nils-herrmann/clean_scirepeval huggingface dataset:
Sales forecasting for computer wholesalers: A comparison of multivariate adaptive regression splines and artificial neural networks

a r t i c l e i n f o Keywords: Sales forecasting Computer wholesaler Multivariate adaptive regression splines Artificial neural networks IT industry Artificial neural networks (ANNs) have been found to be useful for sales/demand forecasting. However, one of the main shortcomings of ANNs is their inability to identify important forecasting variables. This study uses multivariate adaptive regression splines (MARS), a nonlinear and non-parametric regression methodology, to construct sales forecasting models for computer wholesalers. Through the outstanding variable screening ability of MARS, important sales forecasting variables for computer wholesalers can be obtained to enable them to make better sales management decisions. Two sets of real sales data collected from Taiwanese computer wholesalers are used to evaluate the performance of MARS. The experimental results show that the MARS model outperforms backpropagation neural networks, a support vector machine, a cerebellar model articulation controller neural network, an extreme learning machine, an ARIMA model, a multivariate linear regression model, and four two-stage forecasting schemes across various performance criteria. Moreover, the MARS forecasting results provide useful information about the relationships between the forecasting variables selected and sales amounts through the basis functions, important predictor variables, and the MARS prediction function obtained, and hence they have important implications for the implementation of appropriate sales decisions or strategies. In the consumer-centric environment of today's business world, enterprises seeking good sales performance often need to maintain a balance between meeting customer demand and controlling inventory costs. Carrying a larger inventory allows customer demand to be satisfied at all times, but can result in over-stocking, leading to problems such as tied up capital, inventory writedowns, and reduced profit margins. Lower inventory levels, in contrast, may reduce inventory costs, but can result in opportunity costs arising from missed sale opportunities , reduced customer satisfaction, and other problems. Sales forecasting can be used to determine the required inventory level and avoid the problem of under/over-stocking. In addition, sales forecasting can have implications for corporate financial planning, marketing, client management, and other areas of business. Improving the accuracy of sales forecasts has therefore become an important aspect of operating a business. There is an extensive body of literature on sales forecasting in such very few studies center on sales forecasting in the information technology (IT) industry, especially for computer wholesalers. Lu and Wang [34] employed a combination of independent component analysis, growing hierarchical self-organizing maps, and support vector regression analysis …
Optimal Path Planning Based on a Multi-Tree T-RRT* Approach for Robotic Task Planning in Continuous Cost Spaces

This paper presents an integrated approach to robotic task planning in continuous cost spaces. This consists of a low-level path planner and a high-level Planning Domain Definition Language (PDDL)-based task planner. The path planner is based on a multi-tree implementation of the optimal Transitionbased Rapidly-exploring Random Tree (T-RRT*) that searches the environment for paths between all pairs of configuration waypoints. A method for shortcutting paths based on cost function is also presented. The resulting minimized path costs are then passed to a PDDL planner to solve the high-level task planning problem while optimizing the overall cost of the solution plan. This approach is demonstrated on two scenarios consisting of different cost functions: obstacle clearance in a cluttered environment and elevation in a mountain environment. Preliminary results suggest that significant improvements to path quality can be achieved without significant increase to computation time when compared with a T-RRT-based implementation.
Better Driving and Recall When In-car Information Presentation Uses Situationally-Aware Incremental Speech Output Generation

It is established that driver distraction is the result of sharing cognitive resources between the primary task (driving) and any other secondary task. In the case of holding conversations, a human passenger who is aware of the driving conditions can choose to interrupt his speech in situations potentially requiring more attention from the driver, but in-car information systems typically do not exhibit such sensitivity. We have designed and tested such a system in a driving simulation environment. Unlike other systems, our system delivers information via speech (calendar entries with scheduled meetings) but is able to react to signals from the environment to interrupt when the driver needs to be fully attentive to the driving task and subsequently resume its delivery. Distraction is measured by a secondary short-term memory task. In both tasks, drivers perform significantly worse when the system does not adapt its speech, while they perform equally well to control conditions (no concurrent task) when the system intelligently interrupts and resumes.
Limiting fake accounts in large-scale distributed systems through adaptive identity management

Various online, networked systems offer a lightweight process for obtaining identities (e.g., confirming a valid e-mail address), so that users can easily join them. Such convenience comes with a price, however: with minimum effort, an attacker can subvert the identity management scheme in place, obtain a multitude of fake accounts, and use them for malicious purposes. In this work, we approach the issue of fake accounts in large-scale, distributed systems, by proposing a framework for adaptive identity management. Instead of relying on users' personal information as a requirement for granting identities (unlike existing proposals), our key idea is to estimate a trust score for identity requests, and price them accordingly using a proof of work strategy. The research agenda that guided the development of this framework comprised three main items: (i) investigation of a candidate trust score function, based on an analysis of users' identity request patterns, (ii) combination of trust scores and proof of work strategies (e.g. cryptograhic puzzles) for adaptively pricing identity requests, and (iii) reshaping of traditional proof of work strategies, in order to make them more resource-efficient, without compromising their effectiveness (in stopping attackers).
A left ventricular segmentation method on 3D echocardiography using deep learning and snake

Segmentation of left ventricular (LV) endocardium from 3D echocardiography is important for clinical diagnosis because it not only can provide some clinical indices (e.g. ventricular volume and ejection fraction) but also can be used for the analysis of anatomic structure of ventricle. In this work, we proposed a new full-automatic method, combining the deep learning and deformable model, for the segmentation of LV endocardium. We trained convolutional neural networks to generate a binary cuboid to locate the region of interest (ROI). And then, using ROI as the input, we trained stacked autoencoder to infer the LV initial shape. At last, we adopted snake model initiated by inferred shape to segment the LV endocardium. In the experiments, we used 3DE data, from CETUS challenge 2014 for training and testing by segmentation accuracy and clinical indices. The results demonstrated the proposed method is accuracy and efficiency respect to expert's measurements.
Bidirectional Single-Stage Grid-Connected Inverter for a Battery Energy Storage System

The objective of this paper is to propose a bidirectional single-stage grid-connected inverter (BSG-inverter) for the battery energy storage system. The proposed BSG-inverter is composed of multiple bidirectional buck–boost type dc–dc converters (BBCs) and a dc–ac unfolder. Advantages of the proposed BSG-inverter include: single-stage power conversion, low battery and dc-bus voltages, pulsating charging/discharging currents, and individual power control for each battery module. Therefore, the equalization, lifetime extension, and capacity flexibility of the battery energy storage system can be achieved. Based on the developed equations, the power flow of the battery system can be controlled without the need of input current sensor. Also, with the interleaved operation between BBCs, the current ripple of the output inductor can be reduced too. The computer simulations and hardware experimental results are shown to verify the performance of the proposed BSG-inverter.
A Neural Model of the Scintillating Grid Illusion: Disinhibition and Self-Inhibition in Early Vision

A stationary display of white discs positioned on intersecting gray bars on a dark background gives rise to a striking scintillating effectthe scintillating grid illusion. The spatial and temporal properties of the illusion are well known, but a neuronal-level explanation of the mechanism has not been fully investigated. Motivated by the neurophysiology of the Limulus retina, we propose disinhibition and self-inhibition as possible neural mechanisms that may give rise to the illusion. In this letter, a spatiotemporal model of the early visual pathway is derived that explicitly accounts for these two mechanisms. The model successfully predicted the change of strength in the illusion under various stimulus conditions, indicating that low-level mechanisms may well explain the scintillating effect in the illusion.
Euthanasia: another face of murder.

Debate over euthanasia is not a recent phenomenon. Over the years, public opinion, decisions of courts, and legal and medical approaches to the issue of euthanasia has been conflicting. The connection between murder and euthanasia has been attempted in a few debates. Although it is widely accepted that murder is a crime, a clearly defined stand has not been taken on euthanasia. This article considers euthanasia from the medical, legal, and global perspectives and discusses the crime of murder in relation to euthanasia, taking into consideration the issue of consent in the law of crime. This article concludes that in the midst of this debate on euthanasia and murder, the important thing is that different countries need to find their own solution to the issue of euthanasia rather than trying to import solutions from other countries.
A cytokine-mediated link between innate immunity, inflammation, and cancer.

It has been established that cancer can be promoted and/or exacerbated by inflammation and infections. Indeed, chronic inflammation orchestrates a tumor-supporting microenvironment that is an indispensable participant in the neoplastic process. The mechanisms that link infection, innate immunity, inflammation, and cancer are being unraveled at a fast pace. Important components in this linkage are the cytokines produced by activated innate immune cells that stimulate tumor growth and progression. In addition, soluble mediators produced by cancer cells recruit and activate inflammatory cells, which further stimulate tumor progression. However, inflammatory cells also produce cytokines that can limit tumor growth. Here we provide an overview of the current understanding of the role of inflammation-induced cytokines in tumor initiation, promotion, and progression.
A novel active gate driver for silicon carbide MOSFET

A novel active gate driver (AGD) for silicon carbide (SiC) MOSFET is studied in this paper. The gate driver (GD) increases the gate resistance value during the voltage plateau area of the gate-source voltage, in both turn-on and turn-off transitions. The proposed AGD is validated in both simulation and experimental environments and in hard-switching conditions. The simulation is evaluated in MATLAB/Simulink with 100 kHz of switching frequency and 600 V of dc-bus, whereas, the experimental part was realised at 100 kHz and 100 V of dc-bus. The results show that the gate driver can reduce the over-voltage and ringing, with low switching losses.
FACILITATION EFFECTS OF A PREPARATORY SKATEBOARD TRAINING ON THE LEARNING OF SNOWBOARDING

Surfing’s progeny, snowboarding and skateboarding, present similar positional, visual, and kinesthetic reafferential aspects. Such aspects lead us to the assumption of a positive knowledge transfer from skateboarding to snowboarding. In this investigation we analyzed the probability of and theories for the transfer effect under field conditions. Students of the experimental group received five skateboarding lessons. They then joined a student control group for a six-day school snowboarding trip. Both groups were videotaped on the second and sixth days of the trip. Experts rated snowboarding performance of subjects pertaining to either of groups on a scale of one (very bad) to ten (excellent) points. Inter-rater reliability was very good. While there were no significant differences between the groups on the second day, the students of the experimental group significantly outperformed students of the control group in snowboarding on the sixth day (Mcontrol=4.80, SDcontrol=2.10; Mtreat=6.56, SDtreat=2.10; T=-1.78, df=16, pone-tailed=.045, d=-.83). Given a common underlying structure of skateboarding and snowboarding, skateboarding lessons that develop that structure have a facilitation effect on learning how to snowboard successfully.
Exploitative Abuse in Article 82 EC : Back to Basics ?

For the last few years, the EC Commission has been reviewing its application of Article 82EC which prohibits the abuse of a dominant position on the Common Market. The review has resulted in a Communication from the EC Commission which for the first time sets out its enforcement priorities under Article 82EC. The review had been limited to the so-called ‘exclusionary’ abuses and excluded ‘exploitative’ abuses; the enforcement priorities of the EC Commission set out in the Guidance (2008) are also limited to ‘exclusionary’ abuses. This is, however, odd since the EC Commission expresses the objective of Article 82EC as enhancing consumer welfare: exploitative abuses can directly harm consumers unlike exclusionary abuses which can only indirectly harm consumers as the result of exclusion of competitors. This paper questions whether and under which circumstances exploitation can and/or should be found ‘abusive’. It argues that ‘exploitative’ abuse can and should be used as the test of anticompetitive effects on the market under an effects-based approach and thus conduct should only be found abusive if it is ‘exploitative’. Similarly, mere exploitation does not demonstrate harm to competition and without the latter, exploitation on its own should not be found abusive. December 2008
Why Doesn't Jane Protect Her Privacy?

End-to-end encryption has been heralded by privacy and security researchers as an effective defence against dragnet surveillance, but there is no evidence of widespread end-user uptake. We argue that the non-adoption of end-toend encryption might not be entirely due to usability issues identified by Whitten and Tygar in their seminal paper “Why Johnny Can’t Encrypt”. Our investigation revealed a number of fundamental issues such as incomplete threat models, misaligned incentives, and a general absence of understanding of the email architecture. From our data and related research literature we found evidence of a number of potential explanations for the low uptake of end-to-end encryption. This suggests that merely increasing the availability and usability of encryption functionality in email clients will not automatically encourage increased deployment by email users. We shall have to focus, first, on building comprehensive end-user mental models related to email, and email security. We conclude by suggesting directions for future research.
Deep Learning Based Vehicle Make-Model Classification

This paper studies the problems of vehicle make & model classification. Some of the main challenges are reaching high classification accuracy and reducing the annotation time of the images. To address these problems, we have created a fine-grained database using online vehicle marketplaces of Turkey. A pipeline is proposed to combine an SSD (Single Shot Multibox Detector) model with a CNN (Convolutional Neural Network) model to train on the database. In the pipeline, we first detect the vehicles by following an algorithm which reduces the time for annotation. Then, we feed them into the CNN model. It is reached approximately 4% better classification accuracy result than using a conventional CNN model. Next, we propose to use the detected vehicles as ground truth bounding box (GTBB) of the images and feed them into an SSD model in another pipeline. At this stage, it is reached reasonable classification accuracy result without using perfectly shaped GTBB. Lastly, an application is implemented in a use case by using our proposed pipelines. It detects the unauthorized vehicles by comparing their license plate numbers and make & models. It is assumed that license plates are readable.
Loss of heterozygosity on 10q23.3 and mutation of the tumor suppressor gene PTEN in benign endometrial cyst of the ovary: possible sequence progression from benign endometrial cyst to endometrioid carcinoma and clear cell carcinoma of the ovary.

Loss of heterozygosity (LOH) at locus 10q23.3 and mutation of the PTEN tumor suppressor gene occur frequently in both endometrial carcinoma and ovarian endometrioid carcinoma. To investigate the potential role of the PTEN gene in the carcinogenesis of ovarian endometrioid carcinoma and its related subtype, clear cell carcinoma, we examined 20 ovarian endometrioid carcinomas, 24 clear cell carcinomas, and 34 solitary endometrial cysts of the ovary for LOH at 10q23.3 and point mutations within the entire coding region of the PTEN gene. LOH was found in 8 of 19 ovarian endometrioid carcinomas (42.1%), 6 of 22 clear cell carcinomas (27.3%), and 13 of 23 solitary endometrial cysts (56.5%). In 5 endometrioid carcinomas synchronous with endometriosis, 3 cases displayed LOH events common to both the carcinoma and the endometriosis, 1 displayed an LOH event in only the carcinoma, and 1 displayed no LOH events in either lesion. In 7 clear cell carcinomas synchronous with endometriosis, 3 displayed LOH events common to both the carcinoma and the endometriosis, 1 displayed an LOH event in only the carcinoma, and 3 displayed no LOH events in either lesion. In no cases were there LOH events in the endometriosis only. Somatic mutations in the PTEN gene were identified in 4 of 20 ovarian endometrioid carcinomas (20.0%), 2 of 24 clear cell carcinomas (8.3%), and 7 of 34 solitary endometrial cysts (20.6%). These results indicate that inactivation of the PTEN tumor suppressor gene is an early event in the development of ovarian endometrioid carcinoma and clear cell carcinoma of the ovary.
A joint 3D image semantic segmentation and scalable coding scheme with ROI approach

Along with the digital evolution, image post-production and indexing have become one of the most advanced and desired services in the lossless 3D image domain. The 3D context provides a significant gain in terms of semantics for scene representation. However, it also induces many drawbacks including monitoring visual degradation of compressed 3D image (especially upon edges), and increased complexity for scene representation. In this paper, we propose a semantic region representation and a scalable coding scheme. First, the semantic region representation scheme is based on a low resolution version of the 3D image. It provides the possibility to segment the image according to a desirable balance between 2D and depth. Second, the scalable coding scheme consists in selecting a number of regions as a Region of Interest (RoI), based on the region representation, in order to be refined at a higher bitrate. Experiments show that the proposed scheme provides a high coherence between texture, depth and regions and ensures an efficient solution to the problems of compression and scene representation in the 3D image domain.
A Hybrid Patch/Slot Implantable Antenna for Biotelemetry Devices

A single-fed miniaturized hybrid patch/slot implantable antenna is designed and experimentally demonstrated for medical communications service (MICS) (402-405 MHz). By embedding the meander slot and six open slots in the ground, the proposed antenna can obtain effective size reduction at a fixed frequency operation. Compared to traditional planar inverted-F antennas (PIFAs), the proposed method has advantages of good size reduction and also being easy to be optimized to the necessary resonance frequency. The proposed antenna has dimensions of 10×16×1.27 mm3. The simulated and measured bandwidths are 23.9% and 22.8%, respectively.
Asking and Answering Questions during a Programming Change Task

Little is known about the specific kinds of questions programmers ask when evolving a code base and how well existing tools support those questions. To better support the activity of programming, answers are needed to three broad research questions: 1) What does a programmer need to know about a code base when evolving a software system? 2) How does a programmer go about finding that information? 3) How well do existing tools support programmers in answering those questions? We undertook two qualitative studies of programmers performing change tasks to provide answers to these questions. In this paper, we report on an analysis of the data from these two user studies. This paper makes three key contributions. The first contribution is a catalog of 44 types of questions programmers ask during software evolution tasks. The second contribution is a description of the observed behavior around answering those questions. The third contribution is a description of how existing deployed and proposed tools do, and do not, support answering programmers' questions.
Answerer in Questioner's Mind for Goal-Oriented Visual Dialogue

Goal-oriented dialogue has been paid attention for its numerous applications in artificial intelligence. To solve this task, deep learning and reinforcement learning have recently been applied. However, these approaches struggle to find a competent recurrent neural questioner, owing to the complexity of learning a series of sentences. Motivated by theory of mind, we propose “Answerer in Questioner’s Mind” (AQM), a novel algorithm for goal-oriented dialogue. With AQM, a questioner asks and infers based on an approximated probabilistic model of the answerer. The questioner figures out the answerer’s intent via selecting a plausible question by explicitly calculating the information gain of the candidate intentions and possible answers to each question. We test our framework on two goal-oriented visual dialogue tasks: “MNIST Counting Dialog” and “GuessWhat?!.” In our experiments, AQM outperforms comparative algorithms and makes human-like dialogue. We further use AQM as a tool for analyzing the mechanism of deep reinforcement learning approach and discuss the future direction of practical goal-oriented neural dialogue systems.