Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Color Rendition indices for LED lighting in merchandising spaces

This paper aims to develop metrics for the evaluation of color rendition of LED light sources in merchandising spaces. Influential factors for a LED light source, such as correlated color temperature, illuminance, and color rendering index (CRI) have been proposed as part of a sustainable lighting application strategy. However, there are several problems with the CRI. The factor analysis used in this research is focused on the properties of the LED light source, and various combinations of twenty existing and proposed alternative metrics were analysed for the LED illumination evaluation method. LED spectral power distribution should be designed for high luminous efficacy as well as good color rendering. Optimised LED light source increases color saturation and has a good color discrimination capability. Our results indicate that two complementary assessments should be developed to characterise the features of a LED light source for merchandising lighting application. Saturation enhancement or hue shift are core common categories for different types of merchandising lighting. For example, a combination of CRI below 80, high enough CRI R9, and Color Quality Scale (CQS) is recommended in supermarkets, while CRI over 80, sufficiently high CRI R9, and Feeling of Contrast Index (FCI) is recommended for lighting in clothing retail stores.
Impact of Traffic Conditions and Carpool Lane Availability on Peer to Peer Ridesharing Demand

A peer to peer ridesharing system connects drivers who are using their personal vehicles to conduct their daily activities with passengers who are looking for rides. A well-designed and properly implemented ridesharing system can bring about social benefits, such as alleviating congestion and its adverse environmental impacts, as well as personal benefits in terms of shorter travel times and or financial savings for the individuals involved. In this paper, the goal is to study the impact of availability of carpool lanes and traffic conditions on ridesharing demand using an agent based simulation model. Agents will be given the option to use their personal vehicles, or participate in a ridesharing system. An exact many to many ride matching algorithm, where each driver can pick up and drop off multiple passengers and each passenger can complete his or her trip by transferring between multiple vehicle, is used to match drivers with passengers. The proposed approach is implemented in AnyLogic ABS software with a real travel data set of Los Angeles, California. The results of this research will shed light on the types of urban settings that will be more recipient towards ridesharing services.
Information Extraction for Scholarly Document Big Data

CiteSeerX is a digital library search engine that provides free access to over six million scholarly documents crawled from the public web. Their metadata is automatically extracted and tagged. We present key extraction technologies used in CiteSeerX, including document classification and de-duplication, document clustering, header/citation extraction, author disambiguation, and table/algorithm extraction. We also describe developing challenges and future work.
Optimizing Hardware-Based Privacy-Preserving MapReduce

repeat if ( *cost(AES decrypt) > *cost(AES encrypt)) i = i+1; else j = j+1; until ( *cost(AES decrypt) + cost(mapper or reducer) + *cost(AES encrypt) < capacity) configure SoC; ALGORITHM 2. Integrated Combining Module Configuration Algorithm M = # of mappers to one reducer D = # bits of AES decrypt module E = # bits of AES encrypt module = # of cycle for mapper = # of cycle of AES decrypt module = # of cycle of AES encrypt module i = 1; j = 1; = / 128 ∗ ; = / 128 ∗ ; = / ;
Today, I Fired My Sales Force!

Copyright © 2017, Rob Hammond. This article is published under a Creative Commons BY-NC license. Permission is granted to copy and distribute this article for non-commercial purposes, in both printed and electronic formats Well actually, it’s been 18 months since we rolled out the product and distribution changes to capture the new opportunities being created by digital business. And today, I replaced the last of the original team except for Lynn who is calling on our biggest customer. I’m wondering how long I can hold on to even Lynn. She keeps delivering the revenue but margins are shrinking and the relationship just isn’t moving forward. I tried everything to get the team to change. I fought for special incentives, new lead generation resources, extra training, and one-onone sales calls. I showcased wins on team calls, changed our CRM system, and even hired a consultant to come in and train the entire sales force in consultative selling. There were a couple of sparks of hope, but in the end the only thing that worked was to change the people. I feel terrible about the impact on the people and their families, and not to mention the incredible loss of customer relationships and tribal knowledge. But, what else could I do? Sound familiar? To aid business and sales managers who need to chart a path through sales transformation, two theories: innovation diffusion theory and informing science in a complex landscape, are applied to help explain this ubiquitous problem. In addition, results of a global pilot study are presented that provides insights into the sales transformation process and best practices to manage through it. Sales managers, business leaders and sales people alike can use these research insights to illuminate their own journey through sales transformation. Companies literally spend billions training sales people and yet continue to have problems transforming sales teams. This paper describes the results of a pilot survey conducted with sales teams in the Americas and Asia.
Organisational commitment and turnover intention of early career lawyers : The impact of career management help , ethical pressure and career commitment

The research examines the impact of career developing practices and organisational ethical behaviour on early career lawyer’s turnover intentions and commitment. The moderating impact of career commitment is also tested. Respondents included 165 early career lawyers. Results of multiple regression analysis confirm that affective commitment, career management help and ethical pressures predict turnover intention. Early career lawyers are also more affectively committed to their current employing law firm when they receive career management help and ethical pressures are low. A moderating effect was established in the relationships between ethical pressures and both continuance and affective commitment. Higher career commitment strengthened a negative relationship between ethical pressure and affective commitment and strengthened a positive relationship between ethical pressure and continuance commitment.
Towards a robust protocol for enteric methane measurements using a hand held Laser Methane Detector in Ruminents

Direct measuring of enteric methane in breath of ruminants is becoming popular. Since the first peer-reviewed publication (Chagunda et al., 2009) showed the potential application of the proprietary Laser Methane Detector® (LMD) in ruminants, it has been shown to have strong relationship with traditional techniques such as respiration calorimetric chambers. For example, Chagunda et al, (2013) reported sensitivity and specificity for cows of 95.4% and 96.5%, and for sheep, sensitivity was 93.8% and specificity was 78.7%. However, there is no joined-up protocol covering all aspects, including, data collection, data extraction, data handling, and estimating methane volume from the measured concentration.
VALIDATION OF 3 D ENVIRONMENT PERCEPTION FOR LANDING ON SMALL BODIES USING UAV PLATFORMS

This paper focuses on the validation of Guidance, Navigation and Control (GNC) technologies through the use of Unmanned Aerial Vehicle (UAV) platforms. Specifically, the work carried out refers to navigation technologies on small bodies employing a 3D imaging sensor. The envisaged targeted application of such technologies is the approach and landing phases of missions to small bodies, based on target body surface processing. In this work, navigation algorithms have been tested in a simulated environment first, and then using a 3D imaging sensor mounted on-board a UAV that follows a scaled trajectory over an asteroid mockup surface. The results obtained in this work highlight the main pros and cons of using UAVs as testing platforms for GNC technologies, and that UAVs are a valid choice to reduce the gap between simulations and hardware validations.
An Ultra-Wideband Bandpass Filter with Notched Band using Step-Impedance Resonators and Embedded Fold-Slot

In this paper, an ultra-wideband bandpass filter based on slotted step-impedance resonators (SSIR) with embedded fold-slot for notched band is presented. The proposed filter has narrow notch in the passband, resulting from the embedded fold-slot. By tuning the length and width of the embedded fold-slot parameters, the center frequencies and bandwidths of the notched band can be adjusted. The simulated and experimental results show narrow notched band around 250 MHz. The superior stopband performance with better than 15 dB for a frequency range up to 18 GHz has been also obtained with sharp rejection skirt in lower and upper band.
Lucas-Kanade Optical Flow Accelerator

Optical flow algorithms are used to detect the relative direction and magnitude of environmental motion observed in reference to an “observer. ” The “observer” is usually a camera, and motion-quantifying processing is done on the differences between two subsequent captured images. Optical flow has a wide range of applications, especially in robotics. For example, optical flow can be used for pose estimation, obstacle avoidance (by moving away from regions of high optical flow), and image segmentation (by dividing regions into areas of similar optical flow, comparable to stereo vision).
Network Intrusion Detection in Embedded/ IoT Devices using GPGPU - Increasing throughput while reducing power consumption with an integrated GPU

Internet of Things (IoT) devices are low-powered and network connected embedded computers that collect sensor data and perform computations at the edge of a network. These Internet-connected devices often lack sufficient security, with the Mirai botnet being the most highlighted incident to date. To detect attacks, a Network Intrusion Detection System (NIDS) may be used. Intrusion detection is often performed with the costly method of pattern matching, where predefined patterns are matched against observed network traffic, requiring up to 70% of a NIDS’s computational power. This thesis evaluates the suitability of using an embedded device with an integrated GPU as an NIDS. Direct Filter Classification, a state of the art pattern matching algorithm, is improved by moving part of the execution to a GPU. This implementation is then optimized, keeping the quirks of embedded systems in mind. Surprisingly, some optimizations that would intuitively result in an improved execution time, instead increases it. Further attempts at optimizations are performed in the heterogeneous design domain where the CPU and GPU cooperate extensively. Evaluation is performed by comparing the throughput of network traffic possible to analyze per second, and energy consumption of the algorithm in its different forms: CPU-only, GPU-only and a heterogeneous variant. These are later compared to another state of the art pattern matching algorithm. By utilizing a GPU, the throughput was increased by more than 2× while reducing the total energy consumption by more than 50%, compared to a CPU-only variant of DFC. The GPGPU variant of DFC was able to improve the throughput of the widely used pattern matching algorithm Aho-Corasick by more than 50% while only requiring 50% of the energy.
Classification Using Decision Tree Approach towards Information Retrieval Keywords Techniques and a Data Mining Implementation Using WEKA Data Set

Data Mining is an extraction tool for analyzing and retrieving hidden predictive information from large amount of data. The detected patterns give new subsets of data. Classification and prediction are two forms of data analysis that can be used to extract models describing important data classes or to predict future trends. When the target values are used as discrete values, then we use classification tree. Decision tree classification with Waikato Environment for Knowledge Analysis (WEKA) is the simplest way to mining information from huge database. My paper includes the process of WEKA analysis by taking a data set as an example, step by step process of WEKA execution of that data set on different tree algorithms, selection of attributes to be mined and comparison with Knowledge Extraction and Evolutionary Learning. The following classification tree algorithms (AD Tree, Decision stump, NB Tree J48, Random forest, CART,) are used by WEKA for prediction. By comparing the accuracy and correctly classified attributes suitable decision can be figure doubt.
Principal Leadership for Private Schools Improvement: The Singapore Perspective

This paper aims to examine leadership qualities among private school principals in Singapore. Findings from the study indicate that effective principals are able to establish trust, create structures that promote principal-teachers communication and maintain a high level of moral values. Specifically, it presents the findings from the case of ten private schools in Singapore. Included an overview of the study and a discussion of emergent themes and questions related to the roles of the principals and the relations between school and the community. The results from our study indicate that financial goal rank top above all other goals set by the private school principals. The emphasis on maximizing financial gain is a cause of concern as many school principals have lost their intellectual integrity as well as their academic values in their pursuit of financial gains.
A Hierarchical Memory Management for a Load-Balancing Stream Processing Middleware on Tiled Architectures ?

Tiled many-core computer architectures are becoming increasingly popular, providing a viable solution to the complexity of resource management in parallel processors. One of the critical challenges in programming such tiled many-core architectures is the efficient use of available resources. In this paper we present a hierarchical memory management approach for tiled many-core processors. This memory management approach is capable to provide shared memory across multiple OS instances running on different cores. This memory management approach made it possible for us to port LPEL, a dynamic load-balancing middleware for stream processing applications, to the Intel Single-Chip Cloud Computer (SCC), a research processor that shares many similarities with other tiled architectures. This is the first execution middleware running on the SCC that provides dynamic load balancing. An evaluation shows that our framework performs better than an MPI-based implementation.
THE JOINT CROSS-SECTIONAL VARIATION OF EQUITY RETURNS AND VOLATILITIES

This paper analyzes the determinants of the simultaneous cross-sectional variation of return and volatility risk premia. Independently of the model specification employed, the estimated risk premium associated with the default premium beta is always positive and statistically different from zero. Moreover, the risk premium of the market volatility risk premium beta is negative and statistically significant. However, both risk factors are priced economically and statistically differently in the volatility and return segments of the market. On average, common factors in both segments explain 90% of the variability of volatility risk premium portfolios, but only 65% of the variability of equity return portfolios. This version: November 3, 2016
Satisfaction of Women in Fulfilling the Role as Mothers – A Comparative Study between Working and Nonworking Mothers

Women's role in the community and at home have been changing rapidly. Traditionally, men and women had well-defined roles without much overlapping or trespassing- mostly women were confined to home – their primary was "mother" and "care-taker". But, women's rights, education, and gender equality has changed the scenario. Pursuance of quality life, better living standard, cost of living has compelled women to become economic contributors of the home. However, not much significant changes have been observed in gender roles. The question is whether women are satisfied about their role as mother or care-taker. The objective of the study is to explore the difference in the perceived satisfaction in fulfilling the role as a mother and satisfaction in life between working mothers and non-working mothers.
Optimized Real-Time Path Planning for a Robot Manipulator based on PRM and Reinforcement Learning

The Probabilistic RoadMap(PRM) method, one of the popular path planning schemes for a manipulator, can find a collision-free path by connecting the start and goal poses through the roadmap constructed by drawing random nodes in the free configuration space. The PRM shows robust performance for static environments, but rather poor performance for the dynamic environments. On the other hand, reinforcement learning, one of the behavior-based control techniques, can cope with uncertainties in the environment. The agent of reinforcement learning can establish a policy that maximizes the sum of rewards by selecting the optimal actions in any state through iterative interactions with the environment. In this paper, we propose an efficient real-time path planning by combining the PRM and reinforcement learning to cope with uncertain dynamic environments and similar environments. A series of experiments show that the hybrid path planner can generate the collision-free path even for the dynamic environment in which the objects block the pre-planned global path. It is also shown that the hybrid path planner can adapt to the similar environments learned previously without much additional learning.
Contact measurement of turbulent intensity of the pipe flow using UVP

In this study, the authors directly inserted the transducer of the UVP with an accurate movable device into the flow parallel to the flow axis. We investigate the measurement accuracy of the velocity and the possibility of the velocity fluctuation measurement concerning with turbulent intensity using this method. This method is free from the effects of reflection and refraction at the pipe wall unlike the UVP measurement through the pipe wall, although this method has the demerit of contact with liquid. In addition, we adopted micro bubbles as the reflector of the ultra sound. They are environment-friendly compared with fine solid particles which may pollute the environment, and they are economy. These bubbles are generated by the pressurized dissolution method. As the results of the measurement, the time-averaged velocity profiles measured by the UVP contact measurement agreed well with the calculated results by the (1/n) power’s law. The deviations are smaller than those measured by the UVP measurement through the pipe wall. RMS values of velocity fluctuation divided by the crosssectional averaged flow velocity U are presented by the UVP contact measurement. The results agreed with the tendencies of Hosokawa et al.’s and Laufer’s data.
A novel , rapid , and sensitive real-time PCR assay for cost-effective detection and quantification of Staphylococcus aureus in food samples with the ZEN TM double-quenched probe chemistry

Staphylococcus aureus is a prevalent food pathogen. The pathogen is very dangerous as it could survive high cooking temperature and produce heat-stable toxins. Therefore, a fast, sensitive and secure culture-free alternative to detect and correctly quantify S. aureus in food samples to prevent food poisoning associated with it is needed. In the present work, the development of a comparatively inexpensive real-time qPCR assay based on the ZENTM probe chemistry by targeting the thermonuclease gene for a fast, sensitive, cost-effective, and direct quantitative detection of S. aureus in real food samples is reported. The novel assay showed high specificity and sensitivity with the limit of detection (LoD) of as low as 1 copy /reaction. The quantification range varies between 1×101 and 1×107 copies/reaction. This method was able to accurately quantify S. aureus from milk sample with a relative accuracy of 75.88 to 120%. The novel assay successfully detected the presence of as low as 50 copies/mL or copies/g of S. aureus in real food matrices.
A NEW METHOD OF PREDICTIVE DIRECT TORQUE CONTROL FOR DOUBLY FED INDUCTION GENERATOR UNDER UNBALANCED GRID VOLTAGE

In this paper, a new predictive direct torque control (PDTC) method is presented for doubly fed induction generator (DFIG) under unbalanced grid voltage. In PDTC, the future behavior of the system is first predicted using the system model, the appropriate voltage vector is then selected using an optimized cost function in each control period. This method is simple and has excellent steady-state and transient performance, because uses from both predicted and reference values. The main advantages of proposed method are that all of the voltage vectors examine in cost function and the voltage vector that minimizes cost function will be applied during the next sampling period. Also, two control targets presented for unbalanced condition. The first target eliminates electromagnetic torque oscillation and the second target eliminates active power pulsations. The simulation was conducted on a 1 MW DFIG and results shows the effectiveness and robustness of new DTC method. It can be seen that the torque and flux ripples are reduced and their transient response are within a few milliseconds.
Analysis of Chromosome 5 q 31 – 32 and Psoriasis : Confirmation of a Susceptibility Locus but no Association with SNPs within SLC 22 A 4 and SLC 22 A 5

We have previously reported a region on chromosome 5q as a possible susceptibility region for psoriasis. This cytokine cluster-rich region has also been suggested as a susceptibility locus in other autoimmune or inflammatory diseases including Crohn’s disease (CD) and rheumatoid arthritis (RA). Three specific singlenucleotide polymorphisms (SNPs) have been reported to associate with RA and CD and to change the functional activity of two organic cation transporters, solute carrier family 22 member 4/5 (SLC22A4) and (SLC22A5). In this study, we have analyzed these SNPs for an association with psoriasis. We have also performed a denser linkage analysis of this region with an additional 31 microsatellite markers. We were not able to detect any association with any of the three SNPs analyzed. However, our linkage result supports the involvement of this region in the etiology of psoriasis. We obtained a peak non-parametric linkage value of 3.1 for marker D5S436 in a subgroup of patients with joint complaints. This result supports the findings in another study of psoriasis patients originating from Iceland in which the authors obtained a peak logarithm of the odds score of 2.6 for marker D5S2090, only 2 Mb from D5S436. This suggests a psoriasis susceptibility locus on chromosome 5q32 that is involved in the arthritic phenotype of the disease.