nils-herrmann/clean_scirepeval huggingface dataset:
Intrinsic Subspace Evaluation of Word Embedding Representations

We introduce a new methodology for intrinsic evaluation of word representations. Specifically, we identify four fundamental criteria based on the characteristics of natural language that pose difficulties to NLP systems; and develop tests that directly show whether or not representations contain the subspaces necessary to satisfy these criteria. Current intrinsic evaluations are mostly based on the overall similarity or full-space similarity of words and thus view vector representations as points. We show the limits of these point-based intrinsic evaluations. We apply our evaluation methodology to the comparison of a count vector model and several neural network models and demonstrate important properties of these models.
SMART LIVING USING BLUETOOTH- BASED ANDROID SMARTPHONE

With the development of modern technology and Android Smartphone, Smart Living is gradually changing people’s life. Bluetooth technology, which aims to exchange data wirelessly in a short distance using short-wavelength radio transmissions, is providing a necessary technology to create convenience, intelligence and controllability. In this paper, a new Smart Living system called home lighting control system using Bluetooth-based Android Smartphone is proposed and prototyped. First Smartphone, Smart Living and Bluetooth technology are reviewed. Second the system architecture, communication protocol and hardware design aredescribed. Then the design of a Bluetooth-based Smartphone application and the prototype are presented. It is shown that Android Smartphone can provide a platform to implement Bluetooth-based application for Smart Living.
Opportunity model for e-commerce recommendation: right product; right time

Most of existing e-commerce recommender systems aim to recommend the right product to a user, based on whether the user is likely to purchase or like a product. On the other hand, the effectiveness of recommendations also depends on the time of the recommendation. Let us take a user who just purchased a laptop as an example. She may purchase a replacement battery in 2 years (assuming that the laptop's original battery often fails to work around that time) and purchase a new laptop in another 2 years. In this case, it is not a good idea to recommend a new laptop or a replacement battery right after the user purchased the new laptop. It could hurt the user's satisfaction of the recommender system if she receives a potentially right product recommendation at the wrong time. We argue that a system should not only recommend the most relevant item, but also recommend at the right time.
 This paper studies the new problem: how to recommend the right product at the right time? We adapt the proportional hazards modeling approach in survival analysis to the recommendation research field and propose a new opportunity model to explicitly incorporate time in an e-commerce recommender system. The new model estimates the joint probability of a user making a follow-up purchase of a particular product at a particular time. This joint purchase probability can be leveraged by recommender systems in various scenarios, including the zero-query pull-based recommendation scenario (e.g. recommendation on an e-commerce web site) and a proactive push-based promotion scenario (e.g. email or text message based marketing). We evaluate the opportunity modeling approach with multiple metrics. Experimental results on a data collected by a real-world e-commerce website(shop.com) show that it can predict a user's follow-up purchase behavior at a particular time with descent accuracy. In addition, the opportunity model significantly improves the conversion rate in pull-based systems and the user satisfaction/utility in push-based systems.
Detection and Analysis of Irregular Streaks in Dermoscopic Images of Skin Lesions

Irregular streaks are important clues for Melanoma (a potentially fatal form of skin cancer) diagnosis using dermoscopy images. This paper extends our previous algorithm to identify the absence or presence of streaks in a skin lesions, by further analyzing the appearance of detected streak lines, and performing a three-way classification for streaks, Absent, Regular, and Irregular, in a pigmented skin lesion. In addition, the directional pattern of detected lines is analyzed to extract their orientation features in order to detect the underlying pattern. The method uses a graphical representation to model the geometric pattern of valid streaks and the distribution and coverage of the structure. Using these proposed features of the valid streaks along with the color and texture features of the entire lesion, an accuracy of 76.1% and weighted average area under ROC curve (AUC) of 85% is achieved for classifying dermoscopy images into streaks Absent, Regular, or Irregular on 945 images compiled from atlases and the internet without any exclusion criteria. This challenging dataset is the largest validation dataset for streaks detection and classification published to date. The data set has also been applied to the two-class sub-problems of Absent/Present classification (accuracy of 78.3% with AUC of 83.2%) and to Regular/Irregular classification (accuracy 83.6% with AUC of 88.9%). When the method was tested on a cleaned subset of 300 images randomly selected from the 945 images, the AUC increased to 91.8%, 93.2% and 90.9% for the Absent/Regular/Irregular, Absent/Present, and Regular/Irregular problems, respectively.
Protocols for self-organization of a wireless sensor network

We present a suite of algorithms for self-organization of wireless sensor networks, in which there is a scalably large number of mainly static nodes with highly constrained energy resources. The protocols further support slow mobility by a subset of the nodes, energy-efficient routing, and formation of ad hoc subnetworks for carrying out cooperative signal processing functions among a set of the nodes. † This research is supported by DARPA contract number F04701-97-C-0010, and was presented in part at the 37 Allerton Conference on Communication, Computing and Control, September 1999. ‡ Corresponding author.
Constance: An Intelligent Data Lake System

As the challenge of our time, Big Data still has many research hassles, especially the variety of data. The high diversity of data sources often results in information silos, a collection of non-integrated data management systems with heterogeneous schemas, query languages, and APIs. Data Lake systems have been proposed as a solution to this problem, by providing a schema-less repository for raw data with a common access interface. However, just dumping all data into a data lake without any metadata management, would only lead to a 'data swamp'. To avoid this, we propose Constance, a Data Lake system with sophisticated metadata management over raw data extracted from heterogeneous data sources. Constance discovers, extracts, and summarizes the structural metadata from the data sources, and annotates data and metadata with semantic information to avoid ambiguities. With embedded query rewriting engines supporting structured data and semi-structured data, Constance provides users a unified interface for query processing and data exploration. During the demo, we will walk through each functional component of Constance. Constance will be applied to two real-life use cases in order to show attendees the importance and usefulness of our generic and extensible data lake system.
Neural net pruning-why and how

A continuing question in neural net research is the size of network needed to solve a particular problem. If training is started with too small a network for the problem no learning can occur. The researcher must then go through a slow process of deciding that no learning is taking place, increasing the size of the network and training again. If a network that is larger than required is used, then processing is slowed, particularly on a conventional von Neumann computer. An approach to this problem is discussed that is based on learning with a net which is larger than the minimum size network required to solve the problem and then pruning the solution network. The result is a small, efficient network that performs as well or better than the original which does not give a complete answer to the question, since the size of the initial network is still largely based on guesswork but it gives a very useful partial answer and sheds some light on the workings of a neural network in the process.<<ETX>>
1 Tourism Management Strategic use of information technologies in the tourism industry Dr

Information technologies (ITs) prevail all functions of strategic and operational management. As information is the lifeblood of tourism, ITs provide both opportunities and challenges for the industry. Despite the uncertainty experienced in the developments of ITs in tourism, the "only constant will be change". Increasingly, organisations and destinations, which need to compete will be forced to compute. Unless the current tourism industry improves its competitiveness, by utilising the emerging ITs and innovative management methods, there is a danger for exogenous players to enter the marketplace, jeopardising the position of the existing ones. Only creative and innovative suppliers will be able to survive the competition in the new millennium. This paper provides a framework for the utilisation of technology in tourism by adopting a strategic perspective. A continuous business process re-engineering is proposed in order to ensure that a wide range of prerequisites such as vision, rational organisation, commitment and training are in place, so they can enable destinations and principals to capitalise on the unprecedented opportunities emerging through ITs.
Generation of short electrical pulses based on bipolar transistors

A system for the generation of short electrical pulses based on the minority carrier charge storage and the step recovery effect of bipolar transistors is presented. Electrical pulses of about 90 ps up to 800 ps duration are generated with a maximum amplitude of approximately 7 V at 50. The bipolar transistor is driven into saturation and the base-collector and base-emitter junctions become forward biased. The resulting fast switch-off edge of the transistor’s output signal is the basis for the pulse generation. The fast switching of the transistor occurs as a result of the minority carriers that have been injected and stored across the base-collector junction under forward bias conditions. If the saturated transistor is suddenly reverse biased the pn-junction will appear as a low impedance until the stored charge is depleted. Then the impedance will suddenly increase to its normal high value and the flow of current through the junction will turn to zero, abruptly. A differentiation of the output signal of the transistor results in two short pulses with opposite polarities. The differentiating circuit is implemented by a transmission line network, which mainly acts as a high pass filter. Both the transistor technology (pnp or npn) and the phase of the transfer function of the differentating circuit influence the polarity of the output pulses. The pulse duration depends on the transistor parameters as well as on the transfer function of the pulse shaping network. This way of generating short electrical pulses is a new alternative for conventional comb generators based on steprecovery diodes (SRD). Due to the three-terminal structure of the transistor the isolation problem between the input and the output signal of the transistor network is drastically simplified. Furthermore the transistor is an active element in contrast to a SRD, so that its current gain can be used to minimize the power of the driving signal. Correspondence to: M. Gerding (michael.gerding@ruhr-uni-bochum.de)
Practical waveform engineering

RF I-V waveform measurement and engineering systems are now finally enabling practical waveform engineering to be directly undertaken with systems capable of supporting continuous wave stimulus reaching a high level of maturity. On-going research and development activities are presently addressing the multitone requirement. The availability of RF I-V measurement and engineering capability extends the characterization opportunities for both high-frequency/speed transistor technology developers and circuit/system designers; terminal waveforms are the unifying theoretical link between transistor technology, circuit design, and system performance. Design support can involve the direct utilization of such a measurement system in the design investigation/evaluation loop of either power transistor technology or PAs. This eliminates the black box design processes and allows for the development of new intelligent design processes that are completely founded on theoretical waveform analysis. Alternatively, it can also indirectly support the amplifier investigation/evaluation loop by supporting the development or improving the accuracy of nonlinear transistor models or by providing CAD-accessible behavioral or behavioral model parameter datasets. W can now only hope that, triggered by the availability of RF waveform measurement and engineering systems, we will see a renaissance in theoretical waveform mathematical analysis addressing not only power and efficiency but also design sensitivity and linearity.
Design Challenges and Misconceptions in Neural Sequence Labeling

We investigate the design challenges of constructing effective and efficient neural sequence labeling systems, by reproducing twelve neural sequence labeling models, which include most of the state-of-the-art structures, and conduct a systematic model comparison on three benchmarks (i.e. NER, Chunking, and POS tagging). Misconceptions and inconsistent conclusions in existing literature are examined and clarified under statistical experiments. In the comparison and analysis process, we reach several practical conclusions which can be useful to practitioners.
Modeling and Testing of a Digital Distance Relay Using MATLAB / SIMULINK

This paper describes modelling and testing of a digital distance relay for transmission line protection using MATLAB/SIMULINK. SIMULINK’s Power System Blockset (PSB) is used for detailed modelling of a power system network and fault simulation. MATLAB is used to implement programs of digital distance relaying algorithms and to serve as main software environment. The technique is an interactive simulation environment for relaying algorithm design and evaluation. The basic principles of a digital distance relay and some related filtering techniques are also described in this paper. A 345 kV, 100 km transmission line and a MHO type distance relay are selected as examples for fault simulation and relay testing. Some simulation results are given.
Hidden Markov models in computational biology. Applications to protein modeling.

Hidden Markov Models (HMMs) are applied to the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are demonstrated on the globin family, the protein kinase catalytic domain, and the EF-hand calcium binding motif. In each case the parameters of an HMM are estimated from a training set of unaligned sequences. After the HMM is built, it is used to obtain a multiple alignment of all the training sequences. It is also used to search the SWISS-PROT 22 database for other sequences that are members of the given protein family, or contain the given domain. The HMM produces multiple alignments of good quality that agree closely with the alignments produced by programs that incorporate three-dimensional structural information. When employed in discrimination tests (by examining how closely the sequences in a database fit the globin, kinase and EF-hand HMMs), the HMM is able to distinguish members of these families from non-members with a high degree of accuracy. Both the HMM and PROFILESEARCH (a technique used to search for relationships between a protein sequence and multiply aligned sequences) perform better in these tests than PROSITE (a dictionary of sites and patterns in proteins). The HMM appears to have a slight advantage over PROFILESEARCH in terms of lower rates of false negatives and false positives, even though the HMM is trained using only unaligned sequences, whereas PROFILESEARCH requires aligned training sequences. Our results suggest the presence of an EF-hand calcium binding motif in a highly conserved and evolutionary preserved putative intracellular region of 155 residues in the alpha-1 subunit of L-type calcium channels which play an important role in excitation-contraction coupling. This region has been suggested to contain the functional domains that are typical or essential for all L-type calcium channels regardless of whether they couple to ryanodine receptors, conduct ions or both.
PoseCut: Simultaneous Segmentation and 3D Pose Estimation of Humans Using Dynamic Graph-Cuts

We present a novel algorithm for performing integrated segmentation and 3D pose estimation of a human body from multiple views. Unlike other related state of the art techniques which focus on either segmentation or pose estimation individually, our approach tackles these two tasks together. Normally, when optimizing for pose, it is traditional to use some fixed set of features, e.g. edges or chamfer maps. In contrast, our novel approach consists of optimizing a cost function based on a Markov Random Field (MRF). This has the advantage that we can use all the information in the image: edges, background and foreground appearances, as well as the prior information on the shape and pose of the subject and combine them in a Bayesian framework. Previously, optimizing such a cost function would have been computationally infeasible. However, our recent research in dynamic graph cuts allows this to be done much more efficiently than before. We demonstrate the efficacy of our approach on challenging motion sequences. Note that although we target the human pose inference problem in the paper, our method is completely generic and can be used to segment and infer the pose of any specified rigid, deformable or articulated object.
A Sensor System for Automatic Detection of Food Intake Through Non-Invasive Monitoring of Chewing

Objective and automatic sensor systems to monitor ingestive behavior of individuals arise as a potential solution to replace inaccurate method of self-report. This paper presents a simple sensor system and related signal processing and pattern recognition methodologies to detect periods of food intake based on non-invasive monitoring of chewing. A piezoelectric strain gauge sensor was used to capture movement of the lower jaw from 20 volunteers during periods of quiet sitting, talking and food consumption. These signals were segmented into non-overlapping epochs of fixed length and processed to extract a set of 250 time and frequency domain features for each epoch. A forward feature selection procedure was implemented to choose the most relevant features, identifying from 4 to 11 features most critical for food intake detection. Support vector machine classifiers were trained to create food intake detection models. Twenty-fold cross-validation demonstrated per-epoch classification accuracy of 80.98% and a fine time resolution of 30 s. The simplicity of the chewing strain sensor may result in a less intrusive and simpler way to detect food intake. The proposed methodology could lead to the development of a wearable sensor system to assess eating behaviors of individuals.
Accurate modeling of Modbus/TCP for intrusion detection in SCADA systems

The Modbus/TCP protocol is commonly used in SCADA systems for communications between a human–machine interface (HMI) and programmable logic controllers (PLCs). This paper presents a model-based intrusion detection system designed specifically for Modbus/TCP networks. The approach is based on the key observation that Modbus traffic to and from a specific PLC is highly periodic; as a result, each HMI-PLC channel can be modeled using its own unique deterministic finite automaton (DFA). An algorithm is presented that can automatically construct the DFA associated with an HMI-PLC channel based on about 100 captured messages. The resulting DFA-based intrusion detection system looks deep into Modbus/TCP packets and produces a very detailed traffic model. This approach is very sensitive and is able to flag anomalies such as a message appearing out of its position in the normal sequence or a message referring to a single unexpected bit. The intrusion detection approach is tested on a production Modbus system. Despite its high sensitivity, the system has a very low false positive rate—perfect matches of the model to the traffic were observed for five of the seven PLCs tested without a single false alarm over 111 h of operation. Furthermore, the intrusion detection system successfully flagged real anomalies that were caused by technicians who were troubleshooting the HMI system. The system also helped identify a PLC that was configured incorrectly. & 2013 Elsevier B.V. All rights reserved.
ART 2-A: An adaptive resonance algorithm for rapid category learning and recognition

This artic,le introduces Adaptive Resonance Theor) 2-A (ART 2-A), an efjCicient algorithm that emulates the self-organizing pattern recognition and hypothesis testing properties of the ART 2 neural network architect~~rc, hut at a speed two to three orders of magnitude fbster. Analysis and simulations show how’ the ART 2-A systems correspond to ART 2 rivnamics at both the fast-learn limit and at intermediate learning rate.r. Intermediate ieurning rates permit fust commitment of category nodes hut slow recoding, analogous to properties of word frequency effects. encoding specificity ef@cts, and episodic memory. Better noise tolerunce is hereby achieved ti’ithout a loss of leurning stability. The ART 2 and ART 2-A systems are contrasted with the leader algorithm. The speed of ART 2-A makes pructical the use of ART 2 modules in large scale neural computation. Neural networks, Pattern recognition. Category formation. Fast learning, Adaptive resonance.
Sentence-level Emotion Classification with Label and Context Dependence

Predicting emotion categories, such as anger, joy, and anxiety, expressed by a sentence is challenging due to its inherent multi-label classification difficulty and data sparseness. In this paper, we address above two challenges by incorporating the label dependence among the emotion labels and the context dependence among the contextual instances into a factor graph model. Specifically, we recast sentence-level emotion classification as a factor graph inferring problem in which the label and context dependence are modeled as various factor functions. Empirical evaluation demonstrates the great potential and effectiveness of our proposed approach to sentencelevel emotion classification.
Class expression learning for ontology engineering

While the number of knowledge bases in the Semantic Web increases, the maintenance and creation of ontology schemata still remain a challenge. In particular creating class expressions constitutes one of the more demanding aspects of ontology engineering. In this article we describe how to adapt a semi-automatic method for learning OWL class expressions to the ontology engineering use case. Specifically, we describe how to extend an existing learning algorithm for the class learning problem. We perform rigorous performance optimization of the underlying algorithms for providing instant suggestions to the user. We also present two plugins, which use the algorithm, for the popular Protégé and OntoWiki ontology editors and provide a preliminary evaluation on real ontologies.