Miracle-dz/UniFAR-FTUs huggingface dataset:
Traveler Segmentation using Smart Card Data with Deep Learning on Noisy Labels

Automated Fare Collection (AFC) system is widely used in today’s urban transportation facilities across the globe. While Smart Card data collected by AFC systems contain rich information for understanding the city travelers’ transportation behaviors, the anonymous nature of such information hinders further applications with the data which will require clustering the anonymous smart card holders into meaningful customer groups, such as for transportation policy planning and implementation of customized services or targeted incentives. As manual labelling of the travellers is impractical, heuristic rule-based or frequentist methods can be used but they will lead to noisy labels. In this paper, we propose to tackle the problem using a three-stage procedure involving two-sample testing, heuristic labelling, and deep learning with label denoising to mine the temporal patterns of travelers to classify them into several meaningful customer segments of interest. We experimentally validated the robustness of our label denoising approach for deep learning, and showed that the segment sizes predicted using our method on a nine-month Smart Card dataset are in agreement with the reported population gures.
PERFORMANCE ANALYSIS OF COLOR IMAGE ENCRYPTION\DECRYPTION TECHNIQUES P ERFORMANCE ANALYSIS OF COLOR IMAGE ENCRYPTION \ DECRYPTION TECHNIQUES

Digital color image usually has a huge size, thus image security must be very essential and the hacking process must eliminated. This paper proposes a novel technique, which allows the users to encrypt-decrypt different sizes color images, this technique has to provide confidentiality service for images with less computational overhead. The proposed technique is to be implemented and tested, and the obtained experimental results must be compared with other available techniques results. A comparative analysis will be performed for this technique and other available techniques used for encryption-decryption, the encryption time must be calculated in order to find the speedup of the proposed technique comparing with other techniques.
Sains Humanika Significant Human Risk Factors in Aviation Maintenance

Aviation maintenance tasks are part of a complex organization, where individuals perform varied tasks in an environment with time pressures, and sometimes difficult ambient conditions. These situational characteristics in combination with generic human erring tendencies result in varied forms of error. Analysis of existing aviation maintenance data is a crucial step in meeting the aviation industry’s need to improve aviation safety. We are approaches to investigate the significant human risk factors in aviation maintenance industry. Questionnaire through the modified human factors SHELL model was developed to categories the human factors variables that were derived from the literature review and the opinions of aviation personnel who involved in maintenance. The data were obtained from 18 aviation maintenance in Malaysia which has been listed in Malaysia Aviation Industry Report. The total of 315 respondents was received during the period of the survey. Several analysis techniques were used to analyze the survey data through exploratory factor analysis, reliability analysis and confirmatory factor analysis. The result showed that there are significant human risk factors in aviation maintenance and the result also provided approaches of Structural Equation Modeling (SEM) to verify the hypotheses in the path analysis model.
VCSEL With Thermally Induced Birefringence 3 Vertical-Cavity Surface-Emitting Laser With Thermally Induced Birefringence

Birefringent vertical-cavity surface-emitting lasers offer a new opportunity to generate high polarization modulation dynamics. By inducing spin-polarized carriers, the device can be excited to oscillations in the degree of circular polarization with an oscillation frequency nearly equal to the birefringence splitting. A mechanism for frequency tuning of the oscillations directly integrated on the chip is desirable for future applications. With asymmetric heating we demonstrate a reversible tuning of the birefringence splitting of 45GHz with a decrease of the laser output power of less than 3 dB.
Does the Market Kill Bad Ideas ? An Institutional Comparison of Committees and Markets in Network Industries Prabal

In a large number of industries marked with network features, such as telecommunication, internet, software, hardware etc., compatibility among components of the network is essential for completing transactions. For example, the completion of a telephone call requires that there be end-to-end connectivity between the phones and the infrastructure supporting the call. Consumers derive utility from the entire system, rather than from an individual component of the network. The requirement for compatibility is the driving force for standardization of protocols for conducting transactions. However, different network industries vary in the degree of compatibility desired among components and this results in different routes preferred for standardization. The actual process leading to standardization is a relatively neglected issue in the vast literature analyzing the incentives of firms for standardization and its welfare implications. One early exception is Farrell and Saloner (1988), which investigates the comparative performance of formal committees, markets and hybrid mechanisms in systems markets in achieving standardization. Relative to the market mechanism, the formal committee outperforms in its ability to achieve standardization. Farrell(1996) revisits the problem of coordination in a more realistic scenario of private information about quality of protocols. The question that remains unanswered is the incentive of participants in the standards process to reveal their private information and whether such revelation is
Oblivious Transfer based on Physical Unclonable Functions ( Extended Abstract )

Oblivious transfer (OT) is a simple, but powerful cryptographic primitive, on the basis of which secure two-party computation and several oth er cryptographic protocols can be realized. In this paper, we show how OT can b e implemented by Strong Physical Unclonable Functions (PUFs). Special atte ntion is thereby devoted to a recent subclass of Strong PUFs known as SHIC PU Fs. Our results show that the cryptographic potential of these PUFs is perhaps su rprisingly large, and goes beyond the usual identification and key exchange p rotocols.
strains isolated from bovine bulk milk : implications for subsp . detection by PCR and culture

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. strains isolated from bovine bulk milk: implications for subsp. detection by PCR and culture R. Taddei, I. Barbieri, M.L. Pacciarini, F. Fallacara, G.L. Belletti, N. Arrigoni
Purification and study of anti-cancer effects of Serratia marcescens serralysin

Background and Objectives: Serralysin is an extracellular metalloprotease from Serratia marcescens which has been the subject of extensive biological investigations. The goal of this study was to extract and purify serralysin from S. marcescens and to investigate its cytotoxic activity on the colorectal cancer cell line. Materials and Methods: The presence of the serralysin gene was confirmed using PCR. The supernatant of bacterial culture was collected and precipitated using ammonium sulfate. The precipitated protein was dialyzed and subjected to ion exchange chromatography for further purification. Casein assay and skim milk assay was used to confirm the enzymatic activity. SDS-PAGE was used to visualize the presence of serralysin. Metalloprotease inhibition activity was performed using 50 mM EDTA. Cytotoxic activity of serralysin was assessed on MTT assay. Results: The PCR product corresponding to serralysin was estimated to be approximately 1500 bp. A transparent zone around the bacterial colonies on skim milk agar and casein digestion confirmed the proteolytic activity of serralysin. A 52 kDa band in SDS-PAGE corresponding to serralysin was observed before and after purification processes. MTT assay showed IC50 values 24.78 μg/ml and 19.16 μg/ml after 24 h and 48 h exposure of Caco-2 cells to serralysin, respectively. Conclusion: Our results showed that native serralysin has anticancer potential and may be a candidate for further pharmaceutical research and development. Further in vivo and in vitro mechanistic studies are suggested to confirm the biological activities.
COMPUTER VISION BASED MONTE CARLO LOCALIZATION FOR AUTONOMOUS AERIAL VEHICLES

A robot’s knowledge of its location is a fundamental information on mobile robotics, allowing greater autonomy in decision-making problems. Thus, this paper proposes a technique for localization of Unmanned Aerial Vehicles based on Computer Vision sensing and using the Monte Carlo Localization method. Only natural landmarks already present in the environment are used, avoiding the need for manual insertion of recognizable landmarks. For the identification of landmarks the Scale Invariant Feature Transform algorithm (SIFT) is used. We present results from a model of a small autonomous aerial vehicle in a nonstructured environment. Keywords— MCL, SIFT, UAV
Creating digital age learners through school ICT projects : What can the Tech Angels project teach us ?

This article surveys recent work on school ICT projects. It looks at the ideas that are informing this work and surveys some of the strategies being used in the drive to turn schools into “ICT-rich learning environments”. In order to illustrate how these ideas play out in “real-world” situations, the article also profiles one New Zealand school-based ICT initiative as a case study—the Tech Angels project at Wellington Girls’ College, focussing in particular on how this school’s experiences can help other schools considering setting up similar programmes.
IMPROVED SEGMENTATION OF RETINAL IMAGES TO DIAGNOSE VASCULAR DISEASE USING CHARGED FLUID METHOD

Vessel segmentation still remains a challenging medical image analysis problem despite considerable effort in research. Many factors come together to make this problem difficult to be addressed. The images under consideration often come with noise and blur, and suffer from uneven illumination (or biased field in magnetic resonance imaging [MRI]) problems. In addition, although vessels in an image are similar to each other in general, they have different widths and orientations and sometimes different appearances in terms of intensity, color or local shape, which may become more complicated when disease is present. The application will develop a new deformable model, the charged fluid model (CFM) that uses the simulation of a charged fluid to segment anatomic structures in magnetic resonance images of the retinal. Conceptually, the charged fluid behaves like a liquid such that it flows through and around different obstacles. The simulation evolves in two steps governed by Poisson's equation. The first step distributes the elements of the charged fluid within the propagating interface until an electrostatic equilibrium is achieved. The second step advances the propagating front of the charged fluid such that it deforms into a new shape in response to the image gradient. The improved image is used for further analysis of vascular diseases Keywords—Charged Fluid Method, Bias field, Magnetic Resonance Images, Image segmentation.
Growing System Effects on Silage Type Selection for the Finishing Phase of Beef Steers : Feedlot Growth Performance , Carcass Characteristics , Nutrient Digestibility , and Feeding Behavior

The effects of beef cattle growing systems on type of silage during the finishing phase were assessed based on growth performance, carcass characteristics, digestibility, and feeding behavior. Steers (n = 128; BW = 394 ± 21 kg) were used in a 2 × 2 factorial treatment arrangement: a) growing system (Grazing [forage sorghum bmr-6 AF7401]; or Bunk-fed [65% concentrate diet]); and b) finishing diet silage source (Corn silage [BH8895]; or Sorghum silage [bmr-6 AF7401]) 20% inclusion (DM basis). Pen (n = 8 per treatment) was the experimental unit during the finishing phase and a split plot experimental design was used. Only descriptive data was recorded during the growing system phase. No interactions (growing system × finishing phase silage type) were observed (P > 0.16). Steers that grazed had greater ADG (P < 0.01), DMI (P < 0.01), and G:F (P < 0.01) during the finishing phase, than bunk-fed steers. Greater HCW (P < 0.01), less DP (P < 0.01), and less 12th-rib fat (P < 0.01) were observed for steers grown on the forage sorghum grazing system than Bunkfed steers during growing phase. Regardless of growing system, steers fed the corn silage-based finishing diet had less DMI (P < 0.01), greater G:F (P < 0.01), and tended (P = 0.06) to have greater ADG during the finishing phase than steers fed the sorghum silage-based finishing diet. The corn silage-based finishing diet had greater digestibility of nutrients (P < 0.01) than the sorghum silage-based finishing diet, except by fiber, which was not different (P ≥ 0.12) among treatments. Sorghum grazing during growing phase positively affected growth performance during the finishing phase. Replacing corn silage with sorghum silage in beef finishing diets at inclusions used in current study requires diet energy adjustments. Campanili PR, Sarturi JO*, Trojan SJ, Ballou MA, Sugg JD, Ovinge LA and Lemos BJ Department of Animal and Food Sciences, Texas Tech University, USA *Address for Correspondence Jhones O. Sarturi, Department of Animal and Food Sciences, Texas Tech University, Lubbock, TX 79409, USA, E-mail: j.sarturi@ttu.edu Submission: 05 September 2018 Accepted: 28 September 2018 Published: 05 October 2018 Copyright: © 2018 Campanili PR, et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Research Article Open Access
Using K-Means in SVR-Based Text Difficulty Estimation

A challenge for second language learners, educators, and test creators is the identiﬁcation of authentic materials at the right level of difﬁculty. In this work, we present an approach to automatically measure text difﬁculty, integrated into Auto-ILR, a web-based system that helps ﬁnd text material at the right level for learners in 18 languages. The Auto-ILR subscription service scans web feeds, extracts article content, evaluates the difﬁculty, and notiﬁes users of documents that match their skill level. Dif-ﬁculty is measured on the standard ILR scale with language-speciﬁc support vector machine regression (SVR) models built from vectors incorporating length features, term frequencies, relative entropy, and K-means clustering. 1
Some Aspects of Measurement Error in the United States Objective Yield Survey

In this paper we consider aspects of measurement error in the United States Objective Yield Survey for corn, which is conducted by the United States Department of Agriculture National Agriculture Statistics Service. Various models are used to assess measurement error in variables that appear in forecasting models for end-of-season yield. Two major components of yield are number of ears (per sample unit) and average grain weight per ear. Variables that are used in forecasting number of ears are simple counts, such as number of stalks, and
ECONOMIC THEORY , APPLICATIONS AND ISSUES THE UNIVERSITY OF QUEENSLAND Working Paper No . 78 Estimating Input-Mix Efficiency in a Parametric Framework : Application to State-Level Agricultural Data for the United States

This paper contributes to the productivity literature by demonstrating novel econometric methods to estimate input-mix efficiency (IME) in a parametric framework. Input-mix efficiency is defined as the potential improvement in productivity with change in input mix. Any change in input-mix (e.g., land to labor ratio) will result in change in productivity. We minimize a nonlinear input-aggregator function (e.g., Constant Elasticity of Substitution) to derive an expression for input-mix efficiency. We estimate a Bayesian stochastic frontier for obtaining mix efficiency using US state-level agricultural data for the period 1960 – 2004. We note significant variation in input-mix efficiency across the states and regions, attributable to diverse topographic, geographic and infrastructure conditions. Furthermore, comparisons of allocative and mix efficiencies provide insightful policy implications. For example, the production incentives such as taxes and subsidies could help farmers in adjusting their input mix in response to changes in input prices, which can affect the US agricultural productivity significantly. We provide a simple way of estimating mix efficiency in an aggregate-input, aggregate-output framework. This framework can be extended by i) using flexible functional forms; ii) introducing various timeand region-varying input aggregators; and iii) defining more sophisticated weights for input aggregators.
ScienceDirect Changeable , Agile , Reconfigurable & Virtual Production An approach to determine simulation model complexity

Discrete event simulation (DES) is an essential tool for planning, operating and evaluating manufacturing systems. Estimation of simulation model complexity provides several advantages in the planning phase of a simulation project. For this purpose some measures of the simulation model’s complexity are indispensable. The paper presents an approach to determine the complexity of DES models by combining several parameters describing simulation models. The potentials of the proposed approach are examined via industrial cases. © 2016 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the Changeable, Agile, Reconfigurable & Virtual Production Conference.
Estimating Effort of Incremental Integration Software Testing and Design Metrics

Estimating effort is an important component of planning software engineering tasks. Preventive incremental integration software testing is one of such tasks. In the moment when this testing begins software design already exists and can be used in effort estimating. Seven design metrics already proposed in the literature on software engineering have been selected in this paper in order to analyze their applicability to estimating effort of incremental integration testing. A number of programs has been developed to collect the data needed for this analysis. In addition, the conditions under which these data have been collected are shown. Based upon the data, the metrics have been analyzed by using the correlation between each of the metrics and the actual effort spent on this testing. The results point to best metrics to be used for the estimation purpose. Key Words: Software Testing, Incremental Integration Testing, Estimating Effort, Design Metrics.
Supervised Linear Distance Metric Learning with Convex Optimization Convex and Nonsmooth Optimization Project

In distance-based machine learning applications, different distance metrics (Euclidean distance v.s. distance along curved lines) capture the information of different interests, and thus can largely improve the model performance. Emerging as Mahalanobis distances, the distance metrics can be learned by different algorithms in various machine learning aspects. In particular, for this report, we take the path of convex optimizations over the space of positive semidefinite matrix. We start from the earliest work that applys convex optimization to this task and discuss its shortages; then present the main following work that makes effective advances on the earliest work by making up different deficiencies. Analysis and experiments demonstrate the deficiencies and show how other algorithms remedy the corresponding defects. Additional algorithmic derivations are also provided where the original paper omits. Investigations on the optimization techniques utilized by these papers illustrate that algorithms vary a lot based on the characteristics of the problem and an efficient design can be a piece of art. We also compared the performance between the generic solver CVX and the original optimization algorithms provided by the papers.
Neurocognitive Robot Assistant for Robust Fall Detection

Falls represent a major problem in the public health care domain, especially among the elderly population. Therefore, there is a motivation to provide technological solutions for assisted living in home environments. We introduce a neurocognitive robot assistant that monitors a person in a household environment. In contrast to the use of a static-view sensor, a mobile humanoid robot will keep the moving person in view and track his/her position and body motion characteristics. A learning neural system is responsible for processing the visual information from a depth sensor and denoising the live video stream to reliably detect fall events in real time. Whenever a fall event occurs, the humanoid will approach the person and ask whether assistance is required. The robot will then take an image of the fallen person that can be sent to the person’s caregiver for further human evaluation and agile intervention. In this paper, we present a number of experiments with a mobile robot in a home-like environment along with an evaluation of our fall detection framework. Our experimental results show the promising contribution of our system to assistive robotics for fall detection of the elderly at home.
SUSCEPTIBILIDAD A INSECTICIDAS EN POBLACIONES DE Plutella xylostella (L.) (LEPIDOPTERA: PLUTELLIDAE) DEL ESTADO DE GUANAJUATO, MÉXICO SUSCEPTIBILITY TO INSECTICIDES IN POPULATIONS OF Plutella xylostella (L.) (LEPIDOPTERA: PLUTELLIDAE) FROM THE STATE OF GUANAJUATO, MÉXICO

Considering that Plutella xylostella (L.) is the most important pest of cruciferous crops in the State of Guanajuato, susceptibility of early third instar larvae of P. xylostella to emamectin benzoate, indoxacarb, fipronil, and spinosad was determined in three insect populations (North, West, and East) of the State of Guanajuato and in one susceptible population in the laboratory. The values of resistance ratio (RR) to emamectin benzoate, indoxacarb, and fipronil were low: LC50 (0.7 to 2.0×) and LC95 (0.7 to 3.3×); therefore the evaluated populations are considered susceptible to abovementioned insecticides. For spinosad, the RR of LC50 varied from 7.5 to 12.5×; whereas for LC95, RR in the North, West, and East populations were 20.7, 24.7, and 85.3×, respectively. Considering these values and the field efficacy of spinosad, it is concluded that the North and West populations are susceptible to spinosad, while the East population shows an incipient resistance to this insecticide.