nils-herrmann/clean_scirepeval huggingface dataset:
Credit Card Fraud Detection: A Novel Approach Using Aggregation Strategy and Feedback Mechanism

With the rapid development of electronic commerce, the number of transactions by credit cards are increasing rapidly. As online shopping becomes the most popular transaction mode, cases of transaction fraud are also increasing. In this paper, we propose a novel fraud detection method that composes of four stages. To enrich a cardholder’s behavioral patterns, we first utilize the cardholders’ historical transaction data to divide all cardholders into different groups such that the transaction behaviors of the members in the same group are similar. We thus propose a window-sliding strategy to aggregate the transactions in each group. Next, we extract a collection of specific behavioral patterns for each cardholder based on the aggregated transactions and the cardholder’s historical transactions. Then we train a set of classifiers for each group on the base of all behavioral patterns. Finally, we use the classifier set to detect fraud online and if a new transaction is fraudulent, a feedback mechanism is taken in the detection process in order to solve the problem of concept drift. The results of our experiments show that our approach is better than others.
Broadband Dual-Polarized Omnidirectional Antenna for 2G/3G/LTE/WiFi Applications

A broadband dual-polarized omnidirectional antenna is presented in this letter for the 2G/3G/LTE/WiFi communication systems. It consists of one vertically polarized (VP) element and one horizontally polarized (HP) element. The VP element is composed of a ground plane and a center-fed discone coupled by a circular patch with four shorting legs, while the HP element is composed of a printed dipole array, a feeding network, and four pairs of parasitical branches. Through such deliberate design, a broad bandwidth from 820 MHz to more than 6 GHz (about more than 152%) for VP and from 1530 to 2950 MHz (about 63%) for HP can be achieved while keeping the antenna compact. Good port isolation, omnidirectional patterns in horizontal plane, along with acceptable cross polarization and gain are also obtained. Details of the antenna design as well as experimental results are presented and discussed.
Design of PCB leaky-wave antennas for Wide angle beam steering

A PCB based leaky-wave antenna (LWA) design for wide angle beam steering is presented. This design employs longitudinal slots inserted into a substrate-integrated waveguide for operation in the mm-wave V-band. It is optimized for a reduced open-stopband and linear through broadside beam steering. The base design is outlined and adapted for three high frequency laminates with different dielectric constants. It is further described how the dielectric constant directly affects the beam steering properties of the LWA and how to scale the achieved beam angle for the given design by only changing the PCB material. The three LWA designs achieve beam steering from about 45° at Sr =2.2, over 60° at Sr =6.15 to 75° at Sr =10.2. The simulated radiation patterns show a flat gain of up to 18.0 dBi, 17.85 dBi and 15.27 dBi respectively. Furthermore, the effect of the modified PCB permittivity on the radiation efficiency as well as multiuser support with a beam per user is investigated.
Privacy Preserving Group Nearest Neighbor Search

Group k-nearest neighbor (kGNN) search allows a group of n mobile users to jointly retrievek points from a location-based service provider (LSP) that minimizes the aggregate distance to them. We identify four protection objectives in the privacy preserving kGNN search: (i) every user’s location should be protected from LSP; (ii) the group’s query and the query answer should be protected from LSP; (iii) LSP’s private database information should be protected from users, i.e., the users cannot learn more information beyond the answer they requested; (iv) every user’s location should be protected from the other users in the group. We propose the first approach to meet the four privacy goals in the kGNN query. Our approach provides an accurate query answer and does not rely on heavy pre-computation on LSP like previous works. Our approach considers the most hostile environment that any n − 1 users in the query group may collude to infer the location of the remaining user. Though we consider kGNN, the proposed privacy preserving approach can be easily adopted to any group query because it treats the query answering (i.e., kGNN) as a black box. Theoretical and experimental analysis suggest that our approach is highly efficient in both user computation and communication while incurring some reasonable overhead on LSP.
Learning Multi-Way Relations via Tensor Decomposition With Neural Networks

How can we classify multi-way data such as network traffic logs with multi-way relations between source IPs, destination IPs, and ports? Multi-way data can be represented as a tensor, and there have been several studies on classification of tensors to date. One critical issue in the classification of multi-way relations is how to extract important features for classification when objects in different multi-way data, i.e., in different tensors, are not necessarily in correspondence. In such situations, we aim to extract features that do not depend on how we allocate indices to an object such as a specific source IP; we are interested in only the structures of the multi-way relations. However, this issue has not been considered in previous studies on classification of multi-way data. We propose a novel method which can learn and classify multi-way data using neural networks. Our method leverages a novel type of tensor decomposition that utilizes a target core tensor expressing the important features whose indices are independent of those of the multi-way data. The target core tensor guides the tensor decomposition into more effective results and is optimized in a supervised manner. Our experiments on three different domains show that our method is highly accurate, especially on higher order data. It also enables us to interpret the classification results along with the matrices calculated with the novel tensor decomposition.
Component Model of Addiction Treatment: A Pragmatic Transdiagnostic Treatment Model of Behavioral and Substance Addictions

Behavioral addictions such as gambling, video games, sex, and shopping share many clinical features with substance use addictions including etiology, course, and neurobiology. Yet, the treatment of behavioral and substance use addictions tends to be separated. However, we argue that a more effective and efficient treatment approach is to conceptualize behavioral and substance use addictions as different expressions of a common underlying disorder and, in treatment, to address the underlying mechanisms common to both. To this end, the article presents a developing transdiagnostic treatment model of addictions that targets underlying similarities between behavioral and substance use addictions, called the component model of addiction treatment (CMAT). The CMAT is transdiagnostic in that it can be used in the treatment of both behavioral and substance use addictions. It is pragmatic in that it targets component vulnerabilities, which are enduring, yet malleable, individual psychological, cognitive, and neurobiological characteristics that are common to all addictive disorders and have been demonstrated to be modifiable. A working model of CMAT is presented, including proposed component vulnerabilities: lack of motivation, urgency, maladaptive expectancies, deficits in self-control, deficits in social support, and compulsivity, as well as their potential intervention possibilities. Future directions and potential implications of the CMAT are discussed.
Automatic Number Plate Recognition System ( ANPR ) : A Survey

Traffic control and vehicle owner identification has become major problem in every country. Sometimes it becomes difficult to identify vehicle owner who violates traffic rules and drives too fast. Therefore, it is not possible to catch and punish those kinds of people because the traffic personal might not be able to retrieve vehicle number from the moving vehicle because of the speed of the vehicle. Therefore, there is a need to develop Automatic Number Plate Recognition (ANPR) system as a one of the solutions to this problem. There are numerous ANPR systems available today. These systems are based on different methodologies but still it is really challenging task as some of the factors like high speed of vehicle, non-uniform vehicle number plate, language of vehicle number and different lighting conditions can affect a lot in the overall recognition rate. Most of the systems work under these limitations. In this paper, different approaches of ANPR are discussed by considering image size, success rate and processing time as parameters. Towards the end of this paper, an extension to ANPR is suggested.
A DC Voltage-Multiplier Circuit Working as a High-Voltage Pulse Generator

The intensive use of semiconductor devices enabled the development of a repetitive high-voltage pulse-generator topology from the dc voltage-multiplier (VM) concept. The proposed circuit is based on an odd VM-type circuit, where a number of dc capacitors share a common connection with different voltage ratings in each one, and the output voltage comes from a single capacitor. Standard VM rectifier and coupling diodes are used for charging the energy-storing capacitors, from an ac power supply, and two additional on/off semiconductors in each stage, to switch from the typical charging VM mode to a pulse mode with the dc energy-storing capacitors connected in series with the load. Results from a 2-kV experimental prototype with three stages, delivering a 10- s pulse with a 5-kHz repetition rate into a resistive load, are discussed. Additionally, the proposed circuit is compared against the solid-state Marx generator topology for the same peak input and output voltages.
An Affordable Augmented Reality Based Rehabilitation System for Hand Motions

Repetitive practices can facilitate the rehabilitation of the motor functions of the upper extremity for stroke survivors whose motor functions have been impaired. An affordable rehabilitation system for hand motion would be useful to provide intensive training to the patients. Augmented Reality (AR) can provide an intuitive interface to the users with programmable environment and realism feelings. In this research, a low cost viable rehabilitation system for hand motion is developed based on the AR technology. In this application, seamless interaction between the virtual environment and the real hand is supported with markers and a self-designed data-glove. The data-glove is low cost ((
Game-Enhanced Second Language Vocabulary Acquisition Strategies: A Systematic Review

This paper is a synthesis of 17 qualitative, quantitative, and mixed-method studies published over the last decade. The synthesis suggests that game-enhanced learning provides a set of effective strategies such as language repetitions, contextual clues, interaction with native speakers and peers, and imagery to practice and use second language vocabulary in the authentic context. Some of the strategies such as word lists, dictionaries, and vocabulary exercises are more beneficial when combined with native speakers or peers’ interactions. Due to high interactivity of games, note taking and media strategies provide less support for vocabulary learning. The lack of high quality studies and empirical data makes it difficult to draw conclusions about which video games strategies provide the most benefit. The synthesis of research identifies that generally game-enhanced practices are helpful for second language vocabulary enhancement.
Control of Inertial Stabilization Systems Using Robust Inverse Dynamics Control and Adaptive Control

This paper presents an advanced controller design for an Inertial stabilization system. The system has a 2-DOF gimbal which will be attached to an aviation vehicle. Due to dynamics modeling errors, and friction and disturbances from the outside environment, the tracking accuracy of an airborne gimbal may severely degrade. So, an advanced controller is needed. Robust inverse dynamics control and the adaptive control are used in the inner loop or gimbal servo-system to control the gimbal motion. An indirect line of sight (LOS) stabilization will be controlled by the outer loop controller. A stabilizer is mounted on the base of the system to measure base rate and orientation of the gimbal in reference to the fixed reference frame. It can withstand high angular slew rates. The experimental results illustrate that the proposed controllers are capable enough to overcome the disturbances and the impact of LOS disturbances on the tracking performance.
A Regularization Post Layer: An Additional Way How to Make Deep Neural Networks Robust

Neural Networks (NNs) are prone to overfitting. Especially, the Deep Neural Networks in the cases where the training data are not abundant. There are several techniques which allow us to prevent the overfitting, e.g., L1/L2 regularization, unsupervised pre-training, early training stopping, dropout, bootstrapping or cross-validation models aggregation. In this paper, we proposed a regularization post-layer that may be combined with prior techniques, and it brings additional robustness to the NN. We trained the regularization post-layer in the crossvalidation (CV) aggregation scenario: we used the CV held-out folds to train an additional neural network post-layer that boosts the network robustness. We have tested various post-layer topologies and compared results with other regularization techniques. As a benchmark task, we have selected the TIMIT phone recognition which is a well-known and still favorite task where the training data are limited, and the used regularization techniques play a key role. However, the regularization postlayer is a general method, and it may be employed in any classification task.
A 2.3 pJ/bit frequency-stable impulse OOK transmitter powered directly by an RF energy harvesting circuit with −19.5 dBm sensitivity

The proposed 2.5-GHz-band impulse transmitter technology realizes frequency-stable impulse generation against PVT variation and superior energy-per-bit operation, and it can be powered directly from a - 19.5-dBm-sensitivity RF energy harvesting circuit without any regulators that are generally essential to power RF circuits. The transmitter occupies 0.38 mm2 in a 65nm CMOS technology. The maximum frequency difference among measured output return-loss peak of 9 chips with 3 different process corners under 0.5 V supply is about 50 MHz without any frequency calibration. Our prototype achieves 1 Mb/s signal transmission under 2.3 μW power consumption from 0.5 V supply thanks to pulse-level duty cycling operation of maximally digital architecture.
Toward preprototype user acceptance testing of new information systems: implications for software project management

Errors in requirements specifications have been identified as a major contributor to costly software project failures. It would be highly beneficial if information systems developers could verify requirements by predicting workplace acceptance of a new system based on user evaluations of its specifications measured during the earliest stages of the development project, ideally before building a working prototype. However, conventional wisdom among system developers asserts that prospective users must have direct hands-on experience with at least a working prototype of a new system before they can provide assessments that accurately reflect future usage behavior after workplace implementation. The present research demonstrates that this assumption is only partially true. Specifically, it is true that stable and predictive assessments of a system's perceived ease of use should be based on direct behavioral experience using the system. However, stable and behaviorally predictive measures of perceived usefulness can be captured from target users who have received information about a system's functionality, but have not had direct hands-on usage experience. This distinction is key because, compared to ease of use, usefulness is generally much more strongly linked to future usage intentions and behaviors in the workplace. Two longitudinal field experiments show that preprototype usefulness measures can closely approximate hands-on based usefulness measures, and are significantly predictive of usage intentions and behavior up to six months after workplace implementation. The present findings open the door toward research on how user acceptance testing may be done much earlier in the system development process than has traditionally been the case. Such preprototype user acceptance tests have greater informational value than their postprototype counterparts because they are captured when only a relatively small proportion of project costs have been incurred and there is greater flexibility to modify a new system's design attributes. Implications are discussed for future research to confirm the robustness of the present findings and to better understand the practical potential and limitations of preprototype user acceptance testing.
Parkinsons disease patients perspective on context aware wearable technology for auditive assistance

In this paper we present a wearable assistive technology for the freezing of gait (FOG) symptom in patients with Parkinsons disease (PD), with emphasis on subjective user appreciation. Patients with advanced PD often suffer from FOG, which is a sudden and transient inability to move. It often causes falls, interferes with daily activities and significantly impairs quality of life. Because gait deficits in PD patients are often resistant to pharmacologic treatment, effective non+pharmacologic treatments are of special interest.
Clustering by Local Gravitation

The objective of cluster analysis is to partition a set of data points into several groups based on a suitable distance measure. We first propose a model called local gravitation among data points. In this model, each data point is viewed as an object with mass, and associated with a local resultant force (LRF) generated by its neighbors. The motivation of this paper is that there exist distinct differences between the LRFs (including magnitudes and directions) of the data points close to the cluster centers and at the boundary of the clusters. To capture these differences efficiently, two new local measures named centrality and coordination are further investigated. Based on empirical observations, two new clustering methods called local gravitation clustering and communication with local agents are designed, and several test cases are conducted to verify their effectiveness. The experiments on synthetic data sets and real-world data sets indicate that both clustering approaches achieve good performance on most of the data sets.
Impact of dietary fiber intake on glycemic control, cardiovascular risk factors and chronic kidney disease in Japanese patients with type 2 diabetes mellitus: the Fukuoka Diabetes Registry

Dietary fiber is beneficial for the treatment of type 2 diabetes mellitus, although it is consumed differently in ethnic foods around the world. We investigated the association between dietary fiber intake and obesity, glycemic control, cardiovascular risk factors and chronic kidney disease in Japanese type 2 diabetic patients.



A total of 4,399 patients were assessed for dietary fiber intake using a brief self-administered diet history questionnaire. The associations between dietary fiber intake and various cardiovascular risk factors were investigated cross-sectionally.



Body mass index, fasting plasma glucose, HbA1c, triglyceride and high-sensitivity C-reactive protein negatively associated with dietary fiber intake after adjusting for age, sex, duration of diabetes, current smoking, current drinking, total energy intake, fat intake, saturated fatty acid intake, leisure-time physical activity and use of oral hypoglycemic agents or insulin. The homeostasis model assessment insulin sensitivity and HDL cholesterol positively associated with dietary fiber intake. Dietary fiber intake was associated with reduced prevalence of abdominal obesity, hypertension and metabolic syndrome after multivariate adjustments including obesity. Furthermore, dietary fiber intake was associated with lower prevalence of albuminuria, low estimated glomerular filtration rate and chronic kidney disease after multivariate adjustments including protein intake. Additional adjustments for obesity, hypertension or metabolic syndrome did not change these associations.



We demonstrated that increased dietary fiber intake was associated with better glycemic control and more favorable cardiovascular disease risk factors including chronic kidney disease in Japanese type 2 diabetic patients. Diabetic patients should be encouraged to consume more dietary fiber in daily life.
Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks

Learning to detect fraud in large-scale accounting data is one of the long-standing challenges in financial statement audits or fraud investigations. Nowadays, the majority of applied techniques refer to handcrafted rules derived from known fraud scenarios. While fairly successful, these rules exhibit the drawback that they often fail to generalize beyond known fraud scenarios and fraudsters gradually find ways to circumvent them. To overcome this disadvantage and inspired by the recent success of deep learning we propose the application of deep autoencoder neural networks to detect anomalous journal entries. We demonstrate that the trained network’s reconstruction error obtainable for a journal entry and regularized by the entry’s individual attribute probabilities can be interpreted as a highly adaptive anomaly assessment. Experiments on two real-world datasets of journal entries, show the effectiveness of the approach resulting in high f1-scores of 32.93 (dataset A) and 16.95 (dataset B) and less false positive alerts compared to state of the art baseline methods. Initial feedback received by chartered accountants and fraud examiners underpinned the quality of the approach in capturing highly relevant accounting anomalies.
Towards a flexible functional split for cloud-RAN networks

Very dense deployments of small cells are one of the key enablers to tackle the ever-growing demand on mobile bandwidth. In such deployments, centralization of RAN functions on cloud resources is envisioned to overcome severe inter-cell interference and to keep costs acceptable. However, RAN back-haul constraints need to be considered when designing the functional split between RAN front-ends and centralized equipment. In this paper we analyse constraints and outline applications of flexible RAN centralization.
60 GHz Aperture-Coupled Dielectric Resonator Antennas Fed by a Half-Mode Substrate Integrated Waveguide

Dielectric resonator antennas (DRAs) fed by a halfmode substrate integrated waveguide (HMSIW) are proposed and studied in this paper. The investigated antenna configuration consists of a dielectric resonator (DR) mounted on the conducting back plane of an HMSIW. Energy is coupled from the interior HMSIW to the DR through an aperture between them. Using this excitation scheme, a 60 GHz linearly polarized HMSIW-fed DRA is first designed by applying a transverse rectangular slot to feed a dielectric cylinder. This design experimentally exhibits a bandwidth of 24.2% for S11 < -10 dB. In particular, a gain higher than 5.5 dB and a radiation efficiency between 80% and 92% are obtained over the whole operation band, indicating that the HMSIW can be an efficient feed for DRAs operating around 60 GHz. In addition to this linearly polarized example, a DRA of circular polarization, coupled through a pair of cross slots, is also designed, presenting a measured 3-dB axial ratio bandwidth of 4.0%.