Reviewerly/exHarmony huggingface dataset:
Insured MPC: Efficient Secure Computation with Financial Penalties

Fairness in Secure Multiparty Computation (MPC) is known to be impossible to achieve in the presence of a dishonest majority. Previous works have proposed combining MPC protocols with cryptocurrencies in order to financially punish aborting adversaries, providing an incentive for parties to honestly follow the protocol. The focus of existing work is on proving that this approach is possible and unfortunately they present monolithic and mostly inefficient constructions. In this work, we put forth the first UC secure modular construction of “Insured MPC”, where either the output of the private computation (which describes how to distribute funds) is fairly delivered or a proof that a set of parties has misbehaved is produced, allowing for financial punishments. Moreover, both the output and the proof of cheating are publicly verifiable, allowing third parties to independently validate an execution. We present an efficient compiler that implements Insured MPC from an MPC protocol with certain properties, a standard (non-private) Smart Contract and a publicly verifiable homomorphic commitment scheme. As an intermediate step, we propose the first construction of a publicly verifiable homomorphic commitment scheme with composability guarantees.
Interpretable duplicate question detection models based on attention mechanism

Recently, there exist growing concerns about the interpretability of deep learning models. While few of these models have been applied to a duplicate question detection task, which aims at finding semantically equivalent question pairs of question answering forum. In this paper, based on an attention mechanism, we propose two modularized interpretable deep neural network models for such tasks. During the word precessing procedure, a filter operation is employed to enhance the relevant information contained in the pre-trained word embeddings. Regarding the word matching and sentence representation process, vanilla attention and structured attention mechanisms are utilized, respectively. Benefiting from the interpretability of attention techniques, our models can illustrate how the words match between sentence pairs and what aspects of the sentences are extracted to have an effect on the final decision. The attention visualization furnishes us with detailed representation at word and sentence level. And experimental results show that our models are comparable with other reported models.
From Machine Reading Comprehension to Dialogue State Tracking: Bridging the Gap

Dialogue state tracking (DST) is at the heart of task-oriented dialogue systems.However, the scarcity of labeled data is an obstacle to building accurate and robust state tracking systems that work across a variety of domains.Existing approaches generally require some dialogue data with state information and their ability to generalize to unknown domains is limited.In this paper, we propose using machine reading comprehension (RC) in state tracking from two perspectives: model architectures and datasets.We divide the slot types in dialogue state into categorical or extractive to borrow the advantages from both multiplechoice and span-based reading comprehension models.Our method achieves near the current state-of-the-art in joint goal accuracy on Mul-tiWOZ 2.1 given full training data.More importantly, by leveraging machine reading comprehension datasets, our method outperforms the existing approaches by many a large margin in few-shot scenarios when the availability of in-domain data is limited.Lastly, even without any state tracking data, i.e., zero-shot scenario, our proposed approach achieves greater than 90% average slot accuracy in 12 out of 30 slots in MultiWOZ 2.1.
On-chip arbitrary-mode spot size conversion

Abstract Manipulating on-chip optical modes via components in analogy with free-space devices provides intuitional light control, and this concept has been adopted to implement single-lens–assisted spot size conversion using integrated device. However, the reported schemes have been demonstrated only for fundamental mode, while high-order or irregular modes are preferred in specific applications. The 4-f system is widely used in Fourier optics for optical information processing. Under the inspiration of the 4-f system and the beam expander in bulk optics, a spot size converter (SSC) with two metamaterial-based graded-index waveguides is proposed and demonstrated. The proposed device is capable of widening an arbitrary mode while preserving its profile shape. Compared with conventional SSC using adiabatic taper, the footprint can be reduced by 91.5% under a same intermode crosstalk. Experimentally, an expansion ratio of five is demonstrated for regular modes. Furthermore, for an irregular mode, the functionality is numerically verified without structure modification. This work offers a universal solution to on-chip spot size conversion and may broaden the on-chip application prospects of Fourier optics.
A Comparison of Hour-Ahead Solar Irradiance Forecasting Models Based on LSTM Network

The intermittence and fluctuation character of solar irradiance places severe limitations on most of its applications. The precise forecast of solar irradiance is the critical factor in predicting the output power of a photovoltaic power generation system. In the present study, Model I-A and Model II-B based on traditional long short-term memory (LSTM) are discussed, and the effects of different parameters are investigated; meanwhile, Model II-AC, Model II-AD, Model II-BC, and Model II-BD based on a novel LSTM-MLP structure with two-branch input are proposed for hour-ahead solar irradiance prediction. Different lagging time parameters and different main input and auxiliary input parameters have been discussed and analyzed. The proposed method is verified on real data over 5 years. The experimental results demonstrate that Model II-BD shows the best performance because it considers the weather information of the next moment, the root mean square error (RMSE) is 62.1618 W/m 2 , the normalized root mean square error (nRMSE) is 32.2702%, and the forecast skill (FS) is 0.4477. The proposed algorithm is 19.19% more accurate than the backpropagation neural network (BPNN) in terms of RMSE.
A benchmark for clothes variation in person re‐identification

Person re-identification (re-ID) has drawn attention significantly in the computer vision society due to its application and research significance. It aims to retrieve a person of interest across different camera views. However, there are still several factors that hinder the applications of person re-ID. In fact, most common data sets either assume that pedestrians do not change their clothing across different camera views or are taken under constrained environments. Those constraints simplify the person re-ID task and contribute to early development of person re-ID, yet a person has a great possibility to change clothes in real life. To facilitate the research toward conquering those issues, this paper mainly introduces a new benchmark data set for person re-identification. To the best of our knowledge, this data set is currently the most diverse for person re-identification. It contains 107 persons with 9,738 images, captured in 15 indoor/outdoor scenes from September 2019 to December 2019, varying according to viewpoints, lighting, resolutions, human pose, seasons, backgrounds, and clothes especially. We hope that this benchmark data set will encourage further research on person re-identification with clothes variation. Moreover, we also perform extensive analyses on this data set using several state-of-the-art methods. Our dataset is available at https://github.com/nkicsl/NKUP-dataset.
FPGA implementation of hardware architecture with AES encryptor using sub-pipelined S-box techniques for compact applications

Advanced Encryption Standard (AES) is a thriving cryptographic algorithm that can be utilized to guarantee security in electronic information. It remains to uphold to be resistive from most of the attacks. In this work, AES-128 encryption iterative architecture is designed to achieve minimum area and less hardware utilization. Reduced area is attained by introducing a renovated S-box structure into the AES algorithm. Furthermore, hardware utilization is minimized by incorporating the Vedic multiplier in the Mix column transformation of the AES Encryption process. The proposed encryption architecture is of 128-bit size and was executed on the Xilinx Spartan FPGA series, namely, Spartan 3, Virtex-4 and Virtex-5 devices. The optimization result exhibits that the proposed S-box technique has a smaller area than other existing conventional works.
Knowledge Graph Construction and Application of Power Grid Equipment

Recent development of artificial intelligence (AI) technology enquires the traditional power grid system involving additional information and connectivity of all devices for the smooth transit to the next generation of smart grid system. In an AI-enhanced power grid system, each device has its unique name, function, property, location, and many more. A large number of power grid devices can form a complex power grid knowledge graph through serial and parallel connection relationships. The scale of power grid equipment is usually extremely large, with thousands and millions of power devices. Finding the proper way of understanding and operating these devices is difficult. Furthermore, the collection, analysis, and management of power grid equipment become major problems in power grid management. With the development of AI technology, the combination of labeling technology and knowledge graph technology provides a new solution understanding the internal structure of a power grid. As a result, this study focuses on knowledge graph construction techniques for large scale power grid located in China. A semiautomatic knowledge graph construction technology is proposed and applied to the power grid equipment system. Through a series of experimental simulations, we show that the efficiency of daily operations, maintenance, and management of the power grid can be largely improved.
Low Latency End-to-End Streaming Speech Recognition with a Scout Network

The attention-based Transformer model has achieved promising results for speech recognition (SR) in the offline mode. However, in the streaming mode, the Transformer model usually incurs significant latency to maintain its recognition accuracy when applying a fixed-length look-ahead window in each encoder layer. In this paper, we propose a novel low-latency streaming approach for Transformer models, which consists of a scout network and a recognition network. The scout network detects the whole word boundary without seeing any future frames, while the recognition network predicts the next subword by utilizing the information from all the frames before the predicted boundary. Our model achieves the best performance (2.7/6.4 WER) with only an average of 639 ms latency on the test-clean and test-other data sets of Librispeech.
A stochastic multi-agent approach for medical-image segmentation: Application to tumor segmentation in brain MR images

According to functional or anatomical modalities, medical imaging provides a visual representation of complex structures or activities in the human body. One of the most common processing methods applied to those images is segmentation, in which an image is divided into a set of regions of interest. Human anatomical complexity and medical image acquisition artifacts make segmentation of medical images very complex. Thus, several solutions have been proposed to automate image segmentation. However, most existing solutions use prior knowledge and/or require strong interaction with the user. In this paper, we propose a multi-agent approach for the segmentation of 3D medical images. This approach is based on a set of autonomous, interactive agents that use a modified region growing algorithm and cooperate to segment a 3D image. The first organization of agents allows region seed placement and region growing. In a second organization, agent interaction and collaboration allow segmentation refinement by merging the over-segmented regions. Experiments are conducted on magnetic resonance images of healthy and pathological brains. The obtained results are promising and demonstrate the efficiency of our method.
Interval type-2 fuzzy logic system based similarity evaluation for image steganography

Similarity measure, also called information measure, is a concept used to distinguish different objects. It has been studied from different contexts by employing mathematical, psychological, and fuzzy approaches. Image steganography is the art of hiding secret data into an image in such a way that it cannot be detected by an intruder. In image steganography, hiding secret data in the plain or non-edge regions of the image is significant due to the high similarity and redundancy of the pixels in their neighborhood. However, the similarity measure of the neighboring pixels, i.e., their proximity in color space, is perceptual rather than mathematical. This paper proposes an interval type 2 fuzzy logic system (IT2 FLS) to determine the similarity between the neighboring pixels by involving an instinctive human perception through a rule-based approach. The pixels of the image having high similarity values, calculated using the proposed IT2 FLS similarity measure, are selected for embedding via the least significant bit (LSB) method. We term the proposed procedure of steganography as IT2 FLS LSB method. Moreover, we have developed two more methods, namely, type 1 fuzzy logic system based least significant bits (T1FLS LSB) and Euclidean distance based similarity measures for least significant bit (SM LSB) steganographic methods. Experimental simulations were conducted for a collection of images and quality index metrics, such as PSNR, UQI, and SSIM are used. All the three steganographic methods are applied on datasets and the quality metrics are calculated. The obtained stego images and results are shown and thoroughly compared to determine the efficacy of the IT2 FLS LSB method. Finally, we have done a comparative analysis of the proposed approach with the existing well-known steganographic methods to show the effectiveness of our proposed steganographic method.
Supermasks in Superposition

We present the Supermasks in Superposition (SupSup) model, capable of sequentially learning thousands of tasks without catastrophic forgetting. Our approach uses a randomly initialized, fixed base network and for each task finds a subnetwork (supermask) that achieves good performance. If task identity is given at test time, the correct subnetwork can be retrieved with minimal memory usage. If not provided, SupSup can infer the task using gradient-based optimization to find a linear superposition of learned supermasks which minimizes the output entropy. In practice we find that a single gradient step is often sufficient to identify the correct mask, even among 2500 tasks. We also showcase two promising extensions. First, SupSup models can be trained entirely without task identity information, as they may detect when they are uncertain about new data and allocate an additional supermask for the new training distribution. Finally the entire, growing set of supermasks can be stored in a constant-sized reservoir by implicitly storing them as attractors in a fixed-sized Hopfield network.
PALM: Pre-training an Autoencoding&amp;Autoregressive Language Model for Context-conditioned Generation

Self-supervised pre-training, such as BERT (Devlin et al., 2018), MASS (Song et al., 2019) and BART (Lewis et al., 2019), has emerged as a powerful technique for natural language understanding and generation.Existing pre-training techniques employ autoencoding and/or autoregressive objectives to train Transformer-based models by recovering original word tokens from corrupted text with some masked tokens.The training goals of existing techniques are often inconsistent with the goals of many language generation tasks, such as generative question answering and conversational response generation, for producing new text given context.This work presents PALM with a novel scheme that jointly pre-trains an autoencoding and autoregressive language model on a large unlabeled corpus, specifically designed for generating new text conditioned on context.The new scheme alleviates the mismatch introduced by the existing denoising scheme between pre-training and fine-tuning where generation is more than reconstructing original text.An extensive set of experiments show that PALM achieves new state-of-theart results on a variety of language generation benchmarks covering generative question answering (Rank 1 on the official MARCO leaderboard), abstractive summarization on CNN/DailyMail as well as Gigaword, question generation on SQuAD, and conversational response generation on Cornell Movie Dialogues.
Collaborative Learning of Gesture Recognition and 3D Hand Pose Estimation with Multi-order Feature Analysis

Gesture recognition and 3D hand pose estimation are two highly correlated tasks, yet they are often handled separately. In this paper, we present a novel collaborative learning network for joint gesture recognition and 3D hand pose estimation. The proposed network exploits joint-aware features that are crucial for both tasks, with which gesture recognition and 3D hand pose estimation boost each other to learn highly discriminative features. In addition, a novel multi-order multi-stream feature analysis method is introduced which learns posture and multi-order motion information from the intermediate feature maps of videos effectively and efficiently. Due to the exploitation of joint-aware features in common, the proposed technique is capable of learning gesture recognition and 3D hand pose estimation even when only gesture or pose labels are available, and this enables weakly supervised network learning with much reduced data labeling efforts. Extensive experiments show that our proposed method achieves superior gesture recognition and 3D hand pose estimation performance as compared with the state-of-the-art.
Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses

This paper focuses on learning transferable adversarial examples specifically against defense models (models to defense adversarial attacks). In particular, we show that a simple universal perturbation can fool a series of state-of-the-art defenses. Adversarial examples generated by existing attacks are generally hard to transfer to defense models. We observe the property of regional homogeneity in adversarial perturbations and suggest that the defenses are less robust to regionally homogeneous perturbations. Therefore, we propose an effective transforming paradigm and a customized gradient transformer module to transform existing perturbations into regionally homogeneous ones. Without explicitly forcing the perturbations to be universal, we observe that a well-trained gradient transformer module tends to output input-independent gradients (hence universal) benefiting from the under-fitting phenomenon. Thorough experiments demonstrate that our work significantly outperforms the prior art attacking algorithms (either image-dependent or universal ones) by an average improvement of 14.0% when attacking 9 defenses in the transfer-based attack setting. In addition to the cross-model transferability, we also verify that regionally homogeneous perturbations can well transfer across different vision tasks (attacking with the semantic segmentation task and testing on the object detection task). The code is available here: https://github.com/LiYingwei/Regional-Homogeneity .
Machine learning approaches classify clinical malaria outcomes based on haematological parameters

Abstract Background Malaria is still a major global health burden, with more than 3.2 billion people in 91 countries remaining at risk of the disease. Accurately distinguishing malaria from other diseases, especially uncomplicated malaria (UM) from non-malarial infections (nMI), remains a challenge. Furthermore, the success of rapid diagnostic tests (RDTs) is threatened by Pfhrp2/3 deletions and decreased sensitivity at low parasitaemia. Analysis of haematological indices can be used to support the identification of possible malaria cases for further diagnosis, especially in travellers returning from endemic areas. As a new application for precision medicine, we aimed to evaluate machine learning (ML) approaches that can accurately classify nMI, UM, and severe malaria (SM) using haematological parameters. Methods We obtained haematological data from 2,207 participants collected in Ghana: nMI ( n = 978), SM ( n = 526), and UM ( n = 703). Six different ML approaches were tested, to select the best approach. An artificial neural network (ANN) with three hidden layers was used for multi-classification of UM, SM, and uMI. Binary classifiers were developed to further identify the parameters that can distinguish UM or SM from nMI. Local interpretable model-agnostic explanations (LIME) were used to explain the binary classifiers. Results The multi-classification model had greater than 85% training and testing accuracy to distinguish clinical malaria from nMI. To distinguish UM from nMI, our approach identified platelet counts, red blood cell (RBC) counts, lymphocyte counts, and percentages as the top classifiers of UM with 0.801 test accuracy (AUC = 0.866 and F1 score = 0.747). To distinguish SM from nMI, the classifier had a test accuracy of 0.96 (AUC = 0.983 and F1 score = 0.944) with mean platelet volume and mean cell volume being the unique classifiers of SM. Random forest was used to confirm the classifications, and it showed that platelet and RBC counts were the major classifiers of UM, regardless of possible confounders such as patient age and sampling location. Conclusion The study provides proof of concept methods that classify UM and SM from nMI, showing that the ML approach is a feasible tool for clinical decision support. In the future, ML approaches could be incorporated into clinical decision-support algorithms for the diagnosis of acute febrile illness and monitoring response to acute SM treatment particularly in endemic settings.
Morphological segmentation method for Turkic language neural machine translation

Dictionaries play an important role in neural machine translation (NMT). However, a large dictionary requires a significant amount of memory, which limits the application of NMT and can cause a memory error. This limitation can be solved by segmenting each word into morphemes in parallel source corpora. Therefore, this study introduces a new morphological segmentation approach for Turkic languages based on the complete set of endings (CSE), which reduces the vocabulary volume of the source corpora. Herein, we demonstrate the proposed CSE-based morphological segmentation method for the Kazakh, Kyrgyz, and Uzbek languages and present the results of computational NMT experiments for the Kazakh language. The NMT experiment results show that in comparison with byte-pair encoding (BPE)-based segmentation, the proposed CSE-based segmentation increases the bilingual evaluation understudy score of 0.5 and 0.2 points on average for Kazakh–English and English–Kazakh pairs, respectively. Furthermore, in comparison with the BPE-based segmentation, the proposed CSE-based segmentation approach reduced the vocabulary size in NMT by more than a factor of two. This feature of the proposed segmentation approach will be crucial for NMT as the size of the source corpora is increased to improve translation quality.