Reviewerly/exHarmony huggingface dataset:
Weak sub-network pruning for strong and efficient neural networks

Pruning methods to compress and accelerate deep convolutional neural networks (CNNs) have recently attracted growing attention, with the view of deploying pruned networks on resource-constrained hardware devices. However, most existing methods focus on small granularities, such as weight, kernel and filter, for the exploration of pruning. Thus, it will be bound to iteratively prune the whole neural networks based on those small granularities for high compression ratio with little performance loss. To address these issues, we theoretically analyze the relationship between the activation and gradient sparsity, and the channel saliency. Based on our findings, we propose a novel and effective method of weak sub-network pruning (WSP). Specifically, for a well-trained network model, we divide the whole compression process into two non-iterative stages. The first stage is to directly obtain a strong sub-network by pruning the weakest one. We first identify the less important channels from all the layers and determine the weakest sub-network, whereby each selected channel makes a minimal contribution to both the feed-forward and feed-backward processes. Then, a one-shot pruning strategy is executed to form a strong sub-network enabling fine tuning, while significantly reducing the impact of the network depth and width on the compression efficiency, especially for deep and wide network architectures. The second stage is to globally fine-tune the strong sub-network using several epochs to restore its original recognition accuracy. Furthermore, our proposed method impacts on the fully-connected layers as well as the convolutional layers for simultaneous compression and acceleration. Comprehensive experiments on VGG16 and ResNet-50 involving a variety of popular benchmarks, such as ImageNet-1K, CIFAR-10, CUB-200 and PASCAL VOC, demonstrate that our WSP method achieves superior performance on classification, domain adaption and object detection tasks with small model size. Our source code is available at https://github.com/QingbeiGuo/WSP.git.
Embedding Rasa in edge Devices: Capabilities and Limitations

Over the past few years, there has been a boost in the use of commercial virtual assistants. Obviously, these proprietary tools are well-performing, however the functionality they offer is limited, users are "vendor-locked", while possible user privacy issues rise. In this paper we argue that low-cost, open hardware solutions may also perform well, given the proper setup. Specifically, we perform an initial assessment of a low-cost virtual agent employing the Rasa framework integrated into a Raspberry Pi 4. We set up three different architectures, discuss their capabilities and limitations and evaluate the dialogue system against three axes: assistant comprehension, task success and assistant usability. Our experiments show that our low-cost virtual assistant performs in a satisfactory manner, even when a small-sized training dataset is used.
Binary Giza Pyramids Construction For Feature Selection

Feature selection (FS) is considered a challenging machine learning problem that handles the large size of features. The main purpose of FS is to remove irrelevant and redundant variables to improve the performance of the learning algorithms. Consequently, the FS process is considered as an optimization problem where metaheuristics approaches prove efficiency in solving it. In this paper, we propose new binary versions of a new ancient inspired metaheuristic approach called Giza Pyramids Construction (GPC) to select the most relevant subset of features. The proposed binary versions of the algorithm called BGPC-S and BGPC-V are implemented with two transfer functions, with the main objective of maximizing classification accuracy and minimizing the number of selected features. The two versions of BGPC were compared to six well-known binary metaheuristics for feature selection problem, namely Binary Atom Search Optimisation (BASO), Binary Bat Algorithm (BBA), Binary Differential Evolution (BDE), Binary Grey Wolf Optimizer (BGWO), Binary Particle Swarm Optimization (BPSO), and Binary Harris Hawks Optimizer (BHHO), and evaluated over 20 datasets from the UCI repository. Experiments have demonstrated that the proposed approaches outperformed the other algorithms in terms of classification accuracy and the number of selected features.
Optical measurement, characterization and quantification for plastic deformations and stress in three-dimensional structures: Recent developments, challenges, and prospects

Plastic deformations and the associated stress evolution are closely related to the destruction of diverse engineering structures. Accurate measurement, characterization, quantification, and visualization of plastic deformations and the associated stress are critical to the evaluation and prediction of the failure of engineering structures. To date, various optical methods have been developed to explore plasticity-related phenomena. However, it is challenging for traditional optical methods to accurately characterize, quantify, and visualize the plastic deformation and associated stress in three-dimensional (3D) structures. In this article, we review the recent developments of digital image correlation, digital volume correlation, moiré methods, optical interferometry, and photoelastic techniques in terms of measurement, characterization, and visualization of plasticity-related phenomena in materials. The challenges and prospects of combining optical techniques with promising 3D printing to improve the efficiency and accuracy in 3D measurement, characterization, quantification, and visualization of plastic deformations and the associated stress in complex 3D structures are discussed.
An Efficient Learning Weight of Elman Neural Network with Chicken Swarm Optimization Algorithm

Data classification is one of the most frequently used tasks carried out to label information into predefined classes. The most commonly used models for data classification are Feed Forward Artificial Neural Networks (FFANN), and recurrent neural networks. The connection paths of these two structures are different from each other. Generally, the trained algorithm for these structures is back propagation (BP) algorithm which has many defects. For instance, due to the uncertain number of hidden layer neuron and fixed learning rate, it is easy to fall into local minimum, and it will never reach global minimum error function, it may stay at local minimum. Therefore, to make the slow learning process faster, it is necessary to carefully select the initial weight value. Therefore Meta heuristic search techniques play an important role for selection initial weights for the network. Chicken Swarm Optimization algorithm is one of the Meta heuristic technique effectively for selecting the initial weights values to converge to the optimal solution. However, this study proposed the Chicken Swarm Optimization for efficiently learn the initial weights value of Elman Neural Network algorithm. To validate the proposed algorithm, it is compared with existing algorithms such as Back Propagation Neural Network, Artificial Bee Colony Back Propagation and Genetic Algorithm Neural Network, and verified by two classification problems namely: IRIS and 7-bit parity. Simulation results shows that proposed algorithm outperforms with existing algorithms in terms of accuracy and mean square error.
A Spatiotemporal and Multivariate Attribute Correlation Extraction Scheme for Detecting Abnormal Nodes in WSNs

Many heterogeneous sensors exhibit a strong correlation in both space and time that can be used to enhance the abnormal node detection problem in a wireless sensor network (WSN).Corruption in these correlations has been shown effective in detecting false data injection attacks.However, extracting such correlations is considered a challenging task in WSNs.In this paper, we adopt a new crosscorrelation-based method to extract the sensor relationships.It utilizes the observed spatiotemporal (ST) and multivariate-attribute (MVA) sensor correlations to decide whether the sensor is subject to abnormalities or represents actual events.Based on the ST correlations, the cross-correlation is extracted in both space and time by conducting shape-based logical subclustering and two-phase analysis methods.In the first analysis phase, the system uses a variable-size sliding window and a median absolute deviation (MAD) measure.If the collected sensor data streams output a certain percentage of anomalous points, the MAD will flag these points as anomalous measurements, and all the sensor data and the window size will be passed to the second analysis phase.The latter performs both tumbling-window and sliding-window analyses to extract multicriteria cross-correlation measures.Finally, all the extracted sensor time-series features will be fed to the shape-based clustering to generate a sensor similarity-like graph.The latter reflects the similarity degree of the sensor with the other nodes.The nodes with a low similarity degree below the threshold will be identified as candidate abnormal nodes.Based on the observed MVA correlations, a set consisting of a few rules is introduced to check whether the detected candidate abnormal nodes represent actual events.Finally, if abnormal nodes exist, then such nodes are reported.Our experiments using two real-world datasets demonstrate that our proposed approach detects abnormal nodes with an average accuracy of 96.50%, an average precision of 88.69%, and a recall rate of 93.00%.
Towards an Efficient Framework for Data Extraction from Chart Images

In this paper, we fill the research gap by adopting state-of-the-art computer vision techniques for the data extraction stage in a data mining system. As shown in Fig. 1, this stage contains two subtasks, namely, plot element detection and data conversion. For building a robust box detector, we comprehensively compare different deep learning-based methods and find a suitable method to detect box with high precision. For building a robust point detector, a fully convolutional network with feature fusion module is adopted, which can distinguish close points compared to traditional methods. The proposed system can effectively handle various chart data without making heuristic assumptions. For data conversion, we translate the detected element into data with semantic value. A network is proposed to measure feature similarities between legends and detected elements in the legend matching phase. Furthermore, we provide a baseline on the competition of Harvesting raw tables from Infographics. Some key factors have been found to improve the performance of each stage. Experimental results demonstrate the effectiveness of the proposed system.
Revisiting Cryptanalysis on ChaCha From Crypto 2020 and Eurocrypt 2021

ChaCha has been one of the most prominent ARX designs of the last few years because of its use in several systems. The cryptanalysis of ChaCha involves a differential attack that exploits the idea of Probabilistic Neutral Bits (PNBs). For a long period, the single-bit distinguisher in this differential attack was found up to 3rd round. At Crypto 2020, Beierle et al. introduced for the first time the single bit distinguishers for 3.5th round, which contributed significantly to regaining the flow of the research work in this direction. This discovery became the primary factor behind the huge improvement in the key recovery attack complexity in that work. This was followed by another work at Eurocrypt 2021, where a single bit distinguisher at 3.5th round helped to produce a 7th round distinguisher of ChaCha and a further improvement in the key recovery. In this paper, first, we provide the theoretical framework for the distinguisher given by Beierle et al. We mathematically derive the observed differential correlation for the particular position where the output difference is observed at 3.5th round. Also, Beierle et al. mentioned the issue of the availability of proper IVs to produce such distinguishers, and pointed out that not all keys have such IVs available. Here we provide a theoretical insight of this issue. Next, we revisit the work of Coutinho <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (Eurocrypt 2021). Using Differential-Linear attacks against ChaCha, they claimed the distinguisher and the key recovery with complexities 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">218</sup> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{228.51}$ </tex-math></inline-formula> respectively. We show that the differential correlation for the 3.5th round is much smaller than the claim of Coutinho et al. This makes the attack complexities much higher than their claim.
Self-adversarial Learning for Detection of Clustered Microcalcifications in Mammograms

Microcalcification (MC) clusters in mammograms are one of the primary signs of breast cancer. In the literature, most MC detection methods follow a two-step paradigm: segmenting each MC and analyzing their spatial distributions to form MC clusters. However, segmentation of MCs cannot avoid low sensitivity or high false positive rate due to their variability in size (sometimes <0.1 mm), brightness, and shape (with diverse surroundings). In this paper, we propose a novel self-adversarial learning framework to differentiate and delineate the MC clusters in an end-to-end manner. The class activation mapping (CAM) mechanism is employed to directly generate the contours of MC clusters with the guidance of MC cluster classification and box annotations. We also propose the self-adversarial learning strategy to equip CAM with better detection capability of MC clusters by using the backbone network itself as a discriminator. Experimental results suggest that our method can achieve better performance for MC cluster detection with the contouring of MC clusters and classification of MC types.
Dial4JaCa – A Demonstration

Multi-Agent Systems (MAS) have become a powerful approach to the development of complex AI systems. However, the community faces the challenge of developing communication interfaces between software agents and humans, for instance, using natural language. In this paper, we demonstrate an approach to the integration of MAS and chatbot technologies named Dial4JaCa. Dial4JaCa enables the implementation of MAS integrated with chatbots, which can be applied to a variety of domains. In this particular work, we demonstrate how it is possible to integrate a JaCaMo application (agent system) with Dialogflow (a chatbot platform) using Dial4JaCa.
Forecast of the Employment Situation of College Graduates Based on the LSTM Neural Network

Scientific and reasonable forecast model of graduates' employment data can efficaciously embody the complex characteristics of graduates' employment data and embody the nonlinear dynamic interaction of influencing elements of graduates' employment situation. It has a strong and steady characteristic learning capability, thus selecting the main influence data that influence the change of graduates' employment data. In this paper, according to the situation embodied by students' employment, a data mining analysis model is set up by using the statistical method based on the model of cluster analysis technology to forecast the employment situation of graduates. In this paper, a forecast technique of graduates' employment situation based on the long short-term memory (LSTM) recurrent neural network is conceived, including network structure design, network training, and forecast process implementation algorithm. In addition, aiming at minimizing the forecasting error, an LSTM forecasting model parameter optimization algorithm based on multilayer grid search is conceived. It also verifies the applicability and correctness of the LSTM forecasting model and its parameter optimization algorithm in the analysis of graduates' employment situation.
Analytical study of Text Summarization Techniques

Summarization of Text is extracting important information from a body of text and present it in the form of a concise summary. The need for summarization has increased in recent times. The importance of having a simple concise summary of information in the news, business and research domains is paramount. Automatic text summarization is a well known task in the NLP (Natural Language Processing) field. Text Summarization techniques can be large divided into two groups: Extractive summarization and Abstractive summarization. Extractive summarization is based on identifying key sentences or phrases from the source text and grouping them to produce a summary without rewriting or paraphrasing the original text. Abstractive summarization is based on utilizing a deeper understanding of the source text and generating new sentences, not present in the original text, which improves the summary by reducing redundancy and focusing on the meaning of the source text. In this study we implement and compare the performance of various automatic summarization methods in order to gain insight into how long the methods take to implement and how accurate and human-like the generated summaries are. We aim to learn the pros and cons of the various techniques used by utilizing summary scoring as well as manual inspection of generated summary.
Detection of Malicious Intent in Non-cooperative Drone Surveillance

In this paper, a Bayesian approach is proposed for the early detection of a drone threatening or anomalous behaviour in a surveyed region. This is in relation to revealing, as early as possible, the drone intent to either leave a geographical area where it is authorised to fly (e.g. to conduct inspection work) or reach a prohibited zone (e.g. runway protection zones at airports or a critical infrastructure site). The inference here is based on the noisy sensory observations of the target state from a non-cooperative surveillance system such as a radar. Data from Aveillant’s Gamekeeper radar from a live drone trial is used to illustrate the efficacy of the introduced approach.
Introspective Robot Perception Using Smoothed Predictions from Bayesian Neural Networks

This work focuses on improving uncertainty estimation in the field of object classification from RGB images and demonstrates its benefits in two robotic applications. We employ a Bayesian Neural Network (BNN), and evaluate two practical inference techniques to obtain better uncertainty estimates, namely Concrete Dropout (CDP) and Kronecker-factored Laplace Approximation (LAP). We show a performance increase using more reliable uncertainty estimates as unary potentials within a Conditional Random Field (CRF), which is able to incorporate contextual information as well. Furthermore, the obtained uncertainties are exploited to achieve domain adaptation in a semi-supervised manner, which requires less manual efforts in annotating data. We evaluate our approach on two public benchmark datasets that are relevant for robot perception tasks.
Deep learning framework for geological symbol detection on geological maps

Dynamic legend generation for geological maps aims to detect and identify geological map symbols within the current viewshed and generate a corresponding real-time legend to help users quickly obtain the name and meaning of symbols. Detection and recognition entail high complexity and uncertainty because of the diversity of symbol types and the randomness of symbol distribution, and thus the generation of dynamic legends for geological maps is challenging. A new framework based on deep learning is proposed in this study, combining the deep convolutional neural network (CNN) and graph convolutional network (GCN) to realize the extraction and recognition of geological map symbols. Within the framework, a CNN-based model called single symbol detection network (SSDN) is developed to detect and identify single geological map symbols, and a novel GCN combined with L2 distance attention (DAGCN) is proposed to deal with the difficulty of extracting compound symbols caused by the randomness of symbol distribution. This work systematically solves the problem of geological symbol detection with the aid of object detection technology based on deep learning, providing foundation for the dynamic legend generation. Experiments show that the framework of the proposed method is effective, and a new benchmark is established for geological symbol detection on geological maps. All of our data and code are publicly available.
Coordination of two robotic manipulators for object retrieval in clutter

We consider the problem of retrieving a target object from a confined space by two robotic manipulators where overhand grasps are not allowed. If other movable obstacles occlude the target, more than one object should be relocated to clear the path to reach the target object. With two robots, the relocation could be done efficiently by simultaneously performing relocation tasks. However, the precedence constraint between the tasks (e.g, some objects at the front should be removed to manipulate the objects in the back) makes the simultaneous task execution difficult. We propose a coordination method that determines which robot relocates which object so as to perform tasks simultaneously. Given a set of objects to be relocated, the objective is to maximize the number of switches between the robots in performing relocation tasks. Thus, one robot can pick an object in the clutter while the other robot places an object in hand to the outside of the clutter. However, the object to be relocated may not be accessible to all robots, so switching could not always be achieved. Our method is based on the uniform-cost search so the number of switches can be maximized. We also propose a greedy variant whose computation time is shorter. From experiments, we show that our method reduces the completion time of the mission by at least 22.9% (at most 27.3%) compared to the methods with no consideration of switching.
Automatic question-answer pair generation using Deep Learning

Automatic question-answer pair generation is gaining importance because of the time being saved when compared to manual creation of questions. It not only saves time but also reduces the effort involved in the manual question generation process. It is useful in many fields such as school assignments, law practicing, self-assessments and many more. Our main objective is to create wh-type questions from a paragraph and find its accurate answer as well. This research work introduces a Question Answer Generation (QAG) system by using a deep learning approach for combining Answer Extraction (AE), Question Generation (QG) and Question Answering (QA) models. This research work aims to use a pre-trained language model - Bidirectional Encoder Representation from Transformers (BERT) and fine-tune it as per the proposed research objective.