nils-herrmann/clean_scirepeval huggingface dataset:
An efficient IP address lookup algorithm based on a small balanced tree using entry reduction

Due to a tremendous increase in internet traffic, backbone routers must have the capability to forward massive incoming packets at several gigabits per second. IP address lookup is one of the most challenging tasks for high-speed packet forwarding. Some high-end routers have been implemented with hardware parallelism using ternary content addressable memory (TCAM). However, TCAM is much more expensive in terms of circuit complexity as well as power consumption. Therefore, efficient algorithmic solutions are essentially required to be implemented using network processors as low cost solutions. Among the state-of-the-art algorithms for IP address lookup, a binary search based on a balanced tree is effective in providing a low-cost solution. In order to construct a balanced search tree, the prefixes with the nesting relationship should be converted into completely disjointed prefixes. A leaf-pushing technique is very useful to eliminate the nesting relationship among prefixes [V. Srinivasan, G. Varghese, Fast address lookups using controlled prefix expansion, ACM Transactions on Computer Systems 17 (1) (1999) 1–40]. However, it creates duplicate prefixes, thus expanding the search tree. This paper proposes an efficient IP address lookup algorithm based on a small balanced tree using entry reduction. The leaf-pushing technique is used for creating the completely disjointed entries. In the leaf-pushed prefixes, there are numerous pairs of adjacent prefixes with similarities in prefix strings and output ports. The number of entries can be significantly reduced by the use of a new entry reduction method which merges pairs with these similar prefixes. After sorting the reduced disjointed entries, a small balanced tree is constructed with a very small node size. Based on this small balanced tree, a native binary search can be effectively used in address lookup issue. In addition, we propose a new multi-way search algorithm to improve a binary search for IPv4 address lookup. As a result, the proposed algorithms offer excellent lookup performance along with reduced memory requirements. Besides, these provide good scalability for large amounts of routing data and for the address migration toward IPv6. Using both various IPv4 and IPv6 routing data, the performance evaluation results demonstrate that the proposed algorithms have better performance in terms of lookup speed, memory requirement and scalability for the growth of entries and IPv6, as compared with other algorithms based on a binary search.  +82 2 313 8053.
SemEval-2016 Task 10: Detecting Minimal Semantic Units and their Meanings (DiMSUM)

This task combines the labeling of multiword expressions and supersenses (coarse-grained classes) in an explicit, yet broad-coverage paradigm for lexical semantics. Nine systems participated; the best scored 57.7% F1 in a multi-domain evaluation setting, indicating that the task remains largely unresolved. An error analysis reveals that a large number of instances in the data set are either hard cases, which no systems get right, or easy cases, which all systems correctly solve.
Algorithmic Authority: the Ethics, Politics, and Economics of Algorithms that Interpret, Decide, and Manage

This panel will explore algorithmic authority as it manifests and plays out across multiple domains. Algorithmic authority refers to the power of algorithms to manage human action and influence what information is accessible to users. Algorithms increasingly have the ability to affect everyday life, work practices, and economic systems through automated decision-making and interpretation of "big data". Cases of algorithmic authority include algorithmically curating news and social media feeds, evaluating job performance, matching dates, and hiring and firing employees. This panel will bring together researchers of quantified self, healthcare, digital labor, social media, and the sharing economy to deepen the emerging discourses on the ethics, politics, and economics of algorithmic authority in multiple domains.
Bidirectional Dynamics for Protein Secondary Structure Prediction

Connectionist models for learning in sequential domains are typically dynamical systems that use hidden states to store contextual information. In principle, these models can adapt to variable time lags and perform complex sequential mappings. In spite of several successful applications (mostly based on hidden Markov models), the general class of sequence learning problems is still far from being satisfactorily solved. In particular, learning sequential translations is generally a hard task and current models seem to exhibit a number of limitations. One of these limitations, at least for some application domains, is the causality assumption. A dynamical system is said to be causal if the output at (discrete) time t does not depend on future inputs. Causality is easy to justify in dynamics that attempt to model the behavior of many physical systems. Clearly, in these cases the response at time t cannot depend on stimulae that the system has not yet received as input. As it turns out, non-causal dynamics over infinite time horizons cannot be realized by any physical or computational device. For certain categories of finite sequences, however, information from both the past and the future can be very useful for analysis and predictions at time t. This is the case, for example, of DNA and protein sequences where the structure and function of a region in the sequence may strongly depend on events located both upstream and downstream of the region, sometimes at considerable distances. Another good example is provided by the off-line translation of a language into another one. Even in the so-called “simultaneous” translation, it is well known that interpreters are constantly forced to introduce small delays in order to acquire “future” information within a sentence to resolve semantic ambiguities and preserve syntactic correctness. Non-causal dynamics are sometimes used in other disciplines (for example, Kalman smoothing in optimal control or non-causal digital filters in signal processing). However, as far as connectionist models are concerned, the causality assumption is shared among all the types of models which are capable of mapping input sequences to output sequences, including recurrent neural networks and input-output HMMs (IOHMMs) (Bengio & Frasconi, 1996). In this paper, we develop a new family of non-causal adaptive architectures where the underlying dynamics are factored using a pair of chained hidden state variables. The two
Comprehensive and Efficient Protection of Kernel Control Data

Protecting kernel control data (e.g., function pointers and return addresses) has been a serious issue plaguing rootkit defenders. In particular, rootkit authors only need to compromise one piece of control data to launch their attacks, while defenders need to protect thousands of such values widely scattered across kernel memory space. Worse, some of this data (e.g., return addresses) is volatile and can be dynamically generated at run time. Existing solutions, however, offer either incomplete protection or excessive performance overhead. To overcome these limitations, we present indexed hooks, a scheme that greatly facilitates kernel control-flow enforcement by thoroughly transforming and restricting kernel control data to take only legal jump targets (allowed by the kernel's control-flow graph). By doing so, we can severely limit the attackers' possibility of exploiting them as an infection vector to launch rootkit attacks. To validate our approach, we have developed a compiler-based prototype that implements this technique in the FreeBSD 8.0 kernel, transforming 49 025 control transfer instructions (~7.25% of the code base) to use indexed hooks instead of direct pointers. Our evaluation results indicate that our approach is generic, effective, and can be implemented on commodity hardware with a low performance overhead (<;5% based on benchmarks).
New circularly polarized slot radiator for substrate integrated waveguide (SIW) planar array

A circularly polarized slot radiator in millimetre band is presented. Low reflection and good polarization performance is obtained in comparison with state of art slot elements. A high gain array prototype has been implemented in substrate integrated waveguides (SIW) technology, in order to check the new element performance. A cosecant amplitude coverage in the elevation plane, as well as a 6° tilted monopulse pattern in the azimuth plane are obtained. A gain peak value of 28.6 dBi and 79% of efficiency at 36.7 GHz have been obtained in the specified operation band (36.7 – 37 GHz) for a manufactured antenna prototype. Likewise, the top measured axial ratio is 1.95 dB at 36.85 GHz.
Group recommendations with rank aggregation and collaborative filtering

The majority of recommender systems are designed to make recommendations for individual users. However, in some circumstances the items to be selected are not intended for personal usage but for a group; e.g., a DVD could be watched by a group of friends. In order to generate effective recommendations for a group the system must satisfy, as much as possible, the individual preferences of the group's members.
 This paper analyzes the effectiveness of group recommendations obtained aggregating the individual lists of recommendations produced by a collaborative filtering system. We compare the effectiveness of individual and group recommendation lists using normalized discounted cumulative gain. It is observed that the effectiveness of a group recommendation does not necessarily decrease when the group size grows. Moreover, when individual recommendations are not effective a user could obtain better suggestions looking at the group recommendations. Finally, it is shown that the more alike the users in the group are, the more effective the group recommendations are.
Menstrual-like vaginal bleeding in prepubertal girls: an unexplained condition

Unexplained menstrual-like cyclic vaginal bleeding, lasting for several days and without any uterine and vaginal anomaly, is a rare condition in prepubertal girls. Only small series (containing four to eleven cases) have been described in literature. We report our in nine girls presenting with vaginal bleeding without any abnormality at gynaecological, hormonal and echographic examination.
A Wearable Hip Assist Robot Can Improve Gait Function and Cardiopulmonary Metabolic Efficiency in Elderly Adults

The aims of this paper were to investigate the effectiveness of a newly developed wearable hip assist robot, that uses an active assist algorithm to improve gait function, muscle effort, and cardiopulmonary metabolic efficiency in elderly adults. Thirty elderly adults (15 males/ 15 females) participated in thispaper. The experimental protocol consisted of overground gait at comfortable speed under three different conditions: free gait without robot assistance, robot-assisted gait with zero torque (RAG-Z), and full RAG. Under all conditions, muscle effort was analyzed using a 12-channel surface electromyography system. Spatio-temporal data were collected at 120 Hz using a 3-D motion capture system with six infrared cameras. Metabolic cost parameters were collected as oxygen consumption per unit (ml/min/kg) and aerobic energy expenditure (Kcal/min). In the RAG condition, participants demonstrated improved gait function, decreased muscle effort, and reduced metabolic cost. Although the hip assist robot only provides assistance at the hip joint, our results demonstrated a clear reduction in knee and ankle muscle activity in addition to decreased hip flexor and extensor activity. Our findings suggest that this robot has the potential to improve stabilization of the trunk during walking in elderly adults.
Active Learning for Structure in Bayesian Networks

The task of causal structure discovery from empirical data i s a fundamental problem in many areas. Experimental data is crucial for accomplishing this task. However, experiments are typically expensive, and must be selected with great car e. This paper uses active learningto determine the experiments that are most informative towards uncovering the underlyin g structure. We formalize the causal learning task as that of learning the structure of a causal Bayesian network. We consider an active learner that is allowed to conduct experimen ts, where itintervenesin the domain by setting the values of certain variables. We provide a theoretical framework for the a ctive learning problem, and an algorithm that actively choos es the experiments to perform based on the model learned so far. Experimental results show that active learning can sub stantially reduce the number of observations required to de termine the structure of a domain.
A Multi-Object Rectified Attention Network for Scene Text Recognition

Irregular text is widely used. However, it is considerably difficult to recognize because of its various shapes and distorted patterns. In this paper, we thus propose a multi-object rectified attention network (MORAN) for general scene text recognition. The MORAN consists of a multi-object rectification network and an attention-based sequence recognition network. The multi-object rectification network is designed for rectifying images that contain irregular text. It decreases the difficulty of recognition and enables the attention-based sequence recognition network to more easily read irregular text. It is trained in a weak supervision way, thus requiring only images and corresponding text labels. The attentionbased sequence recognition network focuses on target characters and sequentially outputs the predictions. Moreover, to improve the sensitivity of the attentionbased sequence recognition network, a fractional pickup method is proposed for an attention-based decoder in the training phase. With the rectification mechanism, the MORAN can read both regular and irregular scene text. Extensive experiments on various benchmarks are conducted, which show that the MORAN achieves state-of-the-art performance. The source code is available1.
A 27.6 MHz 297 μW 33 ppm/°C CMOS relaxation oscillator with an adjustable temperature compensation scheme

A 27.6 MHz 297 μW relaxation oscillator is presented in this paper by using an 180-nm CMOS technology. The proposed oscillator employs an adjustable temperature compensation feedforward scheme, in which the charging current can be set steady by a four-bit digital trimming signal. We have demonstrated a frequency variation lower than 33.5 ppm/°C which could be close to 0 ppm/°C in theory if the precision is high enough. In practical production, it is effective to calibrate the mismatching and deviation of fabrication because of the novel adjustable temperature compensation scheme.
Modelling Railway Interlocking Tables Using Coloured Petri Nets

Interlocking tables are the functional specification defining the routes, on which the passage of the train is allowed. Associated with the route, the states and actions of all related signalling equipment are also specified. This paper formally models the interlocking tables using Coloured Petri Nets (CPN). The CPN model comprises two parts: Signaling Layout and Interlocking Control. The Signaling Layout part is used to simulate the passage of the train. It stores geographic information of the signalling layout in tokens. The Interlocking Control part models actions of the controller according to the functions specified in the interlocking tables. The arc inscriptions in the model represent the content of the interlocking tables. Following our modelling approach we can reuse the same CPN net structure to model any new or modified interlocking system regardless of its size. Experimental results are presented to provide increased confidence in the model correctness.
COMPUTING MACHINERY AND INTELLIGENCE

I propose to consider the question, “Can machines think?” This should begin with definitions of the meaning of the terms “machine” and “think.” The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words“machine”and “think” are to be found by examining how they are commonly used it is di cult to escape the conclusion that the meaning and the answer to the question, “Can machines think?” is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words. The new form of the problem can be described in terms of a game which we call the “imitation game.” It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart front the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either “X is A and Y is B” or “X is B and Y is A.” The interrogator is allowed to put questions to A and B thus:
Surface reconstruction from unorganized points

Computer Graphics, 26, 2, July 1992 Surface Reconstruction from Unorganized Points Hugues Hoppe* Tony DeRose* Tom Duchampt John McDonald$ Werner Stuetzle~ University of Washington Seattle, WA 98195 We describe and demonstrate an algorithm that takes as input an unorganized set of points {xl, . . . . x.} c IR3 on or near an unknown manifold M, and produces as output a simplicial surface that approximates M. Neither the topology, the presence of boundaries, nor the geometry of M are assumed to be known in advance — all are inferred automatically from the data. This problem natu rally arises in a variety of practical situations such as range scanning an object from multiple view points, recovery of biological shapes from two-dimensional slices, and interactive surface sketching.
DeepPose: Human Pose Estimation via Deep Neural Networks

We propose a method for human pose estimation based on Deep Neural Networks (DNNs). The pose estimation is formulated as a DNN-based regression problem towards body joints. We present a cascade of such DNN regres- sors which results in high precision pose estimates. The approach has the advantage of reasoning about pose in a holistic fashion and has a simple but yet powerful formula- tion which capitalizes on recent advances in Deep Learn- ing. We present a detailed empirical analysis with state-of- art or better performance on four academic benchmarks of diverse real-world images.
Learning realistic human actions from movies

The aim of this paper is to address recognition of natural human actions in diverse and realistic video settings. This challenging but important subject has mostly been ignored in the past due to several problems one of which is the lack of realistic and annotated video datasets. Our first contribution is to address this limitation and to investigate the use of movie scripts for automatic annotation of human actions in videos. We evaluate alternative methods for action retrieval from scripts and show benefits of a text-based classifier. Using the retrieved action samples for visual learning, we next turn to the problem of action classification in video. We present a new method for video classification that builds upon and extends several recent ideas including local space-time features, space-time pyramids and multi-channel non-linear SVMs. The method is shown to improve state-of-the-art results on the standard KTH action dataset by achieving 91.8% accuracy. Given the inherent problem of noisy labels in automatic annotation, we particularly investigate and show high tolerance of our method to annotation errors in the training set. We finally apply the method to learning and classifying challenging action classes in movies and show promising results.
A coarse-to-fine approach for fast deformable object detection

We present a method that can dramatically accelerate object detection with part based models. The method is based on the observation that the cost of detection is likely to be dominated by the cost of matching each part to the image, and not by the cost of computing the optimal configuration of the parts as commonly assumed. Therefore accelerating detection requires minimizing the number of part-to-image comparisons. To this end we propose a multiple-resolutions hierarchical part based model and a corresponding coarse-to-fine inference procedure that recursively eliminates from the search space unpromising part placements. The method yields a ten-fold speedup over the standard dynamic programming approach and is complementary to the cascade-of-parts approach of . Compared to the latter, our method does not have parameters to be determined empirically, which simplifies its use during the training of the model. Most importantly, the two techniques can be combined to obtain a very significant speedup, of two orders of magnitude in some cases. We evaluate our method extensively on the PASCAL VOC and INRIA datasets, demonstrating a very high increase in the detection speed with little degradation of the accuracy.
Engaging by design: How engagement strategies in popular computer and video games can inform instructional design

Computer and video games are a prevalent form of entertainment in which the purpose of the design is to engage players. Game designers incorporate a number of strategies and tactics for engaging players in “gameplay.” These strategies and tactics may provide instructional designers with new methods for engaging learners. This investigation presents a review of game design strategies and the implications of appropriating these strategies for instructional design. Specifically, this study presents an overview of the trajectory of player positioning or point of view, the role of narrative, and methods of interactive design. A comparison of engagement strategies in popular games and characteristics of engaged learning is also presented to examine how strategies of game design might be integrated into the existing framework of engaged learning. A game is a series of interesting choices. —Sid Meier
Automated Flower Classification over a Large Number of Classes

We investigate to what extent combinations of features can improve classification performance on a large dataset of similar classes. To this end we introduce a 103 class flower dataset. We compute four different features for the flowers, each describing different aspects, namely the local shape/texture, the shape of the boundary, the overall spatial distribution of petals, and the colour. We combine the features using a multiple kernel framework with a SVM classifier. The weights for each class are learnt using the method of Varma and Ray, which has achieved state of the art performance on other large dataset, such as Caltech 101/256. Our dataset has a similar challenge in the number of classes, but with the added difficulty of large between class similarity and small within class similarity. Results show that learning the optimum kernel combination of multiple features vastly improves the performance, from 55.1% for the best single feature to 72.8% for the combination of all features.
Exploring the Limits of Language Modeling

In this work we explore recent advances in Recurrent Neural Networks for large scale Language Modeling, a task central to language understanding. We extend current models to deal with two key challenges present in this task: corpora and vocabulary sizes, and complex, long term structure of language. We perform an exhaustive study on techniques such as character Convolutional Neural Networks or Long-Short Term Memory, on the One Billion Word Benchmark. Our best single model significantly improves state-of-the-art perplexity from 51.3 down to 30.0 (whilst reducing the number of parameters by a factor of 20), while an ensemble of models sets a new record by improving perplexity from 41.0 down to 24.2. We also release these models for the NLP and ML community to study and improve upon.