Reviewerly/exHarmony huggingface dataset:
Mitigating Gender Bias in Machine Translation with Target Gender Annotations

When translating The secretary asked for details. to a language with grammatical gender, it might be necessary to determine the gender of the subject secretary. If the sentence does not contain the necessary information, it is not always possible to disambiguate. In such cases, machine translation systems select the most common translation option, which often corresponds to the stereotypical translations, thus potentially exacerbating prejudice and marginalisation of certain groups and people. We argue that the information necessary for an adequate translation can not always be deduced from the sentence being translated or even might depend on external knowledge. Therefore, in this work, we propose to decouple the task of acquiring the necessary information from the task of learning to translate correctly when such information is available. To that end, we present a method for training machine translation systems to use word-level annotations containing information about subject’s gender. To prepare training data, we annotate regular source language words with grammatical gender information of the corresponding target language words. Using such data to train machine translation systems reduces their reliance on gender stereotypes when information about the subject’s gender is available. Our experiments on five language pairs show that this allows improving accuracy on the WinoMT test set by up to 25.8 percentage points.
Tencent Neural Machine Translation Systems for the WMT20 News Translation Task

This paper describes Tencent Neural Machine Translation systems for the WMT 2020 news translation tasks. We participate in the shared news translation task on English ↔ Chinese and English → German language pairs. Our systems are built on deep Transformer and several data augmentation methods. We propose a boosted in-domain finetuning method to improve single models. Ensemble is used to combine single models and we propose an iterative transductive ensemble method which can further improve the translation performance based on the ensemble results. We achieve a BLEU score of 36.8 and the highest chrF score of 0.648 on Chinese → English task.
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning

Reinforcement learning provides a general framework for flexible decision making and control, but requires extensive data collection for each new task that an agent needs to learn. In other machine learning fields, such as natural language processing or computer vision, pre-training on large, previously collected datasets to bootstrap learning for new tasks has emerged as a powerful paradigm to reduce data requirements when learning a new task. In this paper, we ask the following question: how can we enable similarly useful pre-training for RL agents? We propose a method for pre-training behavioral priors that can capture complex input-output relationships observed in successful trials from a wide range of previously seen tasks, and we show how this learned prior can be used for rapidly learning new tasks without impeding the RL agent's ability to try out novel behaviors. We demonstrate the effectiveness of our approach in challenging robotic manipulation domains involving image observations and sparse reward functions, where our method outperforms prior works by a substantial margin.
Single trace side-channel attack on key reconciliation in quantum key distribution system and its efficient countermeasures

Although quantum mechanics guarantees the security of the quantum key distribution system, it is crucial to examine whether the implementation flaws can lead to the disclosure of sensitive information. In this paper, we propose the side-channel attack on the key reconciliation in the quantum key distribution system. In this system, the sifted key and the syndrome are used to derive a shared secret key between two users. From our attack, these can be fully recovered through a single power consumption trace measured during the syndrome computation on the sender’s side. Additionally, we propose efficient countermeasures to thwart such side-channel attacks.
Application of deep learning to predict underestimation in ductal carcinoma in situ of the breast with ultrasound

To develop an ultrasound-based deep learning model to predict postoperative upgrading of pure ductal carcinoma in situ (DCIS) diagnosed by core needle biopsy (CNB) before surgery.Of the 360 patients with DCIS diagnosed by CNB and identified retrospectively, 180 had lesions upstaged to ductal carcinoma in situ with microinvasion (DCISM) or invasive ductal carcinoma (IDC) postoperatively. Ultrasound images obtained from the hospital database were divided into a training set (n=240) and validation set (n=120), with a ratio of 2:1 in chronological order. Four deep learning models, based on the ResNet and VggNet structures, were established to classify the ultrasound images into postoperative upgrade and pure DCIS. We obtained the area under the receiver operating characteristic curve (AUROC), specificity, sensitivity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) to estimate the performance of the predictive models. The robustness of the models was evaluated by a 3-fold cross-validation.Clinical features were not significantly different between the training set and the test set (P value >0.05). The area under the receiver operating characteristic curve of our models ranged from 0.724 to 0.804. The sensitivity, specificity, and accuracy of the optimal model were 0.733, 0.750, and 0.742, respectively. The three-fold cross-validation results showed that the model was very robust.The ultrasound-based deep learning prediction model is effective in predicting DCIS that will be upgraded postoperatively.
Exploring Decomposition for Solving Pattern Mining Problems

This article introduces a highly efficient pattern mining technique called Clustering-based Pattern Mining (CBPM). This technique discovers relevant patterns by studying the correlation between transactions in the transaction database based on clustering techniques. The set of transactions is first clustered, such that highly correlated transactions are grouped together. Next, we derive the relevant patterns by applying a pattern mining algorithm to each cluster. We present two different pattern mining algorithms, one applying an approximation-based strategy and another based on an exact strategy. The approximation-based strategy takes into account only the clusters, whereas the exact strategy takes into account both clusters and shared items between clusters. To boost the performance of the CBPM, a GPU-based implementation is investigated. To evaluate the CBPM framework, we perform extensive experiments on several pattern mining problems. The results from the experimental evaluation show that the CBPM provides a reduction in both the runtime and memory usage. Also, CBPM based on the approximate strategy provides good accuracy, demonstrating its effectiveness and feasibility. Our GPU implementation achieves significant speedup of up to 552× on a single GPU using big transaction databases.
C-Learning: Learning to Achieve Goals via Recursive Classification

We study the problem of predicting and controlling the future state distribution of an autonomous agent. This problem, which can be viewed as a reframing of goal-conditioned reinforcement learning (RL), is centered around learning a conditional probability density function over future states. Instead of directly estimating this density function, we indirectly estimate this density function by training a classifier to predict whether an observation comes from the future. Via Bayes' rule, predictions from our classifier can be transformed into predictions over future states. Importantly, an off-policy variant of our algorithm allows us to predict the future state distribution of a new policy, without collecting new experience. This variant allows us to optimize functionals of a policy's future state distribution, such as the density of reaching a particular goal state. While conceptually similar to Q-learning, our work lays a principled foundation for goal-conditioned RL as density estimation, providing justification for goal-conditioned methods used in prior work. This foundation makes hypotheses about Q-learning, including the optimal goal-sampling ratio, which we confirm experimentally. Moreover, our proposed method is competitive with prior goal-conditioned RL methods.
A UAV Dynamic Path Planning Algorithm

In this paper, we propose a UAV dynamic path planning algorithm to solve the path planning problem of a single UAV in a dynamic environment. The contributions of this paper mainly include the following two folds: (1) Using a combination of global and local path planning to improve planning efficiency. (2) The improved Q-Learning algorithm and artificial potential field method are combined to solve the problem that effective path planning cannot be performed between two path nodes. Finally, simulation results with Matlab proves the effectiveness of the algorithm.
Robust Target Detection and Tracking Algorithm Based on Roadside Radar and Camera

Intelligent transportation systems (ITSs) play an increasingly important role in traffic management and traffic safety. Smart cameras are the most widely used sensors in ITSs. However, cameras suffer from a reduction in detection and positioning accuracy due to target occlusion and external environmental interference, which has become a bottleneck restricting ITS development. This work designs a stable perception system based on a millimeter-wave radar and camera to address these problems. Radar has better ranging accuracy and weather robustness, which is a better complement to camera perception. Based on an improved Gaussian mixture probability hypothesis density (GM-PHD) filter, we also propose an optimal attribute fusion algorithm for target detection and tracking. The algorithm selects the sensors’ optimal measurement attributes to improve the localization accuracy while introducing an adaptive attenuation function and loss tags to ensure the continuity of the target trajectory. The verification experiments of the algorithm and the perception system demonstrate that our scheme can steadily output the classification and high-precision localization information of the target. The proposed framework could guide the design of safer and more efficient ITSs with low costs.
A Hand Gesture Based Wheelchair for Physically Handicapped Person with Emergency Alert System

Many people in the world today suffer from paralysis, physical illness, injury or any other disability. They have walking difficulties. A control system is developed with the aid of hand motion recognition for physically handicapped person with his gesture movement. The system is divided into unit for movement and wheelchair. This paper consists of gaining control of a wheel chair. This wheelchair can be operated by quick, joystick, hand movements. It consists of an accelerometer sensor that controls the users hand motion of the moving stools and clarifies the user movement and moves accordingly. When we changed the ways, the sensor registers values are changed & that values are given to atmega328p controller. Depending on the direction of the acceleration, Arduino controller controls the wheelchair ways like Left, Right, Forward, & Backward. This system also consists of ultrasonic sensor for obstacle avoidance. This program has the function of introducing control of Wheelchair direction reorganization of the hand gesture. For many applications the proposed wheelchair is used including schools, hospitals, old homes and airports. In addition, the SMS will be sent to the family members in case of emergency by pressing a single switch.
Multi-Dataset, Multitask Learning of Egocentric Vision Tasks

For egocentric vision tasks such as action recognition, there is a relative scarcity of labeled data.This increases the risk of overfitting during training.In this paper, we address this issue by introducing a multitask learning scheme that employs related tasks as well as related datasets in the training process.Related tasks are indicative of the performed action, such as the presence of objects and the position of the hands.By including related tasks as additional outputs to be optimized, action recognition performance typically increases because the network focuses on relevant aspects in the video.Still, the training data is limited to a single dataset because the set of action labels usually differs across datasets.To mitigate this issue, we extend the multitask paradigm to include datasets with different label sets.During training, we effectively mix batches with samples from multiple datasets.Our experiments on egocentric action recognition in the EPIC-Kitchens, EGTEA Gaze+, ADL and Charades-EGO datasets demonstrate the improvements of our approach over single-dataset baselines.On EGTEA we surpass the current state-of-the-art by 2.47%.We further illustrate the cross-dataset task correlations that emerge automatically with our novel training scheme.
A comprehensive EHR timeseries pre-training benchmark

Pre-training (PT) has been used successfully in many areas of machine learning. One area where PT would be extremely impactful is over electronic health record (EHR) data. Successful PT strategies on this modality could improve model performance in data-scarce contexts such as modeling for rare diseases or allowing smaller hospitals to benefit from data from larger health systems. While many PT strategies have been explored in other domains, much less exploration has occurred for EHR data. One reason for this may be the lack of standardized benchmarks suitable for developing and testing PT algorithms. In this work, we establish a PT benchmark dataset for EHR timeseries data, establishing cohorts, a diverse set of fine-tuning tasks, and PT-focused evaluation regimes across two public EHR datasets: MIMIC-III and eICU. This benchmark fills an essential hole in the field by enabling a robust manner of iterating on PT strategies for this modality. To show the value of this benchmark and provide baselines for further research, we also profile two simple PT algorithms: a self-supervised, masked imputation system and a weakly-supervised, multi-task system. We find that PT strategies (in particular weakly-supervised PT methods) can offer significant gains over traditional learning in few-shot settings, especially on tasks with strong class imbalance. Our full benchmark and code are publicly available at https://github.com/mmcdermott/comprehensive_MTL_EHR
Promoting Creativity of Engineering Students in the Foreign Language Classroom

The article focuses on the issue of promoting the creative abilities of Food Science majors through the Foreign (English) language course. English language course is one of the non-core academic disciplines which provides plenty of educational opportunities for future engineers to stimulate their creative abilities. Study of the theory and practice behind engineering education and foreign language teaching helped to identify the following gap: the need for promoting the creativity of engineering students through non-core academic disciplines and insufficient attention for its implementation through the English language course. In the process of bridging this gap, a key research question arose: how to encourage professional creativity of engineering students in the English language classroom? The purpose of the article is to study and identify the most versatile and effective learning techniques and strategies promoting creative abilities for Food Science majors. Teaching experience in various universities helped to identify the following effective learning strategies and techniques: the method of interdisciplinary projects, business games, presentations, the interdisciplinary case-study method, and authentic texts. All of these strategies are versatile and can be easily used through the English language course to develop a creative component of professional competence for future engineers.
Ranked Enumeration of Conjunctive Query Results

We study the problem of enumerating answers of Conjunctive Queries ranked according to a given ranking function. Our main contribution is a novel algorithm with small preprocessing time, logarithmic delay, and non-trivial space usage during execution. To allow for efficient enumeration, we exploit certain properties of ranking functions that frequently occur in practice. To this end, we introduce the notions of decomposable and compatible (w.r.t. a query decomposition) ranking functions, which allow for partial aggregation of tuple scores in order to efficiently enumerate the output. We complement the algorithmic results with lower bounds that justify why restrictions on the structure of ranking functions are necessary. Our results extend and improve upon a long line of work that has studied ranked enumeration from both a theoretical and practical perspective.
Rotated rectangular aperture imaging through multi-frame blind deconvolution with Hyper-Laplacian priors

Rotated rectangular aperture imaging has many advantages in large aperture telephoto systems due to its lower cost and lower complexity. This technology makes it possible to build super large aperture telescopes. In this paper, we combine the ideas of deblurring with rotated rectangular aperture imaging and propose an image synthesis algorithm based on multi-frame deconvolution. In the specific reconstruction process, Hyper-Laplacian priors and sparse priors are used, and an effective solution is developed. The simulation and real shooting experiments show that our algorithm has excellent performance in visual effect and objective evaluation. The synthetic images are significantly sharper than the results of the existing methods.
Towards Key-recovery-attack Friendly Distinguishers: Application to GIFT-128

When analyzing a block cipher, the first step is to search for some valid distinguishers, for example, the differential trails in the differential cryptanalysis and the linear trails in the linear cryptanalysis. A distinguisher is advantageous if it can be utilized to attack more rounds and the amount of the involved key bits during the key-recovery process is small, as this leads to a long attack with a low complexity. In this article, we propose a two-step strategy to search for such advantageous distinguishers. This strategy is inspired by the intuition that if a differential is advantageous only when some properties are satisfied, then we can predefine some constraints describing these properties and search for the differentials in the small set.As applications, our strategy is used to analyze GIFT-128, which was proposed in CHES 2017. Based on some 20-round differentials, we give the first 27-round differential attack on GIFT-128, which covers one more round than the best previous result. Also, based on two 17-round linear trails, we give the first linear hull attack on GIFT-128, which covers 22 rounds. In addition, we also give some results on two GIFT-128 based AEADs GIFT-COFB and SUNDAE-GIFT.
Application of ultrasonic dual-mode artificially intelligent architecture in assisting radiologists with different diagnostic levels on breast masses classification

Purpose We aimed to compare the diagnostic performance and interobserver variability in breast tumor classification with or without the aid of an innovative dual-mode artificial intelligence (AI) architecture, which can automatically integrate information from ultrasonography (US) and shear-wave elastography (SWE). Methods Diagnostic performance assessment was performed with a test subset, containing 599 images (from September 2018 to February 2019) from 91 patients including 64 benign and 27 malignant breast tumors. Six radiologists (three inexperienced, three experienced) were assigned to read images independently (independent diagnosis) and then make a secondary diagnosis with the knowledge of AI results. Sensitivity, specificity, accuracy, receiver-operator characteristics (ROC) curve analysis and Cohen's κ statistics were calculated. Results In the inexperienced radiologists' group, the average area under the ROC curve (AUC) for diagnostic performance increased from 0.722 to 0.765 (p = 0.050) with secondary diagnosis using US-mode and from 0.794 to 0.834 (p = 0.019) with secondary diagnosis using dual-mode compared with independent diagnosis. In the experienced radiologists' group, the average AUC for diagnostic performance was significantly higher with AI system using the US-mode (0.812 vs. 0.833, p = 0.039), but not for dual-mode (0.858 vs. 0.866, p = 0.458). Using the US-mode, interobserver agreement among all radiologists improved from fair to moderate (p = 0.003). Using the dual-mode, substantial agreement was seen among the experienced radiologists (0.65 to 0.74, p = 0.017) and all radiologists (0.62 to 0.73, p = 0.001). Conclusion AI assistance provides a more pronounced improvement in diagnostic performance for the inexperienced radiologists; meanwhile, the experienced radiologists benefit more from AI in reducing interobserver variability.
Maneuvering Control of Hexrotor UAV Equipped With a Cable-Driven Gripper

This study proposes a control method for the maneuvering control of an aircraft system equipped with a cable-driven gripper. The UAV chosen for the study is a hex-rotor due to its durability and redundancy. The reason for opting for the cable-driven robotic arm is because of its extended range as compared to a fixed robotic arm. The aircraft system also contains an Ultra HD camera which helps to locate and identify the target object. Since the UAV grabs the object, the extra weight introduces some instability in the system. To help stabilize the dynamic behavior of the gripper and reduce external wind disturbance, we need an effective and robust control mechanism. To tackle these issues, this paper designs a control method based on the Model Reference Adaptive Control with an integrator (MRACI) with reference dynamics like an observer. On one hand, first, verify the performance of the proposed controller using computer-based simulations. On the other hand, it also applies the designed method to hardware-based results. The simulations and experimental results show that the designed controller works as intended and the errors are within an acceptable range.
Graph Learning Based Head Movement Prediction for Interactive 360 Video Streaming

Ultra-high definition (UHD) 360 videos encoded in fine quality are typically too large to stream in its entirety over bandwidth (BW)-constrained networks. One popular approach is to interactively extract and send a spatial sub-region corresponding to a viewer’s current field-of-view (FoV) in a head-mounted display (HMD) for more BW-efficient streaming. Due to the non-negligible round-trip-time (RTT) delay between server and client, accurate head movement prediction foretelling a viewer’s future FoVs is essential. In this paper, we cast the head movement prediction task as a sparse directed graph learning problem: three sources of relevant information—collected viewers’ head movement traces, a 360 image saliency map, and a biological human head model—are distilled into a view transition Markov model. Specifically, we formulate a constrained maximum a posteriori (MAP) problem with likelihood and prior terms defined using the three information sources. We solve the MAP problem alternately using a hybrid iterative reweighted least square (IRLS) and Frank-Wolfe (FW) optimization strategy. In each FW iteration, a linear program (LP) is solved, whose runtime is reduced thanks to warm start initialization. Having estimated a Markov model from data, we employ it to optimize a tile-based 360 video streaming system. Extensive experiments show that our head movement prediction scheme noticeably outperformed existing proposals, and our optimized tile-based streaming scheme outperformed competitors in rate-distortion performance.