nils-herrmann/clean_scirepeval huggingface dataset:
The Palladio component model for model-driven performance prediction

One aim of component-based software engineering (CBSE) is to enable the prediction of extra-functional properties, such as performance and reliability, utilising a well-defined composition theory. Nowadays, such theories and their accompanying prediction methods are still in a maturation stage. Several factors influencing extra-functional properties need additional research to be understood. A special problem in CBSE stems from its specific development process: Software components should be specified and implemented independently from their later context to enable reuse. Thus, extra-functional properties of components need to be specified in a parametric way to take different influencing factors like the hardware platform or the usage profile into account. Our approach uses the Palladio Component Model (PCM) to specify component-based software architectures in a parametric way. This model offers direct support of the CBSE development process by dividing the model creation among the developer roles. This paper presents our model and a simulation tool based on it, which is capable of making performance predictions. Within a case study, we show that the resulting prediction accuracy is sufficient to support the evaluation of architectural design decisions.
A Flood Forecasting Model Based on Deep Learning Algorithm via Integrating Stacked Autoencoders with BP Neural Network

Artificial neural network (ANN) has been widely applied in flood forecasting and got good results. However, it can still not go beyond one or two hidden layers for the problematic non-convex optimization. This paper proposes a deep learning approach by integrating stacked autoencoders (SAE) and back propagation neural networks (BPNN) for the prediction of stream flow, which simultaneously takes advantages of the powerful feature representation capability of SAE and superior predicting capacity of BPNN. To further improve the non-linearity simulation capability, we first classify all the data into several categories by the K-means clustering. Then, multiple SAE-BP modules are adopted to simulate their corresponding categories of data. The proposed approach is respectively compared with the support-vector-machine (SVM) model, the BP neural network model, the RBF neural network model and extreme learning machine (ELM) model. The experimental results show that the SAE-BP integrated algorithm performs much better than other benchmarks.
How do People Evaluate Electronic Word-Of-Mouth? Informational and Normative Based Determinants of Perceived Credibility of Online Consumer Recommendations in China

This study empirically examined the informational and normative based determinants of perceived credibility of online consumer recommendations in China. Past literature demonstrated that informational influence is important in affecting reader’s evaluation of incoming information and the effectiveness of a communication. This study extends from the previous Word-of-Mouth studies by including the normative factors. Since online consumer discussion is characterized by its social aggregation, we argue that several normative cues could be salient and play significant roles in shaping a reader’s credibility evaluation towards the eWOM recommendation. The informational determinants (argument strength, source credibility and confirmation with receiver’s prior belief) and the normative determinants (recommendation consistency and rating) are investigated via an online survey to users of a famous online consumer discussion site in China (myetone.com). Results supported our proposed research model which substantiates the effects of perceived eWOM review credibility from both informational-based and normative-based determinants. This research provides researcher and practitioners with insights on receiver’s eWOM evaluation.
An exploration of relations between visual appeal, trustworthiness and perceived usability of homepages

Extremely high correlations between repeated judgments of visual appeal of homepages shown for 50 milliseconds have been interpreted as evidence for a mere exposure effect [Lindgaard et al. 2006]. Continuing that work, the present research had two objectives. First, it investigated the relationship between judgments differing in cognitive demands. Second, it began to identify specific visual attributes that appear to contribute to different judgments. Three experiments are reported. All used the stimuli and viewing time as before. Using a paradigm known to disrupt processing beyond the stimulus offset, Experiment 1 was designed to ensure that the previous findings could not be attributed to such continued processing. Adopting a within-subject design, Experiment 2 investigated the extent to which judgments differing in cognitive demands (visual appeal, perceived usability, trustworthiness) may be driven by the visual characteristics of a Web page. It also enabled analyses of visual attributes that contributed most to the different judgments. Experiment 3 replicated Experiment 2 but using a between-subject design to ensure that no practice effect could occur. The results suggest that all three types of judgments are largely driven by visual appeal, but that cognitively demanding judgments are processed in a qualitatively different manner than visual appeal, and that they rely on somewhat different visual attributes. A model accounting for the results is provided.
Consciousness in the universe: a review of the 'Orch OR' theory.

The nature of consciousness, the mechanism by which it occurs in the brain, and its ultimate place in the universe are unknown. We proposed in the mid 1990's that consciousness depends on biologically 'orchestrated' coherent quantum processes in collections of microtubules within brain neurons, that these quantum processes correlate with, and regulate, neuronal synaptic and membrane activity, and that the continuous Schrödinger evolution of each such process terminates in accordance with the specific Diósi-Penrose (DP) scheme of 'objective reduction' ('OR') of the quantum state. This orchestrated OR activity ('Orch OR') is taken to result in moments of conscious awareness and/or choice. The DP form of OR is related to the fundamentals of quantum mechanics and space-time geometry, so Orch OR suggests that there is a connection between the brain's biomolecular processes and the basic structure of the universe. Here we review Orch OR in light of criticisms and developments in quantum biology, neuroscience, physics and cosmology. We also introduce a novel suggestion of 'beat frequencies' of faster microtubule vibrations as a possible source of the observed electro-encephalographic ('EEG') correlates of consciousness. We conclude that consciousness plays an intrinsic role in the universe.
Robotics API : Object-Oriented Software Development for Industrial Robots

Industrial robots are flexible machines that can be equipped with various sensors and tools to perform complex tasks. However, current robot programming languages are reaching their limits. They are not flexible and powerful enough to master the challenges posed by the intended future application areas. In the research project SoftRobot, a consortium of science and industry partners developed a software architecture that enables object-oriented software development for industrial robot systems using general-purpose programming languages. The requirements of current and future applications of industrial robots have been analysed and are reflected in the developed architecture. In this paper, an overview is given about this architecture as well as the goals that guided its development. A special focus is put on the design of the object-oriented Robotics API, which serves as a framework for developing complex robotic applications. It allows specifying real-time critical operations of robots and tools, including advanced concepts like sensor-based motions and multi-robot synchronization. The power and usefulness of the architecture is illustrated by several application examples. Its extensibility and reusability is evaluated and a comparison to other robotics frameworks is drawn.
Mereotopology: A Theory of Parts and Boundaries

The term ‘ontology’ has recently acquired a certain currency within the knowledge engineering community, especially in relation to the ARPA knowledge-sharing initiative (see Gruber (to appear), Mars (ed.) 1994, Guarino 1994, Guarino, Carrara and Giaretta 1994, 1994a). The term is used in a number of different senses, however, not all of them clear or mutually compatible. Here I follow philosophical tradition in conceiving ontology as the science which deals with the nature and the organisation of reality. Ontology thus conceived may be formal, in the sense that it is directed towards formal structures and relations in reality. This formal ontology is contrasted with the various material ontologies (of physics, chemistry, medicine, and so on) which study the nature and organisation of specific sub-regions of reality. Formal structures, for example the structures governing the relation of part to whole, are shared in common by all material domains. Both formal and material ontologies may be pursued with the aid of the machinery of axiomatic theories, and it is axiomatic formal ontology that has proved to be of most interest for the ontology-building purposes of the knowledge engineer.
The digital marketing skills gap: Developing a Digital Marketer Model for the communication industries

Scholarly literature suggests digital marketing skills gaps in industry, but these skills gaps are not clearly identified. The research aims to specify any digital marketing skills gaps encountered by professionals working in communication industries. In-depth interviews were undertaken with 20 communication industry professionals. A focus group followed, testing the rigour of the data. We find that a lack of specific technical skills; a need for best practice guidance on evaluation metrics, and a lack of intelligent futureproofing for dynamic technological change and development are skills gaps currently challenging the communication industry. However, the challenge of integrating digital marketing approaches with established marketing practice emerges as the key skills gap. Emerging from the key findings, a Digital Marketer Model was developed, highlighting the key competencies and skills needed by an excellent digital marketer. The research concludes that guidance on best practice, focusing upon evaluation metrics, futureproofing and strategic integration, needs to be developed for the communication industry. The Digital Marketing Model should be subject to further testing in industry and academia. Suggestions for further research are discussed.
Social media and the police: tweeting practices of british police forces during the August 2011 riots

With this paper we take a first step to understand the appropriation of social media by the police. For this purpose we analyzed the Twitter communication by the London Metropolitan Police (MET) and the Greater Manchester Police (GMP) during the riots in August 2011. The systematic comparison of tweets demonstrates that the two forces developed very different practices for using Twitter. While MET followed an instrumental approach in their communication, in which the police aimed to remain in a controlled position and keep a distance to the general public, GMP developed an expressive approach, in which the police actively decreased the distance to the citizens. In workshops and interviews, we asked the police officers about their perspectives, which confirmed the identified practices. Our study discusses benefits and risks of the two approaches and the potential impact of social media on the evolution of the role of police in society.
Genome-wide prediction of cis-regulatory regions using supervised deep learning methods

In the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. Thus, Identifying active cis-regulatory regions in the human genome is critical for understanding gene regulation and assessing the impact of genetic variation on phenotype. The developments of high-throughput sequencing and machine learning technologies make it possible to predict cis-regulatory regions genome wide. Based on rich data resources such as the Encyclopedia of DNA Elements (ENCODE) and the Functional Annotation of the Mammalian Genome (FANTOM) projects, we introduce DECRES based on supervised deep learning approaches for the identification of enhancer and promoter regions in the human genome. Due to their ability to discover patterns in large and complex data, the introduction of deep learning methods enables a significant advance in our knowledge of the genomic locations of cis-regulatory regions. Using models for well-characterized cell lines, we identify key experimental features that contribute to the predictive performance. Applying DECRES, we delineate locations of 300,000 candidate enhancers genome wide (6.8% of the genome, of which 40,000 are supported by bidirectional transcription data), and 26,000 candidate promoters (0.6% of the genome). The predicted annotations of cis-regulatory regions will provide broad utility for genome interpretation from functional genomics to clinical applications. The DECRES model demonstrates potentials of deep learning technologies when combined with high-throughput sequencing data, and inspires the development of other advanced neural network models for further improvement of genome annotations.
Stability control in aerial manipulation

Aerial manipulation, grasping, and perching in small unmanned aerial vehicles (UAVs) require specific control systems to compensate for changing inertial properties. Grasped objects, external forces from terrain objects, or manipulator movements themselves may destabilize or otherwise alter the flight characteristics of small UAVs during operation resulting in undesirable outcomes. Traditional control methods that assume static mass and inertial properties must be modified to produce stable control of a quadrotor system. This paper presents work towards a control scheme to achieve dynamic stability of an aerial vehicle while under the influence of manipulators and grasped objects. A quadrotor with attached multi-degree of freedom manipulators is implemented in simulation and constructed for testing. Compensation of the inertial changes due to in-flight manipulator movements is investigated. A control scheme is developed and results are presented.
Inferring the Root Cause in Road Traffic Anomalies

We propose a novel two-step mining and optimization framework for inferring the root cause of anomalies that appear in road traffic data. We model road traffic as a time-dependent flow on a network formed by partitioning a city into regions bounded by major roads. In the first step we identify link anomalies based on their deviation from their historical traffic profile. However, link anomalies on their own shed very little light on what caused them to be anomalous. In the second step we take a generative approach by modeling the flow in a network in terms of the origin-destination (OD) matrix which physically relates the latent flow between origin and destination and the observable flow on the links. The key insight is that instead of using all of link traffic as the observable vector we only use the link anomaly vector. By solving an L1 inverse problem we infer the routes (the origin-destination pairs) which gave rise to the link anomalies. Experiments on a very large GPS data set consisting on nearly eight hundred million data points demonstrate that we can discover routes which can clearly explain the appearance of link anomalies. The use of optimization techniques to explain observable anomalies in a generative fashion is, to the best of our knowledge, entirely novel.
Party over policy: The dominating impact of group influence on political beliefs.

Four studies demonstrated both the power of group influence in persuasion and people's blindness to it. Even under conditions of effortful processing, attitudes toward a social policy depended almost exclusively upon the stated position of one's political party. This effect overwhelmed the impact of both the policy's objective content and participants' ideological beliefs (Studies 1-3), and it was driven by a shift in the assumed factual qualities of the policy and in its perceived moral connotations (Study 4). Nevertheless, participants denied having been influenced by their political group, although they believed that other individuals, especially their ideological adversaries, would be so influenced. The underappreciated role of social identity in persuasion is discussed.
Ultrasonic Blind Walking Stick

God gifted sense of vision to the human being is an important aspect of our life. But there are some unfortunate people who lack the ability of visualizing things. The visually impaired have to face many challenges in their daily life. The problem gets worse when there is an obstacle in front of them. Blind stick is an innovative stick designed for visually disabled people for improved navigation. The paper presents a theoretical system concept to provide a smart ultrasonic aid for blind people. The system is intended to provide overall measures – Artificial vision and object detection. The aim of the overall system is to provide a low cost and efficient navigation aid for a visually impaired person who gets a sense of artificial vision by providing information about the environmental scenario of static and dynamic objects around them. Ultrasonic sensors are used to calculate distance of the obstacles around the blind person to guide the user towards the available path. Output is in the form of sequence of beep sound which the blind person can hear.
Symbol-Based Control of a Ball-on-Plate Mechanical System

Degree candidate: Phillip Yip Degree and year: Master of Science, 2004 Thesis directed by: Assistant Professor Dimitrios Hristu-Varsakelis Department of Mechanical Engineering Modern control systems often consist of networks of components that must share a common communication channel. Not all components of the networked control system can communicate with one another simultaneously at any given time. The “attention” that each component receives is an important factor that affects the system’s overall performance. An effective controller should ensure that sensors and actuators receive sufficient attention. This thesis describes a “ball-on-plate” dynamical system that includes a digital controller, which communicates with a pair of language-driven actuators, and an overhead camera. A control algorithm was developed to restrict the ball to a small region on the plate using a quantized set of language-based commands. The size of this containment region was analytically determined as a function of the communication constraints and other control system parameters. The effectiveness of the proposed control law was evaluated in experiments and mathematical simulations. SYMBOL-BASED CONTROL OF A BALL-ON-PLATE MECHANICAL SYSTEM by Phillip Yip Thesis submitted to the Faculty of the Graduate School of the University of Maryland, College Park in partial fulfillment of the requirements for the degree of Master of Science 2004 Advisory Commmittee: Assistant Professor Dimitrios Hristu-Varsakelis, Chair/Advisor Professor Balakumar Balachandran Professor Amr Baz c   To my family
MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild

This paper addresses the problem of 3D human pose estimation in the wild. A significant challenge is the lack of training data, i.e., 2D images of humans annotated with 3D poses. Such data is necessary to train state-of-the-art CNN architectures. Here, we propose a solution to generate a large set of photorealistic synthetic images of humans with 3D pose annotations. We introduce an image-based synthesis engine that artificially augments a dataset of real images with 2D human pose annotations using 3D Motion Capture (MoCap) data. Given a candidate 3D pose our algorithm selects for each joint an image whose 2D pose locally matches the projected 3D pose. The selected images are then combined to generate a new synthetic image by stitching local image patches in a kinematically constrained manner. The resulting images are used to train an end-to-end CNN for full-body 3D pose estimation. We cluster the training data into a large number of pose classes and tackle pose estimation as a K-way classification problem. Such an approach is viable only with large training sets such as ours. Our method outperforms the state of the art in terms of 3D pose estimation in controlled environments (Human3.6M) and shows promising results for in-the-wild images (LSP). This demonstrates that CNNs trained on artificial images generalize well to real images.
Automatic transcription of guitar tablature from audio signals in accordance with player's proficiency

We describe a method for automatically transcribing guitar tablatures from audio signals in accordance with the player's proficiency for use as support for a guitar player's practice. The system estimates the multiple pitches in each time frame and the optimal fingering considering playability and player's proficiency. It combines a conventional multipitch estimation method with a basic dynamic programming method. The difficulty of the fingerings can be changed by tuning the parameter representing the relative weights of the acoustical reproducibility and the fingering easiness. Experiments conducted using synthesized guitar audio signals to evaluate the transcribed tablatures in terms of the multipitch estimation accuracy and fingering easiness demonstrated that the system can simplify the fingering with higher precision of multipitch estimation results than the conventional method.
The web of topics: discovering the topology of topic evolution in a corpus

In this paper we study how to discover the evolution of topics over time in a time-stamped document collection. Our approach is uniquely designed to capture the rich topology of topic evolution inherent in the corpus. Instead of characterizing the evolving topics at fixed time points, we conceptually define a topic as a quantized unit of evolutionary change in content and discover topics with the time of their appearance in the corpus. Discovered topics are then connected to form a topic evolution graph using a measure derived from the underlying document network. Our approach allows inhomogeneous distribution of topics over time and does not impose any topological restriction in topic evolution graphs. We evaluate our algorithm on the ACM corpus.
 The topic evolution graphs obtained from the ACM corpus provide an effective and concrete summary of the corpus with remarkably rich topology that are congruent to our background knowledge. In a finer resolution, the graphs reveal concrete information about the corpus that were previously unknown to us, suggesting the utility of our approach as a navigational tool for the corpus.
Platform as a Service - A Conjoint Study on Consumers' Preferences

Platform as a Service (PaaS) solutions are changing the way that software is produced, distributed, consumed, and priced. PaaS, also known as cloud platform, offer an execution environment based on software platforms. To be competitive on the market, PaaS providers have to be aware of drivers of successful platforms and design or adjust their business models accordingly. Surprisingly, prior research has made little attempt to investigate consumers’ preferences on PaaS that influence developers’ choice on PaaS solutions. This paper examines this understudied issue through a conjoint study. First a comprehensive literature analysis on PaaS has been conducted in order to build the study design on a rigorous foundation. The conducted conjoint survey contained ten attributes together with 26 corresponding attribute levels and has been completed by 103 participants. Based on the results, a prioritized list of customers’ preferences for PaaS has been created.