Reviewerly/exHarmony huggingface dataset:
ON-TRAC Consortium Systems for the IWSLT 2022 Dialect and Low-resource Speech Translation Tasks

This paper describes the ON-TRAC Consortium translation systems developed for two challenge tracks featured in the Evaluation Campaign of IWSLT 2022: low-resource and dialect speech translation.For the Tunisian Arabic-English dataset (low-resource and dialect tracks), we build an end-to-end model as our joint primary submission, and compare it against cascaded models that leverage a large fine-tuned wav2vec 2.0 model for ASR.Our results show that in our settings pipeline approaches are still very competitive, and that with the use of transfer learning, they can outperform end-to-end models for speech translation (ST).For the Tamasheq-French dataset (low-resource track) our primary submission leverages intermediate representations from a wav2vec 2.0 model trained on 234 hours of Tamasheq audio, while our contrastive model uses a French phonetic transcription of the Tamasheq audio as input in a Conformer speech translation architecture jointly trained on automatic speech recognition, ST and machine translation losses.Our results highlight that self-supervised models trained on smaller sets of target data are more effective to low-resource end-to-end ST fine-tuning, compared to large off-the-shelf models.Results also illustrate that even approximate phonetic transcriptions can improve ST scores.
Using LARA to create image-based and phonetically annotated multimodal texts for endangered languages

We describe recent extensions to the open source Learning And Reading Assistant (LARA) supporting image-based and phonetically annotated texts.We motivate the utility of these extensions both in general and specifically in relation to endangered and archaic languages, and illustrate with examples from the revived Australian language Barngarla, Icelandic Sign Language, Irish Gaelic, Old Norse manuscripts and Egyptian hieroglyphics.
Scene-Text Aware Image and Text Retrieval with Dual-Encoder

We tackle the tasks of image and text retrieval using a dual-encoder model in which images and text are encoded independently.This model has attracted attention as an approach that enables efficient offline inferences by connecting both vision and language in the same semantic space.However, whether an image encoder as part of a dual-encoder model can interpret scene-text, i.e., the textual information in images, is unclear.We propose pre-training methods that encourage a joint understanding of the scene-text and surrounding visual information.The experimental results demonstrate that our methods improve the retrieval performances of the dual-encoder models.
Neural Machine Translation for Fact-checking Temporal Claims

Computational fact-checking aims at supporting the verification process of textual claims by exploiting trustworthy sources.However, there are large classes of complex claims that cannot be automatically verified, for instance those related to temporal reasoning.To this aim, in this work, we focus on the verification of economic claims against time series sources.Starting from given textual claims in natural language, we propose a neural machine translation approach to produce respective queries expressed in a recently proposed temporal fragment of the Datalog language.The adopted deep neural approach shows promising preliminary results for the translation of 10 categories of claims extracted from real use cases.
SSN_ARMM@ LT-EDI -ACL2022: Hope Speech Detection for Equality, Diversity, and Inclusion Using ALBERT model

In recent years social media has become one of the major forums for expressing human views and emotions.With the help of smartphones and high-speed internet, anyone can express their views on Social media.However, this can also lead to the spread of hatred and violence in society.Therefore it is necessary to build a method to find and support helpful social media content.In this paper, we studied Natural Language Processing approach for detecting Hope speech in a given sentence.The task was to classify the sentences into 'Hope speech' and 'Non-hope speech'.The dataset was provided by LT-EDI organizers with text from Youtube comments.Based on the task description, we developed a system using the pre-trained language model BERT to complete this task.Our model achieved 1st rank in the Kannada language with a weighted average F1 score of 0.750, 2nd rank in the Malayalam language with a weighted average F1 score of 0.740, 3rd rank in the Tamil language with a weighted average F1 score of 0.390 and 6th rank in the English language with a weighted average F1 score of 0.880.
Medical Devices Management System Based on Blockchain Technology

Abstract The recent use of digital Distributed Ledger Technology (DLT) in the healthcare domain can surpass the existing limitations in the centralized IT systems, such as the lack of security, access control or immutability of the electronic health information. If we discuss about clinical information, this innovative informatics advance gives back the control to the data owner. In decentralized environments, smart contracts allow trustable agreements grounded by irreversible transactions, which permit transparency and traceability. Moreover, smart contracts are the living heart of the decentralized applications that run on DTL. In this work, our attention is focused on medical equipments that helps health staff to diagnose and treat patients keeping much of their clinical data taken in dynamics. Hence, we propose a smart contract-based decentralized application framework for the management of devices, targeting also medical services, meant to facilitate the interaction of the involved entities. Our testing environment is the Ethereum platform, extensively used recently in the healthcare domain, being itself a smart operating system that allows decentralized applications to run on it.
Self-protected adiabatic quantum computation

Recent experiments show the existence of collective decoherence in quantum systems. We study the possibility of quantum computation in a decoherence-free subspace which is robust against such kinds of decoherence processes. This passive protection protocol can be especially advantageous for continuous quantum computation such as quantum annealers. As an example we propose to use decoherence-protected adiabatic quantum computation for the Grover search problem.
Paint and Distill

3D object detection task from lidar or camera sensors is essential for autonomous driving. Pioneer attempts at multi-modality fusion complement the sparse lidar point clouds with rich semantic texture information from images at the cost of extra network designs and overhead. In this work, we propose a novel semantic passing framework, named SPNet, to boost the performance of existing lidar-based 3D detection models with the guidance of rich context painting, with no extra computation cost during inference. Our key design is to first exploit the potential instructive semantic knowledge within the ground-truth labels by training a semantic-painted teacher model and then guide the pure-lidar network to learn the semantic-painted representation via knowledge passing modules at different granularities: class-wise passing, pixel-wise passing and instance-wise passing. Experimental results show that the proposed SPNet can seamlessly cooperate with most existing 3D detection frameworks with 1$\sim$5% AP gain and even achieve new state-of-the-art 3D detection performance on the KITTI test benchmark. Code is available at: https://github.com/jb892/SPNet.
Region of Interest Encryption Based on Novel 2D Hyperchaotic Signal and Bagua Coding Algorithm

In recent years, image processing has attracted a lot of attention due to its high accuracy to detect and classify objects in images. Therefore, based on the encryption algorithm, this paper adds deep learning to construct an algorithm that uses a hyperchaotic system for the region of interest image encryption and explores its security. First, we design a novel two-dimensional hyperchaotic map, and for the first time, we use a coding architecture called Bagua coding. We combine the above two points to enhance the effect of the permutation process, and consequently, the complexity of our encryption scheme. The algorithm also uses the features extraction on the plaintext and SHA-256 to generate secret key, coupled with the advanced exclusive-or operation and bit shift calculation to encrypt the plaintext. Next, we import YoloV3 and UNet for object detection and selection. Users can automatically select the region of interest on the image and use an encryption algorithm to encrypt the selected part of the irregular region. Finally, we perform security analysis on ciphertext image. The security analysis results on the generated ciphertext image validate our proposed encryption framework against statistical and differential attack.
Model-Based Offline Planning with Trajectory Pruning

The recent offline reinforcement learning (RL) studies have achieved much progress to make RL usable in real-world systems by learning policies from pre-collected datasets without environment interaction. Unfortunately, existing offline RL methods still face many practical challenges in real-world system control tasks, such as computational restriction during agent training and the requirement of extra control flexibility. The model-based planning framework provides an attractive alternative. However, most model-based planning algorithms are not designed for offline settings. Simply combining the ingredients of offline RL with existing methods either provides over-restrictive planning or leads to inferior performance. We propose a new light-weighted model-based offline planning framework, namely MOPP, which tackles the dilemma between the restrictions of offline learning and high-performance planning. MOPP encourages more aggressive trajectory rollout guided by the behavior policy learned from data, and prunes out problematic trajectories to avoid potential out-of-distribution samples. Experimental results show that MOPP provides competitive performance compared with existing model-based offline planning and RL approaches.
Rethinking Crowdsourcing Annotation: Partial Annotation With Salient Labels for Multilabel Aerial Image Classification

Annotated images are required for supervised model training and evaluation in aerial image classification. Manually annotating images is arduous and expensive, especially for aerial images, which often cover a large land area with multiple labels. A recent trend for conducting such annotation tasks is through crowdsourcing, where images are annotated by volunteers or paid workers (e.g., annotation volunteers for Open Street Map) online from scratch. However, for crowdsourcing image annotations, the quality cannot be guaranteed, and incompleteness and incorrectness are two major concerns. To address such concerns, we have a rethinking of crowdsourcing annotations: Our simple hypothesis is that if annotators only partially annotate multi-label images with salient labels they are confident in, there will be fewer annotation errors and annotators will spend less time on uncertain labels. As a pleasant surprise, with the same annotation budget, we show that a multi-label aerial image classifier supervised by images with salient annotations can outperform models supervised by fully annotated images. Our contributions are 2-fold: An active learning way is proposed to acquire salient labels for multi-label aerial images; and a novel Adaptive Temperature Associated Model (ATAM) specifically using partial annotations is proposed for multi-label aerial image classification. When tested on practical crowdsourcing aerial data, the Open Street Map (OSM) dataset, the proposed ATAM can achieve higher accuracy than state-of-the-art classification methods trained on fully annotated images. The proposed idea is promising for crowdsourcing aerial image annotation. Our code will be publicly available.
Deep fake detection using a sparse auto encoder with a graph capsule dual graph CNN

Deepfake (DF) is a kind of forged image or video that is developed to spread misinformation and facilitate vulnerabilities to privacy hacking and truth masking with advanced technologies, including deep learning and artificial intelligence with trained algorithms. This kind of multimedia manipulation, such as changing facial expressions or speech, can be used for a variety of purposes to spread misinformation or exploitation. This kind of multimedia manipulation, such as changing facial expressions or speech, can be used for a variety of purposes to spread misinformation or exploitation. With the recent advancement of generative adversarial networks (GANs) in deep learning models, DF has become an essential part of social media. To detect forged video and images, numerous methods have been developed, and those methods are focused on a particular domain and obsolete in the case of new attacks/threats. Hence, a novel method needs to be developed to tackle new attacks. The method introduced in this article can detect various types of spoofs of images and videos that are computationally generated using deep learning models, such as variants of long short-term memory and convolutional neural networks. The first phase of this proposed work extracts the feature frames from the forged video/image using a sparse autoencoder with a graph long short-term memory (SAE-GLSTM) method at training time. The first phase of this proposed work extracts the feature frames from the forged video/image using a sparse autoencoder with a graph long short-term memory (SAE-GLSTM) method at training time. The proposed DF detection model is tested using the FFHQ database, 100K-Faces, Celeb-DF (V2) and WildDeepfake. The evaluated results show the effectiveness of the proposed method.
Study of industrial interactive design system based on virtual reality teaching technology in industrial robot

Abstract Across numerous disciplines, virtual reality (VR) had been used to aid decision-making in training, design, and evaluation processes. Both the educational and industrial groups have contributed to a vast knowledge based on a variety of VR topics during the last two decades. VR has been expanded to industry in recent years, but the majority of its applications do not involve industrial robots. To study the application of VR technology in industrial design, it is better to combine the design activities with computer-integrated manufacturing system and bring new opportunities for the innovation of industrial design. Therefore, in this article, an application of industrial interactive design system based on VR technology in the education domain is explored. First, the function and scheme design of industrial robot assembly and adjustment system are designed, and the model is established. Finally, SolidWorks and 3DsMAX are selected as three-dimensional model development tools. Unity 3D is used as the VR development engine; HTC VIVE is used as VR equipment. The study shows that the design of the machine motion instruction interpreter is effective, and the specific steps of the system to realize real-time control are also given. The feasibility of the system is verified through the analysis of typical applications of industrial robots.
Design-time Performability Optimization of Runtime Adaptation Strategies

Self-Adaptive Systems (SASs) adapt themselves to environmental changes during runtime to maintain Quality of Service (QoS) goals. Designing and optimizing the adaptation strategy of an SAS regarding its impact on quality properties is a challenging problem. Usually the design space of adaptation strategies is too large to be explored manually and, hence, requires automated support to find optimal strategies. Most approaches address this problem with optimization at runtime requiring the system is already implemented. However, one expects design-time optimized adaptation strategies to more effectively maintain QoS goals than purely runtime optimized strategies. Also formal guarantees benefit from designed and analysed strategies. We claim that design-time analysis and optimization of adaptation strategies improve in particular quality properties such as performability. To address the research gap between runtime optimization and the ability to make statements on the achieved quality, we envision an approach that builds upon the concept of Model-Based Quality Analysis (MBQA). Many approaches in MBQA address single aspects such as formal languages for adaptation strategies, architectural description languages or QoS prediction. However, they lack integration, which leads, for example to prediction approaches assuming rather static systems. In this paper, we envision an unified approach by considering several sub-approaches as building blocks for performability-based optimization of adaptation strategies at design-time.
Facial Expression Recognition from a Single Face Image Based on Deep Learning and Broad Learning

With advances in computer vision and artificial intelligence technology, facial expression recognition research has become a prominent topic. Current research is grappling with how to enable computers to fully understand expression features and improve recognition rates. Most single face image datasets are based on the psychological classification of the six basic human expressions used for network training. By outlining the problem of facial recognition by comparing traditional methods, deep learning, and broad learning techniques, this review highlights the remaining challenges and future directions of deep learning and broad learning research. The deep learning method has made it easier and more effective to extract expression features and improve facial expression recognition accuracy by end-to-end feature learning, but there are still many difficulties in robustness and real-time performance. The broad learning system (BLS) is a broad network structure that is expanded by increasing the number of feature nodes and enhancement nodes appropriately to reinforce the structure and is also effective in facial expression recognition. However, outliers and noises in unbalanced datasets need BLS to solve in the future. Finally, we present several problems that still need to be addressed in facial expression recognition.
Hybrid machine learning approach for anomaly detection

This research aims to &lt;span lang="EN-US"&gt;improve anomaly detection performance by developing two variants of hybrid models combining supervised and unsupervised machine learning techniques. Supervised models cannot detect new or unseen types of anomaly. Hence in variant 1, a supervised model that detects normal samples is followed by an unsupervised learning model to screen anomaly. The unsupervised model is weak in differentiating between noise and fraud. Hence in variant 2, the hybrid model incorporates an unsupervised model that detects anomaly is followed by a supervised model to validate an anomaly. Three different datasets are used for model evaluation. The experiment is begun with 5 supervised models and 3 unsupervised models. After performance evaluation, 2 supervised models with the highest F1-Score and one unsupervised model with the best recall value are selected for hybrid model development. The variant 1 hybrid model recorded the best recall value across all the experiments, indicating that it is the best at detecting actual fraud and less likely to miss it compared to other models. The variant 2 hybrid model can improve the precision score significantly compared to the original unsupervised model, indicating that it is better in separating noise from fraud,&lt;/span&gt;
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The ZpZp2-additive codes are subgroups of Zpα1×Zp2α2, and can be seen as linear codes over Zp when α2=0, Zp2-additive codes when α1=0, or Z2Z4-additive codes when p=2. A ZpZp2-linear generalized Hadamard (GH) code is a GH code over Zp which is the Gray map image of a ZpZp2-additive code. In this paper, we generalize some known results for ZpZp2-linear GH codes with p=2 to any p≥3 prime when α1≠0. First, we give a recursive construction of ZpZp2-additive GH codes of type (α1,α2;t1,t2) with t1,t2≥1. We also present many different recursive constructions of ZpZp2-additive GH codes having the same type, and show that we obtain permutation equivalent codes after applying the Gray map. Finally, according to some computational results, we see that, unlike Z4-linear GH codes, when p≥3 prime, the Zp2-linear GH codes are not included in the family of ZpZp2-linear GH codes with α1≠0. Indeed, we observe that the constructed codes are not equivalent to the Zps-linear GH codes for any s≥2.
Quantum computational advantage attested by nonlocal games with the cyclic cluster state

We propose a set of Bell-type nonlocal games that can be used to prove an unconditional quantum advantage in an objective and hardware-agnostic manner. In these games, the circuit depth needed to prepare a cyclic cluster state and measure a subset of its Pauli stabilizers on a quantum computer is compared to that of classical Boolean circuits with the same, nearest-neighboring gate connectivity. Using a circuit-based trapped-ion quantum computer, we prepare and measure a six-qubit cyclic cluster state with an overall fidelity of 60.6% and 66.4%, before and after correcting for measurement-readout errors, respectively. Our experimental results indicate that while this fidelity readily passes conventional (or depth-0) Bell bounds for local hidden-variable models, it is on the cusp of demonstrating a higher probability of success than what is possible by depth-1 classical circuits. Our games offer a practical and scalable set of quantitative benchmarks for quantum computers in the pre-fault-tolerant regime as the number of qubits available increases.
Achieving Fault Tolerance on Capped Color Codes with Few Ancillas

The notion of a ``distinguishable fault set'' is developed and applied to considerably reduce the number of ancillas required for fault-tolerant quantum computation on a capped color code while also generalizing the concept of fault-tolerant gadgets.
SwiftNet: Real-time Video Object Segmentation

In this work we present SwiftNet for real-time semisupervised video object segmentation (one-shot VOS), which reports 77.8% J &F and 70 FPS on DAVIS 2017 validation dataset, leading all present solutions in overall accuracy and speed performance. We achieve this by elaborately compressing spatiotemporal redundancy in matching-based VOS via Pixel-Adaptive Memory (PAM). Temporally, PAM adaptively triggers memory updates on frames where objects display noteworthy inter-frame variations. Spatially, PAM selectively performs memory update and match on dynamic pixels while ignoring the static ones, significantly reducing redundant computations wasted on segmentation-irrelevant pixels. To promote efficient reference encoding, light-aggregation encoder is also introduced in SwiftNet deploying reversed sub-pixel. We hope SwiftNet could set a strong and efficient baseline for real-time VOS and facilitate its application in mobile vision. The source code of SwiftNet can be found at https://github.com/haochenheheda/SwiftNet.