nils-herrmann/clean_scirepeval huggingface dataset:
Short-Interval Detailed Production Scheduling in 300mm Semiconductor Manufacturing using Mixed Integer and Constraint Programming

Fully automated 300mm manufacturing requires the adoption of a real-time lot dispatching paradigm. Automated dispatching has provided significant improvements over manual dispatching by removing variability from the thousands of dispatching decisions made every day in a fab. Real-time resolution of tool queues, with consideration of changing equipment states, process restrictions, physical and logical location of WIP, supply chain objectives and a myriad of other parameters, is required to ensure successful dispatching in the dynamic fab environment. However, the real-time dispatching decision in semiconductor manufacturing generally remains a reactive, heuristic response in existing applications, limited to the current queue of each tool. The shortcomings of this method of assigning WIP to tools, aptly named "opportunistic scavenging" as stated in G. Sullivan (1987), have become more apparent in lean manufacturing environments where lower WIP levels present fewer obvious opportunities for beneficial lot sequencing or batching. Recent advancements in mixed integer programming (MIP) and constraint programming (CP) have raised the possibility of integrating optimization software, commonly used outside of the fab environment to compute optimal solutions for scheduling scenarios ranging from order fulfillment systems to crew-shift-equipment assignments, with a real-time dispatcher to create a short-interval scheduler. The goal of such a scheduler is to optimize WIP flow through various sectors of the fab by expanding the analysis beyond the current WIP queue to consider upstream and downstream flow across the entire tool group or sector. This article describes the production implementation of a short-interval local area scheduler in IBM's leading-edge 300mm fab located in East Fishkill, New York, including motivation, approach, and initial results
A Simple Steganography Algorithm Based on Lossless Compression Technique in WSN

Image security has wide applications in data transferring from source to destination system. However, cryptography is a data secrecy technique between sender and receiver, the steganography increases the level of security and acts a protective layer to the hidden information within the source image. In this paper, a compression scheme based image security algorithm for wireless sensor network is proposed to hide a secret color image within the source color image. The main contribution of this paper is to propose a compression scheme which is based on level matrix and integer matrix, and increases the compression level significantly. The performance of the proposed system is evaluated in terms of peak signal to noise ratio (PSNR), mean square error (MSE), number of pixels change rate (NPCR) and unified average changing intensity (UACI). The proposed method achieves 42.65% PSNR, 27.16% MSE, 99.9% NPCR and 30.99% UACI.
ECNU: Using Traditional Similarity Measurements and Word Embedding for Semantic Textual Similarity Estimation

This paper reports our submissions to semantic textual similarity task, i.e., task 2 in Semantic Evaluation 2015. We built our systems using various traditional features, such as string-based, corpus-based and syntactic similarity metrics, as well as novel similarity measures based on distributed word representations, which were trained using deep learning paradigms. Since the training and test datasets consist of instances collected from various domains, three different strategies of the usage of training datasets were explored: (1) use all available training datasets and build a unified supervised model for all test datasets; (2) select the most similar training dataset and separately construct a individual model for each test set; (3) adopt multi-task learning framework to make full use of available training sets. Results on the test datasets show that using all datasets as training set achieves the best averaged performance and our best system ranks 15 out of 73.
Co-FAIS: Cooperative fuzzy artificial immune system for detecting intrusion in wireless sensor networks

Due to the distributed nature of Denial-of-Service attacks, it is tremendously challenging to identify such malicious behavior using traditional intrusion detection systems in Wireless Sensor Networks (WSNs). In the current paper, a bio-inspired method is introduced, namely the cooperative-based fuzzy artificial immune system (Co-FAIS). It is a modular-based defense strategy derived from the danger theory of the human immune system. The agents synchronize and work with one another to calculate the abnormality of sensor behavior in terms of context antigen value (CAV) or attackers and update the fuzzy activation threshold for security response. In such a multi-node circumstance, the sniffer module adapts to the sink node to audit data by analyzing the packet components and sending the log file to the next layer. The fuzzy misuse detector module (FMDM) integrates with a danger detector module to identify the sources of danger signals. The infected sources are transmitted to the fuzzy Q-learning vaccination modules (FQVM) in order for particular, required action to enhance system abilities. The Cooperative Decision Making Modules (Co-DMM) incorporates danger detector module with the fuzzy Q-learning vaccination module to produce optimum defense strategies. To evaluate the performance of the proposed model, the Low Energy Adaptive Clustering Hierarchy (LEACH) was simulated using a network simulator. The model was subsequently compared against other existing soft computing methods, such as fuzzy logic controller (FLC), artificial immune system (AIS), and fuzzy Q-learning (FQL), in terms of detection accuracy, counter-defense, network lifetime and energy consumption, to demonstrate its efficiency and viability. The proposed method improves detection accuracy and successful defense rate performance against attacks compared to conventional empirical methods. & 2014 Elsevier Ltd. All rights reserved.
FPGA Implementations of the AES Masked Against Power Analysis Attacks

Power analysis attacks are a serious treat for implementations of modern cryptographic algorithms. Masking is a particularly appealing countermeasure against such attacks since it increases the security to a well quantifiable level and can be implemented without modifying the underlying technology. Its main drawback is the performance overhead it implies. For example, due to prohibitive memory costs, the straightforward application of masking to the AES algorithm, with precomputed tables, is hardly practical. In this paper, we exploit both the increased size of state-of-the-art reconfigurable hardware devices and previous optimization techniques to minimize the memory occupation of software S-boxes, in order to provide an efficient FPGA implementation of the AES algorithm, masked against side-channel attacks. We describe two high throughput architectures, based on 32-bit and 128-bit datapaths that are suitable for Xilinx Virtex-5 devices. In this way, we demonstrate the possibility to efficiently combine technological advances with algorithmic optimizations in this context.
Constituency Parsing with a Self-Attentive Encoder

We demonstrate that replacing an LSTM encoder with a self-attentive architecture can lead to improvements to a state-ofthe-art discriminative constituency parser. The use of attention makes explicit the manner in which information is propagated between different locations in the sentence, which we use to both analyze our model and propose potential improvements. For example, we find that separating positional and content information in the encoder can lead to improved parsing accuracy. Additionally, we evaluate different approaches for lexical representation. Our parser achieves new state-ofthe-art results for single models trained on the Penn Treebank: 93.55 F1 without the use of any external data, and 95.13 F1 when using pre-trained word representations. Our parser also outperforms the previous best-published accuracy figures on 8 of the 9 languages in the SPMRL dataset.
Client Profiling for an Anti-Money Laundering System

We present a data mining approach for profiling bank clients in order to support the process of detection of antimoney laundering operations. We first present the overall system architecture, and then focus on the relevant component for this paper. We detail the experiments performed on real world data from a financial institution, which allowed us to group clients in clusters and then generate a set of classification rules. We discuss the relevance of the founded client profiles and of the generated classification rules. According to the defined overall agent-based architecture, these rules will be incorporated in the knowledge base of the intelligent agents responsible for the signaling of suspicious transactions.
Classification of grass pollen through the quantitative analysis of surface ornamentation and texture.

Taxonomic identification of pollen and spores uses inherently qualitative descriptions of morphology. Consequently, identifications are restricted to categories that can be reliably classified by multiple analysts, resulting in the coarse taxonomic resolution of the pollen and spore record. Grass pollen represents an archetypal example; it is not routinely identified below family level. To address this issue, we developed quantitative morphometric methods to characterize surface ornamentation and classify grass pollen grains. This produces a means of quantifying morphological features that are traditionally described qualitatively. We used scanning electron microscopy to image 240 specimens of pollen from 12 species within the grass family (Poaceae). We classified these species by developing algorithmic features that quantify the size and density of sculptural elements on the pollen surface, and measure the complexity of the ornamentation they form. These features yielded a classification accuracy of 77.5%. In comparison, a texture descriptor based on modelling the statistical distribution of brightness values in image patches yielded a classification accuracy of 85.8%, and seven human subjects achieved accuracies between 68.33 and 81.67%. The algorithmic features we developed directly relate to biologically meaningful features of grass pollen morphology, and could facilitate direct interpretation of unsupervised classification results from fossil material.
Hard and soft news : A review of concepts , operationalizations and key findings

Over 30 years, a large body of research on what is often called ‘hard’ and ‘soft news’ has accumulated in communication studies. However, there is no consensus about what hard and soft news exactly is, or how it should be defined or measured. Moreover, the concept has not been clearly differentiated from or systematically related to concepts addressing very similar phenomena – tabloidization and ‘infotainment’. Consequently, the results of various studies are hard to compare and different scientific discourses on related issues remain unconnected. Against this backdrop, this article offers a conceptual analysis of the concept based on studies in English and other languages. We identify key dimensions of the concept and make suggestions for a standardized definition and multi-dimensional measurement of harder and softer news. In doing so, we propose to distinguish thematic, focus and style features as basic dimensions that – in their combination – make up harder and softer types of news.
An efficient cooperative hybrid routing protocol for hybrid wireless mesh networks

Hybrid wireless mesh networks are the most generic types of wireless mesh networks. Unlike static mesh routers, which have multiple radio interfaces and almost no energy constraint, mobile mesh clients are usually equipped with a single radio interface and have energy limitations. A cooperative hybrid routing protocol (CHRP) combining advantages of proactive and reactive routing protocols by letting them work cooperatively is proposed in this paper, which can adapt to features of both routers and clients. In CHRP, in order to make a proper route selection, channel condition, interference and constrained energy of clients are considered in the node-aware routing metric. Besides, a cross-layer approach is used in CHRP. Both gateway and client oriented data flows are considered comprehensively. The simulation results using ns-3 show the advantage of the proposed CHRP in terms of average packet loss rate, average latency, average network throughput, average energy consumption of clients and the minimum residual energy of clients.
Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition

This paper addresses the problem of online tracking and classification of multiple objects in an image sequence. Our proposed solution is to first track all objects in the scene without relying on object-specific prior knowledge, which in other systems can take the form of hand-crafted features or user-based track initialization. We then classify the tracked objects with a fast-learning image classifier, that is based on a shallow convolutional neural network architecture and demonstrate that object recognition improves when this is combined with object state information from the tracking algorithm. We argue that by transferring the use of prior knowledge from the detection and tracking stages to the classification stage, we can design a robust, general purpose object recognition system with the ability to detect and track a variety of object types. We describe our biologically inspired implementation, which adaptively learns the shape and motion of tracked objects, and apply it to the Neovision2 Tower benchmark data set, which contains multiple object types. An experimental evaluation demonstrates that our approach is competitive with the state-of-the-art video object recognition systems that do make use of object-specific prior knowledge in detection and tracking, while providing additional practical advantages by virtue of its generality.
Soft decoding of JPEG 2000 compressed images using bit-rate-driven deep convolutional neural networks

Lossy image compression methods always introduce various unpleasant artifacts into the compressed results, especially at low bit-rates. In recent years, many effective soft decoding methods for JPEG compressed images have been proposed. However, to the best of our knowledge, very few works have been done on soft decoding of JPEG 2000 compressed images. Inspired by the outstanding performance of Convolution Neural Network (CNN) in various computer vision tasks, we presents a soft decoding method for JPEG 2000 by using multiple bit-rate-driven deep CNNs. More specifically, in training stage, we train a series of deep CNNs using lots of high quality training images and the corresponding JPEG 2000 compressed images at different coding bit-rates. In testing stage, for an input compressed image, the CNN trained with the nearest coding bit-rate is selected to perform soft decoding. Extensive experiments demonstrate the effectiveness of the presented soft decoding framework, which greatly improves the visual quality and objective scores of JPEG 2000 compressed images.
Key Estimation in Electronic Dance Music

In this paper we study key estimation in electronic dance music, an umbrella term referring to a variety of electronic music subgenres intended for dancing at nightclubs and raves. We start by defining notions of tonality and key before outlining the basic architecture of a template-based key estimation method. Then, we report on the tonal characteristics of electronic dance music, in order to infer possible modifications of the method described. We create new key profiles combining these observations with corpus analysis, and add two pre-processing stages to the basic algorithm. We conclude by comparing our profiles to existing ones, and testing our modifications on independent datasets of pop and electronic dance music, observing interesting improvements in the performance or our algorithms, and suggesting paths for future research.
Multi-spatial Classifier for Blue Whale Images using Photo-identification Method

The process of photo-identification images of the blue whale (Balaenoptera musculus), is made manually; this process classifies images blue whale through its dorsal fin characteristics. The features are extracted visually, which can generate errors at the moment of classified. In this work an image classifier blue whale is presented, which have features such as color pigmentation, background image, type of dorsal fin, among others; these common characteristics generate high statistical dependence. This statistical dependence causes the data extracted through a segmented image of the blue whale, are to be observed through a hyperplane. Using statistical techniques and the K-Nearest Neighbors classifier, the classification of three types of dorsal fin is obtained with an accuracy of 71.66%, assessing the value of K = 7, taking reference catalog of species CICIMARIPN.
Stochastic Contextual Bandits with Known Reward Functions

Many sequential decision-making problems in communication networks such as power allocation in energy harvesting communications, mobile computational offloading, and dynamic channel selection can be modeled as contextual bandit problems which are natural extensions of the well-known multi-armed bandit problem. In these problems, each resource allocation or selection decision can make use of available side-information such as harvested power, specifications of the jobs to be offloaded, or the number of packets to be transmitted. In contextual bandit problems, at each step of a sequence of trials, an agent observes the side information or context, pulls one arm and receives the reward for that arm. We consider the stochastic formulation where the context-reward tuples are independently drawn from an unknown distribution in each trial. The goal is to design strategies for minimizing the expected cumulative regret or reward loss compared to a distribution-aware genie. We analyze a setting where the reward is a known non-linear function of the context and the chosen arm’s current state which is the case with many networking applications. This knowledge of the reward function enables us to exploit the obtained reward information to learn about rewards for other possible contexts. We first consider the case of discrete and finite context-spaces and propose DCB( ), an algorithm that yields regret which scales logarithmically in time and linearly in the number of arms that are not optimal for any context. This is in contrast with existing algorithms where the regret scales linearly in the total number of arms. Also, the storage requirements of DCB( ) do not grow with time. DCB( ) is an extension of the UCB1 policy for the standard multi-armed bandits to contextual bandits using sophisticated proof techniques for regret analysis. We then study continuous context-spaces with Lipschitz reward functions and propose CCB( , δ), an algorithm that uses DCB( ) as a subroutine. CCB( , δ) reveals a novel regret-storage trade-off that is parametrized by δ. Tuning δ to the time horizon allows us to obtain sub-linear regret bounds, while requiring sub-linear storage. Joint learning for all the contexts results in regret bounds that are unachievable by any existing contextual bandit algorithm for continuous context-spaces. Similar performance bounds are also shown to hold for unknown horizon case by employing a doubling trick.
Energy-Aware Genetic Algorithms for Task Scheduling in Cloud Computing

For the cloud computing, task scheduling problems are of paramount importance. It becomes more challenging when takes into account energy consumption, traditional make span criteria and users QoS as objectives. This paper considers independent tasks scheduling in cloud computing as a bi-objective minimization problem with make span and energy consumption as the scheduling criteria. We use Dynamic Voltage Scaling (DVS) to minimize energy consumption and propose two algorithms. These two algorithms use the methods of unify and double fitness to define the fitness function and select individuals. They adopt the genetic algorithm to parallel find the reasonable scheduling scheme. The simulation results demonstrate the two algorithms can efficiently find the right compromise between make span and energy consumption.
Effects of concurrent endurance and strength training on running economy and .VO(2) kinetics.

It has been suggested that endurance training influences the running economy (CR) and the oxygen uptake (.VO(2)) kinetics in heavy exercise by accelerating the primary phase and attenuating the .VO(2) slow component. However, the effects of heavy weight training (HWT) in combination with endurance training remain unclear. The purpose of this study was to examine the influence of a concurrent HWT+endurance training on CR and the .VO(2) kinetics in endurance athletes.



Fifteen triathletes were assigned to endurance+strength (ES) or endurance-only (E) training for 14 wk. The training program was similar, except ES performed two HWT sessions a week. Before and after the training period, the subjects performed 1) an incremental field running test for determination of .VO(2max) and the velocity associated (V(.VO2max)), the second ventilatory threshold (VT(2)); 2) a 3000-m run at constant velocity, calculated to require 25% of the difference between .VO(2max) and VT(2), to determine CR and the characteristics of the VO(2) kinetics; 3) maximal hopping tests to determine maximal mechanical power and lower-limb stiffness; 4) maximal concentric lower-limb strength measurements.



After the training period, maximal strength were increased (P < 0.01) in ES but remained unchanged in E. Hopping power decreased in E (P < 0.05). After training, economy (P < 0.05) and hopping power (P < 0.001) were greater in ES than in E. .VO(2max), leg hopping stiffness and the .VO(2) kinetics were not significantly affected by training either in ES or E.



Additional HWT led to improved maximal strength and running economy with no significant effects on the .VO(2) kinetics pattern in heavy exercise.
Naive Bayes for Text Classification with Unbalanced Classes

Multinomial naive Bayes (MNB) is a popular method for document classification due to its computational efficiency and relatively good predictive performance. It has recently been established that predictive performance can be improved further by appropriate data transformations [1, 2]. In this paper we present another transformation that is designed to combat a potential problem with the application of MNB to unbalanced datasets. We propose an appropriate correction by adjusting attribute priors. This correction can be implemented as another data normalization step, and we show that it can significantly improve the area under the ROC curve. We also show that the modified version of MNB is very closely related to the simple centroid-based classifier and compare the two methods empirically.
Hierarchical Text Generation and Planning for Strategic Dialogue

End-to-end models for goal-orientated dialogue are challenging to train, because linguistic and strategic aspects are entangled in latent state vectors. We introduce an approach to learning representations of messages in dialogues by maximizing the likelihood of subsequent sentences and actions, which decouples the semantics of the dialogue utterance from its linguistic realization. We then use these latent sentence representations for hierarchical language generation, planning and reinforcement learning. Experiments show that our approach increases the endtask reward achieved by the model, improves the effectiveness of long-term planning using rollouts, and allows self-play reinforcement learning to improve decision making without diverging from human language. Our hierarchical latentvariable model outperforms previous work both linguistically and strategically.