nils-herrmann/clean_scirepeval huggingface dataset:
A Systematic Literature Review of Requirements Negotiation Methods from 2010 till 2015

Requirements engineering is a fundamental part of the software engineering process. When the stakeholders of the software project disagree on the requirements, requirements negotiation methods can be used to reach that agreement. This avoids rework and extra costs. A number of new requirements negotiation methods are created by researchers over the last years. This paper provides a review on which negotiation method can be used under which circumstances. Such a review is relevant for both practitioners and researchers. Practitioners could use the results to make decisions on which method might be the best choice for their project contexts. To researchers, our study would help them see trends in the requirements negotiation field. Moreover, researchers are now informed about those requirements negotiation sub-areas which have been actively researched and the sub-areas where much evaluation and empirical work is needed for our knowledge to become complete. Riaz et al. did such a review on requirements negotiation methods till the year 2010. Therefore we compared the results of Riaz et al. with the new findings. This paper carried out a systematic literature review on requirements negotiation methods which are published between 2010 and 2015 and compared the results of Riaz et al. with the new findings. In total, we found 12 new requirements negotiation methods. For each of those 12 methods, we identified the type of requirement conflicts that the methods resolves. We found that in the last five years there is more attention given to create new requirements negotiation methods than before and the methods also cover more types of requirement conflicts now than they did in the past. We also found that requirements contradiction conflicts and quality attribute conflicts are getting more attention now than they were getting before 2010, while resource conflicts and feasibility conflicts still are getting almost no attention at all. Furthermore, we found that almost all requirements negotiation methods are suitable for resolving viewpoint conflicts. Finally, we found that Theory W (WinWin approach) is still very popular for use in the design of requirements negotiation methods. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. 23 Twente Student Conference on IT June 22, 2015, Enschede, The Netherlands.
ME R-CNN: Multi-Expert Region-based CNN for Object Detection

Recent CNN-based object detection methods have drastically improved their performances but still use a single classifier as opposed to ”multiple experts” in categorizing objects. The main motivation of introducing multi-experts is twofold: i) to allow different experts to specialize in different fundamental object shape priors and ii) to better capture the appearance variations caused by different poses and viewing angles. The proposed approach, referred to as multi-expert Region-based CNN (ME R-CNN), consists of three experts each responsible for objects with particular shapes: horizontally elongated, square-like, and vertically elongated. Each expert is a network with multiple fully connected layers and all the experts are preceded by a shared network which consists of multiple convolutional layers. On top of using selective search which provides a compact, yet effective set of region of interests (RoIs) for object detection, we augmented the set by also employing the exhaustive search for training. Incorporating the exhaustive search can provide complementary advantages: i) it captures the multitude of neighboring RoIs missed by the selective search, and thus ii) provide significantly larger amount of training examples to achieve the enhanced accuracy.
Internet of Things as a Service (iTaaS): Challenges and Solutions for Management of Sensor Data on the Cloud and the Fog

Building upon cloud, IoT and smart sensors technologies we design and develop an IoT as a Service (iTaaS) framework, that transforms a user’s mobile device (e.g. a smart phone) to an IoT gateway which allows for fast and efficient data streams transmission to the cloud. We develop a two-fold solution, based on micro-services for the IoT (users’ smart devices) and the cloud side (back-end services). iTaaS includes configurations for (a) the IoT side to support data collection from IoT devices to a gateway on a real time basis and, (b) the cloud back-end side to support data sharing, storage and processing. iTaaS provides the technology foreground to enable immediate application deployments in the domain of interest. An obvious and promising implementation of this technology is e-Health and remote health monitoring. As a proof of concept we implement a real time remote patient monitoring system that integrates the proposed framework and uses Bluetooth Low Energy (BLE) pulse oximeter and heart rate monitoring sensing devices. The experimental analysis shows fast data collection, as (for our experimental setup) data is transmitted from the IoT side (i.e. the gateway) to the cloud in less than 130 ms. We also stress the back-end system with high user concurrency (e.g. with 40 users per second) and high data streams (e.g. 240 data records per second) and we show that the requests are executed at around 1 s, a number that signifies a satisfactory performance by considering the number of requests, the network latency and the relatively small size of the Virtual Machines implementing services on the cloud (2 GB RAM, 1 CPU and 20 GB hard disk size).
Revisiting the social enhancement hypothesis: Extroversion indirectly predicts number of Facebook friends operating through Facebook usage

An online survey of college-age Facebook users (N = 209) found that extroversion, narcissism, openness, and agreeableness predicted friending more people on Facebook. However, only extroversion continued to exert an effect when these and other personality variables were examined together in one regression model, while controlling for frequency of Facebook usage and gender. Also, a path analysis model showed that extroversion directly predicted number of Facebook friends and the number of months people were active on Facebook. In addition, extroversion indirectly influenced number of Facebook friends, operating through months active on Facebook and hours per week spent on Facebook in a parallel mediation effect. Findings offer support for the social enhancement hypothesis, which argues that extroverted people benefit the most from social media. Researchers for decades have tried to understand how personality shapes social interactions among people. Social-networking sites, such as Facebook, offer an opportunity to study these interactions. Research has found that personality shapes how people communicate online, much as it does offline (e.g. Amichai-Hamburger & Vinitzky, 2010; Back et al., 2010). Based on this foundation, it is well established that personality has an effect in how people use social-networking sites. However, what has received less study is how this effect plays out – what path does this effect takes? This current study used a survey of college-age Facebook users (N = 209) to begin to fill this gap by exploring a mediation model that explains how personality and Facebook usage predict number of Facebook friends. To create this model, this study drew on personality theory and the social enhancement hypothesis, which argues that people who are popular offline complement this popularity online (Kraut et al. However, no earlier studies have directly tested a mediation model that examines the effect of personality on friending mediated by how frequently people use Facebook. Therefore, this study offered new knowledge about the role personality plays in human behavior by moving beyond merely describing the influence to explaining its path in the microcosm of a social-networking site. First, I offer a brief history of the role of friending on Facebook. Then I review the literature on how personality theory and the social enhancement hypothesis have been applied to social media to show support for the hypotheses and research question in this study. Then I outline how the proposed model was tested and explain how the results fit existing literature and expand knowledge of …
Group swimming and aquatic exercise programme for children with autism spectrum disorders: a pilot study.

To evaluate the effectiveness of a 14-week aquatic exercise programme for children with autism spectrum disorders (ASD).



Non-randomized control trial.



Twelve children participated in this pilot study with seven participants in the aquatic exercise group and five in the control group. The programme was held twice per week for 40 minutes per session. Swimming skills, cardiorespiratory endurance, muscular endurance, mobility skills and participant and parent satisfaction were measured before and after the intervention.



No significant between-group changes were found. Within-group improvements for swimming skills were found for the intervention group. Programme attendance was high. Parents and children were very satisfied with the programme activities and instructors.



This pilot programme was feasible and showed potential for improving swimming ability in children with ASD. Exercise intensity was low for some participants, most likely contributing to a lack of significant findings on fitness outcomes.
UIMA Ruta Workbench: Rule-based Text Annotation

UIMA Ruta is a rule-based system designed for information extraction tasks, but it is also applicable for many natural language processing use cases. This demonstration gives an overview of the UIMA Ruta Workbench, which provides a development environment and tooling for the rule language. It was developed to ease every step in engineering rule-based applications. In addition to the full-featured rule editor, the user is supported by explanation of the rule execution, introspection in results, automatic validation and rule induction. Furthermore, the demonstration covers the usage and combination of arbitrary components for natural language processing.
Telemetry-mining: a machine learning approach to anomaly detection and fault diagnosis for space systems

For any space mission, safety and reliability are the most important issues. To tackle this problem, we have studied anomaly detection and fault diagnosis methods for spacecraft systems based on machine learning (ML) and data mining (DM) technology. In these methods, the knowledge or model which is necessary for monitoring a spacecraft system is (semi-)automatically acquired from the spacecraft telemetry data. In this paper, we first overview the anomaly detection/diagnosis problem in the spacecraft systems and conventional techniques such as limit-check, expert systems and model-based diagnosis. Then we explain the concept of ML/DM-based approach to this problem, and introduce several anomaly detection/diagnosis methods which have been developed by us
Acculturation: When Individuals and Groups of Different Cultural Backgrounds Meet.

In cross-cultural psychology, one of the major sources of the development and display of human behavior is the contact between cultural populations. Such intercultural contact results in both cultural and psychological changes. At the cultural level, collective activities and social institutions become altered, and at the psychological level, there are changes in an individual's daily behavioral repertoire and sometimes in experienced stress. The two most common research findings at the individual level are that there are large variations in how people acculturate and in how well they adapt to this process. Variations in ways of acculturating have become known by the terms integration, assimilation, separation, and marginalization. Two variations in adaptation have been identified, involving psychological well-being and sociocultural competence. One important finding is that there are relationships between how individuals acculturate and how well they adapt: Often those who integrate (defined as being engaged in both their heritage culture and in the larger society) are better adapted than those who acculturate by orienting themselves to one or the other culture (by way of assimilation or separation) or to neither culture (marginalization). Implications of these findings for policy and program development and for future research are presented.
Information Security in Health Care Centre Using Cryptography and Steganography

As the volume of medicinal information stored electronically increase, so do the need to enhance how it is secured. The inaccessibility to patient record at the ideal time can prompt death toll and also well degrade the level of health care services rendered by the medicinal professionals. Criminal assaults in social insurance have expanded by 125% since 2010 and are now the leading cause of medical data breaches. This study therefore presents the combination of 3DES and LSB to improve security measure applied on medical data. Java programming language was used to develop a simulation program for the experiment. The result shows medical data can be stored, shared, and managed in a reliable and secure manner using the combined model.  Information Security; Health Care; 3DES; LSB; Cryptography; Steganography 1.0 INTRODUCTION In health industries, storing, sharing and management of patient information have been influenced by the current technology. That is, medical centres employ electronical means to support their mode of service in order to deliver quality health services. The importance of the patient record cannot be over emphasised as it contributes to when, where, how, and how lives can be saved. About 91% of health care organizations have encountered no less than one data breach, costing more than $2 million on average per organization [1-3]. Report also shows that, medical records attract high degree of importance to hoodlums compare to Mastercard information because they infer more cash base on the fact that bank
Opportunities for silicon at mmWave and Terahertz frequencies

This paper summarizes opportunities for silicon process technologies at mmwave and terahertz frequencies and demonstrates key building blocks for 94-GHz and 600-GHz imaging arrays. It reviews potential applications and summarizes state-of-the-art terahertz technologies. Terahertz focal-plane arrays (FPAs) for video-rate imaging applications have been fabricated in commercially available CMOS and SiGe process technologies respectively. The 3times5 arrays achieve a responsivity of up to 50 kV/W with a minimum NEP of 400 pW/radicHz at 600 GHz. Images of postal envelopes are presented which demonstrate the potential of silicon integrate 600-GHz terahertz FPAs for future low-cost terahertz camera systems.
SCALEDEEP: A scalable compute architecture for learning and evaluating deep networks

Deep Neural Networks (DNNs) have demonstrated state-of-the-art performance on a broad range of tasks involving natural language, speech, image, and video processing, and are deployed in many real world applications. However, DNNs impose significant computational challenges owing to the complexity of the networks and the amount of data they process, both of which are projected to grow in the future. To improve the efficiency of DNNs, we propose ScaleDeep, a dense, scalable server architecture, whose processing, memory and interconnect subsystems are specialized to leverage the compute and communication characteristics of DNNs. While several DNN accelerator designs have been proposed in recent years, the key difference is that ScaleDeep primarily targets DNN training, as opposed to only inference or evaluation. The key architectural features from which ScaleDeep derives its efficiency are: (i) heterogeneous processing tiles and chips to match the wide diversity in computational characteristics (FLOPs and Bytes/FLOP ratio) that manifest at different levels of granularity in DNNs, (ii) a memory hierarchy and 3-tiered interconnect topology that is suited to the memory access and communication patterns in DNNs, (iii) a low-overhead synchronization mechanism based on hardware data-flow trackers, and (iv) methods to map DNNs to the proposed architecture that minimize data movement and improve core utilization through nested pipelining. We have developed a compiler to allow any DNN topology to be programmed onto ScaleDeep, and a detailed architectural simulator to estimate performance and energy. The simulator incorporates timing and power models of ScaleDeep's components based on synthesis to Intel's 14nm technology. We evaluate an embodiment of ScaleDeep with 7032 processing tiles that operates at 600 MHz and has a peak performance of 680 TFLOPs (single precision) and 1.35 PFLOPs (half-precision) at 1.4KW. Across 11 state-of-the-art DNNs containing 0.65M-14.9M neurons and 6.8M-145.9M weights, including winners from 5 years of the ImageNet competition, ScaleDeep demonstrates 6x-28x speedup at iso-power over the state-of-the-art performance on GPUs.
Smart Home: Cognitive Interactive People-Centric Internet of Things

This article proposes to integrate two entities, the Internet of Things and a cognitive dynamic system (CDS), and studies a smart home scenario as the application of interest. As a people-centric IoT, the smart home aims to enhance the intelligence level of the living environment and improve the quality of human life. Cognition, the distinct principles of which are perception–action cycle, memory, attention, intelligence, and language, can play a key role to pave the way for building truly smart homes. With cognition as its foundation, the engineering paradigm of CDS provides step-by-step guidelines for systematic development of smart homes. Hence, CDS can significantly contribute to the interactive IoT ecosystem.
Incremental tensor analysis: Theory and applications

How do we find patterns in author-keyword associations, evolving over time? Or in data cubes (tensors), with product-branchcustomer sales information? And more generally, how to summarize high-order data cubes (tensors)? How to incrementally update these patterns over time? Matrix decompositions, like principal component analysis (PCA) and variants, are invaluable tools for mining, dimensionality reduction, feature selection, rule identification in numerous settings like streaming data, text, graphs, social networks, and many more settings. However, they have only two orders (i.e., matrices, like author and keyword in the previous example).
 We propose to envision such higher-order data as tensors, and tap the vast literature on the topic. However, these methods do not necessarily scale up, let alone operate on semi-infinite streams. Thus, we introduce a general framework, incremental tensor analysis (ITA), which efficiently computes a compact summary for high-order and high-dimensional data, and also reveals the hidden correlations. Three variants of ITA are presented: (1) dynamic tensor analysis (DTA); (2) streaming tensor analysis (STA); and (3) window-based tensor analysis (WTA). In paricular, we explore several fundamental design trade-offs such as space efficiency, computational cost, approximation accuracy, time dependency, and model complexity.
 We implement all our methods and apply them in several real settings, such as network anomaly detection, multiway latent semantic indexing on citation networks, and correlation study on sensor measurements. Our empirical studies show that the proposed methods are fast and accurate and that they find interesting patterns and outliers on the real datasets.
Consumer perceptual mapping towards e-banking channel: (A study of bank BRI customer in Indonesia)

Implementation of e-banking services system provides various advantages for the company are cost and time efficiencies, and be able to create differentiation and able to target market segments with low cost. To determine the underlying motives customers to select and prefer one channel and the other required a systematic exploration of the customer perception. This study aims to understand the customers' perception of various e-banking channels and how their motives in choosing the channel usage. By understanding customer perceptions about various banking channels, it is expected to be more helpful in assisting banks in Indonesia to introduce e-banking. A convinience sampling technique used to select 234 customers who surveyed in this study. Findings suggest that Automatic Teller Machine was percieved to be low cost, low complexity and most usefulness. EFT is also almost similar to the ATM, get the low cost and low complexity but low on security. Perception on Internet banking was secure and usefulness and high on privacy. Meanwhile perception on SMS banking was easy to access and also high on privacy. And phone banking was perceived to be the most expensive and inaccurate. Future studies are expected in addition to using other multivariate techniques would also be able to add other attributes are more influential.
Validation of the FOUR Score (Spanish Version) in acute stroke: an interobserver variability study.

Methods to assess impaired consciousness in acute stroke typically include the Glasgow Coma Scale (GCS), but the verbal component has limitations in aphasic or intubated patients. The FOUR (Full Outline of UnResponsiveness) score, a new coma scale, evaluates 4 components: eye and motor responses, brainstem reflexes and respiration. We aimed to study the interobserver variability of the FOUR score in acute stroke patients.



We prospectively enrolled consecutive patients with acute stroke admitted from February to July 2008 to the stroke unit of our Neurology Department. Patients were evaluated by neurology residents and nurses using the FOUR score and the GCS. For both scales, we obtained paired and total weighted kappa values (Kw) and intraclass correlation coefficients (ICC). NIH stroke scale was also recorded on admission.



We obtained a total of 75 paired evaluations in 60 patients (41 cerebral infarctions, 15 cerebral hemorrhages and 4 transient ischemic attacks). Thirty-three (55%) patients were alert, 17 (28.3%) drowsy and 10 (16.7%) stuporous or comatose. The overall rater agreement was excellent in the FOUR score (Kw 0.93; 95% CI 0.89-0.97) with an ICC of 0.94 (95% CI 0.91-0.96) and in the GCS (Kw 0.96; 95% CI 0.94-0.98) with an ICC of 0.96 (95% CI 0.93-0.97). A good correlation was found between the FOUR score and the GCS (rho 0.83; p < 0.01) and between the FOUR score and the NIH stroke scale (rho -0.78; p < 0.001).



The FOUR score is a reliable scale for evaluating the level of consciousness in acute stroke patients, showing a good correlation with the GCS and the NIH stroke scale.
Aneesah : A Conversational Natural Language Interface to Databases

This paper presents the design and development of a novel Natural Language Interface to Database (NLIDB). The developed prototype is called Aneesah the NLIDB, which is capable of allowing users to interactively/conversely access desired information stored in a relational database. This paper introduces the novel conversational agent enabled architecture of Aneesah NLIDB and describes the scripting techniques that has been adopted for its development. The proposed framework for Aneesah NLIDB is based on pattern matching techniques implemented to converse with users, handle complexities and ambiguities for building dynamic SQL queries from multiple dialogues in order to extract database information. The preliminary evaluation results gathered following a pilot study reveal promising results. Index Terms – Natural Language Interface to Databases (NLIDB), Conversational Agents (CA), Knowledge base, Artificial Intelligence (AI), Pattern Matching (PM).