nils-herrmann/clean_scirepeval huggingface dataset:
PowerScope: A Tool for Profiling the Energy Usage of Mobile Applications

In this paper, we describe the design and implementation of PowerScope, a tool for profiling energy usage by applications. PowerScope maps energy consumption to program structure, in much the same way that CPU profilers map processor cycles to specific processes and procedures. Our approach combines hardware instrumentation to measure current level with kernel software support to perform stati stical sampling of system activity. Postprocessing softwar e maps the sample data to program structure and produces a profile of energy usage by process and procedure. Using PowerScope, we have been able to reduce the energy consumption of an adaptive video playing application by 46%.
Embedded Vision System for Real-Time Object Tracking using an Asynchronous Transient Vision Sensor

This paper presents an embedded vision system for object tracking applications based on a 128times128 pixel CMOS temporal contrast vision sensor. This imager asynchronously responds to relative illumination intensity changes in the visual scene, exhibiting a usable dynamic range of 120 dB and a latency of under 100 mus. The information is encoded in the form of address-event representation (AER) data. An algorithm for object tracking with 1 millisecond timestamp resolution of the AER data stream is presented. As a real-world application example, vehicle tracking for a traffic-monitoring is demonstrated in real time. The potential of the proposed algorithm for people tracking is also shown. Due to the efficient data pre-processing in the imager chip focal plane, the embedded vision system can be implemented using a low-cost, low-power digital signal processor
A generic abstract syntax model for embedded languages

Representing a syntax tree using a data type often involves having many similar-looking constructors. Functions operating on such types often end up having many similar-looking cases. Different languages often make use of similar-looking constructions. We propose a generic model of abstract syntax trees capable of representing a wide range of typed languages. Syntactic constructs can be composed in a modular fashion enabling reuse of abstract syntax and syntactic processing within and across languages. Building on previous methods of encoding extensible data types in Haskell, our model is a pragmatic solution to Wadler's "expression problem". Its practicality has been confirmed by its use in the implementation of the embedded language Feldspar.
Diet Networks: Thin Parameters for Fat Genomics

Learning tasks such as those involving genomic data often poses a serious challenge: the number of input features can be orders of magnitude larger than the number of training examples, making it difficult to avoid overfitting, even when using the known regularization techniques. We focus here on tasks in which the input is a description of the genetic variation specific to a patient, the single nucleotide polymorphisms (SNPs), yielding millions of ternary inputs. Improving the ability of deep learning to handle such datasets could have an important impact in medical research, more specifically in precision medicine, where highdimensional data regarding a particular patient is used to make predictions of interest. Even though the amount of data for such tasks is increasing, this mismatch between the number of examples and the number of inputs remains a concern. Naive implementations of classifier neural networks involve a huge number of free parameters in their first layer (number of input features times number of hidden units): each input feature is associated with as many parameters as there are hidden units. We propose a novel neural network parametrization which considerably reduces the number of free parameters. It is based on the idea that we can first learn or provide a distributed representation for each input feature (e.g. for each position in the genome where variations are observed in data), and then learn (with another neural network called the parameter prediction network) how to map a feature’s distributed representation (based on the feature’s identity not its value) to the vector of parameters specific to that feature in the classifier neural network (the weights which link the value of the feature to each of the hidden units). This approach views the problem of producing the parameters associated with each feature as a multi-task learning problem. We show experimentally on a population stratification task of interest to medical studies that the proposed approach can significantly reduce both the number of parameters and the error rate of the classifier.
An Efficient Oblivious Database for the Public Cloud

Hardware enclaves such as Intel SGX are a promising technology to increase the security of databases outsourced to the cloud. These enclaves provide an execution environment isolated from the hypervisor/OS, and encryption of data in memory. However, for applications that use large amounts of memory—including most realistic databases—enclaves do not protect against access pattern leaks, where an attacker observes which locations in memory are accessed. The näıve way to address this issue, using Oblivious RAM (ORAM) primitives, adds prohibitive overhead. In this paper, we propose ObliDB, a database that co-designs both its data structures (e.g., oblivious B+ trees) and physical operators to accelerate oblivious relational queries, giving up to 329× speedup over näıve ORAM. On analytics workloads, ObliDB ranges from competitive to 19× faster than previous oblivious systems designed only for analytics, such as Opaque, and comes within 2.6× of Spark SQL. Moreover, ObliDB also supports point queries, insertions, and deletions with latencies of 3–10ms, which is 7–22× faster than previously published oblivious data structures, and makes ObliDB suitable for transactional workloads too. To our knowledge, ObliDB is the first general-purpose oblivious database to approach practical performance.  Saba Eskandarian and Matei Zaharia. An Efficient Oblivious Database for the Public Cloud. PVLDB, 11 (4): xxxx-yyyy, 2017.
See-through-wall imaging using ultra wideband short-pulse radar system

See-through-wall imaging radar is a unique application of ultra wideband communication that can provide soldiers and law enforcement officers with an enhanced situation awareness. We have developed an ultra-wideband high-resolution short pulse imaging radar system operating around 10 GHz, where two essential considerations are addressed: the effect of penetrating the walls; the pulse fidelity through the UWB components and antennas of the radar. Modeled and measured wall parameters, and the effect of antenna types on signal fidelity are discussed in detail.
Human posture recognition using curved segments for image retrieval

This paper presents a human posture recognition method from a single image. We rst segment an image into homogeneous regions and extract curve segments corresponding to human body parts. Each body part is considered as a 2D ribbon. From the smooth curve segments in skin regions, 2D ribbons are extracted and a human body model is constructed. We assign a predeened posture type to the image according to the constructed body model. For the user input query to retrieve images containing human of speciic posture, the system convert the query to a body model. The body model is compared to other body models saved in the local storage of target images and images of good matches are retrieved. When a face detection result is available for the given image, it is also used to increase the reliability of body model. For the query human posture, our system retrieves images of the corresponding posture. As another application, the proposed method provides an initial location of a human body to track in a video sequence.
Interactive Topic Modeling for Exploring Asynchronous Online Conversations: Design and Evaluation of ConVisIT

Since the mid-2000s, there has been exponential growth of asynchronous online conversations, thanks to the rise of social media. Analyzing and gaining insights from such conversations can be quite challenging for a user, especially when the discussion becomes very long. A promising solution to this problem is topic modeling, since it may help the user to understand quickly what was discussed in a long conversation and to explore the comments of interest. However, the results of topic modeling can be noisy, and they may not match the user’s current information needs. To address this problem, we propose a novel topic modeling system for asynchronous conversations that revises the model on the fly on the basis of users’ feedback. We then integrate this system with interactive visualization techniques to support the user in exploring long conversations, as well as in revising the topic model when the current results are not adequate to fulfill the user’s information needs. Finally, we report on an evaluation with real users that compared the resulting system with both a traditional interface and an interactive visual interface that does not support human-in-the-loop topic modeling. Both the quantitative results and the subjective feedback from the participants illustrate the potential benefits of our interactive topic modeling approach for exploring conversations, relative to its counterparts.
Tinmith-Metro: New Outdoor Techniques for Creating City Models with an Augmented Reality Wearable Computer

This paper presents new techniques for capturing and viewing on site 3D graphical models for large outdoor objects. Using an augmented reality wearable computer, we have developed a software system, known as TinmithMetro. Tinmith-Metro allows users to control a 3D constructive solid geometry modeller for building graphical objects of large physical artefacts, for example buildings, in the physical world. The 3D modeller is driven by a new user interface known as Tinmith-Hand, which allows the user to control the modeller using a set of pinch gloves and hand tracking. These techniques allow user to supply their AR renderers with models that would previously have to be captured with manual, time-consuming, and/or expensive methods.
A round- and computation-efficient three-party authenticated key exchange protocol

In three-party authenticated key exchange protocols, each client shares a secret only with a trusted server with assists in generating a session key used for securely sending messages between two communication clients. Compared with two-party authenticated key exchange protocols where each pair of parties must share a secret with each other, a three-party protocol does not cause any key management problem for the parties. In the literature, mainly there exist three issues in three-party authenticated key exchange protocols are discussed that need to be further improved: (1) to reduce latency, communication steps in the protocol should be as parallel as possible; (2) as the existence of a security-sensitive table on the server side may cause the server to become compromised, the table should be removed; (3) resources required for computation should be as few as possible to avoid the protocol to become an efficiency bottleneck. In various applications over networks, a quick response is required especially by light-weight clients in the mobile e-commerce. In this paper, a roundand computation-efficient three-party authenticated key exchange protocol is proposed which fulfils all of the above mentioned requirements.
Text Classification: A Sequential Reading Approach

We propose to model the text classification process as a sequential decision process. In this process, an agent learns to classify documents into topics while reading the document sentences sequentially and learns to stop as soon as enough information was read for deciding. The proposed algorithm is based on a modelisation of Text Classification as a Markov Decision Process and learns by using Reinforcement Learning. Experiments on four different classical mono-label corpora show that the proposed approach performs comparably to classical SVM approaches for large training sets, and better for small training sets. In addition, the model automatically adapts its reading process to the quantity of training information provided.
A generative layout approach for rooted tree drawings

In response to the large number of existing tree layouts, generic “meta-layouts” have recently been proposed. These generic approaches utilize layout design spaces to pinpoint a tree drawing with desired characteristics in the wealth of available drawing options and parameters. While design-space-based generic layouts work well for the confined set of implicit space-filling tree layouts, they have so far eluded their extension to explicit node-link diagrams. In order to produce both, implicit and explicit tree layouts, this paper parts with the descriptive nature of the design spaces and instead takes a generative approach based on operators. As these operators can be combined into operator sequences and be used at different stages of the layout process, a small operator set already suffices to yield a large number of different tree layouts. To this end, we present a generic tree layout pipeline and give examples of suitable layout operators to plug into the pipeline. A prototypical implementation of our pipeline and operators is presented, and it is illustrated with space-filling and node-link examples. Furthermore, the paper presents results from a user study evaluating our generative approach as it is realized by the prototype.
Twitter Part-of-Speech Tagging for All: Overcoming Sparse and Noisy Data

Part-of-speech information is a pre-requisite in many NLP algorithms. However, Twitter text is difficult to part-of-speech tag: it is noisy, with linguistic errors and idiosyncratic style. We present a detailed error analysis of existing taggers, motivating a series of tagger augmentations which are demonstrated to improve performance. We identify and evaluate techniques for improving English part-of-speech tagging performance in this genre. Further, we present a novel approach to system combination for the case where available taggers use different tagsets, based on voteconstrained bootstrapping with unlabeled data. Coupled with assigning prior probabilities to some tokens and handling of unknown words and slang, we reach 88.7% tagging accuracy (90.5% on development data). This is a new high in PTB-compatible tweet part-of-speech tagging, reducing token error by 26.8% and sentence error by 12.2%. The model, training data and tools are made available.
Implementation of Photometric Stereo with General , Unknown Lighting

This report discusses the implementation of the shape recovery in case of four harmonics as described in the paper titled Photometric Stereo with General, Unknown Lighting (Basri, Jacobs, Kemelmacher), 2006. The goal is to recover the harmonic images from a set of number of images of an object taken from the same viewpoint but with different illumination. These harmonic images are then used to infer the surface normal and albedos of the object being imaged. photometric stereo; singular value decomposition(SVD); eigenvalue decomposition; albedo; surface normal; Lorentz transformation
Silicon Implementation of SHA-3 Finalists : BLAKE , Grøstl , JH , Keccak and Skein

Hardware implementation quality is an important factor in selecting the NIST SHA-3 competition finalists. However, a comprehensive methodology to benchmark five final round SHA-3 candidates in ASIC is challenging. Many factors need to be considered, including application scenarios, target technologies and optimization goals. This work describes detailed steps in the silicon implementation of a SHA-3 ASIC. The plan of ASIC prototyping with all the SHA-3 finalists, as an integral part of our SHA-3 ASIC evaluation project, is motivated by our previously proposed methodology, which defines a consistent and systematic approach to move a SHA-3 hardware benchmark process from FPGA prototyping to ASIC implementation. We have designed the remaining five SHA-3 candidates in 0.13 μm IBM process using standard-cell CMOS technology. The fabricated chip is due from foundry in May 2011. In this submission, we discuss our proposed methodology for SHA-3 ASIC evaluation and report the latest results based on pre-silicon post-layout simulation of the five SHA-3 finalists with Round 3 tweaks.
Semantic Segmentation for High Spatial Resolution Remote Sensing Images Based on Convolution Neural Network and Pyramid Pooling Module

Semantic segmentation provides a practical way to segment remotely sensed images into multiple ground objects simultaneously, which can be potentially applied to multiple remote sensed related aspects. Current classification algorithms in remotely sensed images are mostly limited by different imaging conditions, the multiple ground objects are difficult to be separated from each other due to high intraclass spectral variances and interclass spectral similarities. In this study, we propose an end-to-end framework to semantically segment high-resolution aerial images without postprocessing to refine the segmentation results. The framework provides a pixel-wise segmentation result, comprising convolutional neural network structure and pyramid pooling module, which aims to extract feature maps at multiple scales. The proposed model is applied to the ISPRS Vaihingen benchmark dataset from the ISPRS 2D Semantic Labeling Challenge. Its segmentation results are compared with previous state-of-the-art method UZ_1, UPB and three other methods that segment images into objects of all the classes (including clutter/background) based on true orthophoto tiles, and achieve the highest overall accuracy of 87.8% over the published performances, to the best of our knowledge. The results validate the efficiency of the proposed model in segmenting multiple ground objects from remotely sensed images simultaneously.
Construction of the Literature Graph in Semantic Scholar

We describe a deployed scalable system for organizing published scientific literature into a heterogeneous graph to facilitate algorithmic manipulation and discovery. The resulting literature graph consists of more than 280M nodes, representing papers, authors, entities and various interactions between them (e.g., authorships, citations, entity mentions). We reduce literature graph construction into familiar NLP tasks (e.g., entity extraction and linking), point out research challenges due to differences from standard formulations of these tasks, and report empirical results for each task. The methods described in this paper are used to enable semantic features in www.semanticscholar.org.
A Dual-Loop Antenna Design for Hepta-Band WWAN/LTE Metal-Rimmed Smartphone Applications

A simple direct-fed dual-loop antenna capable of providing hepta-band WWAN/LTE operation under surroundings of an unbroken metal rim in smartphone applications is proposed. The greatest highlight of this proposed antenna is that it provides a simple and effective multiband antenna solution for an unbroken metal-rimmed smartphone. The unbroken metal rim with 5 mm in height embraces the system circuit board of 130 × 70 mm2. Two no-ground portions of 10 × 70 mm2 and 5 × 70 mm2 are set on the top and bottom edge of the system circuit board, respectively. In-between the two separate no-ground portions, there is a system ground of 115 × 70 mm2 connected with the unbroken metal rim via a small grounded patch which divides the unbroken metal rim into two strips. Finally, a dual-loop antenna is formed by combining the inner system ground and two strips. This proposed dual-loop antenna is capable of covering GSM850/900/DCS/PCS/UMTS2100/LTE 2300/2500 operating bands. Detailed design considerations of the proposed antenna are described and both experimental and simulation results are also presented and discussed.
"Green" electronics: biodegradable and biocompatible materials and devices for sustainable future.

"Green" electronics represents not only a novel scientific term but also an emerging area of research aimed at identifying compounds of natural origin and establishing economically efficient routes for the production of synthetic materials that have applicability in environmentally safe (biodegradable) and/or biocompatible devices. The ultimate goal of this research is to create paths for the production of human- and environmentally friendly electronics in general and the integration of such electronic circuits with living tissue in particular. Researching into the emerging class of "green" electronics may help fulfill not only the original promise of organic electronics that is to deliver low-cost and energy efficient materials and devices but also achieve unimaginable functionalities for electronics, for example benign integration into life and environment. This Review will highlight recent research advancements in this emerging group of materials and their integration in unconventional organic electronic devices.
Suture techniques in rhinoplasty.

Suture techniques are an indispensable means to biologically sculpt the cartilage of the nose. Here the authors review their use in tip-plasty and present a 4-suture algorithm that allows for simple, complete control in sculpting the shape of all nasal tips in primary rhinoplasty. After a standard cephalic trim of the lateral crus leaving it 6 mm wide, one or more of the four suture techniques are applied. One of the newest techniques that has yielded excellent results is the hemi-transdomal suture, a variation of the conventional transdomal suture. This technique narrows the dome but also everts the lateral crus slightly to avoid concavities of the nostril rim. The 4-suture algorithm is useful in both the open and closed approaches. A more general use of sutures is described and referred to as the "universal horizontal mattress suture," which can be applied to remove all unwanted convexities or concavities and can be used not only to straighten the cartilage but also strengthen it. This suture has applications for the crooked septum, the collapsed lateral crus (external valve), and the collapsed internal valve, as well as for converting ear cartilage grafts into straighter, stronger grafts than previously thought possible.
The short- and long-term fates of memory items retained outside the focus of attention.

When a test of working memory (WM) requires the retention of multiple items, a subset of them can be prioritized. Recent studies have shown that, although prioritized (i.e., attended) items are associated with active neural representations, unprioritized (i.e., unattended) memory items can be retained in WM despite the absence of such active representations, and with no decrement in their recognition if they are cued later in the trial. These findings raise two intriguing questions about the nature of the short-term retention of information outside the focus of attention. First, when the focus of attention shifts from items in WM, is there a loss of fidelity for those unattended memory items? Second, could the retention of unattended memory items be accomplished by long-term memory mechanisms? We addressed the first question by comparing the precision of recall of attended versus unattended memory items, and found a significant decrease in precision for unattended memory items, reflecting a degradation in the quality of those representations. We addressed the second question by asking subjects to perform a WM task, followed by a surprise memory test for the items that they had seen in the WM task. Long-term memory for unattended memory items from the WM task was not better than memory for items that had remained selected by the focus of attention in the WM task. These results show that unattended WM representations are degraded in quality and are not preferentially represented in long-term memory, as compared to attended memory items.