nils-herrmann/clean_scirepeval huggingface dataset:
An analysis of developer metrics for fault prediction

Software product metrics have been widely used as independent variables for constructing a fault prediction model. However, fault injection depends not only on characteristics of the products themselves, but also on characteristics of developers involved in the project.  The goal of this paper is to study the effects of developer features on software reliability.  This paper proposes developer metrics such as the number of code churns made by each developer, the number of commitments made by each developer and the number of developers for each module. By using the eclipse project dataset, we experimentally analyzed the relationship between the number of faults and developer metrics. Second, the effective of developer metrics for performance improvements of fault prediction models were evaluated.  The result revealed that the modules touched by more developer contained more faults. Compared with conventional fault prediction models, developer metrics improved the prediction performance.  We conclude that developer metrics are good predictor of faults and we must consider the human factors for improving the software reliability.
SmartStore: a new metadata organization paradigm with semantic-awareness for next-generation file systems

Existing storage systems using hierarchical directory tree do not meet scalability and functionality requirements for exponentially growing datasets and increasingly complex queries in Exabyte-level systems with billions of files. This paper proposes semantic-aware organization, called SmartStore, which exploits metadata semantics of files to judiciously aggregate correlated files into semantica-ware groups by using information retrieval tools. Decentralized design improves system scalability and reduces query latency for complex queries (range and top-k queries), which is conducive to constructing semantic-aware caching, and conventional filename-based query. SmartStore limits search scope of complex query to a single or a minimal number of semantically related groups and avoids or alleviates brute-force search in entire system. Extensive experiments using real-world traces show that SmartStore improves system scalability and reduces query latency over basic database approaches by one thousand times. To the best of our knowledge, this is the first study implementing complex queries in large-scale file systems.
A low-power wide dynamic range envelope detector

We report a 75dB, 2.8μW, 100Hz-10kHz envelope detector in a 1.5μm 2.8V CMOS technology. The envelope detector performs input-dc-insensitive voltage-to-currentconverting rectification followed by novel nanopower current-mode peak detection. The use of a subthreshold widelinear-range transconductor (WLR OTA) allows greater than 1.7Vpp input voltage swings. We show theoretically that this optimal performance is technologyindependent for the given topology and may be improved only by spending more power. A novel circuit topology is used to perform 140nW peak detection with controllable attack and release time constants. The lower limits of envelope detection are determined by the more dominant of two effects: The first effect is caused by the inability of amplified high-frequency signals to exceed the deadzone created by exponential nonlinearities in the rectifier. The second effect is due to an output current caused by thermal noise rectification. We demonstrate good agreement of experimentally measured results with theory. The envelope detector is useful in low power bionic implants for the deaf, hearing aids, and speech-recognition front ends. Extension of the envelope detector to higherfrequency applications is straightforward if power consumption is increased.
Attacking Object Detectors via Imperceptible Patches on Background

Recent works succeeded to generate adversarial perturbations on the entire image or the object of interests to corrupt CNN based object detectors. In this paper, we focus on exploring the vulnerability of the Single Shot Module (SSM) commonly used in recent object detectors, by adding small perturbations to patches in the background outside the object. The SSM is referred to the Region Proposal Network used in a two-stage object detector or the single-stage object detector itself. The SSM is typically a fully convolutional neural network which generates output in a single forward pass. Due to the excessive convolutions used in SSM, the actual receptive field is larger than the object itself. As such, we propose a novel method to corrupt object detectors by generating imperceptible patches only in the background. Our method can find a few background patches for perturbation, which can effectively decrease true positives and dramatically increase false positives. Efficacy is demonstrated on 5 two-stage object detectors and 8 singlestage object detectors on the MS COCO 2014 dataset. Results indicate that perturbations with small distortions outside the bounding box of object region can still severely damage the detection performance.
Hierarchical Self-Organization in Genetic programming

This paper presents an approach to automatic discovery of functions in Genetic Programming. The approach is based on discovery of useful building blocks by analyzing the evolution trace, generalizing blocks to define new functions, and finally adapting the problem representation onthe-fly. Adaptating the representation determines a hierarchical organization of the extended function set which enables a restructuring of the search space so that solutions can be found more easily. Measures of complexity of solution trees are defined for an adaptive representation framework. The minimum description length principle is applied to justify the feasibilityof approaches based on a hierarchy of discovered functions and to suggest alternative ways of defining a problem’s fitness function. Preliminary empirical results are presented.
Survey on Security Issues in Cloud Computing and Associated Mitigation Techniques

Cloud Computing holds the potential to eliminate the requirements for setting up of high-cost computing infrastructure for IT-based solutions and services that the industry uses. It promises to provide a flexible IT architecture, accessible through internet from lightweight portable devices. This would allow multi-fold increase in the capacity and capabilities of the existing and new software. In a cloud computing environment, the entire data resides over a set of networked resources, enabling the data to be accessed through virtual machines. Since these data-centres may be located in any part of the world beyond the reach and control of users, there are multifarious security and privacy challenges that need to be understood and addressed. Also, one can never deny the possibility of a server breakdown that has been witnessed, rather quite often in the recent times. There are various issues that need to be addressed with respect to security and privacy in a cloud computing environment. This extensive survey paper aims to elaborate and analyze the numerous unresolved issues threatening the cloud computing adoption and diffusion affecting the various stake-holders associated with it.
SteerFit: Automated Parameter Fitting for Steering Algorithms

In the context of crowd simulation, there is a diverse set of algorithms that model steering. The performance of steering approaches, both in terms of quality of results and computational efficiency, depends on internal parameters that are manually tuned to satisfy application-specific requirements. This paper investigates the effect that these parameters have on an algorithm's performance. Using three representative steering algorithms and a set of established performance criteria, we perform a number of large scale optimization experiments that optimize an algorithm's parameters for a range of objectives. For example, our method automatically finds optimal parameters to minimize turbulence at bottlenecks, reduce building evacuation times, produce emergent patterns, and increase the computational efficiency of an algorithm. We also propose using the Pareto Optimal front as an efficient way of modelling optimal relationships between multiple objectives, and demonstrate its effectiveness by estimating optimal parameters for interactively defined combinations of the associated objectives. The proposed methodologies are general and can be applied to any steering algorithm using any set of performance criteria.
Variational Auto-encoded Deep Gaussian Processes

We develop a scalable deep non-parametric generative model by augmenting deep Gaussian processes with a recognition model. Inference is performed in a novel scalable variational framework where the variational posterior distributions are reparametrized through a multilayer perceptron. The key aspect of this reformulation is that it prevents the proliferation of variational parameters which otherwise grow linearly in proportion to the sample size. We derive a new formulation of the variational lower bound that allows us to distribute most of the computation in a way that enables to handle datasets of the size of mainstream deep learning tasks. We show the efficacy of the method on a variety of challenges including deep unsupervised learning and deep Bayesian optimization.
NL2KB: Resolving Vocabulary Gap between Natural Language and Knowledge Base in Knowledge Base Construction and Retrieval

Words to express relations in natural language (NL) statements may be different from those to represen t properties in knowledge bases (KB). The vocabulary g p becomes barriers for knowledge base construction and retrieval. With the demo system called NL2KB in this paper, users can browse which properties in KB side may be mapped to for a given relati onal pattern in NL side. Besides, they can retrieve the sets of relational patterns in NL side for a given property in KB side. We describe how the mapping is established in detail. Although the mined patterns are used for Chinese knowledge base applications, t he methodology can be extended to other languages.
A Survey on Potential Applications of Honeypot Technology in Intrusion Detection Systems

Information security in the sense of personal and institutional has become a top priority in digitalized modern world in parallel to the new technological developments. Many methods, tools and technologies are used to provide the information security of IT systems. These are considered, encryption, authentication, firewall, and intrusion detection and prevention systems. Moreover, honeypot systems are proposed as complementary structures. This paper presents the overall view of the publications in IDS, IPS and honeypot systems. Recently, honeypot systems are anymore used in connection with intrusion detection systems. So this paper describes possible implementation of honeypot technologies combined with IDS/IPS in a network. Studies in the literature have shown intrusion detection systems cannot find the 0-day vulnerabilities. The system provided by the honeypots and intrusion detection systems in the network, might detect new exploit and hacker attempt. Index Terms – Information security, Intrusion detection system (IDS), Intrusion prevention system (IPS), Honeypot, Network Security.
Attacks on Fitness Trackers Revisited: A Case-Study of Unfit Firmware Security

Fitness trackers – wearables that continuously record a wearer’s step count and related activity data – are quickly gaining in popularity. Apart from being useful for individuals seeking a more healthy lifestyle, their data is also being used in court and by insurance companies to adjust premiums. For these use cases, it is essential to ensure authenticity and integrity of data. Here we demonstrate a flaw in the way firmware for Withings’ Activité is verified, allowing an adversary to compromise the tracker itself. This type of attack has so far not been applied to fitness trackers. Vendors have started mitigating previous attacks, which manipulated data by interfering with wireless channels, or by physically moving the tracker to fool sensors. Hardware similarities amongst different trackers suggest findings can be transferred to other tracker as well.
Semantic image retrieval system based on object relationships

Semantic-based image retrieval has recently become popular as an avenue to improve retrieval accuracy. The “semantic gap” between the visual features and the high-level semantic features could be narrowed down by utilizing this kind of retrieval method. However, most of the current methods of semantic-based image retrieval utilize visual semantic features and do not consider spatial relationships. We build a system for content-based image retrieval from image collections on the web and tackle the challenges of distinguishing between images that contain similar objects, in order to capture the semantic meaning of a search query. In order to do so, we utilize a combination of segmentation into objects as well as the relationships of these objects with each other.
Temporal Sequence Learning, Prediction, and Control: A Review of Different Models and Their Relation to Biological Mechanisms

In this review, we compare methods for temporal sequence learning (TSL) across the disciplines machine-control, classical conditioning, neuronal models for TSL as well as spike-timing-dependent plasticity (STDP). This review introduces the most influential models and focuses on two questions: To what degree are reward-based (e.g., TD learning) and correlation-based (Hebbian) learning related? and How do the different models correspond to possibly underlying biological mechanisms of synaptic plasticity? We first compare the different models in an open-loop condition, where behavioral feedback does not alter the learning. Here we observe that reward-based and correlation-based learning are indeed very similar. Machine control is then used to introduce the problem of closed-loop control (e.g., actor-critic architectures). Here the problem of evaluative (rewards) versus nonevaluative (correlations) feedback from the environment will be discussed, showing that both learning approaches are fundamentally different in the closed-loop condition. In trying to answer the second question, we compare neuronal versions of the different learning architectures to the anatomy of the involved brain structures (basal-ganglia, thalamus, and cortex) and the molecular biophysics of glutamatergic and dopaminergic synapses. Finally, we discuss the different algorithms used to model STDP and compare them to reward-based learning rules. Certain similarities are found in spite of the strongly different timescales. Here we focus on the biophysics of the different calcium-release mechanisms known to be involved in STDP.
A Survey of Credit Card Fraud Detection Techniques: Data and Technique Oriented Perspective

Credit card plays a very important rule in today's economy. It becomes an unavoidable part of household, business and global activities. Although using credit cards provides enormous benefits when used carefully and responsibly,significant credit and financial damagesmay be causedby fraudulent activities. Many techniques have been proposed to confront thegrowthin credit card fraud. However, all of these techniques have the same goal of avoiding the credit card fraud; each one has its own drawbacks, advantages and characteristics. In this paper, after investigating difficultiesof credit card fraud detection, we seek to review the state of the art in credit card fraud detection techniques, datasets and evaluation criteria.The advantages and disadvantages of fraud detection methods are enumerated and compared.Furthermore, a classification of mentioned techniques into two main fraud detection approaches, namely, misuses (supervised) and anomaly detection (unsupervised) is presented. Again, a classification of techniques is proposed based on capability to process the numerical and categorical datasets. Different datasets used in literatureare then described and grouped into real and synthesized data and the effective and common attributesare extracted for further usage.Moreover, evaluation employed criterions in literature are collected and discussed.Consequently, open issues for credit card fraud detection are explained as guidelinesfor
An evaluation of alternative architectures for transaction processing in the cloud

Cloud computing promises a number of advantages for the deployment of data-intensive applications. One important promise is reduced cost with a pay-as-you-go business model. Another promise is (virtually) unlimited throughput by adding servers if the workload increases. This paper lists alternative architectures to effect cloud computing for database applications and reports on the results of a comprehensive evaluation of existing commercial cloud services that have adopted these architectures. The focus of this work is on transaction processing (i.e., read and update workloads), rather than analytics or OLAP workloads, which have recently gained a great deal of attention. The results are surprising in several ways. Most importantly, it seems that all major vendors have adopted a different architecture for their cloud services. As a result, the cost and performance of the services vary significantly depending on the workload.
Extending the Internet of Things to the Future Internet through IPv6 support

Emerging Internet of Things (IoT)/Machine-to-Machine (M2M) systems require a transparent access to information and services through a seamless integration into the Future Internet. This integration exploits infrastructure and services found on the Internet by the IoT. On the one hand, the so-called Web of Things aims for direct Web connectivity by pushing its technology down to devices and smart things. On the other hand, the current and Future Internet offer stable, scalable, extensive, and tested protocols for node and service discovery, mobility, security, and auto-configuration, which are also required for the IoT. In order to integrate the IoT into the Internet, this work adapts, extends, and bridges using IPv6 the existing IoT building blocks (such as solutions from IEEE 802.15.4, BT-LE, RFID) while maintaining backwards compatibility with legacy networked embedded systems from building and industrial automation. Specifically, this work presents an extended Internet stack with a set of adaptation layers from non-IP towards the IPv6based network layer in order to enable homogeneous access for applications and
Compact LTCC Bandpass Filter With Wide Stopband Using Discriminating Coupling

This paper presents a novel compact low-temperature cofired ceramic (LTCC) bandpass filter (BPF) with wide stopband and high selectivity. The proposed circuit consists of two coupled λ<sub>g</sub>/4 transmission-line resonators. A special coupling region is selected to realize a novel discriminating coupling scheme for generating a transmission zero (TZ) at the third harmonic frequency. The mechanism is analyzed and the design guideline is described. The source-load coupling is introduced to generate two TZs near the passband and one in the stopband. Thus, wide stopband can be obtained without extra circuits. Due to the LTCC multilayer structures, the filter size is 0.058 λ<sub>g</sub>×0.058 λ<sub>g</sub>×0.011 λ<sub>g</sub>, or 2.63 mm × 2.61 mm × 0.5 mm. The simulated and measured results of the demonstrated LTCC BPF are presented to validate the proposed design.
A computational approach to motion perception

In this paper it is shown that the computation of the optical flow from a sequence of timevarying images is not, in general, an underconstrained problem. A local algorithm for the computation of the optical flow which uses second order derivatives of the image brightness pattern, and that avoids the aperture problem, is presented. The obtained optical flow is very similar to the true motion field — which is the vector field associated with moving features on the image plane — and can be used to recover 3D motion information. Experimental results on sequences of real images, together with estimates of relevant motion parameters, like time-to-crash for translation and angular velocity for rotation, are presented and discussed. Due to the remarkable accuracy which can be achieved in estimating motion parameters, the proposed method is likely to be very useful in a number of computer vision applications.
Same-day genomic and epigenomic diagnosis of brain tumors using real-time nanopore sequencing

Molecular classification of cancer has entered clinical routine to inform diagnosis, prognosis, and treatment decisions. At the same time, new tumor entities have been identified that cannot be defined histologically. For central nervous system tumors, the current World Health Organization classification explicitly demands molecular testing, e.g., for 1p/19q-codeletion or IDH mutations, to make an integrated histomolecular diagnosis. However, a plethora of sophisticated technologies is currently needed to assess different genomic and epigenomic alterations and turnaround times are in the range of weeks, which makes standardized and widespread implementation difficult and hinders timely decision making. Here, we explored the potential of a pocket-size nanopore sequencing device for multimodal and rapid molecular diagnostics of cancer. Low-pass whole genome sequencing was used to simultaneously generate copy number (CN) and methylation profiles from native tumor DNA in the same sequencing run. Single nucleotide variants in IDH1, IDH2, TP53, H3F3A, and the TERT promoter region were identified using deep amplicon sequencing. Nanopore sequencing yielded ~0.1X genome coverage within 6 h and resulting CN and epigenetic profiles correlated well with matched microarray data. Diagnostically relevant alterations, such as 1p/19q codeletion, and focal amplifications could be recapitulated. Using ad hoc random forests, we could perform supervised pan-cancer classification to distinguish gliomas, medulloblastomas, and brain metastases of different primary sites. Single nucleotide variants in IDH1, IDH2, and H3F3A were identified using deep amplicon sequencing within minutes of sequencing. Detection of TP53 and TERT promoter mutations shows that sequencing of entire genes and GC-rich regions is feasible. Nanopore sequencing allows same-day detection of structural variants, point mutations, and methylation profiling using a single device with negligible capital cost. It outperforms hybridization-based and current sequencing technologies with respect to time to diagnosis and required laboratory equipment and expertise, aiming to make precision medicine possible for every cancer patient, even in resource-restricted settings.
Teaching Machines to Read and Comprehend

Teaching machines to read natural language documents remains an elusive challenge. Machine reading systems can be tested on their ability to answer questions posed on the contents of documents that they have seen, but until now large scale training and test datasets have been missing for this type of evaluation. In this work we define a new methodology that resolves this bottleneck and provides large scale supervised reading comprehension data. This allows us to develop a class of attention based deep neural networks that learn to read real documents and answer complex questions with minimal prior knowledge of language structure.
Ring-Light Photometric Stereo

We propose a novel algorithm for uncalibrated photometric stereo. While most of previous methods rely on various assumptions on scene properties, we exploit constraints in lighting configurations. We first derive an ambiguous reconstruction by requiring lights to lie on a view centered cone. This reconstruction is upgraded to Euclidean by constraints derived from lights of equal intensity and multiple view geometry. Compared to previous methods, our algorithm deals with more general data and achieves high accuracy. Another advantage of our method is that we can model weak perspective effects of lighting, while previous methods often assume orthographical illumination. We use both synthetic and real data to evaluate our algorithm. We further build a hardware prototype to demonstrate our approach.