nils-herrmann/clean_scirepeval huggingface dataset:
An Intruder Tracking and Attendance Monitoring System Using Mifare Technology

Main objective of this paper is to track the student in campus and enhance attendance monitoring system .The proposed system consist of mifare tag and reader. Mifare readers would be installed at various location in campus and also in classes. When student comes in the vicinity of reader then location can be found which will sends that location to server. Server check the timetable of that student if he/ she will not attend lecture according to timetable then alert will be send to the parent. This system ensure that attendance and exam records of the students would be track properly and efficiently. The system can also generate detention list of the students .It is small scale application which is completely automated, easy to control, time saving and reliable. In this paper advantages and disadvantages of the proposed Mifare technology will
Thompson Sampling for Dynamic Pricing

In this paper we apply active learning algorithms for dynamic pricing in a prominent e-commerce website. Dynamic pricing involves changing the price of items on a regular basis, and uses the feedback from the pricing decisions to update prices of the items. Most popular approaches to dynamic pricing use a passive learning approach, where the algorithm uses historical data to learn various parameters of the pricing problem, and uses the updated parameters to generate a new set of prices. We show that one can use active learning algorithms such as Thompson sampling to more efficiently learn the underlying parameters in a pricing problem. We apply our algorithms to a real e-commerce system and show that the algorithms indeed improve revenue compared to pricing algorithms that use passive learning.
Design of High Performance Permanent-Magnet Synchronous Wind Generators

This paper is devoted to the analysis and design of high performance permanent-magnet synchronous wind generators (PSWGs). A systematic and sequential methodology for the design of PMSGs is proposed with a high performance wind generator as a design model. Aiming at high induced voltage, low harmonic distortion as well as high generator efficiency, optimal generator parameters such as pole-arc to pole-pitch ratio and stator-slot-shoes dimension, etc. are determined with the proposed technique using Maxwell 2-D, Matlab software and the Taguchi method. The proposed double three-phase and six-phase winding configurations, which consist of six windings in the stator, can provide evenly distributed current for versatile applications regarding the voltage and current demands for practical consideration. Specifically, windings are connected in series to increase the output voltage at low wind speed, and in parallel during high wind speed to generate electricity even when either one winding fails, thereby enhancing the reliability as well. A PMSG is designed and implemented based on the proposed method. When the simulation is performed with a 6 Ω load, the output power for the double three-phase winding and six-phase winding are correspondingly 10.64 and 11.13 kW. In addition, 24 Ω load experiments show that the efficiencies of double three-phase winding and six-phase winding are 96.56% and 98.54%, respectively, verifying the proposed high performance operation.
Restructuring speech representations using a pitch-adaptive time-frequency smoothing and an instantaneous-frequency-based F0 extraction: Possible role of a repetitive structure in sounds

A set of simple new procedures has been developed to enable the real-time manipulation of speech parameters. The proposed method uses pitch-adaptive spectral analysis combined with a surface reconstruction method in the time± frequency region. The method also consists of a fundamental frequency (F0) extraction using instantaneous frequency calculation based on a new concept called `fundamentalness'. The proposed procedures preserve the details of time± frequency surfaces while almost perfectly removing ®ne structures due to signal periodicity. This close-to-perfect elimination of interferences and smooth F0 trajectory allow for over 600% manipulation of such speech parameters as pitch, vocal tract length, and speaking rate, while maintaining high reproductive quality. Ó 1999 Elsevier Science B.V. All rights reserved.
Fracking Sarcasm using Neural Network

Precise semantic representation of a sentence and definitive information extraction are key steps in the accurate processing of sentence meaning, especially for figurative phenomena such as sarcasm, Irony, and metaphor cause literal meanings to be discounted and secondary or extended meanings to be intentionally profiled. Semantic modelling faces a new challenge in social media, because grammatical inaccuracy is commonplace yet many previous state-of-the-art methods exploit grammatical structure. For sarcasm detection over social media content, researchers so far have counted on Bag-of-Words(BOW), N-grams etc. In this paper, we propose a neural network semantic model for the task of sarcasm detection. We also review semantic modelling using Support Vector Machine (SVM) that employs constituency parsetrees fed and labeled with syntactic and semantic information. The proposed neural network model composed of Convolution Neural Network(CNN) and followed by a Long short term memory (LSTM) network and finally a Deep neural network(DNN). The proposed model outperforms state-of-the-art textbased methods for sarcasm detection, yielding an F-score of .92.
Automated Segmentation of Math-Zones from Document Images

With an aim to high-level understanding of the mathematical contents in a document image the requirement of math-zone extraction and recognition technique is obvious. In this paper we present fully auotmatic segmentation of displayed-math zones from the document image, using only the spatial layout information of math-formulas and equations, so as to help commercial OCR systems which cannot discern math-zones and also for the identification and arrangement of math symbols by others.
CAPNet: Continuous Approximation Projection For 3D Point Cloud Reconstruction Using 2D Supervision

Knowledge of 3D properties of objects is a necessity in order to build effective computer vision systems. However, lack of large scale 3D datasets can be a major constraint for datadriven approaches in learning such properties. We consider the task of single image 3D point cloud reconstruction, and aim to utilize multiple foreground masks as our supervisory data to alleviate the need for large scale 3D datasets. A novel differentiable projection module, called ‘CAPNet’, is introduced to obtain such 2D masks from a predicted 3D point cloud. The key idea is to model the projections as a continuous approximation of the points in the point cloud. To overcome the challenges of sparse projection maps, we propose a loss formulation termed ‘affinity loss’ to generate outlierfree reconstructions. We significantly outperform the existing projection based approaches on a large-scale synthetic dataset. We show the utility and generalizability of such a 2D supervised approach through experiments on a real-world dataset, where lack of 3D data can be a serious concern. To further enhance the reconstructions, we also propose a test stage optimization procedure to obtain reconstructions that display high correspondence with the observed input image.
Improving malware detection time by using RLE and N-gram

Malware is a widespread problem and despite the common use of anti-virus software, the diversity of malware is still increasing. A major challenge facing the anti-virus industry is how to effectively detect thousands of malware samples that are received every day. In this paper, a novel approach based Run Length Encoding (RLE) algorithm and n-gram are proposed to improve malware detect on dynamic analysis of based on API sequences.
HDTLR: A CNN based Hierarchical Detector for Traffic Lights

Reliable traffic light detection is one crucial key component for autonomous driving in urban areas. This includes the extraction of direction arrows contained within the traffic lights as an autonomous car will need this information for selecting the traffic light corresponding to its current lane. Current state of the art traffic light detection systems are not able to provide such information. Within this work we present a hierarchical traffic light detection algorithm, which is able to detect traffic lights and determine their state and contained direction information within one CNN forward pass. This Hierarchical DeepTLR (HDTLR) outperforms current state of the art traffic light detection algorithms in state aware detection and can detect traffic lights with direction information down to a size of 4 pixel in width at a frequency of 12 frames per second.
Cannabis use and symptoms of anxiety in adolescence and the moderating effect of the serotonin transporter gene.

There is substantial evidence for the assumption that particularly heavy cannabis usett is associated with a variety of psychopathologic conditions. Little is known about the relationship between cannabis and anxiety. Prior studies have concluded that cannabis use alone is not sufficient for the development of long-term anxiety, and it has been suggested that cannabis is simply a risk factor that operates in conjunction with other risk factors. One such risk factor may be an individuals' genetic vulnerability. The present study examines the relationship between cannabis use and symptoms of anxiety by taking a developmental molecular-genetic perspective with a focus on a polymorphism involved in the regulation of serotonin. Specifically, we concentrated on changes in cannabis use and symptoms of anxiety over time and differences herein for individuals with and without the short allele of the 5-HTTLPR genotype. Data were from 1424 adolescents over a period of 5 years. We used different statistical analyses to test co-development of cannabis use and symptoms of anxiety throughout adolescence and the possible role of the 5-HTTLPR genotype in this process. Results from different analyses showed that cannabis use is associated with an increase in symptoms of anxiety, but only in carriers of the short allele of the 5-HTTLPR genotype, not in non-carriers. The findings of the present study show first evidence that the links between cannabis use and symptoms of anxiety are conditional on the individuals' genetic make-up.
Automated Machine Vision System for Liquid Particle Inspection of Pharmaceutical Injection

The particle matter inspection for pharmaceutical injection is inevitable in the field of pharmaceutical manufacturing, as it has the direct impact on the quality of the drugs. It is a challenge to inspect the contaminated injection online using an imaging system. This paper introduces a novel and effective inspection machine consisting of three modules, a mechanical system with 120 carousel grips, an image acquisition system with multihigh resolution cameras and a multilight sources station, and a distributed industrial electrical computer control system. Particle visual inspection machine first acquires image sequence using the high-speed image acquisition system. The image capture process at each camera module is alternately synchronized with different LED illumination techniques (light transmission method and light reflection method), enabling independent capture of particle images from the same container. Then, a set of novel algorithms for image registration and fast segmentation are proposed to minimize false rejections even in sensitive conditions, which enable the identification of all the tiny potential defects. Finally, a particle tracking and classification algorithm based on an adaptive local weighted-collaborative sparse model is also presented. The experiments demonstrate that the proposed inspection system can effectively detect the particles in the pharmaceutical infusion solution online, and achieve a performance rate of above 97% average accuracy.
A Low-delay Protocol for Multihop Wireless Body Area Networks

Wireless body area networks (WBANs) form a new and interesting area in the world of remote health monitoring. An important concern in such networks is the communication between the sensors. This communication needs to be energy efficient and highly reliable while keeping delays low. Mobility also has to be supported as the nodes are positioned on different parts of the body that move with regard to each other. In this paper, we present a new cross-layer communication protocol for WBANs: CICADA or Cascading Information retrieval by Controlling Access with Distributed slot Assignment. The protocol sets up a network tree in a distributed manner. This tree structure is subsequently used to guarantee collision free access to the medium and to route data towards the sink. The paper analyzes CICADA and shows simulation results. The protocol offers low delay and good resilience to mobility. The energy usage is low as the nodes can sleep in slots where they are not transmitting or receiving.
Cost Minimization Algorithms for Data Center Management

Due to the increasing usage of cloud computing applications, it is important to minimize energy cost consumed by a data center, and simultaneously, to improve quality of service via data center management. One promising approach is to switch some servers in a data center to the idle mode for saving energy while to keep a suitable number of servers in the active mode for providing timely service. In this paper, we design both online and offline algorithms for this problem. For the offline algorithm, we formulate data center management as a cost minimization problem by considering energy cost, delay cost (to measure service quality), and switching cost (to change servers's active/idle mode). Then, we analyze certain properties of an optimal solution which lead to a dynamic programming based algorithm. Moreover, by revising the solution procedure, we successfully eliminate the recursive procedure and achieve an optimal offline algorithm with a polynomial complexity. For the online algorithm, We design it by considering the worst case scenario for future workload. In simulation, we show this online algorithm can always provide near-optimal solutions.
Synthesizing information systems knowledge: A typology of literature reviews

In this article we develop a typology of review types and provide a descriptive insight into the most common reviews found in top IS journals. Our assessment reveals that the number of IS reviews has increased over the years. The majority of the 139 reviews are theoretical in nature, followed by narrative reviews, meta-analyses, descriptive reviews, hybrid reviews, critical reviews, and scoping reviews. Considering the calls for IS research to develop a cumulative tradition, we hope more review articles will be published in the future and encourage researchers who start a review to use our typology to position
Group discussion as interactive dialogue or as serial monologue: the influence of group size.

Current models draw a broad distinction between communication as dialogue and communication as monologue. The two kinds of models have different implications for who influences whom in a group discussion. If the discussion is like interactive dialogue, group members should be influenced most by those with whom they interact in the discussion; if it is like serial monologue, they should be influenced most by the dominant speaker. The experiments reported here show that in small, 5-person groups, the communication is like dialogue and members are influenced most by those with whom they interact in the discussion. However, in large, 10-person groups, the communication is like monologue and members are influenced most by the dominant speaker. The difference in mode of communication is explained in terms of how speakers in the two sizes of groups design their utterances for different audiences.
Food waste reduction practices in German food retail

The purpose of this paper is to investigate food retailers food waste reduction practices in Germany. The focus is on selling and redistributing agricultural produce with visual impairments and other surplus food items. In addition, drivers and barriers regarding the implementation of both waste reduction practices are explored.



In total, 12 in-depth interviews with managerial actors in the food retail sector and a food bank spokesperson were recorded, transcribed and analyzed through a qualitative content analysis.



In contrast to organic retailers, conventional retailers were reluctant to include agricultural produce with visual impairments in their product assortments, due to fears of negative consumer reactions. Another obstacle was EU marketing standards for specific produce. All retailers interviewed engaged in redistribution of surplus food. Logistics and the regulatory framework were the main barriers to food redistribution.



The present study adds to the existing body of literature on food waste reduction practices as it explores selling produce with visual impairments and elaborates on the legal background of food redistribution in German retail. The results are the foundation for providing recommendations to policy makers and charitable food organizations.
In-Band Full-Duplex Relaying: A Survey, Research Issues and Challenges

Recent advances in self-interference cancellation techniques enable in-band full-duplex wireless systems, which transmit and receive simultaneously in the same frequency band with high spectrum efficiency. As a typical application of in-band full-duplex wireless, in-band full-duplex relaying (FDR) is a promising technology to integrate the merits of in-band full-duplex wireless and relaying technology. However, several significant research challenges remain to be addressed before its widespread deployment, including small-size full-duplex device design, channel modeling and estimation, cross-layer/joint resource management, interference management, security, etc. In this paper, we provide a brief survey on some of the works that have already been done for in-band FDR, and discuss the related research issues and challenges. We identify several important aspects of in-band FDR: basics, enabling technologies, information-theoretical performance analysis, key design issues and challenges. Finally, we also explore some broader perspectives for in-band FDR.
Ubiquitous enterprise service adaptations based on contextual user behavior

Recent advances in mobile technologies and infrastructures have created the demand for ubiquitous access to enterprise services from mobile handheld devices. Further, with the invention of new interaction devices, the context in which the services are being used becomes an integral part of the activity carried out with the system. Traditional human–computer interface (HCI) theories are now inadequate for developing these context-aware applications, as we believe that the notion of context should be extended to different categories: computing contexts, user contexts, and physical contexts for ubiquitous computing. This demands a new paradigm for system requirements elicitation and design in order to make good use of such extended context information captured from mobile user behavior. Instead of redesigning or adapting existing enterprise services in an ad hoc manner, we introduce a methodology for the elicitation of context-aware adaptation requirements and the matching of context-awareness features to the target context by capability matching. For the implementation of such adaptations, we propose the use of three tiers of views: user interface views, data views, and process views. This approach centers on a novel notion of process views to ubiquitous service adaptation, where mobile users may execute a more concise version or modified procedure of the original process according to their behavior under different contexts. The process view also serves as the key mechanism for integrating user interface views and data views. Based on this model, we analyze the design and implementation issues of some common ubiquitous access situations and show how to adapt them systematically into a context-aware application by considering the requirements of a ubiquitous enterprise information system.
Price-Based Global Market Segmentation for Services

In business-to-business marketing, managers are often tasked with developing effective global pricing strategies for customers characterized by different cultures and different utilities for product attributes. The challenges of formulating international pricing schedules are especially evident in global markets for service offerings, where intensive customer contact, extensive customization requirements, and reliance on extrinsic cues for service quality make pricing particularly problematic. The purpose of this article is to develop and test a model of the antecedents of business customers’ price elasticities of demand for services in an international setting. The article begins with a synthesis of the services, pricing, and global marketing literature streams and then identifies factors that account for differences in business customers’ price elasticities for service offerings across customers in Asia Pacific, Europe, and North America. The findings indicate that price elasticities depend on service quality, service type, and level of service support and that horizontal segments do exist, which provides support for pricing strategies transcending national borders. The article concludes with a discussion of the managerial implications of these results for effective segmentation of global markets for services.
To ban or not to ban: Differences in mobile phone policies at elementary, middle, and high schools

The present study was to examine differences in mobile phone policies at elementary, middle and high schools. We surveyed 245 elementary, middle and high schools teachers in Shenzhen of China, using a specially designed 18-item questionnaire. Teachers’ responses indicate that, across elementary, middle and high schools, significant differences exist in (1) students’ percentages of using mobile phones among students, (2) students’ dependence of mobile phones, (3) the number of schools banning students’ mobile phone use, (4) oral and written forms used by schools to ban students’ mobile phone use, and (5) policy reinforcement strategies used by schools. However, no school-level differences was found in (1) students’ fondness of using mobile phones, (2) teachers’ assessment of low-level effectiveness of mobile phone policies, and (3) teachers’ policy improvement recommendations. Significance and implications of the findings are discussed.
Mining mood-specific movie similarity with matrix factorization for context-aware recommendation

Context-aware recommendation seeks to improve recommendation performance by exploiting various information sources in addition to the conventional user-item matrix used by recommender systems. Recommendations should also usually strive to satisfy a specific purpose. Within the Moviepilot mood track of the context-aware movie recommendation challenge, we propose a novel movie similarity measure that is specific to the movie property demanded by the challenge, i.e., movie mood. Our measure is further exploited by a joint matrix factorization model for recommendation. We experimentally validate the effectiveness of the proposed algorithm in exploiting mood-specific movie similarity for the recommendation with respect to several evaluation metrics, demonstrating that it could outperform several state-of-the-art approaches. In particular, mood-specific movie similarity is demonstrated to be more beneficial than general mood-based movie similarity, for the purpose of mood-specific recommendation.