Reviewerly/exHarmony huggingface dataset:
Implementation of Virtual Reality for Training in Education and Industry: A Forklift Case

Virtual reality industry training environments proposals are more common in recent years. Due to the versatility of a virtual environment, the interaction with specialized and expensive equipment could be simulated. This type of solution can be applied to many fields and specific processes. Lots of proposals of virtual reality usage have similarities in how they aim for learning to be meaningful for a user. In any teaching process, either in industry or school, there are learning concepts and abilities related to some specific tasks. By a review of proposals in several fields, this work identifies concepts and characteristics that should be taken into consideration when creating a training virtual reality simulator, and then those concepts are applied to a real case. The case implemented is virtual reality for forklift operator training. The review showed the advantages of applications in different fields and highlighted two key concepts to apply in a proposal: fidelity and flexibility. Based on fidelity and flexibility, there are modifications of interactions and instructions. Those concepts were applied to the virtual environment for forklift operation, changing visual elements in the 3D environment and modifying interactions by adding a driving wheel controller. After the development stage, usability and functionality test were applied to a group of students to evaluate user experience and guidance. Tests also allowed to identify what to improve in the virtual environment. These types of tools should stick to reality as much as possible, but extra objects are wanted when those elements improve the learning process.
Elderly People Activity Recognition in Smart Grid Monitoring Environment

Elderly people activity recognition has become a vital necessity in many countries, because most of the elderly people live alone and are vulnerable. Thus, more research to advance in the monitoring systems used to recognize the activities of elderly people is required. Many researchers have proposed different monitoring systems for activity recognition using wired and wireless wearable sensing devices. However, the activity classification accuracy achieved so far should be improved to meet the challenges of more precise activity monitoring. Our study proposes a smart Human Activity Recognition system architecture utilizing an open source dataset generated by wireless, batteryless sensors used by 14 healthy aged persons and unsupervised and supervised machine learning algorithms. In this paper, we also propose using a smart grid for checking regularly the wearable sensing device operational status to address the well-known reliability challenges of these devices, such as wireless charging and data trustworthiness. As the data from the sensing device is very noisy, we employ the K-means++ clustering to identify outliers and use advanced ensemble classification techniques, such as the stacking classifier for which a meta model built using the random forest algorithm gave better results than all other base models considered. We also employ a bagging classifier, which is an ensemble meta-estimator fitting the prediction outputs of the base classifiers and aggregating them to produce the ensemble output. The best classification accuracy of 99.81 was achieved by the stacking classifier in training and 99.78% in testing, respectively. Comparisons for finding the best model were conducted using the recall, F1 score, and precision values.
Hand Gesture Recognition to Facilitate Tasks for the Disabled

Deaf and dumb people communicate with gestures. Number of applications have been developed using gesture recognition computer vision, machine learning etc. A simple technique to recognize gestures is presented in this paper. Here Python libraries such as Opencv, Numpy are used. The technique involves capture of live gestures and recognition of the same using Opencv functions. Parameters such as the area ratio and convexity defects are considered to differentiate between different gestures. The system will also display text message and an audio file will be used for vocalizing the different gestures. A functionality where gestures are recognized for entertainment purpose is presented. This can be of use to blind people for the purpose of entertainment such as playing music, radio or listening to news. Here Multiprocessing library is used. The tasks are assigned to specific gestures.
Wildlife Monitoring and Surveillance

Animal detection and classifications using computer vision systems have been practically unusable in uncontrolled contexts for years, due to concerns about the accuracy of the algorithms used. As a result, object detection, recognition, and classification, among other things, are receiving a lot of attention. Visual monitoring of animals in their natural habitat is currently one of the most popular methods in the field of computer vision (CV) systems. However, real-time animal detection and classification methods are still unavailable. Deep Learning improvements in Computer Vision have been built and developed through time, primarily over one specific method - a Convolutional Neural Network(CNN). This project is proposed using CNN to detect and classify animals in digital images. The feature of the animal in the input image is extracted to aid decision making, which is then plugged into the classification part for model analysis. According to the experimental results, the proposed algorithm has a positive effect on overall animal classification performance. An accuracy of around 64% was achieved when the image size was 50 &#x00D7; 50..
How do internet fraudsters think? A qualitative examination of pro-criminal attitudes and cognitions among internet fraudsters in Nigeria

As the use of the internet is expanding, crimes committed on the internet are also increasing. The means through which fraudsters carry out their schemes are widely known. The negative impacts arising from the activities of internet fraudsters are felt globally. Yet little is understood about the thinking patterns that allow perpetrators to carry on with such crimes. This may also explain why interventions are scarce. In this study, we interviewed 12 male youngsters who are actively engaged in internet fraud activities (11 in the community and 1 in prison custody) in Nigeria. The aim was to identify pro-criminal attitudes and cognitions that are supportive of perpetrating internet fraud. Thematic analysis was used to unpack findings from the interview transcripts. We found that peers were commonly identified to play a role in introducing youths to the crime. There was also tacit support from communities and families. Pro-criminal cognitions such as neutralization, entitlement, minimization, rationalizations, and dehumanization were found in the narratives of the youngsters. Interestingly, youngsters often rationalized and justified their actions using socio-political/socio-historical antecedents, a position we found was popular in the cybercrime space, especially in Nigeria. Our findings have implications for the designing and implementation of rehabilitation intervention for internet fraudsters.
An Algorithmic Reduction Theory for Binary Codes: LLL and More

In this article, we propose an adaptation of the algorithmic reduction theory of lattices to binary codes. This includes the celebrated LLL algorithm (Lenstra, Lenstra, Lovasz, 1982), as well as adaptations of associated algorithms such as the Nearest Plane Algorithm of Babai (1986). Interestingly, the adaptation of LLL to binary codes can be interpreted as an algorithmic version of the bound of Griesmer (1960) on the minimal distance of a code. Using these algorithms, we demonstrate—both with a heuristic analysis and in practice—a small polynomial speed-up over the Information-Set Decoding algorithm of Lee and Brickell (1988) for random binary codes. This appears to be the first such speed-up that is not based on a time-memory trade-off. The above speed-up should be read as a very preliminary example of the potential of a reduction theory for codes, for example in cryptanalysis.
Optimization of Artificial Neural Network: A Bat Algorithm-Based Approach

Artificial Neural Networks (ANNs) are dominant machine learning tools over the last two decades and are part of almost every computational intelligence task. ANNs have several parameters such as number of hidden layers, number of hidden neurons in each layer, variations in inter-connections between them, etc. Proposing an appropriate architecture for a particular problem while considering all parametric terms is an extensive and significant task. Metaheuristic approaches like particle swarm optimization, ant colony optimization, and cuckoo search algorithm have large contributions in the field of optimization. The main objective of this work is to design a new method that can help in the optimization of ANN architecture. This work takes advantage of the Bat algorithm and combines it with an ANN to find optimal architecture with minimal testing error. The proposed methodology had been tested on two different benchmark datasets and demonstrated results better than other similar methods.
Hydrothermal alteration mapping using ASTER data, Takab-Baneh area, NW Iran: a key for further exploration of polymetal deposits

A large number of polymetallic deposits exist in the Takab-Baneh area, NW Iran. Different methods such as minimum noise fraction (MNF), pixel purity index (PPI), n-D visualization, mixture tuned matched filtering (MTMF), relative absorption band depth (RBD), band ratio (BR), and principal component analysis (PCA) were used for extraction of alteration zone characteristics from ASTER data in the Takab-Baneh area. The overall accuracy of MTMF is around 97% for phyllic, and 98% for argillic propylitic, which validates the potential and suitability of ASTER imagery for classification of alteration type. Subsequently, the alteration maps were confirmed by ground-truthing, i.e., field observation and petrographic studies. The results show a wide distribution of three main phyllic, argillic and propylitic alteration zones, that can be considered as targets for future exploration in NW Iran.
A dynamic constraint representation approach based on cross-domain dictionary learning for expression recognition

Facial expression recognition (FER) is an active research area that has attracted much attention from both academics and practitioners of different fields. In this paper, we investigate an interesting and challenging issue in FER, where the training and testing samples are from a cross-domain dictionary. In this context, the data and feature distribution are inconsistent, and thus most of the existing recognition methods may not perform well. Given this, we propose an effective dynamic constraint representation approach based on cross-domain dictionary learning for expression recognition. The proposed approach aims to dynamically represent testing samples from source and target domains, thereby fully considering the feature elasticity in a cross-domain dictionary. We are therefore able to use the proposed approach to predict class information of unlabeled testing samples. Comprehensive experiments carried out using several public datasets confirm that the proposed approach is superior compared to some state-of-the-art methods.
Continuous TBI Monitoring From Spontaneous Speech Using Parametrized Sinc Filters and a Cascading GRU

Traumatic Brain Injury (TBI) is caused by a head injury that affects the brain, impairing cognitive and communication function and resulting in speech and language disorders. Over 80,000 individuals in the US suffer from long-term TBI disabilities and continuous monitoring after TBI is essential to facilitate rehabilitation and prevent regression. Prior work has demonstrated the feasibility of TBI monitoring from speech by leveraging advancements in Artificial Intelligence (AI) and speech processing technology. However, much of prior work explored TBI detection using scripted speech tasks such as diadochokinesis tests or reading a passage. Such scripted approaches require active user involvement that significantly burdens participants. Moreover, they are episodic, are not realistic, and do not provide a longitudinal picture of the user's TBI condition. This study proposes a continuous TBI monitoring from changes in acoustic features of spontaneous speech collected passively using the smartphone. Low-level acoustic features are extracted using parametrized Sinc filters (pSinc) that are then classified TBI (yes/no) using a cascading Gated Recurrent Unit (cGRU). The cGRU model utilizes a cell gate unit in the GRU to store and incorporate each individual's prediction history as prior knowledge into the model. In rigorous evaluation, our proposed method outperformed prior TBI classification methods on conversational speech recorded during patient-therapist discourses following TBI, achieving 83.87% balanced accuracy. Furthermore, unique words that are important in TBI prediction were identified using SHapley Additive exPlanations (SHAP). A correlation was also found between features acquired by the proposed method and coordination deficits following TBI.
Variational joint self‐attention for image captioning

The image captioning task has attracted great attention from many researchers, and significant progress has been made in the past few years. Existing image captioning models, which mainly apply attention-based encoder-decoder architecture, achieve great developments image captioning. These attention-based models, however, are limited in the caption generation due to the potential errors resulting from the inaccurate detection of objects and incorrect attention to the objects. To alleviate the limitation, a Variational Joint Self-Attention model (VJSA) is proposed to learn a latent semantic alignment between the given image and its label description for guiding better image captioning. Unlike the existing image captioning models, VJSA first uses a self-attention module to encode the effective relationship information of intra-sequence and inter-sequences relationships. And then the variational neural inference module learns a distribution over the latent semantic alignment between the image and its corresponding description. In the decoding, the learned semantic alignment guides the decoder to generate the higher quality image caption. The results of the experiments reveal that the VJSA outperforms the compared models, and the performances of various metrics show that the proposed model is effective and feasible in image caption generation.
A classification using RDFLIB and SPARQL on RDF dataset

Web Semantics is one of the prominent domain in the current era of web. This web comprises of linked open data, which is an interconnection of related data over web and the base of data representation is Resource Description Framework (RDF). Applying machine learning algorithms will enhance the capabilities of semantic web over the concepts to visualize and analyze data over web. It will integrate the capabilities of web semantics and machine learning. In this paper, classification on a sample RDF dataset has been implemented and their results have been analyzed. This will give researcher a baseline to work with machine learning algorithms on semantic web data. The machine learning algorithms like decision tree classifier, random forest classifier along with artificial neural network classification has been analyzed in this paper along with their accuracy measures. The integration of machine learning and semantic web, based on RDF based knowledge graphs is the singularity of the proposed work and represented using experimental results.
Local Privacy Protection for Sensitive Areas in Multiface Images

The privacy protection for face images aims to prevent attackers from accurately identifying target persons through face recognition. Inspired by goal-driven reasoning (reverse reasoning), this paper designs a goal-driven algorithm of local privacy protection for sensitive areas in multiface images (face areas) under the interactive framework of face recognition algorithm, regional growth, and differential privacy. The designed algorithm, named privacy protection for sensitive areas (PPSA), is realized in the following manner: Firstly, the multitask cascaded convolutional network (MTCNN) was adopted to recognize the region and landmark of each face. If the landmark overlaps a subgraph divided from the original image, the subgraph will be taken as the seed for regional growth in the face area, following the growth criterion of the fusion similarity measurement mechanism (FSMM). Different from single-face privacy protection, multiface privacy protection needs to deal with an unknown number of faces. Thus, the allocation of the privacy budget ε directly affects the operation effect of the PPSA algorithm. In our scheme, the total privacy budget ε is divided into two parts: ε_1 and ε_2. The former is evenly allocated to each seed, according to the estimated number of faces ρ contained in the image, while the latter is allocated to the other areas that may consume the privacy budget through dichotomization. Unlike the Laplacian (LAP) algorithm, the noise error of the PPSA algorithm will not change with the image size, for the privacy protection is limited to the face area. The results show that the PPSA algorithm meets the requirements ε-Differential privacy, and image classification is realized by using different image privacy protection algorithms in different human face databases. The verification results show that the accuracy of the PPSA algorithm is improved by at least 16.1%, the recall rate is improved by at least 2.3%, and F1-score is improved by at least 15.2%.
Deep-Hist: Breast cancer diagnosis through histopathological images using convolution neural network

Breast cancer diagnosis utilizes histopathological images to get best results as per standards. For detailed diagnosis of breast cancer, microscopic analysis is necessary. During analysis, pathologists examine breast cancer tissues under different magnification levels and it takes a long time, can be hampered by human interpretation and requires expertise of different magnifications. A single patient usually requires dozens of such images during examination. Since, labelling the data is a computationally expensive task, it is assumed that the images for all patients have the same label in conventional image-based classification and is not usually tested practically. In this study, we are intending to investigate the significance of machine learning techniques in computer aided diagnostic systems based on analysis of histopathological breast cancer images. Publicly available BreakHis data set containing around 8,000 histopathological images of breast tumours is used for conducting experiments. The recently proposed non-parametric approach is proven to show interesting results when compared in detail with machine learning approaches. Our proposed model ’Deep-Hist’ is magnification independent and achieves &gt; 92.46% accuracy with Stochastic Gradient Descent (SGD) which is better than the pretrained models for image classification. Hence, our approach can be used in processing data for use in research and clinical environments to provide second opinions very close to the experts’ intuition.
Dual Discriminator GAN: Restoring Ancient Yi Characters

In China, the damage of ancient Yi books are serious. Due to the lack of ancient Yi experts, the repairation of ancient Yi books is progressing very slowly. The artificial intelligence is successful in the field of image and text, so it is feasible for the automatic restoration of ancient books. In this article, a generative adversarial networks with dual discriminator (DDGAN) is designed to restore incomplete characters in the ancient Yi literature. The DDGAN integrates the deep convolution generative adversarial network with an ancient Yi comparison discriminator. Through two training stages, it could iteratively optimizes the ancient Yi character generation networks to obtain the text generator According to the loss of comparison discriminator, DDGAN mode could be optimized. The DDGAN model can generate characters to restore the missing stroke in the ancient Yi. The experiment shows that the proposed method achieves a restoration rate of 77.3% when no more than one third of the characters are missing. This work is effective for the protection of Yi ancient books.
Indian Currency Classification Using Deep Learning Techniques

Progression and evolution of technology has superseeded mechanical human workload in almost every domain with the operation of machines. The currency paper recognition is applicable in various domains of automatic selling goods systems and in banking systems. In the modern transition world for the automatic current recurring systems, the precise identification of paper currency notes is indeed an essential need. Machines often find it difficult in identifying and recognising the currencies in the market when the currency notes have turned bleary and damaged. It is hard for visually disabled people without any technological support or assistance to predict and analyze genuine currency notes. The accuracy of currency notes analysis identification have been refined and boosted throughout with the assistance of these models. Our research methodologies are in line and meeting the desired expectations. This paper presents an Indian Paper Currency Prediction Analysis, proposes an optimized model to recognise the currencies effectively. The Deep Learning approach of CNN model technique has improved the effective analysis of currency recognition with improved accuracy, high speed and efficiency along with complete automatic readily procedure with no human intervention and minimal complexity. This paper represents a strategy which is parted into two divisions, Keras trained a DL Model as well as hosted a Flask based web app on Heroku.Our proposed algorithm design and experimental based results are useful for majorly visually impaired people for differentiating all sorts of available denominations.
Troubleshooting image segmentation models with human-in-the-loop

Image segmentation lays the foundation for many high-stakes vision applications such as autonomous driving and medical image analysis. It is, therefore, of great importance to not only improve the accuracy of segmentation models on well-established benchmarks, but also enhance their robustness in the real world so as to avoid sparse but fatal failures. In this paper, instead of chasing state-of-the-art performance on existing benchmarks, we turn our attention to a new challenging problem: how to efficiently expose failures of “top-performing” segmentation models in the real world and how to leverage such counterexamples to rectify the models. To achieve this with minimal human labelling effort, we first automatically sample a small set of images that are likely to falsify the target model from a large corpus of web images via the maximum discrepancy competition principle. We then propose a weakly labelling strategy to further reduce the number of false positives, before time-consuming pixel-level labelling by humans. Finally, we fine-tune the model to harness the identified failures, and repeat the whole process, resulting in an efficient and progressive framework for troubleshooting segmentation models. We demonstrate the feasibility of our framework using the semantic segmentation task in PASCAL VOC, and find that the fine-tuned model exhibits significantly improved generalization when applied to real-world images with greater content diversity. The code is available at https://github.com/VITA-Group/Troubleshooting_Image_Segmentation.