Reviewerly/exHarmony huggingface dataset:
Efficient Online-friendly Two-Party ECDSA Signature

Two-party ECDSA signatures have received much attention due to their widespread deployment in cryptocurrencies. Depending on whether or not the message is required, we could divide two-party signing into two different phases, namely, offline and online. Ideally, the online phase should be made as lightweight as possible. At the same time, the cost of the offline phase should remain similar to that of a normal signature generation. However, the existing two-party protocols of ECDSA are not optimal: either their online phase requires decryption of a ciphertext, or their offline phase needs at least two executions of multiplicative-to-additive conversion which dominates the overall complexity. This paper proposes an online-friendly two-party ECDSA with a lightweight online phase and a single multiplicative-to-additive function in the offline phase. It is constructed by a novel design of a re-sharing of the secret key and a linear sharing of the nonce. Our scheme significantly improves previous protocols based on either oblivious transfer or homomorphic encryption. We implement our scheme and show that it outperforms prior online-friendly schemes (i.e., those have lightweight online cost) by a factor of roughly 2 to 9 in both communication and computation. Furthermore, our two-party scheme could be easily extended to the 2-out-of-n threshold ECDSA.
Finite Rate QLDPC-GKP Coding Scheme that Surpasses the CSS Hamming Bound

Quantum error correction has recently been shown to benefit greatly from specific physical encodings of the code qubits. In particular, several researchers have considered the individual code qubits being encoded with the continuous variable GottesmanKitaev-Preskill (GKP) code, and then imposed an outer discrete-variable code such as the surface code on these GKP qubits. Under such a concatenation scheme, the analog information from the inner GKP error correction improves the noise threshold of the outer code. However, the surface code has vanishing rate and demands a lot of resources with growing distance. In this work, we concatenate the GKP code with generic quantum low-density parity-check (QLDPC) codes and demonstrate a natural way to exploit the GKP analog information in iterative decoding algorithms. We first show the noise thresholds for two lifted product QLDPC code families, and then show the improvements of noise thresholds when the iterative decoder - a hardware-friendly min-sum algorithm (MSA) - utilizes the GKP analog information. We also show that, when the GKP analog information is combined with a sequential update schedule for MSA, the scheme surpasses the well-known CSS Hamming bound for these code families. Furthermore, we observe that the GKP analog information helps the iterative decoder in escaping harmful trapping sets in the Tanner graph of the QLDPC code, thereby eliminating or significantly lowering the error floor of the logical error rate curves. Finally, we discuss new fundamental and practical questions that arise from this work on channel capacity under GKP analog information, and on improving decoder design and analysis.
Complex Network-based features extraction in RGB-D human action recognition

Analysis of human behavior through visual information has been one of the active research areas in computer vision community during the last decade. Vision-based human action recognition (HAR) is a crucial part of human behavior analysis, which is also of great demand in a wide range of applications. HAR was initially performed via images from a conventional camera; however, depth sensors have recently embedded as an additional informative resource to cameras. In this paper, we have proposed a novel approach to largely improve the performance of human action recognition using Complex Network-based feature extraction from RGB-D information. Accordingly, the constructed complex network is employed for single-person action recognition from skeletal data consisting of 3D positions of body joints. The indirect features help the model cope with the majority of challenges in action recognition. In this paper, the meta-path concept in the complex network has been presented to lessen the unusual actions structure challenges. Further, it boosts recognition performance. The extensive experimental results on two widely adopted benchmark datasets, the MSR-Action Pairs, and MSR Daily Activity3D indicate the efficiency and validity of the method.
Edge supervision and multi-scale cost volume for stereo matching

Recently, methods based on Convolutional Neural Network have achieved huge progress in stereo matching. However, it is still difficult to find accurate matching points in inherently ill-posed regions (e.g., weak texture areas and around object edges), in which the accuracy of disparity estimate can be improved by the corresponding geometric constraints. To tackle this problem, we innovatively generate the depth ground-truth boundary dataset by mining the instance segmentation and semantic segmentation datasets and propose RDNet, which incorporates edge cues into stereo matching. The network learns geometric information through a separate processing branch edge stream, which can process feature information in parallel with the stereo stream. The edge stream removes noise and only focuses on processing the relevant boundary information. Besides, we introduce a multi-scale cost volume in hierarchical cost aggregation to enlarge the receptive fields and capture structural and global representations that can significantly improve the ability of scene understanding and disparity estimation accuracy. Moreover, a disparity refinement network with several dilated convolutions is applied to further improve the accuracy of the final disparity estimation. The proposed method is evaluated on Sceneflow, KITTI 2015 and KITTI 2012 benchmark datasets, and the qualitative and quantitative results demonstrate that the proposed RDNet significantly achieves the state-of-the-art stereo matching performance.
A fake review identification framework considering the suspicion degree of reviews with time burst characteristics

With the rapid development of e-commerce, online reviews have played an increasingly important role in consumers' shopping intentions and behaviors. Therefore, how to effectively identify fake reviews has become one of the important issues that need to be resolved. Since the existing methods do not fully consider the time burst characteristics of reviews, this paper proposes a suspicion degree determining method based on the three-dimensional time series. Besides, combining the suspicion degree feature, review text features, and reviewer's behavior features together, this paper proposes a more comprehensive fake review identification framework. The yelp and amazon public data sets are carried out to verify the effectiveness of the proposed method, and the experimental results show that the proposed method outperforms the most advanced methods.
Synthetic Data Generation using Imitation Training

We propose a strategic approach to generate synthetic data in order to improve machine learning algorithms such as Deep Neural Networks (DNN). Utilization of synthetic data has shown promising results yet there are no specific rules or recipes on how to generate and cook synthetic data. We propose imitation training as a guideline of synthetic data generation to add more underrepresented entities and balance the data distribution for DNN to handle corner cases and resolve long tail problems. The proposed imitation training has a circular process with three main steps: First, the existing system is evaluated and failure cases such as false positive and false negative detections are sorted out; Secondly, synthetic data imitating such failure cases is created with domain randomization; Thirdly, we train a net-work with the existing data and the newly added synthetic data; We repeat these three steps until the evaluation metric converges. We validated the approach by experimenting on object detection in autonomous driving.
DDA-Net: Unsupervised cross-modality medical image segmentation via dual domain adaptation

Deep convolutional networks are powerful tools for single-modality medical image segmentation, whereas generally require semantic labelling or annotation that is laborious and time-consuming. However, domain shift among various modalities critically deteriorates the performance of deep convolutional networks if only trained by single-modality labelling data.In this paper, we propose an end-to-end unsupervised cross-modality segmentation network, DDA-Net, for accurate medical image segmentation without semantic annotation or labelling on the target domain. To close the domain gap, different images with domain shift are mapped into a shared domain-invariant representation space. In addition, spatial position information, which benefits the spatial structure consistency for semantic information, is preserved by an introduced cross-modality auto-encoder.We validated the proposed DDA-Net method on cross-modality medical image datasets of brain images and heart images. The experimental results show that DDA-Net effectively alleviates domain shift and suppresses model degradation.The proposed DDA-Net successfully closes the domain gap between different modalities of medical image, and achieves state-of-the-art performance in cross-modality medical image segmentation. It also can be generalized for other semi-supervised or unsupervised segmentation tasks in some other field.
D-Fence: A Flexible, Efficient, and Comprehensive Phishing Email Detection System

Phishing continues to be a major security concern for organizations around the globe. Past works proposed classifiers to detect phishing emails; however many of them are based on rules, whereas others are typically standalone models focusing on one specific component of emails (say, URL strings). In this work, we take a different approach and propose a multi-modular and comprehensive phishing email detection system, called D-Fence. The different modules of D-Fence — structure module, text module, and URL module — detect phishing attempts in different components of an email. This allows D-Fence to cover larger attack surfaces while also offering flexible (model) configurations with reduced computational overhead. We carry out experiments on a large-scale real-world email dataset comprising mails from multiple enterprises. Our evaluations demonstrate the effectiveness of D-Fence in detecting phishing emails that do not have malicious intentions manifesting in all email components; D-Fence achieves a high recall of 0.99 at a low false-positive rate of 1 in 10K. Furthermore, we perform systematic evaluations to find and evaluate cost-efficient model configurations for D-Fence; the results reveal that D-Fence maintains high detection capability while bringing significant savings in computational time.
Multi-User Dynamic Searchable Symmetric Encryption With Corrupted Participants

We study the problem of multi-user dynamic searchable symmetric encryption (DMUSSE) where a data owner stores its encrypted documents on an untrusted remote server and wishes to selectively allow multiple users to access them by issuing keyword search queries. Specifically, we consider the case where some of the users may be corrupted and <i>colluding with the server</i> to extract additional information about the dataset (beyond what they have access to). We provide the first formal security definition for the dynamic setting as well as forward and backward privacy definitions. We then propose <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> <small>SE</small> , the first provably secure DMUSSE scheme and instantiate it in two versions, one based on oblivious data structures and one based on update queues, with different performance trade-offs. Furthermore, we extend <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> <small>SE</small> to support verifiability of results. To achieve this, users need a secure digest initially computed by the data owner and changed after every update. We efficiently accommodate this, without relying on a trusted third party, by adopting a blockchain-based approach for the digests’ dissemination and deploy our schemes over the permissioned Hyperledger Fabric blockchain. We prototype both versions and experimentally evaluate their practical performance, both as stand-alone systems and running on top of Hyperledger Fabric.
Concurrent Detection and Identification of Multiple Objects using YOLO Algorithm

The main purpose of this research paper is to study and report how the object detection and identification of multiple objects in an image or a video frameworks in real life. As part of this research, we have clearly presented about image classification, image localization, and image detection. The entire work is carried out with the aid of YOLOv3 (You Only Look Once, Version 3) is an object detection algorithm which helps to detect real-time objects. YOLOv3 gives accurate results. It helps in declaring the localizers, classifiers, and detection. It uses a single neural network to the full image to detect the objects using bounding boxes thereby resulting in accurate outputs. The experiments and results show that YOLOv3 is extremely accurate. This rapidness in detecting objects is helpful in situations where humans can't detect with their naked eye cannot track multiple items simultaneously in an image or video frame.
Personal Interest Attention Graph Neural Networks for Session-Based Recommendation

Session-based recommendations aim to predict a user's next click based on the user's current and historical sessions, which can be applied to shopping websites and APPs. Existing session-based recommendation methods cannot accurately capture the complex transitions between items. In addition, some approaches compress sessions into a fixed representation vector without taking into account the user's interest preferences at the current moment, thus limiting the accuracy of recommendations. Considering the diversity of items and users' interests, a personalized interest attention graph neural network (PIA-GNN) is proposed for session-based recommendation. This approach utilizes personalized graph convolutional networks (PGNN) to capture complex transitions between items, invoking an interest-aware mechanism to activate users' interest in different items adaptively. In addition, a self-attention layer is used to capture long-term dependencies between items when capturing users' long-term preferences. In this paper, the cross-entropy loss is used as the objective function to train our model. We conduct rich experiments on two real datasets, and the results show that PIA-GNN outperforms existing personalized session-aware recommendation methods.
Multimodal Human-Agent Dialogue Corpus with Annotations at Utterance and Dialogue Levels

The behaviors of general users for a dialogue system differ greatly from those for a human interlocutor. We have been collecting a multimodal dialogue corpus between a human participant and a virtual agent operated by the Wizard-of-Oz method. This paper presents the collected corpus, Hazumi, which was released in August 2020 and March 2021. The corpus consists of three versions: Hazumi1712, Hazumi1902, and Hazumi1911. The version numbers correspond to the periods during which the data were collected. The three versions contain the dialogue data of 29, 30, and 30 participants, respectively, each of whom spoke with the agent for about 15 to 20 minutes. The corpus contains multimodal recordings, along with several annotations given to every exchange, feature files extracted from the recorded data, and the results of questionnaires conducted before and after the dialogues. The third version Hazumi1911 also contains the physiological signals of the participants during the dialogues and additional questionnaire items. We also show several analyses conducted using this corpus. We anticipate that the corpus will be useful for developing user-adaptive multimodal dialogue systems.
API beauty is in the eye of the clients: 2.2 million Maven dependencies reveal the spectrum of client–API usages

Hyrum’s law states a common observation in the software industry: “With a sufficient number of users of an API, it does not matter what you promise in the contract: all observable behaviors of your system will be depended on by somebody”. Meanwhile, recent research results seem to contradict this observation when they state that “for most APIs, there is a small number of features that are actually used”. In this work, we perform a large scale empirical study of client–API relationships in the Maven ecosystem, in order to investigate this seeming paradox between the observations in industry and the research literature. We study the 94 most popular libraries in Maven Central, as well as the 829,410 client artifacts that declare a dependency to these libraries and that are available in Maven Central, summing up to 2.2M dependencies. Our analysis indicates the existence of a wide spectrum of API usages, with enough clients, most API types end up being used at least once. Our second key observation is that, for all libraries, there is a small set of API types that are used by the vast majority of its clients. The practical consequences of this study are two-fold: (i) it is possible for API maintainers to find an essential part of their API on which they can focus their efforts; (ii) API developers should limit the public API elements to the set of features for which they are ready to have users.
Nonlocal sets of orthogonal multipartite product states with less members

We study the constructions of nonlocal orthogonal product states in multipartite systems that cannot be distinguished by local operations and classical communication. We first present two constructions of nonlocal orthogonal product states in tripartite systems $$\mathcal {C}^{d}\otimes \mathcal {C}^{d}\otimes \mathcal {C}^{d}~(d\ge 3)$$ and $$\mathcal {C}^d\otimes \mathcal {C}^{d+1}\otimes \mathcal {C}^{d+2}~(d\ge 3)$$ . Then for general tripartite quantum system $$\mathcal {C}^{n_{1}}\otimes \mathcal {C}^{n_{2}}\otimes \mathcal {C}^{n_{3}}$$ $$(3\le n_{1}\le n_{2}\le n_{3})$$ , we obtain $$2(n_{2}+n_{3}-1)-n_{1}$$ nonlocal orthogonal product states. Finally, we put forward a new construction approach in $$\mathcal {C}^{d_{1}}\otimes \mathcal {C}^{d_{2}}\otimes \cdots \otimes \mathcal {C}^{d_{n}}$$ $$(d_1,d_2,\cdots d_n\ge 3,\, n>6)$$ multipartite systems. Remarkably, our indistinguishable sets contain less nonlocal product states than the existing ones, which improves the recent results and highlights their related applications in quantum information processing.
Green Energy Forecast-Based Bi-Objective Scheduling of Tasks Across Distributed Clouds

Current large-scale green cloud data centers (GCDCs) tend to consume a huge amount of energy and generate enormous carbon emissions. Existing studies have tried to solve this problem by either realizing prediction of green energy, or optimizing task scheduling. In contrast, this work seamlessly combines green energy prediction and task scheduling to jointly optimize revenue and energy cost of GCDCs. Specifically, this work designs a prediction method, named Savitzky-Golay and Long Short-Term Memory network (SG-LSTM), to realize noise filtering and forecast green energy. Based on such prediction, a bi-objective optimization method, named Decomposition-based Multi-objective evolutionary algorithm with Gaussian mutation and Crowding distance (DMGC), is developed to optimize the revenue and energy cost of GCDCs. Its performance is demonstrated over real-life datasets including Google cluster traces, wind speeds, solar irradiance and prices of electricity. Experimental results show that SG-LSTM outperforms its two peers, back propagation neural network and gated recurrent unit, in terms of root mean square errors and mean absolute errors. In addition, DMGC surpasses its such peers as NSGA-II, SPEA2, and MOEA/D in terms of revenue, energy cost and average execution time. Particularly, DMGC's revenue is 18%, 20% and 13.1% higher, energy cost is 16%, 19.8% and 15.2% lower, and average execution time is 60.02%, 38.47% and 24.17% lower than those of NSGA-II, SPEA2, and MOEA/D, respectively.
A Fast Routing Capsule Network With Improved Dense Blocks

Routing algorithm in most existing Capsule Networks (CapsNets) is always a challenging problem due to its heavy computational burden. To address this issue, we propose a “fast routing” algorithm, where the high-level capsules are activated by the statistical characteristic of votes from low-level capsule vectors. In this way, the votes and their distribution are both considered, and iterations in the traditional “dynamic routing” algorithm are eliminated. The comparison experiment on MNIST dataset reveals that CapsNet with fast routing promotes time efficiency of CapsNet with dynamic routing (DR) by 71.2%, as well as improves classification accuracy by 6%. Moreover, improved dense blocks (IDB) are developed to make a powerful feature extraction, where layer position is utilized to calculate its filters to encourage feature reuse, while reducing redundant features. Finally, the proposed CapsNet with “fast routing” and IDB, namely fast routing CapsNet (FR-CapsNet), outperforms state-of-the-art capsule models in multiple benchmark datasets.
FIXAR: A Fixed-Point Deep Reinforcement Learning Platform with Quantization-Aware Training and Adaptive Parallelism

Deep reinforcement learning (DRL) is a powerful technology to deal with decision-making problem in various application domains such as robotics and gaming, by allowing an agent to learn its action policy in an environment to maximize a cumulative reward. Unlike supervised models which actively use data quantization, DRL still uses the single-precision floating-point for training accuracy while it suffers from computationally intensive deep neural network (DNN) computations. In this paper, we present a deep reinforcement learning acceleration platform named FIXAR, which employs fixed-point data types and arithmetic units for the first time using a SW/HW co-design approach. We propose a quantization-aware training algorithm in fixed-point, which enables to reduce the data precision by half after a certain amount of training time without losing accuracy. We also design a FPGA accelerator that employs adaptive dataflow and parallelism to handle both inference and training operations. Its processing element has configurable datapath to efficiently support the proposed quantized-aware training. We validate our FIXAR platform, where the host CPU emulates the DRL environment and the FPGA accelerates the agent’s DNN operations, by running multiple benchmarks in continuous action spaces based on a latest DRL algorithm called DDPG. Finally, the FIXAR platform achieves 25293.3 inferences per second (IPS) training throughput, which is 2.7 times higher than the CPU-GPU platform. In addition, its FPGA accelerator shows 53826.8 IPS and 2638.0 IPS/W energy efficiency, which are 5.5 times higher and 15.4 times more energy efficient than those of GPU, respectively. FIXAR also shows the best IPS throughput and energy efficiency among other state-of-the-art acceleration platforms using FPGA, even it targets one of the most complex DNN models.