Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization of Process Parameters in Turning Operation of AISI-1016 Alloy Steels with CBN Using Taguchi Method And Anova

This paper investigates the parameters affecting the roughness of surfaces produced in the turning process for the material AISI-1016 Steel. Design of experiments was conducted for the analysis of the influence of the turning parameters such as cutting speed, feed rate and depth of cut on the surface roughness. The results of the machining experiments for AISI-1016 were used to characterize the main factors affecting surface roughness by the Analysis of Variance (ANOVA) method. The feed rate was found to be the most significant parameter influencing the surface roughness in the turning process. Keywords - AISI-1016 steel, ANOVA, surface roughness, Taguchi method, turning. I. INTRODUCTION Today's modern machining industries face challenge to achieve high quality in terms of work piece dimensional accuracy, surface finish , less wear on cutting tools, economy of machining in terms of cost saving. Surface roughness of the machined part is the most important criteria to judge the quality of operation. The literature survey has revealed that several researchers have attempted to calculate the optimum cutting conditions in a turning operation. Brewer and Rueda developed various monograms to assist in the selection of optimum conditions (1). Some of the other techniques which have been used to optimize the machining parameters include goal programming (2) and geometrical programming (3) Now a day's more attention is given to accuracy and surface roughness of the product in the industries. Surface roughness is the most important criteria in determining the machinability of the material . Surface roughness and dimensional accuracy are the major factors needed to predict the machining performances of any machining operation (4). Optimization of machining parameters increases the utility for machining parameters increases the utility for machining economics and also increases the product quality to greater extent (5).
Design and Implement of a Programmable Logic Controller (PLC) for Classical Control Laboratory

The design and implementation of a classical control system laboratory based on PLC control system is introduced in this paper. To design and implement the system, two parts must be produced. The first is software for PLC and the second is hardware for experiments related to the programs. The PLC control system used in the design is LS industrial system company GM7-DR40A 24/16 Digital I/O and single Analog I/O module, two photoelectric sensors from Atonic company’: the first with the model BR100-DDT-P, and the second BEN10M-TFR. An approximate sensor with 5-sides is detected, four of CMOS BCD-7-Segment driven by CD4511B, two relays: 2-poles and 3-poles, six voltages and an ammeter measurement, DC motor and 24 VDC power supply and many connecters and pinions. Satisfactory results are obtained by executing twenty four experiments for classical control theory that fulfill the requirements of control theory in undergraduate stage and replace the old experiments executed by PID controller where the practice system is implemented by PLC control now.
Exploring Reaction Time Differences to Aggression Words as a Function of Attachment Related Avoidance and Anxiety

A previous study using an Emotional Stroop task (EST) and a Visual Lexical Decision task (VLDT) determined that threat-related subliminal primes (for participants high in attachment anxiety) 1) heighted accessibility of representations of attachment figures, and 2) inhibited attachment representations (high attachment avoidance) when the threat prime was the word separation. The current study also used both tasks (EST and VLDT) to explore differences in reaction time (RT) without a subliminal prime for neutral, physical, and relational aggressive words as a function of attachment avoidance and attachment anxiety. Results showed that for the EST, higher avoidance reflected slower RTs to physical aggression words, whereas higher anxiety reflected faster RTs to all three word types. Also, for the VLDT, higher avoidance showed a faster RT to physical aggression words and a slower RT to relational aggression words compared to low avoidance. Furthermore, high anxiety reflected faster RTs to all three word types compared to low anxiety. Theoretical and practical implications of my results are discussed further in the General Discussion.
Mending Fractured Models Using Surface Patches

Repairing and preserving cultural artefacts is a painstaking and time consuming process requiring vast amounts of effort on behalf of area specialists. Digitizing such artefacts not only enhances their endurance in time but also yields for the opportunity to automatically repair them using three dimensional algorithms. The initial step of our approach attempts to join models along their matching fractured surfaces, with respect to surface continuity and detail preservation, by creating a surface patch between them. In particular, our method extracts and smoothes the outlines of fractured surfaces and determines sets of best matching vertices among them, which, subsequently, are used as a guideline to create a surface patch that joins the two parts. We demonstrate our method using real digitized broken artefacts as well as manually created broken models.
ON THE SPECTRA OF SCALE-FREE AND SMALL-WORLD NETWORKS СПЕКТРАЛЕН АНАЛИЗ НА SCALE-FREE И SMALL-WORLD МРЕЖИ Dipl

This paper reviews the most commonly used models of complex systems and networks Erdos-Renyi, Watts-Strogatz and Barabasi-Albert models. These models address different sets of properties and phenomena of real-life networks. Spectral analysis of the reviewed models has been made and also a dataset of Autonomous Systems (AS) links is been reviewed. Spectral analysis for the AS-links graph has been made and compared the spectra of the reviewed models. Based on this works, a new model can be developed, that better describes the unique characteristics of the biggest and most complex network Internet.
Probabilities and Information Gain for Earthquake Forecasting

The paper discusses some issues arising out of attempts to calculate and score regular regional forecasts for earthquake probabilities. Given a conditional intensity model for earthquake occurrence, the model is first used to simulate occurrence patterns over the forecast interval. Then the simulations are used to estimate the required occurrence probabilities. A simple binomial score is suggested for monitoring and evaluating the performance of the probability forecasts. It is shown that an upper bound for the average score is provided by the information (or entropy) rate of the model. Similarly the improvement in the score over a standard model (constant rate Poisson with independent magnitudes) is bounded above by the entropy gain. Rates can be per unit time or per event. The performances of the ETAS and Stress Release models are described, and used to illustrate how the effectiveness of the forecasts depends on the type of model, the timing of the forecasts and the choice of forecast interval. keywords: probability forecasts; information; entropy; earthquake prediction; point processes.
Foot Pain, Function and Plantar Pressure Distribution in Rheumatoid Patients Wearing Insoles an Observational Study

Rheumatoid arthritis (RA) is a chronic autoimmune disease where feet are involved in up to 90% of patients [1]. Rheumatoid foot results from chronic inflammation followed by structural and biomechanical changes. It may differ much from one patient to other and even from one foot to the contralateral [2]. The most common manifestations are metatarsophalangeal joints subluxations, widening of the forefoot, hallux valgus, claw toe deformities, subtalar and talonavicular synovitis, ankle valgus deformity, flat foot and posterior tibial tendon rupture [1,2]. This condition results in in pain, alterations in the loading pattern of the foot during weight bearing, limitations in daily activities and a reduction in quality of life [3]. Foot orthoses, especially insoles, are commonly used as an adjuvant therapy for the rheumatoid foot aiming to correct or improve plantar load and stabilize foot and ankle [3,4]. Many modifications can be applied to the insole such as medial contoured arch support, metatarsal and heel pads, soft cushioning in overweight areas [5]. Systematic review of the effect of foot orthoses in RA observed significant evidence of foot pain reduction but it is still uncertain if it could result in function improvement, foot deformity prevention or plantar pressure reduction [6]. The aim of this study was to evaluate the effect of insoles in RA patients regarding foot pain, function and plantar pressure distribution.
Gender discrimination in educational personnel: a case study of Gweru urban district secondary schools, Zimbabwe

Gender discrimination in educational institutions persists, despite the vigorous pursuit of policies and programmes to reduce the varying degrees of gender inequity in Zimbabwe. Zimbabwe is a signatory to international agreements and conventions which promote gender equity with a thrust towards increased access to education for girls and females. The creation of a healthy educational and social environment in which all people irrespective of gender are encouraged to fully utilize their skills and talents would be an effective tool to eliminate discriminatory practices and tendencies in the education system. Using a survey design and document analysis, this study investigated the practices perceived to constitute discrimination among educational personnel in secondary schools in Gweru Urban District. A total of 120 educational personnel drawn from 12 secondary schools responded to questionnaire and a sample out of 20 with face-to-face interviews. Analyzed data revealed that gender discrimination in learning institutions exists and is manifested in the appointments, promotion and assignment of duties and responsibilities. As a result, women seem to be negatively affected, in the low uptake of administrative positions. The government and the Ministries of Education need to implement professional development programmes on gender issues which raise awareness on the status, role, and contribution of both females and males in education and educational management. This effort would promote a cultural climate, within which females see themselves as leaders and males see women as capable leaders and decision-makers. This effort would promote gender equality and gender empowerment, and minimize gender prejudices in educational institutions. It is envisaged that this would help build a critical mass of females in decision-making processes as well as gender equity initiatives which are visible and vibrant with gender disaggregated data available in all educational institutions.
Multiple-Person Tracking by Detection

Detection and tracking of multiple person is challenging problem mainly due to complexity of scene and large intra-class variations. In this paper, I present a novel on-line method for multiple person tracking based on tracking-by-detection approach. An object tracking component is deployed to increase the performance of the method and decrease the number of detector failures. Furthermore I use a fusion component to associate the responses of the detection and tracking components. The proposed system was evaluated on available datasets and the results shows that it is suitable to use for this task.
Characterizing Carbon Materials with Raman Spectroscopy

Carbon nanomaterials have revolutionized the field of material science in recent years. Individual carbon nanomaterials offer a wide range of useful properties pertaining to electrical conductance, thermal resistance, and exceptional strength, making them very interesting materials to a broad range of industries. The high-level of interest in the processing, modification, and customization of these materials has created a strong demand for techniques that can be used to characterize carbon nanomaterials. Raman spectroscopy is one technique that has proven to be very well suited to many of the characterization needs with these materials. Raman spectroscopy is most sensitive to highly symmetric covalent bonds with little or no natural dipole moment. The carbon-carbon bonds that make up these materials fit this criterion perfectly and as a result Raman spectroscopy is highly sensitive to these materials and able to provide a wealth of information about their structure. As we shall see, Raman spectroscopy is capable of discerning even slight changes in structure making it a very valuable tool in the characterization of carbon nanomaterials.
Genetic mapping of potential QTLs associated with drought tolerance in wheat.

Water shortage is a major constraint to crop production in many parts of the world. Drought tolerance in wheat may be improved by manipulating and detecting quantitative trait loci (QTLs) related to drought tolerance. In the present project microsatellite markers were used for mapping QTLs for photosynthesis, cell membrane stability and relative water content in wheat. An F2 population derived from the intraspecific cross, Chakwal-86 (drought tolerant cultivar) x 6544-6 (drought sensitive genotype) was grown under drought stress in a hydroponic system. The correlation analysis revealed that net photosynthetic rate positively correlated with stomatal conductance, transpiration rate and cell membrane stability. Relative water content also positively correlated with cell membrane stability. Composite interval mapping detected one QTL for net photosynthesis, one for relative water content and two for cell membrane stability on the 2A wheat chromosome. These QTLs may be used to tailor drought tolerant wheat through marker assisted selection or genetic engineering.
Andro_MD: Android Malware Detection based on Convolutional Neural Networks

Android OS maintains its dominance in smart terminal markets, which brings growing threats of malicious applications (apps). The research on Android malware detection has attracted attention from both academia and industry. How to improve the malware detection performance, what classifiers should be selected, and what features should be employed are all critical issues that need to be solved. Convolutional Neural Networks (CNN) is a typical deep learning technique that has achieved great performance in image and speech recognitions. In this work, we present an Android malware detection framework Andro_MD that can train and classify samples with a deep learning technique. The framework includes dataset construction and feature preprocessing, training and classification by CNN, and evaluation by experiments. First, an Android app dataset is constructed with 21,000 samples collected from third-party markets and 34,570 features of 7 categories. Second, we employ sequential and parallel models to train the extracted features and classify the malware apps. Finally, extensive experimental results show the effectiveness and feasibility of the proposed method. Comparisons with similar work and traditional classifiers show that Andro_MD has a better performance on malware detection, and its accuracy can achieve 99.25% with a FPR of 0.53%. The "request permissions" and "used permissions" feature categories have better performances with limited dimensions.
An Integrative Systems Model for Oil and Gas Pipeline Data Prediction and Monitoring Using a Machine Intelligence and Sequence Learning Neural Technique

Oil and gas pipeline vandalism is a recurrent problem in oil rich zones of Nigeria and its West African neighbors and remains a challenge for multinationals to set ahead control measures to avert possible damages to operations both in infrastructure and business profit margins. In this paper, an integrative systems model comprising of a machine intelligence technique called Hierarchical Temporal Memory (HTM) and a sequence learning neural network called the Online-Sequential Extreme Learning Machine (OS-ELM) is proposed for monitoring and prediction of pipeline pressure data. The system models the continual prediction of pipeline oil/gas pressure signals useful for secure monitoring and control to avert acts of vandalism in oil and gas installations. The HTM uses a spatial pooler operated in temporal aggregated fashion and is defined as HTM-SP. The OS-ELM technique uses an explicit hierarchical training scheme so that the best cost estimates may be found after a stipulated number of trial runs. We study the performance of three OS-ELM neural activations: the sigmoid (sig), sinusoidal (sin) and radial basis function (rbf) activations. The results indicate improvement factors of 1.297, 1.297 and 1.300 of the HTM-SP over the OS-ELM sigmoid, sinusoidal and radial basis activations respectively.
Poster : Using Avatars to Safeguard Privacy during Online Registrations

People often have to go through a registration process to avail different types of services, such as email accounts, loyalty memberships and even accessing public Wi-Fi hotspots. Most of the forms used for such registrations, ask for a lot of personal information about the individual which are often beyond what is required to deliver the particular service [1]. More pertinently, users are often aware that their personal information is at risk and have been repeatedly found to be cynical about how it is used by organizations that collect it [2]. Obfuscation has been proposed as a privacy protection mechanism to safeguard users’ personal information [3]. Such mechanisms intercept the user’s personal information before it is processed by the service provider, including social networks, and obfuscate it by either encrypting the data [4] or substituting it with randomly generated fake data [5]. Most obfuscation mechanisms, however, function “under-the-hood” and therefore do not enhance users’ understanding of how their information is protected. Users have to blindly trust that the obfuscation mechanism can effectively safeguard their personal information and have no control over how it actually works. Consequently, such mechanisms have not been widely adopted, possibly due to the so called “privacy paradox”, which suggests that users often refrain from using privacy protection mechanisms, even if they are aware of the risks of disclosing personal information [6]. Therefore, new techniques need to be investigated to improve the usability of privacy protection mechanisms to make them more engaging for users, to encourage adoption, as well as provide them with greater control and flexibility when safeguarding their privacy.
Attentive Encoder-based Extractive Text Summarization

In previous work on text summarization, encoder-decoder architectures and attention mechanisms have both been widely used. Attention-based encoder-decoder approaches typically focus on taking the sentences preceding a given sentence in a document into account for document representation, failing to capture the relationships between a sentence and sentences that follow it in a document in the encoder. We propose an attentive encoder-based summarization (AES) model to generate article summaries. AES can generate a rich document representation by considering both the global information of a document and the relationships of sentences in the document. A unidirectional recurrent neural network (RNN) and a bidirectional RNN are considered to construct the encoders, giving rise to unidirectional attentive encoder-based summarization (Uni-AES) and bidirectional attentive encoder-based summarization (Bi-AES), respectively. Our experimental results show that Bi-AES outperforms Uni-AES. We obtain substantial improvements over a relevant start-of-the-art baseline.
MICROFLUIDIC PURIFICATION OF EXTRACELLULAR VESICLES FROM RAW BLOOD SAMPLES

We introduce a novel use of porous polymer monoliths as nanoporous membranes to purify extracellular vesicles in microfluidic chips. Using in-situ UV-initiated photopolymerization, membrane elements can be patterned in the desired region in microchannels by simple contact mask lithography with reasonable resolution. Pore size can be manipulated by changing the ratio of porogenic solvent to prepolymer solution, and was tuned to a size proper for the filtration of vesicles (~100 nm). Employing the membrane as a filter with a driving force of either pressure bias or electrophoretic injection, vesicles were extracted from the blood of melanoma grown mice and their contained RNA was analyzed.
Knowledge Base Suitable for Answering Questions in Natural Language

This paper presents three knowledge bases widely used by researchers coping with natural language processing: OpenCyc, DBpedia and YAGO. They are characterized from the point of view of questions answering system. In this paper a short description of the aforementioned system implementation is also presented. Key–Words: Knowledge base, Natural language processing
Adaptive VNF Scaling and Flow Routing with Proactive Demand Prediction

With the evolution of Network Function Virtual-izaiton (NFV), enterprises are increasingly outsourcing their network functions to the cloud. However, using virtualized network functions (VNFs) to provide flexible services in today's cloud is challenging due to the inherent difficulty in intelligently scaling VNFs to cope with traffic fluctuations. To best utilize cloud resources, NFV providers need to dynamically scale the VNF deployments and reroute traffic demands for their customers. Since most existing work is reactive in nature, we seek a proactive approach to provision new instances for overloaded VNFs ahead of time based on the estimated flow rates. We formulate the VNF provisioning problem in order that the cost incurred by inaccurate prediction and VNF deployment is minimized. In the proposed online algorithm, we first employ an efficient online learning method which aims at minimizing the error in predicting the service chain demands. We then derive the requested instances with adaptive processing capacities and call two other algorithms for new instance assignment and service chain rerouting, respectively, while achieving good competitive ratios. The joint online algorithm is proven to provide good performance guarantees by both theoretical analysis and trace-driven simulation.
Regulatory Intervention in Consumer Search Markets : The Case of Credit Cards

We build a framework to understand the effects of regulatory interventions in credit markets, such as caps on interest rates and higher compliance costs for lenders. We focus on the credit card market, in which we observe a large dispersion of interest rates at which U.S. consumers borrow. Our framework includes two main features that may explain this dispersion: endogenous search effort/inattention and product differentiation. Our calibration suggests that low search effort accounts for almost all the dispersion in interest rates, whereas product differentiation is negligible. The calibrated model implies that price caps may curb lenders’ market power, but may also reduce borrowers’ search effort, with potentially ambiguous aggregate welfare effects. PRELIMINARY AND INCOMPLETE Comments Welcome We thank Victor Stango for sharing data with us, Scott Nelson, Matthijs Wildenbeest, and Glenn Ellison for useful comments and suggestions. William Matcham provided excellent research assistance. Alessandro Gavazza gratefully acknowledges support from the European Research Council (ERC-Consolidator grant award no. 771004). Department of Economics, Royal Holloway, University of London. Egham Hill, Egham TW20 0EX, United Kingdom. Email: manolis.galenianos@gmail.com. Department of Economics, London School of Economics. Houghton Street, London WC2A 2AE, United Kingdom. Email: a.gavazza@lse.ac. uk.