Reviewerly/exHarmony huggingface dataset:
Exploiting Retraining-Based Mixed-Precision Quantization for Low-Cost DNN Accelerator Design

For successful deployment of deep neural networks (DNNs) on resource-constrained devices, retraining-based quantization has been widely adopted to reduce the number of DRAM accesses. By properly setting training parameters, such as batch size and learning rate, bit widths of both weights and activations can be uniformly quantized down to 4 bit while maintaining full precision accuracy. In this article, we present a retraining-based mixed-precision quantization approach and its customized DNN accelerator to achieve high energy efficiency. In the proposed quantization, in the middle of retraining, an additional bit (extra quantization level) is assigned to the weights that have shown frequent switching between two contiguous quantization levels since it means that both quantization levels cannot help to reduce quantization loss. We also mitigate the gradient noise that occurs in the retraining process by taking a lower learning rate near the quantization threshold. For the proposed novel mixed-precision quantized network (MPQ-network), we have implemented a customized accelerator using a 65-nm CMOS process. In the accelerator, the proposed processing elements (PEs) can be dynamically reconfigured to process variable bit widths from 2 to 4 bit for both weights and activations. The numerical results show that the proposed quantization can achieve 1.37 × better compression ratio for VGG-9 using CIFAR-10 data set compared with a uniform 4-bit (both weights and activations) model without loss of classification accuracy. The proposed accelerator also shows 1.29× of energy savings for VGG-9 using the CIFAR-10 data set over the state-of-the-art accelerator.
Diagnosis of osteoporosis using intelligence of optimized extreme learning machine with improved artificial algae algorithm

Osteoporosis is the silent killer disease that mostly occur in elderly people because of bone fragility and fracture. Early and accurate diagnosis of osteoporosis saves the patient life. This work focuses on developing an efficient classifier model to support this issue. For this, the proven Extreme learning machine (ELM) is selected. Then a novel hybrid metaheuristic optimization algorithm is developed on fusing two nature inspired metaheuristic optimization algorithms namely Artificial algae algorithm with multi-light source and Monarch butterfly optimization algorithm. It is named as HMBA algorithm. To further increase the diagnostic accuracy of ELM, it is optimized using HMBA. This proposed HMBA-ELM classifier model is used to diagnose osteoporosis from normal subjects. The discrimination efficiency of proposed classifier is compared with other similar classifiers based on the results produced. It is found that the proposed HMBA- ELM has yielded outstanding results mainly in terms of (sensitivity ​± ​SD/specificity ​± ​SD/precision ​± ​SD/MCR±SD/accuracy ±SD) as (99.45 ​± ​0.69/99.77 ​± ​0.31/96.32 ​± ​0.12/0.30 ​± ​0.18/99.70 ​± ​0.21), (98.11 ​± ​0.91/99.56 ​± ​0.28/90.03 ​± ​0.19/0.51 ​± ​0.09/99.49 ​± ​0.18) and (99.26 ​± ​1.13/99.54 ​± ​0.33/97.38 ​± ​0.22/0.4 ​± ​0.31/99.6 ​± ​0.32) respectively for three osteoporosis datasets namely Femoral neck, Lumbar spine and Femoral & Spine. This is highest among all other approaches with less computation time.
Q-Bully: A Reinforcement Learning based Cyberbullying Detection Framework

With the ever-increasing involvement of people into social media and online gaming, Cyberbullying has become a serious issue affecting almost all parts of the demographic. Cyberbullying can cause severe mental and emotional impacts on a person, especially on minors; hence, there is a requirement of having intelligent automated systems to detect questionable content present on social media platforms and remove it. In this paper, we introduce our novel algorithm Q-Bully which can automatically detect cyberbullying on various social media and online gaming platforms using Reinforcement Learning along with Natural Language Processing techniques. Previously the techniques used to detect cyberbullying have a good accuracy related to the text they have been trained on and do not incorporate new word patterns without complete retraining of model. In this paper, we incorporated the use of Reinforcement Learning and have conducted an experimental study in which we feed the messages and posts of bullies as well as victims to a Reinforcement Learning Agent for classification. We compare our model with the other baseline models on based on F1 scores (0.86 a benchmark dataset of 16K annotated tweets) and are able to infer that our model outperforms other state-of-the-art models when the dataset is highly dynamic and populated with words which are deliberately misspelled to trick the conventional detection systems.
Imbalanced metric learning for crashing fault residence prediction

As the software crash usually does great harm, locating the fault causing the crash (i.e., the crashing fault) has always been a hot research topic. As the stack trace in the crash reports usually contains abundant information related the crash, it is helpful to find the root cause of the crash. Recently, researchers extracted features of the crash, then constructed the classification model on the features to predict whether the crashing fault resides in the stack trace. This process can accelerate the debugging process and save debugging efforts. In this work, we apply a state-of-the-art metric learning method called IML to crash data for crashing fault residence prediction. This method uses Mahalanobis distance based metric learning to learn high-quality feature representation by reducing the distance between crash instances with the same label and increasing the distance between crash instances with different labels. In addition, this method designs a new loss function that includes four types of losses with different weights to cope with the class imbalanced issue of crash data. The experiments on seven open source software projects show that our IML method performs significantly better than nine sampling based and five ensemble based imbalanced learning methods in terms of three performance indicators.
Model Personalization for Human Activity Recognition

Human activity recognition (HAR) has applications to various fields. However, not accounting for the personal differences among various subjects can lead to significant accuracy degradation. To address this problem, we propose a lightweight personalization process that enables an HAR model adapt for various users, some of them ever unseen before. Indeed, by adding a small amount of new labeled data, the model we propose can be personalized and boost the HAR accuracy when dealing with a wide range of target users. Furthermore, we also propose an innovative training algorithm to support personalization during the training stage. Our evaluations on three public real-world datasets demonstrate the superiority of our personalization approach, i.e. 6-14% improvement on the target-domain accuracy, while using only five labeled data points per-class.
Sarcasm Detection using Genetic Optimization on LSTM with CNN

The challenging problem of 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> Century is to detect sarcasm in vivid data available on a large scale. Over 20 years of study in this field, the past 10 years have shown a significant progress not only in semantic features, but also an upward trend has also been observed in the various machine-learning approaches to analyze and process the data. To enlist a few, theories of sarcasm, it's syntactical and semantic properties; lexical features have been an area of interest for almost all of them. In this paper, we propose a unique deep neural network model whose Bidirectional LSTM undergo Hyper parameters optimization using genetic algorithm followed by a Convolution Neural Network for sarcasm detection. We put forward the results in a robust way, which may result in a better future work in this field.
Near-Optimal Polynomial for Modulus Reduction Using L2-Norm for Approximate Homomorphic Encryption

Since Cheon <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> introduced an approximate homomorphic encryption scheme for complex numbers called Cheon-Kim-Kim-Song (CKKS) scheme, it has been widely used and applied in real-life situations, such as privacy-preserving machine learning. The polynomial approximation of a modulus reduction is the most difficult part of the bootstrapping for the CKKS scheme. In this article, we cast the problem of finding an approximate polynomial for a modulus reduction into an L2-norm minimization problem. As a result, we find an approximate polynomial for the modulus reduction without using the sine function, which is the upper bound for the approximation of the modulus reduction. With the proposed method, we can reduce the degree of the polynomial required for an approximate modulus reduction, while also reducing the error compared with the most recent result reported by Han <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (CT-RSA’ 20). Consequently, we can achieve a low-error approximation, such that the maximum error is less than 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−40</sup> for the size of the message <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m/q\approx 2^{-10}$ </tex-math></inline-formula> . By using the proposed method, the constraint of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q = \mathcal {O}(m^{3/2})$ </tex-math></inline-formula> is relaxed as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(m)$ </tex-math></inline-formula> , and thus the level loss in bootstrapping can be reduced. The solution to the cast problem is determined in an efficient manner without iteration.
From known to the unknown: Transferring knowledge to answer questions about novel visual and semantic concepts

Current Visual Question Answering (VQA) systems can answer intelligent questions about ‘known’ visual content. However, their performance drops significantly when questions about visually and linguistically ‘unknown’ concepts are presented during inference (‘Open-world’ scenario). A practical VQA system should be able to deal with novel concepts in real world settings. To address this problem, we propose an exemplar-based approach that transfers learning (i.e., knowledge) from previously ‘known’ concepts to answer questions about the ‘unknown’. We learn a highly discriminative joint embedding (JE) space, where visual and semantic features are fused to give a unified representation. Once novel concepts are presented to the model, it looks for the closest match from an exemplar set in the JE space. This auxiliary information is used alongside the given Image-Question pair to refine visual attention in a hierarchical fashion. Our novel attention model is based on a dual-attention mechanism that combines the complementary effect of spatial and channel attention. Since handling the high dimensional exemplars on large datasets can be a significant challenge, we introduce an efficient matching scheme that uses a compact feature description for search and retrieval. To evaluate our model, we propose a new dataset for VQA, separating unknown visual and semantic concepts from the training set. Our approach shows significant improvements over state-of-the-art VQA models on the proposed Open-World VQA dataset and other standard VQA datasets.
Novel Artificial Immune Networks-based optimization of shallow machine learning (ML) classifiers

Artificial Immune Networks (AIN) is a population-based evolutionary algorithm that is inspired by theoretical immunology. It applies ideas and metaphors from the biological immune system to solve multi-disciplinary problems. This paper presents a novel application of the AIN for optimizing shallow machine learning (ML) classification algorithms. AIN accomplishes this task by searching the best hyper-parameter set for a specific classification algorithm (also termed model selection), which minimizes training error and enhances the generalization capability of the algorithm. We present a convergence analysis of the proposed algorithm and employ it in conjunction with selected, well-known ML classifiers, namely, an extreme learning machine (ELM), a support vector machine (SVM) and an echo state network (ESN). The performance is evaluated in terms of classification accuracy and learning time, using a range of benchmark datasets, and compared against grid search as well as evolutionary strategy (ES)-based optimization techniques. An empirical study with different datasets demonstrates improved classification accuracy of SVM, from 2% to 5%, for ESN from 3% to 6%, whereas in the case of ELM from 3% to 9%. Comparative simulation results demonstrate the potential of AIN as an alternative optimizer for shallow ML algorithms.
Top-aware reinforcement learning based recommendation

Reinforcement learning (RL) techniques have recently been introduced to recommender systems. Most existing research works focus on designing policy and learning algorithms of the recommender agent but seldom care about the top-aware issue, i.e., the performance on the top positions is not satisfying, which is crucial for real applications. To address the drawback, we propose a Supervised deep Reinforcement learning Recommendation framework named as SRR. Within this framework, we utilize a supervised learning (SL) model to partially guide the learning of recommendation policy, where the supervision signal and RL signal are jointly employed and updated in a complementary fashion. We empirically find that suitable weak supervision helps to balance the immediate reward and the long-term reward, which nicely addresses the top-aware issue in RL based recommendation. Moreover, we perform a further investigation on how different supervision signals impact on recommendation policy. Extensive experiments are carried out on two real-world datasets under both the offline and simulated online evaluation settings, and the results demonstrate that the proposed methods indeed resolve the top-aware issue without much performance sacrifice in the long-run, compared with the state-of-the-art methods.
ClothingNet: Cross-Domain Clothing Retrieval With Feature Fusion and Quadruplet Loss

Cross-domain clothing retrieval is an active research topic because of its massive potential applications in fashion industry.Due to the large number of garment categories or styles, and different clothing appearances caused by different camera angles, different shooting conditions, different messy background environments, or different postures of the dressed human body, the retrieval accuracy of traditional consumer-to-shop scheme is always low.In this paper, based on the framework of deep convolution neural network, a novel cross-domain clothing retrieval method is proposed by using feature fusion and quadruplet loss function, which is named as ClothingNet.First, the pre-trained deep neural network Resnet-50 is adopted to extract feature map of clothing images.The extracted high-level image features can thus be merged with middle-level features, and the final feature representation of clothing images can be obtained by constraining the fusion feature values to a certain range in term of L 2 norm.This fusion feature provides a comprehensive description of the differences between clothing images.For effectively training our ClothingNet, the crossdomain clothing images are organized in form of a quadruplet for calculating its loss function, and the network parameters can be optimized according to back propagation scheme via stochastic gradient descent of loss function.Our proposed method is validated on two public datasets for clothing retrieval, DARN and DeepFashion, showing that the top-50 retrieval accuracy is 35.67% and 53.52% respectively.Experimental results illustrate the effectiveness of our clothing retrieval method.
Joint Channel Coding and Modulation via Deep Learning

Channel coding and modulation are two fundamental building blocks of physical layer wireless communications. We propose a neural network based end-to-end communication system, where both the channel coding and the modulation blocks are modeled as neural networks. Our proposed architecture combines Turbo Autoencoder together with feed-forward neural networks for modulation, and hence called TurboAE-MOD. Turbo Autoencoder was introduced in [1] and consists of a neural network based channel encoder (convolutional neural networks with an interleaver) and a neural network based decoder (iterations of convolutional neural networks with interleavers and de-interleavers in between). By allowing joint training of the channel coding and modulation in an end-to-end manner, we demonstrate that TurboAE-MOD performs comparable to modern codes stacked with canonical modulations for moderate block lengths. We also demonstrate that TurboAE-MOD learns interesting modulation patterns that are amenable to meaningful interpretations.
Multi-Stage Feature Pyramid Stereo Network-Based Disparity Estimation Approach for Two to Three-Dimensional Video Conversion

Disparity estimation is a popular topic in computer vision and has drawn increasing attention in recent years. In this article, we propose a new multi-stage network for the purpose of two to three-dimensional video conversion that contains two training stages: an initial disparity estimation as the first training stage and depth-image-based rendering (DIBR) as an extra component to form the second training stage. In the first training stage, we propose a revised end-to-end feature pyramid stereo network, in which the original non-pyramid structure is replaced by a bottom-up convolutional neural network pyramid for disparity regression. It utilizes the spatial information by concatenating different scale features to boost the performance on boundary consistency. Mirror connections between feature extraction and disparity regression on the corresponding layers are also added to improve the quality of the results. In the second stage, we propose an improved disocclusion filling technique in the DIBR branch and connect the non-neural-network method to the disparity estimation network. This two-stage training strategy can work effectively to generate the improved disparity estimation for two to three-dimensional video conversion. Extensive experiments are conducted and some selected state-of-the-art algorithms are compared with our proposed approach on the popular KITTI2015 and Scene Flow datasets. The results demonstrate that our estimated disparity map can generate high quality 3D images.
Universal Online Sketch for Tracking Heavy Hitters and Estimating Moments of Data Streams

Traffic measurement is key to many network management tasks such as performance monitoring and cyber-security. Its aim is to inspect the packet stream passing through a network device, classify them into flows according to the header fields, and obtain statistics about the flows. For processing big streaming data in size-limited SRAM of line cards, many space-sublinear algorithms have been proposed, such as CountMin and CountSketch. However, most of them are designed for specific measurement tasks. Implementing multiple independent sketches places burden for online operations of a network device. It is highly desired to design a universal sketch that not only tracks individual large flows (called heavy hitters) but also reports overall traffic distribution statistics (called moments). The prior work UnivMon successfully tackled this ambitious quest. However, it incurs large and variable per-packet processing overhead, which may result in a significant throughput bottleneck in high-rate packet streaming, given that each packet requires 65 hashes and 64 memory accesses on average and many times of that in the worst case. To address this performance issue, we need to fundamentally redesign the solution architecture from hierarchical sampling to new progressive sampling and from CountSketch to new ActiveCM+, which ensure that per-packet overhead is a small constant (4 hash and 4 memory accesses) in the worst case, making it much more suitable for online operations, especially for pipeline implementation. The new design also makes effort to reduce memory footprint or equivalently improve measurement accuracy under the same memory. Our experiments show that our solution incurs just one sixteenth per-packet overhead of UnivMon, while improving measurement accuracy by three times under the same memory.
Handwritten Character Recognition to Obtain Editable Text

Developing an android application for character recognition to read the text from an image is a big area of research. Nowadays, there is a trend of storing information from the handwritten documents for future use. A simple way to store the information is image capturing of the handwritten document and save it in image format. The method to transform handwritten data into electronic format is 'Optical Character Recognition'. It involves several steps including pre-processing, segmentation, feature extraction and post-processing. Many researchers have been used OCR for recognizing character. This system uses the android phone to capture the image of the document and further steps are done by OCR. The main challenge is to recognize the characters from different styles of handwriting. Thus, a system is designed that recognizes the handwritten data to obtain an editable text. The output of this system depends upon the data that has to be written by the writer. Our system offers 90% accuracy for handwritten documents and gives the easiest way to edit or share the recognized data.
Enhanced and Effective Computerized Classification of X-Ray Images

According to the World health organization (WHO) accidents became very common. Due to the increase in accidents automating of fracture detections became very essential. To detect the fracture it is necessary to classify the X-ray image first. This paper proposed an effective methodology for the classification of x-ray images to classify the automated explanation to provide efficient and effective results to the physicians and radiologists for making a decision. An attempt was made and a framework presented in this paper, which involves images being pre-processed using M3 filter for denoising, segmentation by K-means clustering, preceded by Statistical feature extraction. Classification of X-ray images are categorized into chest, spine, foot, palm, skull, the head is carried out by comparing the K-Nearest Neighbour (KNN) algorithm, Support Vector Machine(SVM) and Back Propagation Neural Network(BPNN). Overall 88% of accuracy in the analysis of X-ray images is acquired in BPNN Compared to other techniques.