nils-herrmann/clean_scirepeval huggingface dataset:
Tracking Nonstationary Visual Appearances by Data-Driven Adaptation

Without any prior about the target, the appearance is usually the only cue available in visual tracking. However, in general, the appearances are often nonstationary which may ruin the predefined visual measurements and often lead to tracking failure in practice. Thus, a natural solution is to adapt the observation model to the nonstationary appearances. However, this idea is threatened by the risk of adaptation drift that originates in its ill-posed nature, unless good data-driven constraints are imposed. Different from most existing adaptation schemes, we enforce three novel constraints for the optimal adaptation: (1) negative data, (2) bottom-up pair-wise data constraints, and (3) adaptation dynamics. Substantializing the general adaptation problem as a subspace adaptation problem, this paper presents a closed-form solution as well as a practical iterative algorithm for subspace tracking. Extensive experiments have demonstrated that the proposed approach can largely alleviate adaptation drift and achieve better tracking results for a large variety of nonstationary scenes.
Learning Adaptive Metric for Robust Visual Tracking

Matching the visual appearances of the target over consecutive image frames is the most critical issue in video-based object tracking. Choosing an appropriate distance metric for matching determines its accuracy and robustness, and thus significantly influences the tracking performance. Most existing tracking methods employ fixed pre-specified distance metrics. However, this simple treatment is problematic and limited in practice, because a pre-specified metric does not likely to guarantee the closest match to be the true target of interest. This paper presents a new tracking approach that incorporates adaptive metric learning into the framework of visual object tracking. Collecting a set of supervised training samples on-the-fly in the observed video, this new approach automatically learns the optimal distance metric for more accurate matching. The design of the learned metric ensures that the closest match is very likely to be the true target of interest based on the supervised training. Such a learned metric is discriminative and adaptive. This paper substantializes this new approach in a solid case study of adaptive-metric differential tracking, and obtains a closed-form analytical solution to motion estimation and visual tracking. Moreover, this paper extends the basic linear distance metric learning method to a more powerful nonlinear kernel metric learning method. Extensive experiments validate the effectiveness of the proposed approach, and demonstrate the improved performance of the proposed new tracking method.
A 1.4 MHz 0.21 mW MPEG-2/4 AAC single chip decoder

A low power and full ASIC MPEG-2/4 AAC single chip decoder is presented. The decoding blocks are partitioned into four dedicated hardware modules with the complexity and operational type analysis. Through algorithm, architecture, RTL and circuit level lower power techniques, the proposed AAC decoder is operated at 1.4 MHz for the 44.1 KHz sampling frequency and consumes only 0.21 mW using TSMC 0.13 µm library.
Integrating unlabeled images for image retrieval based on relevance feedback

Retrieval techniques based on pure similarity metrics are often suffered from the scales of image features. An alternative approach is to learn a mapping based on queries and relevance feedback by supervised learning. However, the learning is plagued by the insufficiency of labeled training images. Different from most current research in image retrieval, this paper investigates the possibility of taking advantage of unlabeled images in the given image database to make a hybrid statistical learning feasible. Assuming a generative model of the database, the proposed approach casts image retrieval as a transductive learning problem in a probabilistic framework. Our experiments show that the proposed approach has a satisfactory performance in image retrieval applications.
Right Hemisphere Activation of Joke-related Information: An Event-related Brain Potential Study

Two studies tested the hypothesis that the right hemisphere engages in relatively coarse semantic coding that aids high-level language tasks such as joke comprehension. Scalp-recorded event-related brain potentials (ERPs) were collected as healthy adults read probe words (CRAZY) preceded either by jokes or nonfunny controls (Everyone had so much fun jumping into the swimming pool, we decided to put in a littlewater/platform). Probes were related to the meaning of the jokes, but not the controls. In Experiment 1a, with central presentation, probes following jokes (related) elicited less negative ERPs 300700 msec postonset (N400) than did probes following nonfunny controls (unrelated). This finding suggests related probes were primed by the jokes. In addition, unrelated probes elicited a larger anterior positivity 700 900 msec than did related, as irrelevant stimuli impacted control processes invoked by task demands. In Experiment 1b, probes (CRAZY) were preceded only by sentence-final words from jokes (water) or controls (platform). No ERP effects were observed in Experiment 1b, suggesting the N400 priming effect and the anterior positivity observed in Experiment 1a reflect semantic activations at the discourse level. To assess hemispheric differences in semantic activations, in Experiment 2, ERPs were recorded as participants read probe words presented in their left and right visual fields (LVF and RVF, respectively). Probes elicited a smaller N400 component when preceded by jokes than controls. This N400 priming effect was larger with presentation to the LVF, suggesting joke-relevant information was more active in the right hemisphere. The anterior positivity was observed with RVF but not LVF presentation, suggesting an important role for the left hemisphere in controlled retrieval in language comprehension.
Classification of Plant Leaves by Near-infrared Spectroscopy Using ANN and Wavelet

Near-infrared (NIR) spectroscopy is a kind of non-destructive technology, which has many advantages. Infrared spectrum is highly characteristic and can be used to analyze and identify. However, researches and applications of NIR spectroscopy in plant classification and growth monitoring are still rare. This paper conducted NIR spectroscopy experiments on Cinnamomum camphora and Aceraceaedie leaves to obtain their spectrum curves, and wavelet is used to perform pre-processing of obtained data, with efficient compression of spectrum data, then the classification model of the leaves is established using artificial neural network (ANN), with good classification results, which showed that NIR spectroscopy can be used in classification of different plants.
Discriminative subvolume search for efficient action detection

Actions are spatio-temporal patterns which can be characterized by collections of spatio-temporal invariant features. Detection of actions is to find the re-occurrences (e.g. through pattern matching) of such spatio-temporal patterns. This paper addresses two critical issues in pattern matching-based action detection: (1) efficiency of pattern search in 3D videos and (2) tolerance of intra-pattern variations of actions. Our contributions are two-fold. First, we propose a discriminative pattern matching called naive-Bayes based mutual information maximization (NBMIM) for multi-class action categorization. It improves the state-of-the-art results on standard KTH dataset. Second, a novel search algorithm is proposed to locate the optimal subvolume in the 3D video space for efficient action detection. Our method is purely data-driven and does not rely on object detection, tracking or background subtraction. It can well handle the intra-pattern variations of actions such as scale and speed variations, and is insensitive to dynamic and clutter backgrounds and even partial occlusions. The experiments on versatile datasets including KTH and CMU action datasets demonstrate the effectiveness and efficiency of our method.
Study on loom's monitor system based on DSP and CAN bus

In order to improve the management level, promote informationization and automation in textile enterprise, a kind of loom's monitor system based on DSP and CAN bus for rapier loom is discussed in this article. The system is designed by connecting the computers from every workshops (detect real-time computer) with computer from offices (monitor computer) through intranet. It has functions of data recording, data analyzing, data monitoring and alarming. It can trace dynamically the processing of rapier loom's running. For illustration, there gives the detail design for the monitor system, discuss the architecture of network, the whole software design of the system and the key to design database. Empirical results show that it gives an approach for the textile enterprise to realize information and modernization.
3D model-based hand tracking using stochastic direct search method

Tracking the articulated hand motion in a video sequence is a challenging problem in which the main difficulty arises from the complexity of searching for an optimal motion estimate in a high dimensional configuration space induced by the articulated motion. Considering that the complexities of this problem mmay be reduced by learning th lower dimensional manifold of the articulation motion in the configuration space, we propose a new representation for the nonlinear manifold of the articulated motion, with a stochastic simplex algorithm that facilitates very efficient search. Contrary to traditional methods of representing the manifolds through clustering and transition matrix construction, we maintain the set of all training samples. To perform the search of best matching configuration with respect to the input image, we combine sequential Monte Carlo technique with the Nelder-Mead simplex search which is efficient and effective when the gradient is not readily accessible. This new approach has been successfully applied to hand tracking and our experiments show the efficiency and robustness of our algorithm.
An Efficient Carrier Recovery Algorithm for both High-Order Square QAM and Cross-QAM

In this paper, a carrier recovery algorithm for high-order cross-QAM has been proposed, in which less than twelve symbol points in the constellation have been used. The proposed algorithm can be applied to square QAM signals as well. The simulation results demonstrate that the proposed algorithm can deal with carrier frequency offset up to 600KHz@32QAM or 400KHz@128QAM over AWGN channels when the bit rate is 5Mbit/s. The simulation experiments are also done for 64QAM and 256QAM signals, and results show that the proposed algorithm has good performance over a large acquisition span of frequency.
Real-time, transcranial monitoring of safe blood-brain barrier opening in non-human primates

The delivery of drugs to specific neural targets faces two fundamental problems: most drugs do not cross the blood-brain barrier and those that do spread to all parts of the brain. To date there exists only one non-invasive methodology with the potential to solve these problems: selective blood-brain barrier disruption using micro-bubble enhanced focused ultrasound. We have recently developed a single-element 500 kHz spherical transducer ultrasound setup for use in the nonhuman primate. Here, we tested the accuracy of the system by targeting the caudate nucleus of the basal ganglia in two macaque monkeys. Our results show that average in-plane error of the system is on the order of 2 mm and targeting error in depth, i.e., along the ultrasound path, is even smaller averaging 1.2 mm. We have also developed a real-time treatment monitoring based on backscattered emissions spectral analyses. This technique helped us determining a safe and reliable acoustic parameters window for BBB opening.
Approach of remotely sensed data processing task scheduling problem based on ant colony optimization

With the development of remote sensing technology, remote sensing data frequency-intensive has received and processed, the demand of remote sensing applications has kept an increasing growth. The management and planning for multi-source remote sensing data processing became very complicated with the evolution of remote sensed application requests. The way of effect management and scheduling can improve utility of processing resources and sufficiently exert abilities of remote sensing processing center. Based on the multi-objective optimization characteristic of the problem, this paper presents the mathematical model of the problem. An ant colony optimization algorithm is proposed for solving this problem. At last, experiments results show the effectiveness of our approach compared with the results of heuristic algorithm and simulated annealing algorithm.
Investigation of distributive data mining calculating architecture

A distributive calculating architecture is presented to realize data mining efficiently. The architecture is a hierarchical computational method from the conception, which stores the information in every sub-node with the ideas of database partition, a united center distribute unit can be responsible for the collecting and maintenance of the information in every sub-node, by the scan of database, information is distributed to different nodes, this architecture can maintain a global set enumerate tree, the local large item-sets can be constructed by using any effective algorithm, it mainly solves the problem of highly effective data distribution and data skew, the detailed explaining and theoretical proving of the calculating architecture is given, and how to solve the data skew problem highly and effectively is also discussed in this paper. The partial implementation of this algorithm shows the correctness and feasibility, the calculating architecture can be used for distribute database and most applicable for distribute calculation, which can be used in highly and effectively data mining in distributive and parallel environment.
Research on capability requirements generation of Weapon System-of-systems based on CRTAM model

The thoughts and methods of Capability-Based requirement analysis have gone increasingly deep into the development of Weapon System-of-Systems (WSoS). Effective analysis and programming of capability are fundamental tasks for requirement analysis of WSoS. Under the background of operation, there are repeating capabilities among different operational tasks. Acquiring Capability Index Requirements (CIRs) from various Operational Task Requirements (OTRs) is difficult. To resolve this problem, the value of each capability index is measured as an interval and Meta-activity model is proposed, the operational activity is decomposed to Meta-activity collections and the capability requirement of a single operational task is captured using Meta-activity-Capability mapping. Based on this, the WSoS Capability indices are divided into five kinds and four types of Capability Requirements Transverse Aggregation Model (CRTAM) are built to deal with the CIRs acquired from the tasks decomposition. A Capabilities Indices Requirements List (CIRL) under the multi-tasks condition is gained. Finally, the method is validated by an example of anti-stealth air defense systems of systems (ASADSoS).
The camera calibration of binocular vision measurement based on OpenCV

This paper has built a binocular stereo vision measurement system by connecting two CMOS cameras to the IPC using digital image acquisition card. This method sets fixed points through taking images from video camera. It uses the camera model of open source computer vision library (OpenCV) to eliminate the effects of radial and tangential distortion, and then the coordinates and proportion relations between actual objects and images are obtained. First of all, taking the image of checkerboard calibration template as the input in Visual Studio 2010 development platform, this method could get the internal and external parameters of camera. Then, the method of re-projection could obtain calibration error. Finally, the calibration error is analyzed to determine if they are practical. Experiments show that: the process of calibration is efficient and fast; the total average error is within 0.2 pixels. So it completely satisfies the requirement of measurement system.
The power of words: Enhancing music mood estimation with textual input of lyrics

Music mood estimation (MME) is a key technology in mood-based music recommendation. While mainstream MME research nowadays relies on audio music analysis, exploring the significance of lyrics text in predicting song emotion is gaining attention in recent years. One major impediment to MME research is the lack of a clearly labeled and publicly available dataset annotating the emotion ratings of lyrics text and audio separately. In light of this, we compiled a dataset of 600 pop songs (iPop) from the mood ratings of 246 participants who experienced three different song sessions, lyrics text (L), audio music track (M), and the combination of lyrics text and audio music track (C). We then applied statistical analysis to estimate how lyrics text and audio contribute to a song's overall valence-arousal (V-A) mood ratings. Our results show that lyrics text are not only a valid measure for estimating a song's mood ratings but also provide supplementary information that can improve audio-only MME systems. Furthermore, a detailed examination suggests that lyrics text (L) ratings are better estimators of the overall mood ratings of a song (C) in cases where L and M ratings conflict. We then construct a MME system that employs both features extracted from lyrics text and audio music track and validate the conclusions acquired in our statistical analysis. In estimating either V or A rating, the model with lyrics text plus audio track features performs better than only the model with only lyrics text or audio track features. These results validate the statement acquired by the statistical analysis.
TVM: Tabular VM migration for reducing hop violations of service chains in cloud datacenters

Network Function Virtualization is a recent and significant development applied in cloud datacenters, which can help service providers manage network services more flexibly and economically than using middle-boxes. User requests may demand different numbers of services in a chain, where the related flows can be directed to different physical machines (PMs) hosting required virtual network functions (VNFs) in a designated order. Sometimes, with the limited resources in current PMs, the deployment of service chains may result in great hop counts and cause hop (latency) violations against the service level agreements (SLAs) of user requests. Therefore, we propose a dynamic service-chain management approach called Tabular VM Migration (TVM) to reduce hop violations of service chains in cloud datacenters. Different from other VM management policies, TVM considers the order of a set of VMs in a service chain and migrates key VMs which may cause hop violations onto the selected PMs. Thus, hop violations are reduced with few migrations. Simulation results indicate that TVM performs better than other common VM management policies, and reduce at most 16.68% of total number of hop violations induced by a static deployment approach (SOVWin).
An RSU-assisted Cluster Head Selection and Backup Protocol

In VANET, vehicles moving on the highway usually form clusters, and the wireless communications between vehicles are generally conducted by cluster heads. In most cluster-based protocols, however, the cluster head selection carried out through inter-cluster communications has some limitations, e.g., large communication and computation cost, quite complex algorithms, requirement of additional devices and so on. Another potential problem is that the connection between two adjacent cluster heads may be lost due to vehicle's high mobility, which drastically degrades the communication quality. Observing the fact that the road side unit can gather and process more traffic information, in this paper, we propose an RSU-assisted cluster head selection and backup protocol to tackle the above problems. Our protocol can simplify cluster-based protocols with the help of the road side unit. We model the vehicle's movement in a mathematical way and validate the feasibility of our protocol through extensive simulations. The results show that it can greatly improve the data reception rate, and the cluster head backup accuracy is high.
Low-complexity MIMO detection using a list projection technique

It is well known that the maximum-likelihood (ML) detection for multiple-input-multiple-output (MIMO) systems requires high computational complexity. As a result, many suboptimal algorithms, achieving a tradeoff between computational complexity and performance, have been proposed. Among these algorithms, the sphere decoding (SD) detector is considered as a very efficient solution. However, the throughput of the SD is not constant and it greatly depends on for channel conditions. In general, the variation of the throughput is very large. As a result, its hardware implementation can be difficult or inefficient. In this paper, a low-complexity ML-like detection algorithm is proposed. While the proposed algorithm has a constant throughput, it can be computationally more efficient than the SD detection. The other advantage that the SD decoder doesn't have is that the proposed algorithm allows an easy tradeoff between complexity and performance.
Wavefront aberration correction using circulation feedback control based on image boundary definition

In wavefront correction the pixels position error will affect wavefront corrector precision. To solve this problem a circulation feedback control method based on image boundary definition was proposed and an experiment is done on wavefront correcting based on liquid crystal spatial light modulator (LC SLM). The measured wavefront aberration is transformed a gray image to search edge pixels and centroid coordinates by boundary detection method of the digital image processing. On this basis the wavefront aberration is corrected by circulation feedback control. The experimental results show that the PV value of the static wavefront aberration is 0.66λ, the RMS is 0.13λ and the Strehl ratio is 0.54 before correcting and the PV is 0.17λ, the RMS is 0.02λ and the Strehl ratio is 0.98 after correcting. The PV value is less than λ/4 and the Strehl ratio is near diffraction limit, which proves that the above method is effective.
Discriminant-EM algorithm with application to image retrieval

In many vision applications, the practice of supervised learning faces several difficulties, one of which is that insufficient labeled training data result in poor generalization. In image retrieval, we have very few labeled images from query and relevance feedback so that it is hard to automatically weight image features and select similarity metrics for image classification. This paper investigates the possibility of including an unlabeled data set to make up the insufficiency of labeled data. Different from most current research in image retrieval, the proposed approach tries to cast image retrieval as a transductive learning problem, in which the generalization of an image classifier is only defined on a set of images such as the given image database. Formulating this transductive problem in a probabilistic framework the proposed algorithm, Discriminant EM (D-EM) not only estimates the parameters of a generative model but also finds a linear transformation to relax the assumption of probabilistic structure of data distributions as well as select good features automatically. Our experiments show that D-EM has a satisfactory performance in image retrieval applications. D-EM algorithm has the potential to many other applications.