Reviewerly/exHarmony huggingface dataset:
Advanced NLP Framework for Text Processing

Natural language processing (NLP) used in platform such as machine translation, chatbot, and text generation etc., Text augmentation helps in providing the suitable answer for the given instance. Nlpaug is a text augmentation library which helps in augmenting the text. In this paper different NLP framework is provided and implemented. Advanced open source library makes use of neural network for effective result generation. The objectives of this paper are to apply and implement the advanced NLP framework for text processing and compare various open source library for natural language processing. Advanced library used in this paper are nlpaug, texthero, nltk, spacy, gensim, sklearn. Text augmentation can be done in more predominate manner with the help of nlpaug. Further, nlpaug can be used to train the conversational chatbot effectively. However, nlpaug library and other library is having the drawback such as understanding the meanings emojis and any other special characters from the string.
SQL Injection Attacks and Prevention

SQL Injection Attacks generally target the information stored in the database. Therefore, it becomes necessary for administrators to have mechanisms installed to detect and prevent them. Several mechanisms have been proposed for the same. In this paper, we have chosen some models proposing several SQLI detection and prevention systems, analyzed them and formed an overall comparative analysis of all of them, choosing one that we ascertain comparatively more optimal.
Machine Learning-Based Sentiment Analysis for the Social Media Platforms

One of the most widely used social media platforms is Twitter. which is being used to express one’s opinions and sentiments. We can see a constant change in the pattern of tweets such as due to world limit users have started to use slangs, abbreviations and emoticons etc. which makes it difficult to identify the sentiments behind the tweets. Through this literature review we aim to describe methodologies, algorithms and procedure involved in creating a Machine Learning model that can analyze the sentiments. We have used dataset from “sentiment140 dataset” AND “TWITTER AND REDDIT SENTIMENT ANALYSIS DATASET”. This paper proposes the use of Logistic Regression Model, Linear SVC Model and Bernoulli NB Model for Sentiment Analysis. By comparing the three models with two different datasets we found that Linear SVC has greatest accuracy of 81%.
Designing Pair of Nonlinear Components of a Block Cipher over Gaussian Integers

In block ciphers, the nonlinear components, also known as substitution boxes (S-boxes), are used with the purpose of inducing confusion in cryptosystems. For the last decade, most of the work on designing S-boxes over the points of elliptic curves has been published. The main purpose of these studies is to hide data and improve the security levels of crypto algorithms. In this work, we design pair of nonlinear components of a block cipher over the residue class of Gaussian integers (GI). The fascinating features of this structure provide S-boxes pair at a time by fixing three parameters. But the prime field dependent on the Elliptic curve (EC) provides one S-box at a time by fixing three parameters and . The newly designed pair of S-boxes are assessed by various tests like nonlinearity, bit independence criterion, strict avalanche criterion, linear approximation probability, and differential approximation probability.
Improving Targeted Multimodal Sentiment Classification with Semantic Description of Images

Targeted multimodal sentiment classification (TMSC) aims to identify the sentiment polarity of a target mentioned in a multimodal post. The majority of current studies on this task focus on mapping the image and the text to a high-dimensional space in order to obtain and fuse implicit representations, ignoring the rich semantic information contained in the images and not taking into account the contribution of the visual modality in the multimodal fusion representation, which can potentially influence the results of TMSC tasks. This paper proposes a general model for Improving Targeted Multimodal Sentiment Classification with Semantic Description of Images (ITMSC) as a way to tackle these issues and improve the accuracy of multimodal sentiment analysis. Specifically, the ITMSC model can automatically adjust the contribution of images in the fusion representation through the exploitation of semantic descriptions of images and text similarity relations. Further, we propose a target-based attention module to capture the target-text relevance, an image-based attention module to capture the image-text relevance, and a target-image matching module based on the former two modules to properly align the target with the image so that fine-grained semantic information can be extracted. Our experimental results demonstrate that our model achieves comparable performance with several state-of-the-art approaches on two multimodal sentiment datasets. Our findings indicate that incorporating semantic descriptions of images can enhance our understanding of multimodal content and lead to improved sentiment analysis performance.
An ontology to represent firefighters data requirements during building fire emergencies

Firefighters require accurate and timely information regarding a building and its environment to perform their duty safely and successfully during a fire emergency. However, due to the chaotic nature of building fires, firefighters often receive erroneous, conflicting, or delayed information that can affect the outcome of an emergency. In this paper, we propose a solution in the form of an ontology that defines building and environmental data that is needed by firefighters during a building fire emergency. The ontology development followed the METHONTOLOGY method and was implemented using the web ontology language (OWL) in Protégé 5.5.0. Built-in reasoners in Protégé and an ontology pitfall scanning tool were employed to verify the structure and consistency of the new ontology. To validate the ontology efficacy, we developed a prototype web application to represent and visualise relevant information based on the ontology and used that as a basis for conducting interviews with firefighters. Finally, a specification document that describes the ontology was created and published online. The proposed ontology can be a basis for developing intelligent tools and systems that support firefighters.
UCONV-Conformer: High Reduction of Input Sequence Length for End-to-End Speech Recognition

Optimization of modern ASR architectures is among the highest priority tasks since it saves many computational resources for model training and inference. The work proposes a new Uconv-Conformer architecture <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> based on the standard Conformer model. It consistently reduces the input sequence length by 16 times, which results in speeding up the work of the intermediate layers. To solve the convergence issue connected with such a significant reduction of the time dimension, we use upsampling blocks like in the U-Net architecture to ensure the correct CTC loss calculation and stabilize network training. The Uconv-Conformer architecture appears to be not only faster in terms of training and inference speed but also shows better WER compared to the baseline Conformer. Our best Uconv-Conformer model shows 47.8% and 23.5% inference acceleration on the CPU and GPU, respectively. Relative WER reduction is 7.3% and 9.2% on LibriSpeech test_clean and test_other respectively.
FedPrompt: Communication-Efficient and Privacy-Preserving Prompt Tuning in Federated Learning

Federated learning (FL) has enabled global model training on decentralized data in a privacy-preserving way. However, for tasks that utilize pre-trained language models (PLMs) with massive parameters, there are considerable communication costs. Prompt tuning, which tunes soft prompts without modifying PLMs, has achieved excellent performance as a new learning paradigm. In this paper, we want to combine these methods and explore the effect of prompt tuning under FL. We propose "FedPrompt" studying prompt tuning in a model split aggregation way using FL, and prove that split aggregation greatly reduces the communication cost, only 0.01% of the PLMs’ parameters, with little decrease on accuracy both on IID and Non-IID data distribution. We further conduct backdoor attacks by data poisoning on FedPrompt. Experiments show that attack achieve a quite low attack success rate and can not inject backdoor effectively, proving the robustness of FedPrompt.
Palette Load Balancing: Locality Hints for Serverless Functions

Function-as-a-Service (FaaS) serverless computing enables a simple programming model with almost unbounded elasticity. Unfortunately, current FaaS platforms achieve this flexibility at the cost of lower performance for data-intensive applications compared to a serverful deployment. The ability to have computation close to data is a key missing feature. We introduce Palette load balancing, which offers FaaS applications a simple mechanism to express locality to the platform, through hints we term "colors". Palette maintains the serverless nature of the service - users are still not allocating resources - while allowing the platform to place successive invocations related to each other on the same executing node. We compare a prototype of the Palette load balancer to a state-of-the-art locality-oblivious load balancer on representative examples of three applications. For a serverless web application with a local cache, Palette improves the hit ratio by 6x. For a serverless version of Dask, Palette improves run times by 46% and 40% on Task Bench and TPC-H, respectively. On a serverless version of NumS, Palette improves run times by 37%. These improvements largely bridge the gap to serverful implementation of the same systems.
Cross-Head Supervision for Crowd Counting with Noisy Annotations

Noisy annotations such as missing annotations and location shifts often exist in crowd counting datasets due to multi-scale head sizes, high occlusion, etc.These noisy annotations severely affect the model training, especially for density map-based methods.To alleviate the negative impact of noisy annotations, we propose a novel crowd counting model with one convolution head and one transformer head, in which these two heads can supervise each other in noisy areas, called Cross-Head Supervision.The resultant model, CHS-Net, can synergize different types of inductive biases for better counting.In addition, we develop a progressive cross-head supervision learning strategy to stabilize the training process and provide more reliable supervision.Extensive experimental results on Shang-haiTech and QNRF datasets demonstrate superior performance over state-of-the-art methods.
Quantum Deep Recurrent Reinforcement Learning

Recent advances in quantum computing (QC) and machine learning (ML) have drawn significant attention to the development of quantum machine learning (QML). Reinforcement learning (RL) is one of the ML paradigms which can be used to solve complex sequential decision making problems. Classical RL has been shown to be capable to solve various challenging tasks. However, RL algorithms in the quantum world are still in their infancy. One of the challenges yet to solve is how to train quantum RL in the partially observable environments. In this paper, we approach this challenge through building QRL agents with quantum recurrent neural networks (QRNN). Specifically, we choose the quantum long short-term memory (QLSTM) to be the core of the QRL agent and train the whole model with deep Q-learning. We demonstrate the results via numerical simulations that the QLSTM-DRQN can solve standard benchmark such as Cart-Pole with more stable and higher average scores than classical DRQN with similar architecture and number of model parameters.
Solving Jigsaw Puzzle of Large Eroded Gaps Using Puzzlet Discriminant Network

Solving Jigsaw puzzles has recently become an emerging research topic. Traditionally, boundary similarities are utilized for puzzle reassembly. In this paper, we solve Jigsaw Puzzles of Large Eroded Gaps (JPLEG), where boundary similarities are weak and image semantics are the only feasible clues. Inspired by human strategy in solving a puzzle, we introduce the concept of puzzlet, where fragments are gradually combined to form puzzlets of different sizes until the completion of the puzzle. Two sets of Puzzlet Discriminant Networks are designed to visually perceive whether these puzzlets are correctly reassembled. The puzzle reassembly is then formulated as a combinatorial optimization problem, and solved using a genetic algorithm. The proposed method is evaluated on two large datasets, which shows that it significantly outperforms the state-of-the-art methods for puzzle solving.
Wav2Seq: Pre-Training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages

We introduce Wav2Seq, the first self-supervised approach to pre-train both parts of encoder-decoder models for speech data. We induce a pseudo language as a compact discrete representation, and formulate a self-supervised pseudo speech recognition task — transcribing audio inputs into pseudo subword sequences. This process stands on its own, or can be applied as low-cost second-stage pre-training. We experiment with automatic speech recognition (ASR), spoken named entity recognition, and speech-to-text translation. We set new state-of-the-art results for end-to-end spoken named entity recognition, and show consistent improvements on 8 language pairs for speech-to-text translation, even when competing methods use additional text data for training. On ASR, our approach enables encoder-decoder methods to benefit from pre-training for all parts of the network, and shows comparable performance to highly optimized recent methods.
A Depth-Progressive Initialization Strategy for Quantum Approximate Optimization Algorithm

The quantum approximate optimization algorithm (QAOA) is known for its capability and universality in solving combinatorial optimization problems on near-term quantum devices. The results yielded by QAOA depend strongly on its initial variational parameters. Hence, parameter selection for QAOA becomes an active area of research, as bad initialization might deteriorate the quality of the results, especially at great circuit depths. We first discuss the patterns of optimal parameters in QAOA in two directions: the angle index and the circuit depth. Then, we discuss the symmetries and periodicity of the expectation that is used to determine the bounds of the search space. Based on the patterns in optimal parameters and the bounds restriction, we propose a strategy that predicts the new initial parameters by taking the difference between the previous optimal parameters. Unlike most other strategies, the strategy we propose does not require multiple trials to ensure success. It only requires one prediction when progressing to the next depth. We compare this strategy with our previously proposed strategy and the layerwise strategy for solving the Max-cut problem in terms of the approximation ratio and the optimization cost. We also address the non-optimality in previous parameters, which is seldom discussed in other works despite its importance in explaining the behavior of variational quantum algorithms.
Short-Segment Speaker Verification Using ECAPA-TDNN with Multi-Resolution Encoder

Time-domain approaches have shown the potential to improve the performance of speaker verification, but still predominant approaches utilize hand-crafted features such as the mel filterbank energies. Although these features are based on speech perception models and exhibited impressive performances, the fixed frame size does not allow good temporal and spectral resolutions at the same time and there is information loss when taking the magnitude spectrum and during frequency rescaling. In this paper, we propose to incorporate multi-resolution time-domain information into the ECAPA-TDNN speaker verification system. We construct a multi-resolution encoder to extract multiple features in different temporal resolutions, and let the extracted features drive the adapter modules. Experimental results showed that the proposed method outperformed other recently proposed approaches when the input length was 2 seconds or shorter for the VoxCeleb dataset. The proposed approach also showed superior performance on the Google Speech Commands dataset v2.
Phase Retrieval for Rydberg Quantum Arrays

Rydberg-aided atomic electrometry has recently garnered significant research interest for detecting external electric fields. However, the inability of Rydberg probes to detect phase remains a serious impediment to their realistic deployment. In phased array or synthetic aperture applications, if a Rydberg atom probe is used in place of an antenna, then measurements of only electric field intensity are possible at each spatial sample. In this paper, we cast the extraction of useful information from these Rydberg probe measurements as a novel phase retrieval problem. The resulting optimization is non-convex, which we address by developing a three-stage alternating projections algorithm. Our numerical experiments demonstrate the effectiveness of the proposed algorithm in terms of the beamformed array output.
Who Will Purchase This Item Next? Reverse Next Period Recommendation in Grocery Shopping

Recommender systems have become an essential instrument to connect people to the items that they need. Online grocery shopping is one scenario where this is very clear. So-called user-centered recommendations take a user as input and suggest items based on the user’s preferences. Such user-centered recommendations have received significant attention and uptake. Instead, we focus on an item-centered recommendation task, again in the grocery-shopping scenario. In the reverse next-period recommendation ( RNPR ) task, we are given an item and have to identify potential users who would like to consume it in the next period. We consider three sub-tasks of the overall reverse next-period recommendation ( RNPR ) task, (i) Expl- RNPR , (ii) Rep- RNPR , and (iii) Mixed- RNPR , where we consider different types of target users, i.e., (i) explore users, who are new to a given item, (ii) repeat users, who previously purchased a given item, and (iii) both explore users and repeat users. To address the Expl- RNPR task, we propose a habit-interest fusion model that employs frequency information to capture the repetition-exploration habits of users and that uses pre-trained item embeddings to model the user’s interests. For the Mixed- RNPR task, we propose a repetition-exploration user ranking algorithm to decouple the repetition and exploration task and investigate the tradeoff between targeting different types of users for a given item. Furthermore, to reduce the computational cost at inference, we analyze the repetition behavior from both user and item perspectives and then introduce a repetition-based candidate filtering method for each sub-task. We conduct experiments on two public grocery-shopping datasets. Our experimental results not only demonstrate the difference between repetition and exploration, but also the effectiveness of the proposed methods.
Research on the Current Situation and Future Development Trend of Immersive Virtual Reality in the Field of Education

In recent years, emerging technologies such as immersive virtual reality (IVR) and Metaverse are pointing to new directions for the future of education. To summarise the current research status and development trend of immersive virtual reality in the field of education, this paper used the CiteSpace tool to search the SSCI literature included in the Web of Science under the theme of “Immersive virtual reality” or “Educational technology”. The results show a total of 1293 SSCI publications between 1996 and 2022, with the number of studies in this area increasing year on year. Current research hot spots focus on the application of IVR technology, evaluation and effectiveness research, and curriculum design and teaching. Analysis of node network diagrams and knowledge graphs suggest that future trends in the field will be to deepen immersive experiences, increase interactivity, and continue to explore more pedagogical value and application scenarios. In addition, there are increasingly close partnerships between research institutions and large-scale collaborative research is taking place internationally. In future research, researchers can explore scenarios for the application of Metaverse and the design of assessment systems that can facilitate the development of immersive education.
Improving OCR Accuracy for Kazakh Handwriting Recognition Using GAN Models

This paper aims to increase the accuracy of Kazakh handwriting text recognition (KHTR) using the generative adversarial network (GAN), where a handwriting word image generator and an image quality discriminator are constructed. In order to obtain a high-quality image of handwritten text, the multiple losses are intended to encourage the generator to learn the structural properties of the texts. In this case, the quality discriminator is trained on the basis of the relativistic loss function. Based on the proposed structure, the resulting document images not only preserve texture details but also generate different writer styles, which provides better OCR performance in public databases. With a self-created dataset, images of different types of handwriting styles were obtained, which will be used when training the network. The proposed approach allows for a character error rate (CER) of 11.15% and a word error rate (WER) of 25.65%.
An integrated clustering and BERT framework for improved topic modeling

Topic modelling is a machine learning technique that is extensively used in Natural Language Processing (NLP) applications to infer topics within unstructured textual data. Latent Dirichlet Allocation (LDA) is one of the most used topic modeling techniques that can automatically detect topics from a huge collection of text documents. However, the LDA-based topic models alone do not always provide promising results. Clustering is one of the effective unsupervised machine learning algorithms that are extensively used in applications including extracting information from unstructured textual data and topic modeling. A hybrid model of Bidirectional Encoder Representations from Transformers (BERT) and Latent Dirichlet Allocation (LDA) in topic modeling with clustering based on dimensionality reduction have been studied in detail. As the clustering algorithms are computationally complex, the complexity increases with the higher number of features, the PCA, t-SNE and UMAP based dimensionality reduction methods are also performed. Finally, a unified clustering-based framework using BERT and LDA is proposed as part of this study for mining a set of meaningful topics from the massive text corpora. The experiments are conducted to demonstrate the effectiveness of the cluster-informed topic modeling framework using BERT and LDA by simulating user input on benchmark datasets. The experimental results show that clustering with dimensionality reduction would help infer more coherent topics and hence this unified clustering and BERT-LDA based approach can be effectively utilized for building topic modeling applications.