Reviewerly/exHarmony huggingface dataset:
Hashing based Efficient Inference for Image-Text Matching

Image-text matching has been a popular research topic which bridges vision and language through semantic understanding.Recent works mainly focus on exploring the interactions between images and sentences to improve the performance without considering inference efficiency.Specifically, for the large scale databases, it is unacceptable to perform such time-consuming mechanisms between a query (text/image) and each candidate datapoint (image/text) in the whole retrieval set during inference.To tackle this problem, we propose a novel hashing based efficient inference module called HEI, which can be plugged into the existing framework to speed up the inference step without reducing the retrieval performance.In details, HEI learns to map the original datapoints into short binary hash codes and coarsely preserve the heterologous matching relationship.Thus, in the inference phase, the proposed HEI module uses the hash codes to quickly select a few candidate datapoints from the retrieval set for a given query.Then, the image-text matching model fine ranks the candidate set to find the matching datapoint.Extensive experiments on two widely used benchmark MS-COCO and Flickr30k with four baseline methods demonstrate the efficiency and effectiveness of our proposed HEI module.
Scaling Within Document Coreference to Long Texts

State of the art end-to-end coreference resolution models use expensive span representations and antecedent prediction mechanisms.These approaches are expensive both in terms of their memory requirements as well as compute time, and are particularly ill-suited for long documents.In this paper, we propose an approximation to end-to-end models which scales gracefully to documents of any length.Replacing span representations with token representations, we reduce the time/memory complexity via token windows and nearest neighbor sparsification methods for more efficient antecedent prediction.We show our approach's resulting reduction of training and inference time compared to state-of-the-art methods with only a minimal loss in accuracy.
The Classical Language Toolkit: An NLP Framework for Pre-Modern Languages

This paper announces version 1.0 of the Classical Language Toolkit (CLTK), an NLP framework for pre-modern languages.The vast majority of NLP, its algorithms and software, is created with assumptions particular to living languages, thus neglecting certain important characteristics of largely non-spoken historical languages.Further, scholars of pre-modern languages often have different goals than those of living-language researchers.To fill this void, the CLTK adapts ideas from several leading NLP frameworks to create a novel software architecture that satisfies the unique needs of premodern languages and their researchers.Its centerpiece is a modular processing pipeline that balances the competing demands of algorithmic diversity with pre-configured defaults.The CLTK currently provides pipelines, including models, for almost 20 languages.
GIS-Based Distribution System Planning for New PV Installations

Solar panel installations have increased significantly in Japan in recent decades. Due to this, world trends, such as clean/renewable energy, are being implemented in power systems all across Japan—particularly installations of photovoltaic (PV) panels in general households. In this work, solar power was estimated using solar radiation data from geographic information system (GIS) technology. The solar power estimation was applied to the actual distribution system model of the Jono area in Kitakyushu city, Japan. In this work, real power consumption data was applied to a real world distribution system model. We studied the impact of high installation rates of solar panels in Japanese residential areas. Additionally, we considered the voltage fluctuations in the distribution system model by assessing the impact of cloud shadows using a novel cloud movement simulation algorithm that uses real world GIS data. The simulation results revealed that the shadow from the cloud movement process directly impacted the solar power generation in residential areas, which caused voltage fluctuations of the overall distribution system. Thus, we advocate distribution system planning with a large number of solar panels.
EBERT: Efficient BERT Inference with Dynamic Structured Pruning

Pruning has been demonstrated as an effective way of reducing computational complexity for deep networks, especially CNNs for computer vision tasks.In this paper, we investigate the opportunity to accelerate the inference of large-scale pre-trained language model via pruning.We propose EBERT, a dynamic structured pruning algorithm for efficient BERT inference.Unlike previous methods that randomly prune the model weights for static inference, EBERT dynamically determines and prunes the unimportant heads in multi-head self-attention layers and the unimportant structured computations in feed-forward network for each input sample at run-time.Experimental results show that our proposed EBERT outperforms other state-of-the-art methods on different tasks.
Structure-Aware Pre-Training for Table-to-Text Generation

Table-to-text generation is a subtask of datato-text generation which aims to generate naltural language text based on input table.Pretraining techniques have achieved great success on table-to-text generation.However, the pre-trained models used in previous works are typically trained on free-form natural language text while the input of table-to-text task is structured table.In this paper, we propose STTP, a pre-trained model that is trained with tables and their contexts.The STTP model can understand the structured input table and generate fluent text.Experiments on two datasets show the efficacy of our model.
Intelligent Multi-Robot System for Collaborative Object Transportation Tasks in Rough Terrains

Human missions on other planets require constructing outposts and infrastructures, and one may need to consider relocating such large objects according to changes in mission spots. A multi-robot system would be a good option for such a transportation task because it can carry massive objects and provide better system reliability and redundancy when compared to a single robot system. This paper proposes an intelligent and decentralized approach for the multi-robot system using a genetic fuzzy system to perform an object transportation mission that not only minimizes the total travel distance of the multi-robot system but also guarantees the stability of the whole system in a rough terrain environment. The proposed fuzzy inference system determines the multi-robot system’s input for transporting an object to a target position and is tuned in the training process by a genetic algorithm with an artificially generated structured environment employing multiple scenarios. It validates the optimality of the proposed approach by comparing the training results with the results obtained by solving the formulated optimal control problem subject to path inequality constraints. It highlights the performance of the proposed approach by applying the trained fuzzy inference systems to operate the multi-robot system in unstructured environments.
An Adaptive Hybrid Framework for Cross-domain Aspect-based Sentiment Analysis

Cross-domain aspect-based sentiment analysis aims to utilize the useful knowledge in a source domain to extract aspect terms and predict their sentiment polarities in a target domain. Recently, methods based on adversarial training have been applied to this task and achieved promising results. In such methods, both the source and target data are utilized to learn domain-invariant features through deceiving a domain discriminator. However, the task classifier is only trained on the source data, which causes the aspect and sentiment information lying in the target data can not be exploited by the task classifier. In this paper, we propose an Adaptive Hybrid Framework (AHF) for cross-domain aspect-based sentiment analysis. We integrate pseudo-label based semi-supervised learning and adversarial training in a unified network. Thus the target data can be used not only to align the features via the training of domain discriminator, but also to refine the task classifier. Furthermore, we design an adaptive mean teacher as the semi-supervised part of our network, which can mitigate the effects of noisy pseudo labels generated on the target data. We conduct experiments on four public datasets and the experimental results show that our framework significantly outperforms the state-of-the-art methods.
Geometric measure of entanglement of multi-qubit graph states and its detection on a quantum computer

Multi-qubit graph states generated by the action of controlled phase shift operators on a separable quantum state of a system, in which all the qubits are in arbitrary identical states, are examined. The geometric measure of entanglement of a qubit with other qubits is found for the graph states represented by arbitrary graphs. The entanglement depends on the degree of the vertex representing the qubit, the absolute values of the parameter of the phase shift gate, and the parameter of state the gate is acting on. Also, the geometric measure of entanglement of the graph states is quantified on the quantum computer $\textrm{ibmq\_athens}$. The results obtained on the quantum device are in good agreement with analytical ones.
Data Augmentation with Adversarial Training for Cross-Lingual NLI

Due to recent pretrained multilingual representation models, it has become feasible to exploit labeled data from one language to train a crosslingual model that can then be applied to multiple new languages.In practice, however, we still face the problem of scarce labeled data, leading to subpar results.In this paper, we propose a novel data augmentation strategy for better cross-lingual natural language inference by enriching the data to reflect more diversity in a semantically faithful way.To this end, we propose two methods of training a generative model to induce synthesized examples, and then leverage the resulting data using an adversarial training regimen for more robustness.In a series of detailed experiments, we show that this fruitful combination leads to substantial gains in cross-lingual inference.
A Survey on Detection and Prevention of SQL and NoSQL Injection Attack on Server-side Applications

Attacks concerning data can be considered as an intense security threat.A couple of major cyberattacks on eminent database-driven web applications are SQL and NoSQL injection.Confidential data might be revealed to the hacker if the database is injected with malicious codes.Due to inadequate user input validation SQL injection brings a serious threat to the database by leaking proprietary information.Relational and non relational databases are very much vulnerable to these threats.NoSQL database shows higher performance than SQL database regarding efficient storage criteria and data retrieval time.It is flexible for handling big data and is considered to be more secure.Despite these facts and its growing popularity NoSQL databases are also vulnerable to injection attacks.Because of using a different query language, NoSQL injection is irrelevant to traditional SQL injection.Still, SQL and NoSQL injections are quite similar in this sense that both of the attacks rely on suspicious input execution on the server.So, it is a critical issue for non-relational databases as well.In this paper, numerous injection attacks are discussed along with detection and the countermeasures against SQL and NoSQL injection.
High-Capacity Reversible Data Hiding in Encrypted Images Based on Prediction Error Compression and Block Selection

The popularity of cloud computing has impelled more users to upload personal data into the cloud server. The need for secure transmission and privacy protection has become a new challenge and has attracted considerable attention. In this paper, we propose a high-capacity reversible data hiding scheme in encrypted images (RDHEI) that compresses prediction errors in usable blocks of block-based encrypted images. On the content owner side, the original image is divided into <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>2</mn> <mo>×</mo> <mn>2</mn> </math> sized blocks, and each block is encrypted by block-based modulation. On the data hider side, an efficient block-based predictor is utilized to generate prediction errors. The Huffman coding technique is introduced to compress prediction errors in the usable blocks to embed abundant additional data. On the receiver side, the additional data can be totally extracted with a data hiding key and the original image can be losslessly recovered with an image encryption key. Experimental results demonstrate that the embedding rate of our proposed scheme is significantly improved compared to those of state-of-the-art schemes.
Temporal Segmentation of Fine-gained Semantic Action: A Motion-Centered Figure Skating Dataset

Temporal Action Segmentation (TAS) has achieved great success in many fields such as exercise rehabilitation, movie editing, etc. Currently, task-driven TAS is a central topic in human action analysis. However, motion-centered TAS, as an important topic, is little researched due to unavailable datasets. In order to explore more models and practical applications of motion-centered TAS, we introduce a Motion-Centered Figure Skating (MCFS) dataset in this paper. Compared with existing temporal action segmentation datasets, the MCFS dataset is fine-grained semantic, specialized and motion-centered. Besides, RGB-based and Skeleton-based features are provided in the MCFS dataset. Experimental results show that existing state-of-the-art methods are difficult to achieve excellent segmentation results (including accuracy, edit and F1 score) in the MCFS dataset. This indicates that MCFS is a challenging dataset for motion-centered TAS. The latest dataset can be downloaded at https://shenglanliu.github.io/mcfs-dataset/.
Spatio-Temporal Difference Descriptor for Skeleton-Based Action Recognition

In skeletal representation, intra-frame differences between body joints, as well as inter-frame dynamics between body skeletons contain discriminative information for action recognition. Conventional methods for modeling human skeleton sequences generally depend on motion trajectory and body joint dependency information, thus lacking the ability to identify the inherent differences of human skeletons. In this paper, we propose a spatio-temporal difference descriptor based on a directional convolution architecture that enables us to learn the spatio-temporal differences and contextual dependencies between different body joints simultaneously. The overall model is built on a deep symmetric positive definite (SPD) metric learning architecture designed to learn discriminative manifold features with the well-designed non-linear mapping operation. Experiments on several action datasets show that our proposed method achieves up to 3% accuracy improvement over state-of-the-art methods.
ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction

Natural reading orders of words are crucial for information extraction from form-like documents.Despite recent advances in Graph Convolutional Networks (GCNs) on modeling spatial layout patterns of documents, they have limited ability to capture reading orders of given word-level node representations in a graph.We propose Reading Order Equivariant Positional Encoding (ROPE), a new positional encoding technique designed to apprehend the sequential presentation of words in documents.ROPE generates unique reading order codes for neighboring words relative to the target word given a word-level graph connectivity.We study two fundamental document entity extraction tasks including word labeling and word grouping on the public FUNSD dataset and a large-scale payment dataset.We show that ROPE consistently improves existing GCNs with a margin up to 8.4% F1-score.
Towards Trustworthy Predictions from Deep Neural Networks with Fast Adversarial Calibration

To facilitate a wide-spread acceptance of AI systems guiding decision making in real-world applications, trustworthiness of deployed models is key. That is, it is crucial for predictive models to be uncertainty-aware and yield well-calibrated (and thus trustworthy) predictions for both in-domain samples as well as under domain shift. Recent efforts to account for predictive uncertainty include post-processing steps for trained neural networks, Bayesian neural networks as well as alternative non-Bayesian approaches such as ensemble approaches and evidential deep learning. Here, we propose an efficient yet general modelling approach for obtaining well-calibrated, trustworthy probabilities for samples obtained after a domain shift. We introduce a new training strategy combining an entropy-encouraging loss term with an adversarial calibration loss term and demonstrate that this results in well-calibrated and technically trustworthy predictions for a wide range of domain drifts. We comprehensively evaluate previously proposed approaches on different data modalities, a large range of data sets including sequence data, network architectures and perturbation strategies. We observe that our modelling approach substantially outperforms existing state-of-the-art approaches, yielding well-calibrated predictions under domain drift.
Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach

NLP has a rich history of representing our prior understanding of language in the form of graphs.Recent work on analyzing contextualized text representations has focused on handdesigned probe models to understand how and to what extent do these representations encode a particular linguistic phenomenon.However, due to the inter-dependence of various phenomena and randomness of training probe models, detecting how these representations encode the rich information in these linguistic graphs remains a challenging problem.In this paper, we propose a new informationtheoretic probe, Bird's Eye, which is a fairly simple probe method for detecting if and how these representations encode the information in these linguistic graphs.Instead of using classifier performance, our probe takes an information-theoretic view of probing and estimates the mutual information between the linguistic graph embedded in a continuous space and the contextualized word representations.Furthermore, we also propose an approach to use our probe to investigate localized linguistic information in the linguistic graphs using perturbation analysis.We call this probing setup Worm's Eye.Using these probes, we analyze BERT models on their ability to encode a syntactic and a semantic graph structure, and find that these models encode to some degree both syntactic as well as semantic information; albeit syntactic information to a greater extent.Our implementation is available in https://github.com/yifan-h/Graph_Probe-Birds_Eye.
A Knowledge-Guided Framework for Frame Identification

Frame Identification (FI) is a fundamental and challenging task in frame semantic parsing.The task aims to find the exact frame evoked by a target word in a given sentence.It is generally regarded as a classification task in existing work, where frames are treated as discrete labels or represented using one-hot embeddings.However, the valuable knowledge about frames is neglected.In this paper, we propose a Knowledge-Guided Frame Identification framework (KGFI) that integrates three types frame knowledge, including frame definitions, frame elements and frameto frame relations, to learn better frame representation, which guides the KGFI to jointly map target words and frames into the same embedding space and subsequently identify the best frame by calculating the dot-product similarity scores between the target word embedding and all of the frame embeddings.The extensive experimental results demonstrate KG-FI significantly outperforms the state-of-theart methods on two benchmark datasets.
Measuring the accuracy of LSTM and BiLSTM models in the application of artificial intelligence by applying chatbot programme

Python programme contains a question and answer system that derived from data sets that have used and implemented the chatbot in this modern era. where the data collected is in the form of corpuses containing extensive metadata-rich fictional conversations derived from extracted film scripts, commonly called cornell movie dialogue corpus. The various models have been used chatbots in python programmes, and LSTM and BiLSTM models were specifically used in this study. Where the form of accuracy will be reported as a result of the implementation of LSTM and BiLSTM models in the chatbot programme. The programme performance will be influenced by the data from the model selection, because the level of accuracy is determined by the target programme being taken. So this is the main factor that determines which model to choose. Based on considerations required for choosing the programme model, in the end the LSTM and the BiLSTM models are chosen and will be applied to the programme. Based on the LSTM and BiLSTM chatbot programmes that have been tested, it can be concluded that the best parameters come from a pair of BiLSTM chatbots using the BiLTSM model with an average accuracy value of 0.995217.
Semantic Frame Induction using Masked Word Embeddings and Two-Step Clustering

Recent studies on semantic frame induction show that relatively high performance has been achieved by using clustering-based methods with contextualized word embeddings.However, there are two potential drawbacks to these methods: one is that they focus too much on the superficial information of the frameevoking verb and the other is that they tend to divide the instances of the same verb into too many different frame clusters.To overcome these drawbacks, we propose a semantic frame induction method using masked word embeddings and two-step clustering.Through experiments on the English FrameNet data, we demonstrate that using the masked word embeddings is effective for avoiding too much reliance on the surface information of frameevoking verbs and that two-step clustering can improve the number of resulting frame clusters for the instances of the same verb.
A Survey on Cloud Computing for Information Storing

Cloud computing is a technique for storing the information virtually. It may comprise of database, storage, tools, servers, networking and software services. It deals with virtual storing and retrieving of data from anywhere by the help of internet. This paper uses cloud computing technique in education for uploading study materials, videos, sharing information to the students and for conducting tests. The symmetric key encryption technique is used in this concept, where one key is utilized for both decryption and encryption. The advanced encryption standard algorithm (AES) was used for securing the data in the cloud, where it gives high security and faster execution time. This technique is mainly based on improving the concept of virtual classroom by using cloud computing.