Reviewerly/exHarmony huggingface dataset:
Current Advances in Neural Networks

This article reviews current advances and developments in neural networks. This requires recalling some of the earlier work in the field. We emphasize Bayesian approaches and their benefits compared to more standard maximum likelihood treatments. Several representative experiments using varied modern neural architectures are presented.
Another Look at Extraction and Randomization of Groth’s zk-SNARK

Due to the simplicity and performance of zk-SNARKs they are widely used in real-world cryptographic protocols, including blockchain and smart contract systems. Simulation Extractability (SE) is a necessary security property for a NIZK argument to achieve Universal Composability (UC), a common requirement for such protocols. Most of the works that investigate SE focus on its strong variant which implies proof non-malleability. In this work we investigate a relaxed weaker notion, that allows proof randomization, while guaranteeing statement non-malleability, which we argue to be a more natural security property. First, we show that it is already achievable by Groth16, arguably the most efficient and widely deployed SNARK nowadays. Second, we show that because of this, Groth16 can be efficiently transformed into a black-box weakly SE NIZK, which is sufficient for UC protocols. To support the second claim, we present and compare two practical constructions, both of which strike different performance tradeoffs:
SLA Violation Detection and Compensation in Cloud Environment using Blockchain

The increased use of cloud computing technology and its wide adoption in the software industry has led to almost all modern consumer services being heavily dependent on the performance of the computing resources provided by the Cloud Service Provider (CSP). SLA which is agreed upon by both users and the CSP, plays a key role in keeping the performance of provisioned resources above a certain threshold. This SLA is enforced by CSP who uses the logs generated by the underlying machinery to monitor the performance of the machines and compensate the user. Similarly, a cautious customer might maintain its own monitoring mechanism to continuously evaluate the performance of the virtualized resources allocated to them. This creates distrust between the CSP and the client as both of them might not acknowledge the credibility of the other's monitoring mechanism and come at disagreement with each other on this. In this work, we propose a framework for SLA-violation detection and compensation using Hyperledger Fabric blockchain. Furthermore, we carry out the scalability and efficiency analysis Hyperledger Fabric network for the proposed system.
Fast High-Quality Tabletop Rearrangement in Bounded Workspace

In this paper, we examine the problem of rearranging many objects on a tabletop in a cluttered setting using overhand grasps. Efficient solutions for the problem, which capture a common task that we solve on a daily basis, are essential in enabling truly intelligent robotic manipulation. In a given instance, objects may need to be placed at temporary positions (“buffers”) to complete the rearrangement, but allocating these buffer locations can be highly challenging in a cluttered environment. To tackle the challenge, a two-step baseline planner is first developed, which generates a primitive plan based on inherent combinatorial constraints induced by start and goal poses of the objects and then selects buffer locations assisted by the primitive plan. We then employ the “lazy” planner in a tree search framework which is further sped up by adapting a novel preprocessing routine. Simulation experiments show our methods can quickly generate high-quality solutions and are more robust in solving large-scale instances than existing state-of-the-art approaches. source: github.com/arc-l/TRLB
An End-to-End Bidirectional Authentication System for Pallet Pooling Management Through Blockchain Internet of Things (BIoT)

Pallet pooling is regarded as a sustainable and cost-effective measure for the industry, but challenging to advocate due to weak data and pallet authentication.In order to establish trust between end-users and pallet pooling services, we propose an end-to-end, bidirectional authentication system for transmitted data and pallets based on blockchain and Internet-of-things (IoT) technologies. In addition, secure data authentication fosters the pallet authenticity in the whole supply chain network, which is achieved by considering the tag, location, and object-specific features. To evaluate the object-specific features, the scale invariant feature transform (SIFT) approach is adopted to match key-points and descriptors between two pallet images. According to the case study, it is found that the proposed system provides a low bandwidth blocking rate and a high probability of restoring complete data payloads. Consequently, positive influences on end-user satisfaction, quality of service, operational errors, and pallet traceability are achieved through the deployment of the proposed system.
PDC: Piecewise Depth Completion utilizing Superpixels

Depth completion from sparse LiDAR and high-resolution RGB data is one of the foundations for autonomous driving techniques. Current approaches often rely on CNN-based methods with several known drawbacks: flying pixel at depth discontinuities, overfitting to both a given data set as well as error metric, and many more. Thus, we propose our novel Piecewise Depth Completion (PDC), which works completely without deep learning. PDC segments the RGB image into superpixels corresponding the regions with similar depth value. Superpixels corresponding to same objects are gathered using a cost map. At the end, we receive detailed depth images with state of the art accuracy. In our evaluation, we can show both the influence of the individual proposed processing steps and the overall performance of our method on the challenging KITTI dataset.
Privacy-Preserving Communication-Efficient Federated Multi-Armed Bandits

Communication bottleneck and data privacy are two critical concerns in federated multi-armed bandit (MAB) problems, such as situations in decision-making and recommendations of connected vehicles via wireless. In this paper, we design the privacy-preserving communication-efficient algorithm in such problems and study the interactions among privacy, communication and learning performance in terms of the regret. To be specific, we design privacy-preserving learning algorithms and communication protocols and derive the learning regret when networked private agents are performing online bandit learning in a master-worker, a decentralized and a hybrid structure. Our bandit learning algorithms are based on epoch-wise sub-optimal arm eliminations at each agent and agents exchange learning knowledge with the server/each other at the end of each epoch. Furthermore, we adopt the differential privacy (DP) approach to protect the data privacy at each agent when exchanging information; and we curtail communication costs by making less frequent communications with fewer agents participation. By analyzing the regret of our proposed algorithmic framework in the master-worker, decentralized and hybrid structures, we theoretically show trade-offs between regret and communication costs/privacy. Finally, we empirically show these trade-offs which are consistent with our theoretical analysis.
Robustface Recognition by Fusing Fuzzy Type 2 Induced Multiple Facial Fused Image

This paper projects a new decision making model for face recognition from the original image fused with their true and partial diagonal images by integrating the type-2 fuzzy set based approach to mitigate the factors that pretend the face recognition accuracy. The G2DFLD based feature vectors corresponding to a test image are given input to neural network based classifier trained with the feature vectors of the fused images to generate the merit weights with respect to different classes (subjects) under consideration. A new scheme has been introduced in the present approach to generate a score by employing a fuzzy type-2 set based treatment. These scores with respect to each of the classes under consideration are rendered from the feature vectors of the test image and those of the diagonally fused training samples. For each class, the score is fused weighted by the corresponding merit weights to generate the concluding score. These class-wise concluding scores are deliberated in recognizing the test face image. Faces from the well-known databases (gallery) with varied pose, illumination and occlusion are used to evaluate the performance of the model. It has been found that our model exhibits more accurate classification performance than existing similar kind of image level fusion method.
A novel bio-inspired texture descriptor based on biodiversity and taxonomic measures

Texture can be defined as the change of image intensity that forms repetitive patterns resulting from the physical properties of an object's roughness or differences in a reflection on the surface. Considering that texture forms a system of patterns in a non-deterministic way, biodiversity concepts can help its characterization from an image. This paper proposes a novel approach to quantify such a complex system of diverse patterns through species diversity, richness, and taxonomic distinctiveness. The proposed approach considers each image channel as a species ecosystem and computes species diversity and richness as well as taxonomic measures to describe the texture. Furthermore, the proposed approach takes advantage of ecological patterns' invariance characteristics to build a permutation, rotation, and translation invariant descriptor. Experimental results on three datasets of natural texture images and two datasets of histopathological images have shown that the proposed texture descriptor has advantages over several texture descriptors and deep methods.
Joint usage of global and local attentions in hourglass network for human pose estimation

Human pose estimation is a challenging research task in the field of computer vision. The current mainstream work has made great progress in pose estimation, but these works still do not pay enough attention to the negative impact of background on human pose estimation. In this work, we propose a human pose estimation framework characterized by the joint usage of both global and local attention module in an hourglass backbone network. The global attention module aims to reduces the negative impact of background. The local attention module is designed to help refine each joint. We tested our method on two benchmark datasets for human pose estimation, and the experimental results show that the proposed model is superior to current mainstream algorithms.
Behind the Scenes

Few-Shot Event Classification (FSEC) aims at developing a model for event prediction, which can generalize to new event types with a limited number of annotated data. Existing FSEC studies have achieved high accuracy on different benchmarks. However, we find they suffer from trigger biases that signify the statistical homogeneity between some trigger words and target event types, which we summarize as trigger overlapping and trigger separability. The biases can result in context-bypassing problem, i.e., correct classifications can be gained by looking at only the trigger words while ignoring the entire context. Therefore, existing models can be weak in generalizing to unseen data in real scenarios. To further uncover the trigger biases and assess the generalization ability of the models, we propose two new sampling methods, Trigger-Uniform Sampling (TUS) and COnfusion Sampling (COS), for the meta tasks construction during evaluation. Besides, to cope with the context-bypassing problem in FSEC models, we introduce adversarial training and trigger reconstruction techniques. Experiments show these techniques help not only improve the performance, but also enhance the generalization ability of models.
Device-free hand gesture recognition exploiting Machine Learning applied to RFID

Gesture recognition is a recently emerged technology that aims to redesign the interaction between humans and machines. Existing solutions usually recognize the gestures based on camera vision, wearable sensors or specialized signals (e.g., WiFi, acoustic and visible light), but they cope with the drawbacks of high energy consumption or reduced SNR with respect to the surrounding environment, which prevents them from accurately sensing finger movements. In this work, we propose a device-free gesture recognition system that use machine learning techniques on RSSI and phase values from the backscattered signals of an array of tags placed on a plastic plate to recognize different hand gestures. The system has been tested with RFC and SVM algorithms, providing excellent results both in case of stationary hand that in case of moving hand.
Knowledge Graph Representation Learning as Groupoid

Knowledge graph (KG) representation learning which aims to encode entities and relations into low-dimensional spaces, has been widely used in KG completion and link prediction. Although existing KG representation learning models have shown promising performance, the theoretical mechanism behind existing models is much less well-understood. It is challenging to accurately portray the internal connections between models and build a competitive model systematically. To overcome this problem, a unified KG representation learning framework, called GrpKG, is proposed in this paper to model the KG representation learning from a generic groupoid perspective. We discover that many existing models are essentially the same in the sense of groupoid isomorphism and further provide transformation methods between different models. Moreover, we explore the applications of GrpKG in the model classification as well as other processes. The experiments on several benchmark data sets validate the effectiveness and superiority of our framework by comparing two proposed models (GrpQ8 and GrpM2) with the state-of-the-art models.
Dynamic Training Data Dropout for Robust Deep Face Recognition

Learning with noise is a practically challenging problem in deep face recognition. Despite the success of large margin softmax loss functions, these methods are designed for clean face databases. Considering the inevitable noise in the large scale databases, we first analyze the performance of noise in the training databases. For noise-robust deep face recognition, we propose a dynamic training data dropout (DTDD) method to dynamically filter the noise in the training database and gradually form a stable refined database for model learning. Specifically, we leverage the information provided by the model predictions of accumulated training epochs, which can distinguish regular samples and noise effectively and accurately. The proposed DTDD method is easy and stable for implementation, and can be combined with existing state-of-the-art loss functions and network architectures. Extensive experiments on CASIA-WebFace, VGGFace2, and MS-Celeb-1 M databases empirically demonstrate that our proposed method can robustly train deep face recognition models in the presence of label noise and low quality images.
A Dual-stream Framework for 3D Mask Face Presentation Attack Detection

Face presentation attack detection (PAD) plays a vital role in face recognition systems. Many previous face antispoofing methods mainly focus on the 2D face representation attacks, which however, suffer from great performance degradation when facing high-fidelity 3D mask attacks. To address this issue, we propose a novel dual-stream framework consisting of the vanilla convolution stream and the central difference convolution stream. These two streams complement each other and learn more comprehensive features for 3D mask attacks detection. Moreover, we extend 3D PAD to a multi-classification task that contains real face, plaster attack and transparent attack, and utilize various data augmentations and label smoothing techniques to improve the generalizability on unseen attacks. The proposed method achieved the second place in the Chalearn 3D High-Fidelity Mask Face Presentation Attack Detection Challenge@ICCV2021 with a score of 3.15 (ACER).
Generating Adversarial Examples With Distance Constrained Adversarial Imitation Networks

Recent studies have shown that neural networks are vulnerable to adversarial examples that are designed by adding small perturbations to clean examples in order to trick the classifier to misclassify. Various approaches based on optimization have been proposed for generating adversarial examples with minimal perturbation. Model training based methods such as Adversarial Transformation Network (ATN) provide a fundamentally new way to directly transform an input into an adversarial example, which promises fast generation of adversarial examples. However, the adversarial examples may have suboptimal quality with significantly large perturbations or low attack success rate at small perturbations. In this article, we propose a distance constrained <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> dversarial <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b> mitation <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> etwork ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AIN</b> ), which enhances ATN and is capable of generating both targeted and untargeted examples with an explicit distance constraint. AIN can not only generate large scale adversarial examples efficiently as achieved in ATN, but also imitate the behavior of state-of-the-art optimization-based methods, hence achieving improved quality. Extensive experiments show that AIN significantly outperforms ATN and other Generative Adversarial Networks (GAN) based methods in the quality of generated adversarial examples, and is much more efficient than optimization based methods while achieving comparable quality.
Low-resource extraction with knowledge-aware pairwise prototype learning

Knowledge Extraction (KE) aims at extracting structured information from raw texts, such as relation extraction and event extraction. One of the major issues for KE is the low-resource problem due to deficient samples. Previous work addresses the low-resource issue mostly via data-driven methods, such as transfer learning, while neglecting correlation knowledge among classes. For example, inherent correlation of entailment between the relation pair and causality between the event pair can also be utilized for low-resource KE. Consequently, we propose to leverage correlation knowledge via pairwise prototype learning on the hypersphere with a novel framework called Knowledge-aware Hyperspherical Prototype Network (K-HPN). K-HPN is able to recognize inherent correlation among classes, where each class is represented as a prototype on the hypersphere. The experimental results demonstrate that K-HPN outperforms previous methods of KE, particularly with low-resource training data regimes.
A Comprehensive Guideline for Bengali Sentiment Annotation

Sentiment Analysis (SA) is a Natural Language Processing (NLP) and an Information Extraction (IE) task that primarily aims to obtain the writer’s feelings expressed in positive or negative by analyzing a large number of documents. SA is also widely studied in the fields of data mining, web mining, text mining, and information retrieval. The fundamental task in sentiment analysis is to classify the polarity of a given content as Positive, Negative, or Neutral . Although extensive research has been conducted in this area of computational linguistics, most of the research work has been carried out in the context of English language. However, Bengali sentiment expression has varying degree of sentiment labels, which can be plausibly distinct from English language. Therefore, sentiment assessment of Bengali language is undeniably important to be developed and executed properly. In sentiment analysis, the prediction potential of an automatic modeling is completely dependent on the quality of dataset annotation. Bengali sentiment annotation is a challenging task due to diversified structures (syntax) of the language and its different degrees of innate sentiments (i.e., weakly and strongly positive/negative sentiments). Thus, in this article, we propose a novel and precise guideline for the researchers, linguistic experts, and referees to annotate Bengali sentences immaculately with a view to building effective datasets for automatic sentiment prediction efficiently.
Analyzing and Mitigating JPEG Compression Defects in Deep Learning

With the proliferation of deep learning methods, many computer vision problems which were considered academic are now viable in the consumer setting. One drawback of consumer applications is lossy compression, which is necessary from an engineering standpoint to efficiently and cheaply store and transmit user images. Despite this, there has been little study of the effect of compression on deep neural networks and benchmark datasets are often losslessly compressed or compressed at high quality. Here we present a unified study of the effects of JPEG compression on a range of common tasks and datasets. We show that there is a significant penalty on common performance metrics for high compression. We test several methods for mitigating this penalty, including a novel method based on artifact correction which requires no labels to train.
BAASH

Distributed resiliency becomes paramount to alleviate the growing costs of data movement and I/Os while preserving the data accuracy in HPC systems. This paper proposes to adopt blockchain-like decentralized protocols to achieve such distributed resiliency. The key challenge for such an adoption lies in the mismatch between blockchain's targeting systems (e.g., shared-nothing, loosely-coupled, TCP/IP stack) and HPC's unique design on storage subsystems, resource allocation, and programming models. We present BAASH, Blockchain-As-A-Service for HPC, deployable in a plug-n-play fashion. BAASH bridges the HPC-blockchain gap with two key components: (i) Lightweight consensus protocols for the HPC's shared-storage architecture, (ii) A new fault-tolerant mechanism compensating for the MPI to guarantee the distributed resiliency. We have implemented a prototype system and evaluated it with more than two million transactions on a 500-core HPC cluster. Results show that the prototype of the proposed techniques significantly outperforms vanilla blockchain systems and exhibits strong reliability with MPI.
DR-TANet: Dynamic Receptive Temporal Attention Network for Street Scene Change Detection

Street scene change detection continues to capture researchers' interests in the computer vision community. It aims to identify the changed regions of the paired street-view images captured at different times. The state-of-the-art network based on the encoder-decoder architecture leverages the feature maps at the corresponding level between two channels to gain sufficient information of changes. Still, the efficiency of feature extraction, feature correlation calculation, even the whole network requires further improvement. This paper proposes the temporal attention and explores the impact of the dependency-scope size of temporal attention on the performance of change detection. In addition, based on the Temporal Attention Module (TAM), we introduce a more efficient and light-weight version - Dynamic Receptive Temporal Attention Module (DRTAM) and propose the Concurrent Horizontal and Vertical Attention (CHVA) to improve the accuracy of the network on specific challenging entities. On street scene datasets ‘GSV’, ‘TSUNAMI’ and ‘VL-CMU-CD’, our approach gains excellent performance, establishing new state-of-the-art scores without bells and whistles, while maintaining high efficiency applicable in autonomous vehicles.