nils-herrmann/clean_scirepeval huggingface dataset:
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks

One long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent. To measure progress towards that goal, we argue for the usefulness of a set of proxy tasks that evaluate reading comprehension via question answering. Our tasks measure understanding in several ways: whether a system is able to answer questions via chaining facts, simple induction, deduction and many more. The tasks are designed to be prerequisites for any system that aims to be capable of conversing with a human. We believe many existing learning systems can currently not solve them, and hence our aim is to classify these tasks into skill sets, so that researchers can identify (and then rectify) the failings of their systems. We also extend and improve the recently introduced Memory Networks model, and show it is able to solve some, but not all, of the tasks.
Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation

Recent advances in semantic image segmentation have mostly been achieved by training deep convolutional neural networks (CNNs). We show how to improve semantic segmentation through the use of contextual information, specifically, we explore 'patch-patch' context between image regions, and 'patch-background' context. For learning from the patch-patch context, we formulate Conditional Random Fields (CRFs) with CNN-based pairwise potential functions to capture semantic correlations between neighboring patches. Efficient piecewise training of the proposed deep structured model is then applied to avoid repeated expensive CRF inference for back propagation. For capturing the patch-background context, we show that a network design with traditional multi-scale image input and sliding pyramid pooling is effective for improving performance. Our experimental results set new state-of-the-art performance on a number of popular semantic segmentation datasets, including NYUDv2, PASCAL VOC 2012, PASCAL-Context, and SIFT-flow. In particular, we achieve an intersection-overunion score of 78:0 on the challenging PASCAL VOC 2012 dataset.
A Spectral Algorithm for Learning Hidden Markov Models

Hidden Markov Models (HMMs) are one of the most fundamental a nd widely used statistical tools for modeling discrete time series. Typically, they are learned using sea rch heuristics (such as the Baum-Welch / EM algorithm), which suffer from the usual local optima issues. While in gen eral these models are known to be hard to learn with samples from the underlying distribution, we provide t h first provably efficient algorithm (in terms of sample and computational complexity) for learning HMMs under a nat ur l separation condition. This condition is roughly analogous to the separation conditions considered for lear ning mixture distributions (where, similarly, these model s are hard to learn in general). Furthermore, our sample compl exity results do not explicitly depend on the number of distinct (discrete) observations — they implicitly depend on this number through spectral properties of the underlyin g HMM. This makes the algorithm particularly applicable to se ttings with a large number of observations, such as those in natural language processing where the space of observati on is sometimes the words in a language. Finally, the algorithm is particularly simple, relying only on a singula r value decomposition and matrix multiplications.
A Compressive Sensing Data Acquisition and Imaging Method for Stepped Frequency GPRs

A novel data acquisition and imaging method is presented for stepped-frequency continuous-wave ground penetrating radars (SFCW GPRs). It is shown that if the target space is sparse, i.e., a small number of point like targets, it is enough to make measurements at only a small number of random frequencies to construct an image of the target space by solving a convex optimization problem which enforces sparsity through lscr 1 minimization. This measurement strategy greatly reduces the data acquisition time at the expense of higher computational costs. Imaging results for both simulated and experimental GPR data exhibit less clutter than the standard migration methods and are robust to noise and random spatial sampling. The images also have increased resolution where closely spaced targets that cannot be resolved by the standard migration methods can be resolved by the proposed method.
ISAR - radar imaging of targets with complicated motion

ISAR imaging is described for general motion of a radar target. ISAR imaging may be seen as a 3D to 2D projection, and the importance of the ISAR image projection plane is stated. For general motion, ISAR images are often smeared when using FFT processing. Time frequency methods are used to analyze such images, and to form sharp images. A given smeared image is shown to be the result of changes both in scale and in the projection plane orientation.
Learning From Agile Software Development – Part One

B eing agile is a declaration of prioritizing for project maneuverability with respect to shifting requirements, shifting technology, and a shifting understanding of the situation. Other priorities that might override agility include predictability , cost, schedule, process-accreditation, or use of specific tools. Most managers run a portfolio of projects having a mix of those priorities. They need to prioritize agility, predictability , and cost sensitivity in varying amounts and therefore need to mix strategies. This article focuses on borrowing ideas from the agile suite to fit the needs of plan-driven and cost-sensitive programs. Our industry now has enough information to sensibly discuss such blending. illustrate the differences in world view, and some authors have discussed the question of their coexistence and principles underlying successful development strategies [3, 8, 13]. Many project strategies revolve around spending money for either information or flexibility [3, 14]. In a money-for-information (MFI) proposition , the team can choose to expend resources now to gain information earlier. If the information is not considered valuable enough, the resources are applied to other work. The question is how much the team is willing to expend in exchange for that information. In a money-for-flexibility (MFF) proposition , the team may opt to expend resources to preserve later flexibility. If the future is quite certain, the resources are better spent pursuing the most probable outcome, or on MFI issues. Different project strategies are made by deciding which issues are predictable, unpredictable but resolvable, or unresolv-able, deciding which of those are MFI or MFF propositions, and how best to allocate resources for each. Predictable issues can be investigated using breakdown techniques. Such an issue might be creating a schedule for work similar to that successfully performed in the past. Unpredictable but resolvable issues can be investigated through study techniques such as prototypes and simulators. Such issues include system performance limits. These are also MFI propositions. Agile and plan-driven teams are likely to use similar strategies for these issues as part of basic project risk management. Unresolvable issues tend to be sociological , such as which upcoming standard will gain market acceptance, or how long key employees will stay around. These issues cannot be resolved in advance, and so are not MFI propositions, but are MFF propositions. Agile and plan-driven teams are intrinsically likely to use different strategies for these issues. Agile teams will set up to absorb these changes, while plan-driven …
Category use and category learning.

Categorization models based on laboratory research focus on a narrower range of explanatory constructs than appears necessary for explaining the structure of natural categories. This mismatch is caused by the reliance on classification as the basis of laboratory studies. Category representations are formed in the process of interacting with category members. Thus, laboratory studies must explore a range of category uses. The authors review the effects of a variety of category uses on category learning. First, there is an extensive discussion contrasting classification with a predictive inference task that is formally equivalent to classification but leads to a very different pattern of learning. Then, research on the effects of problem solving, communication, and combining inference and classification is reviewed.
The Type of Information Overload Affects Electronic Knowledge Repository continuance

In the present competitive organizational environment more organizations are implementing knowledge management initiatives to gain strategic advantage. One such initiative is that of implementing electronic knowledge repositories (EKR) which often leads to a rapid increase in the quantity of information employees must process daily, raising concerns of employees being overloaded. This is especially true for current EKRs using distributive technology, enabling customizable individual workspaces which can result in loose knowledge structures. This paper identifies a new type of information overload (IO), extending the concept as occurring in both knowledge seekers and contributors and uses cognitive dissonance theory to provide evidence that IO can change employees’ perception of EKR usage. This research paper provides the first empirical evidence that overload has no sufficient affect on EKR continuance intention directly, but has a significant negative affect on the two main success measures: Perceived usefulness and satisfaction of the system.
Dr.Fill: Crosswords and an Implemented Solver for Singly Weighted CSPs

We describe Dr.Fill, a program that solves American-style crossword puzzles. From a technical perspective, Dr.Fill works by converting crosswords to weighted csps, and then using a variety of novel techniques to find a solution. These techniques include generally applicable heuristics for variable and value selection, a variant of limited discrepancy search, and postprocessing and partitioning ideas. Branch and bound is not used, as it was incompatible with postprocessing and was determined experimentally to be of little practical value. Dr.Fill’s performance on crosswords from the American Crossword Puzzle Tournament suggests that it ranks among the top fifty or so crossword solvers in the world.
Bluetooth: vision, goals, and architecture

A few years ago it was recognized that the vision of a truly low-cost, low-power radio-based cable replacement was feasible. Such a ubiquitous link would provide the basis for portable devices to communicate together in an ad hoc fashion by creating personal area networks which have similar advantages to their office environment counterpart - the local area network (LAN). Bluetooth is an effort by a consortium of companies to design a royalty free technology specification enabling this vision. This article describes the vision and goals of the Bluetooth program and introduces the radio-based technology.
High Performance Clustering Based on the Similarity Join

A broad class of algorithms for knowledge discovery in databases (KDD) relies heavily on similarity queries, i.e. range queries or nearest neighbor queries, in multidimensional feature spaces. Many KDD algorithms perform a similarity query for each point stored in the database. This approach causes serious performance degenerations if the considered data set does not fit into main memory. Usual cache strategies such as LRU fail because the locality of KDD algorithms is typically not high enough. In this paper, we propose to replace repeated similarity queries by the similarity join, a database primitive prevalent in multimedia database systems. We present a schema to transform query intensive KDD algorithms into a representation using the similarity join as a basic operation without affecting the correctness of the result of the considered algorithm. In order to perform a comprehensive experimental evaluation of our approach, we apply the proposed transformation to the clustering algorithm DBSCAN and to the hierarchical cluster structure analysis method OPTICS. Our technique allows the application of any similarity join algorithm, which may be based on index structures or not. In our experiments, we use a similarity join algorithm based on a variant of the X-tree. The experiments yield substantial performance improvements of our technique over the original algorithms. The traditional techniques are outperformed by factors of up to 33 for the X-tree and 54 for the R*-tree.
Space-Vector PWM Control Synthesis for an H-Bridge Drive in Electric Vehicles

This paper deals with a synthesis of space-vector pulsewidth modulation (SVPWM) control methods applied for an H-bridge inverter feeding a three-phase permanent-magnet synchronous machine (PMSM) in electric-vehicle (EV) applications. First, a short survey of existing architectures of power converters, particularly those adapted to degraded operating modes, is presented. Standard SVPWM control methods are compared with three innovative methods using EV drive specifications in the normal operating mode. Then, a rigorous analysis of the margins left in the control strategy is presented for a semiconductor switch failure to fulfill degraded operating modes. Finally, both classic and innovative strategies are implemented in numerical simulation; their results are analyzed and discussed.
Global diversity of the Ganoderma lucidum complex (Ganodermataceae, Polyporales) inferred from morphology and multilocus phylogeny.

Species of the Ganoderma lucidum complex are used in many types of health products. However, the taxonomy of this complex has long been chaotic, thus limiting its uses. In the present study, 32 collections of the complex from Asia, Europe and North America were analyzed from both morphological and molecular phylogenetic perspectives. The combined dataset, including an outgroup, comprised 33 ITS, 24 tef1α, 24 rpb1 and 21 rpb2 sequences, of which 19 ITS, 20 tef1α, 20 rpb1 and 17 rpb2 sequences were newly generated. A total of 13 species of the complex were recovered in the multilocus phylogeny. These 13 species were not strongly supported as a single monophyletic lineage, and were further grouped into three lineages that cannot be defined by their geographic distributions. Clade A comprised Ganoderma curtisii, Ganoderma flexipes, Ganoderma lingzhi, Ganoderma multipileum, Ganoderma resinaceum, Ganoderma sessile, Ganoderma sichuanense and Ganoderma tropicum, Clade B comprised G. lucidum, Ganoderma oregonense and Ganoderma tsugae, and Clade C comprised Ganoderma boninense and Ganoderma zonatum. A dichotomous key to the 13 species is provided, and their key morphological characters from context, pores, cuticle cells and basidiospores are presented in a table. The taxonomic positions of these species are briefly discussed. Noteworthy, the epitypification of G. sichuanense is rejected.
Deep Visuo-Tactile Learning: Estimation of Material Properties from Images

Estimation of materials properties, such as softness or roughness from visual perception is an essential factor in deciding our way of interaction with our environment in e.g., object manipulation tasks or walking. In this research, we propose a method for deep visuo-tactile learning in which we train a encoder-decoder network with an intermediate layer in an unsupervised manner with images as input and tactile sequences as output. Materials properties are then represented in the intermediate layer as a continuous feature space and are estimated from image information. Unlike past studies utilizing tactile sensors focusing on classification for object recognition or recognizing material properties, our method does not require manually designing class labels or annotation, does not cause unknown objects to be classified into known discrete classes, and can be used without a tactile sensor after training. To collect data for training, we have attached a uSkin tactile sensor and a camera to the end-effector of a Sawyer robot to stroke surfaces of 21 different material surfaces. Our results after training show that features are indeed expressed continuously, and that our method is able to handle unknown objects in its feature space.
Bilevel Model-Based Discriminative Dictionary Learning for Recognition

Most supervised dictionary learning methods optimize the combinations of reconstruction error, sparsity prior, and discriminative terms. Thus, the learnt dictionaries may not be optimal for recognition tasks. Also, the sparse codes learning models in the training and the testing phases are inconsistent. Besides, without utilizing the intrinsic data structure, many dictionary learning methods only employ the  $\ell _{0}$  or  $\ell _{1}$  norm to encode each datum independently, limiting the performance of the learnt dictionaries. We present a novel bilevel model-based discriminative dictionary learning method for recognition tasks. The upper level directly minimizes the classification error, while the lower level uses the sparsity term and the Laplacian term to characterize the intrinsic data structure. The lower level is subordinate to the upper level. Therefore, our model achieves an overall optimality for recognition in that the learnt dictionary is directly tailored for recognition. Moreover, the sparse codes learning models in the training and the testing phases can be the same. We further propose a novel method to solve our bilevel optimization problem. It first replaces the lower level with its Karush-Kuhn–Tucker conditions and then applies the alternating direction method of multipliers to solve the equivalent problem. Extensive experiments demonstrate the effectiveness and robustness of our method.
UTHealth at SemEval-2016 Task 12: an End-to-End System for Temporal Information Extraction from Clinical Notes

The 2016 Clinical TempEval challenge addresses temporal information extraction from clinical notes. The challenge is composed of six sub-tasks, each of which is to identify: (1) event mention spans, (2) time expression spans, (3) event attributes, (4) time attributes, (5) events’ temporal relations to the document creation times (DocTimeRel), and (6) narrative container relations among events and times. In this article, we present an end-to-end system that addresses all six sub-tasks. Our system achieved the best performance for all six sub-tasks when plain texts were given as input. It also performed best for narrative container relation identification when gold standard event/time annotations were given.
Deep Interest Network for Click-Through Rate Prediction

Click-through rate prediction is an essential task in industrial applications, such as online advertising. Recently deep learning based models have been proposed, which follow a similar Embedding&MLP paradigm. In these methods large scale sparse input features are first mapped into low dimensional embedding vectors, and then transformed into fixed-length vectors in a group-wise manner, finally concatenated together to fed into a multilayer perceptron (MLP) to learn the nonlinear relations among features. In this way, user features are compressed into a fixed-length representation vector, in regardless of what candidate ads are. The use of fixed-length vector will be a bottleneck, which brings difficulty for Embedding&MLP methods to capture user's diverse interests effectively from rich historical behaviors. In this paper, we propose a novel model: Deep Interest Network (DIN) which tackles this challenge by designing a local activation unit to adaptively learn the representation of user interests from historical behaviors with respect to a certain ad. This representation vector varies over different ads, improving the expressive ability of model greatly. Besides, we develop two techniques: mini-batch aware regularization and data adaptive activation function which can help training industrial deep networks with hundreds of millions of parameters. Experiments on two public datasets as well as an Alibaba real production dataset with over 2 billion samples demonstrate the effectiveness of proposed approaches, which achieve superior performance compared with state-of-the-art methods. DIN now has been successfully deployed in the online display advertising system in Alibaba, serving the main traffic.
RRE: A Game-Theoretic Intrusion Response and Recovery Engine

Preserving the availability and integrity of networked computing systems in the face of fast-spreading intrusions requires advances not only in detection algorithms, but also in automated response techniques. In this paper, we propose a new approach to automated response called the response and recovery engine (RRE). Our engine employs a game-theoretic response strategy against adversaries modeled as opponents in a two-player Stackelberg stochastic game. The RRE applies attack-response trees (ART) to analyze undesired system-level security events within host computers and their countermeasures using Boolean logic to combine lower level attack consequences. In addition, the RRE accounts for uncertainties in intrusion detection alert notifications. The RRE then chooses optimal response actions by solving a partially observable competitive Markov decision process that is automatically derived from attack-response trees. To support network-level multiobjective response selection and consider possibly conflicting network security properties, we employ fuzzy logic theory to calculate the network-level security metric values, i.e., security levels of the system's current and potentially future states in each stage of the game. In particular, inputs to the network-level game-theoretic response selection engine, are first fed into the fuzzy system that is in charge of a nonlinear inference and quantitative ranking of the possible actions using its previously defined fuzzy rule set. Consequently, the optimal network-level response actions are chosen through a game-theoretic optimization process. Experimental results show that the RRE, using Snort's alerts, can protect large networks for which attack-response trees have more than 500 nodes.
The Ring of Gyges : Using Smart Contracts for Crime

Thanks to their anonymity (pseudonymity) and lack of trusted intermediaries, cryptocurrencies such as Bitcoin have created or stimulated growth in many businesses and communities. Unfortunately, some are criminal, e.g., money laundering, marketplaces for illicit goods, and ransomware. Next-generation cryptocurrencies such as Ethereum will include rich scripting languages in support of smart contracts, programs that autonomously intermediate transactions. We illuminate the extent to which these new cryptocurrencies, by enabling criminal activities to be conducted anonymously and with minimal trust assumptions, may fuel new criminal ecosystems. Specifically, we show how what we call criminal smart contracts (CSCs) can facilitate leakage of confidential information, theft of cryptographic keys, and various realworld crimes (murder, arson, terrorism). We show significantly that CSCs for leakage of secrets are efficiently realizable in existing scripting languages such as that in Ethereum. We show that CSCs for key theft can be achieved using cryptographic primitives, such as Succinct Non-interactive ARguments of Knowledge (SNARKs), that are already expressible in these languages and for which efficient supporting language extensions are anticipated. We demonstrate similarly that authenticated data feeds, another anticipated feature of smart contract systems, can facilitate CSCs for real-world crimes. Our results illuminate the scope of possible abuses in nextgeneration cryptocurrencies. They highlight the urgency of creating policy and technical safeguards and thereby realizing the great promise of smart contracts for beneficial goals.