nils-herrmann/clean_scirepeval huggingface dataset:
Enabling Supply Chain Agility through IOS Integration and Supply Chain Flexibility

In today’s turbulent environment, competitive pressures and market unpredictability have dramatically lashed business profits. Agility is an essential ability for firms facing such an environment. However, coping with the hostile business environment requires not only the agility from individual firms but also the collaboration from their supply chain partners. Supply chain agility (SCA) therefore is vital to the competitiveness and performance of a firm and its supply chain partners. Exploring how to effectively enable SCA is significant for both practice and theory development. Recently, firms are increasingly relying on integrated information systems and analytical tools, such as business intelligent systems, and close collaboration with their supplier chain partners to enhance their responsiveness. This paper seeks to broaden the understanding about the enabling roles of IOS integration, analytical ability of inter-organizational information systems (IOS), and supply chain flexibility on SCA based on the dynamic capabilities view and real options theory. With a sample of 147 matched-pair data gathered from the top 2000 Taiwanese manufacturing firms, our results support the effect of IOS integration on supply chain flexibility, including offering and sourcing flexibilities, which in turn facilitate SCA. We also propose and show the moderating effect of analytical ability of IOS on the process of enabling SCA. These results contribute to a better understanding of how SCA may be achieved in theory and in practice. The model and findings of this study should be able to serve as a basis for future research for studying SCA.
Mobile-sandbox: having a deeper look into android applications

Smartphones in general and Android in particular are increasingly shifting into the focus of cybercriminals. For understanding the threat to security and privacy it is important for security researchers to analyze malicious software written for these systems. The exploding number of Android malware calls for automation in the analysis. In this paper, we present Mobile-Sandbox, a system designed to automatically analyze Android applications in two novel ways: (1) it combines static and dynamic analysis, i.e., results of static analysis are used to guide dynamic analysis and extend coverage of executed code, and (2) it uses specific techniques to log calls to native (i.e., "non-Java") APIs. We evaluated the system on more than 36,000 applications from Asian third-party mobile markets and found that 24% of all applications actually use native calls in their code.
Exact indexing of dynamic time warping

The problem of indexing time series has attracted much interest. Most algorithms used to index time series utilize the Euclidean distance or some variation thereof. However, it has been forcefully shown that the Euclidean distance is a very brittle distance measure. Dynamic time warping (DTW) is a much more robust distance measure for time series, allowing similar shapes to match even if they are out of phase in the time axis. Because of this flexibility, DTW is widely used in science, medicine, industry and finance. Unfortunately, however, DTW does not obey the triangular inequality and thus has resisted attempts at exact indexing. Instead, many researchers have introduced approximate indexing techniques or abandoned the idea of indexing and concentrated on speeding up sequential searches. In this work, we introduce a novel technique for the exact indexing of DTW. We prove that our method guarantees no false dismissals and we demonstrate its vast superiority over all competing approaches in the largest and most comprehensive set of time series indexing experiments ever undertaken.
Evaluating Sustainability of Architectural Designs Using Building Information Modeling

BIM (Building Information Modeling) has been recently implemented by many Architecture, Engineering, and Construction firms due to its productivity gains and long term benefits. This paper presents the development and implementation of a sustainability assessment framework for an architectural design using BIM technology in extracting data from the digital building model needed for determining the level of sustainability. The sustainability assessment is based on the LEED (Leadership in Energy and Environmental Design) Green Building Rating System, a widely accepted national standards for sustainable building design in the United States. The architectural design of a hotel project is used as a case study to verify the applicability of the framework.
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks

Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks. One appealing alternative is rendering synthetic data where ground-truth annotations are generated automatically. Unfortunately, models trained purely on rendered images fail to generalize to real images. To address this shortcoming, prior work introduced unsupervised domain adaptation algorithms that have tried to either map representations between the two domains, or learn to extract features that are domain-invariant. In this work, we approach the problem in a new light by learning in an unsupervised manner a transformation in the pixel space from one domain to the other. Our generative adversarial network (GAN)-based method adapts source-domain images to appear as if drawn from the target domain. Our approach not only produces plausible samples, but also outperforms the state-of-the-art on a number of unsupervised domain adaptation scenarios by large margins. Finally, we demonstrate that the adaptation process generalizes to object classes unseen during training.
Key-Value Retrieval Networks for Task-Oriented Dialogue

Neural task-oriented dialogue systems often struggle to smoothly interface with a knowledge base. In this work, we seek to address this problem by proposing a new neural dialogue agent that is able to effectively sustain grounded, multi-domain discourse through a novel key-value retrieval mechanism. The model is end-to-end differentiable and does not need to explicitly model dialogue state or belief trackers. We also release a new dataset of 3,031 dialogues that are grounded through underlying knowledge bases and span three distinct tasks in the in-car personal assistant space: calendar scheduling, weather information retrieval, and point-of-interest navigation. Our architecture is simultaneously trained on data from all domains and significantly outperforms a competitive rulebased system and other existing neural dialogue architectures on the provided domains according to both automatic and human evaluation metrics.
Going Native: Using a Large-Scale Analysis of Android Apps to Create a Practical Native-Code Sandboxing Policy

Current static analysis techniques for Android applications operate at the Java level—that is, they analyze either the Java source code or the Dalvik bytecode. However, Android allows developers to write code in C or C++ that is cross-compiled to multiple binary architectures. Furthermore, the Java-written components and the native code components (C or C++) can interact. Native code can access all of the Android APIs that the Java code can access, as well as alter the Dalvik Virtual Machine, thus rendering static analysis techniques for Java unsound or misleading. In addition, malicious apps frequently hide their malicious functionality in native code or use native code to launch kernel exploits. It is because of these security concerns that previous research has proposed native code sandboxing, as well as mechanisms to enforce security policies in the sandbox. However, it is not clear whether the large-scale adoption of these mechanisms is practical: is it possible to define a meaningful security policy that can be imposed by a native code sandbox without breaking app functionality? In this paper, we perform an extensive analysis of the native code usage in 1.2 million Android apps. We first used static analysis to identify a set of 446k apps potentially using native code, and we then analyzed this set using dynamic analysis. This analysis demonstrates that sandboxing native code with no permissions is not ideal, as apps’ native code components perform activities that require Android permissions. However, our analysis provided very encouraging insights that make us believe that sandboxing native code can be feasible and useful in practice. In fact, it was possible to automatically generate a native code sandboxing policy, which is derived from our analysis, that limits many malicious behaviors while still allowing the correct execution of the behavior witnessed during dynamic analysis for 99.77% of the benign apps in our dataset. The usage of our system to generate policies would reduce the attack surface available to native code and, as a further benefit, it would also enable more reliable static analysis of Java code.
Spanner: Google's Globally Distributed Database

Spanner is Google’s scalable, multiversion, globally distributed, and synchronously replicated database. It is the first system to distribute data at global scale and support externally-consistent distributed transactions. This article describes how Spanner is structured, its feature set, the rationale underlying various design decisions, and a novel time API that exposes clock uncertainty. This API and its implementation are critical to supporting external consistency and a variety of powerful features: nonblocking reads in the past, lock-free snapshot transactions, and atomic schema changes, across all of Spanner.
“ I Want That ” : Human-inthe-Loop Control of a Wheelchair-Mounted Robotic Arm

Wheelchair-mounted robotic arms have been commercially available for a decade. In order to operate these robotic arms, a user must have a high level of cognitive function. Our research focuses on replacing a manufacturer-provided, menu-based interface with a vision-based system while adding autonomy to reduce the cognitive load. Instead of manual task decomposition and execution, the user explicitly designates the end goal, and the system autonomously retrieves the object. In this paper, we present the complete system which can autonomously retrieve a desired object from a shelf. We also present the results of a 15-week study in which 12 participants from our target population used our system, totaling 198 trials.
Predictive analytics with aviation big data

In this paper, we describe a novel analytics system that enables query processing and predictive analytics over streams of big aviation data. As part of an Internal Research and Development project, Boeing Research and Technology (BR&T) Advanced Air Traffic Management (AATM) built a system that makes predictions based upon descriptive patterns of massive aviation data. Boeing AATM has been receiving live Aircraft Situation Display to Industry (ASDI) data and archiving it for over two years. At the present time, there is not an easy mechanism to perform analytics on the data. The incoming ASDI data is large, compressed, and requires correlation with other flight data before it can be analyzed. The service exposes this data once it has been uncompressed, correlated, and stored in a data warehouse for further analysis using a variety of descriptive, predictive, and possibly prescriptive analytics tools. The service is being built partially in response to requests from Boeing Commercial Aviation (BCA) for analysis of capacity and flow in the US National Airspace System (NAS). The service utilizes a custom tool developed by Embry Riddle Aeronautical University (ERAU) that correlates the raw ASDI feed, IBM Warehouse with DB2 for data management, WebSphere Message Broker for real-time message brokering, SPSS Modeler for statistical analysis, and Cognos BI for front-end business intelligence (BI) visualization tools. This paper describes a scalable service architecture, implementation and value it adds to the aviation domain.
The biology of bone graft repair.

Cancellous and cortical autografts histologically have three differences: (1) cancellous grafts are revascularized more rapidly and completely than cortical grafts; (2) creeping substitution of cancellous bone initially involves an appositional bone formation phase, followed by a resorptive phase, whereas cortical grafts undergo a reverse creeping substitution process; (3) cancellous grafts tend to repair completely with time, whereas cortical grafts remain as admixtures of necrotic and viable bone. Physiologic skeletal metabolic factors influence the rate, amount, and completeness of bone repair and graft incorporation. The mechanical strengths of cancellous and cortical grafts are correlated with their respective repair processes: cancellous grafts tend to be strengthened first, whereas cortical grafts are weakened. Bone allografts are influenced by the same immunologic factors as other tissue grafts. Fresh bone allografts may be rejected by the host's immune system. The histoincompatibility antigens of bone allografts are presumably the proteins or glycoproteins on cell surfaces. The matrix proteins may or may not elicit graft rejection. The rejection of a bone allograft is considered to be a cellular rather than a humoral response, although the humoral component may play a part. The degree of the host response to an allograft may be related to the antigen concentration and total dose. The rejection of a bone allograft is histologically expressed by the disruption of vessels, an inflammatory process including lymphocytes, fibrous encapsulation, peripheral graft resorption, callus bridging, nonunions, and fatigue fractures.
Opinion Extraction, Summarization and Tracking in News and Blog Corpora

Humans like to express their opinions and are eager to know others’ opinions. Automatically mining and organizing opinions from heterogeneous information sources are very useful for individuals, organizations and even governments. Opinion extraction, opinion summarization and opinion tracking are three important techniques for understanding opinions. Opinion extraction mines opinions at word, sentence and document levels from articles. Opinion summarization summarizes opinions of articles by telling sentiment polarities, degree and the correlated events. In this paper, both news and web blog articles are investigated. TREC, NTCIR and articles collected from web blogs serve as the information sources for opinion extraction. Documents related to the issue of animal cloning are selected as the experimental materials. Algorithms for opinion extraction at word, sentence and document level are proposed. The issue of relevant sentence selection is discussed, and then topical and opinionated information are summarized. Opinion summarizations are visualized by representative sentences. Text-based summaries in different languages, and from different sources, are compared. Finally, an opinionated curve showing supportive and nonsupportive degree along the timeline is illustrated by an opinion tracking system.
cyNeo4j: connecting Neo4j and Cytoscape

We developed cyNeo4j, a Cytoscape App to link Cytoscape and Neo4j databases to utilize the performance and storage capacities Neo4j offers. We implemented a Neo4j NetworkAnalyzer, ForceAtlas2 layout and Cypher component to demonstrate the possibilities a distributed setup of Cytoscape and Neo4j have.



The app is available from the Cytoscape App Store at http://apps.cytoscape.org/apps/cyneo4j, the Neo4j plugins at www.github.com/gsummer/cyneo4j-parent and the community and commercial editions of Neo4j can be found at http://www.neo4j.com.



georg.summer@gmail.com.
Traces and Projections : Exploring the Patterns of Storytelling

This paper describes a framework for building story traces (compact global views of a narrative) and story projections (selections of key story elements) and their applications in digital storytelling. Word and sense properties are extracted using the WordNet lexical database enhanced with Prolog inference rules and a number of lexical transformations. Inference rules are based on navigation in various WordNet relation chains (hypernyms, meronyms, entailment and causality links, etc.) and derived inferential closures expressed as boolean combinations of node and edge properties used to direct the navigation. The resulting abstract story traces provide a compact view of the underlying story’s key content elements and a means for automated indexing and classification of story collections [1, 2]. Ontology driven projections act as a kind of “semantic lenses” and provide a means to select a subset of a story whose key sense elements are subsumed by a set of concepts, predicates and properties expressing the focus of interest of a user. Finally, we discuss applications of these techniques in story understanding, classification of digital story collections, story generation and story-related question answering. The main contribution of the paper consists in the use of a lexical knowledge base together with an advanced rule based inference mechanism for understanding stories, and the use of the information extracted by this process for various applications.
A Secure Steganography Method Based on Integer Lifting Wavelet Transform

Steganography plays an important role in secret communication in digital worlds and open environments like Internet. Undetectability and imperceptibility of confidential data are major challenges of steganography methods. This article presents a secure steganography method in frequency domain based on partitioning approach. The cover image is partitioned into 8×8 blocks and then integer wavelet transform through lifting scheme is performed for each block. The symmetric RC4 encryption method is applied to secret message to obtain high security and authentication. Tree Scan Order is performed in frequency domain to find proper location for embedding secret message. Secret message is embedded in cover image with minimal degrading of the quality. Experimental results demonstrate that the proposed method has achieved superior performance in terms of high imperceptibility of stego-image and it is secure against statistical attack in comparison with existing methods.
Entrusted Loans : A Close Look at China ’ s Shadow Banking System

We perform transaction-level analyses of entrusted loans – the largest component of shadow banking in China. There are two types – affiliated and non-affiliated. The latter involve a much higher interest rate than the former and official bank loan rates, and largely flow into the real estate industry. Both involve firms with privileged access to cheap capital to channel funds to less privileged firms and increase when credit is tight. The pricing of entrusted loans, especially that of non-affiliated loans, incorporates fundamental and informational risks. Stock market reactions suggest that both affiliated and non-affiliated loans are fairly-compensated investments.
Distributed Circuit Modeling of Galvanic and Capacitive Coupling for Intrabody Communication

Modeling of intrabody communication (IBC) entails the understanding of the interaction between electromagnetic fields and living tissues. At the same time, an accurate model can provide practical hints toward the deployment of an efficient and secure communication channel for body sensor networks. In the literature, two main IBC coupling techniques have been proposed: galvanic and capacitive coupling. Nevertheless, models that are able to emulate both coupling approaches have not been reported so far. In this paper, a simple model based on a distributed parameter structure with the flexibility to adapt to both galvanic and capacitive coupling has been proposed. In addition, experimental results for both coupling methods were acquired by means of two harmonized measurement setups. The model simulations have been subsequently compared with the experimental data, not only to show their validity but also to revise the practical frequency operation range for both techniques. Finally, the model, along with the experimental results, has also allowed us to provide some practical rules to optimally tackle IBC design.
Level Playing Field for Million Scale Face Recognition

Face recognition has the perception of a solved problem, however when tested at the million-scale exhibits dramatic variation in accuracies across the different algorithms [11]. Are the algorithms very different? Is access to good/big training data their secret weapon? Where should face recognition improve? To address those questions, we created a benchmark, MF2, that requires all algorithms to be trained on same data, and tested at the million scale. MF2 is a public large-scale set with 672K identities and 4.7M photos created with the goal to level playing field for large scale face recognition. We contrast our results with findings from the other two large-scale benchmarks MegaFace Challenge and MS-Celebs-1M where groups were allowed to train on any private/public/big/small set. Some key discoveries: 1) algorithms, trained on MF2, were able to achieve state of the art and comparable results to algorithms trained on massive private sets, 2) some outperformed themselves once trained on MF2, 3) invariance to aging suffers from low accuracies as in MegaFace, identifying the need for larger age variations possibly within identities or adjustment of algorithms in future testing.
Recurrent Memory Networks for Language Modeling

Recurrent Neural Networks (RNNs) have obtained excellent result in many natural language processing (NLP) tasks. However, understanding and interpreting the source of this success remains a challenge. In this paper, we propose Recurrent Memory Network (RMN), a novel RNN architecture, that not only amplifies the power of RNN but also facilitates our understanding of its internal functioning and allows us to discover underlying patterns in data. We demonstrate the power of RMN on language modeling and sentence completion tasks. On language modeling, RMN outperforms Long Short-Term Memory (LSTM) network on three large German, Italian, and English dataset. Additionally we perform indepth analysis of various linguistic dimensions that RMN captures. On Sentence Completion Challenge, for which it is essential to capture sentence coherence, our RMN obtains 69.2% accuracy, surpassing the previous state of the art by a large margin.1
Towards energy-aware scheduling in data centers using machine learning

As energy-related costs have become a major economical factor for IT infrastructures and data-centers, companies and the research community are being challenged to find better and more efficient power-aware resource management strategies. There is a growing interest in "Green" IT and there is still a big gap in this area to be covered.
 In order to obtain an energy-efficient data center, we propose a framework that provides an intelligent consolidation methodology using different techniques such as turning on/off machines, power-aware consolidation algorithms, and machine learning techniques to deal with uncertain information while maximizing performance. For the machine learning approach, we use models learned from previous system behaviors in order to predict power consumption levels, CPU loads, and SLA timings, and improve scheduling decisions. Our framework is vertical, because it considers from watt consumption to workload features, and cross-disciplinary, as it uses a wide variety of techniques.
 We evaluate these techniques with a framework that covers the whole control cycle of a real scenario, using a simulation with representative heterogeneous workloads, and we measure the quality of the results according to a set of metrics focused toward our goals, besides traditional policies. The results obtained indicate that our approach is close to the optimal placement and behaves better when the level of uncertainty increases.