Miracle-dz/UniFAR-FTUs huggingface dataset:
Benchmarking Academic Programs in Business Analytics

Interest in business analytics (BA) is currently popular. Professional consultancies and software houses are both touting it as the next wave in business, claiming that the need for BA skills is large and growing. Universities are beginning to respond by offering undergraduate majors and minors, Master of Science degrees, certificates, and concentrations within their Master of Business Administration programs. But what subjects are being covered in these programs? We surveyed some of the largest, most established, and best-known programs (predominantly in the United States, but some international) and interviewed representatives of these programs to better understand the requirements for students entering, the required and elective course topics covered, and job opportunities for graduates. In this article, we summarize our findings and provide some conclusions about analytics programs, including the current landscape, suggestions for development, and our vision for the future. We believe this report is useful to institutions that offer analytics programs, to those considering such offerings, and to the employers who are hiring analytics professionals. These employers need to better understand the skills that professionals are acquiring. Finally, it should help prospective students who seek to understand the analytics programs being offered to find the best match for their skills and interests.
MIMO fast fully adaptive processing in Over-the-Horizon Radar

High frequency Over-the-Horizon Radar (OTHR) provides an economical means to track noncooperative air targets over large expanses of land and ocean. However, OTHR in Canada is confounded by the presence of radar clutter from the region of the aurora borealis. Given the time-varying nature of auroral clutter, this paper proposes joint adaptive transmit and receive beamforming as a key tool to deal with this clutter. This beamforming is an alternative adaptive process based on the previously proposed fast-fully adaptive (FFA) scheme extended to couple transmit and receive beamforming, specifically for slow-time multiple-input multiple-output (MIMO) radar. We test the efficacy of this algorithm using measured OTHR data.
Multilingual generation of administrative forms

We will demonstrate the GIST system, which generates social security forms in English, Italian and German. The system is intended for use by the technical authors and translators who design forms. A knowledge specification tool allows the author to build a model of the form in the knowledge representation language LOOM. From the LOOM model, a text drafter generates equivalent texts in the three supported languages, guided by some broad stylistic parameters which the author can control. The output texts serve as drafts which the authors and translators can modify or extend.
Direct congestion control scheme (DCCS) for differentiated services IP networks

IETF proposes the differentiated services (DiffServ) architecture to implement QoS in the Internet. However, recent studies have shown that under various conditions, the existing DiffServ mechanism may have problems of unfairness and inefficient resource utilization, thereby failing to achieve the desired QoS for TCP flows running over assured services. This paper considers that the idea of TCP friendly congestion control should be used in traffic conditioning (TC) for AF PHB (assured forwarding per-hop behavior), especially when the customer expects to achieve better performance by out-of-profile traffic. This paper also proposes using a direct congestion control scheme (DCCS) and dynamic traffic conditioning (DTC) to achieve fairness between responsive and unresponsive aggregates as well as better allocations for responsive flows (TCP) with different packet lengths, micro-flow number, round trip time (RTT) etc. Moreover, a better resource utilization can be achieved at the same time.
Discovering Patterns using Process Mining

Process mining provides an important bridge between data mining and business process analysis, his techniques allow for extracting information from event logs. In general, there are two steps in process mining, correlation definition or discovery and then process inference or composition. Firstly, the authors' work consists to mine small patterns from a log traces of two applications; SKYPE, and VIBER, those patterns are the representation of the execution traces of a business process. In this step, the authors use existing techniques; The patterns are represented by finite state automaton or their regular expression; The final model is the combination of only two types of small patterns whom are represented by the regular expressions ab* and ab*c*. Secondly, the authors compute these patterns in parallel, and then combine those small patterns using the composition rules, they have two parties the first is the mine, they discover patterns from execution traces and the second is the combination of these small patterns. The patterns mining and the composition is illustrated by the automaton existing techniques. The Execution traces are the different actions effected by users in the SKYPE and VIBER. The results are general and precise. It minimizes the execution time and the loss of information.
Reducing DRAM Latency at Low Cost by Exploiting Heterogeneity

In modern systems, DRAM-based main memory is signi? cantly slower than the processor.Consequently, processors spend a long time waiting to access data from main memory, makingthe long main memory access latency one of the most critical bottlenecks to achieving highsystem performance. Unfortunately, the latency of DRAM has remained almost constant inthe past decade. This is mainly because DRAM has been optimized for cost-per-bit, ratherthan access latency. As a result, DRAM latency is not reducing with technology scaling, andcontinues to be an important performance bottleneck in modern and future systems.This dissertation seeks to achieve low latency DRAM-based memory systems at low costin three major directions. The key idea of these three major directions is to enable and ex-ploit latency heterogeneity in DRAM architecture. First, based on the observation that longbitlines in DRAM are one of the dominant sources of DRAM latency, we propose a newDRAM architecture, Tiered-Latency DRAM (TL-DRAM), which divides the long bitline intotwo shorter segments using an isolation transistor, allowing one segment to be accessed withreduced latency. Second, we propose a ? ne-grained DRAM latency reduction mechanism,Adaptive-Latency DRAM, which optimizes DRAM latency for the common operating conditions for individual DRAM module. We observe that DRAM manufacturers incorporate a very large timing margin as a provision against the worst-case operating conditions, whichis accessing the slowest cell across all DRAM products with the worst latency at the highesttemperature, even though such a slowest cell and such an operating condition are rare. Ourmechanism dynamically optimizes DRAM latency to the current operating condition of theaccessed DRAM module, thereby reliably improving system performance. Third, we observethat cells closer to the peripheral logic can be much faster than cells farther from the peripherallogic (a phenomenon we call architectural variation). Based on this observation, we propose anew technique, Architectural-Variation-Aware DRAM (AVA-DRAM), which reduces DRAMlatency at low cost, by pro? ling and identifying only the inherently slower regions in DRAMto dynamically determine the lowest latency DRAM can operate at without causing failures.This dissertation provides a detailed analysis of DRAM latency by using both circuit-levelsimulation with a detailed DRAM model and FPGA-based pro? ling of real DRAM modules.Our latency analysis shows that our low latency DRAM mechanisms enable significant latencyreductions, leading to large improvement in both system performance and energy e? fficiencyacross a variety of workloads in our evaluated systems, while ensuring reliable DRAM operation.
Multistyle classification of speech under stress using wavelet packet energy and entropy features

Physco-physiological conditions like stress and emotional state may affect human's speech production. Different techniques have been proposed such as facial expressions, speech production variation, and physiological signals to detect the emotional/stressed states of a person. For past 2 decades, the determination of an emotional/stressed state through speech has been undergone substantial research and development. Various techniques are used in the literature to classify emotional/stressed states on the basis of speech. In this paper, a feature extraction method using two different wavelet packet filterbank structures which are based on barkscale and equivalent rectangular bandwidth (ERB) scale for identifying the emotional/stressed states of a person. In this study speech samples are taken from Speech Under Simulated and Actual Stress (SUSAS) database. Linear Discriminant analysis (LDA) based classifier is used to test usefulness of suggested features. Experimental result shows that the suggested methods can be used to identify the emotional/stressed states of a person.
Level-shifted PWM for Cascaded Multilevel Inverters with Even Power Distribution

Cascaded H-bridge multilevel inverters are commonly controlled using multicarrier Phase-Shifted PWM, since it allows an even power distribution among the converter power cells, which can lead to lower harmonic distortion in the input current if an adequate transformer is used. However it is known that other multicarrier methods, like Phase Disposition PWM (PD- PWM) have better output voltage quality with lower distortion. Nevertheless, when used with cascaded inverters, this method produces uneven usage of the converter cells, that disables the harmonic cancelations at the transformer input. This paper presents a modified PD-PWM technique, that combines the benefits of both modulation methods, achieving good output voltage and input current quality.
Self-assembly of cell springs using smooth muscle-like cells differentiated from multipotent cells

This paper describes the self-assembly of “cell springs” using smooth muscle-like cells differentiated from multipotent dedifferentiated fat (DFAT) cells. Using a microfluidic co-axial device, DFAT cells suspended in extracellular matrix (ECM) mixture of fibrin and collagen were encapsulated within tubular alginate microfibers and formed into 3D fiber-shaped micro-constructs. Such DFAT cell fibers were then induced to differentiate into smooth muscle cells (SMCs) under various conditions. Upon differentiation induction, DFAT cell fibers spontaneously contracted into coiled spring structures within 3 days. After 3 weeks of differentiation, the differentiated DFAT cell fibers showed synchronized contraction upon electrical stimulation.
Comparison of cognitive performance in mild cognitive impairment and dementia patients with that in normal children and adults

Background: It is well known that there is rapid cognitive development in childhood and cognitive decline during aging, but the volume of these changes using the same clinical tool is not well documented in the literature. The aim of our study was to investigate and compare the cognitive performance of mild cognitive impairment (MCI) and dementia patients with that of children, adolescents and adults, using a worldwide screening tool, the Mini Mental State Examination (MMSE), and considering the age, educational level and mental status of the participants.
Remarks on quaternion neural network based controller with application to an inverted pendulum

In this paper, a quaternion neural network trained by using back-propagation algorithm is applied to controlling an inverted pendulum as a part of utilizing the quaternion neural network for dynamic system control. Experimental results show that the quaternion neural network can control the inverted pendulum with a large initial angle which a state feedback controller can not handle. Moreover, the quaternion framework makes the neural computing more efficient. In the control problem of the inverted pendulum, the quaternion neural network finishes its learning with a fewer number of trails compared with the conventional real number neural network which has a more complex network topology and more parameters in real number being employed. These results indicate the feasibility of using the quaternion neural network for controlling dynamic systems.
Enaminones and norepinephrine employ convergent mechanisms to depress excitatory synaptic transmission in the rat nucleus accumbens in vitro

We recently reported that anticonvulsant anilino enaminones depress excitatory postsynaptic currents (EPSCs) in the nucleus accumbens (NAc) indirectly via γ‐aminobutyric acid (GABA) acting on GABAB receptors [ S.B. Kombian et al. (2005)Br. J. Pharmacol. , 145, 945–953]. Norepinephrine (NE) and dopamine (DA), both known to be involved in seizure disorders, also depress EPSCs in this nucleus. The current study explored a possible interaction between enaminones and adrenergic and/or dopaminergic mechanisms that may contribute to their synaptic depression and anticonvulsant effect. Using whole‐cell recording in rat forebrain slices containing the NAc, we show that NE‐induced, but not DA‐induced, EPSC depression occludes E139‐induced EPSC depressant effect. UK14,304, a selective α2 receptor agonist, mimicked the synaptic effect of NE and also occluded E139 effects. Phentolamine, a non‐selective α‐adrenergic antagonist that blocked NE‐induced EPSC depression, also blocked the E139‐induced EPSC depression. Furthermore, yohimbine, an α2‐adrenoceptor antagonist, also blocked the E139‐induced EPSC depression, while prazosin, a selective α1‐adrenergic antagonist, and propranolol, a non‐selective β‐adrenoceptor antagonist, did not block the E139 effect. Similar to the E139‐induced EPSC depression, the NE‐induced EPSC depression was also blocked by the GABAB receptor antagonist, CGP55845. By contrast, however, neither SCH23390 nor sulpiride, D1‐like and D2‐like DA receptor antagonists, respectively, blocked the E139‐induced synaptic depression. These results suggest that NE and E139, but not DA, employ a similar mechanism to depress EPSCs in the NAc, and support the hypothesis that E139, like NE, may act on α2‐adrenoceptors to cause the release of GABA, which then mediates synaptic depression via GABAB receptors.
"Let me show you what i want": engaging individuals with cognitive disabilities and their families in design

In this paper I describe an ongoing multi-stage, participatory design study with individuals with cognitive disabilities and their family caregivers. I use a technology probe to inspire families to co-design a picture-based remote communication system. The technology platform is a PDA.smart phone. I will present early findings from this study and discuss how it was a successful approach to engage with individuals with significant cognitive disabilities as co-designers of their own technology.
Initiation and tracking of dim target via fusion of feature probabilities with CNN-UM

CNN-UM-based automatic target initiation and tracking algorithm with the fusion of feature probabilities has been developed. The probability for each feature is created in the region of influence depending on the reliability and the strength of the feature. For computing the confidence of target, a fusion algorithm employing products of weighted sums of feature probabilities is proposed. New target initiation is done through the temporal accumulation of such confidence and the target decision is done with the confidence value obtained through the probability fusion. Due to the utilization of multiple features of target, robustness of target detection is possessed. On-chip experimental results are included in this paper.
CSIRO Non-carbon Dioxide Greenhouse Gas Research. Part 1: 1975–90

There are a number atmospheric gases, in addition to carbon dioxide (CO2), that affect the absorption and emission of infrared radiation throughout the atmosphere, the so-called ‘non-CO2 greenhouse gases’, and they have a significant impact on climate. In addition, some of these non-CO2 greenhouse gases contain chlorine and/or bromine, and contribute to halogen-catalysed stratospheric ozone depletion. In the mid 1970s, CSIRO at Aspendale became the first southern hemisphere laboratory to initiate research into the atmospheric abundance, trends, sources and sinks of non-CO2 greenhouse gases, and today (2017) is currently observing and modelling the past and present biogeochemical cycling of over eighty of these species, arguably the most comprehensive program of its type globally. The resultant CSIRO data are used to derive global and regional emissions of non-CO2 greenhouse gases and their impact on climate and stratospheric ozone via resultant changes to the planetary radiative budget and the abundance of ‘equivalent chlorine’ (weighted sum of chlorine and bromine) in the stratosphere. These data and their impacts are reported nationally to relevant Commonwealth and State Departments—environment, energy, industry, agriculture—and to relevant Australian industries—refrigeration, air-conditioning, aluminium production. They are reported internationally to United Nations agencies responsible for implementing the Vienna Convention for the Protection of the Ozone Layer (1985) and the Framework Convention on Climate Change (1992), who periodically assess the science of climate change and ozone depletion. As the world strives to reduce its greenhouse gas emissions through national, policy-driven, initiatives framed to meet agreed obligations under these international agreements, atmospheric measurement programs, such as those operated by the CSIRO and the Bureau of Meteorology in Australia, are critical in independently verifying the success or otherwise of such endeavours. This paper describes the initial fifteen years (1975–90) of activities in CSIRO that set up the framework for the current, globally significant, CSIRO non-CO2 greenhouse gas research program.
VASCULAR ENDOTHELIAL CELLS RELE TO FLUID FLOW THAT AFFECT SMOO EXPR

INTRODUCTION Vascular endothelial cells line the lumen of blood vessels, while smooth muscle cells are found within the vessel wall among a matrix of collagen and structural fibers. In healthy vessels, endothelial cells experience laminar flow (shear stresses ranging from 10 to 12 dyne/cm), whereas turbulent flow (shear stress ranging from 20 to 30 dyne/cm) can occur in diseases such as atherosclerosis. Atherosclerosis development is described by the response to injury theory: endothelial cells suffer an injury resulting in altered morphology, metabolism, or other cell functions, which stimulates smooth muscle cell proliferation and migration into the lumen, ultimately causing vascular thickening and hardening [1]. Cyclooxygenase (COX)-2, endothelial cell nitric oxide synthase (ecNOS), and platelet-derived growth factor (PDGF-β) are genes with known responses to fluid shear stress and are important in the development of atherosclerosis. Specifically, COX-2 is involved in the production of prostacyclin (PGI2), an inhibitor of platelet aggregation and smooth muscle cell growth and migration [2]. ecNOS catalyzes the reaction of arginine to citrulline along with nitric oxide (NO), which acts as a vasodilator and mimics PGI2 in other ways, as a product [3]. Activation of PDGF-β leads to smooth muscle cell proliferation, migration, and differentiation across the lumen and may ultimately lead to vascular wall hardening. The objective of this study, therefore, was to determine the effects of such soluble and transferable factors (released by endothelial cells in response to fluid flow) on smooth muscle cell function and gene expression. Elucidating this mechanism may lead to a better understanding of atherosclerosis development, and potentially, improved disease treatment.
Malicious circuitry detection using transient power analysis for IC security

Malicious modification of integrated circuits (ICs) in untrusted foundry, referred to as “Hardware Trojan”, has emerged as a serious security threat. Since it is extremely difficult to detect the presence of such Trojan circuits using conventional testing strategies, side-channel analysis has been considered as an alternative. In this paper, we proposed a non-destructive side-channel approach that characterizes and compares transient power signature using principle component analysis to achieve the hardware Trojan detection. The approach is validated with hardware measurement results using an FPGA-based test setup for large design including a 128-bit AES cipher. Experimental results show that this approach can discover small (<;1.1% area) Trojans under large noise and variations.
An Empirical Research on the Evaluation System of Cross-border E-commerce

With the development of e-commerce, abroad online shopping has become popular in China. However, as a newly emerged form of shopping, incomplete information and lacking of professional evaluation on abroad online shops cost consumers a lot in searching, exploring and comparison. Meanwhile, cross-border e-commerce platforms do not know for sure how to attract abroad customers effectively. In this research, we try to establish assessment criteria for cross-border e-commerce from the perspective of customers, combined with experts’ opinion. Firstly, based on participant experiencing nearly 100 abroad websites and content analysis on consumer review, we generalize 4 main criteria and 16 sub-criteria that may influence consumers’ satisfaction. Then, based on an expert interview and Analytic Hierarchy Process, we found that the most weighted factors successively are commodity quality, relative price, deliver speed and payment methods.
Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data

The accurate and timely monitoring of regional urban extent is helpful for analyzing urban sprawl and studying environmental issues related to urbanization. This paper proposes a classification scheme for large-scale urban extent mapping by combining the Day/Night Band of the Visible Infrared Imaging Radiometer Suite on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS DNB) and the Normalized Difference Vegetation Index from the Moderate Resolution Imaging Spectroradiometer products (MODIS NDVI). A Back Propagation (BP) neural network based one-class classification method, the Present-Unlabeled Learning (PUL) algorithm, is employed to classify images into urban and non-urban areas. Experiments are conducted in mainland China (excluding surrounding islands) to detect urban areas in 2012. Results show that the proposed model can successfully map urban area with a kappa of 0.842 on the pixel level. Most of the urban areas are identified with a producer’s accuracy of 79.63%, and only 10.42% the generated urban areas are misclassified with a user’s accuracy of 89.58%. At the city level, among 647 cities, only four county-level cities are omitted. To evaluate the effectiveness of the proposed scheme, three contrastive analyses are conducted: (1) comparing the urban map obtained in this paper with that generated by the Defense Meteorological Satellite Program/Operational Linescan System Nighttime Light Data (DMSP/OLS NLD) and MODIS NDVI and with that extracted from MCD12Q1 in MODIS products; (2) comparing the performance of the integration of NPP-VIIRS DNB and MODIS NDVI with single input data; and (3) comparing the classification method used in this paper (PUL) with a linear method (Large-scale Impervious Surface Index (LISI)). According to our analyses, the proposed classification scheme shows great potential to map regional urban extents in an effective and efficient manner.