nils-herrmann/clean_scirepeval huggingface dataset:
A taxonomy of ETL activities

Extract-Transform-Load (ETL) activities are software modules responsible for populating a data warehouse with operational data, which have undergone a series of transformations on their way to the warehouse. The whole process is very complex and of signifi-cant importance for the design and maintenance of the data ware-house. A plethora of commercial ETL tools are already available in the market. However, each one of them follows a different ap-proach for the modeling of ETL activities; i.e., of the building blocks of an ETL workflow. As a result, so far there is no standard or unified approach for describing such activities. In this paper, we are working towards the identification of generic properties that characterize ETL activities. In doing so, we follow a black-box approach and provide a taxonomy that characterizes ETL activities in terms of the relationship of their input to their output and provide a normal form that is based on interpreted semantics for the black box activities. Finally, we show how the proposed taxonomy can be used in the construction of larger modules, i.e., ETL archetype patterns, which can be used for the composition and optimization of ETL workflows.
Deep Neural Networks

With ever-increasing computational demand for deep learning, it is critical to investigate the implications of the numeric representation and precision of DNN model weights and activations on computational efficiency. In this work, we explore unconventional narrow-precision floating-point representations as it relates to inference accuracy and efficiency to steer the improved design of future DNN platforms. We show that inference using these custom numeric representations on production-grade DNNs, including GoogLeNet and VGG, achieves an average speedup of 7.6× with less than 1% degradation in inference accuracy relative to a state-of-the-art baseline platform representing the most sophisticated hardware using single-precision floating point. To facilitate the use of such customized precision, we also present a novel technique that drastically reduces the time required to derive the optimal precision configuration.
Consumer behavior in social commerce: A literature review

August 2015 Received in revised form 26 January 2016  The emergence of social commerce has brought substantial changes to both businesses and consumers. Hence, understanding consumer behavior in the context of social commerce has become critical for companies that aim to better influence consumers and harness the power of their social ties. Given that research on this issue is new and largely fragmented, it will be theoretically important to evaluate what has been studied and derive meaningful insights through a structured review of the literature. In this study, we conduct a systematic review of social commerce studies to explicate how consumers behave on social networking sites. We classify these studies, discuss noteworthy theories, and identify important research methods. More importantly, we draw upon the stimulus–organism–response model and the five-stage consumer decision-making process to propose an integrative framework for understanding consumer behavior in this context. We believe that this framework can provide a useful basis for future social commerce research.
Faster and Timing-Attack Resistant AES-GCM

We present a bitsliced implementation of AES encryption in counter mode for 64-bit Intel processors. Running at 7.59 cycles/byte on a Core 2, it is up to 25% faster than previous implementations, while simultaneously offering protection against timing attacks. In particular, it is the only cache-timing-attack resistant implementation offering competitive speeds for stream as well as for packet encryption: for 576-byte packets, we improve performance over previous bitsliced implementations by more than a factor of 2. We also report more than 30% improved speeds for lookup-table based Galois/Counter mode authentication, achieving 10.68 cycles/byte for authenticated encryption. Furthermore, we present the first constant-time implementation of AES-GCM that has a reasonable speed of 21.99 cycles/byte, thus offering a full suite of timing-analysis resistant software for authenticated encryption.
A Single Photon Avalanche Diode Implemented in 130-nm CMOS Technology

We report on the first implementation of a single photon avalanche diode (SPAD) in 130 nm complementary metal-oxide-semiconductor (CMOS) technology. The SPAD is fabricated as p+/n-well junction with octagonal shape. A guard ring of p-well around the p+ anode is used to prevent premature discharge. To investigate the dynamics of the new device, both active and passive quenching methods have been used. Single photon detection is achieved by sensing the avalanche using a fast comparator. The SPAD exhibits a maximum photon detection probability of 41% and a typical dark count rate of 100 kHz at room temperature. Thanks to its timing resolution of 144 ps full-width at half-maximum (FWHM), the SPAD has several uses in disparate disciplines, including medical imaging, 3D vision, biophotonics, low-light illumination imaging, etc.
Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking

Tracking-by-detection methods have demonstrated competitive performance in recent years. In these approaches, the tracking model heavily relies on the quality of the training set. Due to the limited amount of labeled training data, additional samples need to be extracted and labeled by the tracker itself. This often leads to the inclusion of corrupted training samples, due to occlusions, misalignments and other perturbations. Existing tracking-by-detection methods either ignore this problem, or employ a separate component for managing the training set. We propose a novel generic approach for alleviating the problem of corrupted training samples in tracking-by-detection frameworks. Our approach dynamically manages the training set by estimating the quality of the samples. Contrary to existing approaches, we propose a unified formulation by minimizing a single loss over both the target appearance model and the sample quality weights. The joint formulation enables corrupted samples to be downweighted while increasing the impact of correct ones. Experiments are performed on three benchmarks: OTB-2015 with 100 videos, VOT-2015 with 60 videos, and Temple-Color with 128 videos. On the OTB-2015, our unified formulation significantly improves the baseline, with a gain of 3:8% in mean overlap precision. Finally, our method achieves state-of-the-art results on all three datasets.
Process Implanting: A New Active Introspection Framework for Virtualization

Previous research on virtual machine introspection proposed "out-of-box" approach by moving out security tools from the guest operating system. However, compared to the traditional "in-the-box" approach, it remains a challenge to obtain a complete semantic view due to the semantic gap between the guest VM and the hyper visor. In this paper, we present Process Implanting, a new active VM introspection framework, to narrow the semantic gap by implanting a process from the host into the guest VM and executing it under the cover of an existing running process. With the protection and coordination from the hyper visor, the implanted process can run with a degree of stealthiest and exit gracefully without leaving negative impact on the guest operating system. We have designed and implemented a proof-of-concept prototype on KVM which leverages hardware virtualization. We also propose and demonstrate application scenarios for Process Implanting in the area of VM security.
Deep Neural Network based Human Activity Recognition for the Order Picking Process

Although the fourth industrial revolution is already in pro-gress and advances have been made in automating factories, completely automated facilities are still far in the future. Human work is still an important factor in many factories and warehouses, especially in the field of logistics. Manual processes are, therefore, often subject to optimization efforts. In order to aid these optimization efforts, methods like human activity recognition (HAR) became of increasing interest in industrial settings. In this work a novel deep neural network architecture for HAR is introduced. A convolutional neural network (CNN), which employs temporal convolutions, is applied to the sequential data of multiple intertial measurement units (IMUs). The network is designed to separately handle different sensor values and IMUs, joining the information step-by-step within the architecture. An evaluation is performed using data from the order picking process recorded in two different warehouses. The influence of different design choices in the network architecture, as well as pre- and post-processing, will be evaluated. Crucial steps for learning a good classification network for the task of HAR in a complex industrial setting will be shown. Ultimately, it can be shown that traditional approaches based on statistical features as well as recent CNN architectures are outperformed.
Does Impulsivity Relate to Perceived Dependence on and Actual Use of the Mobile Phone ?

Several authors have studied the risks arising from the growth in mobile phone use (e.g. large debts incurred by young people, banned or dangerous use of cellular phones). The aim of this study is to analyse whether impulsivity, which has often been related to various forms of addictive behaviours, is associated with massive use of and dependence on the mobile phone. In this study, 108 female undergraduate psychology students were screened using a questionnaire evaluating actual use of and perceived dependence on the mobile phone, and with the French adaptation of the UPPS Impulsive Behavior Scale. This scale identifies four distinct components associated with impulsive behaviour: Urgency, lack of Premeditation, lack of Perseverance, and Sensation Seeking. The results showed that a relationship can be established between the use of and perceived dependence on the cellular phone and two facets of impulsivity: Urgency and lack of Perseverance.
Two years of short URLs internet measurement: security threats and countermeasures

URL shortening services have become extremely popular. However, it is still unclear whether they are an effective and reliable tool that can be leveraged to hide malicious URLs, and to what extent these abuses can impact the end users. With these questions in mind, we first analyzed existing countermeasures adopted by popular shortening services. Surprisingly, we found such countermeasures to be ineffective and trivial to bypass. This first measurement motivated us to proceed further with a large-scale collection of the HTTP interactions that originate when web users access live pages that contain short URLs. To this end, we monitored 622 distinct URL shortening services between March 2010 and April 2012, and collected 24,953,881 distinct short URLs. With this large dataset, we studied the abuse of short URLs. Despite short URLs are a significant, new security risk, in accordance with the reports resulting from the observation of the overall phishing and spamming activity, we found that only a relatively small fraction of users ever encountered malicious short URLs. Interestingly, during the second year of measurement, we noticed an increased percentage of short URLs being abused for drive-by download campaigns and a decreased percentage of short URLs being abused for spam campaigns. In addition to these security-related findings, our unique monitoring infrastructure and large dataset allowed us to complement previous research on short URLs and analyze these web services from the user's perspective.
License Plate Extraction in Low Resolution Video

Extensive study has been conducted in the detection of license plate for the applications in intelligent transportation system (ITS). However, these results are all based on images acquired at a resolution of 640 times 480. In this paper, a new method is proposed to extract license plate from the surveillance video which is shot at lower resolution (320 times 240) as well as degraded by video compression. Morphological operations of bottom-hat and morphology gradient are utilized to detect the LP candidates, and effective schemes are applied to select the correct one. The average rates of correct extraction and false alarms are 96.62% and 1.77%, respectively, based on the experiments using more than four hours of video. The experimental results demonstrate the effectiveness and robustness of the proposed method
Road region estimation using a sequence of monocular images

In advanced driving assistance systems, it is important to be able to detect the region covered by the road in the images. This paper proposes a method for estimating the road region in images captured by a vehicle-mounted monocular camera. Our proposed method first estimates all of relevant parameters for the camera motion and the 3D road plane from correspondence points between successive images. By calculating a homography matrix from the estimated camera motion and the estimated road plane parameters, and then warping the image at the previous frame, the road region can be determined. To achieve robustness in various road scenes, our method selects the threshold for determining the road region adaptively and incorporates the assumption of a simple road region boundary. In our experiments, it has been shown that the proposed method is able to estimate the road region in real road environments.
The State-of-the-Art in IC Reverse Engineering

This paper gives an overview of the place of reverse engineering (RE) in the semiconductor industry, and the techniques used to obtain information from semiconductor products. The continuous drive of Moores law to increase the integration level of silicon chips has presented major challenges to the reverse engineer, obsolescing simple teardowns, and demanding the adoption of new and more sophisticated technology to analyse chips. Hardware encryption embedded in chips adds a whole other level of difficulty to IC analysis. This paper covers product teardowns, and discusses the techniques used for system-level analysis, both hardware and software; circuit extraction, taking the chip down to the transistor level, and working back up through the interconnects to create schematics; and process analysis, looking at how a chip is made, and what it is made of. Examples are also given of each type of RE. The paper concludes with a case study of the analysis of an IC with embedded encryption hardware.
First I "like" it, then I hide it: Folk Theories of Social Feeds

Many online platforms use curation algorithms that are opaque to the user. Recent work suggests that discovering a filtering algorithm's existence in a curated feed influences user experience, but it remains unclear how users reason about the operation of these algorithms. In this qualitative laboratory study, researchers interviewed a diverse, non-probability sample of 40 Facebook users before, during, and after being presented alternative displays of Facebook's News Feed curation algorithm's output. Interviews revealed 10 "folk theories' of automated curation, some quite unexpected. Users who were given a probe into the algorithm's operation via an interface that incorporated "seams,' visible hints disclosing aspects of automation operations, could quickly develop theories. Users made plans that depended on their theories. We conclude that foregrounding these automated processes may increase interface design complexity, but it may also add usability benefits.
Combining PSO and k-means to enhance data clustering

In this paper we propose a clustering method based on combination of the particle swarm optimization (PSO) and the k-mean algorithm. PSO algorithm was showed to successfully converge during the initial stages of a global search, but around global optimum, the search process will become very slow. On the contrary, k-means algorithm can achieve faster convergence to optimum solution. At the same time, the convergent accuracy for k-means can be higher than PSO. So in this paper, a hybrid algorithm combining particle swarm optimization (PSO) algorithm with k-means algorithm is proposed we refer to it as PSO-KM algorithm. The algorithm aims to group a given set of data into a user specified number of clusters. We evaluate the performance of the proposed algorithm using five datasets. The algorithm performance is compared to K-means and PSO clustering.
The third Dialog State Tracking Challenge

In spoken dialog systems, dialog state tracking refers to the task of correctly inferring the user's goal at a given turn, given all of the dialog history up to that turn. This task is challenging because of speech recognition and language understanding errors, yet good dialog state tracking is crucial to the performance of spoken dialog systems. This paper presents results from the third Dialog State Tracking Challenge, a research community challenge task based on a corpus of annotated logs of human-computer dialogs, with a blind test set evaluation. The main new feature of this challenge is that it studied the ability of trackers to generalize to new entities - i.e. new slots and values not present in the training data. This challenge received 28 entries from 7 research teams. About half the teams substantially exceeded the performance of a competitive rule-based baseline, illustrating not only the merits of statistical methods for dialog state tracking but also the difficulty of the problem.
Pseudo-CMOS: A novel design style for flexible electronics

Flexible electronics have attracted much attention since they enable promising applications such as low-cost RFID tags and e-paper. Thin-film transistors (TFTs) are considered as an ideal candidate to implement flexible electronics on low-cost substrates. Most TFT technologies, however, have only mono-type -- either n- or p-type -- devices and thus modern design technologies for silicon-based electronics cannot be directly applied. In this paper, we propose a novel design style Pseudo-CMOS for flexible electronics that uses only mono-type TFTs while achieving comparable performance with the complementary-type designs. The manufacturing cost and complexity can therefore be significantly reduced while the circuit yield and reliability are also enhanced with the built-in capability of post-fabrication tuning. Some standard cells have been designed and fabricated in p-type organic and n-type InGaZnO (IGZO) TFT technologies which successfully verify the superiority of the proposed Pseudo-CMOS design style. To the best of our knowledge, this is the first design solution that has proven superior performance for both types of TFT technologies.
Fast Traffic Sign Recognition via High-Contrast Region Extraction and Extended Sparse Representation

In this paper, we propose a high-performance traffic sign recognition (TSR) framework to rapidly detect and recognize multiclass traffic signs in high-resolution images. This framework includes three parts: a novel region-of-interest (ROI) extraction method called the high-contrast region extraction (HCRE), the split-flow cascade tree detector (SFC-tree detector), and a rapid occlusion-robust traffic sign classification method based on the extended sparse representation classification (ESRC). Unlike the color-thresholding or extreme region extraction methods used by previous ROI methods, the ROI extraction method of the HCRE is designed to extract ROI with high local contrast, which can keep a good balance of the detection rate and the extraction rate. The SFC-tree detector can detect a large number of different types of traffic signs in high-resolution images quickly. The traffic sign classification method based on the ESRC is designed to classify traffic signs with partial occlusion. Instead of solving the sparse representation problem using an overcomplete dictionary, the classification method based on the ESRC utilizes a content dictionary and an occlusion dictionary to sparsely represent traffic signs, which can largely reduce the dictionary size in the occlusion-robust dictionaries and achieve high accuracy. The experiments demonstrate the advantage of the proposed approach, and our TSR framework can rapidly detect and recognize multiclass traffic signs with high accuracy.
Improving Coreference Resolution by Learning Entity-Level Distributed Representations

A long-standing challenge in coreference resolution has been the incorporation of entity-level information – features defined over clusters of mentions instead of mention pairs. We present a neural network based coreference system that produces high-dimensional vector representations for pairs of coreference clusters. Using these representations, our system learns when combining clusters is desirable. We train the system with a learning-to-search algorithm that teaches it which local decisions (cluster merges) will lead to a high-scoring final coreference partition. The system substantially outperforms the current state-of-the-art on the English and Chinese portions of the CoNLL 2012 Shared Task dataset despite using few hand-engineered features.
Averaging covariance matrices for EEG signal classification based on the CSP: An empirical study

This paper presents an empirical comparison of covariance matrix averaging methods for EEG signal classification. Indeed, averaging EEG signal covariance matrices is a key step in designing brain-computer interfaces (BCI) based on the popular common spatial pattern (CSP) algorithm. BCI paradigms are typically structured into trials and we argue that this structure should be taken into account. Moreover, the non-Euclidean structure of covariance matrices should be taken into consideration as well. We review several approaches from the literature for averaging covariance matrices in CSP and compare them empirically on three publicly available datasets. Our results show that using Riemannian geometry for averaging covariance matrices improves performances for small dimensional problems, but also the limits of this approach when the dimensionality increases.
Multi-Predicate Semantic Role Labeling

The current approaches to Semantic Role Labeling (SRL) usually perform role classification for each predicate separately and the interaction among individual predicate’s role labeling is ignored if there is more than one predicate in a sentence. In this paper, we prove that different predicates in a sentence could help each other during SRL. In multi-predicate role labeling, there are mainly two key points: argument identification and role labeling of the arguments shared by multiple predicates. To address these issues, in the stage of argument identification, we propose novel predicate-related features which help remove many argument identification errors; in the stage of argument classification, we adopt a discriminative reranking approach to perform role classification of the shared arguments, in which a large set of global features are proposed. We conducted experiments on two standard benchmarks: Chinese PropBank and English PropBank. The experimental results show that our approach can significantly improve SRL performance, especially in Chinese PropBank.