nils-herrmann/clean_scirepeval huggingface dataset:
Elimination of subband-coding artifacts using the dithering technique

Subband image coding produces several types of artifacts, including contouring and ringing. We use the dithering technique to remove these artifacts. By dithering the subband signals, we can replace signal-dependent artifacts with signal-independent white noise which is less annoying to human eyes. The subjective quality of the coded images is hence improved. In addition, signal-independent white noise can be easily removed by existing noise-reduction methods. The dithering technique can also be applied to transform coding, including the discrete cosine transform, to remove the blocking artifact. Experiment results that illustrate the effectiveness of dithering are given.<<ETX>>
Integrated feature selection and higher-order spatial feature extraction for object categorization

In computer vision, the bag-of-visual words image representation has been shown to yield good results. Recent work has shown that modeling the spatial relationship between visual words further improves performance. Previous work extracts higher-order spatial features exhaustively. However, these spatial features are expensive to compute. We propose a novel method that simultaneously performs feature selection and feature extraction. Higher-order spatial features are progressively extracted based on selected lower order ones, thereby avoiding exhaustive computation. The method can be based on any additive feature selection algorithm such as boosting. Experimental results show that the method is computationally much more efficient than previous approaches, without sacrificing accuracy.
Retrieval of hand-drawn sketches with partial matching

Sketching is a natural way of input that provides an effective means of illustration. A sketch consists of multiple strokes that can be captured by pen-based devices to be stored in a database for future retrieval. In our prior work, we proposed a sketch retrieval method that is suitable for whole matching. However, the problem gets complicated if the retrieval is performed for partially matched sketches. In this paper, we propose a method to solve the partial matching problem for retrieving sketches. In the matching stage, the score is first computed by considering the stroke feature similarity alone. For a sketch consisting of multiple strokes, spatial relations between every pair of strokes (bistroke) can be used together with the bistroke features for finding the correspondence to further improve the retrieval performance. A potential application of sketch retrieval for partial matching is to retrieve materials from the lecture notes for reviewing without the trouble of describing it using keywords.
Spoken Attributes: Mixing Binary and Relative Attributes to Say the Right Thing

In recent years, there has been a great deal of progress in describing objects with attributes. Attributes have proven useful for object recognition, image search, face verification, image description, and zero-shot learning. Typically, attributes are either binary or relative: they describe either the presence or absence of a descriptive characteristic, or the relative magnitude of the characteristic when comparing two exemplars. However, prior work fails to model the actual way in which humans use these attributes in descriptive statements of images. Specifically, it does not address the important interactions between the binary and relative aspects of an attribute. In this work we propose a spoken attribute classifier which models a more natural way of using an attribute in a description. For each attribute we train a classifier which captures the specific way this attribute should be used. We show that as a result of using this model, we produce descriptions about images of people that are more natural and specific than past systems.
Face-graph matching for classifying groups of people

When people gather for a group photo, they are together for a social reason. Past work has shown that these social relationships affect how people position themselves in a group photograph. We propose classifying the type of group photo based on the spatial arrangement and the predicted attributes of the faces in the image. We propose a matching algorithm for finding images from a training set that have both similar arrangement of faces and attribute correspondence. We formulate the problem as a bipartite matching problem where the faces from each of the pair of images are nodes in the graph. Our work demonstrates that face arrangement, when combined with attribute (age and gender) correspondence, is a useful cue in capturing an approximate social essence of the group of people, and lets us understand why the group of people gathered for the photo.
Geometry-assisted statistical modeling for face mosaicing

The modeling of facial appearance has many applications. This paper proposes an approach to generating a statistical face model based on video mosaicing. Unlike traditional video mosaicing, we use the geometry of a face to improve the mosaicing result. Given a face sequence, each frame is unwrapped onto certain portion of the surface of a sphere, as determined by spherical projection and the minimization procedure using the Levenberg-Marquardt algorithm. A statistical model containing a mean image and a number of eigenimages, instead of only one image template, is used to represent the face mosaic. Good experimental results have been observed.
Unsupervised Image Layout Extraction

We propose a novel unsupervised learning algorithm to extract the layout of an image by learning latent object-related aspects. Unlike traditional image segmentation algorithms that segment an image using feature similarity, our method is able to learn high-level object characteristics (aspects) from a large number of unlabeled images containing similar objects to facilitate image segmentation. Our method does not require human to annotate the training set and works without supervision. We use a graphical model to address the learning of aspects and layout extraction together. In particular, aspect-feature dependency from multiple images is learned via the expectation-maximization algorithm. We demonstrate that, by associating latent aspects to spatial structure, the proposed method achieves much better layout extraction results than using probabilistic latent semantic analysis.
Statistically optimal synthesis banks for subband coders

In maximally decimated filter banks, the perfect reconstruction or biorthogonal solution is not necessarily the best choice when subband quantizers are present. Under suitable statistical assumptions, expressions for the best synthesis bank can be derived in terms of the analysis bank and other statistical quantities. We explore this topic for subband coders and the special case of transform coders. We highlight the statistical conditions under which the biorthogonal solution is still the best. We derive expressions for the Wiener filter matrix in terms of the joint statistics of appropriate signals. Special cases where the optimal synthesis filter bank is the biorthogonal system followed by a scalar post filter are also considered.<<ETX>>
Image description with a goal: Building efficient discriminating expressions for images

Many works in computer vision attempt to solve different tasks such as object detection, scene recognition or attribute detection, either separately or as a joint problem. In recent years, there has been a growing interest in combining the results from these different tasks in order to provide a textual description of the scene. However, when describing a scene, there are many items that can be mentioned. If we include all the objects, relationships, and attributes that exist in the image, the description would be extremely long and not convey a true understanding of the image. We present a novel approach to ranking the importance of the items to be described. Specifically, we focus on the task of discriminating one image from a group of others. We investigate the factors that contribute to the most efficient description that achieves this task. We also provide a quantitative method to measure the description quality for this specific task using data from human subjects and show that our method achieves better results than baseline methods.
Jointly estimating demographics and height with a calibrated camera

One important problem in computer vision is to provide a demographic description a person from an image. In practice, many of the state-of-the-art methods use only an analysis of the face to estimate the age and gender of a person of interest. We present a model that combines two problems, height estimation and demographic classification, which allows each to serve as context for the other. Our idea is to use a calibrated camera for measuring the height of people in the scene. Height is measured by jointly inferring across anthropometric dimensions, age, and gender using publicly available statistics. The height estimate provides context for recognizing the age and gender of the subject, and likewise age and gender conditions the distribution of the anthropometric features for estimating height. The performance of our method is explored on a new database of 127 people captured with a calibrated camera with recorded height, age, and gender. We show that estimating height leads to improvements in age and gender classification, and vice versa. To the best of our knowledge, our model produces the most accurate automatic height estimates reported, with the error having a standard deviation of 26.7 mm.
Optimal thresholding for key generation based on biometrics

In this paper, we introduce a novel method for key generation based on biometrics. Given the biometrics, a set of reliable features are extracted. Each feature is compared with multiple thresholds to generate a multi-bit key. By cascading the multi-bit keys, we obtain one biokey that can be used for security applications. In order to generate a reliable bio-key, an optimal thresholding method is proposed to minimize the authentication error rate, in terms of the false accept rate (FAR) and the false reject rate (FRR). The experimental results show that the proposed approach of key generation is user-friendly and reliable.
Reinterpreting the Application of Gabor Filters as a Manipulation of the Margin in Linear Support Vector Machines

Linear filters are ubiquitously used as a preprocessing step for many classification tasks in computer vision. In particular, applying Gabor filters followed by a classification stage, such as a support vector machine (SVM), is now common practice in computer vision applications like face identity and expression recognition. A fundamental problem occurs, however, with respect to the high dimensionality of the concatenated Gabor filter responses in terms of memory requirements and computational efficiency during training and testing. In this paper, we demonstrate how the preprocessing step of applying a bank of linear filters can be reinterpreted as manipulating the type of margin being maximized within the linear SVM. This new interpretation leads to sizable memory and computational advantages with respect to existing approaches. The reinterpreted formulation turns out to be independent of the number of filters, thereby allowing the examination of the feature spaces derived from arbitrarily large number of linear filters, a hitherto untestable prospect. Further, this new interpretation of filter banks gives new insights, other than the often cited biological motivations, into why the preprocessing of images with filter banks, like Gabor filters, improves classification performance.
Landmark recognition: A unary approach

In this paper, we consider the problem of automatic landmark image recognition. Specifically, we identify a fundamental issue that lurks in such applications as modern landmark recognition that arises as a natural consequence of a current state-of-the-art techinque, namely one-versus-all SVM. Then, we provide a unary classification approach that retains much of the benefits of one-versus-all SVM's whilst avoids their shortcomings in the context of landmark recognition tasks. Finally, we provide empirical evidence of the improvements based on our experiments.
Polymer nanocomposite for improving light harvesting of perovskite solar cells

We have successfully demonstrated the use of polymer-Au-nanoparticle (Au-NP) nanocomposites as hole transport layer (HTL) materials for improving the performance of perovskite solar cells. In addition to the enhanced electrical properties of the HTL material itself, the embedded Au-NPs can also serve as light scattering centers to extend the optical pathway of the incident light, resulting in enhanced photon absorption efficiency of the device. As a result, the power conversion efficiency of the device has been improved to 10.71% from 7.81%.
SDN-based dynamic multipath forwarding for inter-data center networking

Since traffic engineering (TE) in Software Defined Networking (SDN) can be much more efficiently and intelligently implemented, Multipath in SDN becomes a new option. However, Ternary Content Addressable Memory (TCAM) size become the bottleneck of SDN. In this paper, we propose an SDN-based Dynamic Flowentry-Saving Multipath (DFSM) mechanism for inter-DC WAN traffic forwarding. DFSM adopts source-destination-based multipath forwarding and latency-aware flow-based traffic splitting to save flow entries and achieve better load balancing. Our evaluations indicate that DFSM saves 15% to 30% system flow entries in different topologies compared to label-based tunneling, and also reduces average latency by 10% to 48% by consuming 8% to 20% more flow entries than Equal-Cost Multipath (ECMP) in less-interconnected topologies. In addition, compared to even traffic splitting, DFSM reduces the standard deviation of path latencies from 14% to 7%.
Data mining for understanding and improving decision-making affecting ground delay programs

Difficulty of deciding on control action depends on the weather and traffic conditions. Weather signature on different days can categorize days into days with little decision difficulty, days with moderate decision difficulty and days with high decision difficulty. This paper examines performance of different data mining methods in the three regions of decision difficulty. Not surprisingly, data mining methods have the best performance in the region of little decision difficulty and have the poorest performance in the region of most decision difficulty. In applications where data mining methods have differing performance in differing regions, it would be more useful to characterize the region specific performance instead of characterizing performance by a single parameter.
E2CC: Building energy efficient ClassCloud using DRBL

Green Computing is a growing research topic in recent years. Its goal is to increase energy efficiency and reduced resource consumption. Building infrastructure for Cloud Computing is one of the methods to achieve these goals. The key concept of Cloud Computing is to provide a resource sharing model based on virtualization, distributed filesystem and web services. In this paper, we will propose an energy efficient architecture for Cloud Computing using Diskless Remote Boot in Linux (DRBL) and Xen. We call this architecture ClassCloud because it is suitable to build an experimental cloud infrastructure in PC classrooms. According to our power consumption experiments, the diskless design of DRBL provides a way to implement a power economization computing platform. Computers booting from network without hard disk installed will reduce power consumption from 7% to 33% compared to computers with hard disk installed.
The measurement of viscosity in rubber mixing process based on fuzzy-GA modeling

Rubber mixing is a complicated process and online measurement of viscosity is very difficult to achieve. To cope with the problem, a soft sensing approach based on fuzzy-GA modeling is proposed. During modeling, T-S fuzzy model is employed to approximate the non-linearity of rubber mixing process, an improved Gustafon-Kessel fuzzy clustering algorithm based on similarity assessing is proposed to determine the optimum number of clusters and real-coded GA (genetic algorithm) is adopted to optimize model parameters. All these techniques make the fuzzy model simple and accurate. Based on the approach, a test is conducted. The results show that the proposed approach provides a result near laboratory measurement, and the error is lower and acceptable. It decreases the time involved with tests in laboratory and can be seen as a powerful tool for online measurement of viscosity.
On video communications in MIMO systems: channel coding and error resilient source coding

The problem of efficient video communications over MIMO wireless systems is of great significance, due to the high capacity of the multiple antenna system. The high data rates provided by the MIMO system can be traded off with diversity gain by using different channel coding schemes. Also, by using different video source coding methods, high compression gain can be traded off with the error resilience gain. One should jointly consider source coding and channel coding when designing a MIMO wireless video system. However, little is known so far about what combinations of channel coding and source coding methods have the best overall performance in a MIMO system, In this paper, by comparing the performances of several different typical combinations through simulation studies, we show that no single combination is the best for the entire range of channel conditions, rather different combinations may be best for a sub-range.
Profiling Skype video calls: Rate control and video quality

Video telephony has recently gained its momentum and is widely adopted by end-consumers. But there have been very few studies on the network impacts of video calls and the user Quality-of-Experience (QoE) under different network conditions. In this paper, we study the rate control and video quality of Skype video calls. We first measure the behaviors of Skype video calls on a controlled network testbed. By varying packet loss rate, propagation delay and bandwidth, we observe how Skype adjusts its rates, FEC redundancy and video quality. We find that Skype is robust against mild packet losses and propagation delays, and can efficiently utilize the available network bandwidth. We also find that Skype employs an overly aggressive FEC protection strategy. Based on the measurement results, we develop rate control model, FEC model, and video quality model for Skype. Extrapolating from the models, we conduct numerical analysis to study the network impacts of Skype. We demonstrate that user back-offs upon quality degradation serve as an effective user-level rate control scheme. We also show that Skype video calls are indeed TCP-friendly and respond to congestion quickly when the network is overloaded.
Distributed simulation based on web enabling HLA

Based on the analysis of the characteristic of HLA and Web Services, this paper advances a distributed simulation based on web enabling HLA and gives two development methods of the web enabling HLA. This method gives a good solution on building good opening and high model scalability distributed simulation environment and supports the interoperability among heterogeneous simulations and enabling reusability of legacy models and simulations. The characteristics of the framework and key technologies involved are analyzed.
Full Verifiability for Outsourced Decryption in Attribute Based Encryption

Attribute based encryption (ABE) is a popular cryptographic technology to protect the security of users’ data. However, the decryption cost and ciphertext size restrict the application of ABE in practice. For most existing ABE schemes, the decryption cost and ciphertext size grow linearly with the complexity of access structure. This is undesirable to the devices with limited computing capability and storage space. Outsourced decryption is considered as a feasible method to reduce the user's decryption overhead, which enables a user to outsource a large number of decryption operations to the cloud service provider (CSP). However, outsourced decryption cannot guarantee the correctness of transformation done by the cloud, so it is necessary to check the correctness of outsourced decryption to ensure security for users’ data. Current research mainly focuses on verifiability of outsourced decryption for the authorized users. It still remains a challenging issue that how to guarantee the correctness of outsourced decryption for unauthorized users. In this paper, we propose an ABE scheme with verifiable outsourced decryption (called full verifiability for outsourced decryption), which can simultaneously check the correctness for transformed ciphertext for the authorized users and unauthorized users. The proposed ABE scheme with verifiable outsourced decryption is proved to be selective CPA-secure in the standard model.
Effective Web Service Selection via Communities Formed by Super-Agents

In this paper, we propose a novel community-based approach for web service selection where super-agents with more capabilities serve as community managers. They maintain communities and build community-based reputation for a service based on the opinions from all community members that have similar interests and judgement criteria. The community-based reputation is useful for consumer agents in selecting satisfactory services when they do not have much personal experience with the services. Experimental results show that our approach results in more effective service selection. A practical reward mechanism is also introduced to create incentives for super-agents to contribute their resources and provide truthful community-based reputation information, as strong support for our approach.
IT-QEAS: An improved trilateration localization method through quality evaluation and adaptvie optimization selection strategy

Wireless localization can provide position information for limitary, aerospace, emergency scheduling, rescue and relief work, fault diagnosis and many other related location-based services system in various application fields. Location information always supports critical decision-making of these application systems. So the localization accuracy is of great importance. As availability, low computation complexity, and low cost, trilateration localization method is applied in various systems. However, as there are numerous negative factors like non-line-of-sight measurement, complex multipath fading, which leads to high level localization error. To improve localization accuracy, an improved trilateration localization method is proposed, which is called improved trilateration localization method with distance estimation quality evaluation and anchor node adaptive selection (IT-QEAS). In this method, the qualities of distances estimation between unknown node and anchor nodes are evaluated, and the anchor nodes with higher quality distance estimation are selected adaptively to form trilateration localization equations, and finally, location result with high accuracy could be gained. The experimental result with actual localization data is shown that the proposed method can get higher localization accuracy with low computation complexity.
Public emergency oriented SOA-based logistics system

An SOA-based emergency logistics system based on GPS / GIS is presented for that the emergency logistics system can not meet the requirement of managing public emergency frequently. The intelligent vehicle system based on GPS and monitoring platform is designed for real-time data interaction. Ontology-based emergency logistics repository, emergency logistics framework, early-warning rule base and emergency rule base are implemented in this system. Early-warning system and emergency decision-making system can use reasoning engine and optimization sub-system of routes, inventory policy and distribution to make prejudgment and decision in order to reduce casualties and property losses.