Reviewerly/exHarmony huggingface dataset:
Two-stage Visual Cues Enhancement Network for Referring Image Segmentation

Referring Image Segmentation (RIS) aims at segmenting the target object from an image referred by one given natural language expression. The diverse and flexible expressions and complex visual contents in the images raise the RIS model with higher demands for investigating fine-grained matching behaviors between words in expressions and objects presented in images. However, such matching behaviors are hard to be learned and captured when the visual cues of referents (i.e. referred objects) are insufficient, as the referents of weak visual cues tend to be easily confused by cluttered background at boundary or even overwhelmed by salient objects in the image. And the insufficient visual cues issue can not be handled by the cross-modal fusion mechanisms as done in previous work.In this paper, we tackle this problem from a novel perspective of enhancing the visual information for the referents by devising a Two-stage Visual cues enhancement Network (TV-Net), where a novel Retrieval and Enrichment Scheme (RES) and an Adaptive Multi-resolution feature Fusion (AMF) module are proposed. Specifically, RES retrieves the most relevant image from an external data pool with regard to both the visual and textual similarities, and then enriches the visual information of the referent with the retrieved image for better multimodal feature learning. AMF further enhances the visual detailed information by incorporating the high-resolution feature maps from lower convolution layers of the image. Through the two-stage enhancement, our proposed TV-Net enjoys better performances in learning fine-grained matching behaviors between the natural language expression and image, especially when the visual information of the referent is inadequate, thus produces better segmentation results. Extensive experiments are conducted to validate the effectiveness of the proposed method on the RIS task, with our proposed TV-Net surpassing the state-of-the-art approaches on four benchmark datasets.
OpenHands: Making Sign Language Recognition Accessible with Pose-based Pretrained Models across Languages

AI technologies for Natural Languages have made tremendous progress recently.However, commensurate progress has not been made on Sign Languages, in particular, in recognizing signs as individual words or as complete sentences.We introduce OpenHands 1 , a library where we take four key ideas from the NLP community for low-resource languages and apply them to sign languages for wordlevel recognition.First, we propose using pose extracted through pretrained models as the standard modality of data in this work to reduce training time and enable efficient inference, and we release standardized pose datasets for different existing sign language datasets.Second, we train and release checkpoints of 4 posebased isolated sign language recognition models across 6 languages (American, Argentinian, Chinese, Greek, Indian, and Turkish), providing baselines and ready checkpoints for deployment.Third, to address the lack of labelled data, we propose self-supervised pretraining on unlabelled data.We curate and release the largest pose-based pretraining dataset on Indian Sign Language (Indian-SL).Fourth, we compare different pretraining strategies and for the first time establish that pretraining is effective for sign language recognition by demonstrating (a) improved fine-tuning performance especially in low-resource settings, and (b) high crosslingual transfer from Indian-SL to few other sign languages.We open-source all models and datasets in OpenHands with a hope that it makes research in sign languages reproducible and more accessible.
A CNN-Based Advertisement Recommendation through Real-Time User Face Recognition

The advertising market’s use of smartphones and kiosks for non-face-to-face ordering is growing. An advertising video recommender system is needed that continuously shows advertising videos that match a user’s taste and displays other advertising videos quickly for unwanted advertisements. However, it is difficult to make a recommender system to identify users’ dynamic preferences in real time. In this study, we propose an advertising video recommendation procedure based on computer vision and deep learning, which uses changes in users’ facial expressions captured at every moment. Facial expressions represent a user’s emotions toward advertisements. We can utilize facial expressions to find a user’s dynamic preferences. For such a purpose, a CNN-based prediction model was developed to predict ratings, and a SIFT algorithm-based similarity model was developed to search for users with similar preferences in real time. To evaluate the proposed recommendation procedure, we experimented with food advertising videos. The experimental results show that the proposed procedure is superior to benchmark systems such as a random recommendation, an average rating approach, and a typical collaborative filtering approach in recommending advertising videos to both existing users and new users. From these results, we conclude that facial expressions are a critical factor for advertising video recommendations and are helpful in properly addressing the new user problem in existing recommender systems.
Pluto: Exposing Vulnerabilities in Inter-Contract Scenarios

Attacks on smart contracts have caused considerable losses to digital assets. Many techniques based on symbolic execution, fuzzing, and static analysis are used to detect contract vulnerabilities. Most of the current analyzers only consider vulnerability detection intra-contract scenarios. However, Ethereum contracts usually interact with others by calling their functions. A bug hidden in a path that depends on information from external contract calls is defined as an inter-contract vulnerability. Failure to deal with this kind of bug can result in potential false negatives and false positives. In this work, we propose Pluto, which supports vulnerability detection in inter-contract scenarios. It first builds an Inter-contract Control Flow Graph (ICFG) to extract semantic information among contract calls. Afterward, it symbolically explores the ICFG and deduces Inter-Contract Path Constraints (ICPC) to check the reachability of execution paths more accurately. Finally, Pluto detects whether there is a vulnerability based on some predefined rules. For evaluation, we compare Pluto with five state-of-the-art tools, including Oyente, Mythril, Securify, ILF, and Clairvoyance on a labeled benchmark and 39,443 real-world Ethereum smart contracts. The result shows that other tools can only detect 10% of the inter-contract vulnerabilities, while Pluto can detect 80% of them on the labeled dataset. Beyond that, Pluto has detected 451 confirmed vulnerabilities on real-world contracts, including 36 vulnerabilities in inter-contract scenarios. Two bugs have been assigned with unique CVE identifiers by the US National Vulnerability Database (NVD). On average, Pluto costs 16.9 seconds to analyze a contract, which is as fast as the state-of-the-art tools.
A Deep Learning-Based Inventory Management and Demand Prediction Optimization Method for Anomaly Detection

The rapid development of emerging technologies such as machine learning and data mining promotes a lot of smart applications, e.g., Internet of things (IoT). The supply chain management and communication are a key research direction in the IoT environment, while the inventory management (IM) has increasingly become a core part of the whole life cycle management process of the supply chain. However, the current situations of a long supply chain life cycle, complex supply chain management, and frequently changing user demands all lead to a sharp rise in logistics and communication cost. Hence, as the core part of the supply chain, effective and predictable IM becomes particularly important. In this way, this work intends to reduce the cost during the life cycle of the supply chain by optimizing the IM process. Specifically, the IM process is firstly formulated as a mathematical model, in which the objective is to jointly minimize the logistic cost and maximize the profit. On this basis, a deep inventory management (DIM) method is proposed to address this model by using the long short-term memory (LSTM) theory of deep learning (DL). In particular, DIM transforms the time series problem into a supervised learning one and it is trained using the back propagation pattern, such that the training process can be finished efficiently. The experimental results show that the average inventory demand prediction accuracy of DIM exceeds about 80%, which can reduce the inventory cost by about 25% compared with the other state-of-the-art methods and detect the anomaly inventory actions quickly.
srBERT: automatic article classification model for systematic review using BERT

Systematic reviews (SRs) are recognized as reliable evidence, which enables evidence-based medicine to be applied to clinical practice. However, owing to the significant efforts required for an SR, its creation is time-consuming, which often leads to out-of-date results. To support SR tasks, tools for automating these SR tasks have been considered; however, applying a general natural language processing model to domain-specific articles and insufficient text data for training poses challenges.The research objective is to automate the classification of included articles using the Bidirectional Encoder Representations from Transformers (BERT) algorithm. In particular, srBERT models based on the BERT algorithm are pre-trained using abstracts of articles from two types of datasets, and the resulting model is then fine-tuned using the article titles. The performances of our proposed models are compared with those of existing general machine-learning models.Our results indicate that the proposed srBERTmy model, pre-trained with abstracts of articles and a generated vocabulary, achieved state-of-the-art performance in both classification and relation-extraction tasks; for the first task, it achieved an accuracy of 94.35% (89.38%), F1 score of 66.12 (78.64), and area under the receiver operating characteristic curve of 0.77 (0.9) on the original and (generated) datasets, respectively. In the second task, the model achieved an accuracy of 93.5% with a loss of 27%, thereby outperforming the other evaluated models, including the original BERT model.Our research shows the possibility of automatic article classification using machine-learning approaches to support SR tasks and its broad applicability. However, because the performance of our model depends on the size and class ratio of the training dataset, it is important to secure a dataset of sufficient quality, which may pose challenges.
A Fast Lightweight 3D Separable Convolutional Neural Network With Multi-Input Multi-Output for Moving Object Detection

Advances in moving object detection have been driven by the active application of deep learning methods.However, many existing models render superior detection accuracy at the cost of high computational complexity and slow inference speed.This fact has hindered the development of such models in mobile and embedded vision tasks, which need to be carried out in a timely fashion on a computationally limited platform.In this paper, we propose a super-fast (inference speed-154 fps) and lightweight (model size-1.45MB) end-to-end 3D separable convolutional neural network with a multi-input multi-output (MIMO) strategy named "3DS_MM" for moving object detection.To improve detection accuracy, the proposed model adopts 3D convolution which is more suitable to extract both spatial and temporal information in video data than 2D convolution.To reduce model size and computational complexity, the standard 3D convolution is decomposed into depthwise and pointwise convolutions.Besides, we proposed a MIMO strategy to increase inference speed, which can take multiple frames as the network input and output multiple frames of detection results.Further, we conducted the scene dependent evaluation (SDE) and scene independent evaluation (SIE) on the benchmark CDnet2014 and DAVIS2016 datasets.Compared to stateof-the-art approaches, our proposed method significantly increases the inference speed, reduces the model size, meanwhile achieving the highest detection accuracy in the SDE setup and maintaining a competitive detection accuracy in the SIE setup.
On the Relation of Trust and Explainability: Why to Engineer for Trustworthiness

Recently, requirements for the explainability of software systems have gained prominence. One of the primary motivators for such requirements is that explainability is expected to facilitate stakeholders’ trust in a system. Although this seems intuitively appealing, recent psychological studies indicate that explanations do not necessarily facilitate trust. Thus, explainability requirements might not be suitable for promoting trust.One way to accommodate this finding is, we suggest, to focus on trustworthiness instead of trust. While these two may come apart, we ideally want both: a trustworthy system and the stakeholder’s trust. In this paper, we argue that even though trustworthiness does not automatically lead to trust, there are several reasons to engineer primarily for trustworthiness – and that a system’s explainability can crucially contribute to its trustworthiness.
QuillBot as an online tool: Students’ alternative in paraphrasing and rewriting of English writing

QuillBot is an online application to paraphrase writing, avoid plagiarism, summarize long sentences and improve grammar to be more precise and look professional. The objective of this research is to review the QuillBot as an Artificial Intelligence (AI) tool system for students’ in paraphrasing and rewriting English writing both in the free and premium versions. This research applies descriptive qualitative. The data used is an English abstract article. The results show that QuillBot paraphrasing tools use several ways to paraphrase the text: 1) paraphrasing by using equations or synonyms, 2) paraphrasing by changing the form of the word, 3) paraphrasing by using active or passive sentences, and 4) paraphrasing by changing the order of words in sentences. This paraphrasing uses Standard Mode, which serves to balance changes to the text when users input them but still keeps them from changing the actual (original) meaning of the text, also making the result look more original. QuillBot is one of the paraphrasings and summarizing tools that can be used by students for rewriting any content based on a state-of-the-art AI system. This tool can be the students’ alternative which provides a solution by helping paraphrase when students do not have the idea to paraphrase English writing manually. However, a good knowledge of vocabulary and understanding of English grammar, of course, will help students or other users (s) both in using online or manual paraphrasing to be better or the best quality.
Marathi Text Analysis using Unsupervised Learning and Word Cloud

Managing a large number of textual documents is a critical and significant task and supports many applications ranging from information retrieval to clustering search engine results. Marathi is one of the oldest of the regional languages in the Indo-Aryan language family, dating from about AD 1000. Abundance of Marathi literature has generated a big corpus and need of summarization of information. The objective of this study is to overcome the scalability problem while managing the documents and summarize the Marathi corpus by extracting tokens. The work is better in terms of scalability and supports the consistent quality of cluster for incremental data set. Most of the past and contemporary research works have targeted English corpus document management. Marathi corpus has been mostly exploited by the researchers for exploring stemming, single-document summarization and classifier design on Marathi corpus. Implementing unsupervised learning on the Marathi corpus for summarization of multiple documents through Word Cloud is still an untouched area. Technically speaking, the current work is an application of TF-IDF, cosine-based document similarity measures and cluster dendrograms, in addition to various other Natural Language Processing (NLP) activities. Entropy and precision are used to evaluate the experiments carried on different datasets and results prove the robustness of the proposed approach for Marathi Corpus.
MassiveSumm: a very large-scale, very multilingual, news summarisation dataset

Current research in automatic summarisation is unapologetically anglo-centered-a persistent state-of-affairs, which also predates neural net approaches.High-quality automatic summarisation datasets are notoriously expensive to create, posing a challenge for any language.However, with digitalisation, archiving, and social media advertising of newswire articles, recent work has shown how, with careful methodology application, large-scale datasets can now be simply gathered instead of written.In this paper, we present a large-scale multilingual summarisation dataset containing articles in 92 languages, spread across 28.8 million articles, in more than 35 writing scripts.This is both the largest, most inclusive, existing automatic summarisation dataset, as well as one of the largest, most inclusive, ever published datasets for any NLP task.We present the first investigation on the efficacy of resource building from news platforms in the low-resource language setting.Finally, we provide some first insight on how low-resource language settings impact state-of-the-art automatic summarisation system performance.
Is Domain Adaptation Worth Your Investment? Comparing BERT and FinBERT on Financial Tasks

With the recent rise in popularity of Transformer models in Natural Language Processing, research efforts have been dedicated to the development of domain-adapted versions of BERT-like architectures.In this study, we focus on FinBERT, a Transformer model trained on text from the financial domain.By comparing its performances with the original BERT on a wide variety of financial text processing tasks, we found continual pretraining from the original model to be the more beneficial option.Domain-specific pretraining from scratch, conversely, seems to be less effective.
Single shot i-Toffoli gate in dispersively coupled superconducting qubits

Quantum algorithms often benefit from the ability to execute multi-qubit (&amp;gt;2) gates. To date, such multi-qubit gates are typically decomposed into single- and two-qubit gates, particularly in superconducting qubit architectures. The ability to perform multi-qubit operations in a single step could vastly improve the fidelity and execution time of many algorithms. Here, we propose a single shot method for executing an i-Toffoli gate, a three-qubit gate with two control and one target qubit, using currently existing superconducting hardware. We show numerical evidence for a process fidelity over 99.5% and a gate time of 450 ns for superconducting qubits interacting via tunable couplers. Our method can straight forwardly be extended to implement gates with more than two control qubits at similar fidelities.
The Use of Digital Media Art Using UI and Visual Sensing Image Technology

Visual sensing image technology has narrowed the distance between people and art with the rapid development of digital media, but new art forms continue to appear. Therefore, this exploration is aimed at studying the application of visual image technology based on user interface (UI) and virtual reality (VR) technology in art. This exploration is to explore the development path of digital media art. The concept of UI is briefly discussed. Based on the current means of visual sensing technology, UI, visual sensing image technology, and digital media art are successfully combined after the close relationship between digital media and art is realized. The results show that VR technology, which combines UI and visual sensing technology, has good compatibility with digital media art and can further shorten the distance between digital media art and the public. Moreover, the promotion of this application can greatly increase users’ experience of VR. In addition, most people hold a more positive attitude towards this combination. It reveals that it is essential to apply UI and visual sensing image technology to digital media art.
Estimating Quantum and Private Capacities of Gaussian Channels via Degradable Extensions

We present upper bounds on the quantum and private capacity of single-mode, phase-insensitive bosonic Gaussian channels based on degradable extensions. Our findings are state of the art in the following parameter regions: low temperature and high transmissivity for the thermal attenuator, low temperature for additive Gaussian noise, high temperature and intermediate amplification for the thermal amplifier.
Software Defect Prediction using Deep Learning

An increasing number of defects in software, damages the quality and reliability of that software.The detection of defective instances is becoming increasingly important, and current detection techniques require a great deal of improvement.However, Machine Learning (ML) techniques are effectively used, to detect defects in software.The primary purpose of ML techniques in Software Defect Prediction (SDP) is to predict defects, according to historical data.Establishing a critical SDP model on high-dimensional and limited data is still a challenging task.Thus, in this paper, we proposed an approach to detect defective modules in software using enhanced Convolutional Neural Networks (CNNs).The paper aims to identify the defective instance using the enhanced deep learning method.Our experiments are based on Within Project Defect Prediction (WPDP), where K-fold cross-validation is performed.The proposed approach has been evaluated on nineteen opensource software defect datasets, with respect to different evaluation metrics.Empirical results show that our proposed approach is significantly better than Li's CNN and standard ML model.In addition, we performed the Scott-Knot ESD test, which shows the effectiveness of our proposed approach.
Multi-stage Training with Improved Negative Contrast for Neural Passage Retrieval

In the context of neural passage retrieval, we study three promising techniques: synthetic data generation, negative sampling and fusion.We systematically investigate how these techniques contribute to the performance of the retrieval system and how they complement each other.We propose a multi-stage framework comprising of pre-training with synthetic data, fine-tuning with labeled data and negative sampling at both stages.We study six negative sampling strategies and apply them to the fine-tuning stage and, as a noteworthy novelty, to the synthetic data that we use for pre-training.Also, we explore fusion methods that combine negatives from different strategies.We evaluate our system using two passage retrieval tasks for open-domain QA and using MS MARCO.Our experiments show that augmenting the negative contrast in both stages is effective to improve passage retrieval accuracy and, importantly, they also show that synthetic data generation and negative sampling have additive benefits.Moreover, using fusion of different kinds allows us to reach performance that establishs a new state-of-the-art level in two of the tasks we evaluated.