Miracle-dz/UniFAR-FTUs huggingface dataset:
Supervised Massive Data Analysis for Telecommunication Customer Churn Prediction

Customer churn management becomes increasingly critical for telecommunication companies in the competitive mobile market. For retaining customers before they switch to competitors, an accurate customer churn analysis model is important to predict the potential lost customers in two or three months. Two month window is practical for telecommunication companies to design strategies to retain potential lost customers. However it will bring large uncertainty and increase the difficulty for prediction. There are three main difficulties for customer churn prediction modeling. First, the customer churn data set is substantially imbalanced in reality. Second, the samples in feature space are relatively scattering. Third, the dimension of feature space is high and dimension reduction is necessary for algorithm efficiency. To overcome these difficulties, we propose a new supervised one-side sampling technique to pre-process the imbalanced data set. K-means method is applied to cluster the data set into meaningful clusters and then one-sided sampling is applied in each cluster for removing noise and redundant negative samples. Random forest method is used for dimensional reduction and selecting important variables. C5.0 decision tree is the classifier applied in this study to predict customer churn in two or three months. About 2.7 million 4 Generation (4G) telecommunication customer data are used for experiments. We obtain a precision ratio of 80.42% with a recall ratio of 52.43%. The proposed model provides satisfied prediction results which can be practically used to retain potential lost customers.
Identifying cyber attacks under local model information

This work considers the problem of detecting corrupted components as well as external attacks in a large scale decentralized system. The electric power system, the transportation system, and generally any computer or network system are examples of large scale systems for which cyberattacks have become an important threat. Despite the recent advances in the theory of detection and identification of misbehaving parts, the existing procedures to enforce security are still computationally inefficient and numerically unreliable. We consider the case of linear networks, and we model an external attack as an exogenous input affecting the system. We exploit two complementary methods that relies on two different sets of assumptions to reduce the complexity of the misbehavior detection and identification. The first method takes advantage of the presence in the network of weakly interconnected subparts, it requires the agents to have only a limited knowledge of the network model, and it affords local identification of the misbehaving agents whose behavior deviates more than a threshold. The second method relies on the presence of a set of trustworthy agents (leaders) with better computation and communication capabilities. Only relying on a partial knowledge of the network model, the leaders cooperatively detect and identify the misbehaving agents. The proposed methods are shown to improve the detection time, the numerical reliability, and the computational cost of existing algorithms.
Multicomponent intervention to reduce daily sedentary time: a randomised controlled trial

Objectives To test the efficacy of a multicomponent technology intervention for reducing daily sedentary time and improving cardiometabolic disease risk among sedentary, overweight university employees. Design Blinded, randomised controlled trial. Setting A large south-eastern university in the USA. Participants 49 middle-aged, primarily female, sedentary and overweight adults working in sedentary jobs enrolled in the study. A total of 40 participants completed the study. Interventions Participants were randomised to either: (1) an intervention group (N=23; 47.6+9.9 years; 94.1% female; 33.2+4.5 kg/m2); (2) or wait-list control group (N=17; 42.6+8.9 years; 86.9% female; 31.7+4.9 kg/m2). The intervention group received a theory-based, internet-delivered programme, a portable pedal machine at work and a pedometer for 12 weeks. The wait-list control group maintained their behaviours for 12 weeks. Outcome measures Primary (sedentary and physical activity behaviour measured objectively through StepWatch) and secondary (heart rate, blood pressure, height, weight, waist circumference, per cent body fat, cardiorespiratory fitness, fasting lipids) outcomes were measured at baseline and postintervention (12 weeks). Exploratory outcomes including intervention compliance and process evaluation measures were also assessed postintervention. Results Compared to controls, the intervention group reduced daily sedentary time (mean change (95%CI): −58.7 min/day (−118.4 to 0.99; p<0.01)) after adjusting for baseline values and monitor wear time. Intervention participants logged on to the website 71.3% of all intervention days, used the pedal machine 37.7% of all working intervention days and pedalled an average of 31.1 min/day. Conclusions These findings suggest that the intervention was engaging and resulted in reductions in daily sedentary time among full-time sedentary employees. These findings hold public health significance due to the growing number of sedentary jobs and the potential of these technologies in large-scale worksite programmes. Trial Registration ClinicalTrials.gov #NCT01371084.
Tangencies between families of disjoint regions in the plane

Let C be a family of n convex bodies in the plane, which can be decomposed into k subfamilies of pairwise disjoint sets. It is shown that the number of tangencies between the members of C is at most O(kn), and that this bound cannot be improved. If we only assume that our sets are connected and vertically convex, that is, their intersection with any vertical line is either a segment or the empty set, then the number of tangencies can be superlinear in n, but it cannot exceed n(n log2 n). Our results imply a new upper bound on the number of regular intersection points on the boundary of *C.
Analysis of Odorants in Marking Fluid of Siberian Tiger (Panthera tigris altaica) Using Simultaneous Sensory and Chemical Analysis with Headspace Solid-Phase Microextraction and Multidimensional Gas Chromatography-Mass Spectrometry-Olfactometry

Scent-marking is the most effective method of communication in the presence or absence of a signaler. These complex mixtures result in a multifaceted interaction triggered by the sense of smell. The objective was to identify volatile organic compound (VOC) composition and odors emitted by total marking fluid (MF) associated with Siberian tigers (Panthera tigris altaica). Siberian tiger, an endangered species, was chosen because its MF had never been analyzed. Solid phase microextraction (SPME) for headspace volatile collection combined with multidimensional gas chromatography-mass spectrometry-olfactometry for simultaneous chemical and sensory analyses were used. Thirty-two VOCs emitted from MF were identified. 2-acetyl-1-pyrroline, the sole previously identified compound responsible for the “characteristic” odor of P. tigris MF, was identified along with two additional compounds confirmed with standards (urea, furfural) and four tentatively identified compounds (3-methylbutanamine, (R)-3-methylcyclopentanone, propanedioic acid, and 3-hydroxybutanal) as being responsible for the characteristic aroma of Siberian tiger MF. Simultaneous chemical and sensory analyses improved characterization of scent-markings and identified compounds not previously reported in MF of other tiger species. This research will assist animal ecologists, behaviorists, and zookeepers in understanding how scents from specific MF compounds impact tiger and wildlife communication and improve management practices related to animal behavior. Simultaneous chemical and sensory analyses is applicable to unlocking scent-marking information for other species.
Walking Ahead: The Headed Social Force Model

Human motion models are finding an increasing number of novel applications in many different fields, such as building design, computer graphics and robot motion planning. The Social Force Model is one of the most popular alternatives to describe the motion of pedestrians. By resorting to a physical analogy, individuals are assimilated to point-wise particles subject to social forces which drive their dynamics. Such a model implicitly assumes that humans move isotropically. On the contrary, empirical evidence shows that people do have a preferred direction of motion, walking forward most of the time. Lateral motions are observed only in specific circumstances, such as when navigating in overcrowded environments or avoiding unexpected obstacles. In this paper, the Headed Social Force Model is introduced in order to improve the realism of the trajectories generated by the classical Social Force Model. The key feature of the proposed approach is the inclusion of the pedestrians’ heading into the dynamic model used to describe the motion of each individual. The force and torque representing the model inputs are computed as suitable functions of the force terms resulting from the traditional Social Force Model. Moreover, a new force contribution is introduced in order to model the behavior of people walking together as a single group. The proposed model features high versatility, being able to reproduce both the unicycle-like trajectories typical of people moving in open spaces and the point-wise motion patterns occurring in high density scenarios. Extensive numerical simulations show an increased regularity of the resulting trajectories and confirm a general improvement of the model realism.
Thermal Emission Spectrometer results: Mars atmospheric thermal structure and aerosol distribution

Infrared spectra returned by the Thermal Emission Spectrometer (TES) are well suited for retrieval of the thermal structure and the distribution of aerosols in the Martian atmosphere. Combined nadir- and limb-viewing spectra allow global monitoring of the atmosphere up to 0.01 mbar (65 km). We report here on the atmospheric thermal structure and the distribution of aerosols as observed thus far during the mapping phase of the Mars Global Surveyor mission. Zonal and temporal mean cross sections are used to examine the seasonal evolution of atmospheric temperatures and zonal winds during a period extending from northern hemisphere mid-summer through vernal equinox (L, = 104°-360°). Temperature maps at selected pressure levels provide a characterization of planetary-scale waves. Retrieved atmospheric infrared dust opacity maps show the formation and evolution of regional dust storms during southern hemisphere summer. Response of the atmospheric thermal structure to the changing dust loading is observed. Maps of water-ice clouds as viewed in the thermal infrared are presented along with seasonal trends of infrared water-ice opacity. Uses of these observations for diagnostic studies of the dynamics of the atmosphere are discussed.
Interconnection analysis for standard cell layouts

We present an accurate model and procedures for predicting the common physical design characteristics of standard cell layouts (i.e., the interconnection length and the chip area). The predicted results are obtained from analysis of the net list only, that is, no prior knowledge of the functionality of the design is used. Random and optimized placements, global routing, and detailed routing are each abstracted by procedural models that capture the important features of these processes, and closed-form expressions that define these procedural models are presented. We have verified both the global characteristics (total interconnection length and layout area) and the detailed characteristics (wire length and feedthrough distributions) of the model. On the designs in our test suite, the estimates are very close to the actual layouts.
Actionness-Assisted Recognition of Actions

We elicit from a fundamental definition of action low-level attributes that can reveal agency and intentionality. These descriptors are mainly trajectory-based, measuring sudden changes, temporal synchrony, and repetitiveness. The actionness map can be used to localize actions in a way that is generic across action and agent types. Furthermore, it also groups interacting regions into a useful unit of analysis, which is crucial for recognition of actions involving interactions. We then implement an actionness-driven pooling scheme to improve action recognition performance. Experimental results on three datasets show the advantages of our method on both action detection and action recognition comparing with other state-of-the-art methods.
A Design Methodology for Matching Improvement in Bandgap References

Errors caused by tolerance variations and mismatches among components severely degrade the performance of integrated circuits. These random effects in process parameters significantly impact manufacture costs by decreasing yield and so by including extra-circuits for adjustment. In this paper we propose a design methodology based on the Pelgrom's MOS transistor-mismatching model devices. Our main objective is to calculate the size of each component considering their relation between area and mismatching. Therefore, in order to validate our proposal methodology, we used as a design target a bandgap reference circuit fabricated in 0.35 mum CMOS technology. Its temperature coefficient attains an average value of 40ppm/degC and an average output voltage of 1,20714V. It also includes a straightforward 4-bits trim circuit to achieve more process independence variation. As a result of our methodology, the considerable area of 400 times 350 mum2 was occupied due to our matching design requirements
Inventory Management: Modelling Real-life Supply Chains and Empirical Validity

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Color-based descriptors for image fingerprinting

Typically, content-based image retrieval (CBIR) systems receive an image or an image description as input and retrieve images from a database that are similar to the query image in regard to properties such as color, texture, shape, or layout. A kind of system that did not receive much attention compared to CBIR systems, is one that searches for images that are not similar but exact copies of the same image that have undergone some transformation. In this paper, we present such a system referred to as an image fingerprinting system, since it aims to extract unique and robust image descriptors (in analogy to human fingerprints). We examine the use of color-based descriptors and provide comparisons for different quantization methods, histograms calculated using color-only and/or spatial-color information with different similarity measures. The system was evaluated with receiver operating characteristic (ROC) analysis on a large database of 919 original images consisting of randomly drawn art images and similar images from specific categories, along with 30 transformed images for each original, totaling 27570 images. The transformed images were produced with attacks that typically occur during digital image distribution, including different degrees of scaling, rotation, cropping, smoothing, additive noise and compression, as well as illumination contrast changes. Results showed a sensitivity of 96% at the small false positive fraction of 4% and a reduced sensitivity of 88% when 13% of all transformations involved changing the illuminance of the images. The overall performance of the system is encouraging for the use of color, and particularly spatial chromatic descriptors for image fingerprinting
Meat Quality and Physicochemical Trait Assessments of Berkshire and Commercial 3-way Crossbred Pigs

In this study, we compared qualities and physiochemical traits of meat from Berkshire (black color) pigs with those of meat from 3-way Landrace (white color) × Yorkshire (white color) × Duroc (red color) crossbred pigs (LYD). Meat quality characteristics, including pH, color, drip loss, cooking loss, and free amino acid, fatty acid, vitamin, and mineral contents of longissimus dorsi muscles, were compared. Meat from Berkshire pigs had deeper meat color (redness), higher pH, and lower drip loss and cooking loss than meat from LYD pigs. Moreover, meat from Berkshire pigs had higher levels of phosphoserine, aspartic acid, threonine, serine, asparagine, α-aminoadipic acid, valine, methionine, isoleucine, leucine, tyrosine, histidine, tryptophan, and carnosine and lower levels of glutamic acid, glycine, alanine, and ammonia than did meat from LYD pigs. The fatty acids oleic acid, docosahexaenoic acid (DHA), and monounsaturated fatty acids (MUFA) were present in significantly higher concentrations in Berkshire muscles than they were in LYD muscles. Additionally, Berkshire muscles were significantly enriched with nucleotide components (inosine), minerals (Mg and K), and antioxidant vitamins such as ascorbic acid (C) in comparison with LYD muscles. In conclusion, our results show that in comparison with LYD meat, Berkshire meat has better meat quality traits and is a superior nutritional source of all essential amino acids, monounsaturated fatty acids, vitamin C, and minerals (Mg and K).
OctaLynx D: RISC microprocessor dedicated for dynamic thermal management

The paper describes features of the OctaLynx RISC microprocessor version dedicated to cooperation with the Dynamic Power Management system with awareness of present circuit temperature. The processor can use two independent interrupts triggered by thermal events. Additional function registers contain information about maximum temperature found in the circuit by the hardware part of the power management system and information to control frequency generated by built-in oscillator. The processor was designed with Verilog hardware description language.
Analyzing the effect of demographic characteristics on compulsive buying : A case study of consumers in Iranian shopping centers

Shopping is a normal and routine part of everyday life for most people, but compulsive buyers have different shopping experience. The inability to control an overpowering impulse to buy pervades their lives and results in significant and sometimes severe consequences. This study aims to investigate the compulsive buying behavior and explore its relationship with some demographic variables. A comprehensive data set has been collected through a survey of 1144 customers who had the experience of buying at various shopping centers in Tehran, Iran. According to the result of a validated questionnaire, a compulsive buying latent variable has been calculated for each costumer using the Structural Equation Modeling. Afterward, the relationship between underlying demographic characteristics and the compulsive buying variable has been analyzed using a number of statistical tests. The results demonstrate that some of these antecedents significantly affect compulsive buying tendency. The findings would assist managers, marketers, policy makers, and health care practitioners in their decisions, because individuals who exhibit negative consumption behaviors variously affect these parties. [Behnaz Khodayari, Mohammad Ali Abdolvand, Kambiz Heidarzadeh Hanzaee. Analyzing the effect of demographic characteristics on compulsive buying: A case study of consumers in Iranian shopping centers. Life Sci J 2012;9(4):5740-5746] (ISSN:1097-8135). http://www.lifesciencesite. com. 854
Remotely sensed agricultural modification improves prediction of suitable habitat for a threatened lizard

ABSTRACT The geographical distribution of a species is limited by factors such as climate, resources, disturbances and species interactions. Environmental niche models attempt to encapsulate these limits and represent them spatially but do not always incorporate disturbance factors. We constructed MaxEnt models derived from a remotely sensed vegetation classification with, and without, an agricultural modification variable. Including agricultural modification improved model performance and led to more sites with native vegetation and fewer sites with exotic or degraded native vegetation being predicted suitable for A. parapulchella. Analysis of a relatively well-surveyed sub-area indicated that including agricultural modification led to slightly higher omission rates but markedly fewer likely false positives. Expert assessment of the model based on mapped habitat also suggested that including agricultural modification improved predictions. We estimate that agricultural modification has led to the destruction or decline of approximately 30–35% of the most suitable habitat in the sub-area studied and approximately 20–25% of suitable habitat across the entire study area, located in the Australian Capital Territory, Australia. Environmental niche models for a range of species, particularly habitat specialists, are likely to benefit from incorporating agricultural modification. Our findings are therefore relevant to threatened species planning and management, particularly at finer spatial scales.
A Survey on Fault Tolerance in Wireless Sensor Networks

The reliability of wireless sensor networks (WSN) is affected by faults that may occur due to various reasons such as malfunctioning hardware, software glitches, dislocation, or environmental hazards, e.g. fire or flood. A WSN that is not prepared to deal with such situations may suffer a reduction in overall lifetime, or lead to hazardous consequences in critical application contexts. One of the major fault recovery techniques is the exploitation of redundancy, which is often a default condition in WSNs. Another major approach is the involvement of base stations or other resourceful nodes to maintain operations after failures. In this paper we present a survey of approaches to fault tolerance and detection techniques in WSNs in both theoretical and application driven research. We provide a taxonomy of faults and classify the surveyed approaches according to their ability of detecting and recovering from faults.