Reviewerly/exHarmony huggingface dataset:
Performance and Visual Explainability of Chromagram-based CNN-ANN as Acoustic Fault Classifier of Industrial Equipment

The early and accurate diagnosis and prognosis of the defect of industrial equipment is crucial for predictive and prescriptive maintenance. Fault detection in airborne signals using acoustic sensors is a relatively new initiative in the field of predictive maintenance. Anomalies are detected in the sound using the reconstruction error of a CNN Autoencoder (CNN-AE) prior to downtime incidents. This has been demonstrated in prior research and is used as the foundation of this paper. By using the anomalies, downtime incidents can be predicted and maintenance work can be done proactively. The anomalies detected are extracted, visualized and used in building the fault classifier. The fault classifier is intended to help speed up the maintenance work by recommending the type of fault based on a probability distribution. In this paper, we discuss the design, performance, and visual explanation of the fault classification model. Visualization techniques, namely: intermediate activations, and gradient-weighted class activation mapping (Grad-CAM) are used to explain how the model classifies the input data.
A New Approach to Data Annotation Automation for Online Handwritten Mathematical Expression Recognition based on Recurrent Neural Networks

The modern recognition methods based on deep learning have established high requirements for the size of training data. However, such data is not always publicly available, often undersized, or limited by the number of classes. Preparing ground truth data is very expensive, time-consuming, and error-prone during collecting as well as annotation for many applications, particularly for optical character recognition and handwriting recognition. In many applications, such as recognition of 2-dimensional languages (diagrams, charts, mathematical formulas), annotation is further complicated by the fact that in addition to the large number of symbol classes that vary depending on the application, the spatial relations between symbols or classes must also be annotated. In this work, we propose an approach for automatic annotation of online handwritten mathematical expressions. This iterative approach provides a hierarchical annotation using an LSTM-based recognition model and a small annotated dataset as a starting point and provides an increase in the alphabet, gradually improving the recognition accuracy of new classes of symbols. The proposed approach does not imply prior verification of the gathered dataset and comprises three main stages: training recognition models, automatic annotation using recognition and matching algorithms, and automatic verification. These stages are repeated until the number of new automatically recognized and annotated samples becomes small enough. Samples that have not passed automatic verification are suspicious and require manual verification or refining, which is done at the last stage. In our experiment, more than 85% of the samples were automatically annotated. The annotation accuracy at the symbol level is more than 99%. Experimental results demonstrated that the proposed approach provided time-saving of up to 90% on manual operations. The proposed approach can also be applied to high-noise datasets.
An Efficient Approach to Enhance the Scalability of the HDFS: Extended Hadoop Archive (EHAR)

The Hadoop framework is most popular among data analytics applications. The file system of the Hadoop (HDFS) provides the layered storage facility for the frequent and infrequent data. In HDFS data can be archived using the HAR (Hadoop Archive) technique, but HAR archive are immutable means once the archive created it cannot be modified. One has to rewrite the whole archive if want to append the some new file to the existing archive. This paper introduces extended Hadoop archive (EHAR) that will resolve the scalability issue of the HDFS and also provide the mechanism to append the new files to the existing Hadoop archive. The experimental result shows that the execution time of the proposed approach is 53% to 39% lesser than the native HAR for the different fixed size files and 52% to 38% lesser than the native HAR for the different variable size files.
INTER-Meth: A Methodological Approach for the Integration of Heterogeneous IoT Systems

The Internet of Things (IoT) is a jeopardized ecosystem in which heterogeneity is intrinsic at all levels, from physical devices to communication protocols till high-level application semantics. The absence of IoT standards increases the complexity of integration and interoperability among heterogeneous platforms. This generates a strong demand for proper methodologies in order to fully support the development of heterogeneous, yet interoperable, IoT systems. To fill this gap, in this chapter the INTER-METH engineering methodology is presented. Developed in the context of the European H2020 INTER-IoT project, INTER-METH supports the integration of heterogeneous IoT platforms from the analysis to the maintenance phase. Its abstract and instantiated process schema are described, with particular focus on the analysis and design phases that are fundamental drivers of the whole integration process. Relevant interoperability design patterns, the building blocks of the design phase, will be discussed. The chapter also presents the INTER-CASE tool associated to the methodology which is useful to guide integrator designers in properly following the INTER-METH workflow. Finally, the chapter shows the proposed methodology and its tool in action, with the practical integration of BodyCloud and UniversAAL platforms adopted in the INTER-Health pilot of the INTER-IoT project.
PCMO: Partial Classification from CNN-Based Model Outputs

The partial classification can assign a sample to a class subset when this sample has similar probabilities for multiple classes. However, the extra information for making such predictions usually comes at the cost of retraining the model, changing the model architecture, or applying a new loss function. In an attempt to alleviate this computational burden, we fulfilled partial classification only based on pre-trained CNN-based model outputs (PCMO), by transforming the model outputs to beliefs for predicted sets under the Dempster-Shafer theory. The PCMO method has been executed on six prevalent datasets, four classical CNN-based models, and compared with three existing methods. For instance, experiments with MNIST and CIFAR10 datasets show the superiority of PCMO in terms of average discounted accuracy (\(0.23\%\) and \(7.71\%\) improvement, respectively) when compared to other methods. The performance demonstrated that the PCMO method makes it possible to improve classification accuracy and to make cautious decisions by assigning a sample to a class subset. Moreover, the PCMO method is simple to implement compared to the existing methods, as the PCMO method does not need to retrain the model or conduct any further modifications.
Efficient Single Image De-raining Using Multi-scale Depthwise Separable Dilated Convolution

Single image de-raining based on deep networks has achieved significant progress, which is attributing to complex network structures and the huge number of learnable parameters. However, these complex networks are not suitable for mobile terminals or embedded devices since they are generally resource-constrained. To this end, we propose a novel Efficient De-raining Network (Eff-DerainNet) by introducing multi-scale depthwise separable dilated convolution. Firstly, we propose two novel lightweight modules to construct our network, i.e., MDSD block and MDSD-ConvGRU. The former could extract multi-scale features when parsing the rainy images and the latter is committed to integrating the deep features generated by the whole network. Secondly, we regard the de-raining problem as a multi-stage task and introducing a mature recurrent learning strategy to improve the de-raining performance. Last, we discuss two different supervision forms with four loss functions to maximize the representation ability of our network. Our Eff-DerainNet could achieve competitive performance compared with the latest state-of-the-art deep-learning models, i.e., DRDNet or RCDNet, and bring 20.3- or 12.3-times compression in terms of memory cost. Extensive experiments verify the efficiency and effectiveness of our method in both quantitative assessments and visual quality.
Adaptive window stereo matching algorithm for weak texture region

Stereo matching is one of the key techniques in stereo vision. Due to the existing adaptive window stereo matching algorithm is insufficient to extract features in low-texture regions, a weighted dynamic adaptive window stereo matching algorithm based on pixel gradient value is proposed. Firstly, the Sobel operator is used to calculate the gradient value of each pixel in the image and the phase information is introduced. According to the two, the pixel is divided into strong, medium and weak texture regions, and then different thresholds are assigned to the pixels in different regions. Then the image is converted from the RGB color space to the HSV color space and the matching window is dynamically generated according to the region threshold and color threshold. The HAD cost calculation function was established and the traditional Census algorithm was improved. The disparity map was obtained by nonlinear fusion calculation, and the obtained disparity map was detected by subpixel and filtered by median value. Finally, the high-precision disparity map was obtained. Experimental results show that the proposed algorithm is effective, has high matching accuracy, and has good robustness to optical distortion and edge information conditions.
Multi-View Person Re-IDentification with Wireless Multimedia Sensor Networks

Wireless Multimedia Sensor Networks (WMSN) involves a set of multimedia sensors such as cameras to collect video or audio data about the deployed Region of Interest (RoI). Compared to traditional Wireless Sensor Networks, the visible information of covered targets can be obtained with WMSN. Moreover, due to dense deployment of low-cost cameras, the object such as person may be covered by several cameras. This paper introduces a new multi-view person Re-IDentification (Re-ID) model with WMSN. Moreover, human skeleton extraction based person Re-ID method is applied to extract person’s posture feature. As a result, significant cost by labeling data will be avoided. Finally, Decision-level and Feature-level feature fusion methods with multi-view cameras are proposed to complete Re-Identification process. Multi-view video-based than Re-ID algorithm has better performance than single-view video-based Re-ID algorithm. As we know, this is to first research on person Re-Identification with WMSN.
Analyse k-means algorithm and implementing a new clustering algorithm

Clustering is a technique of machine learning which involves grouping data points. We can use clustering algorithm to cluster each data points into a specific group. K-means algorithm is the most famous and commonly used algorithm for analysis of clusters. The k-means clustering algorithm is a method of partitioning clusters that partition data objects into k different clusters. However, the main drawback of this algorithm is that classical k-means algorithm is mainly sensitive to initial centroids and it is also difficult to determine the total number of clusters. So in order to improve the performance of k-means algorithm, this paper presents a new modified k-means method which uses intra-class distance to cluster the data.
The Different Aspects of Information Security Education

This chapter discusses the importance of information security education for everyone, ranging from organizations to professionals and students, all the way through to individual users of information and communication systems. It discusses the different subject areas in information security and shows how instead of being intimidated by it, different categories of users can obtain varying depths of information security education based on their cyber-activities and need for knowledge. Information security professionals would require an in-depth knowledge in all aspects of information security, and information technology professionals and students would require an overall education in these areas, while most users of information and communication systems would only require a basic education to help protect their information assets in cyberspace.
Geochemistry and tectonic setting for the deposition of IOG siliciclastics at the western margin of Bonai Granite, Singhbhum-Orissa Craton, India

Bagiyabahal and Birtola areas are located in the south-western extension of the Noamundi-Koira Iron Ore Group (IOG) basin. Rock types exposed in the area comprises of siliciclastics and volcanics which occurs unconformably over the basement tonalite-trondhjemite granite-gneiss (Bonai Granite Phase-I). The cover rocks show sheared contact with the porphyritic Bonai Granite Phase-II. The IOG basin margin is suggested to be a part of a ‘volcanic passive margin’ as indicated by the geochemical behaviour of the siliciclastics as well as massive emplacements of mafic intrusives (doleritic sill, dyke and gabbro) and extrusives (basaltic lava flow) along faulted continental blocks. The siliciclastics comprise of U and Au bearing quartz-pebble conglomerate (QPC) and quartzite succession. It was deposited along the western margin of the Bonai granite (phase I) in anoxic conditions as indicated by their low Th/U ratios and presence of detrital uraninite grains. Repeated cycles of sedimentation and volcanism led to the formation of alternate layers of siliciclastics and basic bodies in the area. Major, trace and rare earth elements (REE) geochemical data suggests a semi-humid to humid palaeo-climatic environment of during the deposition in the passive continental margin setting characterized by fault-controlled sedimentation over a rift related faulted continental crust and shelf. Geochemical data suggests chemically weathered provenance dominated by clay minerals. Higher content of U, Th, Au, Cr, REE, platinum group of elements (PGE) and other geochemical ratios suggest a mixed provenance for the deposition of the siliciclastics comprising a predominantly acidic/granitic source possibly from the Bonai Granitic Complex (BGC) along with granite derived reworked quartzose sediments, minor basic and ultrabasic sources of Older Metamorphic Group (OMG). This paper attempts to characterize the geochemical behaviour, tectonic setting and provenance of the siliciclastics of Birtola and Bagiyabahal areas by analyzing drill core and surface samples.
Rethinking Anchor-Object Matching and Encoding in Rotating Object Detection

Rotating object detection is more challenging than horizontal object detection because of the multi-orientation of the objects involved. In the recent anchor-based rotating object detector, the IoU-based matching mechanism has some mismatching and wrong-matching problems. Moreover, the encoding mechanism does not correctly reflect the location relationships between anchors and objects. In this paper, RBox-Diff-based matching (RDM) mechanism and angle-first encoding (AE) method are proposed to solve these problems. RDM optimizes the anchor-object matching by replacing IoU (Intersection-over-Union) with a new concept called RBox-Diff, while AE optimizes the encoding mechanism to make the encoding results consistent with the relative position between objects and anchors more. The proposed methods can be easily applied to most of the anchor-based rotating object detectors without introducing extra parameters. The extensive experiments on DOTA-v1.0 dataset show the effectiveness of the proposed methods over other advanced methods.
Using Semi-Supervised Learning to Detect and Predict Unlabeled Restatements

This paper introduces a semi-supervised machine learning technique, Positive-Unlabeled (PU) Learning, to address the issue of “unlabeled positives” in accounting research. It uses financial statement restatement detection/prediction as a research setting. In this setting, unlabeled positives are audited financial statements that were not identified to be restated when data was collected but were subsequently restated. Extant research in restatement detection/prediction uses supervised learning, which treats unlabeled positives as clean financial reports. This improper handling of unlabeled positives in machine learning studies may affect model performance in restatements detection/prediction. Our analysis shows that PU Learning is significantly superior to supervised learning in detecting unlabeled restatements from past financial statements and predicting restatements from newly issued financial statements based on different metrics. In particular, the best-performing PU Learning method can outperform the benchmark supervised learning model by 113.2% (110.6%) in terms of the recall ratio for detection (prediction) and 34% (51%) in F2 Score. The methodology introduced in this study can also be applied to detecting or predicting other accounting outcomes that have unlabeled positives, such as fraud and accounting misconduct.
Inpatinets’ FWA Detection: Mismatch between the Clinical Path and Medical Condition

We proposed an approach for inpatients’ FWA (fraud, waste, and abuse) detection using multi-view data. In medical insurance, inpatients’ data consisted of daily records of clinical items they consumed and the medical conditions such as gender, age, diagnosis, length of stay, and so on. Even though hospitalized due to the same disease, the clinical daily records varied significantly. To detect the abnormal variations, our method considered to group inpatients in the view of clinical daily records, as well as in the view of their medical conditions, and detect the abnormal variations based on the mismatch of the two clustering results. To accomplish the goal, we accommodated a two-step BERT (Bidirectional Encoder Representations from Transformers) to extract features of the clinical path via the representation learning of the daily consumed items for each admission. The inpatients were then divided into different groups in view of the sequential clinical items’ consumption. We also clustered the inpatients according to their clinical conditions. And the suspicious FWA cases were defined as the ones that had a low probability of cooccurrence and were inconsistent on the properties of two fine-grouping results. We experimented with our approach in multiple insurance claim data with different kinds of surgeries to detect FWA. And 95% of the suspicious cases detected using this approach were confirmed by the medical insurance experts.
Breast Cancer Screening

Breast cancer is a common malignancy worldwide. It is considered top cancer in women and about 13% of women in the general population will develop breast cancer sometimes during their lives, with a gradual increase in incidence as survival increases. Primary prevention of breast cancer is directed toward promoting a healthy lifestyle and reversing modifiable risk factors; these factors include smoking cessation, physical activity, alcohol, and dietary modification. Imaging plays an important role in the diagnosis and management of breast cancer, it is also considered the most valuable tool in screening breast cancer. Mammogram is the most widely used method; it is recommended by many societies and committees as a useful method for early detection of breast cancer. False-positive and over-diagnosis constitute a problem in using screening mammogram. The implementation of a screening program faces many issues that may adversely affect its success such as personal factors, social factors, and accessibility issues. These issues should be identified as the initial step in program implementation. The role of Magnetic Resonance Imaging and Ultrasound is mainly in high-risk patients. The introduction of Artificial Intelligence in Mammogram may add beneficial effects in time and efforts improving its efforts.
Using CNN and Tensorflow to recognise ‘Signal for Help’ Hand Gestures

Domestic violence is a prevalent crime in our society, more so with the introduction of COVID19 restrictions. For the victim, it can be a traumatic experience, so much as to not report the crime. Consequently, the ‘Signal for Help’ hand gestures were recently introduced as a discrete method to enable the victim to confidently express their need for help. This research investigates the classification of these hand gestures using a deep learning approach, which has not previously been implemented in this context. A deep learning approach is chosen due to the favourable results obtained in different contexts on hand gesture classification. Due to the unavailability of a dataset containing images of these hand gestures, a ‘Signal for Help’ dataset containing 112 images is generated as part of this study. These images are pre-processed to be of size 50x50 dimensions. Furthermore, a synthetic version of this dataset is also generated from the pre-processed images containing 2,352 images. The aims of this research are to show that using a synthetic ‘Signal for Help’ dataset improves model performance, and using deep learning is effective in ‘Signal for Help’ hand gesture classification. The results in this research show that using a synthetic ‘Signal for Help’ dataset improves model performance and is effective for ‘Signal for Help’ hand gesture classification.
Experimental methods of pupillographic analysis based on high-speed video recording devices

The prospects for using artificial intelligence in video analytics are becoming more and more undeniable. Researchers are actively working on methods for making decisions based on the results of automated analysis of the sequence of images received from video cameras. The purpose of our study was to develop a method for decision-making based on modeling a person's pupillary response to an information stimulus. A ZWO ASI120MC digital video camera was used for research. The studies carried out made it possible to reliably establish that the state of the optical system of the eye when viewing the calibration slide comes to a physical state common to all people. This made it possible to systematize the pupillograms, referring them to one of two categories (stress state is / is not). Having determined an individual threshold value during calibration, it becomes possible to quickly classify a person's emotional response to the received information impulse. Based on the developed methodology for systematizing the pupillary reaction to an information stimulus by categories, a decision tree for intelligent video surveillance systems was built. In addition to the size of the pupils, the algorithm takes into account the parameters of gaze fixation on stimulus images, the stability of the illumination of the surface of the pupils.
Research on the target cargo identification method based on CNN

The automatic inspection robot can effectively pre-vent cargo from being stolen, replaced and opened without authorization. However, it will be disturbed by external factors such as occlusion, illumination and angle when it takes photos to identify cargo, which will affect the recognition effect. In order to improve the recognition effect of the automatic inspection robot on the target goods, this paper first detects the object in the data preprocessing stage, extracts the goods con-tour, and then carries on the image recognition, which im-proves the recognition rate. Then we compare the loss rate and accuracy rate of four network models: AlexNet, GoogleNet, ResNet and MobileNet. The experimental results show that ResN et algorithm has the highest image recognition accuracy, is more friendly to the recognition of light and occlusion, and is more in line with the actual operation needs of automatic inspection robot. It has reference value for the research of target cargo recognition.