nils-herrmann/clean_scirepeval huggingface dataset:
A miniature DC-70 GHz SP4T switch in 0.13-µm CMOS

This paper presents a miniature DC-70 GHz single-pole four-throw (SP4T) built in a low-cost 0.13-µm CMOS process. The switch is based on a series-shunt design with input and output matching circuits. Deep n-well (also called triple-well) CMOS transistors are used to minimize the substrate coupling. Also, deep trench isolation is used between the different ports to minimize the port-to-port coupling. The SP4T results in a measured insertion loss of less than 3.5 dB up to 67 GHz with an isolation of greater than 25 dB. The measured port-to-port coupling is less than 28 dB up to 67 GHz. The measured P1dB and IIP3 are independent of frequency and are 9–10 dBm and 20–21 dBm, respectively. The active chip area is 0.24×0.23 mm2. To our knowledge, this work represents the widest bandwidth SP4T switch in any CMOS technology to-date.
Cleanix: A Big Data Cleaning Parfait

In this demo, we present Cleanix, a prototype system for cleaning relational Big Data. Cleanix takes data integrated from multiple data sources and cleans them on a shared-nothing machine cluster. The backend system is built on-top-of an extensible and flexible data-parallel substrate - the Hyracks framework. Cleanix supports various data cleaning tasks such as abnormal value detection and correction, incomplete data filling, de-duplication, and conflict resolution. We demonstrate that Cleanix is a practical tool that supports effective and efficient data cleaning at the large scale.
Real-time raindrop detection based on cellular neural networks for ADAS

A core aspect of advanced driver assistance systems (ADAS) is to support the driver with information about the current environmental situation of the vehicle. Bad weather conditions such as rain might occlude regions of the windshield or a camera lens and therefore affect the visual perception. Hence, the automated detection of raindrops has a significant importance for video-based ADAS. The detection of raindrops is highly time critical since video pre-processing stages are required to improve the image quality and to provide their results in real-time. This paper presents an approach for real-time raindrops detection which is based on cellular neural networks (CNN) and support vector machines (SVM). The major idea is to prove the possibility of transforming the support vectors into CNN templates. The advantage of CNN is its ultra fast precessing on embedded platforms such as FPGAs and GPUs. The proposed approach is capable to detect raindrops that might negatively affect the vision of the driver. Different classification features were extracted to evaluate and compare the performance between the proposed approach and other approaches.
3D Body Reconstruction from Photos Based on Range Scan

We present a data-driven shape model for reconstructing human body models from one or more 2D photos. One of the key tasks in reconstructing the 3D model from image data is shape recovery, a task done until now in utterly geometric way, in the domain of human body modeling. In contrast, we adopt a data-driven, parameterized deformable model that is acquired from a collection of range scans of real human body. The key idea is to complement the image-based reconstruction method by leveraging the quality shape and statistic information accumulated from multiple shapes of range-scanned people. In the presence of ambiguity either from the noise or missing views, our technique has a bias towards representing as much as possible the previously acquired ‘knowledge’ on the shape geometry. Texture coordinates are then generated by projecting the modified deformable model onto the front and back images. Our technique has shown to reconstruct successfully human body models from minimum number images, even from a single image input.
High-Frequency PWM Buck Converters Using GaN-on-SiC HEMTs

GaN high electron mobility transistors (HEMTs) are well suited for high-frequency operation due to their lower on resistance and device capacitance compared with traditional silicon devices. When grown on silicon carbide, GaN HEMTs can also achieve very high power density due to the enhanced power handling capabilities of the substrate. As a result, GaN-on-SiC HEMTs are increasingly popular in radio-frequency power amplifiers, and applications as switches in high-frequency power electronics are of high interest. This paper explores the use of GaN-on-SiC HEMTs in conventional pulse-width modulated switched-mode power converters targeting switching frequencies in the tens of megahertz range. Device sizing and efficiency limits of this technology are analyzed, and design principles and guidelines are given to exploit the capabilities of the devices. The results are presented for discrete-device and integrated implementations of a synchronous Buck converter, providing more than 10-W output power supplied from up to 40 V with efficiencies greater than 95% when operated at 10 MHz, and greater than 90% at switching frequencies up to 40 MHz. As a practical application of this technology, the converter is used to accurately track a 3-MHz bandwidth communication envelope signal with 92% efficiency.
It's Mine, Don't Touch!: interactions at a large multi-touch display in a city centre

We present data from detailed observations of CityWall, a large multi-touch display installed in a central location in Helsinki, Finland. During eight days of installation, 1199 persons interacted with the system in various social configurations. Videos of these encounters were examined qualitatively as well as quantitatively based on human coding of events. The data convey phenomena that arise uniquely in public use: crowding, massively parallel interaction, teamwork, games, negotiations of transitions and handovers, conflict management, gestures and overt remarks to co-present people, and "marking" the display for others. We analyze how public availability is achieved through social learning and negotiation, why interaction becomes performative and, finally, how the display restructures the public space. The multi-touch feature, gesture-based interaction, and the physical display size contributed differentially to these uses. Our findings on the social organization of the use of public displays can be useful for designing such systems for urban environments.
Creation and Evaluation of emotion expression with body movement, sound and eye color for humanoid robots

The ability to display emotions is a key feature in human communication and also for robots that are expected to interact with humans in social environments. For expressions based on Body Movement and other signals than facial expressions, like Sound, no common grounds have been established so far. Based on psychological research on human expression of emotions and perception of emotional stimuli we created eight different expressional designs for the emotions Anger, Sadness, Fear and Joy, consisting of Body Movements, Sounds and Eye Colors. In a large pre-test we evaluated the recognition ratios for the different expressional designs. In our main experiment we separated the expressional designs into their single cues (Body Movement, Sound, Eye Color) and evaluated their expressivity. The detailed view at the perception of our expressional cues, allowed us to evaluate the appropriateness of the stimuli, check our implementations for flaws and build a basis for systematical revision. Our analysis revealed that almost all Body Movements were appropriate for their target emotion and that some of our Sounds need a revision. Eye Colors could be identified as an unreliable component for emotional expression.
IndexFinder: A Method of Extracting Key Concepts from Clinical Texts for Indexing

Extracting key concepts from clinical texts for indexing is an important task in implementing a medical digital library. Several methods are proposed for mapping free text into standard terms defined by the Unified Medical Language System (UMLS). For example, natural language processing techniques are used to map identified noun phrases into concepts. They are, however, not appropriate for real time applications. Therefore, in this paper, we present a new algorithm for generating all valid UMLS concepts by permuting the set of words in the input text and then filtering out the irrelevant concepts via syntactic and semantic filtering. We have implemented the algorithm as a web-based service that provides a search interface for researchers and computer programs. Our preliminary experiment shows that the algorithm is effective at discovering relevant UMLS concepts while achieving a throughput of 43K bytes of text per second. The tool can extract key concepts from clinical texts for indexing.
The support vector machine under test

Support vector machines (SVMs) are rarely benchmarked against other classi1cation or regression methods. We compare a popular SVM implementation (libsvm) to 16 classi1cation methods and 9 regression methods—all accessible through the software R—by the means of standard performance measures (classi1cation error and mean squared error) which are also analyzed by the means of bias-variance decompositions. SVMs showed mostly good performances both on classi1cation and regression tasks, but other methods proved to be very competitive.
Why Do People Use Facebook?

The social networking site, Facebook, has gained an enormous amount of popularity. In this article, we review the literature on the factors contributing to Facebook use. We propose a model suggesting that Facebook use is motivated by two primary needs: (1) The need to belong and (2) the need for self-presentation. Demographic and cultural factors contribute to the need to belong, whereas neuroticism, narcissism, shyness, self-esteem and self-worth contribute to the need for self presentation. Areas for future research are discussed.
Biological foundations of language: clues from sign language.

The general objective of the research reviewed here is to examine the biological foundations of human language. We report on language and its formal architecture, as well as its representation in the brain, through studying languages that have arisen outside of the mainstream of spoken languages; the visual gestural systems developed among deaf people. The study of sign languages offers a unique opportunity for insight into the nature of neural mechanisms for language, since sign languages utilize a transmission modality different from that of spoken languages. To understand the extent to which neural mechanisms for language are shaped by the modality of their implementation, we are naturally led to the study of sign language because we cannot solve the problem for language in general within the spoken-auditory modality. In these studies, one can make use of the difference in transmission modality between sign and speech to investigate the biological foundations of language.
Towards View-point Invariant Person Re-identification via Fusion of Anthropometric and Gait Features from Kinect Measurements

In this work, we present view-point invariant person re-identification (Re-ID) by multi-modal feature fusion of 3D soft biometric cues. We exploit the MS KinectTM sensor v.2, to collect the skeleton points from the walking subjects and leverage both the anthropometric features and the gait features associated with the person. The key proposals of the paper are two fold: First, we conduct an extensive study of the influence of various features both individually and jointly (by fusion technique), on the person Re-ID. Second, we present an actual demonstration of the view-point invariant Re-ID paradigm, by analysing the subject data collected in different walking directions. Focusing the latter, we further analyse three different categories which we term as pseudo, quasi and full view-point invariant scenarios, and evaluate our system performance under these various scenarios. Initial pilot studies were conducted on a new set of 20 people, collected at the host laboratory. We illustrate, for the first time, gait-based person re-identification with truly view-point invariant behaviour, i.e. the walking direction of the probe sample being not represented in the gallery samples.
Dropout as a Bayesian Approximation : Insights and Applications

Deep learning techniques are used more and more often, but they lack the ability to reason about uncertainty over the features. Features extracted from a dataset are given as point estimates, and do not capture how much the model is confident in its estimation. This is in contrast to probabilistic Bayesian models, which allow reasoning about model confidence, but often with the price of diminished performance. We show that a multilayer perceptron (MLP) with arbitrary depth and non-linearities, with dropout applied after every weight layer, is mathematically equivalent to an approximation to a well known Bayesian model. This interpretation offers an explanation to some of dropout’s key properties, such as its robustness to over-fitting. Our interpretation allows us to reason about uncertainty in deep learning, and allows the introduction of the Bayesian machinery into existing deep learning frameworks in a principled way. Our analysis suggests straightforward generalisations of dropout for future research which should improve on current techniques.
Implicit LOD for processing, visualisation and classification in Point Cloud Servers

We propose a new paradigm to effortlessly get a portable geometric Level Of Details (LOD) for a point cloud inside a Point Cloud Server. The point cloud is divided into groups of points (patch), then each patch is reordered (MidOc ordering) so that reading points following this order provides more and more details on the patch. This LOD have then multiple applications: point cloud size reduction for visualisation (point cloud streaming) or speeding of slow algorithm, fast density peak detection and correction as well as safeguard for methods that may be sensible to density variations. The LOD method also embeds information about the sensed object geometric nature, and thus can be used as a crude multi-scale dimensionality descriptor, enabling fast classification and on-the-fly filtering for basic classes.
CapsNet comparative performance evaluation for image classification

Image classification has become one of the main tasks in the field of computer vision technologies. In this context, a recent algorithm called CapsNet that implements an approach based on activity vectors and dynamic routing between capsules may overcome some of the limitations of the current state of the art artificial neural networks (ANN) classifiers, such as convolutional neural networks (CNN). In this paper, we evaluated the performance of the CapsNet algorithm in comparison with three well-known classifiers (Fisherfaces, LeNet, and ResNet). We tested the classification accuracy on four datasets with a different number of instances and classes, including images of faces, traffic signs, and everyday objects. The evaluation results show that even for simple architectures, training the CapsNet algorithm requires significant computational resources and its classification performance falls below the average accuracy values of the other three classifiers. However, we argue that CapsNet seems to be a promising new technique for image classification, and further experiments using more robust computation resources and refined CapsNet architectures may produce better outcomes.
Cross-Lingual Word Representations via Spectral Graph Embeddings

Cross-lingual word embeddings are used for cross-lingual information retrieval or domain adaptations. In this paper, we extend Eigenwords, spectral monolingual word embeddings based on canonical correlation analysis (CCA), to crosslingual settings with sentence-alignment. For incorporating cross-lingual information, CCA is replaced with its generalization based on the spectral graph embeddings. The proposed method, which we refer to as Cross-Lingual Eigenwords (CL-Eigenwords), is fast and scalable for computing distributed representations of words via eigenvalue decomposition. Numerical experiments of English-Spanish word translation tasks show that CLEigenwords is competitive with stateof-the-art cross-lingual word embedding methods.
Probabilistic movement modeling for intention inference in human-robot interaction

Intention inference can be an essential step toward efficient humanrobot interaction. For this purpose, we propose the Intention-Driven Dynamics Model (IDDM) to probabilistically model the generative process of movements that are directed by the intention. The IDDM allows to infer the intention from observed movements using Bayes’ theorem. The IDDM simultaneously finds a latent state representation of noisy and highdimensional observations, and models the intention-driven dynamics in the latent states. As most robotics applications are subject to real-time constraints, we develop an efficient online algorithm that allows for real-time intention inference. Two human-robot interaction scenarios, i.e., target prediction for robot table tennis and action recognition for interactive humanoid robots, are used to evaluate the performance of our inference algorithm. In both intention inference tasks, the proposed algorithm achieves substantial improvements over support vector machines and Gaussian processes.
The PADRES Distributed Publish/Subscribe System

Workflow management systems are traditionally centralized, creating a single point of failure and a scalability bottleneck. In collaboration with Cybermation, Inc., we have developed a content-based publish/subscribe platform, called PADRES, which is a distributed middleware platform with features inspired by the requirements of workflow management and business process execution. These features constitute original additions to publish/subscribe systems and include an expressive subscription language, composite subscription processing, a rulebased matching and routing mechanism, historc, query-based data access, and the support for the decentralized execution of business process specified in XML. PADRES constitutes the basis for the next generation of enterprise management systems developed by Cybermation, Inc., including business process automation, monitoring, and execution applications.
GPUvm: Why Not Virtualizing GPUs at the Hypervisor?

Graphics processing units (GPUs) provide orders-ofmagnitude speedup for compute-intensive data-parallel applications. However, enterprise and cloud computing domains, where resource isolation of multiple clients is required, have poor access to GPU technology. This is due to lack of operating system (OS) support for virtualizing GPUs in a reliable manner. To make GPUs more mature system citizens, we present an open architecture of GPU virtualization with a particular emphasis on the Xen hypervisor. We provide design and implementation of fulland para-virtualization, including optimization techniques to reduce overhead of GPU virtualization. Our detailed experiments using a relevant commodity GPU show that the optimized performance of GPU para-virtualization is yet two or three times slower than that of pass-through and native approaches, whereas full-virtualization exhibits a different scale of overhead due to increased memory-mapped I/O operations. We also demonstrate that coarse-grained fairness on GPU resources among multiple virtual machines can be achieved by GPU scheduling; finer-grained fairness needs further architectural support by the nature of non-preemptive GPU workload.
Content-based data leakage detection using extended fingerprinting

Protecting sensitive information from unauthorized disclosure is a major concern of every organization. As an organization’s employees need to access such information in order to carry out their daily work, data leakage detection is both an essential and challenging task. Whether caused by malicious intent or an inadvertent mistake, data loss can result in significant damage to the organization. Fingerprinting is a content-based method used for detecting data leakage. In fingerprinting, signatures of known confidential content are extracted and matched with outgoing content in order to detect leakage of sensitive content. Existing fingerprinting methods, however, suffer from two major limitations. First, fingerprinting can be bypassed by rephrasing (or minor modification) of the confidential content, and second, usually the whole content of document is fingerprinted (including non-confidential parts), resulting in false alarms. In this paper we propose an extension to the fingerprinting approach that is based on sorted k-skip-n-grams. The proposed method is able to produce a fingerprint of the core confidential content which ignores non-relevant (non-confidential) sections. In addition, the proposed fingerprint method is more robust to rephrasing and can also be used to detect a previously unseen confidential document and therefore provide better detection of intentional leakage incidents.
Predicting consumer intentions to shop online: An empirical test of competing theories

The proliferation of commercial Web sites providing consumers with a new medium to purchase products and services has increased the importance of understanding the determinants of consumer intentions to shop online. This study compared the technology acceptance model and two variations of the theory of planned behavior to examine which model best helps to predict consumer intentions to shop online. Data were gathered from 297 Taiwanese customers of online bookstores, and structural equation modeling was used to compare the three models in terms of overall model fit, explanatory power and path significance. Decomposing the belief structures in the theory of planned behavior moderately increased explanatory power for behavioral intention. The results also indicate that the decomposed theory of planned behavior provides an improved method of predicting consumer intentions to shop online. Finally, the implications of this study are discussed.
Semantic-enhanced blockchain technology for smart cities and communities

Large-scale transactional systems still suffer from not viable trust management strategies. Given its intrinsic characteristics, blockchain technology appears as interesting from this perspective. A semantic layer built upon a basic blockchain infrastructure would join the benefits of flexible resource/service discovery and validation by consensus. This paper proposes a novel Service-oriented Architecture (SOA) based on a semantic blockchain. Registration, discovery, selection and payment operations are implemented as smart contracts, allowing decentralized execution and trust. Potential applications include material and immaterial resource marketplaces and trustless collaboration among autonomous entities, spanning many areas of interest for smart cities and communities.
Wide-area scene mapping for mobile visual tracking

We propose a system for easily preparing arbitrary wide-area environments for subsequent real-time tracking with a handheld device. Our system evaluation shows that minimal user effort is required to initialize a camera tracking session in an unprepared environment. We combine panoramas captured using a handheld omnidirectional camera from several viewpoints to create a point cloud model. After the offline modeling step, live camera pose tracking is initialized by feature point matching, and continuously updated by aligning the point cloud model to the camera image. Given a reconstruction made with less than five minutes of video, we achieve below 25 cm translational error and 0.5 degrees rotational error for over 80% of images tested. In contrast to camera-based simultaneous localization and mapping (SLAM) systems, our methods are suitable for handheld use in large outdoor spaces.