nils-herrmann/clean_scirepeval huggingface dataset:
iDriver - Human Machine Interface for Autonomous Cars

Modern cars are equipped with a variety of sensors, advanced driver assistance systems and user interfaces nowadays. To benefit from these systems and to optimally support the driver in his monitoring and decision making process, efficient human-machine interfaces play an important part. This paper describes the second release of iDriver, an iPad software solution which was developed to navigate and remote control autonomous cars, to give access to live sensor data and useful data about the car state, as there are, e.g., current speed, engine and gear state. The software was used and evaluated in our two fully autonomous research cars "Spirit of Berlin" and "Made in Germany".
Solving a Real-World Glass Cutting Problem

We consider the problem of finding two-dimensional cutting patterns for glass sheets in order to produce rectangular elements requested by customers. The number of needed sheets, and therefore the waste, is to be minimized. The cutting facility requires three-staged guillotineable cutting patterns, and in addition, a plan for loading produced elements on transportation wagons. The availability of only three loading docks for wagons imposes additional constraints on the feasibility of cutting patterns. We describe a greedy first fit heuristic, two branchand-bound based heuristics, and different variants of an evolutionary algorithm for the problem, and compare them on a set of real-world instances. The evolutionary algorithm is based on an order representation, specific recombination and mutation operators, and a decoding heuristic. In one variant, branch-and-bound is occasionally applied to locally optimize parts of a solution during decoding.
DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket

Malicious applications pose a threat to the security of the Android platform. The growing amount and diversity of these applications render conventional defenses largely ineffective and thus Android smartphones often remain unprotected from novel malware. In this paper, we propose DREBIN, a lightweight method for detection of Android malware that enables identifying malicious applications directly on the smartphone. As the limited resources impede monitoring applications at run-time, DREBIN performs a broad static analysis, gathering as many features of an application as possible. These features are embedded in a joint vector space, such that typical patterns indicative for malware can be automatically identified and used for explaining the decisions of our method. In an evaluation with 123,453 applications and 5,560 malware samples DREBIN outperforms several related approaches and detects 94% of the malware with few false alarms, where the explanations provided for each detection reveal relevant properties of the detected malware. On five popular smartphones, the method requires 10 seconds for an analysis on average, rendering it suitable for checking downloaded applications directly on the device.
DeepSpace: An Online Deep Learning Framework for Mobile Big Data to Understand Human Mobility Patterns

In the recent years, the rapid spread of mobile device has create the vast amount of mobile data. However, some shallow-structure models such as support vector machine (SVM) have difficulty dealing with high dimensional data with the development of mobile network. In this paper, we analyze mobile data to predict human trajectories in order to understand human mobility pattern via a deep-structure model called “DeepSpace”. To the best of out knowledge, it is the first time that the deep learning approach is applied to predicting human trajectories. Furthermore, we develop the vanilla convolutional neural network (CNN) to be an online learning system, which can deal with the continuous mobile data stream. In general, “DeepSpace” consists of two different prediction models corresponding to different scales in space (the coarse prediction model and fine prediction models). This two models constitute a hierarchical structure, which enable the whole architecture to be run in parallel. Finally, we test our model based on the data usage detail records (UDRs) from the mobile cellular network in a city of southeastern China, instead of the call detail records (CDRs) which are widely used by others as usual. The experiment results show that “DeepSpace” is promising in human trajectories prediction.
The Global Superorganism : An Evolutionary-cybernetic Model of the Emerging Network Society

The organicist view of society is updated by incorporating concepts from cybernetics, evolutionary theory, and complex adaptive systems. Global society can be seen as an autopoietic network of self-producing components, and therefore as a living system or ‘superorganism’. Miller's living systems theory suggests a list of functional components for society's metabolism and nervous system. Powers' perceptual control theory suggests a model for a distributed control system implemented through the market mechanism. An analysis of the evolution of complex, networked systems points to the general trends of increasing efficiency, differentiation and integration. In society these trends are realized as increasing productivity, decreasing friction, increasing division of labor and outsourcing, and increasing cooperativity, transnational mergers and global institutions. This is accompanied by increasing functional autonomy of individuals and organisations and the decline of hierarchies. The increasing complexity of interactions and instability of certain processes caused by reduced friction necessitate a strengthening of society's capacity for information processing and control, i.e. its nervous system. This is realized by the creation of an intelligent global computer network, capable of sensing, interpreting, learning, thinking, deciding and initiating actions: the ‘global brain’. Individuals are being integrated ever more tightly into this collective intelligence. Although this image may raise worries about a totalitarian system that restricts individual initiaSocial Evolution & History / March 2007 58 tive, the superorganism model points in the opposite direction, towards increasing freedom and diversity. The model further suggests some specific futurological predictions for the coming decades, such as the emergence of an automated distribution network, a computer immune system, and a global consensus about values and standards.
SEQ2SQL: GENERATING STRUCTURED QUERIES

Relational databases store a significant amount of the worlds data. However, accessing this data currently requires users to understand a query language such as SQL. We propose Seq2SQL, a deep neural network for translating natural language questions to corresponding SQL queries. Our model uses rewards from inthe-loop query execution over the database to learn a policy to generate the query, which contains unordered parts that are less suitable for optimization via cross entropy loss. Moreover, Seq2SQL leverages the structure of SQL to prune the space of generated queries and significantly simplify the generation problem. In addition to the model, we release WikiSQL, a dataset of 80654 hand-annotated examples of questions and SQL queries distributed across 24241 tables from Wikipedia that is an order of magnitude larger than comparable datasets. By applying policybased reinforcement learning with a query execution environment to WikiSQL, Seq2SQL outperforms a state-of-the-art semantic parser, improving execution accuracy from 35.9% to 59.4% and logical form accuracy from 23.4% to 48.3%.
Learning to Generate Market Comments from Stock Prices

This paper presents a novel encoderdecoder model for automatically generating market comments from stock prices. The model first encodes both shortand long-term series of stock prices so that it can mention shortand long-term changes in stock prices. In the decoding phase, our model can also generate a numerical value by selecting an appropriate arithmetic operation such as subtraction or rounding, and applying it to the input stock prices. Empirical experiments show that our best model generates market comments at the fluency and the informativeness approaching human-generated reference texts.
Social Network Analysis and Information Propagation: A Case Study Using Flickr and YouTube Networks

—Social media and Social Network Analysis (SNA) acquired a huge popularity and represent one of the most important social and computer science phenomena of recent years. One of the most studied problems in this research area is influence and information propagation. The aim of this paper is to analyze the information diffusion process and predict the influence (represented by the rate of infected nodes at the end of the diffusion process) of an initial set of nodes in two networks: Flickr user's contacts and YouTube videos users commenting these videos. These networks are dissimilar in their structure (size, type, diameter, density, components), and the type of the relationships (explicit relationship represented by the contacts links, and implicit relationship created by commenting on videos), they are extracted using NodeXL tool. Three models are used for modeling the dissemination process: Linear Threshold Model (LTM), Independent Cascade Model (ICM) and an extension of this last called Weighted Cascade Model (WCM). Networks metrics and visualization were manipulated by NodeXL as well. Experiments results show that the structure of the network affect the diffusion process directly. Unlike results given in the blog world networks, the information can spread farther through explicit connections than through implicit relations.
Mining with Information Extraction

Text mining concerns looking for patterns in unstructured text. The related task of Information Extraction (IE) is about locating specific items in natural-language documents. This paper presents a framework for text mining, called DISCOTEX (Discovery from Text EXtraction), using a learned information extraction system to transform text into more structured data which is then mined for interesting relationships. The initial version of DISCOTEX integrates an IE module acquired by an IE learning system, and a standard rule induction module. In addition, rules mined from a database extracted from a corpus of texts are used to predict additional information to extract from future documents, thereby improving the recall of the underlying extraction system. Encouraging results are presented on applying these techniques to a corpus of computer job announcement postings from an Internet newsgroup.
Relational dynamic memory networks

Neural networks excel in detecting regular patterns but are less successful in representing and manipulating complex data structures, possibly due to the lack of an external memory. This has led to the recent development of a new line of architectures known as Memory-Augmented Neural Networks (MANNs), each of which consists of a neural network that interacts with an external memory matrix. However, this RAM-like memory matrix is unstructured and thus does not naturally encode structured objects. Here we design a new MANN dubbed Relational Dynamic Memory Network (RDMN) to bridge the gap. Like existing MANNs, RDMN has a neural controller but its memory is structured as multi-relational graphs. RDMN uses the memory to represent and manipulate graph-structured data in response to query; and as a neural network, RDMN is trainable from labeled data. Thus RDMN learns to answer queries about a set of graph-structured objects without explicit programming. We evaluate the capability of RDMN on several important prediction problems, including software vulnerability, molecular bioactivity and chemical-chemical interaction. Results demonstrate the efficacy of the proposed model.
Powering the planet: chemical challenges in solar energy utilization.

Global energy consumption is projected to increase, even in the face of substantial declines in energy intensity, at least 2-fold by midcentury relative to the present because of population and economic growth. This demand could be met, in principle, from fossil energy resources, particularly coal. However, the cumulative nature of CO(2) emissions in the atmosphere demands that holding atmospheric CO(2) levels to even twice their preanthropogenic values by midcentury will require invention, development, and deployment of schemes for carbon-neutral energy production on a scale commensurate with, or larger than, the entire present-day energy supply from all sources combined. Among renewable energy resources, solar energy is by far the largest exploitable resource, providing more energy in 1 hour to the earth than all of the energy consumed by humans in an entire year. In view of the intermittency of insolation, if solar energy is to be a major primary energy source, it must be stored and dispatched on demand to the end user. An especially attractive approach is to store solar-converted energy in the form of chemical bonds, i.e., in a photosynthetic process at a year-round average efficiency significantly higher than current plants or algae, to reduce land-area requirements. Scientific challenges involved with this process include schemes to capture and convert solar energy and then store the energy in the form of chemical bonds, producing oxygen from water and a reduced fuel such as hydrogen, methane, methanol, or other hydrocarbon species.
Autonomous vehicles testing methods review

Driving test is critical to the deployment of autonomous vehicles. It is necessary to review the related works since the methodologies summaries are rare, which will help to set up an integrated method for autonomous driving test in different development stages, and help to provide a reliable, quick, safe, low cost and reproducible method and accelerate the development of autonomous vehicle. In this paper, we review the related autonomous driving test works, including autonomous vehicle functional verification, vehicle integrated testing, system validation in different architectures. This review work will be helpful for autonomous vehicle development.
Three New Probabilistic Models for Dependency Parsing: An Exploration

After presenting a novel O(n) parsing algorithm for dependency grammar, we develop three contrasting ways to stochasticize it. We propose (a) a lexical affinity model where words struggle to modify each other, (b) a sense tagging model where words fluctuate randomly in their selectional preferences, and (c) a generative model where the speaker fleshes out each word’s syntactic and conceptual structure without regard to the implications for the hearer. We also give preliminary empirical results from evaluating the three models’ parsing performance on annotated Wall Street Journal training text (derived from the Penn Treebank). In these results, the generative model performs significantly better than the others, and does about equally well at assigning partof-speech tags.
Person-to-Person Transmission of Nipah Virus in a Bangladeshi Community

An encephalitis outbreak was investigated in Faridpur District, Bangladesh, in April-May 2004 to determine the cause of the outbreak and risk factors for disease. Biologic specimens were tested for Nipah virus. Surfaces were evaluated for Nipah virus contamination by using reverse transcription-PCR (RT-PCR). Thirty-six cases of Nipah virus illness were identified; 75% of case-patients died. Multiple peaks of illness occurred, and 33 case-patients had close contact with another Nipah virus patient before their illness. Results from a case-control study showed that contact with 1 patient carried the highest risk for infection (odds ratio 6.7, 95% confidence interval 2.9-16.8, p < 0.001). RT-PCR testing of environmental samples confirmed Nipah virus contamination of hospital surfaces. This investigation provides evidence for person-to-person transmission of Nipah virus. Capacity for person-to-person transmission increases the potential for wider spread of this highly lethal pathogen and highlights the need for infection control strategies for resource-poor settings.
Introduction to Machine Learning

In present times, giving a computer to carry out any task requires a set of specific instructions or the implementation of an algorithm that defines the rules that need to be followed. The present day computer system has no ability to learn from past experiences and hence cannot readily improve on the basis of past mistakes. So, giving a computer or instructing a computer controlled programme to perform a task requires one to define a complete and correct algorithm for task and then programme the algorithm into the computer. Such activities involve tedious and time consuming effort by specially trained teacher or person. Jaime et al (Jaime G. Carbonell, 1983) also explained that the present day computer systems cannot truly learn to perform a task through examples or through previous solved task and they cannot improve on the basis of past mistakes or acquire new abilities by observing and imitating experts. Machine Learning research endeavours to open the possibility of instruction the computer in such a new way and thereby promise to ease the burden of hand writing programmes and growing problems of complex information that get complicated in the computer. When approaching a task-oriented acquisition task, one must be aware that the resultant computer system must interact with human and therefore should closely match human abilities. So, learning machine or programme on the other hand will have to interact with computer users who make use of them and consequently the concept and skills they acquireif not necessarily their internal mechanism must be understandable to humans. Also Alpaydin (Alpaydin, 2004) stated that with advances in computer technology, we currently have the ability to store and process large amount of data, as well as access it from physically distant locations over computer network. Most data acquisition devices are digital now and record reliable data. For example, a supermarket chain that has hundreds of stores all over the country selling thousands of goods to millions of customers. The point of sale terminals record the details of each transaction: date, customer identification code, goods bought and their amount, total money spent and so forth, This typically amounts to gigabytes of data every day. This store data becomes useful only when it is analysed and tuned into information that can be used or be predicted. We do not know exactly which people are likely to buy a particular product or which author to suggest to people who enjoy reading Hemingway. If we knew, we would not need any analysis of the data; we would just go ahead and write down code. But because we do not, we can only collect data and hope to extract the answers to these and similar question from 1
Detection of APT Malware through External and Internal Network Traffic Correlation

This master thesis presents overview on advanced persistent threat (APT) definition and explanation of it. One of the most dangerous APT named: ”Snake” will be presented along with other similar APT’s. Various virtual environments like e.g. VirtualBox will be investigated in order to understand how APT malware behaves in these environments. The central focus of this master thesis lies on detection of futuristic APT malware based on cross-referencing communication patterns in order to detect APT malware. A prototype detection tool will be created and tested in order to detect similar APT’s like Snake. Additionally a prototype malware will be supplied as well, which contain similar stealth communication techniques as the Snake APT malware. This prototype malware will be tested with the current state of commercial firewall applications in order to prove its effectiveness. In the end challenges and solutions will be presented for future research work.
Action-driven 3D indoor scene evolution

We introduce a framework for action-driven evolution of 3D indoor scenes, where the goal is to simulate how scenes are altered by human actions, and specifically, by object placements necessitated by the actions. To this end, we develop an action model with each type of action combining information about one or more human poses, one or more object categories, and spatial configurations of objects belonging to these categories which summarize the object-object and object-human relations for the action. Importantly, all these pieces of information are learned from annotated photos. Correlations between the learned actions are analyzed to guide the construction of an action graph. Starting with an initial 3D scene, we probabilistically sample a sequence of actions from the action graph to drive progressive scene evolution. Each action triggers appropriate object placements, based on object co-occurrences and spatial configurations learned for the action model. We show results of our scene evolution that lead to realistic and messy 3D scenes, as well as quantitative evaluations by user studies which compare our method to manual scene creation and state-of-the-art, data-driven methods, in terms of scene plausibility and naturalness.
A Soft-Structured Agile Framework for Larger Scale Systems Development

One of the more important issues in the development of larger scale complex systems (product development period of two or more years) is accommodating changes to requirements. Requirements gathered for larger scale systems evolve during lengthy development periods due to changes in software and business environments, new user needs and technological advancements. Agile methods, which focus on accommodating change even late in the development lifecycle, can be adopted for the development of larger scale systems. However, as currently applied, these practices are not always suitable for the development of such systems. We propose a soft-structured framework combining the principles of agile and conventional software development that addresses the issue of rapidly changing requirements for larger scale systems. The framework consists of two parts: (1) a soft-structured requirements gathering approach that reflects the agile philosophy i.e., the Agile Requirements Generation Model and (2) a tailored development process that can be applied to either small or larger scale systems.
CMMI Guided Process Improvement for DevOps Projects: An Exploratory Case Study

Very recently, an increasing number of software companies adopted DevOps to adapt themselves to the ever-changing business environment. While it is important to mature adoption of the DevOps for these companies, no dedicated maturity models for DevOps exist. Meanwhile, maturity models such as CMMI models have demonstrated their effects in the traditional paradigm of software industry, however, it is not clear whether the CMMI models could guide the improvements with the context of DevOps. This paper reports a case study aiming at evaluating the feasibility to apply the CMMI models to guide process improvement for DevOps projects and identifying possible gaps. Using a structured method(i.e., SCAMPI C), we conducted a case study by interviewing four employees from one DevOps project. Based on evidence we collected in the case study, we managed to characterize the maturity/capability of the DevOps project, which implies the possibility to use the CMMI models to appraise the current processes in this DevOps project and guide future improvements. Meanwhile, several gaps also are identified between the CMMI models and the DevOps mode. In this sense, the CMMI models could be taken as a good foundation to design suitable maturity models so as to guide process improvement for projects adopting the DevOps.