Reviewerly/exHarmony huggingface dataset:
Using Probabilistic Models for Data Compression

Our research objective is to improve the Huffman coding efficiency by adjusting the data using a Poisson distribution, which avoids the undefined entropies too. The scientific value added by our paper consists in the fact of minimizing the average length of the code words, which is greater in the absence of applying the Poisson distribution. Huffman Coding is an error-free compression method, designed to remove the coding redundancy, by yielding the smallest number of code symbols per source symbol, which in practice can be represented by the intensity of an image or the output of a mapping operation. We shall use the images from the PASCAL Visual Object Classes (VOC) to evaluate our methods. In our work we use 10,102 randomly chosen images, such that half of them are for training, while the other half is for testing. The VOC data sets display significant variability regarding object size, orientation, pose, illumination, position and occlusion. The data sets are composed by 20 object classes, respectively: aeroplane, bicycle, bird, boat, bottle, bus, car, motorbike, train, sofa, table, chair, tv/monitor, potted plant, person, cat, cow, dog, horse and sheep. The descriptors of different objects can be compared to give a measurement of their similarity. Image similarity is an important concept in many applications. This paper is focused on the measure of similarity in the computer science domain, more specifically information retrieval and data mining. Our approach uses 64 descriptors for each image belonging to the training and test set, therefore the number of symbols is 64. The data of our information source are different from a finite memory source (Markov), where its output depends on a finite number of previous outputs. When dealing with large volumes of data, an effective approach to increase the Information Retrieval speed is based on using Neural Networks as an artificial intelligent technique.
Automatic pancreas segmentation using ResNet-18 deep learning approach

The accurate pancreas segmentation process is essential in the early detection of pancreatic cancer. The pancreas is situated in the abdominal cavity of the human body. The abdominal cavity contains the pancreas, liver, spleen, kidney, and adrenal glands. Sharp and smooth detection of the pancreas from this abdominal cavity is a challenging and tedious job in medical image investigation. Top-down approaches like Novel Modified K-means Fuzzy clustering algorithm (NMKFCM), Scale Invariant Feature Transform (SIFT), Kernel Density Estimator (KDE) algorithms were applied for pancreas segmentation in the early days. Recently, Bottom-up method has become popular for pancreas segmentation in medical image analysis and cancer diagnosis. LevelSet algorithm is used to detect the pancreas from the abdominal cavity. The deep learning, bottom-up approach performance is better than another. Deep Residual Network (ResNet-18) deep learning, bottom-up approach is used to detect accurate and sharp pancreas from CT scan medical images. 18 layers are used in the architecture of ResNet-18. The automatic pancreas and kidney segmentation is accurately extracted from CT scan images. The proposed method is applied to the medical CT scan images dataset of 82 patients. 699 images and 150 images with different angles are used for training and testing purposes, respectively. ResNet-18 attains a dice similarity index value up to 98.29±0.63, Jaccard Index value up to 96.63±01.25, Bfscore value up to 84.65±03.96. The validation accuracy of the proposed method is 97.01%, and the loss rate value achieves up to 0.0010. The class imbalance problem is solved by class weight and data augmentation.
Research on the Obstacle-Avoidance Steering Control Strategy of Tracked Inspection Robots

Tracked inspection robots possess prominent advantages in dealing with severe environment rescue, safety inspection, and other important tasks, and have been used widely. However, tracked robots are affected by skidding and slipping, so it is difficult to achieve accurate control. For example, the control parameters of a tracked robot are the same during driving, but the pressure, shear force and steering resistance of the robot on the road surface are different, which affects the steering characteristics of the robot on complex terrain. Based on analysis of the structural parameters and steering radius of the robot, the traction force and resistance torque models of the tracked robot were established, and the plane dynamics of the robot’s steering were analyzed and solved. The corresponding relationships between the road parameters, relative steering radius, and lateral relative offset of the robot on three typical roads were obtained. Mathematical models of the robot’s track speed and relative steering radius with and without skid and slip were established. Through simulation analysis of Matlab software, the corresponding relationship between the relative steering radius of the robot and the velocity difference of the two tracks were obtained. Taking angular obstacles as an example, three obstacle-avoidance steering control strategies, once turning in situ center, twice turning in situ center, and large-radius steering were developed. The tracked robot and obstacle multi-body dynamic simulation models were constructed using ADAMS simulation software. The simulation results show that all three methods can complete the steering tasks according to the requirements; however, under the influence of skid and slip, the trajectory of the robot deviates from the ideal trajectory, which has a great impact on large-radius steering, even though the large-radius obstacle-avoidance steering control strategy has the advantages of a smooth trajectory, fast steering speed, and high efficiency. The obstacle-avoidance steering experiments were completed by the robot prototype, which verifies the rationality of robot steering theory, which could provide the corresponding theoretical basis for autonomous obstacle-avoidance navigation control of a tracked robot.
Reflectance spectra and AVIRIS-NG airborne hyperspectral data analysis for mapping ultramafic rocks in igneous terrain

The layered Sittampundi Anorthosite Complex is covered by mafic and ultramafic rocks including anorthosite, gabbro, pyroxenite and other igneous rocks. The ultramafic terrain has frequently undergone metamorphism. In the present study, laboratory spectral measurements were carried out from mafic, ultramafic and felsic rocks in the 350–2500 nm spectral range to characterise their diagnostic spectral features and for further utilisation for rock-type mapping. In 2016, the Sittampundi complex was covered by an AVIRIS-NG airborne survey jointly conducted by the Space Application Centre (SAC-ISRO) and Jet Propulsion Laboratory (NASA). The level-2 AVIRIS-NG data was obtained from SAC and used to interpret various rock types. ENVI 5.3 software was used for digital image processing of the AVIRIS-NG airborne hyperspectral data. The continuum-removed spectra of major rock types including anorthosite, meta-anorthosite, gabbro, meta-gabbro, pyroxenite, pegmatite, granite, gneiss and migmatite were critically analysed and their diagnostic absorption features correlated with chemistry and mineralogy. The AVIRIS-NG data analyses include bad band removal, minimum noise fraction transformation (MNF) and band combination. Out of various band combinations, the MNF composite images B456, B546 and B561 provided an enhanced output for the delineation of various rock types in the ultramafic terrain.
Prompting GPT-3 To Be Reliable

Large language models (LLMs) show impressive abilities via few-shot prompting. Commercialized APIs such as OpenAI GPT-3 further increase their use in real-world language applications. However, the crucial problem of how to improve the reliability of GPT-3 is still under-explored. While reliability is a broad and vaguely defined term, we decompose reliability into four main facets that correspond to the existing framework of ML safety and are well-recognized to be important: generalizability, social biases, calibration, and factuality. Our core contribution is to establish simple and effective prompts that improve GPT-3's reliability as it: 1) generalizes out-of-distribution, 2) balances demographic distribution and uses natural language instructions to reduce social biases, 3) calibrates output probabilities, and 4) updates the LLM's factual knowledge and reasoning chains. With appropriate prompts, GPT-3 is more reliable than smaller-scale supervised models on all these facets. We release all processed datasets, evaluation scripts, and model predictions. Our systematic empirical study not only sheds new insights on the reliability of prompting LLMs, but more importantly, our prompting strategies can help practitioners more reliably use LLMs like GPT-3.
Imagic: Text-Based Real Image Editing with Diffusion Models

Text-conditioned image editing has recently attracted considerable interest. However, most methods are currently either limited to specific editing types (e.g., object overlay, style transfer), or apply to synthetically generated images, or require multiple input images of a common object. In this paper we demonstrate, for the very first time, the ability to apply complex (e.g., non-rigid) text-guided semantic edits to a single real image. For example, we can change the posture and composition of one or multiple objects inside an image, while preserving its original characteristics. Our method can make a standing dog sit down or jump, cause a bird to spread its wings, etc. -- each within its single high-resolution natural image provided by the user. Contrary to previous work, our proposed method requires only a single input image and a target text (the desired edit). It operates on real images, and does not require any additional inputs (such as image masks or additional views of the object). Our method, which we call "Imagic", leverages a pre-trained text-to-image diffusion model for this task. It produces a text embedding that aligns with both the input image and the target text, while fine-tuning the diffusion model to capture the image-specific appearance. We demonstrate the quality and versatility of our method on numerous inputs from various domains, showcasing a plethora of high quality complex semantic image edits, all within a single unified framework.
Model and Training Method of the Resilient Image Classifier Considering Faults, Concept Drift, and Adversarial Attacks

Modern trainable image recognition models are vulnerable to different types of perturbations; hence, the development of resilient intelligent algorithms for safety-critical applications remains a relevant concern to reduce the impact of perturbation on model performance. This paper proposes a model and training method for a resilient image classifier capable of efficiently functioning despite various faults, adversarial attacks, and concept drifts. The proposed model has a multi-section structure with a hierarchy of optimized class prototypes and hyperspherical class boundaries, which provides adaptive computation, perturbation absorption, and graceful degradation. The proposed training method entails the application of a complex loss function assembled from its constituent parts in a particular way depending on the result of perturbation detection and the presence of new labeled and unlabeled data. The training method implements principles of self-knowledge distillation, the compactness maximization of class distribution and the interclass gap, the compression of feature representations, and consistency regularization. Consistency regularization makes it possible to utilize both labeled and unlabeled data to obtain a robust model and implement continuous adaptation. Experiments are performed on the publicly available CIFAR-10 and CIFAR-100 datasets using model backbones based on modules ResBlocks from the ResNet50 architecture and Swin transformer blocks. It is experimentally proven that the proposed prototype-based classifier head is characterized by a higher level of robustness and adaptability in comparison with the dense layer-based classifier head. It is also shown that multi-section structure and self-knowledge distillation feature conserve resources when processing simple samples under normal conditions and increase computational costs to improve the reliability of decisions when exposed to perturbations.
Analysis and Experimentation on the ManTraNet Image Forgery Detector

This work describes the ManTraNet network for image forgery detection.ManTraNet is an end-to-end convolutional neural network composed of two sub-networks, one to extract features linked to traces of manipulation, and another to detect local anomalies between the features.It is trained on pristine and forged images from several datasets.We briefly analyze the results provided by ManTraNet, so as to highlight its qualities and limitations.Overall, ManTraNet yields state-of-the-art results on benchmark datasets with images similar to the one it sees in training, but is unreliable on wild images, due to its opacity and the difficulty distinguishing true detections from false positives.
RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations

Detecting accounting anomalies is a recurrent challenge in financial statement audits. Recently, novel methods derived from Deep-Learning (DL) such as Autoencoder Neural Networks (AENNs) have been proposed to audit the large volumes of a statement’s underlying accounting records. However, due to their vast number of parameters, such models exhibit the drawback of being inherently opaque. At the same time, the concealing of a model’s inner workings often hinders its real-world application in financial audits, since auditors must reasonably explain and justify their audit decisions. Nowadays, various Explainable AI (XAI) techniques have been proposed to address this challenge, e.g., SHapley Additive exPlanations (SHAP). However, in unsupervised DL as often applied in financial audits, these methods explain the model output at the level of encoded variables. As a result, the explanations of AENNs are often hard to comprehend by human auditors. To mitigate this drawback, we propose Reconstruction Error SHapley Additive exPlanations Extension (RESHAPE), which explains the model output on an aggregated attribute level. In addition, we introduce an evaluation framework to compare the versatility of XAI methods in auditing. Our experimental results show empirical evidence that RESHAPE results in versatile explanations compared to state-of-the-art baselines. We envision such attribute-level explanations as a necessary next step in the adoption of unsupervised DL techniques in financial auditing.
Semantic Relationship-based Embedding Models for Text Classification

Embedding representation models characterize each word as a vector of numbers with a&#x0D; fixed length. These models have been used in tasks involving text classification, such as recommen-&#x0D; dation and question-answer systems. Semantic relationships are words with a relationship between&#x0D; them providing a complete idea to a text. Therefore, it is hypothesized that an embedding model&#x0D; involving semantic relationships will provide better performance for tasks that use them. This paper&#x0D; presents three embedding models based on semantic relations extracted fromWikipedia to classify&#x0D; texts. The synonym, hyponym, and hyperonym semantic relationships were the ones considered&#x0D; in this work since previous experiments have shown that they are the ones that provide the most&#x0D; semantic knowledge. Lexical-syntactic patterns present in the literature were implemented and&#x0D; subsequently applied to the Wikipedia corpus to obtain the semantic relationships present in it.&#x0D; Several semantic relationships are used in different models: synonymy, hyponym-hyperonym, and a&#x0D; combination of the first two. A convolutional neural network was trained for text classification to&#x0D; evaluate the performance of each model. The results obtained were evaluated with the metrics of &#x0D; precision, accuracy, recall, and F1-measure. The best values obtained with the second model were&#x0D; accuracy of 0.79 for the 20-Newsgroup corpus. F1-measure and recall of 0.87 respectively for the Reuters corpus.
Automated Design of Salient Object Detection Algorithms with Brain Programming

Despite recent improvements in computer vision, artificial visual systems' design is still daunting since an explanation of visual computing algorithms remains elusive. Salient object detection is one problem that is still open due to the difficulty of understanding the brain's inner workings. Progress on this research area follows the traditional path of hand-made designs using neuroscience knowledge. In recent years two different approaches based on genetic programming appear to enhance their technique. One follows the idea of combining previous hand-made methods through genetic programming and fuzzy logic. The other approach consists of improving the inner computational structures of basic hand-made models through artificial evolution. This research work proposes expanding the artificial dorsal stream using a recent proposal to solve salient object detection problems. This approach uses the benefits of the two main aspects of this research area: fixation prediction and detection of salient objects. We decided to apply the fusion of visual saliency and image segmentation algorithms as a template. The proposed methodology discovers several critical structures in the template through artificial evolution. We present results on a benchmark designed by experts with outstanding results in comparison with the state-of-the-art.
Improved 3D-ResNet sign language recognition algorithm with enhanced hand features

In sign language video, the hand region is small, the resolution is low, the motion speed is fast, and there are cross occlusion and blur phenomena, which have a great impact on sign language recognition rate and speed, and are important factors restricting sign language recognition performance. To solve these problems, this paper proposes an improved 3D-ResNet sign language recognition algorithm with enhanced hand features, aiming to highlight the features of both hands, solve the problem of missing more effective information when relying only on global features, and improve the accuracy of sign language recognition. The proposed method has two improvements. Firstly, the algorithm detects the left and right hand regions based on the improved EfficientDet network, uses the improved Bi-FPN module and dual channel and spatial attention module are used to enhance the detection ability of the network for small targets like hand. Secondly, the improved residual module is used to improve the 3D-ResNet18 network to extract sign language features. The global, the left-hand and the right-hand image sequences are divided into three branches for feature extraction and fusion, so as to strengthen the attention to hand features, strengthen the representation ability of sign language features, and achieve the purpose of improving the accuracy of sign language recognition. In order to verify the performance of this algorithm, a series of experiments are carried out on CSL dataset. For example, in the experiments of hand detection algorithm and sign language recognition algorithm, the performance indicators such as Top-N, mAP, FLOPs and Parm are applied to find the optimal algorithm framework. The experimental results show that the Top1 recognition accuracy of this algorithm reaches 91.12%, which is more than 10% higher than that of C3D, P3D and 3D-ResNet basic networks. From the performance indicators of Top-N, mAP, FLOPs, Parm and so on, the performance of the algorithm in this paper is better than several algorithms in recent three years, such as I3D+BLSTM, B3D ResNet, AM-ResC3D+RCNN and so on. The results show that the hand detection network with enhanced hand features and three-dimensional convolutional neural network proposed in this paper can achieve higher accuracy of sign language recognition.
EXP-SC: A Semantic Communication Model Based on Information Framework Expansion and Knowledge Collision

Semantic communication is not obsessed with improving the accuracy of transmitted symbols, but is concerned with expressing the desired meaning that the symbol sequence exactly carried. However, the generation and measurement of semantic messages are still an open problem. Expansion combines simple things into complex systems and even generates intelligence, which is consistent with the evolution of the human language system. We apply this idea to semantic communication system, quantifying and transmitting semantics by symbol sequences, and investigate the semantic information system in a similar way as Shannon did for digital communication systems. This work was the first to propose the concept of semantic expansion and knowledge collision, which may provide a new paradigm for semantic communications. We believe that expansion and collision will be the cornerstone of semantic information theory.
Computer-Aided Diagnosis by Tissue Image Analysis as an Optical Biopsy in Hysteroscopy

This review of our experience in computer-assisted tissue image analysis (CATIA) research shows that significant information can be extracted and used to diagnose and distinguish normal from abnormal endometrium. CATIA enabled the evaluation and differentiation between the benign and malignant endometrium during diagnostic hysteroscopy. The efficacy of texture analysis in the endometrium image during hysteroscopy was examined in 40 women, where 209 normal and 209 abnormal regions of interest (ROIs) were extracted. There was a significant difference between normal and abnormal endometrium for the statistical features (SF) features mean, variance, median, energy and entropy; for the spatial grey-level difference matrix (SGLDM) features contrast, correlation, variance, homogeneity and entropy; and for the gray-level difference statistics (GLDS) features homogeneity, contrast, energy, entropy and mean. We further evaluated 52 hysteroscopic images of 258 normal and 258 abnormal endometrium ROIs, and tissue diagnosis was verified by histopathology after biopsy. The YCrCb color system with SF, SGLDM and GLDS color texture features based on support vector machine (SVM) modeling correctly classified 81% of the cases with a sensitivity and a specificity of 78% and 81%, respectively, for normal and hyperplastic endometrium. New technical and computational advances may improve optical biopsy accuracy and assist in the precision of lesion excision during hysteroscopy. The exchange of knowledge, collaboration, identification of tasks and CATIA method selection strategy will further improve computer-aided diagnosis implementation in the daily practice of hysteroscopy.