nils-herrmann/clean_scirepeval huggingface dataset:
Integrative analysis of 111 reference human epigenomes

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.
MPEG digital video-coding standards

Modern image and video compression techniques today offer the possibility to store or transmit the vast amount of data necessary to represent digital images and video in an efficient and robust way. New audio visual applications in the field of communication, multimedia and broadcasting became possible based on digital video coding technology. As manifold as applications for image coding are today, as manifold are the different approaches and algorithms and were the first hardware implementations and even systems in the commercial field, such as private teleconferencing systems [chen, hal]. However, with the advances in VLSI-technology it became possible to open more application fields to a larger number of users and therefore the necessity for video coding standards arose. Commercially, international standardization of video communication systems and protocols aims to serve two important purposes: interoperability and economy of scale. Interworking between video communication equipment from different vendors is a desirable feature for users and equipment manufactures alike. It increases the attractiveness for buying and using video
Discovering phases, phase transitions, and crossovers through unsupervised machine learning: A critical examination.

We apply unsupervised machine learning techniques, mainly principal component analysis (PCA), to compare and contrast the phase behavior and phase transitions in several classical spin models-the square- and triangular-lattice Ising models, the Blume-Capel model, a highly degenerate biquadratic-exchange spin-1 Ising (BSI) model, and the two-dimensional XY model-and we examine critically what machine learning is teaching us. We find that quantified principal components from PCA not only allow the exploration of different phases and symmetry-breaking, but they can distinguish phase-transition types and locate critical points. We show that the corresponding weight vectors have a clear physical interpretation, which is particularly interesting in the frustrated models such as the triangular antiferromagnet, where they can point to incipient orders. Unlike the other well-studied models, the properties of the BSI model are less well known. Using both PCA and conventional Monte Carlo analysis, we demonstrate that the BSI model shows an absence of phase transition and macroscopic ground-state degeneracy. The failure to capture the "charge" correlations (vorticity) in the BSI model (XY model) from raw spin configurations points to some of the limitations of PCA. Finally, we employ a nonlinear unsupervised machine learning procedure, the "autoencoder method," and we demonstrate that it too can be trained to capture phase transitions and critical points.
Natural Language Inference with External Knowledge

Modeling informal inference in natural language is very challenging. With the recent availability of large annotated data, it has become feasible to train complex models such as neural networks to perform natural language inference (NLI), which have achieved state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform NLI from the data? If not, how can NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we aim to answer these questions by enriching the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models with external knowledge further improve the state of the art on the Stanford Natural Language Inference (SNLI) dataset.
Vid2speech: Speech reconstruction from silent video

Speechreading is a notoriously difficult task for humans to perform. In this paper we present an end-to-end model based on a convolutional neural network (CNN) for generating an intelligible acoustic speech signal from silent video frames of a speaking person. The proposed CNN generates sound features for each frame based on its neighboring frames. Waveforms are then synthesized from the learned speech features to produce intelligible speech. We show that by leveraging the automatic feature learning capabilities of a CNN, we can obtain state-of-the-art word intelligibility on the GRID dataset, and show promising results for learning out-of-vocabulary (OOV) words.
3-D ICs: a novel chip design for improving deep-submicrometer interconnect performance and systems-on-chip integration

Performance of deep submicron VLSI is being increasingly dominated by the interconnects due to decreasing wire pitch and increasing die size. Additionally, heterogeneous integration of different technologies in one single chip is becoming increasingly desirable, for which planar (2-D) ICs may not be suitable. This paper analyzes the limitations of the existing interconnect technologies and design methodologies and presents a novel 3-dimensional (3-D) chip design strategy that exploits the vertical dimension to alleviate the interconnect related problems and to facilitate heterogeneous integration of technologies to realize a System-on a-Chip (SoC) design. A comprehensive analytical treatment of these 3-D ICs has been presented and it has been shown that by simply dividing a planar chip into separate blocks, each occupying a separate physical level interconnected by short and vertical inter-layer interconnects (VILICs), significant improvement in performance and reduction in wire-limited chip area can be achieved, without the aid of any other circuit or design innovations. A scheme to optimize the interconnect distribution among different interconnect tiers is presented and the effect of transferring the repeaters to upper Si layers has been quantified in this analysis for a two-layer 3-D chip. Furthermore, one of the major concerns in 3-D ICs arising due to power dissipation problems has been analyzed and an analytical model has been presented to estimate the temperatures of the different active layers. It is demonstrated that advancement in heat sinking technology will be necessary in order to extract maximum performance from these chips. Implications of 3D device architecture on several design issues have also been discussed with especial attention to SoC design strategies. Finally, some of the promising technologies for manufacturing 3-D ICs have been outlined.
Near-Optimally Teaching the Crowd to Classify

How should we present training examples to learners to teach them classification rules? This is a natural problem when training workers for crowdsourcing labeling tasks, and is also motivated by challenges in data-driven online education. We propose a natural stochastic model of the learners, modeling them as randomly switching among hypotheses based on observed feedback. We then develop STRICT, an efficient algorithm for selecting examples to teach to workers. Our solution greedily maximizes a submodular surrogate objective function in order to select examples to show to the learners. We prove that our strategy is competitive with the optimal teaching policy. Moreover, for the special case of linear separators, we prove that an exponential reduction in error probability can be achieved. Our experiments on simulated workers as well as three real image annotation tasks on Amazon Mechanical Turk show the effectiveness of our teaching algorithm.
A microscopic look at WiFi fingerprinting for indoor mobile phone localization in diverse environments

WiFi fingerprinting has received much attention for indoor mobile phone localization. In this study, we examine the impact of various aspects underlying a WiFi fingerprinting system. Specifically, we investigate different definitions for fingerprinting and location estimation algorithms across different indoor environments ranging from a multi-storey office building to shopping centers of different sizes. Our results show that the fingerprint definition is as important as the choice of location estimation algorithm and there is no single combination of these two that works across all environments or even all floors of a given environment. We then consider the effect of WiFi frequency bands (e.g., 2.4GHz and 5GHz) and the presence of virtual access points (VAPs) on location accuracy with WiFi fingerprinting. Our results demonstrate that 5GHz signals are less prone to variation and thus yield more accurate location estimation. We also find that the presence of VAPs improves location estimation accuracy.
Open Set Logo Detection and Retrieval

Current logo retrieval research focuses on closed set scenarios. We argue that the logo domain is too large for this strategy and requires an open set approach. To foster research in this direction, a large-scale logo dataset, called Logos in the Wild, is collected and released to the public. A typical open set logo retrieval application is, for example, assessing the effectiveness of advertisement in sports event broadcasts. Given a query sample in shape of a logo image, the task is to find all further occurrences of this logo in a set of images or videos. Currently, common logo retrieval approaches are unsuitable for this task because of their closed world assumption. Thus, an open set logo retrieval method is proposed in this work which allows searching for previously unseen logos by a single query sample. A two stage concept with separate logo detection and comparison is proposed where both modules are based on task specific Convolutional Neural Networks (CNNs). If trained with the Logos in the Wild data, significant performance improvements are observed, especially compared with state-of-the-art closed set approaches.
Automating the approximate record-matching process

Data Quality has many dimensions one of which is accuracy. Accuracy is usually compromised by errors accidentally or intensionally introduced in a database system. These errors result in inconsistent, incomplete, or erroneous data elements. For example, a small variation in the representation of a data object, produces a unique instantiation of the object being represented. In order to improve the accuracy of the data stored in a database system, we need to compare them either with real-world counterparts or with other data stored in the same or a di erent system. In this paper we address the problem of matching records which refer to the same entity by computing their similarity. Exact record matching has limited applicability in this context since even simple errors like character transpositions cannot be captured in the record linking process. Our methodology deploys advanced data mining techniques for dealing with the high computational and inferential complexity of approximate record matching.
A Semi-NMF-PCA Unified Framework for Data Clustering

In this work, we propose a novel way to consider the clustering and the reduction of the dimension simultaneously. Indeed, our approach takes advantage of the mutual reinforcement between data reduction and clustering tasks. The use of a low-dimensional representation can be of help in providing simpler and more interpretable solutions. We show that by doing so, our model is able to better approximate the relaxed continuous dimension reduction solution by the true discrete clustering solution. Experiment results show that our method gives better results in terms of clustering than the state-of-the-art algorithms devoted to similar tasks for data sets with different proprieties.
Security for 4G and 5G cellular networks: A survey of existing authentication and privacy-preserving schemes

This paper presents a comprehensive survey of existing authentication and privacy-preserving schemes for 4G and 5G cellular networks. We start by providing an overview of existing surveys that deal with 4G and 5G communications, applications, standardization, and security. Then, we give a classification of threat models in 4G and 5G cellular networks in four categories, including, attacks against privacy, attacks against integrity, attacks against availability, and attacks against authentication. We also provide a classification of countermeasures into three types of categories, including, cryptography methods, humans factors, and intrusion detection methods. The countermeasures and informal and formal security analysis techniques used by the authentication and privacy preserving schemes are summarized in form of tables. Based on the categorization of the authentication and privacy models, we classify these schemes in seven types, including, handover authentication with privacy, mutual authentication with privacy, RFID authentication with privacy, deniable authentication with privacy, authentication with mutual anonymity, authentication and key agreement with privacy, and three-factor authentication with privacy. In addition, we provide a taxonomy and comparison of authentication and privacypreserving schemes for 4G and 5G cellular networks in form of tables. Based on the current survey, several recommendations for further research are discussed at the end of this paper.
Corrugated substrate integrated waveguide distributed amplifier

A distributed amplifier directly compatible with half-mode substrate integrated waveguide is proposed. In halfmode substrate integrated waveguide (HMSIW), the top and bottom conductors of the printed circuit board form the top and bottom walls of the waveguide, whilst a row of vias form the side-wall. The vias can be replaced by quarter-wave open-circuit stubs to form the half-mode corrugated substrate integrated waveguide (HMCSIW) thereby isolating the top and bottom walls from each other at DC. In the distributed amplifier, FETs are used to periodically couple of pair of HMCSIWs. The HMCSIWs allow convenient biasing of the FETs without additional RF chokes and can be connected to HMSIWs via de-coupling capacitors. Simulations and measurements of a distributed amplifier operating from 4 GHz to 7 GHz demonstrates the feasibility of this approach.
DBLP - Some Lessons Learned

The DBLP Computer Science Bibliography evolved from an early small experimental Web server to a popular service for the computer science community. Many design decisions and details of the public XML-records behind DBLP never were documented. This paper is a review of the evolution of DBLP. The main perspective is data modeling. In DBLP persons play a central role, our discussion of person names may be applicable to many other data bases. All DBLP data are available for your own experiments. You may either download the complete set, or use a simple XML-based API described in an online appendix.
COTD: Reference-Free Hardware Trojan Detection and Recovery Based on Controllability and Observability in Gate-Level Netlist

This paper presents a novel hardware Trojan detection technique in gate-level netlist based on the controllability and observability analyses. Using an unsupervised clustering analysis, the paper shows that the controllability and observability characteristics of Trojan gates present significant inter-cluster distance from those of genuine gates in a Trojan-inserted circuit, such that Trojan gates are easily distinguishable. The proposed technique does not require any golden model and can be easily integrated into the current integrated circuit design flow. Furthermore, it performs a static analysis and does not require any test pattern application for Trojan activation either partially or fully. In addition, the timing complexity of the proposed technique is an order of the number of signals in a circuit. Moreover, the proposed technique makes it possible to fully restore an inserted Trojan and to isolate its trigger and payload circuits. The technique has been applied on various types of Trojans, and all Trojans are successfully detected with 0 false positive and negative rates in less than 14 s in the worst case.
Mobile App Rating Scale: A New Tool for Assessing the Quality of Health Mobile Apps

The use of mobile apps for health and well being promotion has grown exponentially in recent years. Yet, there is currently no app-quality assessment tool beyond "star"-ratings.



The objective of this study was to develop a reliable, multidimensional measure for trialling, classifying, and rating the quality of mobile health apps.



A literature search was conducted to identify articles containing explicit Web or app quality rating criteria published between January 2000 and January 2013. Existing criteria for the assessment of app quality were categorized by an expert panel to develop the new Mobile App Rating Scale (MARS) subscales, items, descriptors, and anchors. There were sixty well being apps that were randomly selected using an iTunes search for MARS rating. There were ten that were used to pilot the rating procedure, and the remaining 50 provided data on interrater reliability.



There were 372 explicit criteria for assessing Web or app quality that were extracted from 25 published papers, conference proceedings, and Internet resources. There were five broad categories of criteria that were identified including four objective quality scales: engagement, functionality, aesthetics, and information quality; and one subjective quality scale; which were refined into the 23-item MARS. The MARS demonstrated excellent internal consistency (alpha = .90) and interrater reliability intraclass correlation coefficient (ICC = .79).



The MARS is a simple, objective, and reliable tool for classifying and assessing the quality of mobile health apps. It can also be used to provide a checklist for the design and development of new high quality health apps.
Path Loss Estimation for Wireless Underground Sensor Network in Agricultural Application

Path loss models are used to estimate the path loss between transmitter and receiver in applications that involve transmission of electromagnetic waves. This paper describes the wireless underground sensor networks, which communicates within the soil and is different from the terrestrial wireless sensor network. An integrated study of electromagnetic waves propagation for a wireless underground sensor network is described in this paper. Here, path loss model and signal strength are presented, because of electromagnetic waves attenuation in the soil medium. Tests were conducted at various soil volumetric water contents and node deployment depths using three different frequencies, when soil consists of 0% sands, 35% silt and 15% clay. The results showed that radio signal path loss was minimum when frequency and moisture content are low. The experimental results revealed that a 20% increase in the soil moisture content increased the path loss by more than 30%.
Flooding-resilient broadcast authentication for VANETs

Digital signatures are one of the fundamental security primitives in Vehicular Ad-Hoc Networks (VANETs) because they provide authenticity and non-repudiation in broadcast communication. However, the current broadcast authentication standard in VANETs is vulnerable to signature flooding: excessive signature verification requests that exhaust the computational resources of victims. In this paper, we propose two efficient broadcast authentication schemes, Fast Authentication (FastAuth) and Selective Authentication (SelAuth), as two countermeasures to signature flooding. FastAuth secures periodic single-hop beacon messages. By exploiting the sender's ability to predict its own future beacons, FastAuth enables 50 times faster verification than previous mechanisms using the Elliptic Curve Digital Signature Algorithm. SelAuth secures multi-hop applications in which a bogus signature may spread out quickly and impact a significant number of vehicles. SelAuth pro- vides fast isolation of malicious senders, even under a dynamic topology, while consuming only 15%--30% of the computational resources compared to other schemes. We provide both analytical and experimental evaluations based on real traffic traces and NS-2 simulations. With the near-term deployment plans of VANET on all vehicles, our approaches can make VANETs practical.
Developing a Speech Activity Detection System for the DARPA RATS Program

This paper describes the speech activity detection (SAD) system developed by the Patrol team for the first phase of the DARPA RATS (Robust Automatic Transcription of Speech) program, which seeks to advance state of the art detection capabilities on audio from highly degraded communication channels. We present two approaches to SAD, one based on Gaussian mixture models, and one based on multi-layer perceptrons. We show that significant gains in SAD accuracy can be obtained by careful design of acoustic front end, feature normalization, incorporation of long span features via data-driven dimensionality reducing transforms, and channel dependent modeling. We also present a novel technique for normalizing detection scores from different systems for the purpose of system combination.
Dual-Band Long-Range Passive RFID Tag Antenna Using an AMC Ground Plane

A dual-band passive radio frequency identification (RFID) tag antenna applicable for a recessed cavity in metallic objects such as heavy equipment, vehicles, aircraft, and containers with long read range is proposed by using an artificial magnetic conductor (AMC) ground plane. The proposed tag antenna consists of a bowtie antenna and a recessed cavity with the AMC ground plane installed on the bottom side of the cavity. The AMC ground plane is utilized to provide dual-band operation at European (869.5 869.7 MHz) and Korean (910 914 MHz) passive UHF RFID bands by replacing the bottom side of the metallic cavity of a PEC-like behavior and, therefore, changing the reflection phase of the ground plane. It is worthwhile to mention that the European and the Korean UHF RFID bands are allocated very closely, and the frequency separation ratio between the two bands is just about 0.045, which is very small. It is demonstrated by experiment that the maximum reading distance of the proposed tag antenna with optimized dimensions can be improved more than 3.1 times at the two RFID bands compared to a commercial RFID tag.