The recent developments in data center topologies offer high bandwidth capacity and multiple paths between processing nodes. The increased use of cloud computing applications imposes challenges on underlying network infrastructure. To meet these challenges we need efficient traffic management that can maximize the utilization of multi-path data center networks. Path diversity not only provides the opportunity of performing load balancing and improve fault tolerance, but also can be exploited to allocate more bandwidth for large flows and improve the performance of network-limited applications. In this paper, we present an architecture based on Multipath TCP (MPTCP) and Software Defined Networking (SDN) to provide better bandwidth allocation for large flows. The SDN controller, which has global knowledge of network topology and traffic measurements, is capable of making educated routing decisions. The proposed SDN architecture enables applications to achieve better throughput for large flows by initiating new MPTCP subflows. To provide this capability, we modify the Linux kernel MPTCP implementation to enable applications to create additional MPTCP subflows on-demand. These MPTCP subflows are placed on least-congested paths by the centralized controller. The evaluation results obtained from running experiments on the GENI testbed environment show a significant improvement in the throughput of large flows.
A Smorgasbord of Features to Combine Phrase-Based and Neural Machine Translation

Superiority of neural machine translation (NMT) and phrase-based statistical machine translation (PBSMT) depends on the translation task. For some translation tasks, such as those involving low-resource language pairs or close languages, NMT may underperform PBSMT. In order to have a translation system that performs consistently better regardless of the translation task, recent work proposed to combine PBSMT and NMT approaches. In this paper, we propose an empirical comparison of the most popular existing approaches that combine PBSMT and NMT. Despite its simplicity, our simple reranking system using a smorgasbord of informative features signiﬁcantly and consistently outperforms other methods, even for translation tasks where PBSMT and NMT produce translations of a very different quality.
Integer-only cross field computation

We propose a new iterative algorithm for computing smooth cross fields on triangle meshes that is simple, easily parallelizable on the GPU, and finds solutions with lower energy and fewer cone singularities than state-of-the-art methods. Our approach is based on a formal equivalence, which we prove, between two formulations of the optimization problem. This equivalence allows us to eliminate the real variables and design an efficient grid search algorithm for the cone singularities. We leverage a recent graph-theoretical approximation of the resistance distance matrix of the triangle mesh to speed up the computation and enable a trade-off between the computation time and the smoothness of the output.
Extended StirTrace benchmarking of biometric and forensic qualities of morphed face images

Since its introduction in 2014, the face morphing forgery (FMF) attack has received significant attention from the biometric and media forensic research communities. The attack aims at creating artificially weakened templates which can be successfully matched against multiple persons. If successful, the attack has an immense impact on many biometric authentication scenarios including the application of electronic machine-readable travel document (eMRTD) at automated border control gates. We extend the StirTrace framework for benchmarking FMF attacks by adding five issues: a novel three-fold definition for the quality of morphed images, a novel FMF realisation (combined morphing), a post-processing operation to simulate the digital image format used in eMRTD (passport scaling 15 kB), an automated face recognition system (VGG face descriptor) as additional means for biometric quality assessment and two feature spaces for FMF detection (keypoint features and fusion of keypoint and Benford features) as additional means for forensic quality assessment. We show that the impact of StirTrace post-processing operations on the biometric quality of morphed face images is negligible except for two noise operators and passport scaling 15 kB, the impact on the forensic quality depends on the type of post-processing, and the new FMF realisation outperforms the previously considered ones.
Association of left atrial pressure with left atrial volume and N-terminal prohormone brain natriuretic peptide in children with cardiomyopathy

Abstract Background Enlargement of the left atrium is a non-invasive marker of diastolic dysfunction of the left ventricle, a determinant of prognosis in children with cardiomyopathy. Similarly, N-terminal prohormone brain natriuretic peptide is a useful marker in the management of children with cardiomyopathy and heart failure. The aim of this study is to evaluate the association of left atrial pressures with left atrial volume and N-terminal prohormone brain natriuretic peptide in children with cardiomyopathy. Methods This was a retrospective study reviewing the medical records of patients <18 years of age, who were diagnosed with cardiomyopathy or acute myocarditis with eventual development of cardiomyopathy. Left atrial volume by transthoracic echocardiogram and pulmonary capillary wedge pressure, a surrogate of left atrial pressure, obtained by means of cardiac catheterisation were analysed. In addition, N-terminal prohormone brain natriuretic peptide levels obtained at the time of the cardiac catheterisation were also reviewed. Statistical analysis was performed to evaluate the association of left atrial pressures with left atrial volume and N-terminal prohormone brain natriuretic peptide levels. Results There was a linear correlation of left atrial pressure estimated in the cardiac catheterisation with indexed left atrial volume (r=0.63; p<0.001) and left atrial volume z-scores (r=0.59; p<0.001). We found no statistically significant association between the left atrial pressure and N-terminal prohormone brain natriuretic peptide levels. Conclusions Left atrial volume measured non-invasively by echocardiography can be used as a surrogate for left atrial pressure in assessing diastolic dysfunction of the left ventricle in children with cardiomyopathy. The larger the size of the left atrium, worse is the diastolic function of the left ventricle.