Reviewerly/exHarmony huggingface dataset:
Count, Crop and Recognise: Fine-Grained Recognition in the Wild

The goal of this paper is to label all the animal individuals present in every frame of a video. Unlike previous methods that have principally concentrated on labelling face tracks, we aim to label individuals even when their faces are not visible. We make the following contributions: (i) we introduce a 'Count, Crop and Recognise' (CCR) multi-stage recognition process for frame level labelling. The Count and Recognise stages involve specialised CNNs for the task, and we show that this simple staging gives a substantial boost in performance; (ii) we compare the recall using frame based labelling to both face and body track based labelling, and demonstrate the advantage of frame based with CCR for the specified goal; (iii) we introduce a new dataset for chimpanzee recognition in the wild; and (iv) we apply a high-granularity visualisation technique to further understand the learned CNN features for the recognition of chimpanzee individuals.
Wearable solution for robust fall detection

This chapter presents an elderly fall-detection monitoring solution that implements both accelerometer and sound-based detection algorithm. The accelerometer-based fall detection is instrumental in the detection of a valid fall occurrence. However, it has been shown that using accelerometer alone is insufficient to accurately detect a fall, as the accelerometer tends to misinterpret some of the daily motion activities and misclassified them as valid falls. The sound sensor is introduced to detect the sound pressure generated from a resultant fall, but sound pressure cannot by itself be used as a reliable indicator of a fall. Thus a fuzzy logic–based fall-detection algorithm is developed to process the output signals from the accelerometer and sound sensor, where a valid fall activity detected by the accelerometer, coupled with a detected sound pressure from the resultant fall, can infer an occurrence of a valid fall. This chapter demonstrates the proposed algorithm is able to improve the accuracy of detecting a valid fall as compared to the accelerometer only fall-detection algorithm by minimizing false fall detections per day from high of 1.37 to low of 0.06.
Parallel Tracking and Reconstruction of States in Heuristic Optimization Systems on GPUs

Modern heuristic optimization systems leverage the parallel processing power of Graphics Processing Units (GPUs). Many states are maintained and evaluated in parallel to improve runtime by orders of magnitudes in comparison to purely CPUbased approaches. A well known example is the parallel Monte Carlo tree search, which is often used in combination with more advanced machine-learning methods these days. However, all approaches require different optimization states in memory to update or manipulate variables and observe their behavior over time. Large real-world problems often require a large number of states that are typically limited by the amount of available memory. This is particularly challenging in cases in which older states (that are not currently being evaluated) are still required for backtracking purposes. In this paper, we propose a new general high-level approach to track and reconstruct states in the scope of heuristic optimization systems on GPUs. Our method has a considerably lower memory consumption compared to traditional approaches and scales well with the complexity of the optimization problem.
Automated genre-based multi-domain sentiment lexicon adaptation using unlabeled data

Sentiment analysis research has evolved over the years to extract relevant information from opinionated raw text. Sentiment lexicon is a compiled list of sentiment words and a core component of sentiment analysis tasks. These words play a key role in domain adaptation. Domain adaptation is challeng ing due to variation in sentiments across the domains. We propose a solution to this research problem by presenting a genre-level sentiment lexicon adaptation approach. The model uses a language domain sense to represent the genre pertaining to the distinct characteristics of the communicated text. The approach addresses the generalization of knowledge at the genre level by learning the multi-source domain lexicon for the selected source domains. The novelty of our approach lies in the genre level relevancy of the source lexicon to the target domains. The model uses unlabeled training data for the source and target domain sentiment lexicon learning. The lexicon adaptation is demonstrated on a long list of target domains that address the three domain adaptation challenges. Experimental results have proved that the model learns the relevant scores and polarities of sentiment words, in addition, it identifies new domain-based sentiment words. The model is evaluated in comparison with standard baselines.
Linguistic Steganography by Sampling-based Language Generation

Linguistic steganography aims to hide secret messages within text carriers. In this paper, we propose a linguistic steganography method by means of sampling-based language generation. Comparing with deterministic text generation using beam-search, the sampling-based approach increases the redundancy of generated texts and benefits the hiding of information. The arithmetic coding (AC) algorithm is adopted to embed messages in our proposed method. Its performance is compared with fixed-length coding (FLC) and variable-length coding (VLC) which were designed for embedding messages during deterministic text generation. Besides, the KL divergence and temperature based strategies are designed to control the embedding rates of FLC, VLC and AC respectively. Experiments using a story generation model show that AC performed better than FLC and VLC when embedding messages during sampling-based text generation. With an embedding rate of 1.45 bits/word, our AC-based steganography method achieved ideal imperceptibility, and the subjective quality of its generated text is as good as the non-steganography one.
Bosom Malignant Diseases (Cancer) Identification by using Deep Learning Technique

In recently observed that breast related diseases affects women present all over the globe, where it emerges as the second most common disease in the world. In 2012, 12 % cancer patients were present and from these patients 25 % are breast cancer patients. In the traditional method to cure the breast cancer is malignant tumor. Most of the doctors manually presumed the bosom malignant growth region. Various examinations have referred that this manual presumed requires more time and it relies upon the operation and machine. Therefore, it is necessary to design a perfect algorithm for the identification of bosom diseases. In this report, we have developed an algorithm to identify the breast cancer patient automatically. This algorithm can automatically detect the tumor of breast cancer by observing the biopsy pictures. Also, the calculation must be very precise, as the lives of individuals are at risk. All the performance operations are done on the microscopy pictures and the data set for this microscopy pictures is designed for the clustering analysis of a picture. The experimental results of the proposed scheme show accuracy 98.3 %, precision 0.65, Recall 0.95, F1 score 0.77 and ROC - AUC 0.692.
Blu

UI designers look for inspirational examples from existing UI designs during the prototyping process. However, they have to reconstruct these example UI designs from scratch to edit content or apply styling. The existing solution attempts to make UI screens into editable vector graphics using image segmentation techniques. In this research, we aim to use deep learning and gestalt laws-based algorithms to convert UI screens to editable blueprints by identifying the constituent UI element categories, their location, dimension, text content, and layout hierarchy. In this paper, we present a proof-of-concept web application that uses the UI screens and annotations from the RICO dataset and generates an editable blueprint vector graphic, and a UI layout tree. With this research, we aim to support UX designers in reconstructing UI screens and communicating UI layout information to developers.
Unusual Event Detection in Surveillance Video Using Transfer Learning

Unusual event detection in surveillance video is one of the active research area in computer vision. In this paper, we study the performance of different classifiers using a two-stream CNN architecture. The CNN along with different classifiers have been used to detect unusual event in a surveillance video. The two-stream two-dimensional convolutional network pretrained on the ImageNet database is employed to extract features from video frames. The extracted features are used to classify the video frame into one of the two classes. Different classifiers including SVM, K-NN, RBFN, Naive Bayes, Logistic Regression, K-means clustering have been utilized for this purpose. It has been observed that SVM gives better performance as compared with other classifiers.
Machine learning for determining accurate outcomes in criminal trials

Abstract Advances in mathematical and computational technologies have brought unique and ground-breaking benefits to diverse fields throughout society (engineering, medicine, economics, etc.). Within legal systems, however, the potential applications of data science and innovative mathematical tools have yet to be embraced with the same ambition. The complex decision-making that is needed for reaching just verdicts is often seen as out of reach for such approaches and, in the case of criminal trials, this inhibits exploration into whether machine learning could have a positive impact. Here, through assigning numerical scores to prosecution and defence evidence, and employing an approach based on dimensionality reduction, we showed that evidence strands presented at historical murder trials could be used to train effective machine-learning algorithms (or models). We tested the evidence quantification approach with the trained model and showed that, through machine learning, criminal cases could be clearly classified (probability &amp;gt;99.9%) as belonging to either a guilty or a not-guilty category. The classification was found to be as expected for all test cases. All guilty test cases that were not wrongful convictions were correctly assigned to the guilty category by our model and, crucially, test cases that were wrongful convictions were correctly assigned to the not-guilty category. This work demonstrated the potential for machine learning to benefit criminal trial decision-making, and should motivate further testing and development of the model and datasets for assisting the judicial process.
Towards Dense 3D Reconstruction for Mixed Reality in Healthcare: Classical Multi-View Stereo vs Deep Learning

Faithfully reproducing surroundings in 3D is a key-component in Mixed Reality for medical training in neonatology, where a user sees a hospital room in a Virtual Reality helmet while retaining tangible interaction with a baby mannequin and various medical tools. Deep learning solutions have high claims against classical methods but their performance in real-life application remains unclear. To fill this blank, we present a comparative study of depth map based Multi-View Stereo methods for dense 3D reconstruction. We compare classical state-of-the-art methods to their learned counterparts and assess their robustness to weakly-textured and reflective surfaces as well as accuracy on thin structures both globally and locally. We also analyze the effect of depth filtering along with computational effort. Our experiments reveal various factors which contribute to the performance gap between the methods that we discuss in detail. This study is the first to evaluate traditional dense geometry reconstruction methods against brand-new deep learning models. It helps to better understand what suits best the challenges of hospital environments. Furthermore, it builds a solid analytic ground to underscore the strengths and weaknesses of the learned methods.
Regularized Adversarial Training for Single-Shot Virtual Try-On

Spatially placing an object onto a background is an essential operation in graphic design and facilitates many different applications such as virtual try-on. The placing operation is formulated as a geometric inference problem for given foreground and background images, and has been approached by spatial transformer architecture. In this paper, we propose a simple yet effective regularization technique to guide the geometric parameters based on user-defined trust regions. Our approach stabilizes the training process of spatial transformer networks and achieves a high-quality prediction with single-shot inference. Our proposed method is independent of initial parameters, and can easily incorporate various priors to prevent different types of trivial solutions. Empirical evaluation with the Abstract Scenes and CelebA datasets shows that our approach achieves favorable results compared to baselines.
Comparative Performance Analysis of K-Means and DBSCAN Clustering algorithms on various platforms

While many data scientists are working hard just to improve a very fractional amount of performance, we wonder if there are any difference in performance of clustering among the platform we normally use. So we selected three datasets and perform K-means and DBSCAN clustering algorithm on the selected datasets in the four most popular platforms- Python, Matlab, R and Wolfram Mathematica. Then we summarized the results and compare the performances of different platforms. For performance metrics we used 2 criteria, first one is how much time it takes to execute clustering function and second one is the overall accuracy of clustering result. In our study we found that algorithm takes different execution time in different platform. Also variation was observed in terms of accuracy on various platform. Sometimes execution time was correlated with dataset size. Finally we suggest the platforms that are to be used for speed and for accuracy.
Research on the Sentiment Analysis Based on Machine Learning and Feature Extraction Algorithm

Sentiment analysis is a comprehensive research in Natural Language Processing (NLP) which is used to target the emotional context of the text. In previous experiments, shallow vocabulary and grammatical features were often considered, and the implicit semantic features were neglected. In order to solve this problem, this paper combines the training ways of the word vectors (Word2vec Doc2veC TF-IDF model) and machine classification model (SVM, I<; NN, etc.) for classification and then extracting features. The main goal is to use the performance of Word2vec Doc2vec and TF-IDF models to convert words into high-dimensional vectors. Through the calculation of the cosine similarity of the word vectors, the semantic similarity of the vocabulary can be obtained. Then these word vectors are used as semantic features and input into the machine learning classification model for training, so as to obtain the emotion classification results. In this paper, compared with the results of dictionary-based text sentiment analysis, the accuracy of machine learning methods are higher.
NoUCSR: Efficient Super-Resolution Network without Upsampling Convolution

Deep learning approaches have been ubiquitous in single image super-resolution ever since the success of SRCNN. However, the superior performance is based on high requirement of computational resources, limiting the application of deep learning approaches in resource-constrained embedded and mobile devices. In this paper, we firstly show that the convolution layers in upsampling block are parameter-and computation-intensive. Secondly, we find that replacing upsampling convolution by concatenating different level features can reduce parameters and inference runtime significantly, while keeping same performance. Finally, we introduce an efficient model without upsampling convolution called NoUCSR, and present variant models optimizing parameter, inference runtime and performance respectively at the constraint of MSRResNet. The experiments show that NoUCSR can achieve a better tradeoff among parameter, inference runtime and performance than state-of-the-art methods.
Exploration of the robustness and generalizability of the additive factors model

Additive Factors Model is a widely used student model, which is primarily used for refining knowledge component models (Q-matrices). We explore the robustness and generalizability of the model. We explicitly formulate simplifying assumptions that the model makes and we discuss methods for visualizing learning curves based on the model. We also report on an application of the model to data from a learning system for introductory programming; these experiments illustrate possibly misleading interpretation of model results due to differences in item difficulty. Overall, our results show that greater care has to be taken in the application of the model and in the interpretation of results obtained with the model.
Autonomous Mobile Robot Navigation Considering the Pedestrian Flow Intersections

In recent years, many service robots move autonomously in an human co-existing environments. In these environments, there are multiple pedestrian flows and their intersections especially, in the indoor passage. At these intersections, the pedestrian's movement tends not to be constant and for autonomous mobile robots, it is difficult to move safely with avoiding pedestrians. In our previous study, path planning considering the occlusion area in the indoor passage realized safe moving at the intersections. However, proposed system required intersection position in the environment beforehand. Therefore, the purpose of this study is development of a robot navigation scheme that realizes its safe movement for pedestrians at the intersection of their flow without special information beforehand. For realizing this objective, our proposed scheme estimates the intersection of the indoor passage without giving shape of the intersection to the robot beforehand. Using estimated results, our robot design the movement path, which is suitable for avoiding its risk at the intersection. The shape of the indoor passage can measure the confluence, but it is difficult to estimate the door that is closed state because the door has two different states: open and closed. In addition, the state of the door actually changes depending on movement of the pedestrian passing through the door, and the pedestrian on the passage side predict and avoid person that will appear from door changing the state of the door. The mobile robot can move the most suitable route by reflecting the walking characteristics of the pedestrian on the robot. In this study, we give type and position of door to the robot beforehand, and we propose the path planning using the walking characteristics of pedestrian for each door. In the simulation experiment and experiment using mobile robot, we confirmed that the proposed method is safer avoidance moving in front of the door.
Leopard: Understanding the Threat of Blockchain Domain Name Based Malware

Recently, as various detection approaches of malicious domains and malware are proposed, the malware which connects to its command and control (C&C) server using techniques like domain flux can be identified effectively. Therefore, cybercriminals seek new alternative methods and discover that DNS based on blockchains can be used to connect C&C servers. Because of the distributed ledger technology, domain names resolved by blockchain DNS, called blockchain domain names (BDNs), are of inherent anonymity and censorship-resistance. We analyzed the work mechanism of this new type of malware. In order to detect malicious BDNs, we propose a prototype system, named Leopard, which analyzes DNS traffic patterns and resource records of BDNs. To our best knowledge, we are the first to propose the automatic detection of malicious BDNs. In Leopard, we extracted 17 features from collected traffic and distinguished between malicious BDNs and domains operated by generic and country-code top-level domains registries from the Alexa top 5000 using a random forest model. In our experiments, we evaluate Leopard on a nine-day real-world dataset. The experimental results show that Leopard can effectively detect malicious BDNs with an AUC of 0.9980 and discover 286 unknown malicious BDNs from the dataset.
Captioning Image Using Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM)

Internet access in Indonesia brings both negative and positive impact. The data shows that there is a significant raise the number of children who access porn image. The researcher tried to implement the Convolutional Neural Network (CNN) algorithm and Long-Short Term Memory (LSTM) algorithm using learning transfer to predict the caption of a pornographic image. The input space given is a pornographic image that would be extracted using the bottleneck layer of CNN encoder. The weight values in the extraction were obtained from the pre-trained model using Inception V3. Image captioning was processed using one hot encoding technique and produced one hot vector. The results of feature extraction by bottleneck layer and one hot vector are used as training datasets by the LSTM network. The accuracy of the model was calculated using BLEU, METEOR and CIDEr matrices with a score range of 0 to 100. The results of BLEU-1 to BLEU-4 shows the score of 55.84; 24.01; 10.57; 4.59 respectively, METEOR at 12.75 and CIDEr at 43.84.
Artificial intelligence in precision health: Systems in practice

The field of medicine has been slow to adopt artificial intelligence (AI) techniques. Although most data in health care is unstructured, clinicians could greatly benefit from a systematic bias-free approach to pattern recognition and conditional probability to generate differential diagnoses and assist the daily task of making quick decisions under uncertainty. As of late, examples of applications and health-tech companies have proliferated. We believe the trend is clear, and the race to ground-breaking innovation is on. This chapter provides a brief history of AI efforts during the past decades, several examples of machine-learning techniques that have been applied to medical diagnosis, and a list of health-care tech companies that are leading the way in genomic discovery, image recognition, differential diagnosis generation, and the general endeavor of bringing AI to precision medicine practice.
Crane: Mitigating Accelerator Under-utilization Caused by Sparsity Irregularities in CNNs

Convolutional neural networks (CNNs) have achieved great success in numerous AI applications. To improve inference efficiency of CNNs, researchers have proposed various pruning techniques to reduce both computation intensity and storage overhead. These pruning techniques result in multi-level sparsity irregularities in CNNs. Together with that in activation matrices, which is induced by employment of ReLU activation function, all these sparsity irregularities cause a serious problem of computation resource under-utilization in sparse CNN accelerators. To mitigate this problem, we propose a method of load-balancing based on a workload stealing technique. We demonstrate that this method can be applied to two major inference data-flows, which cover all state-of-the-art sparse CNN accelerators. Based on this method, we present an accelerator, called Crane, which addresses all kinds of sparsity irregularities in CNNs. We perform a fair comparison between Crane and state-of-the-art prior approaches. Experimental results show that Crane improves performance by 27% ~ 88% and reduces energy consumption by 16% ~ 48%, respectively, compared to the counterparts.