Reviewerly/exHarmony huggingface dataset:
Comprehensive benchmark datasets for Amharic scene text detection and recognition

Ethiopic/Amharic script is one of the oldest African writing systems, which serves at least 23 languages (e.g., Amharic, Tigrinya) in East Africa for more than 120 million people. The Amharic writing system, Abugida, has 282 syllables, 15 punctuation marks, and 20 numerals. The Amharic syllabic matrix is derived from 34 base graphemes/consonants by adding up to 12 appropriate diacritics or vocalic markers to the characters. The syllables with a common consonant or vocalic markers are likely to be visually similar and challenge text recognition tasks. In this work, we presented the first comprehensive public datasets named HUST-ART, HUST-AST, ABE, and Tana for Amharic script detection and recognition in the natural scene. We have also conducted extensive experiments to evaluate the performance of the state of art methods in detecting and recognizing Amharic scene text on our datasets. The evaluation results demonstrate the robustness of our datasets for benchmarking and its potential of promoting the development of robust Amharic script detection and recognition algorithms. Consequently, the outcome will benefit people in East Africa, including diplomats from several countries and international communities.
An Efficient and Robust Cloud-Based Deep Learning With Knowledge Distillation

In recent years, deep neural networks have shown extraordinary power in various practical learning tasks, especially in object detection, classification, natural language processing. However, deploying such large models on resource-constrained devices or embedded systems is challenging due to their high computational cost. Efforts such as model partition, pruning, or quantization have been used at the expense of accuracy loss. Knowledge distillation is a technique that transfers model knowledge from a well-trained model (teacher) to a smaller and shallow model (student). Instead of using a learning model on the cloud, we can deploy distilled models on various edge devices, significantly reducing the computational cost, memory usage and prolonging the battery lifetime. In this work, we propose a novel neuron manifold distillation (NMD) method, where the student models imitate the teacher's output distribution and learn the feature geometry of the teacher model. In addition, to further improve the cloud-based learning system reliability, we propose a confident prediction mechanism to calibrate the model predictions. We conduct experiments with different distillation configurations over multiple datasets. Our proposed method demonstrates a consistent improvement in accuracy-speed trade-offs for the distilled model.
High performance and energy efficient inference for deep learning on multicore ARM processors using general optimization techniques and BLIS

We evolve PyDTNN, a framework for distributed parallel training of Deep Neural Networks (DNNs), into an efficient inference tool for convolutional neural networks. Our optimization process on multicore ARM processors involves several high-level transformations of the original framework, such as the development and integration of Cython routines to exploit thread-level parallelism; the design and development of micro-kernels for the matrix multiplication, vectorized with ARM’s NEON intrinsics, that can accommodate layer fusion; and the appropriate selection of several cache configuration parameters tailored to the memory hierarchy of the target ARM processors. Our experiments evaluate both inference throughput (measured in processed images/s) and inference latency (i.e., time-to-response) as well as energy consumption per image when varying the level of thread parallelism and the processor power modes. The experiments with the new inference engine are reported for the ResNet50 v1.5 model on the ImageNet dataset from the MLPerf suite using the ARM v8.2 cores in the NVIDIA Jetson AGX Xavier board. These results show superior performance compared with the well-spread TFLite from Google and slightly inferior results when compared with ArmNN, the native library from ARM for DNN inference.
Quantum computations for disambiguation and question answering

Abstract Automatic text processing is now a mature discipline in computer science, and so attempts at advancements using quantum computation have emerged as the new frontier, often under the term of quantum natural language processing. The main challenges consist in finding the most adequate ways of encoding words and their interactions on a quantum computer, considering hardware constraints, as well as building algorithms that take advantage of quantum architectures, so as to show improvement on the performance of natural language tasks. In this paper, we introduce a new framework that starts from a grammar that can be interpreted by means of tensor contraction, to build word representations as quantum states that serve as input to a quantum algorithm. We start by introducing an operator measurement to contract the representations of words, resulting in the representation of larger fragments of text. We then go on to develop pipelines for the tasks of sentence meaning disambiguation and question answering that take advantage of quantum features. For the first task, we show that our contraction scheme deals with syntactically ambiguous phrases storing the various different meanings in quantum superposition, a solution not available on a classical setting. For the second task, we obtain a question representation that contains all possible answers in equal quantum superposition, and we implement Grover’s quantum search algorithm to find the correct answer, agnostic to the specific question, an implementation with the potential of delivering a result with quadratic speedup.
Fake News Detection Using SVM Algorithm in Machine Learning

Online news has taken over as the primary source of information in recent years. People don&#x0027;t have enough time to read the newspaper, so they utilise social media to keep up with the latest news. However, sometimes information on the internet is unclear, and it may be intended to deceive. Automated false news identification technologies, such as machine learning models, have become a must in the current system. With hold out cross validation, the performance of machine learning models was evaluated on two fake and real news datasets of varying sizes. On the ISOT dataset and the KD nugget dataset, the suggested novel stacking model obtained testing accuracy of 99.94 percent and 96.05 percent, respectively. While using the dataset, we were unable to obtain an accurate result for identifying fake news from current events, and we were only able to detect fake news. Concerning the specific group. As a result, we&#x0027;re going to use for detecting fake news in real-time tweets from Twitter. The global model is able to capture general sentiment information and is shared across multiple tweets. Greedy &#x0026; Dynamic Blocking Algorithms unique to Trends, such as the Support Vector Machine model. In addition, we collect sentiment knowledge from both labelled and unlabelled samples in each Trend and use it to improve the learning of Trends-specific sentiment categorization. We use restoration over Trends-specific sentiment classifiers in our method for encouraging the exchange of sentiment information between relevant every key word.
Specifying and Reasoning about CPS through the Lens of the NIST CPS Framework

Abstract This paper introduces a formal definition of a Cyber-Physical System (CPS) in the spirit of the CPS Framework proposed by the National Institute of Standards and Technology (NIST). It shows that using this definition, various problems related to concerns in a CPS can be precisely formalized and implemented using Answer Set Programming (ASP). These include problems related to the dependency or conflicts between concerns, how to mitigate an issue, and what the most suitable mitigation strategy for a given issue would be. It then shows how ASP can be used to develop an implementation that addresses the aforementioned problems. The paper concludes with a discussion of the potentials of the proposed methodologies.
Tiny Object Detection based on YOLOv5

In view of the poor accuracy of mainstream object detection algorithms in detecting tiny objects, A tiny object detection algorithm based on improved YOLOv5 is proposed. The main feature extraction network of YOLOv5 was modified to generate four feature images to enhance feature extraction of the original input images. Modified the YOLOv5 Neck part, combined with FPN and PANet, carried out feature fusion for four feature maps containing different semantic information, generated better features, and improved the performance of tiny object detection. GIoU loss function was introduced to replace the IoU loss function in the original algorithm to improve the positioning accuracy of tiny objects. Swish activation function was used to replace the original ReLU activation function to better retain target features. The Mosaic data enhancement method was used to enrich the object detection background, and the learning rate cosine annealing attenuation training method was used to dynamically update the learning rate parameters, and the improved YoloV5 algorithm was fused. In this paper, a comparison test is conducted between the original YoloV5 algorithm and CityPrersons data set. Experimental results show that the improved YoloV5 algorithm can effectively improve the detection accuracy of tiny objects.
AQX: Explaining Air Quality Forecast for Verifying Domain Knowledge using Feature Importance Visualization

Air pollution forecast has become critical because of its direct impact on human health and its increased production caused by rapid industrialization. Machine learning (ML) solutions are being drastically explored in this domain because they can potentially produce highly accurate results with access to historical data. However, experts in the environmental area are skeptical about adopting ML solutions in real-world applications and policy making due to their black-box nature. In contrast, despite having low accuracy sometimes, the existing traditional simulation model (e.g., CMAQ) are widely used and follows well-defined and transparent equations. Therefore, presenting the knowledge learned by the ML model can make it transparent as well as comprehensible. In addition, validating the ML model’s learning with the existing domain knowledge might aid in addressing their skepticism, building appropriate trust, and better utilizing ML models. In collaboration with three experts with an average of five years of research experience in the air pollution domain, we identified that feature (meteorological feature like wind) contribution, towards the final forecast as the major information to be verified with domain knowledge. In addition, the accuracy of ML models compared with traditional simulation models and raw wind trajectories are essential for domain experts to validate the feature contribution. Based on the identified information, we designed and developed AQX, a visual analytics system to help experts validate and verify the ML model’s learning with their domain knowledge. The system includes multiple coordinated views to present the contributions of input features at different levels of aggregation in both temporal and spatial dimensions. It also provides a performance comparison of ML and traditional models in terms of accuracy and spatial map, along with the animation of raw wind trajectories for the input period. We further demonstrated two case studies and conducted expert interviews with two domain experts to show the effectiveness and usefulness of AQX.
Discrete Motion Planner based on Deep Recurrent Neural Network for Mobile Robot Obstacle Avoidance in Dead-End Environments

For autonomous mobile robots, obstacle avoidance is a necessary capability for navigation. We have thus far proposed a discrete motion planner based on a fully-connected feedforward deep neural network. The motion planner resulted in a sufficient capability with discrete motions, such as straight, right turn, and left turn. Once a robot stops, however, it is impossible to restart navigation toward a goal destination. This problem occurs when obstacles form a dead end in an environment. In order for the robot to escape from the dead end, further motions are required. Moreover, the robot is required to take into account a series of motions for the dead end. In this paper, therefore, we propose a discrete motion planner based on a fully-connected deep recurrent neural network. For network training, we use imitation learning from motion instructions. In the experiment, two of the motion planners are applied to the robot. Finally, we show that the robot based on the proposed motion planner successfully moves toward the goal destination while avoiding obstacles even in a dead-end environment.
Adaptive modeling of nonnegative environmental systems based on projectional Differential Neural Networks observer

A new design of a non-parametric adaptive approximate model based on Differential Neural Networks (DNNs) applied for a class of non-negative environmental systems with an uncertain mathematical model is the primary outcome of this study. The approximate model uses an extended state formulation that gathers the dynamics of the DNN and a state projector (pDNN). Implementing a non-differentiable projection operator ensures the positiveness of the identifier states. The extended form allows producing continuous dynamics for the projected model. The design of the learning laws for the weight adjustment of the continuous projected DNN considered the application of a controlled Lyapunov-like function. The stability analysis based on the proposed Lyapunov-like function leads to the characterization of the ultimate boundedness property for the identification error. Applying the Attractive Ellipsoid Method (AEM) yields to analyze the convergence quality of the designed approximate model. The solution to the specific optimization problem using the AEM with matrix inequalities constraints allows us to find the parameters of the considered DNN that minimizes the ultimate bound. The evaluation of two numerical examples confirmed the ability of the proposed pDNN to approximate the positive model in the presence of bounded noises and perturbations in the measured data. The first example corresponds to a catalytic ozonation system that can be used to decompose toxic and recalcitrant contaminants. The second one describes the bacteria growth in aerobic batch regime biodegrading simple organic matter mixture.
Learning Binary Semantic Embedding for Large-Scale Breast Histology Image Analysis

With the progress of clinical imaging innovation and machine learning, the computer-assisted diagnosis of breast histology images has attracted broad attention. Nonetheless, the use of computer-assisted diagnoses has been blocked due to the incomprehensibility of customary classification models. In view of this question, we propose a novel method for <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> earning <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</u> inary <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> emantic <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> mbedding (LBSE). In this study, bit balance and uncorrelation constraints, double supervision, discrete optimization and asymmetric pairwise similarity are seamlessly integrated for learning binary semantic-preserving embedding. Moreover, a fusion-based strategy is carefully designed to handle the intractable problem of parameter setting, saving huge amounts of time for boundary tuning. Based on the above-mentioned proficient and effective embedding, classification and retrieval are simultaneously performed to give interpretable image-based deduction and model helped conclusions for breast histology images. Extensive experiments are conducted on three benchmark datasets to approve the predominance of LBSE in different situations.
Dynamic neighborhood genetic learning particle swarm optimization for high-power-density electric propulsion motor

To maximize the power density of the electric propulsion motor in aerospace application, this paper proposes a novel Dynamic Neighborhood Genetic Learning Particle Swarm Optimization (DNGL-PSO) for the motor design, which can deal with the insufficient population diversity and non-global optimal solution issues. The DNGL-PSO framework is composed of the dynamic neighborhood module and the particle update module. To improve the population diversity, the dynamic neighborhood strategy is first proposed, which combines the local neighborhood exemplar generation mechanism and the shuffling mechanism. The local neighborhood exemplar generation mechanism enlarges the search range of the algorithm in the solution space, thus obtaining high-quality exemplars. Meanwhile, when the global optimal solution cannot update its fitness value, the shuffling mechanism module is triggered to dynamically change the local neighborhood members. The roulette wheel selection operator is introduced into the shuffling mechanism to ensure that particles with larger fitness value are selected with a higher probability and remain in the local neighborhood. Then, the global learning based particle update approach is proposed, which can achieve a good balance between the expansion of the search range in the early stage and the acceleration of local convergence in the later stage. Finally, the optimization design of the electric propulsion motor is conducted to verify the effectiveness of the proposed DNGL-PSO. The simulation results show that the proposed DNGL-PSO has excellent adaptability, optimization efficiency and global optimization capability, while the optimized electric propulsion motor has a high power density of 5.207 kW/kg with the efficiency of 96.12%.
Augmentation-based Pseudo-Ground truth Generation for Deep Learning in Historical Document Segmentation for Greater Levels of Archival Description and Access

The successful use of deep learning solutions for document image segmentation typically relies on a large number of manually labeled ground truth examples, which is expensive to obtain for historical document images that have significant noise effects and variation. At the same time, successful applications of deep learning solutions for document image segmentation have rich potential to facilitate greater levels of description in archival collections (e.g., at and below the item-level). These greater levels of description are critical to increasing access and use of archival collections across an array of research domains. In response, this article investigates whether an augmentation-based approach to generating pseudo-ground truth can be effective with a limited number of labeled images in a document segmentation application. The rationale is that if we can decrease the cost of generating ground truth through augmentation-based approaches, we can use these approaches as part of the description and access pipelines for historical library and archival collections. In this initial exploration, we first generate synthetic images and corresponding pseudo-ground truth using a set of existing degradation-based augmentation models from a small number of labeled actual images. When generating synthetic images, we control the visual quality distortion based on OCR word-level confidence to avoid generating images unlikely to be present in the dataset. Then, we perform several investigations to examine the impact of incorporating pseudo-ground truth data in the training of the deep learning network dhSegment and further evaluate the use of multiple combinations of degradation models. We also assess the generalizability of the approach by applying the trained network on a larger dataset. Our investigations primarily use real-world datasets known to have significant noise effects. Results show that augmentation-based pseudo-ground truth generation is capable of improving segmentation performance with the use of the full original dataset and requires only 30% of the original dataset. Results also show that using more than three degradation models is likely to cause overfitting during training. Furthermore, we show that a segmentation network trained on pseudo-ground truth data has generalization capability.
Fully Automatic Analysis of Muscle B-Mode Ultrasound Images Based on the Deep Residual Shrinkage U-Net

The parameters of muscle ultrasound images reflect the function and state of muscles. They are of great significance to the diagnosis of muscle diseases. Because manual labeling is time-consuming and laborious, the automatic labeling of muscle ultrasound image parameters has become a research topic. In recent years, there have been many methods that apply image processing and deep learning to automatically analyze muscle ultrasound images. However, these methods have limitations, such as being non-automatic, not applicable to images with complex noise, and only being able to measure a single parameter. This paper proposes a fully automatic muscle ultrasound image analysis method based on image segmentation to solve these problems. This method is based on the Deep Residual Shrinkage U-Net(RS-Unet) to accurately segment ultrasound images. Compared with the existing methods, the accuracy of our method shows a great improvement. The mean differences of pennation angle, fascicle length and muscle thickness are about 0.09°, 0.4 mm and 0.63 mm, respectively. Experimental results show that the proposed method realizes the accurate measurement of muscle parameters and exhibits stability and robustness.
Differential cryptanalysis of diffusion and confusion based information confidentiality mechanism

Wireless multimedia communications have progressed significantly in recent years. As a result, there is an increasing demand for more secure media transmission to protect multimedia information. Image encryption systems have been presented throughout the years, but those based on chaotic maps are the most secure and trustworthy due to the inherent properties in such types of multimedia contents involving the pixels strong correlation and data handling capacities. In this research, we have performed differential cryptanalysis of a recently suggested cryptosystem based on a three dimensional (3D) logistic map and 3D Cat map. The originally proposed approach was based on diffusion and confusion strategy. The vulnerabilities in the understudy cryptosystem lead to the successful proposed cryptanalysis attack. The original ciphers are recovered by means of a chosen-plaintext attack. The recovered data is also subjected to some statistical analysis to check the quality.