nils-herrmann/clean_scirepeval huggingface dataset:
A cutting plane method for solving linear generalized disjunctive programming problems

Raman and Grossmann [Raman, R., & Grossmann, I.E. (1994). Modeling and computational techniques for logic based integer programming. Computers and Chemical Engineering, 18(7), 563–578] and Lee and Grossmann [Lee, S., & Grossmann, I.E. (2000). New algorithms for nonlinear generalized disjunctive programming. Computers and Chemical Engineering, 24, 2125–2141] have developed a reformulation of Generalized Disjunctive Programming (GDP) problems that is based on determining the convex hull of each disjunction. Although the with the quires n order to hod relies m an LP else until ng, retrofit utting plane
A highly-efficient 138–170 GHz SiGe HBT frequency doubler for power-constrained applications

This paper presents a 138-170 GHz active frequency doubler implemented in a 0.13 μm SiGe BiCMOS technology with a peak output power of 5.6 dBm and peak power-added efficiency of 7.6%. The doubler achieves a peak conversion gain of 4.9 dB and consumes only 36 mW of DC power at peak drive through the use of a push-push frequency doubling stage optimized for low drive power, along with a low-power output buffer. To the best of our knowledge, this doubler achieves the highest output power, efficiency, and fundamental frequency suppression of all D-band and G-band SiGe HBT frequency doublers to date.
Distributed and Adaptive Medium Access Control for Internet-of-Things-Enabled Mobile Networks

In this paper, we propose a distributed and adaptive hybrid medium access control (DAH-MAC) scheme for a single-hop Internet of Things (IoT)-enabled mobile ad hoc network supporting voice and data services. A hybrid superframe structure is designed to accommodate packet transmissions from a varying number of mobile nodes generating either delay-sensitive voice traffic or best-effort data traffic. Within each superframe, voice nodes with packets to transmit access the channel in a contention-free period (CFP) using distributed time division multiple access, while data nodes contend for channel access in a contention period (CP) using truncated carrier sense multiple access with collision avoidance. In the CFP, by adaptively allocating time slots according to instantaneous voice traffic load, the MAC exploits voice traffic multiplexing to increase the voice capacity. In the CP, a throughput optimization framework is proposed for the DAH-MAC, which maximizes the aggregate data throughput by adjusting the optimal contention window size according to voice and data traffic load variations. Numerical results show that the proposed MAC scheme outperforms existing quality-of-service-aware MAC schemes for voice and data traffic in the presence of heterogeneous traffic load dynamics.
Optimal Bayesian Transfer Learning

Transfer learning has recently attracted significant research attention, as it simultaneously learns from different source domains, which have plenty of labeled data, and transfers the relevant knowledge to the target domain with limited labeled data to improve the prediction performance. We propose a Bayesian transfer learning framework, in the homogeneous transfer learning scenario, where the source and target domains are related through the joint prior density of the model parameters. The modeling of joint prior densities enables better understanding of the “transferability” between domains. We define a joint Wishart distribution for the precision matrices of the Gaussian feature-label distributions in the source and target domains to act like a bridge that transfers the useful information of the source domain to help classification in the target domain by improving the target posteriors. Using several theorems in multivariate statistics, the posteriors and posterior predictive densities are derived in closed forms with hypergeometric functions of matrix argument, leading to our novel closed-form and fast Optimal Bayesian Transfer Learning (OBTL) classifier. Experimental results on both synthetic and real-world benchmark data confirm the superb performance of the OBTL compared to the other state-of-the-art transfer learning and domain adaptation methods.
DocFace: Matching ID Document Photos to Selfies

Numerous activities in our daily life, including purchases, travels and access to services, require us to verify who we are by showing ID documents containing face images, such as passports and driver licenses. An automatic system for matching ID document photos to live face images in real time with high accuracy would speed up the verification process and reduce the burden on human operators. In this paper, we propose a new method, DocFace, for ID document photo matching using the transfer learning technique. We propose to use a pair of sibling networks to learn domain specific parameters from heterogeneous face pairs. Cross validation testing on an ID-Selfie dataset shows that while the best CNN-based general face matcher only achieves a TAR=61.14% at FAR=0.1% on the problem, the DocFace improves the TAR to 92.77%. Experimental results also indicate that given sufficiently large training data, a viable system for automatic ID document photo matching can be developed and deployed.
Multi-output RNN-LSTM for multiple speaker speech synthesis with α-interpolation model

Deep Learning has been applied successfully to speech processing. In this paper we propose an architecture for speech synthesis using multiple speakers. Some hidden layers are shared by all the speakers, while there is a specific output layer for each speaker. Objective and perceptual experiments prove that this scheme produces much better results in comparison with single speaker model. Moreover, we also tackle the problem of speaker interpolation by adding a new output layer (α-layer) on top of the multi-output branches. An identifying code is injected into the layer together with acoustic features of many speakers. Experiments show that the α-layer can effectively learn to interpolate the acoustic features between speakers.
Synthesis of Forgiving Data Extractors

We address the problem of synthesizing a robust data-extractor from a family of websites that contain the same kind of information. This problem is common when trying to aggregate information from many web sites, for example, when extracting information for a price-comparison site.
 Given a set of example annotated web pages from multiple sites in a family, our goal is to synthesize a robust data extractor that performs well on all sites in the family (not only on the provided example pages). The main challenge is the need to trade off precision for generality and robustness. Our key contribution is the introduction of forgiving extractors that dynamically adjust their precision to handle structural changes, without sacrificing precision on the training set.
 Our approach uses decision tree learning to create a generalized extractor and converts it into a forgiving extractor, inthe form of an XPath query. The forgiving extractor captures a series of pruned decision trees with monotonically decreasing precision, and monotonically increasing recall, and dynamically adjusts precision to guarantee sufficient recall. We have implemented our approach in a tool called TREEX and applied it to synthesize extractors for real-world large scale web sites. We evaluate the robustness and generality of the forgiving extractors by evaluating their precision and recall on: (i) different pages from sites in the training set (ii) pages from different versions of sites in the training set (iii) pages from different (unseen) sites. We compare the results of our synthesized extractor to those of classifier-based extractors, and pattern-based extractors, and show that TREEX significantly improves extraction accuracy.
Meerkat and Periscope: I Stream, You Stream, Apps Stream for Live Streams

We conducted a mixed methods study of the use of the Meerkat and Periscope apps for live streaming video and audio broadcasts from a mobile device. We crowdsourced a task to describe the content, setting, and other characteristics of 767 live streams. We also interviewed 20 frequent streamers to explore their motivations and experiences. Together, the data provide a snapshot of early live streaming use practices. We found a diverse range of activities broadcast, which interviewees said were used to build their personal brand. They described live streaming as providing an authentic, unedited view into their lives. They liked how the interaction with viewers shaped the content of their stream. We found some evidence for multiple live streams from the same event, which represent an opportunity for multiple perspectives on events of shared public interest.
Sub-threshold startup charge pump using depletion MOSFET for a low-voltage harvesting application

This paper presents a fully integrated CMOS start-up circuit for a low voltage battery-less harvesting application. The proposed topology is based on a step-up charge pump using depletion transistors instead of enhancement transistors. With this architecture, we can obtain a self-starting voltage below the enhancement transistor's threshold due to its normally-on operation. The key advantages are the CMOS compatibility, inductor-less solution and no extra post-fabrication processing. The topology has been simulated in 0.18μm technology using a transistor-level model and has been compared to the traditional charge pump structure. The depletion-based voltage doubler charge pump enables operation from an input voltage as low as 250mV compared to 400mV in an enhancement-based one. The proposed topology can also achieve other conversion ratios such as 1:-1 inverter or 1:N step-up.
Web-to-Application Injection Attacks on Android: Characterization and Detection

Vulnerable Android applications are traditionally exploited via malicious apps. In this paper, we study an underexplored class of Android attacks which do not require the user to install malicious apps, but merely to visit a malicious website in an Android browser. We call them web-to-app injection (or W2AI) attacks, and distinguish between different categories of W2AI sideeffects. To estimate their prevalence, we present an automated W2AIScanner to find and confirm W2AI vulnerabilities. Analyzing real apps from the official Google Play store – we found 286 confirmed vulnerabilities in 134 distinct applications. Our findings suggest that these attacks are pervasive and developers do not adequately protect apps against them. Our tool employs a novel combination of static analysis and symbolic execution with dynamic testing. We show through experiments that this design significantly enhances the detection accuracy compared with an existing state-of-the-art analysis.
Entropy-based active learning for object recognition

Most methods for learning object categories require large amounts of labeled training data. However, obtaining such data can be a difficult and time-consuming endeavor. We have developed a novel, entropy-based ldquoactive learningrdquo approach which makes significant progress towards this problem. The main idea is to sequentially acquire labeled data by presenting an oracle (the user) with unlabeled images that will be particularly informative when labeled. Active learning adaptively prioritizes the order in which the training examples are acquired, which, as shown by our experiments, can significantly reduce the overall number of training examples required to reach near-optimal performance. At first glance this may seem counter-intuitive: how can the algorithm know whether a group of unlabeled images will be informative, when, by definition, there is no label directly associated with any of the images? Our approach is based on choosing an image to label that maximizes the expected amount of information we gain about the set of unlabeled images. The technique is demonstrated in several contexts, including improving the efficiency of Web image-search queries and open-world visual learning by an autonomous agent. Experiments on a large set of 140 visual object categories taken directly from text-based Web image searches show that our technique can provide large improvements (up to 10 x reduction in the number of training examples needed) over baseline techniques.
Video and accelerometer-based motion analysis for automated surgical skills assessment

Basic surgical skills of suturing and knot tying are an essential part of medical training. Having an automated system for surgical skills assessment could help save experts time and improve training efficiency. There have been some recent attempts at automated surgical skills assessment using either video analysis or acceleration data. In this paper, we present a novel approach for automated assessment of OSATS-like surgical skills and provide an analysis of different features on multi-modal data (video and accelerometer data). We conduct a large study for basic surgical skill assessment on a dataset that contained video and accelerometer data for suturing and knot-tying tasks. We introduce “entropy-based” features—approximate entropy and cross-approximate entropy, which quantify the amount of predictability and regularity of fluctuations in time series data. The proposed features are compared to existing methods of Sequential Motion Texture, Discrete Cosine Transform and Discrete Fourier Transform, for surgical skills assessment. We report average performance of different features across all applicable OSATS-like criteria for suturing and knot-tying tasks. Our analysis shows that the proposed entropy-based features outperform previous state-of-the-art methods using video data, achieving average classification accuracies of 95.1 and 92.2% for suturing and knot tying, respectively. For accelerometer data, our method performs better for suturing achieving 86.8% average accuracy. We also show that fusion of video and acceleration features can improve overall performance for skill assessment. Automated surgical skills assessment can be achieved with high accuracy using the proposed entropy features. Such a system can significantly improve the efficiency of surgical training in medical schools and teaching hospitals.
TCP accelerator for DVB-RCS SATCOM dynamic bandwidth environment with HAIPE

A high assurance IP encryption (HAIPE) compliant protocol accelerator is proposed for military networks consisting of red (or classified) networks and black (or unclassified) networks. The boundary between red and black sides is assumed to be protected via a HAIPE device. However, the IP layer encryption introduces challenges for bandwidth on demand satellite communication. The problems experienced by transmission control protocol (TCP) over satellites are well understood: While standard modems (on the black side) employ TCP performance enhancing proxy (PEP) which has been shown to work well, the HAIPE encryption of TCP headers renders the onboard modem's PEP ineffective. This is attributed to the fact that under the bandwidth-on-demand environment, PEP must use traditional TCP mechanisms such as slow start to probe for the available bandwidth of the link (which eliminates the usefulness of the PEP). Most implementations recommend disabling the PEP when a HAIPE device is used. In this paper, we propose a novel solution, namely broadband HAIPE-embeddable satellite communications terminal (BH-eST), which utilizes dynamic network performance enhancement algorithms for high latency bandwidth-on-demand satellite links protected by HAIPE. By moving the PEP into the red network and exploiting the explicit congestion notification bypass mechanism allowed by the latest HAIPE standard, we have been able to regain PEP's desired network enhancement that was lost due to HAIPE encryption (even though the idea of deploying PEP at the modem side is not new). Our BHeST solution employs direct video broadcast-return channel service (DVB-RCS), an open standard as a means of providing bandwidth-on-demand satellite links. Another issue we address is the estimation of current satellite bandwidth allocated to a remote terminal which is not available in DVB-RCS. Simulation results show that the improvement of our solution over FIX PEP is significant and could reach up to 100%. The improvement over the original TCP is even more (up to 500% for certain configurations).
Stopping Criteria in Contrastive Divergence: Alternatives to the Reconstruction Error

Restricted Boltzmann Machines (RBMs) are general unsupervised learning devices to ascertain generative models of data distributions. RBMs are often trained using the Contrastive Divergence learning algorithm (CD), an approximation to the gradient of the data log-likelihood. A simple reconstruction error is often used to decide whether the approximation provided by the CD algorithm is good enough, though several authors (Schulz et al., 2010; Fischer & Igel, 2010) have raised doubts concerning the feasibility of this procedure. However, not many alternatives to the reconstruction error have been used in the literature. In this manuscript we investigate simple alternatives to the reconstruction error in order to detect as soon as possible the decrease in the log-likelihood during learning. Proceedings of the 2 International Conference on Learning Representations, Banff, Canada, 2014.
An Automata-Theoretic Approach to Linear Temporal Logic

The automata-theoretic approach to linear temporal logic uses the theory of automata as a unifying paradigm for program specification, verification, and synthesis. Both programs and specifications are in essence descriptions of computations. These computations can be viewed as words over some alphabet. Thus,programs and specificationscan be viewed as descriptions of languagesover some alphabet. The automata-theoretic perspective considers the relationships between programs and their specifications as relationships between languages.By translating programs and specifications to automata, questions about programs and their specifications can be reduced to questions about automata. More specifically, questions such as satisfiability of specifications and correctness of programs with respect to their specifications can be reduced to questions such as nonemptiness and containment of automata. Unlike classical automata theory, which focused on automata on finite words, the applications to program specification, verification, and synthesis, use automata on infinite words, since the computations in which we are interested are typically infinite. This paper provides an introduction to the theory of automata on infinite words and demonstrates its applications to program specification, verification, and synthesis.
Strategic Information Disclosure to People with Multiple Alternatives

In this article, we study automated agents that are designed to encourage humans to take some actions over others by strategically disclosing key pieces of information. To this end, we utilize the framework of persuasion games—a branch of game theory that deals with asymmetric interactions where one player (Sender) possesses more information about the world, but it is only the other player (Receiver) who can take an action. In particular, we use an extended persuasion model, where the Sender’s information is imperfect and the Receiver has more than two alternative actions available. We design a computational algorithm that, from the Sender’s standpoint, calculates the optimal information disclosure rule. The algorithm is parameterized by the Receiver’s decision model (i.e., what choice he will make based on the information disclosed by the Sender) and can be retuned accordingly.
 We then provide an extensive experimental study of the algorithm’s performance in interactions with human Receivers. First, we consider a fully rational (in the Bayesian sense) Receiver decision model and experimentally show the efficacy of the resulting Sender’s solution in a routing domain. Despite the discrepancy in the Sender’s and the Receiver’s utilities from each of the Receiver’s choices, our Sender agent successfully persuaded human Receivers to select an option more beneficial for the agent. Dropping the Receiver’s rationality assumption, we introduce a machine learning procedure that generates a more realistic human Receiver model. We then show its significant benefit to the Sender solution by repeating our routing experiment. To complete our study, we introduce a second (supply--demand) experimental domain and, by contrasting it with the routing domain, obtain general guidelines for a Sender on how to construct a Receiver model.
GeoDMA - Geographic Data Mining Analyst

Remote sensing images obtained by remote sensing are a key source of data for studying large-scale geographic areas. From 2013 onwards, a new generation of land remote sensing satellites from USA, China, Brazil, India and Europe will produce in one year as much data as 5 years of the Landsat-7 satellite. Thus, the research community needs new ways to analyze large data sets of remote sensing imagery. To address this need, this paper describes a toolbox for combing land remote sensing image analysis with data mining techniques. Data mining methods are being extensively used for statistical analysis, but up to now have had limited use in remote sensing image interpretation due to the lack of appropriate tools. The toolbox described in this paper is the Geographic Data Mining Analyst (GeoDMA). It has algorithms for segmentation, feature extraction, feature selection, classification, landscape metrics and multi-temporal methods for change detection and analysis. GeoDMA uses decision-tree strategies adapted for spatial data mining. It connects remotely sensed imagery with other geographic data types using access to local or remote databases. GeoDMA has methods to assess the accuracy of simulation models, as well as tools for spatio-temporal analysis, including a visualization of time-series that helps users to find patterns in cyclic events. The software includes a new approach for analyzing spatio-temporal data based on polar coordinates transformation. This method creates a set of descriptive features that improves the classification accuracy of multi-temporal image databases. GeoDMA is tightly integrated with TerraView GIS, so its users have access to all traditional GIS features. To demonstrate GeoDMA, we show two case studies on land use and land cover change.
A Local Feature based on Lagrangian Measures for Violent Video Classification

Lagrangian theory provides a diverse set of tools for continuous motion analysis. Existing work shows the applicability of Lagrangian method for video analysis in several aspects. In this paper we want to utilize the concept of Lagrangian measures to detect violent scenes. Therefore we propose a local feature based on the SIFT algorithm that incooperates appearance and Lagrangian based motion models. We will show that the temporal interval of the used motion information is a crucial aspect and study its influence on the classification performance. The proposed LaSIFT feature outperforms other state-of-the-art local features, in particular in uncontrolled realistic video data. We evaluate our algorithm with a bag-of-word approach. The experimental results show a significant improvement over the state-of-the-art on current violent detection datasets, i.e. Crowd Violence, Hockey Fight.