nils-herrmann/clean_scirepeval huggingface dataset:
A method for business process reengineering based on enterprise ontology

Business Process Reengineering increases enterprise's chance to survive in competition among organizations , but failure rate among reengineering efforts is high, so new methods that decrease failure, are needed, in this paper a business process reengineering method is presented that uses Enterprise Ontology for modelling the current system and its goal is to improve analysing current system and decreasing failure rate of BPR, and cost and time of performing processes, In this method instead of just modelling processes, processes with their : interactions and relations, environment, staffs and customers will be modelled in enterprise ontology. Also in choosing processes for reengineering step, after choosing them, processes which, according to the enterprise ontology, has the most connection with the chosen ones, will also be chosen to reengineer, finally this method is implemented on a company and As-Is and To-Be processes are simulated and compared by ARIS tools, Report and Simulation Experiment
Tree-Based Multi-dimensional Range Search on Encrypted Data with Enhanced Privacy

With searchable encryption, a data user is able to perform meaningful search on encrypted data stored in the public cloud without revealing data privacy. Besides handling simple queries (e.g., keyword queries), complex search functions, such as multi-dimensional (conjunctive) range queries, have also been studied in several approaches to support the search of multi-dimensional data. However, current works supporting multi-dimensional range queries either only achieve linear search complexity or reveal additional private information to the public cloud. In this paper, we propose a tree-based symmetric-key searchable encryption to support multi-dimensional range queries on encrypted data. Besides protecting data privacy, our proposed scheme is able to achieve faster-than-linear search, query privacy and single-dimension privacy simultaneously compared to previous solutions. More specifically, we formally define the security of our proposed scheme, prove that it is selectively secure, and demonstrate its efficiency with experiments over a real-world dataset.
Practical Private Set Intersection Protocols with Linear Complexity

The constantly increasing dependence on anytime-anywhere availability of data and the commensurately increasing fear of losing privacy motivate the need for privacy-preserving techniques. One interesting and common problem occurs when two parties need to privately compute an intersection of their respective sets of data. In doing so, one or both parties must obtain the intersection (if one exists), while neither should learn anything about other set elements. Although prior work has yielded a number of effective and elegant Private Set Intersection (PSI) techniques, the quest for efficiency is still underway. This paper explores some PSI variations and constructs several secure protocols that are appreciably more efficient than the state-of-the-art.
Predicting Categorical Emotions by Jointly Learning Primary and Secondary Emotions through Multitask Learning

Detection of human emotions is an essential part of affect-aware human-computer interaction (HCI). In daily conversations, the preferred way of describing affects is by using categorical emotion labels (e.g., sad, anger, surprise). In categorical emotion classification, multiple descriptors (with different degrees of relevance) can be assigned to a sample. Perceptual evaluations have relied on primary and secondary emotions to capture the ambiguous nature of spontaneous recordings. Primary emotion is the most relevant category felt by the evaluator. Secondary emotions capture other emotional cues also conveyed in the stimulus. In most cases, the labels collected from the secondary emotions are discarded, since assigning a single class label to a sample is preferred from an application perspective. In this work, we take advantage of both types of annotations to improve the performance of emotion classification. We collect the labels from all the annotations available for a sample and generate primary and secondary emotion labels. A classifier is then trained using multitask learning with both primary and secondary emotions. We experimentally show that considering secondary emotion labels during the learning process leads to relative improvements of 7.9% in F1-score for an 8-class emotion classification task.
Mediator Effects of Positive Emotions on Social Support and Depression among Adolescents Suffering from Mobile Phone Addiction.

Depression is a common mental disorder that is widely seen among adolescents suffering from mobile phone addiction. While it is well known that both positive emtions in adolescents wiotions and social support can have a positive impact by helping individuals to maintain a positive attitude, the correlation between positive emotions, social support, and depression among these adolescents remains to be investigated. This study examined the mediator effects of positive emotions on the relationship between social support and depression among adolescents suffering from mobile phone addiction.



For this study, conducted in 2016, we selected 1,346 adolescent students from three middle schools (ranging from Junior Grade One to Senior Grade Three) in Hunan Province of China, to participate in the survey. Participants were selected using the stratified cluster random sampling method, and all participants remained anonymous throughout the study. Each participant completed the Self-made General Situation Questionnaire, the Social Support Rating Scale, the Positive and Negative Affect Schedule, the Center for Epidemiological Studies Depression Scale, and the Mobile Phone Addiction Tendency Scale.



There was significant positive correlation between positive emotions and social support. Both positive emotions and social support demonstrated significant negative correlation with depression. Positive emotions had partial mediator effects on the relationship between social support and depression (P<0.01).



Both social support and positive emotions can lower levels of depression among adolescents suffering from mobile phone addiction. Social support contributes to positive emoth mobile phone addiction, thereby reducing their levels of depression. These findings suggest that more support and care should be given to this particular adolescent population.
Citizens and institutions as information prosumers. The case study of italian municipalities on Twitter

The aim of this paper is to address changes in public communication following the advent of Internet social networking tools and the emerging web 2.0 technologies which are providing new ways of sharing information and knowledge. In particular public administrations are called upon to reinvent the governance of public affairs and to update the means for interacting with their communities. The paper develops an analysis of the distribution, diffusion and performance of the official profiles on Twitter adopted by the Italian municipalities (comuni) up to November 2013. It aims to identify the patterns of spatial distribution and the drivers of the diffusion of Twitter profiles; the performance of the profiles through an aggregated index, called the Twitter performance index (Twiperindex), which evaluates the profiles’ activity with reference to the gravitational areas of the municipalities in order to enable comparisons of the activity of municipalities with different demographic sizes and functional roles. The results show that only a small portion of innovative municipalities have adopted Twitter to enhance e-participation and e-governance and that the drivers of the diffusion seem to be related either to past experiences and existing conditions (i.e. civic networks, digital infrastructures) developed over time or to strong local community awareness. The better performances are achieved mainly This work is licensed under the Creative Commons Attribution-Non commercial Works 3.0 License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/ or send a letter to Creative Commons, 543 Howard Street, 5 Floor, San Francisco, California, 94105, USA.  2015, Vol.10, 1-26 2 by small and medium-sized municipalities. Of course, the phenomenon is very new and fluid, therefore this analysis should be considered as a first step in ongoing research which aims to grasp the dynamics of these new means of public communication.
Cellular planning for next generation wireless mobile network using novel energy efficient CoMP

Cellular planning also called radio network planning is a crucial stage for the deployment of a wireless network. The enormous increase in data traffic requires augmentation of coverage, capacity, throughput, quality of service and optimized cost of the network. The main goal of cellular planning is to enhance the spectral efficiency and hence the throughput of a network. A novel CoMP algorithm has been discussed with two-tier heterogeneous network. Number of clusters has been obtained using V-R (variance ratio) Criterion. The centroid of a cluster obtained using K-means algorithm provides the deployment of BS position. Application of CoMP in this network using DPS approach with sleep mode of power saving, provides higher energy efficiency, SINR and throughput as compared to nominal CoMP. CoMP basically describes a scheme in which a group of base stations (BS) dynamically co-ordinate and co-operate among themselves to convert interference into a beneficial signal. Network planning using stochastic method and Voronoi Tessellation with two-tier network has been applied to a dense region of Surat city in Gujarat state of India. The results show clear improvement in signal-to-interference plus noise ratio (SINR) by 25% and energy efficiency of the network by 28% using the proposed CoMP transmission.
Towards 3D Point cloud based object maps for household environments

This article investigates the problem of acquiring 3D object maps of indoor household environments, in particular kitchens. The objects modeled in these maps include cupboards, tables, drawers and shelves, which are of particular importance for a household robotic assistant. Our mapping approach is based on PCD (point cloud data) representations. Sophisticated interpretation methods operating on these representations eliminate noise and resample the data without deleting the important details, and interpret the improved point clouds in terms of rectangular planes and 3D geometric shapes. We detail the steps of our mapping approach and explain the key techniques that make it work. The novel techniques include statistical analysis, persistent histogram features estimation that allows for a consistent registration, resampling with additional robust fitting techniques, and segmentation of the environment into meaningful regions.
Adaptive Simulation-based Training of AI Decision-makers using Bayesian Optimization

This work studies how an AI-controlled dog-fighting agent with tunable decisionmaking parameters can learn to optimize performance against an intelligent adversary, as measured by a stochastic objective function evaluated on simulated combat engagements. Gaussian process Bayesian optimization (GPBO) techniques are developed to automatically learn global Gaussian Process (GP) surrogate models, which provide statistical performance predictions in both explored and unexplored areas of the parameter space. This allows a learning engine to sample full-combat simulations at parameter values that are most likely to optimize performance and also provide highly informative data points for improving future predictions. However, standard GPBO methods do not provide a reliable surrogate model for the highly volatile objective functions found in aerial combat, and thus do not reliably identify global maxima. These issues are addressed by novel Repeat Sampling (RS) and Hybrid Repeat/Multipoint Sampling (HRMS) techniques. Simulation studies show that HRMS improves the accuracy of GP surrogate models, allowing AI decision-makers to more accurately predict performance and efficiently tune parameters.
An alternative approach for risk assessment in Scrum

Agile processes are widely used for the purpose of software project development sine they have overcome the limitations faced in traditional software project development. Risk management is a crucial part in project development which helps in eliminating the risks prevailing in software project. Even though risk management is associated with agile approach, still there prevails the scope for improvement. Therefore the motto of this paper is to analyze agile methodology, Scrum and highlight the extent to which risk is managed in it and propose a model to overcome its' limitations in risk analysis phase.
LBP based recursive averaging for babble noise reduction applied to automatic speech recognition

Improved automatic speech recognition (ASR) in babble noise conditions continues to pose major challenges. In this paper, we propose a new local binary pattern (LBP) based speech presence indicator (SPI) to distinguish speech and non-speech components. Babble noise is subsequently estimated using recursive averaging. In the speech enhancement system optimally-modified log-spectral amplitude (OMLSA) uses the estimated noise spectrum obtained from the LBP based recursive averaging (LRA). The performance of the LRA speech enhancement system is compared to the conventional improved minima controlled recursive averaging (IMCRA). Segmental SNR improvements and perceptual evaluations of speech quality (PESQ) scores show that LRA offers superior babble noise reduction compared to the IMCRA system. Hidden Markov model (HMM) based word recognition results show a corresponding improvement.
DeepProp: Extracting Deep Features from a Single Image for Edit Propagation

Edit propagation is a technique that can propagate various image edits (e.g., colorization and recoloring) performed via user strokes to the entire image based on similarity of image features. In most previous work, users must manually determine the importance of each image feature (e.g., color, coordinates, and textures) in accordance with their needs and target images. We focus on representation learning that automatically learns feature representations only from user strokes in a single image instead of tuning existing features manually. To this end, this paper proposes an edit propagation method using a deep neural network (DNN). Our DNN, which consists of several layers such as convolutional layers and a feature combiner, extracts strokeadapted visual features and spatial features, and then adjusts the importance of them. We also develop a learning algorithm for our DNN that does not suffer from the vanishing gradient problem, and hence avoids falling into undesirable locally optimal solutions. We demonstrate that edit propagation with deep features, without manual feature tuning, can achieve better results than previous work.
From Hunt the Wumpus to EverQuest: Introduction to Quest Theory

The paper will explore how the landscape types and the quest types are used in various games, how they structure the gameplay, how they act as bones for the game-content (graphics, dialogue, sound) and how they sometimes form the base on which a story is imposed and related to the player. The question then becomes, how does the quest structure influence the story structure? How do the limitations of the quest combinations limit the kinds of story that are possible? How rich can the imposed story be, without breaking the gameplay? Are landscape and quest-structure the dominant factors in quest game design, to which the story-ambitions must defer? The main thesis of the paper is that if we understand the powerful but simple structure the grammar of quests (how they work, how they are used) we can understand both the limits and the potential of these kinds of games.
Zoning based Devanagari Character Recognition

In character recognition, zoning based feature extraction is one of the most popular methods. The character image is divided into predefined number of zones and a feature is computed from each of these zones. Usually, this feature is based on the pattern (black) pixels contained in that zone. Some of such features are average pixel density, sum squared distance, histogram. But in such features, say the average pixel density, different combination location of pixels can all give rise to same average pixel density. This often leads to errors in classification. In this paper, a new technique is presented where the pattern pixel location is also taken into account to contribute as much unique feature as possible. The experimental tests, carried out in the field of Devanagari handwritten numeral and character recognition show that the proposed technique leads to improvement over the traditional zoning methods.. General Terms Character recognition.
deepSA2018 at SemEval-2018 Task 1: Multi-task Learning of Different Label for Affect in Tweets

This paper describes our system implementation for subtask V-oc of SemEval-2018 Task 1: affect in tweets. We use multi-task learning method to learn shared representation, then learn the features for each task. There are five classification models in the proposed multitask learning approach. These classification models are trained sequentially to learn different features for different classification tasks. In addition to the data released for SemEval2018, we use datasets from previous SemEvals during system construction. Our Pearson correlation score is 0.638 on the official SemEval2018 Task 1 test set.
A harmonic suppression circularly polarized patch antenna for an RF ambient energy harvesting system

This paper presented a new design of harmonic suppression microstrip patch antenna with the circular polarization properties at frequency 900MHz for an RF ambient energy harvesting system. The purpose of harmonic suppression antenna is to eliminate the using of a harmonic filter circuit in filtering the unwanted signal at the harmonic frequencies in a rectenna system. By introducing a truncating to corner of the rectangular microstrip patch antenna to achieved the required axial ratio, an antenna with circular polarization property is presented. The dimension of patch is reduced by applying partially ground plane. Then an implementation of a defected ground structure (DGS) with a circular shape slot, the unwanted radiation at harmonic frequencies are successfully suppressed. The patch antenna is designed on an FR4 substrate with εr=4.7 and a thickness of 1.6 mm has -20dB return loss at 900 MHz and suppress the second and third harmonic up to -2dB. The proposed circularly polarized harmonic suppression antenna is then fabricated and measured for the verification of the design.
HRM as a motivator to share knowledge-The importance of seeing the whole picture

Connecting Human Resource Management (HRM) and knowledge transfer through motivation is a new research area. Out of the few existing studies there is a predominance of quantitative studies, which are showing inconclusive results. As a response, this study uses a qualitative micro perspective to investigate how HRM practises influence intrinsicand extrinsic motivation to share knowledge. It is important to divide motivation into intrinsic and extrinsic, as it impacts knowledge sharing differently. Former studies have identified a need to study the whole HRM system, therefore, to capture differences in motivation among employees exposed to the same system, this thesis takes on a single case study approach. Qualitative interviews were held with employees at an MNC that relies on knowledge intensive activities. The findings showed that employees were motivated intrinsically through career development and extrinsically by the performance management system. The supportive climate showed to influence motivation to share knowledge, both directly and indirectly. Job design was shown to work well in combination with other practises. Finally, a key finding was the importance of having an aligned HRM system.
Analysis of wind resistant capability of a HAV

A hybrid aerial vehicle (HAV), featured combination of a quadrotor system and a fixed-wing system, is one of the most promising configurations having both a vertical take-off and landing (VTOL) capability and high cruise performance. In this paper, a HAV has been designed and prototyped to analyze the performance of the HAV. The specifications of the HAV are summarized. The aerodynamic coefficients of the HAV derived from CFD data and the influence of axial flow on the maximum thrust of quad propellers that is calculated using QPROP are demonstrated, respectively. The wind resistant capability of the HAV is analyzed from two aspects according to different wind direction. The factors which have important influence on the wind resistant capability of the HAV are analyzed and the solutions to improve the wind resistant capability of the HAV are presented.
Born Digital: Understanding the First Generation of Digital Natives

Perhaps you have seen them on the train in the morning, adjusting their iPods and texting with their preternaturally agile thumbs. Or perhaps one sits in the cubicle next to yours, cycling through a seemingly endless loop of news feeds, YouTube videos, cell phone calls, and work. Perhaps you have one living in your home, capable of reprogramming the wireless router and leaping insurmountable DVR instructions in a single, tech-savvy bound.
Improving Wireless Physical Layer Security via Cooperating Relays

Physical (PHY) layer security approaches for wireless communications can prevent eavesdropping without upper layer data encryption. However, they are hampered by wireless channel conditions: absent feedback, they are typically feasible only when the source-destination channel is better than the source-eavesdropper channel. Node cooperation is a means to overcome this challenge and improve the performance of secure wireless communications. This paper addresses secure communications of one source-destination pair with the help of multiple cooperating relays in the presence of one or more eavesdroppers. Three cooperative schemes are considered: decode-and-forward (DF), amplify-and-forward (AF), and cooperative jamming (CJ). For these schemes, the relays transmit a weighted version of a reencoded noise-free message signal (for DF), a received noisy source signal (for AF), or a common jamming signal (for CJ). Novel system designs are proposed, consisting of the determination of relay weights and the allocation of transmit power, that maximize the achievable secrecy rate subject to a transmit power constraint, or, minimize the transmit power subject to a secrecy rate constraint. For DF in the presence of one eavesdropper, closed-form optimal solutions are derived for the relay weights. For other problems, since the optimal relay weights are difficult to obtain, several criteria are considered leading to suboptimal but simple solutions, i.e., the complete nulling of the message signals at all eavesdroppers (for DF and AF), or the complete nulling of jamming signal at the destination (for CJ). Based on the designed relay weights, for DF in the presence of multiple eavesdroppers, and for CJ in the presence of one eavesdropper, the optimal power allocation is obtained in closed-form; in all other cases the optimal power allocation is obtained via iterative algorithms. Numerical evaluation of the obtained secrecy rate and transmit power results show that the proposed design can significantly improve the performance of secure wireless communications.