nils-herrmann/clean_scirepeval huggingface dataset:
Wireless Software Defined Networking: A Survey and Taxonomy

One of the primary architectural principles behind the Internet is the use of distributed protocols, which facilitates fault tolerance and distributed management. Unfortunately, having nodes (i.e., switches and routers) perform control decisions independently makes it difficult to control the network or even understand or debug its overall emergent behavior. As a result, networks are often inefficient, unstable, and fragile. This Internet architecture also poses a significant, often insurmountable, challenge to the deployment of new protocols and evolution of existing ones. Software defined networking (SDN) is a recent networking architecture with promising properties relative to these weaknesses in traditional networks. SDN decouples the control plane, which makes the network forwarding decisions, from the data plane, which mainly forwards the data. This decoupling enables more centralized control where coordinated decisions directly guide the network to desired operating conditions. Moreover, decoupling the control enables graceful evolution of protocols, and the deployment of new protocols without having to replace the data plane switches. In this survey, we review recent work that leverages SDN in wireless network settings, where they are not currently widely adopted or well understood. More specifically, we evaluate the use of SDN in four classes of popular wireless networks: cellular, sensor, mesh, and home networks. We classify the different advantages that can be obtained by using SDN across this range of networks, and hope that this classification identifies unexplored opportunities for using SDN to improve the operation and performance of wireless networks.
ARM: Augment-REINFORCE-Merge Gradient for Discrete Latent Variable Models

To backpropagate the gradients through discrete stochastic layers, we encode the true gradients into a multiplication between random noises and the difference of the same function of two different sets of discrete latent variables, which are correlated with these random noises. The expectations of that multiplication over iterations are zeros combined with spikes from time to time. To modulate the frequencies, amplitudes, and signs of the spikes to capture the temporal evolution of the true gradients, we propose the augment-REINFORCE-merge (ARM) estimator that combines data augmentation, the score-function estimator, permutation of the indices of latent variables, and variance reduction for Monte Carlo integration using common random numbers. The ARM estimator provides low-variance and unbiased gradient estimates for the parameters of discrete distributions, leading to state-of-the-art performance in both auto-encoding variational Bayes and maximum likelihood inference, for discrete latent variable models with one or multiple discrete stochastic layers.
Evaluating Word Embedding Models: Methods and Experimental Results

Extensive evaluation on a large number of word embedding models for language processing applications is conducted in this work. First, we introduce popular word embedding models and discuss desired properties of word models and evaluation methods (or evaluators). Then, we categorize evaluators into intrinsic and extrinsic two types. Intrinsic evaluators test the quality of a representation independent of specific natural language processing tasks while extrinsic evaluators use word embeddings as input features to a downstream task and measure changes in performance metrics specific to that task. We report experimental results of intrinsic and extrinsic evaluators on six word embedding models. It is shown that different evaluators focus on different aspects of word models, and some are more correlated with natural language processing tasks. Finally, we adopt correlation analysis to study performance consistency of extrinsic and intrinsic evalutors.
Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines

Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling Machines (S2Ms), a class of neural network models that uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised learning. Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. Synaptic stochasticity plays the dual role of an efficient mechanism for sampling, and a regularizer during learning akin to DropConnect. A local synaptic plasticity rule implementing an event-driven form of contrastive divergence enables the learning of generative models in an on-line fashion. S2Ms perform equally well using discrete-timed artificial units (as in Hopfield networks) or continuous-timed leaky integrate and fire neurons. The learned representations are remarkably sparse and robust to reductions in bit precision and synapse pruning: removal of more than 75% of the weakest connections followed by cursory re-learning causes a negligible performance loss on benchmark classification tasks. The spiking neuron-based S2Ms outperform existing spike-based unsupervised learners, while potentially offering substantial advantages in terms of power and complexity, and are thus promising models for on-line learning in brain-inspired hardware.
Object tracking using firefly algorithm

Firefly algorithm (FA) is a new meta-heuristic optimisation algorithm that mimics the social behaviour of fireflies flying in the tropical and temperate summer sky. In this study, a novel application of FA is presented as it is applied to solve tracking problem. A general optimisation-based tracking architecture is proposed and the parameters’ sensitivity and adjustment of the FA in tracking system are studied. Experimental results show that the FA-based tracker can robustly track an arbitrary target in various challenging conditions. The authors compare the speed and accuracy of the FA with three typical tracking algorithms including the particle filter, meanshift and particle swarm optimisation. Comparative results show that the FA-based tracker outperforms the other three trackers.
Posture recognition with a top-view camera

We describe a system that recognizes human postures with heavy self-occlusion. In particular, we address posture recognition in a robot assisted-living scenario, where the environment is equipped with a top-view camera for monitoring human activities. This setup is very useful because top-view cameras lead to accurate localization and limited inter-occlusion between persons, but conversely they suffer from body parts being frequently self-occluded. The conventional way of posture recognition relies on good estimation of body part positions, which turns out to be unstable in the top-view due to occlusion and foreshortening. In our approach, we learn a posture descriptor for each specific posture category. The posture descriptor encodes how well the person in the image can be `explained' by the model. The postures are subsequently recognized from the matching scores returned by the posture descriptors. We select the state-of-the-art approach of pose estimation as our posture descriptor. The results show that our method is able to correctly classify 79.7% of the test sample, which outperforms the conventional approach by over 23%.
Estimation and comparison of the windage loss of a 60 kW Switched Reluctance Motor for hybrid electric vehicles

A Switched Reluctance Motor (SRM) has been designed with outer dimensions, maximum torque, operating area, and maximum efficiency identical to the rare-earth PM motor used in the Toyota Prius. However, an SRM has salient poles in the rotor; thus, additional windage loss is generated. In this paper, estimation of the windage loss of the SRM is carried out. Then, a comparison to the machine test result is carried out. The influence of the windage loss is identified.
Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks

Stroke is an acute cerebral vascular disease, which is likely to cause long-term disabilities and death. Acute ischemic lesions occur in most stroke patients. These lesions are treatable under accurate diagnosis and treatments. Although diffusion-weighted MR imaging (DWI) is sensitive to these lesions, localizing and quantifying them manually is costly and challenging for clinicians. In this paper, we propose a novel framework to automatically segment stroke lesions in DWI. Our framework consists of two convolutional neural networks (CNNs): one is an ensemble of two DeconvNets (Noh et al., 2015), which is the EDD Net; the second CNN is the multi-scale convolutional label evaluation net (MUSCLE Net), which aims to evaluate the lesions detected by the EDD Net in order to remove potential false positives. To the best of our knowledge, it is the first attempt to solve this problem and using both CNNs achieves very good results. Furthermore, we study the network architectures and key configurations in detail to ensure the best performance. It is validated on a large dataset comprising clinical acquired DW images from 741 subjects. A mean accuracy of Dice coefficient obtained is 0.67 in total. The mean Dice scores based on subjects with only small and large lesions are 0.61 and 0.83, respectively. The lesion detection rate achieved is 0.94.
Semantic Indexing Using WordNet Senses

We describe in this paper a boolean Information l~.etrieval system that adds word semantics to the classic word based indexing. Two of the main tasks of our system, namely the indexing and retrieval components, are using a combined wordbased and sense-based approach. The key to our system is a methodology for building semantic representations of open text, at word and collocation level. This new technique, called semantic indexing, shows improved effectiveness over the classic word based indexing techniques. 1 I n t r o d u c t i o n The main problem with the traditional boolean word-based approach to Information Retrieval (IR) is that it usually returns too many results or wrong results to be useful.
DELTACON: A Principled Massive-Graph Similarity Function

How much did a network change since yesterday? How different is the wiring between Bob’s brain (a lefthanded male) and Alice’s brain (a right-handed female)? Graph similarity with known node correspondence, i.e. the detection of changes in the connectivity of graphs, arises in numerous settings. In this work, we formally state the axioms and desired properties of the graph similarity functions, and evaluate when state-ofthe-art methods fail to detect crucial connectivity changes in graphs. We propose DELTACON, a principled, intuitive, and scalable algorithm that assesses the similarity between two graphs on the same nodes (e.g. employees of a company, customers of a mobile carrier). Experiments on various synthetic and real graphs showcase the advantages of our method over existing similarity measures. Finally, we employ DELTACON to real applications: (a) we classify people to groups of high and low creativity based on their brain connectivity graphs, and (b) do temporal anomaly detection in the who-emails-whom Enron graph.
Knowledge Transfer In International Acquisitions

This paper reports on a multimethod study of knowledge transfer in international acquisitions. Using questionnaire data we show that the transfer of technological know-how is facilitated by communication, visits & meetings, and by time elapsed since acquisition, while the transfer of patents is associated with the articulability of the knowledge, the size of the acquired unit, and the recency of the acquisition. Using case study data, we show that the immediate post-acquisition period is characterized by imposed one-way transfers of knowledge from the acquirer to the acquired, but over time this gives way to high-quality reciprocal knowledge transfer.
Satisfiability Solvers

The past few years have seen an enormous progress in the performance of Boolean satisfiability (SAT) solvers. Despite the worst-case exponential run time of all known algorithms, satisfiability solvers are increasingly leaving their mark as a general-purpose tool in areas as diverse as software and hardware verification [29–31, 228], automatic test pattern generation [138, 221], planning [129, 197], scheduling [103], and even challenging problems from algebra [238]. Annual SAT competitions have led to the development of dozens of clever implementations of such solvers [e. and the creation of an extensive suite of real-world instances as well as challenging hand-crafted benchmark problems [cf. 115]. Modern SAT solvers provide a " black-box " procedure that can often solve hard structured problems with over a million variables and several million constraints. In essence, SAT solvers provide a generic combinatorial reasoning and search platform. The underlying representational formalism is propositional logic. However, the full potential of SAT solvers only becomes apparent when one considers their use in applications that are not normally viewed as propositional reasoning tasks. For example, consider AI planning, which is a PSPACE-complete problem. By restricting oneself to polynomial size plans, one obtains an NP-complete reasoning problem , easily encoded as a Boolean satisfiability problem, which can be given to a SAT solver [128, 129]. In hardware and software verification, a similar strategy leads one to consider bounded model checking, where one places a bound on the length of possible error traces one is willing to consider [30]. Another example of a recent application of SAT solvers is in computing stable models used in the answer set programming paradigm, a powerful knowledge representation and reasoning approach . In these applications—planning, verification, and answer set programming—the translation into a propositional representation (the " SAT encoding ") is done automatically
DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks

Survival analysis (time-to-event analysis) is widely used in economics and finance, engineering, medicine and many other areas. A fundamental problem is to understand the relationship between the covariates and the (distribution of) survival times (times-to-event). Much of the previous work has approached the problem by viewing the survival time as the first hitting time of a stochastic process, assuming a specific form for the underlying stochastic process, using available data to learn the relationship between the covariates and the parameters of the model, and then deducing the relationship between covariates and the distribution of first hitting times (the risk). However, previous models rely on strong parametric assumptions that are often violated. This paper proposes a very different approach to survival analysis, DeepHit, that uses a deep neural network to learn the distribution of survival times directly. DeepHit makes no assumptions about the underlying stochastic process and allows for the possibility that the relationship between covariates and risk(s) changes over time. Most importantly, DeepHit smoothly handles competing risks; i.e. settings in which there is more than one possible event of interest. Comparisons with previous models on the basis of real and synthetic datasets demonstrate that DeepHit achieves large and statistically significant performance improvements over previous state-of-the-art methods.
"I am borrowing ya mixing ?" An Analysis of English-Hindi Code Mixing in Facebook

Code-Mixing is a frequently observed phenomenon in social media content generated by multi-lingual users. The processing of such data for linguistic analysis as well as computational modelling is challenging due to the linguistic complexity resulting from the nature of the mixing as well as the presence of non-standard variations in spellings and grammar, and transliteration. Our analysis shows the extent of Code-Mixing in English-Hindi data. The classification of Code-Mixed words based on frequency and linguistic typology underline the fact that while there are easily identifiable cases of borrowing and mixing at the two ends, a large majority of the words form a continuum in the middle, emphasizing the need to handle these at different levels for automatic processing of the data.
Multi-instance Metric Learning

Multi-instance learning, like other machine learning and data mining tasks, requires distance metrics. Although metric learning methods have been studied for many years, metric learners for multi-instance learning remain almost untouched. In this paper, we propose a framework called Multi-Instance MEtric Learning (MIMEL) to learn an appropriate distance under the multi-instance setting. The distance metric between two bags is defined using the Mahalanobis distance function. The problem is formulated by minimizing the KL divergence between two multivariate Gaussians under the constraints of maximizing the between-class bag distance and minimizing the within-class bag distance. To exploit the mechanism of how instances determine bag labels in multi-instance learning, we design a nonparametric density-estimation-based weighting scheme to assign higher â€œweightsâ€ to the instances that are more likely to be positive in positive bags. The weighting scheme itself has a small workload, which adds little extra computing costs to the proposed framework. Moreover, to further boost the classification accuracy, a kernel version of MIMEL is presented. We evaluate MIMEL, using not only several typical multi-instance tasks, but also two activity recognition datasets. The experimental results demonstrate that MIMEL achieves better classification accuracy than many state-of-the-art distance based algorithms or kernel methods for multi-instance learning.
Electric bicycles - A performance evaluation

In this article, the term "electric bicycle" is used to describe "electric-motor-powered bicycles," including both fully and partially motor-powered bicycles. Here, the electric bicycle market would benefit from further research both on the battery and on the drive technology and their use with electric bicycles. In the United States, electric bicycles are currently used most commonly for short trips to grocery stores or for leisurely rides.This article provides a systematic, comprehensive classification of electric bicycles that includes an overview of the state of the art of today's commercially available electric bicycles. The power requirements in different typical riding situations are also identified. The results are confirmed by experiments. From the results, the key parameters, needs, and challenges involved in improving the performance of electric bicycle are identified.The article gives the summary of the different results that can serve as a roadmap for such improvements. This summary includes both market trends and regulations and technical-science-related aspects.
Probabilistic neural-network structure determination for pattern classification

Network structure determination is an important issue in pattern classification based on a probabilistic neural network. In this study, a supervised network structure determination algorithm is proposed. The proposed algorithm consists of two parts and runs in an iterative way. The first part identifies an appropriate smoothing parameter using a genetic algorithm, while the second part determines suitable pattern layer neurons using a forward regression orthogonal algorithm. The proposed algorithm is capable of offering a fairly small network structure with satisfactory classification accuracy.
An Adaptive Vision System for Tracking Soccer Players from Variable Camera Settings

In this paper we present ASPOGAMO, a vision system capable of estimating motion trajectories of soccer players taped on video. The system performs well in a multitude of application scenarios because of its adaptivity to various camera setups, such as single or multiple camera settings, static or dynamic ones. Furthermore, ASPOGAMO can directly process image streams taken from TV broadcast, and extract all valuable information despite scene interruptions and cuts between different cameras. The system achieves a high level of robustness through the use of modelbased vision algorithms for camera estimation and player recognition and a probabilistic multi-player tracking framework capable of dealing with occlusion situations typical in team-sports. The continuous interplay between these submodules is adding to both the reliability and the efficiency of the overall system.
K2C: Cryptographic Cloud Storage with Lazy Revocation and Anonymous Access

Security and privacy concerns hinder the adoption of cloud storage and computing in sensitive environments. We present a user-centric privacypreserving cryptographic access control protocol called K2C (Key To Cloud) that enables end-users to securely store, share, and manage their sensitive data in an untrusted cloud storage anonymously. K2C is scalable and supports the lazy revocation. It can be easily implemented on top of existing cloud services and APIs – we demonstrate its prototype based on Amazon S3 API. K2C is realized through our new cryptographic key-updating scheme, referred to as AB-HKU. The main advantage of the AB-HKU scheme is that it supports efficient delegation and revocation of privileges for hierarchies without requiring complex cryptographic data structures. We analyze the security and performance of our access control protocol, and provide an open source implementation. Two cryptographic libraries, Hierarchical Identity-Based Encryption and Key-Policy Attribute-Based Encryption, developed in this project are useful beyond the specific cloud security problem studied.
Sentiment Classification of News Articles

The advent use of new online social media such as articles, blogs, message boards, news channels, and in general Web content has dramatically changed the way people look at various things around them. Today, it’s a daily practice for many people to read news online. People's perspective tends to undergo a change as per the news content they read. The majority of the content that we read today is on the negative aspects of various things e.g. corruption, rapes, thefts etc. Reading such news is spreading negativity amongst the people. Positive news seems to have gone into a hiding. The positivity surrounding the good news has been drastically reduced by the number of bad news. This has made a great practical use of Sentiment Analysis and there has been more innovation in this area in recent days. Sentiment analysis refers to a broad range of fields of text mining, natural language processing and computational linguistics. It traditionally emphasizes on classification of text document into positive and negative categories. Sentiment analysis of any text document has emerged as the most useful application in the area of sentiment analysis. The objective of this project is to provide a platform for serving good news and create a positive environment. This is achieved by finding the sentiments of the news articles and filtering out the negative articles. This would enable us to focus only on the good news which will help spread positivity around and would allow people to think positively.