nils-herrmann/clean_scirepeval huggingface dataset:
CINDY: A cylindrical interactive display and its applications

In this paper, we present the prototype of our developed cylindrical interactive display, CINDY. It is an advanced version of our previous proposed omnidirectional display, Omni-Tube. In addition to the advantages of ease of use, low production cost, high portability and mobility that Omni-Tube possesses, CINDY greatly improves accuracy of the motion detection module and is able to give the users more precise control for interacting. Our system consists of three functional parts: one is an optical device that is designed in such a way as to be easily mounted to any commercial micro projector, another is a rotation IR motion detection module that is responsible to locate users' positions, the other is an image transformation algorithm that manages the optical distortions. CINDY is suitable for various applications, such as virtual interactive exhibition in a museum or interactive games. We believe CINDY will have a significant promise for low-cost portable 3D display.
DeepSheet: A sheet music generator based on deep learning

Sheet music has long been regarded as one of the most effective medias for musicians, music players, and amateurs to communicate with each other. It is also an intuitive way for non-professionals to learn how to play a musical instrument or sing a song. However, not all composers have willingness to share their own sheet music, especially those protected by strict copyright regulations. For amateurs or novice musicians without the capability to distinguish the chords by their own ears, it would be difficult for them to enjoy the happiness of playing other's music, or singing songs. In order to provide the beginners with an effective way to play music or sing songs without a given sheet music, we developed a sheet music generator - “DeepSheet”, which was engineered based on deep learning techniques. Basically, the DeepSheet system is mainly composed of three distinctive components as follows: 1. voice separation of the audio song, 2. chord estimation of the background music, and 3. alignment of lyrics and music beats. The experimental results drawn from more than 150 songs of the Beatles and Queen indicated that the newly developed DeepSheet system has the capability and sensibility to generate sheet music with accuracy of approximately 76%.
Automatic video region-of-interest determination based on user attention model

The paper presents a framework for automatic video region-of-interest determination based on a user attention model. A set of attempts on using video attention features and knowledge of applied media aesthetics are made. The three types of visual attention features used are intensity, color, and motion. Referring to aesthetic principles, these features are combined according to camera motion types on the basis of a newly proposed video analysis unit, frame-segment. We conduct subjective experiments on several kinds of video data and demonstrate the effectiveness of the proposed framework.
MobiUP: An Upsampling-Based System Architecture for High-Quality Video Streaming on Mobile Devices

Nowadays, mobile video streaming enables people to access digital content, such as online TV shows, music videos, sports reports, and news programs, anytime, anywhere. However, current streaming services in mobile networks are subject to the available wireless bandwidth shared among many users and can only provide videos with limited resolutions. Moreover, on recently developed high-resolution mobile devices, such as iPhone, Google Nexus One, Nokia N97, and SonyEricsson X10, the resolution of video streaming is much lower than the devices can actually support. As a result, existing video upsampling schemes usually introduce visual artifacts. In response to the above problem, we bridge the resolution gap between streaming videos and client screens, and propose a novel upsampling-based system architecture, called MobiUP, to enable high-quality video streaming onto mobile devices. To avoid modifying existing codecs for video streaming, MobiUP upsamples videos with decoded frames and appends a limited amount of metadata to the streaming videos for facilitating high-quality and real-time conversion from low resolution to high fullscreen resolution on the client side. In other words, the proposed upsampling architecture complements current systems. Therefore, MobiUP is generic and flexible, and it can be implemented easily on mobile devices for practical use. The implementation results demonstrate that, although the appended metadata is less than 8% of the total transmitted data, it improves the quality of the upsampled video significantly. Meanwhile, the computation time of MobiUP Client is close to that of bilinear upsampling algorithms implemented on mobile devices.
Video Adaptation for Small Display Based on Content Recomposition

The browsing of quality videos on small hand-held devices is a common scenario in pervasive media environments. In this paper, we propose a novel framework for video adaptation based on content recomposition. Our objective is to provide effective small size videos which emphasize the important aspects of a scene while faithfully retaining the background context. That is achieved by explicitly separating the manipulation of different video objects. A generic video attention model is developed to extract user-interest objects, in which a high-level combination strategy is proposed for fusing the adopted three types of visual attention features: intensity, color, and motion. Based on the knowledge of media aesthetics, a set of aesthetic criteria is presented. Accordingly, these objects are well reintegrated with the direct-resized background to optimally match the specific screen sizes. Experimental results demonstrate the efficiency and effectiveness of our approach
Background Extraction Based on Joint Gaussian Conditional Random Fields

Background extraction is generally the first step in many computer vision and augmented reality applications. Most existing methods, which assume the existence of a clean background during the reconstruction period, are not suitable for video sequences such as highway traffic surveillance videos, whose complex foreground movements may not meet the assumption of a clean background. Therefore, we propose a novel joint Gaussian conditional random field (JGCRF) background extraction algorithm for estimating the optimal weights of frame composition for a fixed-view video sequence. A maximum a posteriori problem is formulated to describe the intra- and inter-frame relationships among all pixels of all frames based on their contrast distinctness and spatial and temporal coherence. Because all background objects and elements are assumed to be static, patches that are motionless are good candidates for the background. Therefore, in the algorithm method, a motionless extractor is designed by computing the pixel-wise differences between two consecutive frames and thresholding the accumulation of variation across the frames to remove possible moving patches. The proposed JGCRF framework can flexibly link extracted motionless patches with desired fusion weights as extra observable random variables to constrain the optimization process for more consistent and robust background extraction. The results of quantitative and qualitative experiments demonstrated the effectiveness and robustness of the proposed algorithm compared with several state-of-the-art algorithms; the proposed algorithm also produced fewer artifacts and had a lower computational cost.
Automatic Chapter Generation for Consumer Videos

This paper proposes a novel framework for automatic chapter generation of consumer videos, in which the knowledge of media aesthetics and video production is utilized. For an input video, a set of specific visual and aural aesthetic features, called expressive elements, is first extracted. Then a high-level structure with aesthetic semantics, namely aesthetic intensity, is obtained and used to generate the corresponding video chapters. Experimental results demonstrate the effectiveness and efficiency of the proposed approach.
Baseball event detection using game-specific feature sets and rules

A framework for scrutinizing baseball videos is proposed. By applying the well-defined baseball rules, this work exactly identifies what happens in a game rather than roughly finding some interesting parts. After extracting the information changes on the superimposed caption, a rule-based decision is applied to detect meaningful events. Only three types of information, including number of outs, number of scores, and base-occupation situation, have to be considered in the detection process. The experimental results show the effectiveness of this framework and demonstrate some research opportunities about generating semantic-level summary or indexing for sports videos.
A real-time human identification system through multimodal information

Human identification is very important for many applications. Most existing methods are based on ID cards or biometrical methods, such as fingerprint hard information or clothing color soft biometric trait. In this work, we propose a novel framework for human identification through recognizing human moving trajectory by two modalities, that is, the motion information from bird's eye view depth cameras and the compass sensor signals of a mobile device. Our proposed system has the advantages of ease of installation and support real-time service. We believe the proposed system will have a significant promise for human identification service.
Learning and Recognition of On-Premise Signs From Weakly Labeled Street View Images

Camera-enabled mobile devices are commonly used as interaction platforms for linking the user's virtual and physical worlds in numerous research and commercial applications, such as serving an augmented reality interface for mobile information retrieval. The various application scenarios give rise to a key technique of daily life visual object recognition. On-premise signs (OPSs), a popular form of commercial advertising, are widely used in our living life. The OPSs often exhibit great visual diversity (e.g., appearing in arbitrary size), accompanied with complex environmental conditions (e.g., foreground and background clutter). Observing that such real-world characteristics are lacking in most of the existing image data sets, in this paper, we first proposed an OPS data set, namely OPS-62, in which totally 4649 OPS images of 62 different businesses are collected from Google's Street View. Further, for addressing the problem of real-world OPS learning and recognition, we developed a probabilistic framework based on the distributional clustering, in which we proposed to exploit the distributional information of each visual feature (the distribution of its associated OPS labels) as a reliable selection criterion for building discriminative OPS models. Experiments on the OPS-62 data set demonstrated the outperformance of our approach over the state-of-the-art probabilistic latent semantic analysis models for more accurate recognitions and less false alarms, with a significant 151.28% relative improvement in the average recognition rate. Meanwhile, our approach is simple, linear, and can be executed in a parallel fashion, making it practical and scalable for large-scale multimedia applications.
An Innovative Design for In-Vitro Fertilization Oocyte Retrieval Systems

In-vitro fertilization (IVF) and related technologies are arguably the most challenging of all cell culture applications. This paper introduces a new design of an integrated oocyte retrieval solution before the oocytes are further put into laboratory processing during IVF treatment. It facilitates the surgery operation and addresses the temperature inaccuracy of follicular fluid during the transfer to the patient's body. The mechanical design is implemented into a medical-standard conforming platform. An accurate temperature estimation and optimization scheme is also proposed. Comparison before and after the introduction of the new prototype under various operating conditions reveals a significant improvement in performance. This yields a potential application for the medical/healthcare industry.
A 3-D Printed Ti-6Al-4V 3-DOF Compliant Parallel Mechanism for High Precision Manipulation

This paper presents the synthesis and evaluation of a 3-D printed three degrees-of-freedom (DOF) spatial-motion compliant parallel mechanism (CPM). The CPM was synthesized by the beam-based structural optimization method and a prototype was fabricated by the electron beam melting (EBM) technology with Ti6Al4V material. The mechanical characteristics of 3-D printed compliant mechanism for precision applications, i.e., stiffness property, dynamic response, and large elastic deformation, were experimentally evaluated. Most importantly, a coefficient factor of 1.27 was proposed to determine the effective thickness for 3-D printed compliant mechanisms with 0.5 mm thick flexures. Using the effective thickness, the characteristics of the 3-D printed CPM have shown to agree with the prediction, with a maximum deviation of 10.5%. The Ti6Al4V CPM is able to achieve the large work range up to 4 mm of linear displacement, 6 degrees of angular displacements, fast dynamic response of 119 Hz, good decoupled motions, and high non-actuating stiffness. A 3-DOF manipulator was built based on the 3-D printed CPM and actuated by three voice-coil motors. Experimental results have shown that the 3-DOF manipulator could achieve repeatable motions with resolution of 20 nm for the translation along the Z-axis, 0.14 arcsecond for the rotation about the X-axis, and 0.12 arcsecond for the rotation about the Y-axis. In conclusion, EBM technology is suitable for fabricating compliant mechanisms in precision manipulator systems; the mechanical characteristics of 3-D printed compliant mechanisms are predictable when an effective thickness is used.
An information theoretic divergence for microarray data clustering

The Kullback-Leibler (KL) divergence used in conjunction with the unsupervised Self-Organizing Map (SOM) algorithm has been previously shown to be effective for the gene clustering problem: the patterns of the gene clusters obtained were found to be superior to those obtained by the hierarchical clustering algorithm using the uncentered Pearson correlation measure. Motivated by this initial finding, in this research we study the effectiveness of the KL-divergence in a more general setting where the data points are not necessarily projected to the unit simplex but to a parallel simplex in the positive orthant. Two novel hard and soft clustering algorithms based on the so-called generalized KL-divergence are proposed. We tested the algorithms on both gene and sample clustering problems. Experimental results on real microarray datasets with known class labels (for genes or samples) show that the generalized KL-divergence based algorithms produce comparable or better results to those obtained by similar algorithms based on popular distance measures for microarray data clustering, such as the squared Euclidean distance and the Pearson correlation. Two validation indices, namely the Adjusted Rand Index and the newly developed Variation of Information metric, have been used to validate the results.
Deep learning sparse ternary projections for compressed sensing of images

Compressed sensing (CS) is a sampling theory that allows reconstruction of sparse (or compressible) signals from an incomplete number of measurements, using of a sensing mechanism implemented by an appropriate projection matrix. The CS theory is based on random Gaussian projection matrices, which satisfy recovery guarantees with high probability; however, sparse ternary {0, −1, +1} projections are more suitable for hardware implementation. In this paper, we present a deep learning approach to obtain very sparse ternary projections for compressed sensing. Our deep learning architecture jointly learns a pair of a projection matrix and a reconstruction operator in an end-to-end fashion. The experimental results on real images demonstrate the effectiveness of the proposed approach compared to state-of-the-art methods, with significant advantage in terms of complexity.
Chalcogenide suspended-core fibers: Manufacturing and non-linear properties at 1. 55 µm

Chalcogenide glasses are known for their high non-linear optical properties (100 – 1000 times as high as the non linearity of silica glass). To enhance the non-linear properties, As-Se suspended-core fibers were manufactured. First, high-purity glasses were elaborated and moulded to obtain high-quality preforms. After drawing, the diameter of the suspended core can be less than 2 µm, which corresponds to a non-linear parameter γ of nearly 30 000 W−1·km−1. Self-phase modulation, four-wave mixing and Raman effects can be observed and studied in those fibers at the telecom wavelengths.
Adaptive fuzzy controller for predicting order of concurrent actions in real-time systems

In controlling a real-time system with a large number of controlling points and when the time spent for setting up the reacting actions is longer than the deadline, we have to predict the reacting actions before receiving complete information about them. These actions are chosen from the concurrent past actions based on the rank of their utilities. Thus in predicting the rank we have to estimate the indefinite qualities defined by the uncompleted information. The paper presents a hardware approach to predict the order of concurrent actions based on a fuzzy and adaptive mechanism which measures the possible utilities given in intervals with additional statistical information. In order to process efficiently the incomplete data we design a fuzzy hardware controller (FHC) which specializes on ranking the intervals according to the observed data in the past. The analysis shows that the fuzzy measurement implemented by a hardware specialized controller with parallel architecture is able to reduce significantly the processing time, compared with implementation by software running on a general computer. Moreover, the FHC can adapt to the types of statistical data which is also safer and can be updated easily within the common memory of the FHC.
Comparison of active crowbar protection schemes for DFIGs wind turbines

The doubly fed induction generators (DFIGs) are used in almost 50% of the offshore and onshore wind power plants. The DFIGs offer advantages compared to fix speed induction generators. The DFIGs are sensitive to grid voltage disturbances and require additional protection for the rotor side power electronic converter, like crowbar protection. In the present paper two active crowbar topologies are compared, IGBT with bypass resistor and rectifier with IGBT, which were the most studied in the scientific literature. A DFIG model with analyzed crowbar protections was implemented in Matlab/Simulink. The simulation results reveal that both solutions can limit the rotor current and the dc link voltage values. However, the solution with rectifier offers better results.
A novel CPA-secure hybrid encryption scheme based-on pNE cryptosystem

Although pNE is a new and unique provably secure variant of famous NTRU cryptosystem so far, the message-expansion factor of pNE, similar to other variants of NTRU, is rather larger than that of other traditional public-key cryptosystems. In this paper, beside introducing RISKE, a new probabilistic secret-key cryptosystem based-on characteristics of invertible elements in binary truncated quotient polynomial rings, we combine it with pNE to construct a novel hybrid cryptosystem, named HpNE, which not only is IND-CPA secure but also has smaller message-expansion factor than pNE does.
Cognitive Radio Based Resource Allocation for Sum Rate Maximization in Dual Satellite Systems

Satellites operating on same frequency bands with overlapping coverage can suffer from co-channel interference from the others. Hence, resource allocation and interference management for the multi-satellite system are very important to maintain the reliable communications and effective utilization of the radio resources. Such design typically requires the channel state information (CSI) of both desirable and interfering communication links; however, the large round trip delay in satellite communication renders the estimation of instantaneous CSI a difficult task. In this paper, we study the resource allocation for the uplink communications of two satellites using the cognitive radio concept where the two satellites are treated as the primary and secondary satellites. Many of conventional resource allocation problems for sum rate maximization deal with power management. Our design which does not require the instantaneous CSI knowledge aims to maximize the average sum rate of the secondary satellite by optimize both the secondary users' (SU) powers and angles toward the secondary satellite. To tackle the underlying non-convex resource allocation problem, we propose a block coordinate descent based iterative algorithm where in each iteration, the power allocation is solved optimally by using the dual based algorithm and the angles of SUs are determined to achieve the maximum average sum rate while maintaining the average interference constraints. We then conduct numerical studies and show significant performance improvement of the proposed algorithm compared to other conventional algorithms.
CGSIL: Collaborative Geo-clustering Search-Based Indoor Localization

Many works have been proposed to use Wi-Fi signals to locate the position of the mobile devices. However, most of these approaches require a costly training step to build a Wi-Fi's map for each location. ISIL has been proposed as the first Wi-Fi-based technique that is training-free, in which the localization can be done instantly at any location without the need of training. However, due to its simplicity, ISIL does not provide adequate accuracy. In this paper, we proposed CGSIL, another training-free localization that is two times more accurate than ISIL by utilizing collaborative filtering and geographic clustering on the location information.
Comparative Study of Room-Temperature Ferromagnetism in Undoped and Mn-Doped CdSe Quantum Dots

We report a comparative study of room-temperature ferromagnetism (RTFM) in undoped and Mn-doped CdSe quantum dots (QDs) synthesized by a pyrolysis method. Both undoped and Mn-doped CdSe QDs exhibited the RTFM with a coercive field of ~160 Oe. The magnetic moment of the QDs showed a strong dependence on the QD size, indicating that the RTFM of the QDs was due to an intrinsic effect. The saturation magnetization value of QDs increased with a decreasing of the QD size. Compared with the undoped QDs, the Mn-doped CdSe QDs exhibited the enhancement of saturation magnetization value with a slight doping concentration. By further increasing the Mn doping concentration to 4 at.%, the increase of the saturation magnetization value of the doped-QDs was not observed, which could be attributed to the enhancement of antiferromagnetic coupling between Mn2+ ions.
A 53&#x00B5;W −82dBm sensitivity 920MHz OOK receiver design using bias switch technique on 65nm SOTB CMOS technology

This paper presents an ultra-low power receiver design at 920MHz. We proposed a receiver architecture, in which bias switch technique is applied to reduce power consumption significantly. The receiver was simulated and laid out on 65nm SOTB CMOS technology, consuming only 53μW at 0.6V supply voltage. It achieves a sensitivity of -82dBm with a data rate of 10 - 100 kbps.