Reviewerly/exHarmony huggingface dataset:
A Dynamic Round Triple Data Encryption Standard Cryptographic Technique for Data Security

Cryptographic techniques have been widely employed to protect sensitive data from unauthorized access and manipulation. Among these cryptographic techniques, Data Encryption Standard (DES) has been widely employed, however, it suffers from key and differential attacks. To overcome these attacks, several DES modifications have been proposed in literatures. Most modifications have focused on enhancing DES encryption key; however, the strength of a cryptographic technique is determined by the encryption key used and the number of encryption rounds. It is a known fact that Advanced Encryption Standard (AES) cryptographic technique with 14 encryption rounds is stronger than AES with 12 rounds while AES with 12 rounds is stronger than AES with 10 rounds. Therefore, this study proposed a DES cryptographic technique whose number of rounds is dynamic. Users are expected to specify the number of encryption and decryption rounds to be employed at run time. Moreover, a predefined number of shifting operations which is left circular shift 2 was chosen for each encryption round. As, a trade-off in complexity, the number of Substitution box (S-box) was also reduced to 4, so that the input to the S-boxes would be arranged in four 12-bit blocks for the X-OR operation and not six 8-bit blocks as in the traditional DES. Finally, three keys were used to encrypt, decrypt and encrypt the plaintext ciphertext as in triple DES. The modified DES yielded a better avalanche effect for rounds greater than 16 though its encryption and decryption time were greater than that of the traditional DES.
Few-Shot Learning for Remote Sensing Image Retrieval With MAML

Few-shot remote sensing image retrieval is devoted to add new retrieval categories with a small number of labeled samples, and simultaneously achieve favorable retrieval performance for new categories and keep the primary retrieval performance for the original categories as far as possible. Few-shot learning has received considerable attention, however its applications in image retrieval, especially for remote sensing image retrieval, are still very few. In this paper, we redefine the few-shot image retrieval problem formally and further propose a few-shot retrieval method under model-agnostic meta-learning (MAML) framework, combined with ResNet and GeM as feature extraction module. Moreover, the optimal mean average precision (mAP) is used as ranking loss for defining the loss function of learning model, and a histogram binning approximation of mAP, which is differential, is thus employed so that the whole few-shot retrieval model can be end-to-end trained. The retrieval effectiveness and efficiency of our method are verified on UC-Merced and AID data sets.
CNN-Based Simultaneous Dehazing and Depth Estimation

It is difficult for both cameras and depth sensors to obtain reliable information in hazy scenes. Therefore, image dehazing is still one of the most challenging problems to solve in computer vision and robotics. With the development of convolutional neural networks (CNNs), lots of dehazing and depth estimation algorithms using CNNs have emerged. However, very few of those try to solve these two problems at the same time. Focusing on the fact that traditional haze modeling contains depth information in its formula, we propose a CNN-based simultaneous dehazing and depth estimation network. Our network aims to estimate both a dehazed image and a fully scaled depth map from a single hazy RGB input with end-to-end training. The network contains a single dense encoder and four separate decoders; each of them shares the encoded image representation while performing individual tasks. We suggest a novel depth-transmission consistency loss in the training scheme to fully utilize the correlation between the depth information and transmission map. To demonstrate the robustness and effectiveness of our algorithm, we performed various ablation studies and compared our results to those of state-of-the-art algorithms in dehazing and single image depth estimation, both qualitatively and quantitatively. Furthermore, we show the generality of our network by applying it to some real-world examples.
Localizing 2D Ultrasound Probe from Ultrasound Image Sequences Using Deep Learning for Volume Reconstruction

This paper presents an ultrasound (US) volume reconstruction method only from US image sequences using deep learning. The proposed method employs the convolutional neural network (CNN) to estimate the position of a 2D US probe only from US images. Our CNN model consists of two networks: feature extraction and motion estimation. We also introduce the consistency loss function to enforce. Through a set of experiments using US image sequence datasets with ground-truth motion measured by a motion capture system, we demonstrate that the proposed method exhibits the efficient performance on probe localization and volume reconstruction compared with the conventional method.
Robust corner detection with fractional calculus for magnetic resonance imaging

In medical image processing and analyzing, corners are one of the most important features, which makes point-based registration and diagnosis studies essential. However, existing corner detectors are impressible to noise. This work proposes an order-steerable and robust image corner detector, while a fractional gradient operator consisting of fractional-order forward and backward differentiation and integration is derived. The fractional gradient operator achieving a 90° phase shift as the traditional first derivative does provides a new idea to calculate the gradient in an image. Qualitative and quantitative comparison experiments with exiting detection methods based on integer-order derivative are performed on simulated and real medical slices. The comparison experiments indicate that the present corner detector in this paper allows very strong response for corner detection and provides a better ability of detecting and locating image corners and robustness against noise.
An End-To-End Framework For Pose Estimation Of Occluded Pedestrians

Pose estimation in the wild is a challenging problem, particularly in situations of(i) occlusions of varying degrees, and (ii) crowded outdoor scenes. Most of the existing studies of pose estimation did not report the performance in similar situations. Moreover, pose annotations for occluded parts of the human figures have not been provided in any of the relevant standard datasets, which in turn creates further difficulties to the required studies for pose estimation of the entire Figure for occluded humans. Well known pedestrian detection datasets such as CityPersons contains samples of outdoor scenes but it does not include pose annotations. Here we propose a novel multi-task framework for end-to-end training towards the entire pose estimation of pedestrians including in situations of any kind of occlusion. To tackle this problem, we make use of a pose estimation dataset, MS-COCO, and employ unsupervised adversarial instance-level domain adaptation for estimating the entire pose of occluded pedestrians. The experimental studies show that the proposed framework outperforms the SOTA results for pose estimation, instance segmentation and pedestrian detection in cases of heavy occlusions (HO) and reasonable + heavy occlusions (R+HO) on the two benchmark datasets.
Lightweight Crypto-Assisted Distributed Differential Privacy for Privacy-Preserving Distributed Learning

The appearance of distributed learning allows multiple participants to collaboratively train a global model, where instead of directly releasing their private training data with the server, participants iteratively share their local model updates (parameters) with the server. However, recent attacks demonstrate that sharing local model updates is not sufficient to provide reasonable privacy guarantees, as local model updates may result in significant privacy leakage about local training data of participants. To address this issue, in this paper, we present an alternative approach that combines distributed differential privacy (DDP) with a three-layer encryption protocol to achieve a better privacy-utility tradeoff than the existing DP-based approaches. An unbiased encoding algorithm is proposed to cope with floating-point values, while largely reducing mean squared error due to rounding. Our approach dispenses with the need for any trusted server, and enables each party to add less noise to achieve the same privacy and similar utility guarantees as that of the centralized differential privacy. Preliminary analysis and performance evaluation confirm the effectiveness of our approach, which achieves significantly higher accuracy than that of local differential privacy approach, and comparable accuracy to the centralized differential privacy approach.
Self-Paced Deep Regression Forests with Consideration on Underrepresented Examples

Deep discriminative models (e.g. deep regression forests, deep neural decision forests) have achieved remarkable success recently to solve problems such as facial age estimation and head pose estimation. Most existing methods pursue robust and unbiased solutions either through learning discriminative features, or reweighting samples. We argue what is more desirable is learning gradually to discriminate like our human beings, and hence we resort to self-paced learning (SPL). Then, a natural question arises: can self-paced regime lead deep discriminative models to achieve more robust and less biased solutions? To this end, this paper proposes a new deep discriminative model—self-paced deep regression forests with consideration on underrepresented examples (SPUDRFs). It tackles the fundamental ranking and selecting problem in SPL from a new perspective: fairness. This paradigm is fundamental and could be easily combined with a variety of deep discriminative models (DDMs). Extensive experiments on two computer vision tasks, i.e., facial age estimation and head pose estimation, demonstrate the efficacy of SPUDRFs, where state-of-the-art performances are achieved.
Lower Bounds for Non-Elitist Evolutionary Algorithms via Negative Multiplicative Drift

A decent number of lower bounds for non-elitist population-based evolutionary algorithms has been shown by now. Most of them are technically demanding due to the (hard to avoid) use of negative drift theorems-general results which translate an expected movement away from the target into a high hitting time. We propose a simple negative drift theorem for multiplicative drift scenarios and show that it can simplify existing analyses. We discuss in more detail Lehre's (2010) negative drift in populations method, one of the most general tools to prove lower bounds on the runtime of non-elitist mutation-based evolutionary algorithms for discrete search spaces. Together with other arguments, we obtain an alternative and simpler proof of this result, which also strengthens and simplifies this method. In particular, now only three of the five technical conditions of the previous result have to be verified. The lower bounds we obtain are explicit instead of only asymptotic. This allows us to compute concrete lower bounds for concrete algorithms, but also enables us to show that super-polynomial runtimes appear already when the reproduction rate is only a (1-ω(n-1/2)) factor below the threshold. For the special case of algorithms using standard bit mutation with a random mutation rate (called uniform mixing in the language of hyper-heuristics), we prove the result stated by Dang and Lehre (2016b) and extend it to mutation rates other than Θ(1/n), which includes the heavy-tailed mutation operator proposed by Doerr et al. (2017). We finally use our method and a novel domination argument to show an exponential lower bound for the runtime of the mutation-only simple genetic algorithm on OneMax for arbitrary population size.
Underwater Image Enhancement Using Laplace Decomposition

This letter presents underwater image enhancement using Laplace decomposition. Underwater image undergoes Laplace decomposition resulting in low- and high-frequency bands. Haze is removed from the low-frequency band, and then it is normalized for white balancing. The high-frequency band is amplified for edge preservation. Adding the two frequency images results in an enhanced image. It has improved, in contrast, color, object prominence, edge preservation, reduced artifacts, and naturalness as compared to other methods related to underwater image enhancement.
Balancing Actuation and Computing Energy in Motion Planning

We study a novel class of motion planning problems, inspired by emerging low-energy robotic vehicles, such as insect-size flyers, chip-size satellites, and high-endurance autonomous blimps, for which the energy consumed by computing hardware during planning a path can be as large as the energy consumed by actuation hardware during the execution of the same path. We propose a new algorithm, called Compute Energy Included Motion Planning (CEIMP). CEIMP operates similarly to any other anytime planning algorithm, except it stops when it estimates further computing will require more computing energy than potential savings in actuation energy. We show that CEIMP has the same asymptotic computational complexity as existing sampling-based motion planning algorithms, such as PRM*. We also show that CEIMP outperforms the average baseline of using maximum computing resources in realistic computational experiments involving 10 floor plans from MIT buildings. In one representative experiment, CEIMP outperforms the average baseline 90.6% of the time when energy to compute one more second is equal to the energy to move one more meter, and 99.7% of the time when energy to compute one more second is equal to or greater than the energy to move 3 more meters.
Uncovering Human Multimodal Activity Recognition with a Deep Learning Approach

Recent breakthroughs on deep learning and computer vision have encouraged the use of multimodal human activity recognition aiming at applications in human-robot-interaction. The wide availability of videos at online platforms has made this modality one of the most promising for this task, whereas some researchers have tried to enhance the video data with wearable sensors attached to human subjects. However, temporal information on both video and inertial sensors are still under investigation. Most of the current work focusing on daily activities do not present comparative studies considering different temporal approaches. In this paper, we are proposing a new model build upon a Two-Stream ConvNet for action recognition, enhanced with Long Short-Term Memory (LSTM) and a Temporal Convolution Networks (TCN) to investigate the temporal information on videos and inertial sensors. A feature-level fusion approach prior to temporal modelling is also proposed and evaluated. Experiments have been conducted on the egocentric multimodal dataset and on the UTD-MHAD. LSTM and TCN showed competitive results, with the TCN performing slightly better for most applications. The feature-level fusion approach also performed well on the UTD-MHAD with some overfitting on the egocentric multimodal dataset. Overall the proposed model presented promising results on both datasets compatible with the state-of-the-art, providing insights on the use of deep learning for human-robot-interaction applications.
Efficient Construction of a Control Modular Adder on a Carry-Lookahead Adder Using Relative-Phase Toffoli Gates

Control modular addition is a core arithmetic function, and we must consider the computational cost for actual quantum computers to realize efficient implementation. To achieve a low computational cost in a control modular adder, we focus on minimizing KQ, defined by the product of the number of qubits and the depth of the circuit. In this paper, we construct an efficient control modular adder with small KQ by using relative-phase Toffoli gates in two major types of quantum computers: Fault-Tolerant Quantum Computers (FTQ) on the Logical layer and Noisy Intermediate-Scale Quantum Computers (NISQ). We give a more efficient construction compared to Van Meter and Itoh's, based on a carry-lookahead adder. In FTQ, $T$ gates incur heavy cost due to distillation, which fabricates ancilla for running $T$ gates with high accuracy but consumes a lot of specially prepared ancilla qubits and a lot of time. Thus, we must reduce the number of $T$ gates. We propose a new control modular adder that uses only 20% of the number of $T$ gates of the original. Moreover, when we take distillation into consideration, we find that we minimize $\text{KQ}_{T}$ (the product of the number of qubits and $T$-depth) by running $\Theta\left(n / \sqrt{\log n} \right)$ $T$ gates simultaneously. In NISQ, CNOT gates are the major error source. We propose a new control modular adder that uses only 35% of the number of CNOT gates of the original. Moreover, we show that the $\text{KQ}_{\text{CX}}$ (the product of the number of qubits and CNOT-depth) of our circuit is 38% of the original. Thus, we realize an efficient control modular adder, improving prospects for the efficient execution of arithmetic in quantum computers.
Learning Word Representation for the Cyber Security Vulnerability Domain

There have been ever-increasing amounts of security vulnerabilities discovered and reported in recent years. Much of the information related to these vulnerabilities is currently available to the public, in the form of rich, textual data (e.g. vulnerability reports). Many of the state-of-the-art techniques used today to process such textual data rely on so-called word embeddings. As of today, several pre-trained embeddings have been created, many of which rely on general-purpose training datasets such as Google News and Wikipedia. More recently, other domain-specific word embeddings have been created (e.g. in the context of software development) to cope with terminology and ambiguity limitations of existing general-purpose embeddings. The availability of word embeddings for specialised domains is critical for the effectiveness of domain-specific tasks that rely on this technique. In this paper, we propose a word embedding for the cyber security vulnerability domain. We train our embedding model on multiple, rich and heterogeneous security vulnerability information sources publicly available on the web. The benefits of such specialised word embedding are demonstrated through a qualitative comparison of word similarity and the exemplary task of matching security professionals to vulnerability discovery tasks posted to bug bounty programs. We also introduce a new dataset of words pairs similarity with a human judgement that can be used as a benchmark. Our experimental results show that, in the context of cyber security, our domain-specific word embedding outperforms existing pre-trained embeddings built on general-purpose and software engineering datasets.
Interpretable & Time-Budget-Constrained Contextualization for Re-Ranking.

Search engines operate under a strict time constraint as a fast response is paramount to user satisfaction. Thus, neural re-ranking models have a limited time-budget to re-rank documents. Given the same amount of time, a faster re-ranking model can incorporate more documents than a less efficient one, leading to a higher effectiveness. To utilize this property, we propose TK (Transformer-Kernel): a neural re-ranking model for ad-hoc search using an efficient contextualization mechanism. TK employs a very small number of Transformer layers (up to three) to contextualize query and document word embeddings. To score individual term interactions, we use a document-length enhanced kernel-pooling, which enables users to gain insight into the model. TK offers an optimal ratio between effectiveness and efficiency: under realistic time constraints (max. 200 ms per query) TK achieves the highest effectiveness in comparison to BERT and other re-ranking models. We demonstrate this on three large-scale ranking collections: MSMARCO-Passage, MSMARCO-Document, and TREC CAR. In addition, to gain insight into TK, we perform a clustered query analysis of TK's results, highlighting its strengths and weaknesses on queries with different types of information need and we show how to interpret the cause of ranking differences of two documents by comparing their internal scores.
Transductive Prototypical Network For Few-Shot Classification

Few-shot learning is the key step towards human-level intelligence. Prototypical Network is a promising approach to address the key issue of over-fitting for few-shot learning. Nevertheless, the original Prototypical Network only uses one or few labeled instances to represent the corresponding class, which easily deviates from the real class distribution leading to the imprecise classification results. In this paper, we propose Transductive Prototypical Network (Td-PN), a universal transductive approach that refines the class representations by merging scarce labeled samples and high-confidence ones of target set. Our proposed Td-PN first maps the samples to a classifying-friendly (discriminative) embedding space by redesigning a weighted contrastive loss, then utilizes the transductive inference to obtain the powerful prototype representation for each class. Experiments demonstrate that our approach outperforms the state-of-the-art algorithms.
Quickest Intruder Detection For Multiple User Active Authentication

In this paper, we investigate how to detect intruders with low latency for Active Authentication (AA) systems with multiple-users. We extend the Quickest Change Detection (QCD) framework to the multiple-user case and formulate the Multiple-user Quickest Intruder Detection (MQID) algorithm. Furthermore, we extend the algorithm to the data-efficient scenario where intruder detection is carried out with fewer observation samples. We evaluate the effectiveness of the proposed method on two publicly available AA datasets on the face modality.
Blockchain-Based Secure and Privacy-Preserving Clinical Data Sharing and Integration

This paper is an exploration to securely integrate geo-scattered medical data silos and to provide privacy-preserving data sharing via blockchain and cryptography. We leverage broadcast encryption, key regression, data privacy classification, blockchain and smart contract holistically to address such a challenge. In our design, a patient's medical records are divided into multiple parts with different sensitivities according to personal privacy requirements. Each part is encrypted with a symmetric encryption key and it is broadcast encrypted to a pre-defined user set. Data encryption keys are generated by a key regression scheme, where keys used to encrypt low-sensitivity data parts can be derived from keys used to encrypt high-sensitivity data parts. With such a mechanism, we can enforce strict access and privacy control on medical data. Furthermore, we use Ethereum blockchain to connect various institutions to provide efficient data sharing, and we design smart contracts to implement the business logic of medical data sharing.
A Multiple Attributes Image Quality Database for Smartphone Camera Photo Quality Assessment

Smartphone is the superstar product in digital device market and the quality of smartphone camera photos (SCPs) is becoming one of the dominant considerations when consumers purchase smartphones. How to evaluate the quality of smartphone cameras and the taken photos is urgent issue to be solved. To bridge the gap between academic research accomplishment and industrial needs, in this paper, we establish a new Smartphone Camera Photo Quality Database (SCPQD2020) including 1800 images with 120 scenes taken by 15 smartphones. Exposure, color, noise and texture which are four dominant factors influencing the quality of SCP are evaluated in the subjective study, respectively. Ten popular no-reference (NR) image quality assessment (IQA) algorithms are tested and analyzed on our database. Experimental results demonstrate that the current objective models are not suitable for SCPs, and quality metrics having high correlation with human visual perception are highly needed.