nils-herrmann/clean_scirepeval huggingface dataset:
Terabit/s-Class Optical PCB Links Incorporating 360-Gb/s Bidirectional 850 nm Parallel Optical Transceivers

We report here on the design, fabrication, and characterization of highly integrated parallel optical transceivers designed for Tb/s-class module-to-module data transfer through polymer waveguides integrated into optical printed circuit boards (o-PCBs). The parallel optical transceiver is based on a through-silicon-via silicon carrier as the platform for integration of 24-channel vertical cavity surface-emitting laser and photodiode arrays with CMOS ICs. The Si carrier also includes optical vias (holes) for optical access to conventional surface-emitting 850 nm optoelectronic devices. The 48-channel 3-D transceiver optochips are flip-chip soldered to organic carriers to form transceiver optomodules. Fully functional optomodules with 24 transmitter + 24 receiver channels were assembled and characterized with transmitters operating up to 20 Gb/s/ch and receivers up to 15 Gb/s/ch. At 15 Gb/s, the 48-channel optomodules provide a bidirectional aggregate bandwidth of 360 Gb/s. In addition, o-PCBs have been developed using a 48-channel flex waveguide assembly attached to FR4 electronic boards. Incorporation of waveguide turning mirrors and lens arrays facilitates optical coupling to/from the o-PCB. Assembly of optomodules to the o-PCB using a ball grid array process provides both electrical and optical interconnections. An initial demonstration of the full module-to-module optical link achieved >; 20 bidirectional links at 10 Gb/s. At 15 Gb/s, operation at a bit error ratio of <; 10- 12 was demonstrated for 15 channels in each direction, realizing a record o-PCB link with a 225 Gb/s bidirectional aggregate data rate.
Automatically patching errors in deployed software

We present ClearView, a system for automatically patching errors in deployed software. ClearView works on stripped Windows x86 binaries without any need for source code, debugging information, or other external information, and without human intervention.
 ClearView (1) observes normal executions to learn invariants thatcharacterize the application's normal behavior, (2) uses error detectors to distinguish normal executions from erroneous executions, (3) identifies violations of learned invariants that occur during erroneous executions, (4) generates candidate repair patches that enforce selected invariants by changing the state or flow of control to make the invariant true, and (5) observes the continued execution of patched applications to select the most successful patch.
 ClearView is designed to correct errors in software with high availability requirements. Aspects of ClearView that make it particularly appropriate for this context include its ability to generate patches without human intervention, apply and remove patchesto and from running applications without requiring restarts or otherwise perturbing the execution, and identify and discard ineffective or damaging patches by evaluating the continued behavior of patched applications.
 ClearView was evaluated in a Red Team exercise designed to test its ability to successfully survive attacks that exploit security vulnerabilities. A hostile external Red Team developed ten code injection exploits and used these exploits to repeatedly attack an application protected by ClearView. ClearView detected and blocked all of the attacks. For seven of the ten exploits, ClearView automatically generated patches that corrected the error, enabling the application to survive the attacks and continue on to successfully process subsequent inputs. Finally, the Red Team attempted to make Clear-View apply an undesirable patch, but ClearView's patch evaluation mechanism enabled ClearView to identify and discard both ineffective patches and damaging patches.
Model-based anthropometry: Predicting measurements from 3D human scans in multiple poses

Extracting anthropometric or tailoring measurements from 3D human body scans is important for applications such as virtual try-on, custom clothing, and online sizing. Existing commercial solutions identify anatomical landmarks on high-resolution 3D scans and then compute distances or circumferences on the scan. Landmark detection is sensitive to acquisition noise (e.g. holes) and these methods require subjects to adopt a specific pose. In contrast, we propose a solution we call model-based anthropometry. We fit a deformable 3D body model to scan data in one or more poses; this model-based fitting is robust to scan noise. This brings the scan into registration with a database of registered body scans. Then, we extract features from the registered model (rather than from the scan); these include, limb lengths, circumferences, and statistical features of global shape. Finally, we learn a mapping from these features to measurements using regularized linear regression. We perform an extensive evaluation using the CAESAR dataset and demonstrate that the accuracy of our method outperforms state-of-the-art methods.
A 4D Light-Field Dataset and CNN Architectures for Material Recognition

Material Recognition Ting-Chun Wang1, Jun-Yan Zhu1, Ebi Hiroaki2, Manmohan Chandraker2, Alexei A. Efros1, Ravi Ramamoorthi2 1University of California, Berkeley University of California, San Diego Motivation • Light-field images should help recognize materials since reflectance can be estimated • CNNs have recently been very successful in material recognition • We combine these two and propose a new light-field dataset since no one is currently available
Geometry-and Appearance-based Reasoning of Construction Progress 1 Monitoring

9 Although adherence to project schedules and budgets is most highly valued by project owners, 10 more than 53% of typical construction projects are behind schedule and more than 66% suffer 11 from cost overruns, partly due to inability to accurately capture construction progress. To address 12 these challenges, this paper presents new geometryand appearance-based reasoning methods for 13 detecting construction progress, which has the potential to provide more frequent progress mea14 sures using visual data that are already being collected by general contractors. The initial step 15 of geometry-based filtering detects the state of construction of Building Information Modeling 16 (BIM) elements (e.g. in-progress, completed). The next step of appearance-based reasoning cap17 tures operation-level activities by recognizing different material types. Two methods have been 18 investigated for the latter step: a texture-based reasoning for image-based 3D point clouds and 19 color-based reasoning for laser scanned point clouds. This paper presents two case studies for 20 each reasoning approach for validating the proposed methods. The results demonstrate the effec21 tiveness and practical significances of the proposed methods.
SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks

Saliency in Context (SALICON) is an ongoing effort that aims at understanding and predicting visual attention. Conventional saliency models typically rely on low-level image statistics to predict human fixations. While these models perform significantly better than chance, there is still a large gap between model prediction and human behavior. This gap is largely due to the limited capability of models in predicting eye fixations with strong semantic content, the so-called semantic gap. This paper presents a focused study to narrow the semantic gap with an architecture based on Deep Neural Network (DNN). It leverages the representational power of high-level semantics encoded in DNNs pretrained for object recognition. Two key components are fine-tuning the DNNs fully convolutionally with an objective function based on the saliency evaluation metrics, and integrating information at different image scales. We compare our method with 14 saliency models on 6 public eye tracking benchmark datasets. Results demonstrate that our DNNs can automatically learn features particularly for saliency prediction that surpass by a big margin the state-of-the-art. In addition, our model ranks top to date under all seven metrics on the MIT300 challenge set.
Combating online fraud attacks in mobile-based advertising

Smartphone advertisement is increasingly used among many applications and allows developers to obtain revenue through in-app advertising. Our study aims at identifying potential security risks of mobile-based advertising services where advertisers are charged for their advertisements on mobile applications. In the Android platform, we particularly implement bot programs that can massively generate click events on advertisements on mobile applications and test their feasibility with eight popular advertising networks. Our experimental results show that six advertising networks (75%) out of eight are vulnerable to our attacks. To mitigate click fraud attacks, we suggest three possible defense mechanisms: (1) filtering out program-generated touch events; (2) identifying click fraud attacks with faked advertisement banners; and (3) detecting anomalous behaviors generated by click fraud attacks. We also discuss why few companies were only willing to deploy such defense mechanisms by examining economic misincentives on the mobile advertising industry.
Weakly-Supervised Video Object Grounding from Text by Loss Weighting and Object Interaction

We study weakly-supervised video object grounding: given a video segment and a corresponding descriptive sentence, the goal is to localize objects that are mentioned from the sentence in the video. During training, no object bounding boxes are available, but the set of possible objects to be grounded is known beforehand. Existing approaches in the image domain use Multiple Instance Learning (MIL) to ground objects by enforcing matches between visual and semantic features. A naive extension of this approach to the video domain is to treat the entire segment as a bag of spatial object proposals. However, an object existing sparsely across multiple frames might not be detected completely since successfully spotting it from one single frame would trigger a satisfactory match. To this end, we propagate the weak supervisory signal from the segment level to frames that likely contain the target object. For frames that are unlikely to contain the target objects, we use an alternative penalty loss. We also leverage the interactions among objects as a textual guide for the grounding. We evaluate our model on the newlycollected benchmark YouCook2-BoundingBox and show improvements over competitive baselines.
Promises and Partnership

We examine experimentally the impact of communication on trust and cooperation. Our design admits observation of promises, lies, and beliefs. The evidence is consistent with people striving to live up to others’ expectations so as to avoid guilt, as can be modeled using psychological game theory. When players exhibit such guilt aversion, communication may influence motivation and behavior by influencing beliefs about beliefs. Promises may enhance trustworthy behavior, which is what we observe. We argue that guilt aversion may be relevant for understanding strategic interaction in a variety of settings, and that it may shed light on the role of language, discussions, agreements, and social norms in these contexts.
When Far Becomes Near: Remapping of Space by Tool Use

Far (extrapersonal) and near (peripersonal) spaces are behaviorally defined as the space outside the hand-reaching distance and the space within the hand-reaching distance. Animal and human studies have confirmed this distinction, showing that space is not homogeneously represented in the brain. In this paper we demonstrate that the coding of space as far and near is not only determined by the hand-reaching distance, but it is also dependent on how the brain represents the extension of the body space. We will show that when the cerebral representation of body space is extended to include objects or tools used by the subject, space previously mapped as far can be remapped as near. Patient P.P., after a right hemisphere stroke, showed a dissociation between near and far spaces in the manifestation of neglect. Indeed, in a line bisection task, neglect was apparent in near space, but not in far space when bisection in the far space was performed with a projection lightpen. However, when in the far space bisection was performed with a stick, used by the patient to reach the line, neglect appeared and was as severe as neglect in the near space. An artificial extension of the patient's body (the stick) caused a remapping of far space as near space.
Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions

In this work, we propose a generalized product of experts (gPoE) framework for combining the predictions of multiple probabilistic models. We identify four desirable properties that are important for scalability, expressiveness and robustness, when learning and inferring with a combination of multiple models. Through analysis and experiments, we show that gPoE of Gaussian processes (GP) have these qualities, while no other existing combination schemes satisfy all of them at the same time. The resulting GP-gPoE is highly scalable as individual GP experts can be independently learned in parallel; very expressive as the way experts are combined depends on the input rather than fixed; the combined prediction is still a valid probabilistic model with natural interpretation; and finally robust to unreliable predictions from individual experts.
Data-Driven Synthesis of Cartoon Faces Using Different Styles

This paper presents a data-driven approach for automatically generating cartoon faces in different styles from a given portrait image. Our stylization pipeline consists of two steps: an offline analysis step to learn about how to select and compose facial components from the databases; a runtime synthesis step to generate the cartoon face by assembling parts from a database of stylized facial components. We propose an optimization framework that, for a given artistic style, simultaneously considers the desired image-cartoon relationships of the facial components and a proper adjustment of the image composition. We measure the similarity between facial components of the input image and our cartoon database via image feature matching, and introduce a probabilistic framework for modeling the relationships between cartoon facial components. We incorporate prior knowledge about image-cartoon relationships and the optimal composition of facial components extracted from a set of cartoon faces to maintain a natural, consistent, and attractive look of the results. We demonstrate generality and robustness of our approach by applying it to a variety of portrait images and compare our output with stylized results created by artists via a comprehensive user study.
Opinosis: A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions

We present a novel graph-based summarization framework (Opinosis) that generates concise abstractive summaries of highly redundant opinions. Evaluation results on summarizing user reviews show that Opinosis summaries have better agreement with human summaries compared to the baseline extractive method. The summaries are readable, reasonably well-formed and are informative enough to convey the major opinions.
Chronological Examination of Insider Threat Sabotage: Preliminary Observations

The threat of malicious insiders to organizations is persistent and increasing. We examine 15 real cases of insider threat sabotage of IT systems to identify several key points in the attack time-line, such as when the insider clearly became disgruntled, began attack preparations, and carried out the attack. We also determine when the attack stopped, when it was detected, and when action was taken on the insider. We found that 7 of the insiders we studied clearly became disgruntled more than 28 days prior to attack, but 9 did not carry out malicious acts until less than a day prior to attack. Of the 15 attacks, 8 ended within a day, 12 were detected within a week, and in 10 cases action was taken on the insider within a month. This exercise is a proof-of-concept for future work on larger data sets, and in this paper we detail our study methods and results, discuss challenges we faced, and identify potential new research directions.
A Fast File System for UNIX

A reimplementation of the UNIX file system is described. The reimplementation provides substantially higher throughput rates by using more flexible allocation policies that allow better locality of reference and can be adapted to a wide range of peripheral and processor characteristics. The new file system clusters data that is sequentially accessed and provides tw o block sizes to allo w fast access to lar ge files while not wasting large amounts of space for small files. File access rates of up to ten times f aster than the traditional UNIX file system are e xperienced. Longneeded enhancements to the programmers’ interface are discussed. These include a mechanism to place advisory locks on files, extensions of the name space across file systems, the ability to use long file names, and provisions for administrati ve control of resource usage. Revised February 18, 1984 CR
Distributed Optimal Power Flow for Smart Microgrids

Optimal power flow (OPF) is considered for microgrids, with the objective of minimizing either the power distribution losses, or, the cost of power drawn from the substation and supplied by distributed generation (DG) units, while effecting voltage regulation. The microgrid is unbalanced, due to unequal loads in each phase and non-equilateral conductor spacings on the distribution lines. Similar to OPF formulations for balanced systems, the considered OPF problem is nonconvex. Nevertheless, a semidefinite programming (SDP) relaxation technique is advocated to obtain a convex problem solvable in polynomial-time complexity. Enticingly, numerical tests demonstrate the ability of the proposed method to attain the globally optimal solution of the original nonconvex OPF. To ensure scalability with respect to the number of nodes, robustness to isolated communication outages, and data privacy and integrity, the proposed SDP is solved in a distributed fashion by resorting to the alternating direction method of multipliers. The resulting algorithm entails iterative message-passing among groups of consumers and guarantees faster convergence compared to competing alternatives.
A rapid pattern-recognition method for driving styles using clustering-based support vector machines

A rapid pattern-recognition approach to characterize driver's curve-negotiating behavior is proposed. To shorten the recognition time and improve the recognition of driving styles, a k-means clustering-based support vector machine (kMC-SVM) method is developed and used for classifying drivers into two types: aggressive and moderate. First, vehicle speed and throttle opening are treated as the feature parameters to reflect the driving styles. Second, to discriminate driver curve-negotiating behaviors and reduce the number of support vectors, the k-means clustering method is used to extract and gather the two types of driving data and shorten the recognition time. Then, based on the clustering results, a support vector machine approach is utilized to generate the hyperplane for judging and predicting to which types the human driver are subject. Lastly, to verify the validity of the kMC-SVM method, a cross-validation experiment is designed and conducted. The research results show that the kMC-SVM is an effective method to classify driving styles with a short time, compared with SVM method.
MetaPAD: Meta Pattern Discovery from Massive Text Corpora

Mining textual patterns in news, tweets, papers, and many other kinds of text corpora has been an active theme in text mining and NLP research. Previous studies adopt a dependency parsing-based pattern discovery approach. However, the parsing results lose rich context around entities in the patterns, and the process is costly for a corpus of large scale. In this study, we propose a novel typed textual pattern structure, called meta pattern, which is extended to a frequent, informative, and precise subsequence pattern in certain context. We propose an efficient framework, called MetaPAD, which discovers meta patterns from massive corpora with three techniques: (1) it develops a context-aware segmentation method to carefully determine the boundaries of patterns with a learnt pattern quality assessment function, which avoids costly dependency parsing and generates high-quality patterns; (2) it identifies and groups synonymous meta patterns from multiple facets---their types, contexts, and extractions; and (3) it examines type distributions of entities in the instances extracted by each group of patterns, and looks for appropriate type levels to make discovered patterns precise. Experiments demonstrate that our proposed framework discovers high-quality typed textual patterns efficiently from different genres of massive corpora and facilitates information extraction.
Disparity image segmentation for free-space detection

The paper introduces a novel and efficient algorithm for determining the free-space in road driving assistance scenarios. The input data for the algorithm is gathered from a stereo camera and is processed as a disparity image. Each column of the disparity image is segmented based on its relative extreme points. The idea is inspired from a time series compression article which presents a method for segmenting data measured at equal intervals of time (time series): electro cardiograms, monthly stocking-exchanges, etc. The novelty of the method consists in adapting an idea used in a different area of interest for an image recognition purpose. Compared to existing algorithms in the driving assistance field that share the same goal, the proposed method achieves great adaptability and a linear time performance. The adaptability of the method is worth mentioning as it gives good results both on precise data gathered with a lidar scanner and on noisy disparity inferred with a stereo camera. The algorithm filters most of the errors of measurement while preserving the points of interest that delimit the road, objects or sky. Because the filtering steps preserve the data of interest, additional post-processing steps are no longer required thus minimizing the time complexity.
A Socratic method for validation of measurement-based networking research

Nearly threedecadesof Internetmeasurement hasresultedin large-scaleglobal infrastructuresusedby an increasingnumber of researchers.They have examinedvariousInternetpropertiesin areassuchas network infrastructure(routers,links), tra c (measurement at packet, flow, andsessionlevel) andapplications(DNS, Web, P2P, online socialnetworks etc.) andpresented resultsin diversevenues.Key relatedtopicslike securityandprivacy have alsobeenexplored.Thereis however a lack of clearly articulatedstandardsthat reducetheprobabilityof commonmistakesmadein studiesinvolving measurements, their analysisand modeling. A community-widee ort is likely to fosterfidelity in datasetsobtainedfrom measurements andreusedin subsequent studies.We presenta Socraticapproachasstepstowardsa solutionto this problemby enumeratinga sequenceof questionsthat canbeansweredrelatively quickly by bothmeasurersandreusersof datasets. To illustratetheapplicabilityandappropriateness of thequestionswe answerthemfor anumberof pastandcurrentmeasurement studies.