Reviewerly/exHarmony huggingface dataset:
Research on extracting customer complaint work order's abstract in mobile customer service based on the combination of extractive and generative methods

With the development of text mining technology, how to quickly summarize and form a complaint work order's abstract based on long hotline voice transcription text, which has become a key point to improve the efficiency of agent service processing. Although online companies have a massive amount of hotline transcribed text data, there is no research on automatically generating work order's summary in mobile customer service. To reduce the cost of manual order creation, this paper proposes three methods: extractive methods (including Lead3, Tf-idf+score sorting, TextRank), generative methods (including Seq2Seq, PGN), and the combination method of extractive and generative(including Spaces, Unilm, and T5 models) . The Rouge-l value is up to 0.5071, which indicates that the method proposed is effective.
A ConvNeXt-backbone cGAN for Underwater Power Engineering Image Enhancement

Underwater images is often greatly affected by color distortion, decreased contrast, and blurred details during the construction and maintenance of underwater power engineering. we proposed an underwater power engineering image enhancement model based on conditional generative adversarial networks (cGAN). Firstly, the generator incorporates the ConvNeXt module to enhance the global feature extraction capability by utilizing large convolutional kernels specifically designed for underwater image enhancement tasks. Subsequently, a boundary loss function is formulated to better capture high frequency information related to local textures and styles by comparing the boundary differences in the images. Lastly, a style transfer method is employed to transform land power engineering images and combine them with underwater environmental images to create a novel dataset for underwater power engineering. Case study results demonstrate the effectiveness of the proposed model in improving the quality of underwater power engineering images through adversarial learning, surpassing the enhancement performance of conventional models.
Optimally Spaced Autoencoder

This paper presents a method for image restoration that uses a new object function for a multilayer perceptron (MLP) network. The training algorithm of the MLP aims to maximize the separation between patterns from different classes, while minimizing the distances between patterns from the same class. The trained MLP serves as a transformation encoder, mapping the pattern space into a new space where patterns from different classes are distinctly separated. This encoder accomplishes the ‘optimally spaced coding’ directly. It will enable effective resolution of challenging classification problems and restoration of severely corrupted images.
NCGAN: A Calibrated Oversampling Technique for Fraud Detection

We introduce a new data oversampling technique, the Negative Calibrated Generative Adversarial Network (NCGAN). In fraud detection, high-quality data oversampling is the key to successful classifications. When dealing with ultra imbalanced data in binary classification scenarios, the similarity of synthetic positive data to negative data is an undesirable property. Our method focus on alleviate such undesirable property by calibrating synthetic positive samples with negative samples. We formulate our NCGAN in mathematical notations and conduct experiments to validate that NCGAN is universally effective for linear models, tree models and deep learning models. Compared with traditonal method, our NCGAN will increase the precision and F1 score for machine learning models.
Research on Mid-Platform Scenario Operation and Maintenance System of Computer Cloud Platform Based on Enhanced Risk Prevention

It adopts micro-service architecture to create a safe, efficient and agile intelligent operation and maintenance integrated service platform. The system has the functions of low voltage topology recognition, intelligent anomaly research, accurate automatic order sending, auxiliary power grid planning and so on. Combined with some classical theories of situational awareness model, a situational awareness algorithm model based on improved fuzzy clustering algorithm is established. The overall operation and maintenance service platform builds the operation and maintenance service system based on the cloud computing and middle-desk architecture to realize the unified standardization and integration of the whole-domain management resource data. A trial of the system in a power company showed a 0.3% drop in the error rate.
VTSeg: Video Transformer for Semantic Segmentation

Semantic segmentation is a critical task for any system that requires scene understanding, as it computes the objects’ class at pixel level. We present VTSeg, a novel video semantic segmentation framework that exploits spatio-temporal information between consecutive frames to achieve a higher segmentation quality. VTSeg leverages semi-supervised learning, eliminating the need for additional training data. The architecture employs a unique adaptation of Transformers to video semantic segmentation by making an analogy between image representation spaces and natural languages. The encoder and decoder incorporate an innovative video attention module and generate multiscale features. Additionally, we introduce a novel segmentation head that uses super-resolution techniques. Compared to other video semantic segmentation approaches that include optical flow, we achieve a 3% mIoU improvement on the Virtual KITTI 2 dataset, even when ground truth is used directly for optical flow. Compared to image segmentation networks, VTSeg keeps the same 3% mIoU distance despite using 75% fewer labels. On the Cityscapes validation set, we obtain 76% mIoU using half the input resolution, without including any of the usual quality enhancing techniques such as ImageNet pre-training, test set augmentation, or sliding window inference. VTSeg framework learns powerful representations from both labeled and unlabeled data, making it a suitable video adapter able to boost the results of any image-based semantic segmentation network.
Adversarial2Adversarial: Defending against Adversarial Fingerprint Attacks without Clean Images

A number of denoising-based methods have been proposed to defend against adversarial fingerprint attacks. However, these methods inherently rely on having a clean image that corresponds to each adversarial fingerprint image. In this paper, we propose a novel denoising-based defense method without the need for clean fingerprint images. Our approach leverages a Noise2Noise mechanism, which performs denoising based on the noisy dataset. This enables us to effectively eliminate any adversarial noise that may be embedded in fingerprint images without training on clean fingerprint images. The experimental results on real-world datasets confirm that our method is robust against untrained adversarial fingerprint attacks while outperforming existing methods.
BSS: An Efficient Block Storage System for Block Archive Service in Blockchains

While blockchain enables decentralized services with zero trust, every node needs to store continuously generating block information in their local storage. Because blocks are generating continuously in blockchain, there exists a significant storage space burden on each node. A typical technique is to introduce a special node customized for block archiving. In case of Ethereum, a special node called block archive node to store all historical blocks. With the help of block archive node, each full node is enough to maintain only latest 128 blocks. In this paper, we propose BSS, an efficient block storage system for block archive service in blockchains, which can support any blockchain including Ethereum. BSS is a distributed system consisting of some number of block archive nodes. The storage overhead of BSS is O(1). The main idea is to encode each block into several small chunks and enforce each archive node to store one chunk, not an entire block. BSS also supports Byzantine tolerant chunk storage protocol for possible Byzantine attacks by block archive nodes. It is shown from the experiments on AWS, BSS enables both substantial block availability and notable performance even in Byzantine attacks.
Design and Fabrication of Mobile Visual Emergency Command Hall Based on Virtual Reality

Accidents and disasters occur frequently worldwide every year, causing relatively serious losses to personnel and property. Emergency command halls are a necessary response mechanism in the event of accidents and disasters. Traditional emergency command methods have problems such as poor convenience, immobility, and high cost. By combining the advantages of strong immersion, good interactivity, portability and mobility of virtual reality technology in the new generation of artificial intelligence, a mobile visualization emergency command hall is developed based on virtual reality technology. It combines existing virtual reality development platforms, helmets, glasses, handles, and other software and hardware to achieve the goals of real-time rescue, mobile visualization command, and ensuring the safety of the life and property of the public.
Incorporating BERT with Naive Bayes into Neutral Sentiment Analysis

With the rapid increase in online text data, sentiment analysis has become increasingly important in various applications. However, current research on neutral sentiment analysis is relatively limited, and there is still no effective method for accurately identifying neutral emotions. While Bert has shown superior performance in classifying positive and negative emotions compared to traditional approaches like the Naive Bayes algorithm, its accuracy in identifying neutral sentiment remains a challenge. To address this, we propose a method that combines Bert with the Bayesian algorithm and employs a sentiment dictionary constructed from the text data in the database to calculate correction weights for the two sentiments. This approach aims to improve Bert's accuracy in identifying neutral sentiment texts and thus enhance the overall performance of sentiment analysis.
Deep learning object detection for tracing the plasma portion of whole blood from images of medical sample containers

In this work, we explore the use of a deep learning neural network architecture for doing object detection of medical fluid samples, from color images. The ability of reliable identification and localization of liquid phases inside sample containers can help in automating significant parts of the work carried out in medical laboratories and hospitals. Our focus is on tracing the liquid levels of the plasma portion of whole blood, which may be further used in aliquoting, platelet rich plasma treatments automation, complete blood count analysis and others. The majority of sample containers are transparent and may contain or may not, one or more barcodes attached on the external surface, in addition to the manufacturer’s labels. Consequently, the available clearance window to view the liquid contents is of variable size and shape, and may be sufficiently large only from few view angles of the sample container. To address the clearance window challenge, we introduce a new input representation technique, named vertical image stitching, that takes multiple images of a rotating sample container, extracts a region of interest from each of them and then stitches them together vertically in a single output image, called vertically stitched image. Based on the vertical stitched image, we introduce a new data augmentation technique named vertical stitch permutation, which enables obtaining 10 times more sample variations in the dataset. Finally, we convert the 2D bounding box localization computation into 1D points localization by removing from the loss function the bounding box components corresponding to the box height. We performed experiments on a dataset of 976 unique samples, containing 17 sample containers from 11 manufacturers. We trained YoloV4- tiny with the above mentioned techniques and achieved a mean average precision of 98.09% (at intersection over union threshold of 0.90) with an average intersection over union of 95.18%.
Natural language processing for the automation of operations in an online bookstore

The use of Natural Language Processing Algorithms (NLP) for automation purposes in various applications is frequently encountered recently. Some research managed to identify the dominant emotion from a text using neural networks (ANN), Random Forest (RF) and Support Vectors Machines (SVM) while other studies classified documents with the aim of mechanizing the extraction process of information.This paper presents a study on different Natural Language Processing Algorithms (NLP) used for automation of a virtual bookstore. The objective was the application of artificial intelligence algorithms to streamline the necessary processes of an online bookstore or library, taking into consideration the short descriptions and summaries of the books. The aim is to improve the results obtained by supervised algorithms using various techniques such as aggregating unsupervised classifications. In order to boost their understanding of natural language, GPT is used to enhance the dataset by adding additional context. Finally, some of the methods utilized in improving accuracy can be used to create a personalized recommendation system that suits each reader’s needs.
Research on English Data Intelligent Retrieval Terminal System Based on Computer 3D Image Vision Technology

This paper introduces an embedded software for English information query based on ARM single chip microcomputer LPC2290 and Mini GUI. Integrate the Mini GUI with network communication. Remote access to MySQL using C/S architecture. The hardware and software architecture of English data intelligent retrieval terminal system is presented. The graphical interface of the system and the data extraction of the system are completed based on Mini GUI. Several main implementation links of the system are also explained. This paper presents an intelligent query terminal based on English data, which improves the user interface very well. It can not only realize the intelligent search of English data, but also solve the expensive problem of the traditional intelligent search terminal of English data.
Complex Motion-aware Splatting for Video Frame Interpolation

Video frame interpolation, a crucial component of computer vision, synthesizes additional frames to enhance the frame rate of a video, leading to improved performance with minimal additional cost. Despite recent advancements with deep learning and convolutional neural networks (CNNs), it still remains a challenge to generate precise intermediate frames, especially when complex and fast motions are involved. This paper presents a novel deep learning-based framework for video frame interpolation that incorporates a complex motion detection module and proposes a complex motion-aware splatting (CMS) method. We employ a forward warping approach that uses a complex motion map as a weight map in splatting. The framework further leverages a module that embeds temporal and spatial information from the frame sequence to acquire motion information. The effectiveness of our proposed model is demonstrated through qualitative and quantitative results on a public dataset.
Road Marking Real-Time Detection with a Single Stage Object Detector

Vital guidance information for autonomous driving cars can be extracted from road markings. The main challenges come from the wide range of traffic scenarios, lighting and weather conditions. In this work, we look into the problem of road marking detection with object detection neural networks on the CeyMo Road Marking dataset. We develop an architecture named SE-YOLOv7-tiny, which is based on YOLOv7-tiny, as it is a lightweight object detector that can be deployed on edge devices. To improve model performance, we propose several adjustments to YOLOv7-tiny. Firstly, we replace the Leaky-RELU activation function with Mish. Then we fine-tune data augmentation parameters and introduce into the pipeline the mosaic data augmentation technique. The final adjustment that we propose is to introduce squeeze-and-excitation blocks for features fusion into the neck part of the architecture. We evaluate the inference speed of the obtained models on NVIDIA RTX 2070. The experimental results show that our method achieves a macro F1-score of 89.59% and scenario overall F1-score of 93.32%. Compared with previous state-of-the-art (SSD-Inception-v2) on the object detection task, our model improves macro F1-score by 6.71% and scenario overall F1-score by 8.13%, while achieving twice the FPS.
A Generative Modelling-based Approach for Text-to-Speech Synthesis in Romanian Language

In recent decades, technological advancements in the field of Artificial Intelligence (AI) have opened up new opportunities for generating audio content based on text. In particular, Text-to-Speech (TTS) transformation models have become the most popular method for generating speech from written text. These models can be utilised in applications such as audiobooks, various virtual assistants, and video games, and their performances continue to improve, with generated results becoming increasingly close to human voice. However, there are still many challenges regarding generating as natural voice as possible and appropriate expression of emotions. In this paper we will focus on TTS synthesis, understanding the state-of-the-art and exploring possibilities of training these models for languages other than English, diverse voices, and improving the quality of voice generation. We introduce an approach for Romanian language TTS synthesis using generative deep learning models. Our approach yielded promising results on Romanian language datasets, demonstrating the effectiveness of our methodology.
Hand signal classification system for sign language communication in Virtual Reality

Sign language is an essential form of communication for people with hearing disabilities. However, effective communication traditionally required the use of trained translators. With recent advances in AI and virtual reality, technology seems to provide a viable means of facilitating communication with sign-language users. This paper describes a solution developed around the detection and classification of the user’s hand signals and shows its integration within a system meant to facilitate communication with hearing impaired individuals. The system relies on the capabilities of a virtual reality headset to capture the user’s hand signals and then employs a machine learning model in order to correctly classify them. Within this paper, we discuss some of the relevant technical aspects of the system as well as provide insight into its observed capabilities.
Optical Flow with Semantic Guidance and Uncertainty Estimation for Robust Video Perception

Optical flow plays an important role in many applications, such as autonomous driving, where it facilitates the perception and understanding of the surrounding environment. Since the rise of deep learning approaches for optical flow estimation, much effort has been expended on new architectures and on increasing the accuracy of predicted flow. However, topics such as including semantic knowledge in model training or estimating the uncertainty for the predicted optical flow have received comparatively little attention so far. In this paper we investigate the effect of adding semantic supervision for a state of the art supervised optical flow model [1], as well as the necessary adaptations needed for obtaining a measure of flow uncertainty. In addition, our method is suitable for deployment and integration on various embedded hardware platforms for automated driving. In our experiments, we find that semantic guidance is able to improve the model performance on the KITTI benchmark by 9% on foreground objects, after having been trained only on synthetic data from the target domain. We also obtain improved flow uncertainty estimates over commonly used approaches such as model ensembles and Monte Carlo Dropout.
Enhanced face recognition using rank-level fusion with multi-feature vectors and interval type-3 fuzzy set

In the near past, interval type-3 fuzzy set theory has been introduced as a more effective means of addressing uncertainty in decision-making than its fuzzy set counterparts. This paper suggests an approach to face recognition which integrates multiple feature vectors and interval type-3 fuzzy sets for fuzzy rank-level fusion. Use of multiple feature vectors resolves inter-class resemblances while incorporating interval type-3 fuzzy membership function in generating fuzzy rank reduces the uncertainty due to intra-class divergences. The output of the classifier is regarded as confidence factor. For each feature vector, the fuzzy ranks are generated by formulating an interval type-3 fuzzy membership function for each of top-ordered H classes based on confidence factors utilizing all training feature vectors and those of the training samples of the respective class. Multiple fuzzy ranks, built upon interval type-3 fuzzy set, are generated by employing of multiple feature vectors. To determine the final score of a class, summation of the fuzzy rank complements is combined with the complement of arithmetic mean of respective classifier’s output. Final scores are evaluated to conclude test image's class. Two publicly accessible face databases are used to evaluate the method and it has shown that the solution, advocated here, exhibits an improved effectiveness over its existing related counterparts.
3D Object Classification and Segmentation from Large Scale Area Reconstruction

3D reconstruction is difficult to use in general applications because it treats objects as a whole without distinction. Post-processing to segment them into individual objects is essential. However, when the reconstruction target is a large-scale area, it is difficult to segment directly from the 3D geometry because it is divided into small rectangular tiles for convenience. We propose a method that can accurately classify and segment objects even in the case of locally divided 3D tiled geometry. Instead of using 3D segmentation, we adopt the method of 2D instance segmentation of a global multi-view image and then projecting it onto the 3D geometry. This solves the problem that objects located on the boundaries of tiles are fragmented and not properly segmented.
Robust Machine Learning Systems For Dependable Space Applications

Artificial Intelligence and Machine Learning have attracted lots of attention to the space engineering community the last few years. In this paper, an attempt to utilize the concept of Machine Learning is presented for a robotic mission to other planets (in this paper is focused on Mars) for planetary exploration purposes. Although, building a machine that is able to learn is not achieved (and it is not yet feasible to be built in general), the borrowed ideas, concepts, and products from the research domain on Machine Learning, provided the necessary engineering tools for improving the navigation, kinematics, and control of the rover.