nils-herrmann/clean_scirepeval huggingface dataset:
A journey to highly dynamic, self-adaptive service-based applications

Future software systems will operate in a highly dynamic world. Systems will need to operate correctly despite of unespected changes in factors such as environmental conditions, user requirements, technology, legal regulations, and market opportunities. They will have to operate in a constantly evolving environment that includes people, content, electronic devices, and legacy systems. They will thus need the ability to continuously adapt themselves in an automated manner to react to those changes. To realize dynamic, self-adaptive systems, the service concept has emerged as a suitable abstraction mechanism. Together with the concept of the service-oriented architecture (SOA), this led to the development of technologies, standards, and methods to build service-based applications by flexibly aggregating individual services. This article discusses how those concepts came to be by taking two complementary viewpoints. On the one hand, it evaluates the progress in software technologies and methodologies that led to the service concept and SOA. On the other hand, it discusses how the evolution of the requirements, and in particular business goals, influenced the progress towards highly dynamic self-adaptive systems. Finally, based on a discussion of the current state of the art, this article points out the possible future evolution of the field.
Linking Feature Lines on 3D Triangle Meshes with Artificial Potential Fields

We propose artificial potential fields as a support theory for a feature linking algorithm. This algorithm operates on 3D triangle meshes derived from multiple range scans of an object, and the features of interest are curvature extrema on the object's surface. A problem that arises with detecting these features is that results from standard algorithms are often incomplete in that feature lines are broken and discontinuous. Our novel linking algorithm closes these broken feature lines to form a more complete feature description. The main contribution of this algorithm is the use of artificial potential fields to govern the linking process. In this paper, we discuss the feature detection process itself and then define the linking procedure in the context of potential fields. We present results for both synthetic and scanned models.
Identification of the optic nerve head with genetic algorithms

This work proposes creating an automatic system to locate and segment the optic nerve head (ONH) in eye fundus photographic images using genetic algorithms.



Domain knowledge is used to create a set of heuristics that guide the various steps involved in the process. Initially, using an eye fundus colour image as input, a set of hypothesis points was obtained that exhibited geometric properties and intensity levels similar to the ONH contour pixels. Next, a genetic algorithm was used to find an ellipse containing the maximum number of hypothesis points in an offset of its perimeter, considering some constraints. The ellipse thus obtained is the approximation to the ONH. The segmentation method is tested in a sample of 110 eye fundus images, belonging to 55 patients with glaucoma (23.1%) and eye hypertension (76.9%) and random selected from an eye fundus image base belonging to the Ophthalmology Service at Miguel Servet Hospital, Saragossa (Spain).



The results obtained are competitive with those in the literature. The method's generalization capability is reinforced when it is applied to a different image base from the one used in our study and a discrepancy curve is obtained very similar to the one obtained in our image base. In addition, the robustness of the method proposed can be seen in the high percentage of images obtained with a discrepancy delta<5 (96% and 99% in our and a different image base, respectively). The results also confirm the hypothesis that the ONH contour can be properly approached with a non-deformable ellipse. Another important aspect of the method is that it directly provides the parameters characterising the shape of the papilla: lengths of its major and minor axes, its centre of location and its orientation with regard to the horizontal position.
Mining Sequential Patterns

We are given a large database of customer transactions, where each transaction consists of customer-id, transaction time, and the items bought in the transaction. We introduce the problem of mining sequential patterns over such databases. We present three algorithms to solve this problem, and empirically evaluate their performance using synthetic data. Two of the proposed algorithms, AprioriSome and AprioriAll, have comparable performance, albeit AprioriSome performs a little better when the minimum number of customers that must support a sequential pattern is low. Scale-up experiments show that both AprioriSome and AprioriAll scale linearly with the number of customer transactions. They also have excellent scale-up properties with respect to the number of transactions per customer and the number of items in a transaction.
A compressed LSB steganography method

A least significant bit (LSB) Steganography is mainly characterized by its hiding capacity and imperceptibility which is measured by Peak Signal to Noise Ratio (PSNR). There is a trade-off between these two factors. In this paper, an improved LSB Steganography which uses modulus function for data hiding method has been proposed and implemented which is better than previous methods in imperceptibility keeping the hiding capacity same. The proposed Steganography method breaks the secret data into two components. Each component is hidden into cover image pixels using modulo function. Repetition in secret data component is also considered. Results show that the proposed method is better than existing methods unless there is high randomness in secret data.
Productivity Improvement of Software Development Process Through Scrumban: A Practitioner's Approach

Rather than theoretical approaches in software engineering about software development, as a software process practitioner we got to know several times that, either end user is unable to elaborate the desired system due to the lack of strong imagination capabilities or sometimes a novice business analyst does not able to perfectly articulate similar to end user's need. To mitigate these risk due to the gap of communication and/or thinking on both ends. There are many processes involved, typically in software development processes like- ‘Agile’ /‘Agility’ plays vital role for said challenges. Generally agile focuses on stabilizing the fluctuating requirements of customer. In addition, agility successfully make commitment on the highly rising hunger of technology i.e. recently launched or under development. In an IT industry, it is quite difficult to deliver a solution timely and meet the fluctuating requirements without a distinguished plan. Generally, scrum methodology is applicable in majority of IT organizations who hold client centric culture. Scrum ensures iterative delivery to end user. To make this process bit easier Kanban is recently take place of scrum. Sometimes Kanban unable to go easy as it requires strong team building and interaction between the members. Let us see how the mixture of scrum and Kanban i.e. ‘Scrumban’ improve the process and productivity with higher business benefits.
A Time Series Graph Cut Image Segmentation Scheme for Liver Tumors

Tumor detection in biomedical imaging is a time-consuming process for medical professionals and is not without errors. Thus in recent decades, researchers have developed algorithmic techniques for image processing using a wide variety of mathematical methods, such as statistical modeling, variational techniques, and machine learning. In this paper, we propose a semi-automatic method for liver segmentation of 2D CT scans into three labels denoting healthy, vessel, or tumor tissue based on graph cuts. First, we create a feature vector for each pixel in a novel way that consists of the 59 intensity values in the time series data and propose a simplified perimeter cost term in the energy functional. We normalize the data and perimeter terms in the functional to expedite the graph cut without having to optimize the scaling parameter λ. In place of a training process, predetermined tissue means are computed based on sample regions identified by expert radiologists. The proposed method also has the advantage of being relatively simple to implement computationally. It was evaluated against the ground truth on a clinical CT dataset of 10 tumors and yielded segmentations with a mean Dice similarity coefficient (DSC) of .77 and mean volume overlap error (VOE) of 36.7%. The average processing time was 1.25 minutes per slice. 1 ar X iv :1 80 9. 05 21 0v 1 [ cs .C V ] 1 3 Se p 20 18
Comparative Study of Linear and Nonlinear Features Used in Imagined Vowels Classification Using a Backpropagation Neural Network Classifier

Imagined speech recognition is an alternative means to aid individuals with motor disabilities, illnesses and speech disorders, such as the mute and those under the state of coma, to communicate properly through silent communication. Imagined speech or unspoken speech is speech that is only uttered within the mind without the use of any muscle movement. In this study, the proponents compared the effects of linear and nonlinear features in an imagined speech classification model. This model was designed to classify the five vowels namely /a/ /e/ /i/ /o/ and /u/. Electroencephalogram (EEG) signals were measured in each study volunteers while they were imagining the different vowels. Linear and nonlinear features were extracted from the processed EEG signals, and these features were used as inputs to an Artificial Neural Network (ANN) classifier.
Visualizing cyber security: Usable workspaces

The goal of cyber security visualization is to help analysts increase the safety and soundness of our digital infrastructures by providing effective tools and workspaces. Visualization researchers must make visual tools more usable and compelling than the text-based tools that currently dominate cyber analysts' tool chests. A cyber analytics work environment should enable multiple, simultaneous investigations and information foraging, as well as provide a solution space for organizing data. We describe our study of cyber-security professionals and visualizations in a large, high-resolution display work environment and the analytic tasks this environment can support. We articulate a set of design principles for usable cyber analytic workspaces that our studies have brought to light. Finally, we present prototypes designed to meet our guidelines and a usability evaluation of the environment.
I Know What Your Packet Did Last Hop: Using Packet Histories to Troubleshoot Networks

The complexity of networks has outpaced our tools to debug them; today, administrators use manual tools to diagnose problems. In this paper, we show how packet histories—the full stories of every packet’s journey through the network—can simplify network diagnosis. To demonstrate the usefulness of packet histories and the practical feasibility of constructing them, we built NetSight, an extensible platform that captures packet histories and enables applications to concisely and flexibly retrieve packet histories of interest. Atop NetSight, we built four applications that illustrate its flexibility: an interactive network debugger, a live invariant monitor, a path-aware history logger, and a hierarchical network profiler. On a single modern multi-core server, NetSight can process packet histories for the traffic of multiple 10 Gb/s links. For larger networks, NetSight scales linearly with additional servers and scales even further with straightforward additions to hardwareand hypervisor-based switches.
High throughput heavy hitter aggregation for modern SIMD processors

Heavy hitters are data items that occur at high frequency in a data set. They are among the most important items for an organization to summarize and understand during analytical processing. In data sets with sufficient skew, the number of heavy hitters can be relatively small. We take advantage of this small footprint to compute aggregate functions for the heavy hitters in fast cache memory in a single pass.
 We design cache-resident, shared-nothing structures that hold only the most frequent elements. Our algorithm works in three phases. It first samples and picks heavy hitter candidates. It then builds a hash table and computes the exact aggregates of these elements. Finally, a validation step identifies the true heavy hitters from among the candidates.
 We identify trade-offs between the hash table configuration and performance. Configurations consist of the probing algorithm and the table capacity that determines how many candidates can be aggregated. The probing algorithm can be perfect hashing, cuckoo hashing and bucketized hashing to explore trade-offs between size and speed.
 We optimize performance by the use of SIMD instructions, utilized in novel ways beyond single vectorized operations, to minimize cache accesses and the instruction footprint.
Research paper recommendation with topic analysis

With the collaborative filtering techniques becoming more and more mature, recommender systems are widely used nowadays, especially in electronic commerce and social networks. However, the utilization of recommender system in academic research itself has not received enough attention. A research paper recommender system would greatly help researchers to find the most desirable papers in their fields of endeavor. Due to the textual nature of papers, content information could be integrated into existed recommendation methods. In this paper, we proposed that by using topic model techniques to make topic analysis on research papers, we could introduce a thematic similarity measurement into a modified version of item-based recommendation approach. This novel recommendation method could considerable alleviate the cold start problem in research paper recommendation. Our experiment result shows that our approach could recommend highly relevant research papers.
Distributed GraphLab : A Framework for Machine Learning and Data Mining in the Cloud

While high-level data parallel frameworks, like MapReduce, simplify the design and implementation of large-scale data processing systems, they do not naturally or efficiently support many important data mining and machine learning algorithms and can lead to inefficient learning systems. To help fill this critical void, we introduced the GraphLab abstraction which naturally expresses asynchronous, dynamic, graph-parallel computation while ensuring data consistency and achieving a high degree of parallel performance in the shared-memory setting. In this paper, we extend the GraphLab framework to the substantially more challenging distributed setting while preserving strong data consistency guarantees. We develop graph based extensions to pipelined locking and data versioning to reduce network congestion and mitigate the effect of network latency. We also introduce fault tolerance to the GraphLab abstraction using the classic Chandy-Lamport snapshot algorithm and demonstrate how it can be easily implemented by exploiting the GraphLab abstraction itself. Finally, we evaluate our distributed implementation of the GraphLab abstraction on a large Amazon EC2 deployment and show 1-2 orders of magnitude performance gains over Hadoop-based implementations.
Reliability-aware design to suppress aging

Due to aging, circuit reliability has become extraordinary challenging. Reliability-aware circuit design flows do virtually not exist and even research is in its infancy. In this paper, we propose to bring aging awareness to EDA tool flows based on so-called degradation-aware cell libraries. These libraries include detailed delay information of gates/cells under the impact that aging has on both threshold voltage (Vth) and carrier mobility (μ) of transistors. This is unlike state of the art which considers Vth only. We show how ignoring μ degradation leads to underestimating guard-bands by 19% on average. Our investigation revealed that the impact of aging is strongly dependent on the operating conditions of gates (i.e. input signal slew and output load capacitance), and not solely on the duty cycle of transistors. Neglecting this fact results in employing insufficient guard-bands and thus not sustaining reliability during lifetime.
 We demonstrate that degradation-aware libraries and tool flows are indispensable for not only accurately estimating guardbands, but also efficiently containing them. By considering aging degradations during logic synthesis, significantly more resilient circuits can be obtained. We further quantify the impact of aging on the degradation of image processing circuits. This goes far beyond investigating aging with respect to path delays solely. We show that in a standard design without any guardbanding, aging leads to unacceptable image quality after just one year. By contrast, if the synthesis tool is provided with the degradation-aware cell library, high image quality is sustained for 10 years (even under worst-case aging and without a guard-band). Hence, using our approach, aging can be effectively suppressed.
An EMG-controlled exoskeleton for hand rehabilitation

The principal goal of this work is the development, testing and experimentation of a device for the hand rehabilitation. The system we designed is intended for people who have partially lost the ability to control correctly the hand musculature, for example after a stroke or a spinal cord injure. Based on EMG signals the system can "understand" the subject volition to move the hand and thanks to its actuators can help the fingers movement in order to perform the task. In this paper we describe the device and discuss the first results conducted on a healthy volunteer.
Engaging robots: easing complex human-robot teamwork using backchanneling

People are increasingly working with robots in teams and recent research has focused on how human-robot teams function, but little attention has yet been paid to the role of social signaling behavior in human-robot teams. In a controlled experiment, we examined the role of backchanneling and task complexity on team functioning and perceptions of the robots' engagement and competence. Based on results from 73 participants interacting with autonomous humanoid robots as part of a human-robot team (one participant, one confederate, and three robots), we found that when robots used backchanneling team functioning improved and the robots were seen as more engaged. Ironically, the robots using backchanneling were perceived as less competent than those that did not. Our results suggest that backchanneling plays an important role in human-robot teams and that the design and implementation of robots for human-robot teams may be more effective if backchanneling capability is provided.
Improving performance of recurrent neural network with relu nonlinearity

In recent years significant progress has been made in successfully training recurrent neural networks (RNNs) on sequence learning problems involving long range temporal dependencies. The progress has been made on three fronts: (a) Algorithmic improvements involving sophisticated optimization techniques, (b) network design involving complex hidden layer nodes and specialized recurrent layer connections and (c) weight initialization methods. In this paper, we focus on recently proposed weight initialization with identity matrix for the recurrent weights in a RNN. This initialization is specifically proposed for hidden nodes with Rectified Linear Unit (ReLU) non linearity. We offer a simple dynamical systems perspective on weight initialization process, which allows us to propose a modified weight initialization strategy. We show that this initialization technique leads to successfully training RNNs composed of ReLUs. We demonstrate that our proposal produces comparable or better solution for three toy problems involving long range temporal structure: the addition problem, the multiplication problem and the MNIST classification problem using sequence of pixels. In addition, we present results for a benchmark action recognition problem.
Monitoring Breathing via Signal Strength in Wireless Networks

This paper shows experimentally that standard wireless networks which measure received signal strength (RSS) can be used to reliably detect human breathing and estimate the breathing rate, an application we call “BreathTaking”. We present analysis showing that, as a first order approximation, breathing induces sinusoidal variation in the measured RSS on a link, with amplitude a function of the relative amplitude and phase of the breathing-affected multipath. We show that although an individual link may not reliably detect breathing, the collective spectral content of a network of devices reliably indicates the presence and rate of breathing. We present a maximum likelihood estimator (MLE) of breathing rate, amplitude, and phase, which uses the RSS data from many links simultaneously. We show experimental results which demonstrate that reliable detection and frequency estimation is possible with 30 seconds of data, within 0.07 to 0.42 breaths per minute (bpm) RMS error in several experiments. The experiments also indicate that the use of directional antennas may improve the systems robustness to external motion.
Weak signals as predictors of real-world phenomena in social media

Global and national events in recent years have shown that online social media can be a force for good (e.g., Arab Spring) and harm (e.g., the London riots). In both of these examples, social media played a key role in group formation and organization, and in the coordination of the group's subsequent collective actions (i.e., the move from rhetoric to action). Surprisingly, despite its clear importance, little is understood about the factors that lead to this kind of group development and the transition to collective action. This paper focuses on an approach to the analysis of data from social media to detect weak signals, i.e., indicators that initially appear at the fringes, but are, in fact, early indicators of such large-scale real-world phenomena. Our approach is in contrast to existing research which focuses on analysing major themes, i.e., the strong signals, prevalent in a social network at a particular point in time. Analysis of weak signals can provide interesting possibilities for forecasting, with online user-generated content being used to identify and anticipate possible offline future events. We demonstrate our approach through analysis of tweets collected during the London riots in 2011 and use of our weak signals to predict tipping points in that context.
Serverless Computing: Design, Implementation, and Performance

We present the design of a novel performance-oriented serverless computing platform implemented in. NET, deployed in Microsoft Azure, and utilizing Windows containers as function execution environments. Implementation challenges such as function scaling and container discovery, lifecycle, and reuse are discussed in detail. We propose metrics to evaluate the execution performance of serverless platforms and conduct tests on our prototype as well as AWS Lambda, Azure Functions, Google Cloud Functions, and IBM's deployment of Apache OpenWhisk. Our measurements show the prototype achieving greater throughput than other platforms at most concurrency levels, and we examine the scaling and instance expiration trends in the implementations. Additionally, we discuss the gaps and limitations in our current design, propose possible solutions, and highlight future research.