Reviewerly/exHarmony huggingface dataset:
TAAN: Task-Aware Attention Network for Few-shot Classification

Few-shot classification aims to recognize unlabeled samples from unseen classes given only a few labeled samples. Current approaches of few-shot learning usually employ a metric-learning framework to learn a feature similarity comparison between a query (test) example and the few support (training) examples. However, these approaches all extract features from samples independently without looking at the entire task as a whole, and so fail to provide an enough discrimination to features. Moreover, the existing approaches lack the ability to select the most relevant features for the task at hand. In this work, we propose a novel algorithm called <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Task-Aware</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Attention</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Network</i> (TAAN) to address the above problems in few-shot classification. By inserting a <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Task-Relevant</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Channel</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Attention</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Module</i> into metric-based few-shot learners, TAAN generates channel attentions for each sample by aggregating the context of the entire support set and identifies the most relevant features for similarity comparison. The experiment demonstrates that TAAN is competitive in overall performance comparing to the recent state-of-the-art systems and improves the performance considerably over baseline systems on both mini-ImageNet and tiered - ImageNet benchmarks.
Context Sentences to Single Vector Compression using Convolutional Transformers for Deep Learning based NLG Tasks

In natural / casual conversations, people tend to have their own unique style of speaking/ chatting; certain phrases only they use, style of typing, customized grammar, nicknames etc. These `imperfections' make the dialogue more human; we are able to identify people from their unique style of chatting. Conversational chatbots trained on clean text won't be able to capture these nuances. We propose a method to capture these quirks in the form of a chatbot with a novel NLG (Natural Language Generation) architecture; In the form of a convolutional transformer. WhatsApp chats between two users were cleaned and exported for training the model. The dataset had Unicode characters for foreign lexicon, emojis, and personalised grammar / texting patterns. The model is able to capture these traits in conversation for short responses.
Personalized Recommendation Model for Mobile E-commerce Users

Aiming at the technical problems of data sparsity and real-time recommendation in mobile e-commerce, this paper proposes a personalized recommendation algorithm based on user purchase preference and the interest of traditional user collaborative filtering recommendation. In online recommendation, the user's real-time behavior characteristics are brought into the behavior model to get the user's purchase tendency. Then combined with the interest degree, the product with the highest purchase probability is recommended to the user. We use the relevant algorithms to mine the information in the mobile e-commerce transaction database to achieve the unity of real-time and accuracy. Finally, the proposed classification model is verified on a real data set. The experimental results show that the proposed method can provide personalized real-time recommendation for mobile users better than other traditional methods to improve the consumer satisfaction.
Analysis on Bilibili Video Categorical Segmentation Using XGBoost

Video media platforms such as YouTube and Bilibili, have been increasingly popular. The industry of video media is developing rapidly and it is one of the main industries that bring monetary to many countries. Bilibili is one of the largest and most popular video sites in China. To make better policies and decisions as well as remain competitive, organizers and uploaders of Bilibili have to understand the features of popular videos. Marketing segmentation can help to achieve this goal. This study presents Bilibili video segmentation with XGBoost, an advanced machine learning algorithm based on tree boosting system. The study was divided into two sections: the categorical analysis and the experiment. The statistical analysis and the experiment are all based on data of videos of the 100 most popular uploaders. In the experiment, the multiclass log loss of XGBoost was 0.519707. The results of this study will be helpful for the management and predictive analysis of Bilibili and the whole video media industry.
"I personally relate it to the traffic light"

Improving the usability and adoption of secure (i.e. end-to-end encrypted) email systems has been a notorious challenge for over two decades. One of the open questions concerns the amount and format of information that should be communicated to users to inform them of the security and privacy properties with respect to different messages or correspondents. Contributing to the ongoing discussion on the usability and effectiveness of security and privacy indicators, particularly in the context of systems targeting non-expert users, this paper sheds light on users' evaluation of traffic light-inspired indicators, as a metaphor to represent different privacy states and guarantees, provided by a new system for email end-to-end encryption called p≡p. Using a mixed-methods approach, based on input gathered from 150 participants in three online studies, we highlight the pros and cons of the traffic light semantic in p≡p's context and beyond, and discuss the potential implications on the perceived security and use of such systems.
Giant Microwave Spontaneous Emission Enhancements in Planar Aperture Waveguide Structures

Abstract Microwave interactions are an invaluable tool for probing atomic states of matter and are currently being extensively explored for the development of scalable quantum computing. However, many of the available methods for spectroscopic analysis and photonic quantum manipulations are inaccessible for microwaves due to the weak spontaneous emission of quantum emitters. In this work, a mechanism for giant resonant enhancement of spontaneous emission from magnetic dipole emitters is suggested that employs a new phenomenon of near field impedance cancellation via magnetic flux confinement. Enhancements of at least 10 11 are theoretically shown for structures with 100 nm features, and much greater potential enhancements are predicted with this method. Subsequently, the realization of integrated systems with enhanced quantum emitters using microwave spoof plasmon waveguides is demonstrated. Taken together, these findings will pave the way for microwave quantum photonics and microwave hybrid quantum computing.
Design Framework for SRAM-Based Computing-In-Memory Edge CNN Accelerators

This paper presents an architectural framework and an evaluation model for Static Random Access Memory (SRAM)-based Computing-in-Memory (CIM) edge Convolutional Neural Network (CNN) accelerators. To provide a baseline for system-level design perspectives, an architectural framework for SRAM-CIM design concerning the key design points in state-of-the-art works is proposed. Furthermore, a configurable evaluation model featuring top-down design flow based on the proposed framework is established to investigate design space explorations. Case studies validated the framework and evaluation model using LeNet-5, AlexNet and VGG-16 to achieve energy-aware optimizations. The optimized memory scale for LeNet-5 is "16 PEs and 120 tiles" with the minimal estimated inference energy of 0.0018J, while for AlexNet and VGG-16, "16 PEs and 120 tiles" is better achieving minimal energy consumption of 0.1733mJ and 0.6825mJ respectively. Estimation results highlight tradeoffs among data represent- tation parameters and memory partitioning parameters. This work provides specific SRAM-CIM design guidelines from a system-level perspective.
Optimization of Loss Function on Human Faces Using Generative Adversarial Networks

Every human has a unique face that may vary with a wide variety of details from person to person. But, in twins, these differences are very less to be noticed but much significance. Hence, this raises a problem in creating digital faces of humans with all distinguishable features to that of a real human. Also, identify the faces that are not completely visible in a picture due to the distorted image or wrong angle of the face. To resolve this problem, in this research, we are building a model that can generate human after training of the models. For these face generation, we have used deep convoluted generative adversarial networks (DCGANs). This model is trained using the images available in the form OFA dataset that is being provided to the model. The images in the dataset are passed through a series of modifications called preprocessing. Next, our model is divided into three segments called generator, discriminator, and loss function optimization. These segments are trained individually and together form our complete model. The generator, generating the image of the human face and later, these images are provided to the discriminator. The discriminator that is also trained using the dataset starts discriminating against the image generated by the generator. The results are of generator, and discriminator is improvised using improvement analysis called loss function. Finally, and based on this result analysis, the generator is going to improve in its next epoch using the dataset image. As the loss values are noted for generator and discriminator, the values go as 1.1314, 0.8530, 0.8263, 0.8010 for the generator and 1.0013, 1.1288, 1.3122, 1.3111 for the discriminator. This shows a good loss reduction in generated images by a generator which means the enhancement in features of faces. The loss values increase in discriminator as it fails to discriminate the image. In the final stage of output, the generated face image is so real to be discriminated between fake and realistic face image. This model provides a solution to many existing problems of face recognition. This model can be used to identify people even if their face is partially visible on the screen by generating a clear image by a few modifications.
Eye Pupil Controlled Transport Riding Wheelchair

A control system of the transport riding wheelchair has been illustrated in this paper. To support traumatized individuals in a sticky situation a navigating electric wheelchair is needed. In this paper eye-ball controlled wheelchair has been suggested to mitigate the dependency of disable person from others. Based on the movement of eye-ball the wheelchair has been controlled. A camera has been spotted in the goggles, to detect the location of the eye pupil and acquire the eye image by computer vision methods. The recommend control system can welfare for paralyzed individuals to refine their existence. In this paper hardware and software, architecture has been described. To detect eye pupil location the Open computer vision (OpenCV) library has been used. The main control mechanism of this whole system has been done by a raspberry pi.
GEAC: Generating and Evaluating Handwritten Arabic Characters Using Generative Adversarial Networks

Generative Adversarial Network (GAN) has made a breakthrough and great success in many research areas in computer vision. Different GANs generate different outputs. In this research work, we apply different GANs to generate handwritten Arabic characters. A basic GAN, Vanilla GAN, Deep Convolutional GAN (DCGAN), Bidirectional GAN (BiGAN), and Wasserstein GAN (WGAN) are used. Then, the results of the generated images are evaluated using native-Arabic human and Fréchet Inception Distance (FID). The qualitative and quantitative results are provided for the images generation and evaluation. In experimental evaluation, WGAN achieves better results in FID with a value of 96.007. On the other hand, DCGAN achieves better results in native-Arabic human evaluation with a value of 35%.
DualPathGAN: Facial reenacted emotion synthesis

Facial reenactment has developed rapidly in recent years, but few methods have been built upon reenacted face in videos. Facial-reenacted emotion synthesis can make the process of facial reenactment more practical. A facial-reenacted emotion synthesis method is proposed that includes a dual-path generative adversarial network (GAN) for emotion synthesis and a residual-mask network to impose structural restrictions to preserve the mouth shape of the source person. To train the dual-path GAN more effectively, a learning strategy based on separated discriminators is proposed. The method is trained and tested on a very challenging imbalanced dataset to evaluate the ability to deal with complex practical scenarios. Compared with general emotion synthesis methods, the proposed method can generate more realistic facial emotion synthesised images or videos with higher quality while retaining the expression contents of the original videos. The DualPathGAN achieves a Fréchet inception distance (FID) score of 9.20, which is lower than the FID score of 11.37 achieved with state-of-the-art methods.
Evaluation of characteristics of wireless sensor networks with analytical modeling

In this paper, a mathematical model of the operation process of the ZigBee standard wireless network for remote monitoring systems based on the Markov chain apparatus was developed. Using the model was evaluated of the operation process of the CSMA/CA algorithm of the MAC level of the IEEE 802.15.4 ZigBee standard. The peculiarity of this mathematical model is that it takes into account the level of loading of network elements and potential distortions in the transmitted packets as a result of the influence of interference. Using the developed mathematical model, the main characteristics of the network operation process, such as dependence of the probability of successful transmission of packets on the system load (on the number of nodes), the dependence of the bandwidth on system load (on the number of nodes) for the case where there is noise in the channel and for the absence of noise in the channel were obtained.
A new self-organizing map based algorithm for multi-label stream classification

Several algorithms have been proposed for offline multi-label classification. However, applications in areas such as traffic monitoring, social networks, and sensors produce data continuously, the so called data streams, posing challenges to batch multi-label learning. With the lack of stationarity in the distribution of data streams, new algorithms are needed to online adapt to such changes (concept drift). Also, in realistic applications, changes occur in scenarios with infinitely delayed labels, where the true classes of the arrival instances are never available. We propose an online unsupervised incremental method based on self-organizing maps for multi-label stream classification in scenarios with infinitely delayed labels. We consider the existence of an initial set of labeled instances to train a self-organizing map for each label. The learned models are then used and adapted in an evolving stream to classify new instances, considering that their classes will never be available. We adapt to incremental concept drifts by online updating the weight vectors of winner neurons and the dataset label cardinality. Predictions are obtained using the Bayes rule and the outputs of each neuron, adapting the prior probabilities and conditional probabilities of the classes in the stream. Experiments using synthetic and real datasets show that our method is highly competitive with several ones from the literature, in both stationary and concept drift scenarios.
Unsupervised Anomaly Detection for Discrete Sequence Healthcare Data

Fraud in healthcare is widespread, as doctors could prescribe unnecessary treatments to increase bills. Insurance companies want to detect these anomalous fraudulent bills and reduce their losses. Traditional fraud detection methods use expert rules and manual data processing. Recently, machine learning techniques automate this process, but hand-labeled data is extremely costly and usually out of date. We propose a machine learning model that automates fraud detection in an unsupervised way. Two deep learning approaches include LSTM neural network for prediction next patient visit and a seq2seq model. For normalization of produced anomaly scores, we propose Empirical Distribution Function (EDF) approach. So, the algorithm works with high class imbalance problems.
Modelling children ever born using performance evaluation metrics: A dataset

Predicting the number of total children ever born in a country is a key component for proper implementation of economic growth policy. Here, performance metrics were used to predict models that appropriately describe the factors that affect children ever born. A comparison of 60% training and 40% validation, 70% training and 30% validation, 80% training and 20% validation also 90% training and 10% validation was performed respectively to examine the three models’ behaviours (Poisson regression, Negative Binomial regression and Generalized Poisson regression) with RMSE, R2, MAE and MSE as performance metrics. Although all the three models had almost identical performance evaluation metrics, the Poisson regression was chosen as the most appropriate model because it is the simplest model.
An Ontological Model for Scientific Miracle in the Holy Quran

The Holy Quran is a cyclopedia document that contains a huge volume of knowledge. Many modern scientific discoveries are much similar to some verses of the Quran, which the Islamic scientists call a scientific miracle in the Holy Quran. However, representing knowledge of scientific miracles in the Holy Quran on the semantic web in a such way that enables sharing and reusing is still a research issue. This research suggests a general structure for these scientific facts mentioned in the Holy Quran and then represents this structure by creating Scientific Miracle Ontology (SMO) using METHONTOLOGY methodology. The results of SMO have been evaluated by competency questions and translated these competency questions into SPARQL queries and the results obtained emphasized that SMO was effective in retrieval relevant concepts and verses of scientific miracles in the Holy Quran.
Inspection of Histological 3D Reconstructions in Virtual Reality

3D reconstruction is a challenging current topic in medical research. We perform 3D reconstructions from serial sections stained by immunohistological methods. This paper presents an immersive visualization solution to quality control (QC), inspect, and analyze such reconstructions. QC is essential to establish correct digital processing methodologies. Visual analytics, such as annotation placement, mesh painting, and classification utility, facilitates medical research insights. We propose a visualization in virtual reality (VR) for these purposes. In this manner, we advance the microanatomical research of human bone marrow and spleen. Both 3D reconstructions and original data are available in VR. Data inspection is streamlined by subtle implementation details and general immersion in VR.
Application of 3D tracking and registration in exhibition hall navigation interaction

Augmented reality technology provides new development ideas for the visualization of data information, and at the same time provides new development space for the convergence of digital visualization technology and multiple industries. The traditional exhibition hall visit mode is single and limited by region and the number of visitors. In response to such problems, this article develops a handheld mobile terminal AR exhibition hall navigation application based on augmented reality technology. The article mainly elaborates on the principle of 3D tracking registration technology and its realization method in the development of exhibition hall navigation interactive application development. The 3D model scene of the exhibition hall is constructed based on the environmental data of the real scene, and Vuforia is used for 3D tracking registration and registration of the real scene. Target positioning, use augmented reality development technology to create system navigation interactive function modules, break the barriers of time and space, achieve the superposition and integration of virtual and reality, complete application development and test analysis. The test results show that the AR exhibition hall navigation application developed by using 3D tracking registration technology has good interactivity and ease of use, and can provide new solutions for the future exhibition and navigation of the smart cultural tourism industry.
Automating Key Phrase Extraction from Fault Logs to Support Post-Inspection Repair of Software Requirements

This research paper aims at developing an automated approach to identify fault prone requirements in a software requirement specification (SRS) document to mitigate the fault propagation to later phases where the same faults are harder to find and fix. This research work proposes an automated approach (i.e., KESRI) for the identification of “problematic areas” (i.e., faulty requirements) from fault logs generated during inspections. Our automated approach uses machine learning-based key phrase extraction (KPE) algorithms (both supervised and unsupervised) that can extract key phrases from fault logs and map them to an SRS document (using semantic analysis) to locate faulty requirements. To validate our proposed approach, an inspection study conducted at North Dakota State University (NDSU) with 41 inspectors using an industrial-strength SRS document that resulted in fault logs. When compared against human experts, our approach achieved F-measure of up to 83% in extracting the relevant key phrases using supervised KPE algorithms. In conclusion, our automated KPE and mapping approach has the potential to reduce manual overhead and assist authors during the fault-fixation post-inspection.
An approach to detect offence in Memes using Natural Language Processing(NLP) and Deep learning

Social media is one of the most popular form of platform which is most common with people of our age. With passage of time, memes have gained a significant popularity and are often shared on social media platforms. Memes usually have hilarious content but can be offensive sometimes, containing some hateful message or character image. Such memes may have detrimental social impact in our society or to any individual. [1] Thus, an automated system for evaluation of offensiveness in the meme content is required. This paper presents an approach to detect offense in memes using Natural Language Processing (NLP) and deep learning. Due to the increasing number of memes over the internet, it is not an easy task to evaluate each meme before it spreads all around. This raises a demand for a system that can automate the process of evaluating memes before they agitate a crowd or spread a humour. This paper presents a model to detect offensive memes, in three steps. First, it will extract the text from the given image, then it will classify the given text as offensive or not offensive. If the text is found to be offensive then in the third step it will further classify offensive text in three categories namely slight offensive, very offensive and hateful offensive. The dataset used for this work consists of 6,992 memes which were labeled as not offensive, slightly offensive, very offensive, and hateful offensive. The model uses very simple architecture with a multi-layer dense network structure involving NLP with RNN and LSTM along with word embeddings such as GloVe and FastText.