nils-herrmann/clean_scirepeval huggingface dataset:
154-2008: Understanding Your Customer: Segmentation Techniques for Gaining Customer Insight and Predicting Risk in the Telecom Industry

The explosion of customer data in the last twenty years has increased the need for data mining aimed at customer relationship management (CRM) and understanding the customer. It is well known that the telecom sector consists of customers with a wide array of customer behaviors. These customers pose different risks, making it imperative to implement different treatment strategies to maximize shareholder profit and improve revenue generation. Segmentation is the process of developing meaningful customer groups that are similar based on individual account characteristics and behaviors. The goal of segmentation is to know your customer better and to apply that knowledge to increase profitability, reduce operational cost, and enhance customer service. Segmentation can provide a multidimensional view of the customer for better treatment targeting. An improved understanding of customer risk and behaviors enables more effective portfolio management and the proactive application of targeted treatments to lift account profitability. In this paper we outline several segmentation techniques using SAS Enterprise MinerTM.  Rapid advances in computer technology and an explosion of data collection systems over the last thirty years make it more critical for business to understand their customers. Companies employing data driven analytical strategies often enjoy a competitive advantage. Many organizations across several industries widely employ analytical models to gain a better understanding of their customers. They use these models to predict a wide array of events such as behavioral risk, fraud, or the likelihood of response. Regardless of the predictive variable, a single model may not perform optimally across the target population because there may be distinct segments with different characteristics inherent in the population. Segmentation may be done judgmentally based on experience, but such segmentation schema is limited to the use of only a few variables at best. True multivariate segmentation with the goal of identifying the different segments in your population is best achieved through the use of cluster analysis. Clustering and profiling of the customer base can answer the following questions: ♦ Who are my customers? ♦ How profitable are my customers? ♦ Who are my least profitable customers? ♦ Why are my customers leaving? ♦ What do my best customers look like? This paper discusses the use of SAS Enterprise Miner to segment a population of customers using cluster analysis and decision trees. Focus is placed on the methodology rather than the results to ensure the integrity and confidentiality of customer data. Other statistical strategies are presented in this paper which could be employed in the pursuit of further customer intelligence. It has been said that statistical cluster analysis is as much art as it is science because it is performed without the benefit of well established statistical criteria. Data Mining and Predictive Modeling SAS Global Forum 2008
The Ease of Doing Business Index as a tool for Investment location decisions

The Ease of Doing Business Index (EDBI) uses 41 variables to compare the business environment of different countries. It is widely used by policy makers, researchers and multinational companies. This paper aims to assess EDBI’s consistency and validity in representing the business environment by using factor analysis. It is found that the EDBI presents a limited consistency and descriptive power of a country’s business environment. The consequence of these findings is that multinational firms should handle carefully the EDBI in their investment decisions. GEE The Ease of Doing Business Index as a tool for Investment location decisions – João Zambujal-Oliveira, Ricardo Pinheiro-Alves
Novelty Detection via Topic Modeling in Research Articles

In today’s world redundancy is the most vital problem faced in almost all domains. Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. The problem becomes more intense when it comes to “Research Articles”. A method of identifying novelty at each sections of the article is highly required for determining the novel idea proposed in the research paper. Since research articles are semistructured, detecting novelty of information from them requires more accurate systems. Topic model provides a useful means to process them and provides a simple way to analyze them. This work compares the most predominantly used topic modelLatent Dirichlet Allocation with the hierarchical Pachinko Allocation Model. The results obtained are promising towards hierarchical Pachinko Allocation Model when used for document retrieval.
Modern Trends in Inductive Power Transfer for Transportation Applications

Inductive power transfer (IPT) has progressed to be a power distribution system offering significant benefits in modern automation systems and particularly so in stringent environments. Here, the same technology may be used in very dirty environments and in a clean room manufacture. This paper reviews the development of simple factory automation (FA) IPT systems for both today's complex applications and onward to a much more challenging application-IPT roadway. The underpinning of all IPT technology is two strongly coupled coils operating at resonance to transfer power efficiently. Over time the air-gap, efficiency, coupling factor, and power transfer capability have significantly improved. New magnetic concepts are introduced to allow misalignment, enabling IPT systems to migrate from overhead monorails to the floor. However, the demands of IPT roadway bring about significant challenges. Here, compared with the best FA practice, air-gaps need to be 100 times larger, power levels greater than ten times, system losses ten times lower to meet efficiency requirements, and systems from different manufacturers must be interoperable over the full range of operation. This paper describes how roadway challenges are being met and outlines the problems that still exist and the solutions designers are finding to them.
The population genetics of the Jewish people

Adherents to the Jewish faith have resided in numerous geographic locations over the course of three millennia. Progressively more detailed population genetic analysis carried out independently by multiple research groups over the past two decades has revealed a pattern for the population genetic architecture of contemporary Jews descendant from globally dispersed Diaspora communities. This pattern is consistent with a major, but variable component of shared Near East ancestry, together with variable degrees of admixture and introgression from the corresponding host Diaspora populations. By combining analysis of monoallelic markers with recent genome-wide variation analysis of simple tandem repeats, copy number variations, and single-nucleotide polymorphisms at high density, it has been possible to determine the relative contribution of sex-specific migration and introgression to map founder events and to suggest demographic histories corresponding to western and eastern Diaspora migrations, as well as subsequent microevolutionary events. These patterns have been congruous with the inferences of many, but not of all historians using more traditional tools such as archeology, archival records, linguistics, comparative analysis of religious narrative, liturgy and practices. Importantly, the population genetic architecture of Jews helps to explain the observed patterns of health and disease-relevant mutations and phenotypes which continue to be carefully studied and catalogued, and represent an important resource for human medical genetics research. The current review attempts to provide a succinct update of the more recent developments in a historical and human health context.
Partial Least Squares for Discrimination

Partial least squares (PLS) was not originally designed as a tool for statistical discrimination. In spite of this, applied scientists routinely use PLS for classification and there is substantial empirical evidence to suggest that it performs well in that role. The interesting question is: why can a procedure that is principally designed for overdetermined regression problems locate and emphasize group structure? Using PLS in this manner has heurestic support owing to the relationship between PLS and canonical correlation analysis (CCA) and the relationship, in turn, between CCA and linear discriminant analysis (LDA). This paper replaces the heuristics with a formal statistical explanation. As a consequence, it will become clear that PLS is to be preferred over PCA when discrimination is the goal and dimension reduction is needed.
A Simple Flood Forecasting Scheme Using Wireless Sensor Networks

This paper presents a forecasting model designed using WSNs( Wireless Sensor Networks) to predict flood in rivers using simple and fast calculations to provide real-time results and save the lives of people who may be affected by the flood. Our prediction model uses multiple variable robust linear regression which is easy to understand and simple and cost effective in implementation, is speed efficient, but has low resource utilization and yet provides real time predictions with reliable accuracy, thus having features which are desirable in any real world algorithm. Our prediction model is independent of the number of parameters, i.e. any number of parameters may be added or removed based on the on-site requirements. When the water level rises, we represent it using a polynomial whose nature is used to determine if the water level may exceed the flood line in the near future. We compare our work with a contemporary algorithm to demonstrate our improvements over it. Then we present our simulation results for the predicted water level compared to the actual water level.
A New Nested Neutral Point-Clamped (NNPC) Converter for Medium-Voltage (MV) Power Conversion

In this paper, a new voltage source converter for medium voltage applications is presented which can operate over a wide range of voltages (2.4-7.2 kV) without the need for connecting the power semiconductor in series. The operation of the proposed converter is studied and analyzed. In order to control the proposed converter, a space-vector modulation (SVM) strategy with redundant switching states has been proposed. SVM usually has redundant switching state anyways. What is the main point we are trying to get to? These redundant switching states help to control the output voltage and balance voltages of the flying capacitors in the proposed converter. The performance of the converter under different operating conditions is investigated in MATLAB/Simulink environment. The feasibility of the proposed converter is evaluated experimentally on a 5-kVA prototype.
Quantitative functional evaluation of a 3D-printed silicone-embedded prosthesis for partial hand amputation: A case report.

A male patient with partial hand amputation of his nondominant hand, with only stumps of the proximal phalanx of the first and fifth finger, was evaluated. The performance of using two alternative 3D printed silicone-embedded personalized prostheses was evaluated using the quantitative Jebsen Hand Function Test.



Custom design and fabrication of 3D printed prostheses appears to be a good technique for improving the clinical treatment of patients with partial hand amputations. Despite its importance the literature shows an absence of studies reporting on quantitative functional evaluations of 3D printed hand prostheses.



We aim at producing the first quantitative assessment of the impact of using 3D printed silicone-embedded prostheses that can be fabricated and customized within the clinical environment.



Alginate molds and computed tomographic scans were taken from the patient's hand. Each candidate prosthesis was modeled in Computer Aided Design software and then fabricated using a combination of 3D printed parts and silicone-embedded components.



Incorporating the patient's feedback during the design loop was very important for obtaining a good aid on his work activities. Although the explored patient-centered design process still requires a multidisciplinary team, functional benefits are large.


CONCLUSION(S)
Quantitative data demonstrates better hand performance when using 3D printed silicone-embedded prosthesis vs not using any aid. The patient accomplished complex tasks such as driving a nail and opening plastic bags. This was impossible without the aid of produced prosthesis.
Electrostatic adhesive brakes for high spatial resolution refreshable 2.5D tactile shape displays

We investigate the mechanism, design, modeling and fabrication of a scalable high resolution, low cost and lightweight refreshable 2.5D tactile pin array controlled by electrostatic adhesive brakes. By replacing linear actuators in motorized shape displays with a high voltage solid-state circuit that can be fabricated with printable electronics techniques, we can decrease the cost and complexity of such devices. Electrostatic adhesive brakes, made by patterning interdigital electrodes on high dielectric constant thin films, are used to hold metal pins' positions and provide contact force to the user's fingertip. We present designs of two high resolution brake modules which are 1.7 mm pitch with 0.8 mm width pins and 4 mm pitch with 1.58 mm width pins with a maximum measured dynamic loading force of 76.3 gf and static loading force of 28 gf on an individual pin (for the later size). A small demonstration of 4 × 2 pin array with a 4 mm pitch size within a row and 2.5 mm pitch size between the rows, using 1.58 mm width pins, was created. We also characterized the refresh time to be 37.5 ms for each brake, which enables refreshable actuated pin displays.
Feature warping for robust speaker verification

We propose a novel feature mapping approach that is robust to channel mismatch, additive noise and to some extent, nonlinear effects attributed to handset transducers. These adverse effects can distort the short-term distribution of the speech features. Some methods have addressed this issue by conditioning the variance of the distribution, but not to the extent of conforming the speech statistics to a target distribution. The proposed target mapping method warps the distribution of a cepstral feature stream to a standardised distribution over a specified time interval. We evaluate a number of the enhancement methods for speaker verification, and compare them against a Gaussian target mapping implementation. Results indicate improvements of the warping technique over a number of methods such as Cepstral Mean Subtraction (CMS), modulation spectrum processing, and short-term windowed CMS and variance normalisation. This technique is a suitable feature post-processing method that may be combined with other techniques to enhance speaker recognition robustness under adverse conditions.
Attitude Detection for One-Round Conversation: Jointly Extracting Target-Polarity Pairs

We tackle Attitude Detection, which we define as the task of extracting the replier's attitude, i.e., a target-polarity pair, from a given one-round conversation. While previous studies considered Target Extraction and Polarity Classification separately, we regard them as subtasks of Attitude Detection. Our experimental results show that treating the two subtasks independently is not the optimal solution for Attitude Detection, as achieving high performance in each subtask is not sufficient for obtaining correct target-polarity pairs. Our jointly trained model AD-NET substantially outperforms the separately trained models by alleviating the target-polarity mismatch problem. Moreover, we proposed a method utilising the attitude detection model to improve retrieval-based chatbots by re-ranking the response candidates with attitude features. Human evaluation indicates that with attitude detection integrated, the new responses to the sampled queries from are statistically significantly more consistent, coherent, engaging and informative than the original ones obtained from a commercial chatbot.
Recognition of Offline Handwritten Chinese Characters Using the Tesseract Open Source OCR Engine

Due to the complex structure and handwritten deformation, the offline handwritten Chinese characters recognition has been one of the most challenging problems. In this paper, an offline handwritten Chinese character recognition tool has been developed based on the Tesseract open source OCR engine. The tool mainly contributes on the following two points: First, a handwritten Chinese character features library is generated, which is independent of a specific user's writing style, Second, by preprocessing the input image and adjusting the Tesseract engine, multiple candidate recognition results are output based on weight ranking. The recognition accuracy rate of this tool is above 88% for both known user test set and unknown user test set. It has shown that the Tesseract engine is feasible for offline handwritten Chinese character recognition to a certain degree.
Influence of emotional engagement and habituation on exposure therapy for PTSD.

This study examined 2 process variables, emotional engagement and habituation, and outcome of exposure therapy for posttraumatic stress disorder. Thirty-seven female assault victims received treatment that involved repeated imaginal reliving of their trauma, and rated their distress at 10-min intervals. The average distress levels during each of 6 exposure sessions were submitted to a cluster analysis. Three distinct groups of clients with different patterns of distress were found: high initial engagement and gradual habituation between sessions, high initial engagement without habituation, and moderate initial engagement without habituation. Clients with the 1st distress pattern improved more in treatment than the other clients. The results are discussed within the framework of emotional processing theory, emphasizing the crucial role of emotional engagement and habituation in exposure therapy.
Transcranial direct current stimulation: State of the art 2008

Effects of weak electrical currents on brain and neuronal function were first described decades ago. Recently, DC polarization of the brain was reintroduced as a noninvasive technique to alter cortical activity in humans. Beyond this, transcranial direct current stimulation (tDCS) of different cortical areas has been shown, in various studies, to result in modifications of perceptual, cognitive, and behavioral functions. Moreover, preliminary data suggest that it can induce beneficial effects in brain disorders. Brain stimulation with weak direct currents is a promising tool in human neuroscience and neurobehavioral research. To facilitate and standardize future tDCS studies, we offer this overview of the state of the art for tDCS.
Feature Extraction Based on Co-occurrence of Adjacent Local Binary Patterns

In this paper, we propose a new image feature based on spatial co-occurrence among micropatterns, where each micropattern is represented by a Local Binary Pattern (LBP). In conventional LBP-based features such as LBP histograms, all the LBPs of micropatterns in the image are packed into a single histogram. Doing so discards important information concerning spatial relations among the LBPs, even though they may contain information about the image’s global structure. To consider such spatial relations, we measure their co-occurrence among multiple LBPs. The proposed feature is robust against variations in illumination, a feature inherited from the original LBP, and simultaneously retains more detail of image. The significant advantage of the proposed method versus conventional LBP-based features is demonstrated through experimental results of face and texture recognition using public databases.
Basic emotions, relations among emotions, and emotion-cognition relations.

From the cognitive theory perspective that emotions are cognition dependent and contain cognitive components, Ortony and Turner (1990) questioned the validity of the concept of basic emotions. They argued that the so-called basic emotions were neither psychologically or biologically "primitive" nor "irreducible building blocks" for generating the "great variety of emotional experiences." In the biosocial theory tradition, researchers have identified multiple noncognitive activators of emotion and demonstrated the usefulness of defining the essential components of emotion as phenomena that do not require cognitive mediators or constituents. In this framework, emotions are seen as basic because their biological and social functions are essential in evolution and adaptation. Particular emotions are called basic because they are assumed to have innate neural substrates, innate and universal expressions, and unique feeling-motivational states. The great variety of emotional experiences is explained as a function of emotion-cognition interactions that result in affective-cognitive structures.
A 6-$\mu$ W Chip-Area-Efficient Output-Capacitorless LDO in 90-nm CMOS Technology

An output-capacitorless low-dropout regulator (LDO) compensated by a single Miller capacitor is implemented in a commercial 90-nm CMOS technology. The proposed LDO makes use of the small transistors realized in nano-scale technology to achieve high stability, fast transient performance and small voltage spikes under rapid load-current changes without the need of an off-chip capacitor connected at the LDO output. Experimental result verifies that the proposed LDO is stable for a capacitive load from 0 to 50 pF (estimated equivalent parasitic capacitance from load circuits) and with load capability of 100 mA. Moreover, the gain-enhanced structure provides sufficient loop gain to improve line regulation to 3.78 mV/V and load regulation to 0.1 mV/mA, respectively. The embedded voltage-spike detection circuit enables pseudo Class-AB operation to drive the embedded power transistor promptly. The measured power consumption is only 6 μW under a 0.75-V supply. The maximum overshoot and undershoot under a 1.2-V supply are less than 66 mV for full load current changes within 100-ns edge time, and the recovery time is less than 5 μs.
Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval

Thanks to the success of deep learning, cross-modal retrieval has made significant progress recently. However, there still remains a crucial bottleneck: how to bridge the modality gap to further enhance the retrieval accuracy. In this paper, we propose a self-supervised adversarial hashing (SSAH) approach, which lies among the early attempts to incorporate adversarial learning into cross-modal hashing in a self-supervised fashion. The primary contribution of this work is that two adversarial networks are leveraged to maximize the semantic correlation and consistency of the representations between different modalities. In addition, we harness a self-supervised semantic network to discover high-level semantic information in the form of multi-label annotations. Such information guides the feature learning process and preserves the modality relationships in both the common semantic space and the Hamming space. Extensive experiments carried out on three benchmark datasets validate that the proposed SSAH surpasses the state-of-the-art methods.
Run-time detection of hardware Trojans: The processor protection unit

Typical SOC designs use processors and therefore, trust in such processor cores is essential. The 2011 Embedded Systems Challenge (ESC 2011)  showed a wide range of possibilities to attack a processor through hardware Trojans. We propose an approach to detect suspicious behavior of a processor and thus assess if the processor is trustworthy or not. A countermeasure, called Processor Protection Unit (PPU) is presented focusing on its design to be particularly resilient against hardware Trojan insertion.