Reviewerly/exHarmony huggingface dataset:
History of Soil Studies in Nevada

Nevada has a rich and long history of soil investigations, which began with the Soil Survey of the Fallon Area in 1909 by the Bureau of Soils and continued with the mapping of all or portions of all 16 counties in Nevada under the leadership of the Soil Conservation Service and later the Natural Resources Conservation Service. This chapter traces the history of soil surveys, briefly describes the nature of soil research, and highlights the Orovada soil series, Nevada’s official state soil.
Deep Video Prediction Through Sparse Motion Regularization

This paper introduces data-dependent sparse motion regularization for dense flow-based video prediction. To achieve video prediction (a form of extrapolation from past frames), the dense flow-based model estimates a motion vector for every pixel in a target frame for backward warping. Due to the sheer amount of motion vectors to be estimated, the model tends to be complex, thereby calling for proper regularization to avoid over-fitting. Most flow-based models adopt smoothness regularization. However, the smoothness requirement is detrimental to preserving the discontinuity of the motion field, which often appears in videos with distinct object motion. To address this issue, our sparse motion regularization discovers distinct sparse motion via weighted K-means clustering and regularizes the model based on minimizing clustering errors in the predicted motion field. When incorporated in an end-to-end trainable deep video prediction model, our scheme outperforms smoothness regularization, achieving superiority over direct generation-based video prediction on UCF-101 and Common Intermediate Format (CIF) datasets.
Evaluating Adaptation Performance of Hierarchical Deep Reinforcement Learning

Deep Reinforcement Learning has been used to exploit specific environments, but has difficulty transferring learned policies to new situations. This issue poses a problem for practical applications of Reinforcement Learning, as real-world scenarios may introduce unexpected differences that drastically reduce policy performance. We propose the use of differentiated sub-policies governed by a hierarchical controller to support adaptation in such scenarios. We also introduce a confidence- based training process for the hierarchical controller which improves training stability and convergence times. We evaluate these methods in a new Capture the Flag environment designed to explore adaptation in autonomous multi-agent settings.
Fast Multi-Stage Motion-Compensated Approach for HDR

Multi-frame High Dynamic Range (HDR) imaging is a technique to improve details while capturing scenes with large variations in luminosity by blending together a stack of images captured using varying exposure values. Local motion between the stack of images often result in ghost artifacts or reduced dynamic range especially in over-exposed or underexposed regions. This paper proposes a method to mitigate these problems using a novel multi-stage algorithm. At each stage of the proposed method, intermediate HDR images are generated from consecutive exposure pairs using a computational unit termed as Basis HDR. The outputs produced by each Basis HDR unit is ensured to be free of ghosts using a novel algorithm to estimate local motion in the scene. Qualitative and quantitative comparison studies show that the proposed method generates images with improved dynamic range, minimal ghosts and a higher MEF-SSIM score as compared to existing state-of-the-art. The proposed method can process a 3-frame HDR on a smartphone in under a second thus making it a fast and reliable algorithm for smartphones.
Rethinking Modal-oriented Label Correlations for Multi-modal Multi-label Learning

Multi-modal multi-label learning provides a fundamental framework for complex objects, which can be represented with multiple modalities and annotated with multiple labels simultaneously. Different modalities can usually provide complementary information, which may lead to improved performance. What's more, exploiting label correlations is crucially important to multi-label learning. However, most existing multi-label learning approaches do not sufficiently consider the complementary information among different modalities. In this paper, we propose a novel end-to-end deep learning framework named Rethinking Modal-oriented Label Correlations (RMLC), which sequentially polish the label prediction with each individual modality. In order to explicitly account for the correlated prediction of multiple labels, RMLC leverages an efficient sequential modal-based exploration to rethink label correlations. The final prediction of each label involves the collaboration between modal-specific prediction and the prediction of other labels based on cross-modal interaction. Comprehensive experiments on benchmark datasets validate the effectiveness and competitiveness of the proposed RMLC approach.
Channel–Spatial fusion aware net for accurate and fast object Detection

A major challenge of object detection is that accurate detector is limited by speed due to enormous network, while the lightweight detector can reach real-time but its weak representation ability leads to the expense of accuracy. To overcome the issue, we propose a channel-spatial fusion awareness module (CSFA) to improve the accuracy by enhancing the feature representation of network at the negligible cost of complexity. Given a feature map, our method exploits two parts sequentially, channel awareness and spatial awareness, to reconstruct feature map without deepening the network. Because of the property of CSFA for easy integrating into any layer of CNN architectures, we assemble this module into ResNet-18 and DLA-34 in CenterNet to form a CSFA detector. Results consistently show that CSFA-Net runs in a fairly fast speed, and achieves state-of-the-art, i.e., mAP of 81.12% on VOC2007 and AP of 43.2% on COCO.
Possibility for Identifying/Extracting Rock Outcrop Using Landsat 8 OLI/TIRS - Case Study of Thua Thien Hue Province

This paper approaches the ratio image method to extract the exposed rock information from the Landsat 8 OLI/TIRS satellite image (2019) according to the object orientation classification. Combining automatic interpretation and interpretation through threshold of image index values according to interpretation key the object orientation classification to separate soil object containing exposed rock and no exposed rock in Thua Thien Hue province. Using the Topsoil Grain Size Index (TGSI), the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI) and other related analytical problems have identified 40 exposed rock storage areas in the study area. The results have been verified in the field and the Kappa index is 85.10%.
Attention-Based Asymmetric Fusion Network for Saliency Prediction in 3D Images

Nowadays the visual saliency prediction has become a fundamental problem in 3D imaging area. In this paper, we proposed a saliency prediction model from the perspective of addressing three aspects of challenges. First, to adequately extract features of RGB and depth information, we designed an asymmetric encoder structure on the base of U-shape architecture. Second, to prevent the semantic information between salient objects and corresponding contexts from diluting in cross-modal distillation stream, we devised a global guidance module to capture high-level feature maps and deliver them into feature maps in shallower layers. Third, to locate and emphasize salient objects, we introduced a channel-wise attention model. Finally we built the refinement stream with integrated fusion strategy, gradually refining the saliency maps from coarse to fine-grained. Experiments on two widely-used datasets demonstrate the effectiveness of the proposed architecture, and the results show that our model outperforms six selective state-of-the-art models.
Linear-Gaussian State-Space Models

SummaryAnother special case where the forward and backward recursions developed in Chap. 5 may be implemented exactly is when the considered state-space model is linear and Gaussian. The corresponding algorithms are commonly known as the Kalman filter and the Kalman smoother. The recursions follow immediately from the generic formulae of Chap. 5, but in this setting they become linear algebra calculations. Various alternative, mathematically equivalently but computationally different, recursions can be obtained. This chapter provides insights into these possibilities and touches upon the practical implementation of such recursions.
The Trusted and Decentralized Network Resource Management

Network resource management relies on trusted infrastructures. However, current trusted infrastructures have centralized or hierarchical structures where root nodes often have privileges over subtrees. Hacked or malicious roots may affect subtrees by modifying IP ownership, routing and so on. Therefore, a trusted and decentralized network resource management becomes crucial. The emergence of blockchain such as Ethereum becomes a leading candidate for achieving trusted and decentralized network resource management. In this paper, based on Ethereum, we design a trusted authentication scheme that includes voting policy, endorsement, credibility model and rebinding mechanism to ensure the credibility of entities. Our scheme can allocate, delegate and transfer network resources in a trusted and decentralized style. The experimental results verify the feasibility and security of our scheme. When an entity is hacked or malicious, its endorsement authentication is revoked and the entity does not have the right to manage the network resources. In addition, the overhead increased by the trusted authentication scheme is within a reasonable range.
Utility-Enhancing Flexible Mechanisms for Differential Privacy

Differential privacy is a mathematical technique that provides strong theoretical privacy guarantees by ensuring the statistical indistinguishability of individuals in a dataset. It has become the de facto framework for providing privacy-preserving data analysis over statistical datasets. Differential privacy has garnered significant attention from researchers and privacy experts due to its strong privacy guarantees. However, the accuracy loss caused by the noise added has been an issue. First, we propose a new noise adding mechanism that preserves \((\epsilon ,\delta )\)-differential privacy. The distribution pertaining to this mechanism can be observed as a generalized truncated Laplacian distribution. We show that the proposed mechanism adds optimal noise in a global context, conditional upon technical lemmas. We also show that the generalized truncated Laplacian mechanism performs better than the optimal Gaussian mechanism. In addition, we also propose an \((\epsilon )\)-differentially private mechanism to improve the utility of differential privacy by fusing multiple Laplace distributions. We also derive the closed-form expressions for absolute expectation and variance of noise for the proposed mechanisms. Finally, we empirically evaluate the performance of the proposed mechanisms and show an increase in all utility measures considered, while preserving privacy.
Spatially-Aware Displays for Computer Assisted Interventions

We present a novel display and visual interaction paradigm, which aims at reducing the complexity of understanding the spatial transformations between the surgeon’s viewpoint, the patient, the pre- or intra-operative 2D and 3D data, and surgical tools during computer assisted interventions. To the best of our knowledge, this is the first work in which the traditional interventional display, for example in surgical navigation systems, is registered both to the patient and to the surgeon’s view. The closest concept is that of traditional Augmented Reality windows in which a semitransparent or video see-through display is positioned between the surgeon and the patient. In such cases, the system was providing an AR view into the patient. In the new concept introduced here, the surgeon keeps his/her own direct view to the patient without any need for additional display or direct view augmentation, but the monitor used in the operating room is now registered to the patient and surgeon’s viewpoint. The display could act as fixed viewing frustum or as a mirror frustum relative to the surgeon’s view. This allows the physicians to effortlessly relate their view of tools and patient to the virtual representation of the patient data. In this paper, the first realization and implementation of such a concept is presented and three clinical partners have tested the system and their first feedback is discussed in detail. They unanimously believe that this concept opens the path for facilitating interactive exploration of data and more intuitive navigation guidance in computer assisted interventions.
MeXT-SE: A Design Tool to Transparently Generate Secure MPSoC

Hardware accelerators are increasingly employed in conjunction with general-purpose processors to meet stringent performance constraints. In these heterogeneous systems, security has become a prime concern. In this article, we present a design approach to generate platform-independent secure multiprocessor systems-on-chip (MPSoC) from a high-level abstraction. The aim of this article is to simplify the implementation of MPSoC, while ensuring security. The proposed design flow starts with a set of abstract and concrete specifications of a system, provided by the user, and ends up generating a generic description of the appropriate hardware design by setting up the communication structure of different components. The resulting abstract architecture is further processed using the vendor tool-chain to generate the target platform's configuration. To enforce security, we proposed a distributed isolation framework for multilevel security, resource assess control including access to and from hardware accelerators. From a user specification, the security layer is transparently generated and security rules and requirements transparently enforced at run-time. Experimentation results show that our proposed tool can generate MPSoC designs capable of providing secure hardware execution with negligible execution overhead.
Comparative Analysis of Majority Language Influence on North Sámi Prosody Using WaveNet-Based modeling

The Finnmark North Sámi is a variety of North Sámi language, an indigenous, endangered minority language spoken in the northernmost parts of Norway and Finland. The speakers of this language are bilingual, and regularly speak the majority language (Finnish or Norwegian) as well as their own North Sámi variety. In this paper we investigate possible influences of these majority languages on prosodic characteristics of Finnmark North Sámi, and associate them with prosodic patterns prevalent in the majority languages. We present a novel methodology that: (a) automatically finds the portions of speech (words) where the prosodic differences based on majority languages are most robustly manifested; and (b) analyzes the nature of these differences in terms of intonational patterns. For the first step, we trained convolutional WaveNet speech synthesis models on North Sámi speech material, modified to contain purely prosodic information, and used conditioning embeddings to find words with the greatest differences between the varieties. The subsequent exploratory analysis suggests that the differences in intonational patterns between the two Finnmark North Sámi varieties are not manifested uniformly across word types (based on part-of-speech category). Instead, we argue that the differences reflect phrase-level prosodic characteristics of the majority languages.
KCNN and BiLSTM-Mhead Attention emotion analysis model of Micro-blogs Based on Multi-feature fusion

For important local features were ignored by the convolutional neural network in Micro-blogs emotion analysis, and contextual information could not be fully learned by the recurrent neural network, a mixed model of KCNNBiLSTM-MA based on multi-feature fusion was proposed. The K convolutional neural network was used to obtain the important Topk features of the partial features of sentences. Then the sentence sequence information and context connection were got from different directions through bidirectional long short-term memory network, and the Multi-head Attention is added to its foundation thus giving more attention to the important parts. Finally, the different features obtained are fused for classification. The experiment proves that the KCNNBiLSTM-MA model has better performance than other emotion analysis models.
Optimizing Accelerator on FPGA for Deep Convolutional Neural Networks

With the development of deep learning, the traditional neural networks architecture has been gradually met the bottleneck of the performance. Convolutional neural networks (CNNs) has been widely concerned because of its high precision advantage. However, CNNs are usually computationally large. And in addition to the widely used GPUs, but which has higher energy. And FPGA is gradually used to achieve CNNs acceleration due to its high performance, high concurrency, fast development cycle and re-configurability characteristics. Although previous works have made considerable progress, few researches have been used to address data dependence in data structure of CNN. Data dependence greatly affects the performance of accelerators. In this paper, we present a way to greatly improve the read efficiency of the accelerated hardware by reconstructing the original digital set and using the preload mechanism. This effectively reduces the problem of data dependence. In this way, pipeline technology can speed up the CNN computing process more effectively. We implemented the accelerator architecture on the XC 7z045 board, and the proposed accelerator has a clear advantage over previous studies to improve the efficiency and processing speed of CNNs.
A Method of Identification of Potential Earthquake Source Zones

We propose a machine learning method for mapping potential earthquake source zones (ESZ). We use two hypotheses: (1) the recurrence of strong earthquakes and (2) the dependence of sources of strong earthquakes on the properties of the geological environment. To solve this problem, we know the catalog of earthquakes and a set of spatial fields of geological and geophysical features. We tested the method of identification of the potential ESZ with \(m\ge 6.0\) for the Caucasus region. The map of the potential earthquake source zones and a geological interpretation of the decision rule are presented.
The impact of scaling the production of a new interface for musical expression on its design

The innovation in the new musical interfaces is largely driven by the ground up endeavors that introduce a level of redundancy. Inspired by the successes of iPhone and other industry innovations that were driven by iteration, consolidation, and scalability, we present a new interface for musical expression and discuss key elements of its implementation and integration into an existing and established laptop ensemble. In 2019, the Linux Laptop Orchestra of Virginia Tech (L2Ork) introduced the L2Orkmote, a custom reverse engineered variant of the Wii Remote and Nunchuk controller that reorganizes sensors and buttons using an additively manufactured housing. The goal was to equip each orchestra member with two of the newly designed L2Orkmotes, which resulted to the production of 40 L2Orkmotes. This large-scale production mandated software improvements, including the development of a robust API that can support such a large number of concurrently connected Bluetooth devices. Considering that new musical interfaces for musical expression (NIMEs) are rarely designed to scale, we report on the design. Additionally, we share the large-scale real-world deployment concurrently utilizing 28 L2Orkmotes, the supporting usability evaluation, and discuss the impact of scaling NIME production on its design.
Biased RSA Private Keys: Origin Attribution of GCD-Factorable Keys

In 2016, Švenda et al. (USENIX 2016, The Million-key Question) reported that the implementation choices in cryptographic libraries allow for qualified guessing about the origin of public RSA keys. We extend the technique to two new scenarios when not only public but also private keys are available for the origin attribution – analysis of a source of GCD-factorable keys in IPv4-wide TLS scans and forensic investigation of an unknown source. We learn several representatives of the bias from the private keys to train a model on more than 150 million keys collected from 70 cryptographic libraries, hardware security modules and cryptographic smartcards. Our model not only doubles the number of distinguishable groups of libraries (compared to public keys from Švenda et al.) but also improves more than twice in accuracy w.r.t. random guessing when a single key is classified. For a forensic scenario where at least 10 keys from the same source are available, the correct origin library is correctly identified with average accuracy of 89% compared to 4% accuracy of a random guess. The technique was also used to identify libraries producing GCD-factorable TLS keys, showing that only three groups are the probable suspects.
MRobust: A Method for Robustness against Adversarial Attacks on Deep Neural Networks

We present a novel black-box adversarial training algorithm to defend against state-of-the-art attack methods in machine learning. In order to search for an adversarial attack, the algorithm analyses small regions around the input that are likely to make significant contributions for the generation of adversarial samples. Unlike some of the literature in the area, the proposed method does not require access to the internal layers of the model and is therefore applicable to applications such as security. We report the experimental results obtained on models of different sizes built for the MNIST and CIFAR10 datasets. The results suggest that known attacks on the resulting models are less transferable than those models trained by state-of-the art attack algorithms.
Inside Beethoven!

On the occasion of the 250th anniversary celebrations of Ludwig van Beethoven in 2020, we developed the musical room installation "Inside Beethoven! The Audience Goes on Stage". It is a stage with virtual musicians. Visitors are invited to enter, take the position of the musicians and listen to each of them at close range The installation emulates the listening perspective of the musicians, thus, providing an uncommon experience to visitors who are mostly used to listen to music from recordings or from a concert audience's perspective. The musicians play Beethoven's septet op. 20 and its trio arrangement op. 38, two exclusive 14 channel music productions. Both versions are aligned and can be switched seamlessly. Digital music stands visualize the score. In this paper we trace the genesis and construction of the installation and point at some conceptual and technical challenges tackled.
A PGF-Mediated Social Media Approach for Cyberpolicing India Abusers in Telangana Academics

In this modern era IT industry plays a vital role and is facing a problem of the cybercrimes. Whenever cybercrimes are touched in an academic environment that also creates problem for the development of the society. Cybercrimes become a serious situation in the society and caused negative effects to the IT industry. An instance where mishandling a cybercrime by the academic concerned public authority in the case of JNTUHJAC ( http://www.jntuhjac.com/www.jntuhjac.com ), becomes slowdown in the IT development. We have introduced a People’s Governance Forum (PGF) through social networking site (SNS) usage that helps to provide social media intelligence (SMI) to the victims and remedial solution to the cybercrime committed by an online organization (JNTUHJAC.com). We present information related to the cybercrime giving awareness to the public through the social media.