nils-herrmann/clean_scirepeval huggingface dataset:
Personalized recommendation engine using HADOOP

More and more E-commerce Websites provide products with different prices which made it hard for consumers to find the products and services they want. In order to overcome this data overload, personalized recommendation engines are used to suggest products and to provide consumers with relevant data to help them decide which products to purchase. Recommendation engines are highly computational and hence ideal for the Hadoop Platform. This system aims at building a book recommendation engine which uses item or user based recommendation from Mahout for recommending books. It will analyze the data and give suggestions based on what similar users did and on the past transaction history of the user.
Adaptive Quantization for Deep Neural Network

In recent years Deep Neural Networks (DNNs) have been rapidly developed in various applications, together with increasingly complex architectures. The performance gain of these DNNs generally comes with high computational costs and large memory consumption, which may not be affordable for mobile platforms. Deep model quantization can be used for reducing the computation and memory costs of DNNs, and deploying complex DNNs on mobile equipment. In this work, we propose an optimization framework for deep model quantization. First, we propose a measurement to estimate the effect of parameter quantization errors in individual layers on the overall model prediction accuracy. Then, we propose an optimization process based on this measurement for finding optimal quantization bit-width for each layer. This is the first work that theoretically analyse the relationship between parameter quantization errors of individual layers and model accuracy. Our new quantization algorithm outperforms previous quantization optimization methods, and achieves 20-40% higher compression rate compared to equal bit-width quantization at the same model prediction accuracy.
Fast high-quality non-blind deconvolution using sparse adaptive priors

We present an efficient approach for high-quality non-blind deconvolution based on the use of sparse adaptive priors. Its regularization term enforces preservation of strong edges while removing noise. We model the image-prior deconvolution problem as a linear system, which is solved in the frequency domain. This clean formulation lends to a simple and efficient implementation. We demonstrate its effectiveness by performing an extensive comparison with existing non-blind deconvolution methods, and by using it to deblur photographs degraded by camera shake. Our experiments show that our solution is faster and its results tend to have higher peak signal-to-noise ratio than the state-of-the-art techniques. Thus, it provides an attractive alternative to perform high-quality non-blind deconvolution of large images, as well as to be used as the final step of blind-deconvolution algorithms.
A Multimodal Classifier Generative Adversarial Network for Carry and Place Tasks From Ambiguous Language Instructions

This letter focuses on a multimodal language understanding method for carry-and-place tasks with domestic service robots. We address the case of ambiguous instructions, that is, when the target area is not specified. For instance “put away the milk and cereal” is a natural instruction where there is ambiguity regarding the target area, considering environments in daily life. Conventionally, this instruction can be disambiguated from a dialogue system, but at the cost of time and cumbersome interaction. Instead, we propose a multimodal approach, in which the instructions are disambiguated using the robot's state and environment context. We develop the Multi-Modal Classifier Generative Adversarial Network (MMC-GAN) to predict the likelihood of different target areas considering the robot's physical limitation and the target clutter. Our approach, MMC-GAN, significantly improves accuracy compared with baseline methods that use instructions only or simple deep neural networks.
Action Recognition and Video Description using Visual Attention

We propose soft attention based models for the tasks of action recognition in videos and generating natural language descriptions of videos. We use multi-layered Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units which are deep both spatially and temporally. Our model learns to focus selectively on parts of the video frames and classifies videos after taking a few glimpses. It is also able to generate sentences describing the videos using spatio-temporal glimpses across them. The model essentially learns which parts in the frames are relevant for the task at hand and attaches higher importance to them. We evaluate the action recognition model on UCF-11 (YouTube Action), HMDB-51 and Hollywood2 datasets and analyze how the model focuses its attention depending on the scene and the action being performed. We evaluate the description generation model on YouTube2Text dataset and visualize the model's attention as it generates words.
Enterprise architecture modelling--the issue of integration

The problem of aligning and integrating business and IT is hampering many companies in their strategic and tactical development. Constructing integrated architecture models contributes to tackling this problem. Unfortunately, no enterprise architecture description language currently exists that fully enables integrated enterprise modelling. A variety of architectural domains are commonly distinguished for which architects use their own modelling techniques and concepts, tool support, visualisation techniques, etc. In this paper we outline such an integrated language and describe concepts that relate architectural domains. This language also serves as a bridge between other existing modelling languages. Furthermore, we present the design of a workbench for enterprise architecture that serves as a modelling tool and a tool integration environment at the same time: it supports both the integration of models in existing modelling languages and the integration of existing modelling tools. q 2005
Smoothing Noisy Data with Spline Functions *

I t is shown how to choose the smoothing parameter when a smoothing periodic spline of degree 2m -1 is used to reconstruct a smooth periodic curve from noisy ordinate data. The noise is assumed "white" , and the true curve is assumed to be in the Sobolev space W,(*ra) of periodic functions with absolutely continuous v-th derivative, v = 0, t . . . . . 2 m I and square integrable 2m-th derivative. The criteria is minimum expected square error, averaged over the data points. The dependency of the optimum smoothing parameter on the sample size, the noise variance, and the smoothness of the true curve is found explicitly.
Play Therapy: Practitioners' Perspectives on Implementation and Effectiveness

The purpose of the present research was to explore practitioners’ perspectives on play therapy as an intervention when working with a child who has experienced trauma, has present PTSD symptoms and has a co-morbid mental health diagnosis. Play therapy has been accepted as an effective intervention to utilize with children who have been exposed to trauma (Schaefer, 1994). However, there is currently limited research evaluating play therapy as an intervention with children who have been traumatized and have developed PTSD or other mental health symptoms/disorders. The current study aimed to supplement the gap in existing research. Two agencies that serve early childhood mental health clients agreed to participate in the present study by completing an online survey. Data was gathered from 22 practitioner respondents. The results indicate that practitioners believe that play therapy is an effective intervention when treating children with trauma histories, PTSD symptoms, and mental health disorders. The results of the present research support findings from previous literature regarding play therapy when used as an intervention for treating trauma and/or mental health disorders. Furthermore, the present research confirms the notion that creating a safe space for their clients using play therapy is an important part of the intervention process. Given the gap in research surrounding play therapy as an intervention when PTSD and a co-morbid mental health disorders occur concurrently, further research would be beneficial to the field of social work and would positively inform the practitioners who work in early intervention settings. PRACTITIONERS’	  PERSPECTIVES 2
Recognizing human actions: a local SVM approach

Local space-time features capture local events in video and can be adapted to the size, the frequency and the velocity of moving patterns. In this paper, we demonstrate how such features can be used for recognizing complex motion patterns. We construct video representations in terms of local space-time features and integrate such representations with SVM classification schemes for recognition. For the purpose of evaluation we introduce a new video database containing 2391 sequences of six human actions performed by 25 people in four different scenarios. The presented results of action recognition justify the proposed method and demonstrate its advantage compared to other relative approaches for action recognition.
A Graph-to-Sequence Model for AMR-to-Text Generation

We model the graph state (g = {h}|vj∈V ) via state transition, hj incorporates larger context through the gated (LSTM-based) state transition. c j t , hj t = LSTM([x i j ; x o j ], [hi j ; ho j ], c j t−1) where x i j and x o j are the sum of all incoming and outgoing edge representations of vj, respectively. Similarly, hi j and ho j are the sum of the hidden states of all incoming and outgoing neighbors: x i j = ∑
Design of Polarization Reconfigurable Antenna Using Metasurface

A planar polarization-reconfigurable metasurfaced antenna (PRMS) designed using metasurface (MS) is proposed. The PRMS antenna consists of a planar MS placed atop of and in direct contact with a planar slot antenna, both having a circular shape with a diameter of 78 mm (0.9 λ0), making it compact and low profile. By rotating the MS around the center with respect to the slot antenna, the PRMS antenna can be reconfigured to linear polarization, left-hand and right-hand circular polarizations. An equivalent circuit is used to explain the reconfigurability of the antenna. The PRMS antenna is studied and designed to operate at around 3.5 GHz using computer simulation. For verification of simulation results, the PRMS antenna is fabricated and measured. The antenna performance, in terms of polarization reconfigurability, axial-ratio bandwidth, impedance bandwidth, realized boresight gain and radiation pattern, is presented. Results show that the PRMS antenna in circular polarizations achieves an operating bandwidth of 3.3-3.7 GHz (i.e., fractional bandwidth 11.4%), a boresight gain of above 5 dBi and high-polarization isolation of larger than 15 dB. While the PRMS antenna in linear polarization achieves a gain of above 7.5 dBi with cross-polarization isolation larger than 50 dB.
A novel booster antenna on flexible substrates for metal proximity NFC applications

In this article a novel booster antenna is proposed in order to increase the read range of a small (1.1×10-4 m2) metal mounted HF (13.56 MHz) RFID. The highly inductive coupling between the reader and booster, and between the RFID and booster is the main propagation method. The communication between the reader and RFID realized through the booster antenna. For this purpose two coils are connected in parallel to each other resonating at the same frequency. The greater coil is coupled to the reader antenna and the smaller coil is coupled to the RFID. The simulation and measurement results show the reliability of the proposed structure.
Determining Semantic Textual Similarity using Natural Deduction Proofs

Determining semantic textual similarity is a core research subject in natural language processing. Since vector-based models for sentence representation often use shallow information, capturing accurate semantics is difficult. By contrast, logical semantic representations capture deeper levels of sentence semantics, but their symbolic nature does not offer graded notions of textual similarity. We propose a method for determining semantic textual similarity by combining shallow features with features extracted from natural deduction proofs of bidirectional entailment relations between sentence pairs. For the natural deduction proofs, we use ccg2lambda, a higherorder automatic inference system, which converts Combinatory Categorial Grammar (CCG) derivation trees into semantic representations and conducts natural deduction proofs. Experiments show that our system was able to outperform other logicbased systems and that features derived from the proofs are effective for learning textual similarity.
Weakly Supervised Object Discovery by Generative Adversarial & Ranking Networks.

The deep generative adversarial networks (GAN) recently have been shown to be promising for different computer vision applications, like image editing, synthesizing high resolution images, generating videos, etc. These networks and the corresponding learning scheme can handle various visual space mappings. We approach GANs with a novel training method and learning objective, to discover multiple object instances for three cases: 1) synthesizing a picture of a specific object within a cluttered scene; 2) localizing different categories in images for weakly supervised object detection; and 3) improving object discovery in object detection pipelines. A crucial advantage of our method is that it learns a new deep similarity metric, to distinguish multiple objects in one image. We demonstrate that the network can act as an encoder-decoder generating parts of an image which contain an object, or as a modified deep CNN to represent images for object detection in supervised and weakly supervised scheme. Our ranking GAN offers a novel way to search through images for object specific patterns. We have conducted experiments for different scenarios and demonstrate the method performance for object synthesizing and weakly supervised object detection and classification using the MS-COCO and PASCAL VOC datasets.
Structure Based Data De-Anonymization of Social Networks and Mobility Traces

We present a novel de-anonymization attack on mobility trace data and social data. First, we design an Unified Similarity (US) measurement, based on which we present a US based De-Anonymization (DA) framework which iteratively de-anonymizes data with an accuracy guarantee. Then, to de-anonymize data without the knowledge of the overlap size between the anonymized data and the auxiliary data, we generalize DA to an Adaptive De-Anonymization (ADA) framework. Finally, we examine DA/ADA on mobility traces and social data sets.
Comparison of vision-based and sensor-based systems for joint angle gait analysis

Gait analysis has become recently a popular research field and been widely applied to clinical diagnosis of neurodegenerative diseases. Various low-cost sensor-based and vision-based systems are developed for capturing the hip and knee joint angles. However, the performances of these systems have not been validated and compared between each other. The purpose of this study is to set up an experiment and compare the performances of a sensor-based system with multiple inertial measurement units (IMUs), a vision-based gait analysis system with marker detection, and a markerless vision-based system on capturing the hip and knee joint angles during normal walking. The obtained measurements were validated with the data acquired from goniometers as ground truth measurement. The results indicate that the IMUs-based sensor system gives excellent performance with small errors, while vision systems produce acceptable results with slightly larger errors.
Mechanical design of humanoid robot platform KHR-3 (KAIST Humanoid Robot 3: HUBO)

KHR-I has been developed on the purpose of research about biped walking. It has 21 DOF without hands and head, which has 12 DOF in legs, 1 DOF in torso, and 8 DOF in arms. The objective of KHR-2 (41 DOF) was to develop the humanoid which can walk on the living-floor with human-like appearance and movement. KHR-3 has the purpose that it has more human-like features, movements and human-friendly character. Mechanical design of KHR-3 is presented on this paper. The design concept, lower body design, upper body design and actuator selection of joints are included in this paper. We have developed and published KHR-I and 2 in last three years. KHR3 platform is based on KHR-2. It has 41 degree of freedom (DOF), 125 cm height, and 55 kg weight. The differences from KHR-2 are mechanical stiffness and detailed design of the frame in the mechanical point of view. Stiffness of the frame is increased and detailed design about joints and link frame has been modified or redesigned. We introduced exterior art design concept on KHR-2 in beginning, and the concept has been implemented on KHR-3 in mechanical design stage
A Wireless and Batteryless Intelligent Carbon Monoxide Sensor

Carbon monoxide (CO) poisoning from natural gas water heaters is a common household accident in Taiwan. We propose a wireless and batteryless intelligent CO sensor for improving the safety of operating natural gas water heaters. A micro-hydropower generator supplies power to a CO sensor without battery (COSWOB) (2.5 W at a flow rate of 4.2 L/min), and the power consumption of the COSWOB is only ~13 mW. The COSWOB monitors the CO concentration in ambient conditions around natural gas water heaters and transmits it to an intelligent gateway. When the CO level reaches a dangerous level, the COSWOB alarm sounds loudly. Meanwhile, the intelligent gateway also sends a trigger to activate Wi-Fi alarms and sends notifications to the mobile device through the Internet. Our strategy can warn people indoors and outdoors, thereby reducing CO poisoning accidents. We also believe that our technique not only can be used for home security but also can be used in industrial applications (for example, to monitor leak occurrence in a pipeline).
Spatial-temporal mode transmission based upon the QR decomposition of a polynomial channel matrix with uncertainties

This paper presents an entirely time domain approach for communicating over frequency selective multiple-input multiple-output (MIMO) channels. The proposed system applies a paraunitary matrix to the received signals, which is specifically designed to transform the effective polynomial channel matrix for the system into an upper triangular polynomial matrix. This then enables the MIMO channel equalisation problem to be transformed into a set of single-input single-output (SISO) equalisation problems, by exploiting the upper triangular structure of the transformed channel matrix, which can then be individually solved using Turbo equalisation. In particular, this paper investigates how the system performs when there is error present in the channel matrix state information and discusses the possible errors that are encountered when formulating the QR decomposition (QRD) of a polynomial matrix.
A 77-GHz 2T6R Transceiver With Injection-Lock Frequency Sextupler Using 65-nm CMOS for Automotive Radar System Application

In this paper, a CMOS multichannel transceiver (TRX) is proposed for angular identification in automotive car radar applications. Two transmitters (TXs) and six receivers (RXs) are placed on the same die using 65-nm CMOS technology with chip size 3.5 × 3 mm2. To generate a millimeter-wave (MMW) frequency-modulated continuous-wave signal, an injection-lock frequency sextupler cascaded a 1-to-8 Wilkinson power dividing network with wideband isolation is designed as the LO-chain to convert external source from 12.5-13.7 to 75-82.2 GHz for both the TXs and RXs. Each TX achieves above 11-dBm output power and the RXs achieve 30-dB conversion gain from 75 to 82 GHz, and the total power dissipation of whole chip is 1.43 W. Compared with other published multichannel TRXs in silicon germanium (SiGe) process, this paper demonstrates compatible performances and the potential of multichannel TRX using CMOS for MMW automotive car radar application.
Is the brain really a small-world network?

It is commonly assumed that the brain is a small-world network (e.g., Sporns and Honey 2006). Indeed, one of the present authors claimed as much 15 years ago (Hilgetag et al. 2000). The small-worldness is believed to be a crucial aspect of efficient brain organization that confers significant advantages in signal processing (e.g., LagoFernández et al. 2000). Correspondingly, the small-world organization is deemed essential for healthy brain function, as alterations of small-world features are observed in patient groups with Alzheimer’s disease (Stam et al. 2007), autism (Barttfeld et al. 2011) or schizophrenia spectrum diseases (Liu et al. 2008; Wang et al. 2012; Zalesky et al. 2011). While the colloquial idea of a small, interconnected world has a long tradition (e.g., Klemperer 1938), the present concept of small-world features of networks is frequently associated with the Milgram experiment (Milgram 1967) that demonstrated surprisingly short paths across social networks (‘six degrees of separation’). The concept was formalized by Watts and Strogatz (1998), who derived small-world networks from regular networks by including a small proportion of random network shortcuts. Such an organization results in short paths across the whole network—almost as small as in random networks—combined with local ‘cliquishness’ (or clustering) of neighboring nodes, due to dense local interconnections. These features can be mathematically summarized by the smallworld coefficient (Humphries et al. 2006), which is defined as the clustering coefficient of a given network (normalized by the clustering coefficient of a same-size random network) divided by the network’s normalized average shortest pathlength. While any network that has a smallworld coefficient larger than one is formally a small-world network, for many researchers, the term has become associated with the specific Watts and Strogatz model that is based on the partial random rewiring of a regular network . Indeed, the estimation of the rewiring probability has been used to directly associate real-world networks with the Watts and Strogatz model (Humphries and Gurney 2008). Incidentally, the small-world coefficient might not faithfully capture the small-world property as originally described by Watts and Strogatz (1998). Therefore, an alternative coefficient has been proposed that compares the clustering of the network to a lattice instead of a random network (Telesford et al. 2011). A large number of empirical network data conform to the small-world features of short paths combined with high clustering, including many neural networks—but do these Electronic supplementary material The online version of this article (doi:10.1007/s00429-015-1035-6) contains supplementary material, which is available to authorized users.