Reviewerly/exHarmony huggingface dataset:
Multimedia Data Privacy Against Machines

With the explosion of multimedia data, machine learning applications are proliferating. Privacy concerns have recently resurfaced, due to a few prominent incidents of user data leakage, which divulged sensitive information to other people. Multimedia data platform providers––social networks, their affiliates or governments with access to users’ content––use algorithms to profile users by extracting or inferring demographic information, personality traits, relationships, opinions, and beliefs. These results, in turn, feed algorithms for targeted advertising but also for personalized content recommendation to maximize user engagement. While this is ostensibly for users’ benefit, they have serious privacy implications. We highlight this new problem of privacy protection against machines in contrast to the traditional problem of privacy protection against humans. We briefly touch upon our initial solution, a human-sensitivity-aware image perturbation model, which is able to modify the computational classification results of sensitive attributes while preserving the remaining attributes. We then point to many exciting open problems in this new area.
Automated grey and white matter segmentation in digitized Aβ human brain tissue slide images

Neuropathologists assess vast brain areas to identify diverse and subtly differentiated morphologies. Alzheimer's disease pathologies have different density distributions in grey matter (GM) and white matter (WM), making the task of separating GM and WM necessary to neuropathologic deep phenotyping. Standard methods of segmentation typically require manual annotations, where a trained observer traces the boundaries of GM and WM on digitized tissue slide images using software like Aperio ImageScope or QuPath. This method can be time-consuming and can prevent the analysis of large amounts of slides in a scalable way. In this paper, we propose a CNN-based approach to automatically segment GM and WM in ultra-high-resolution whole slide images (WSIs) by transforming the segmentation problem into a classification problem. Contrary to the traditional image processing segmentation method, our technique is flexible, robust, and efficient with the accuracy of 77.43% in GM and 79.42% in WM on our hold-out WSIs.
Hierarchical System for Car Make and Model Recognition on Image Using Neural Networks

A hierarchical system for object classification using convolutional neural networks is considered, as an example recognition of a car make and model discussed. The task of finegrained object classification on image, if a presence of an object and its position in an image undefined is important in various areas. The proposed hierarchical architecture can be generalized to solve different object localization and classification tasks. The algorithm includes methods for object detection, classification of object orientation in captured frame, use of different classification methods depending on orientation, and the integration of data from various methods to increase classification precision. The point of this paper is the study of possible components for a proposed system and experimental evaluation of preferred methods. In the experiments, an accuracy of the object detection was 98%, an accuracy of object orientation classification only was up to 96% depending on a network architecture, an accuracy of car make and model classification on all directions was 92.6%.
Algorithm using supervised subspace learning and non‐local representation for pose variation recognition

Pose variation has been one of the challenges of face recognition. To solve this challenge, the authors propose a classification algorithm using supervised subspace learning and non‐local representation (SSLNR). In SSLNR, they first propose a supervised subspace learning algorithm (SSLA). SSLA includes three different terms. The first term is the difference term, which can reduce the intra‐class differences. The second term is the block‐diagonal regularisation term, which promotes the samples to be represented by intra‐class samples. The last one is the noise robust term. Then, the original samples are mapped to the learned subspace by using SSLA. Thus, the intra‐class differences of the samples mapped to the learned subspace are reduced. Finally, those mapped samples are classified by proposed non‐local constraint‐based extended sparse representation classifier. SSLNR is extensively evaluated using four databases, namely Georgia Tech, Label faces in the wild, FEI and CVL. Experimental results show that SSLNR achieves better performance than some state‐of‐the‐art algorithms, such as DARG and RRNN.
Query by Example for Cross-Lingual Event Retrieval

We propose a Query by Example (QBE) setting for cross-lingual event retrieval. In this setting, a user describes a query event using example sentences in one language, and a retrieval system returns a ranked list of sentences that describe the query event, but from a corpus in a different language. One challenge in this setting is that a sentence may mention more than one event. Hence, matching the query sentence with document sentence results in a noisy matching. We propose a Semantic Role Labeling (SRL) based approach to identify event spans in sentences and use a state-of-the-art sentence matching model, Sentence BERT (SBERT) to match event spans in queries and documents without any supervision. To evaluate our approach we construct an event retrieval dataset from ACE which is an existing event detection dataset. Experimental results show that it is valuable to predict event spans in queries and documents and our proposed unsupervised approach achieves superior performance compared to Query Likelihood (QL), Relevance Model 3 (RM3) and SBERT.
Steganographer Detection Via Enhancement-Aware Graph Convolutional Network

Steganographer detection aims to find guilty users who hide secret information in images or other multimedia data in the social network. In existing work, the distances between users are calculated based on the distributions of all images shared by the corresponding users, then users lying an abnormal distance from others are detected as guilty users. This flattened method is difficult to grasp the nuances of the guilty and innocent users. In this paper, we are the first to propose a graph-based deep learning framework for steganographer detection. The proposed Enhancement-aware Graph Convolutional Network (EGCN) represents each user as a weighted complete graph and learns to highlight the differences between guilty users and innocent users based on the structured graph. Compared with the state-of-the-art method and other representative graph-based models, the proposed framework demonstrates its effectiveness across image domains, and even under the context of large-scale social media scenario.
Prediction of Survival Rate from Non-Small Cell Lung Cancer using Improved Random Forest

The major advantage of survival rate prediction is to help patients by giving a better understanding about the success rate of his treatment. In case of lung cancer it is difficult to determine which feature should be used in order to determine this information. In this paper a number of algorithms are applied to the data set to classify the survival rate of Non -Small Cell lung cancer patients along with our proposed method Improved Random forest. The key data features used in these algorithms are overall treatment time, stages, total tumor dose, gender and age. The predictive power of various algorithms are compared. The results show that among the four individual models developed, Improved Random Forest is the most accurate one with an accuracy of 98%. Hence this work provides an effective and powerful approach to predict survival rate of NSCLC patients.
An Accurate Object Detector With Effective Feature Extraction by Intrinsic Prior Knowledge

Deep learning-based object detection algorithms play an increasingly important role in many computer vision tasks. Many detection algorithms are trying to increase the depth of networks to improve their feature expression capacity. However, how to better extract the essential features from a fixed number of training samples is often ignored. Thus, we propose CoReSh network, using the prior knowledge of intrinsic features, to address this problem. First, the relevant optical factors which may affect the detection performance are explored, and the intrinsic features are proven to be able to represent the essential information of objects. Second, a new data augmentation is proposed to increase the feature diversities of training samples through intrinsic decomposition. Third, a deep detection neural network incorporating color, reflectance, and shading image's information is designed. To optimize the performance of our network, different learning rate setting tactics are compared. Besides, a contrast experiment on the Pascal VOC dataset was set up to verify it. The result shows that our network is 10% better than ordinary deep learning-based detection networks in precision, and roughly the same as the network loaded with pre-training weights from ILSVRC. It shows that our network has greatly improved the precision of object detection and it is suitable when the training process lacks sufficient training samples.
A novel public-key encryption scheme based on Bass cyclic units in integral group rings

In this study, we construct a new public key cryptosystem both like El-Gamal and RSA cryptosystem based on Bass cyclic units in integral group rings. Naturally, the underlying hard problems for this system are both decisional Diffie-Hellman assumption and factorization of integers. Since the system is related to RSA scheme indirectly, key generation process is very efficient. This cryptosystem actually supports and strengthens the current public key cryptosystems in [6], [8] and [20]. Encryption algorithm in the system is constructed via non-commutative operations in integral group rings of dihedral groups. Finally, we briefly discuss the security of our cryptosystem against some known attacks.
Semi-Supervised Semantic Segmentation Constrained by Consistency Regularization

In this paper, we propose a self-training based method for semi-supervised semantic segmentation. Our method utilizes k perturbed images of each unlabeled image to generate a new mask through the proposed vote operation which in turn is used as a supervision signal to train the model. The k predicted masks of perturbed images can provide nontrivial knowledge that is not captured by a single prediction and the proposed vote operation enables the model to output a low entropy prediction. Consistency regularization is applied between generated mask and k masks in terms of MSE loss in our network. Extensive experiments are conducted on different datasets to evaluate the effectiveness of our method. We show that the proposed method surpasses previous state-of the-art semi-supervised methods on ISIC 2017 dataset and achieves competitive performance on PASCAL VOC 2012 dataset.
Measuring semantic similarity of documents with weighted cosine and fuzzy logic

Currently, the semantic analysis is used by different fields, such as information retrieval, the biomedical domain, and natural language processing. The primary focus of this research work is on using semantic methods, the cosine similarity algorithm, and fuzzy logic to improve the matching of documents. The algorithms were applied to plain texts in this case CVs (resumes) and job descriptions. Synsets of WordNet were used to enrich the semantic similarity methods such as the Wu-Palmer Similarity (WUP), Leacock-Chodorow similarity (LCH), and path similarity (hypernym/hyponym). Additionally, keyword extraction was used to create a postings list where keywords were weighted. The task of recruiting new personnel in the companies that publish job descriptions and reciprocally finding a company when workers publish their resumes is discussed in this research work. The creation of a new gold standard was required to achieve a comparison of the proposed methods. A web application was designed to match the documents manually, creating the new gold standard. Thereby the new gold standard confirming benefits of enriching the cosine algorithm semantically. Finally, the results were compared with the new gold standard to check the efficiency of the new methods proposed. The measures used for the analysis were precision, recall, and f-measure, concluding that the cosine similarity weighted semantically can be used to get better similarity scores.
Fine-Grained Garment Parsing: A Body Generation Approach

Current human parsing methods segment an image into different semantic parts including background, body parts and garments. A major limitation of today’s human parsing methodologies is that they are not able to provide fine-grained garment segmentation (e.g., left and right sleeves), and it is mainly due to the lack of a dataset with such fine-grained semantic garment part labels. To tackle this, we propose a body generation approach for fine-grained garment parsing. In particular, we first use a body generation module based on image inpainting, to locate the fine-grained garment parts corresponding to where the generated body parts are, e.g., the left sleeve is assumed to be associated with the left arm; we then extract the garment parts from the original whole garment based on the positions above. In our experiments based on a public dataset focusing on top clothing images, our solution can effectively separate a top garment into a left sleeve, a right sleeve and front, as compared to state-of-the-art solutions that parse it as a whole.
Hand Mobility Recovery Through Game Controlled by Gestures

Motivation is one of the key factors when having to do exercises for hand mobility, because they have to be done consistently. This paper proposes a motivational application for people who have suffered hand trauma and aims to help them to do exercises for regaining hand mobility. There are two modules in this application: one for gesture recognition using a web camera and one for a gestures controlled Tetris game. OpenCV, a computer vision library is used for recognizing nine different gestures, and the game is developed in a visual programming language. The recognition rate is high enough in order to fluidly play Tetris. The application is visually attractive, simple to use and may help in increasing motivation to do hand exercises.
Image Classification Based on Convolutional Neural Network and Support Vector Machine

Automatic classification of images is a key task in many areas, including information retrieval, scene detection, internet data filtering, medical applications, etc. When directly operating on the image, the traditional classification method is difficult to achieve good results due to the high-dimensional characteristics of the data. To this end, in this paper, a novel method of image classification named CNN-SVM is proposed. Specifically, we first preprocess original images to extract the vital features and reduce redundant features by using the convolutional neural network (CNN). Then, the cross-validation (CV) is adopted to optimize the penalty parameter C and the kernel parameter σ for the support vector machine (SVM). Finally, the extracted features will be input to an optimized SVM model. In order to validate the superiority of our proposed algorithm, we select a hybrid image set taken from Caltech 265 image archive as our experimental data. The experimental results reflect that CNN-SVM has higher classification accuracy.
Concurrent Morphology-Optimization and Behavior-Learning: Co-Designing Intelligent Quadcopters

In this paper, a novel co-design process is proposed to accomplish the coupled objectives of maximizing the flight range and learning capacity of a quadcopter unmanned aerial vehicle (UAV), with the end goal of producing a UAV that can learn to fly bottoms up (pun intended). The approaches presented here are motivated by the observed coherence in the evolution of morphology and behavior (or rather the capacity to learn behavior) in nature. The co-design process requires concurrent execution of the following two processes: i) optimization of the UAV morphology (e.g., geometry, component choices, etc.) and ii) learning of the flight controller to achieve take-off and preliminary hovering. In order to decrease the computational burden of applying the learning process nested under each morphology design, a novel metric called "Talent'' is defined, which estimates the learning capacity of the system based on the UAV morphology and control theory. A concept of "Capacity Variables'' is also introduced to help in reducing the dimension of the design variable space, and enable swift inverse design. Multi-objective optimization and neuroevolution processes are respectively used to perform the multi-level design and validate the learning capacity of the UAV designs produced thereof. The results of our co-design framework demonstrate the trade-offs accomplished in terms of UAV range performance and talent. More importantly, we show that through coherent morphology-talent optimization, it is possible to produce UAVs that can learn to fly (in this case, take off and hover briefly) with no prior guidance, which is in itself a major step forward in designing autonomous aerial robotic systems.
A Highly Efficient Training-Aware Convolutional Neural Network Compression Paradigm

By reducing parameters and quantization, convolutional neural network compression has become the key steps to reduce model size. However, most of the existing work treats these two methods separately, and the work combining the two methods is cumbersome. We propose a simple but highly efficient training-aware convolutional neural network compression paradigm, which elegantly combines lightweight network design and binary network architecture into a three-stage scheme.In the proposed scheme, we keep the first layer as the standard convolution in order to maintain the representation ability of the network, and then use depthwise separable convolution to replace the standard convolution for middle layers, finally we adopt binary quantization for the remaining convolution layers. Our scheme is training-aware, meaning pruning and quantization are explicit and aware to us, unlike the well-known deep compression method where pruning and quantization are unknown before training.Experimental results show that our proposed compression paradigm can achieve 75.4 times of compression ratio compared to maximal 49 times of deep compression, while maintaining similar performance. Both compression ratio and accuracy are much better than the state-of-the-art binary neural network BNN+.
Optimal Time-Frequency Distribution Selection for LPI Radar Pulse Classification

The work presented in this paper shows the performance of various time-frequency distributions when gathering ELectronic INTelligence (ELINT) from an electromagnetic environment that contains transmissions from radars operating in a Low Probability of Interception (LPI) mode. A radar device varying waveform parameters on a pulse-by-pulse basis to enhance sensing capabilities and/or to avoid interception warrants a method that can assign a unique Pulse Descriptor Word (PDW) to each pulse detected. The simulations presented here makes use of a Deep Learning classifier that is fed by time-frequency representations of noisy LFM and FMCW pulses that each have unique signal parameters. The performance of the radar pulse classifier is conveyed for multiple time-frequency methods. The results show that the time-frequency representation requirements for accurate PDW generation varies for each signal parameter being estimated whilst also having a dependence on the SNR of the intercepted signal.
TOWARDS A FRAMEWORK FOR INTEGRATED AND COLLABORATIVE KNOWLEDGE MANAGEMENT FOR ENGINEERING DESIGN – A CASE STUDY

Abstract In automotive industry, the design process is costly and time-consuming. Car safety is a crucial factor in the development of a vehicle, which is why crash simulation is an essential step in the design process. To improve car crash simulation analysis, it is necessary to reduce the time required and support the resolution of encountered design issues. We propose a knowledge management approach to support car crash simulation analysis and ensure the collaboration of different stakeholders. In a knowledge-intensive context, we used an ontology-based approach to formalise and capture knowledge.
3D Self-Attention for Unsupervised Video Quantization

Unsupervised video quantization is to compress the original videos to compact binary codes so that video retrieval can be conducted in an efficient way. In this paper, we make a first attempt to combine quantization method with video retrieval called 3D-UVQ, which obtains high retrieval accuracy with low storage cost. In the proposed framework, we address two main problems: 1) how to design an effective pipeline to perceive video contextual information for video features extraction; and 2) how to quantize these features for efficient retrieval. To tackle these problems, we propose a 3D self-attention module to exploit the spatial and temporal contextual information, where each pixel is influenced by its surrounding pixels. By taking a further recurrent operation, each pixel can finally capture the global context from all pixels. Then, we propose gradient-based residual quantization which consists of several quantization blocks to approximate the features gradually. Extensive experimental results on three benchmark datasets demonstrate that our method significantly outperforms the state-of-the-arts. Ablation study shows that both the 3D self-attention module and the gradient-based residual quantization can improve the performance of retrieval. Our model is publicly available at https://github.com/brownwolf/3D-UVQ.
Intelligent Video Analytics (IVA) and Surveillance System using Machine Learning and Neural Networks

Drowning in the huge amount of content of recorded and live video data, relying solely on manual observation by security personnel for long hours is not feasible no efficient. There is a need for an intelligent and smart system which can overcome this difficulty by automating this process. It should alert the security personnel when suspicious objects in the frame are detected It should also keep false alarms to a minimum. If this is achieved, the work of surveillance will be optimized and the presence of security personnel can be used else-where more labor force is needed. Video surveillance system plays a significant role in the security systems of modern cities since it can detect abnormal behavior and suspicious objects ahead of time as opposed to the late detection by humans. However, due to the expansion of the surveillance network, a massive amount of data is generated which makes the analytics, storage and retrieval of the data challenging.