Reviewerly/exHarmony huggingface dataset:
Hardware Implementation of Dual-Tree Wavelet Transform Based Image Reconstruction

Real-time implementations of image processing algorithms on embedded platforms are gaining importance. In this paper, we propose an Application Specific Integrated Circuit (ASIC) architecture for the perfect reconstruction of images using wavelets with a view to extending this to denoising and feature extraction of images. An architecture that implements the Dual-Tree Wavelet Transform is presented. The architecture features a 128×128 single-port block memory and its addressing schemes, a simple upsampling/downsampling method and a folding and adding mechanism. It is implemented using 180nm technology. The results show perfect reconstruction of 128×128 grayscale images with up to 1-bit error in pixel values when compared to the corresponding input images.
Neural network promotes the transmission quality of remote health based on 5G technology

In this paper, to improve the wireless channel transmission quality in remote health using 5G, Bi-LSTM (Bi-directional Long Short Term Memory) neural network based channel estimation method is proposed to solve the problem of limited performance of traditional channel estimation methods in complex multipath channel environment. The wireless channel in this paper is processed in an end-to-end way, and compared with the traditional method which estimates CSI before recovering the signals we use deep learning tools to directly obtain the transmitted signal and hiding the CSI in the process. In order to solve the problem of channel distortion, the autoregressive process is used in the model of wireless communication channel. The WINNER II channel parameters are used to generate training data. Besides, we use the iterative training process of neural network to obtain the optimal solution of the channel autoregressive coefficient. From the simulation results, in the complex multipath channel environment, compared with the traditional channel estimation method (LS and MMSE) and the channel estimation method using DNN (Deep Neural Networks), the performance of BER in our network is better. And compared with the performance of NMSE with increasing number of pilots, our network has the best performance on 5G wireless signal transmission, which can consider to be used in remote health.
Cam-Net: Compressed Attentive Multi-Granularity Network For Dynamic Scene Classification

Dynamic scene classification on portable platforms is extremely challenging due to the contradiction between model complexity and computing resources. To resolve this long-standing dilemma, we propose the compressed attentive multi-granularity network (CAM-Net) in a two-step manner. First, we present a novel AM-Net based on multi-granularity attention units to boost the performance of the full-precision model. It captures and enhances both coarse and fine target-related information. Then, we introduce an efficient binary approximation to AM-Net to improve computing efficiency, leading to CAM-Net. Particularly, a grouping guidance approach is adopted to guide the reconstruction of full-precision weights from binary ones. With this guidance, CAM-Net can significantly reduce memory usage as well as CPU consumption, yet only cause a slight decline in accuracy. Extensive experiments have been conducted on three benchmark datasets, i.e., Maryland, YUPENN ++ and ActivityNet, demonstrating the effectiveness and superiority of the proposed method on scene classification.
On the Generalized Cross-Law of Importation in Fuzzy Logic

Recently, Baczyński et al. introduced two pexider-type generalisations of the law of importation in fuzzy logic, i.e., I(C(x, α), y)=I(x, J(α, y) (GLI) and I(C(x, α), y)=J(x, I(α, y) (CLI), where C is a fuzzy conjunction and I, J are fuzzy implications. However, (CLI) has not been adequately investigated so far. In this paper, we firstly show that (CLI) can be derived from the α-migrativity of an R-implication obtained from an α-migrative t-norm. Secondly, the relationships between the satisfaction of the law of importation (LI) by the pairs (C, I) or (C, J) and the satisfaction of (CLI) by the triple (C, I, J) are studied. Moreover, some necessary conditions of (CLI) are given. Finally, we study (CLI) under three different perspectives.
An Energy Saving-Oriented Incentive Mechanism in Colocation Data Centers

The size and amount of colocation data centers (colocations, for short) have been growing rapidly with the increasing popularity of cloud services, which ultimately causes a heavy burden on the power grid and the environment. However, even a colocation owner wishes to reduce its energy consumption, it may not be able to apply some effective energy saving techniques directly to the servers, since the servers belong to and are operated by its tenants. To solve the "uncoordinated relationship" issue between owners and tenants and achieve the energy reduction with a limited cost budget, an energy saving-oriented incentive mechanism, ESCo is proposed in this paper. Different from existing mechanisms that emphasize on minimizing the cost for the owners, our mechanism emphasizes on the maximization of the amount of energy saved by the tenants given a limited budget that the owner wants to pay for the energy saving. An algorithm is developed to realize a stable assignment in the mechanism. Trace-driven simulations based on real-world data are performed to verify the effectiveness of ESCo. The results show that ESCo can achieve 14.47% more energy saving than existing colocation incentive mechanisms.
Visual Tracking Via Temporally-Regularized Context-Aware Correlation Filters

Classical discriminative correlation filter (DCF) model suffers from boundary effects, several modified discriminative correlation filter models have been proposed to mitigate this drawback using enlarged search region, and remarkable performance improvement has been reported by related papers. However, model deterioration is still not well addressed when facing occlusion and other challenging scenarios. In this work, we propose a novel Temporally-regularized Context-aware Correlation Filters (TCCF) model to model the target appearance more robustly. We take advantage of the enlarged search region to obtain more negative samples to make the filter sufficiently trained, and a temporal regularizer, which restricting variation in filter models between frames, is seamlessly integrated into the original formulation. Our model is derived from the new discriminative learning loss formulation, a closed form solution for multidimensional features is provided, which is solved efficiently using Alternating Direction Method of Multipliers (ADMM). Extensive experiments on standard OTB-2015, TempleColor-128 and VOT-2016 benchmarks show that the proposed approach performs favorably against many state-of-the-art methods with real-time performance of 28fps on single CPU.
High Entropy Random Selection Protocols

We study the two party problem of randomly selecting a common string among all the strings of length n. We want the protocol to have the property that the output distribution has high Shannon entropy or high min entropy, even when one of the two parties is dishonest and deviates from the protocol. We develop protocols that achieve high, close to n, Shannon entropy and simultaneously min entropy close to n/2. In the literature the randomness guarantee is usually expressed in terms of “resilience”. The notion of Shannon entropy is not directly comparable to that of resilience, but we establish a connection between the two that allows us to compare our protocols with the existing ones. We construct an explicit protocol that yields Shannon entropy $$n - O(1)$$ and has $$O(\log ^* n)$$ rounds, improving over the protocol of Goldreich et al. (SIAM J Comput 27: 506–544, 1998) that also achieves this entropy but needs O(n) rounds. Both these protocols need $$O(n^2)$$ bits of communication. Next we reduce the number of rounds and the length of communication in our protocols. We show the existence, non-explicitly, of a protocol that has 6 rounds, O(n) bits of communication and yields Shannon entropy $$n- O(\log n)$$ and min entropy $$n/2 - O(\log n)$$ . Our protocol achieves the same Shannon entropy bound as, also non-explicit, protocol of Gradwohl et al. (in: Dwork (ed) Advances in Cryptology—CRYPTO ‘06, 409–426, Technical Report , 2006), however achieves much higher min entropy: $$n/2 - O(\log n)$$ versus $$O(\log n)$$ . Finally we exhibit a very simple 3-round explicit “geometric” protocol with communication length O(n). We connect the security parameter of this protocol with the well studied Kakeya problem motivated by Harmonic Analysis and Analytic Number Theory. We prove that this protocol has Shannon entropy $$n-o(n)$$ . Its relation to the Kakeya problem follows a new and different approach to the random selection problem than any of the previously known protocols.
Perceptual Quality Assessment On DIBR Synthesized Videos With Composite Distortions

DIBR (depth-image-based rendering) synthesized videos has become increasingly popular recently in supporting 3D-TV, Free-viewpoint video (FVV) and interactive graphic applications. Due to the synthesis process, it contains composite distortions, i.e., DCT-like distortions plus geometric distortions. It has led to new challenges for subjective and objective video quality assessment (VQA) community, where such composite distortions have not been taken into consideration. In this work, we construct a new database composed of DIBR synthesized videos, the reference view video is compressed by H.264 and HEVC encoders, which is subsequently synthesized to the virtual view with different DIBR algorithms. We also propose a novel no-reference (NR) synthesized video quality metric with multi-modal feature pooling and evaluate it against representative full reference (FR) and NR objective VQA models on the proposed database. The database and the proposed metric will be made available to the public.
Dynamic Global-Local Attention Network Based On Capsules for Text Classification

Text classification requires a comprehensive consideration of global and local information for the text. However, most methods only treat the global and local features of the text as two separate parts and ignore the relationship between them. In this paper, we propose a Dynamic Global-Local Attention Network based on Capsules (DGLA) that can use global features to dynamically adjust the importance of local features (e.g., sentence-level features or phrase-level features). The global features of the text are extracted by the capsule network, which can capture the mutual positional relationship of the input features to mine more hidden information. Furthermore, we have designed two global-local attention mechanisms within DGLA to measure the importance of two different local features and effectively leverage the advantages of these two attention mechanisms through the residual network. The performance of the model was evaluated on seven benchmark text classification datasets, and DGLA achieved the highest accuracy on all datasets. Ablation experiments show that the global-local attention mechanism can significantly improve the performance of the model.
Resolving the Shortcomings of Human Centered Design in Transcultural Communication

The reach and impact of economies and societies is increasingly global thanks to improved communication systems and technological development. However, rarely is consideration given to the nuances of communication expectations across cultural groups. This shortcoming can negatively impact user design. For human-centered design to be effective, it must consider the perspectives of its audience and their varying attitudes regarding what makes communication effective.
Fine-grained Image Recognition Based on Attention Map and Image Sampling

The difference between fine-grained image classification and general image classification is that the difference between fine-grained images is small, so in fine-grained image classification, the details of the images are extremely important. In this paper, we proposed a network that can retain both the overall image information and the local image information. Our network structure is composed as follows: First using the convolutional layer to obtain the feature map of the image, and then use the trilinear attention method to process the feature map to obtain the average attention map and the single-channel attention map, and then using selective sampling. The sampled image is obtained according to the two attention maps above, and finally the original image and the sampled two images are input to the convolutional neural network for discrimination. Our entire network can be trained end-to-end. We used this network structure to conduct a large number of experiments on the CUB-2011-200, FGVC aircraft and Stanford Cars datasets, and the experimental results all proved the effectiveness of the method.
Resilience of MDCS Watermarking Algorithm in Wireless Network Environment

This paper presents a robust video watermarking algorithm MDCS. Its performances and watermark extraction capability in wireless channel with dropped packets were tested. The experimental part of the paper confirmed the possibility of extraction high quality color watermark in function of insertion parameter T. The limit value of the insertion parameter which allows extraction of watermark without errors was determined. The obtained results confirm the MDCS algorithm's resilience to packet loss and recommend it for copyright protection in the event of significant degradation of the video sequence.
Turin Papyrus Map and Historical Background of Geological Work in Egypt

The history of recorded geological observations of the Egyptian Eastern Desert and Sinai is divided into five stages: Pharaonic, Roman to early Ottoman, nineteenth century, twentieth century to the 1950s and post-1950s. Pharaonic interests were mainly resource-focused (gold, gemstones, hard stone) and are exemplified by the Turin Papyrus geological map. Roman to early Ottoman activities followed similar motivations and transported huge quantities of Egyptian stone across the Mediterranean. The nineteenth century saw the first scientific observations, with the French Expedition, and later with the maps produced by intrepid German explorers, Rohlfs, Zittel and Schweinfurth. This stage concluded with the momentous founding of the Egyptian Geological Survey by Henry G. Lyons. In the first half of the twentieth century, William Fraser Hume and colleagues Ball, Barron and Beadnell conducted fundamental mapping programs that formed the basis for later comprehensive geoscientific research in Egypt. The 1950s begins a stage of resurgence of academic interest in Egyptian geology, particularly inspired by evolving tectonic concepts and the leadership of Egyptian and western scientists, and is marked by an acceleration in the rate of appearance of national geoscience periodicals.
Intelligent Decision-Making Based on Self-Organization

The article deals with main terms of decision support systems based on self-organization. Moreover, the main components and algorithms used in these systems have been described. The authors have provided a structure of the decision support system and it input parameters. The input data include a database, a knowledge base, and a model of the base. The article has concerned a statement of the parametric optimization problem. In terms of the computer-aided design systems, the search of optimal solutions is complicated by the incompleteness of a priori mathematical description of objects. The aim of the parametric optimization algorithm is to find a set of parameters in which the objective function takes the minimum value. The self-organization process has been analysed to build in it in the intelligent decision-making system. The authors have considered bioinspired self-organization algorithms based on the behaviour of bats and monkeys in nature. These algorithms have several advantages, such as scalability, fault tolerance, adaptation, modularity, autonomy, and parallelism. It makes them more effective as compared to classical approaches. Thus, the modified monkey search algorithm has been considered in the article. A model of the optimization problem based on bats behaviour is also provided. To estimate the effectiveness of developed algorithms,the computational experiments have been conducted. The use of bioinspired algorithms in intelligent decision-making systems is a promising area, as confirmed by the experiments.
Video object segmentation using spatio-temporal deep network

Video analysis is increasingly becoming possible with improvement in hardware and deep learning algorithms. Videos contain the spatial as well as the temporal information that come closest to the real-world visual information representation. Albeit the human brain can make better decisions using spatio-temporal data, the images and video frames captured from the same standard RGB camera will vary in quality. Deep learning has resulted in extraordinary performances for image analysis. Image-based deep networks have been modified and extended to work on video, and optical flow between the frames has been utilized to capture temporal variations. There is a gap in understanding whether such networks capture the spatio-temporal information collectively. The network that can capture the information effectively should be capable of good performances despite relatively bad quality video frames. In this work, different deep network architectures are explored and their ability to capture spatio-temporal features is explored. With the understanding of the advantages and disadvantages of the network components, a new network is designed for the task of video object segmentation (VOS). The performance of the proposed network is evaluated using the DAVIS dataset for three tasks: VOS using weak supervision, zero-shot VOS and one-shot VOS. The best performance is reported in comparison to the state-of-the-art on DAVIS dataset and the robustness of the model to noisy labels is demonstrated.
Unsupervised Multiple Granularities Attention-Attribute Learning for Person Re-Identification

Person re-identification (Re-id) aims at matching images of the same person across disjoint camera views. The most challenging aspect lies to design robust features and similarity models that reduce the impact of appearance variations. To address this problem, we develop a joint learning deep un-supervised model that aggregates the representation power of different level features for preserving the invariant knowledge of the same person in different domains. Specifically, we formulate a novel unified Re-id architecture called Unsupervised Joint Attention-Attribute Network (UJ-AAN). Our model is used for joint learning of attention selection and high-level semantic attributes to minimize the distance between different viewpoints of the same person by designing a bilinear feature aggregation module. Furthermore, we introduce attribute-related learning scheme to ensure the domain adaptability of UJ-AAN model in unlabeled domain. Extensive comparative evaluations demonstrate the advantages of the proposed UJ-AAN model over a wide range of state-of-the-art Re-id methods on two auto-detected Re-id benchmarks DukeMTMC-ReID and Market-1501.
Enhanced family tree

Enhanced Family Tree reimagines the possibilities of family trees with an evolving series of exhibits. The authors' works combine genealogical data, visualization, 3D technologies and interactivity to explore and display ancient genealogical relationships. Their new approach may reveal questionable relationships in genealogical records. Moreover, the authors' use of an organic metaphor of a "tree" can be further extended to increase public understanding and engagement. The audience's questions arising from this project show increased curiosity and nuanced questioning about their own family origins and development.
Processing Big Data Across Infrastructures

For a range of major scientific computing challenges that span fundamental and applied science, the deployment of Big Data Applications on a large-scale system, such as an internal or external cloud, a cluster or even distributed public resources ("crowd computing"), needs to be offered with guarantees of predictable performance and utilization cost. Currently, however, this is not possible, because scientific communities lack the technology, both at the level of modelling and analytics, which identifies the key characteristics of BDAs and their impact on performance. There is also little data or simulations available that address the role of the system operation and infrastructure in defining overall performance. Our vision is to fill this gap by producing a deeper understanding of how to optimize the deployment of Big Data Applications on hybrid large-scale infrastructures. Our objective is the optimal deployment of BDAs that run on systems operating on large infrastructures, in order to achieve optimal performance, while taking into account running costs. We describe a methodology to achieve this vision. The methodology starts with the modeling and profiling of applications, as well as with the exploration of alternative systems for their execution, which are hybridization's of cloud, cluster and crowd. It continues with the employment of predictions to create schemes for performance optimization with respect to cost limitations for system utilization. The schemes can accommodate execution by adapting, i.e. extend or change, the system.
Multi-dimensional Stable Matching Problems in Abstract Argumentation

We show how different multi-dimensional extensions of the stable matching (or marriage) problem can be represented by Abstract Argumentation frameworks: the set of stable extensions exactly corresponds to the set of solutions in the original problem. We show how to allow incomplete preference lists and ties in the same problems, and consequently frameworks. All the proposed problems are NP-Complete or NP-Hard: efficient stable-semantics solvers can help their solutions, and in general cross-fertilisation can benefit both the fields.
Wordoids: Boid Based Personalized Word Clustering System in Dark Side Ternary Stars

Personalized systems are required in many domains. However, gathering training data for personalization from individuals, as is necessary with deep learning, is a difficult and time-consuming task. With our proposed method, less or no training data is required to adapt to individuals' preferences, even when they shift over time. We introduce a potential field based method “Dark Side Ternary Stars” which has three components, GAGPL, Wordoids, and EGO. In this paper, we focus on two of them, ”Wordoids”, which adopt extends Boids algorithms to perform individualized classification of keywords by topic and improved our previous work ”GAGPL”, which calculates the individualized semantic orientation of sentences by using learned words per topic. As experimental results, we applied this method to news articles about Japanese professional baseball and we show that our method can obtain individualized semantic orientations and summaries of the article per individual.
FFANN Weight Initialization: A New Method

In this paper a new weight initialization scheme is proposed. The proposed scheme, distributes the biases / thresholds at a equal intervals in an interval ( - λ, λ); while the weights are distributed uniformly in the same interval. The value of the λ is chosen such that the expected value of the net inputs is 0 while the variance is 1. On a set of 10 tasks (5 function approximation and 5 real life benchmark regression tasks), the proposed weight initialization routine was compared to three existing weight initialization routine. From the results obtained we may infer that the proposed weight initialization is almost always better than these three existing routines and is never worse on the basis of generalization performance (result over the test data set).