Reviewerly/exHarmony huggingface dataset:
Effective Post-Training Quantization Of Neural Networks For Inference on Low Power Neural Accelerator

Neural network deployment to the target environment is considered a challenging task especially because of heavy burden of hardware requirements that DNN models lay on computation capabilities and power consumption. In case of low power edge devices, such as GNA - neural coprocessor, quantization becomes the only way to make the deployment possible. This paper draws attention to the post-training quantization for low-power devices and proves that this approach is practically effective. We propose a novel quantization algorithm capable of reducing DNNs precision to 16-bit or 8-bit integer with negligible drop in accuracy (less than 0.1 percent). The elaborated approach is demonstrated on a set of speech recognition networks trained in Kaldi framework with OpenVINO framework as an inference backend that supports quantization and GNA as a target. Quantization influence on original topologies was rigorously measured and analyzed.
Adversarial Dataset Augmentation Using Reinforcement Learning and 3D Modeling

An extensive and diverse dataset is a crucial requirement for the successful training of a deep neural network. Compared to on-site data collection, 3D modeling allows to generate large datasets faster and cheaper. Still, the diversity and perceptual realism of synthetic images remain in the realm of a 3D artist’s experience. Moreover, hard sample mining with 3D modeling poses an open question: which synthetic images are challenging for an object detection model? We present an Adversarial 3D modeling framework for training an object detection model against a reinforcement learning-based adversarial controller. The controller alters the 3D simulator parameters to generate complex synthetic images. The controller aims to minimize the score of the object detection model during the training time. We hypothesize that such an objective of the controller allows to maximize the score of the detection model during inference on real-world data. We evaluate our approach by training a YOLOv3 object detection model using our adversarial framework. A comparison with a similar model trained on random synthetic and real images proves that our framework allows us to achieve better performance than using random real or synthetic data.
Dynamics of an atomic Bose–Einstein condensate interacting with nonlinear quantized field under the influence of Stark effect

In this paper we propose a generalized f-deformed Dicke model for the dynamics of an atomic Bose–Einstein condensate (BEC) in the presence of collisional interactions and Stark effect. The BEC contains N two-level atoms and the nonlinear characteristics of quantized field originates from the f-deformation of the associated raising and lowering operators. After solving the time dependent Schrödinger equation corresponding to the model Hamiltonian, we investigate the dynamics of the system under various physical situations; in particular, two different initial states for the quantized field i.e., coherent and squeezed field. In this regard, at first, we obtain some analytical expressions correspond to the energy transfer and non-classical properties of the constituents of system and then proceed to analyze their dynamics, numerically. Our results show that the dynamical evolution of system can be governed by tuning the parameters describing the considered interactions. In particular, the energy levels can be controlled and shifted by changing the Stark effect. Collapse–revival phenomenon occurs in the patterns of atomic inversion, specially in the case of initial coherent state. However, with initial squeezed field typical revivals accompanied with short time collapses may be observed; moreover, the general behavior of atomic inversion shows irregular oscillatory manner. Short time squeezing takes place and can be exchanged between the field quadratures in the absence or presence of Stark effect. Also, the amount of entanglement depends on the nonlinearities which specify the interactions among the atoms in BEC and photons in the quantized field. Interestingly, the general pattern of Von Neumann entropy as a measure of entanglement is independent of the chosen initial states. However, the amplitude of entropy reduces in the case of squeezed state which means that the system possesses more separable states in this condition. In addition, the time evolution of Bloch sphere confirms irregular behavior of entanglement in the system.
FGRec: A Fine-Grained Point-of-Interest Recommendation Framework by Capturing Intrinsic Influences

Point-of-interest (POI) recommendation has become an important service to help users discover attractive locations. A variety of available check-in data make it possible to build a personalized POI recommender system, but the extreme sparsity of check-in data poses a severe challenge for POI recommendation. Recent studies mainly utilize social information, categorical information and/or geographical information to supplement the highly sparse check-in data. However, these studies often apply shallow methods for the extra information and provide considerably limited improvements on POI recommendation. In this paper, we propose a fine-grained POI recommendation framework, called FGRec to capture the intrinsic influences of social, categorical and geographical information on the check-in behaviors of users. First, we study the social influence in depth by exploiting the multi-hop social friends and top-n nearest neighbor friends, not only the direct friends (i.e., 1-hop friends). Second, we investigate the categorical influence by factorizing both user-POI and user-category matrices simultaneously over the same user embedding space, rather than simply using the popularity of POI categories. Third, we explore the geographical influence by integrating two types of distance (i.e., the distance between user homes and POIs and the distance among POIs) into a unified probability distribution over check-in POIs, instead of modeling them separately. Finally, experimental results on two large-scale real-world datasets demonstrate the effectiveness and superiority of the proposed method.
Universal Adversarial Perturbations in Epileptic Seizure Detection

Adversarial examples have received a lot of attention over the past decade, particularly with the rise of deep neural networks. Adversarial manipulation of sensitive health-related information, e.g., if such information is used for prescribing medicine, may have irreversible consequences, involving patients' lives. In this article, we consider adversarial perturbations in the context of medical and health applications and focus on the epileptic seizure detection problem. We formulate an optimization problem for computing universal adversarial perturbations and show that such universal perturbations may be used to declare the majority of seizure samples as non-seizure, i.e., to fool the classification algorithm, while being imperceptible to the medical expert eye.
Quantifying Uncertainty in Neural Network Ensembles using U-Statistics

Quantifying uncertainty is critically important to many applications of predictive modeling. In this paper we apply a recently developed method that uses U-statistics as a basis for estimating uncertainty in ensemble regressors to the case of neural network ensembles. U-statistics generalize the notion of a sample mean and provide distributional properties to estimates obtained by ensembles of estimators. With this method, we train neural networks on subsamples of the data and use the resulting ensemble to estimate the variance of the point estimates from the ensemble. We demonstrate that neural networks predicting a regression function exhibit the required theoretical properties for use in this ensemble method, and we then perform a coverage probability study of three simulated data sets to show that the empirical coverage probabilities match the theoretical values.
Hrinet: Alternative Supervision Network For High-Resolution Ct Image Interpolation

Image interpolation in the medical area is of high importance as most 3D biomedical volume images are sampled where the distance between consecutive slices is significantly greater than the in-plane pixel size due to radiation dose or scanning time. Image interpolation creates a number of new slices between known slices in order to obtain an isotropic volume image. The results can be used for the higher quality of 3D reconstruction and visualization of human body structures. Semantic interpolation on the manifold has been proved to be very useful for smoothing image interpolation. Nevertheless, all previous methods focused on low-resolution image interpolation, and most of them work poorly on high-resolution images. We propose a novel network, High Resolution Interpolation Network (HRINet), aiming at producing high quality and realistic CT image interpolations. We combine the idea of ACAI and GANs, and propose a novel idea of alternative supervision method by applying supervised and unsupervised training alternatively to raise the accuracy of human organ structures in CT while keeping high quality. We compare an MSE based and a perceptual based loss optimizing methods for high quality interpolation, and show the tradeoff between the structural correctness and sharpness. Our experiments show the great improvement on 25 6 and 512 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> images quantitatively and qualitatively.
Computational Complexity of Nurimisaki and Sashigane

Nurimisaki and Sashigane are Nikoli's pencil puzzles. We study the computational complexity of Nurimisaki and Sashigane puzzles. It is shown that deciding whether a given instance of each puzzle has a solution is NP-complete.
Multi-Agent Pattern Formation: a Distributed Model-Free Deep Reinforcement Learning Approach

In this paper, we investigate how a large-scale system of independently learning agents can collectively form acceptable two-dimensional patterns (pattern formation) from any initial configuration. We propose a decentralized multi-agent deep reinforcement learning architecture MAPF-DQN (Multi-Agent Pattern Formation DQN) in which a set of independent and distributed agents capture their local visual field and learn how to act so as to collectively form target shapes. Agents exploit their individual networks with a central replay memory and target networks that are used to store and update the representation of the environment as well as learning the dynamics of the other agents. We then show that agents trained on random patterns using MAPF-DQN can organize themselves into very complex shapes in large-scale environments. Our results suggest that the proposed framework achieves zero-shot generalization on most of the environments independently of the depth of view of agents.
On the contaminated exponential distribution: A theoretical Bayesian approach for modeling positive-valued insurance claim data with outliers

Analysis of the insurance data has recently been achieved considerable attention for insurance industries. This paper introduces the contaminated exponential (CE) distribution as an alternative platform for analyzing positive-valued insurance dataset with some levels of outliers. The Bayesian approach for obtaining the parameter estimates is presented. In order to check the performance of the proposed methodology, some simulation studies by implementing the Gibbs sampling are conducted. Finally, four examples of actual insurance claim data with various sample sizes have been analyzed to illustrate the superiority of the CE distribution in analyzing data and identifying outliers.
TestRoutes

High test-to-code traceability can be an important aspect of quality assurance and can contribute to bug localization and code maintenance. Several existing techniques and a considerable effort from the scientific community already made significant advances in the field. Despite this, readily accessible data on traceability links is very scarce. To contribute to related research, we present a manually curated test-to-code traceability dataset containing the traceability information on 220 test cases. This method-level data was gathered from 4 open-source software systems written in the Java language, distinguishing not only focal information on test cases but also highlighting the utilized helper methods on both the test and production aspects of code. The data includes more than 2000 of such method classifications.
A Curriculum Learning Based Approach to Captioning Ultrasound Images

We present a novel curriculum learning approach to train a natural language processing (NLP) based fetal ultrasound image captioning model. Datasets containing medical images and corresponding textual descriptions are relatively rare and hence, smaller-sized when compared to the datasets of natural images and their captions. This fact inspired us to develop an approach to train a captioning model suitable for small-sized medical data. Our datasets are prepared using real-world ultrasound video along with synchronised and transcribed sonographer speech recordings. We propose a “dual-curriculum” method for the ultrasound image captioning problem. The method relies on building and learning from curricula of image and text information for the ultrasound image captioning problem. We compare several distance measures for creating the dual curriculum and observe the best performance using the Wasserstein distance for image information and tf-idf metric for text information. The evaluation results show an improvement in all performance metrics when using curriculum learning over stochastic mini-batch training for the individual task of image classification as well as using a dual curriculum for image captioning.
Local Policy Optimization for Trajectory-Centric Reinforcement Learning

The goal of this paper is to present a method for simultaneous trajectory and local stabilizing policy optimization to generate local policies for trajectory-centric model-based reinforcement learning (MBRL). This is motivated by the fact that global policy optimization for non-linear systems could be a very challenging problem both algorithmically and numerically. However, a lot of robotic manipulation tasks are trajectory-centric, and thus do not require a global model or policy. Due to inaccuracies in the learned model estimates, an open-loop trajectory optimization process mostly results in very poor performance when used on the real system. Motivated by these problems, we try to formulate the problem of trajectory optimization and local policy synthesis as a single optimization problem. It is then solved simultaneously as an instance of nonlinear programming. We provide some results for analysis as well as achieved performance of the proposed technique under some simplifying assumptions.
On the Expressive Power of Linear Algebra on Graphs

There is a long tradition in understanding graphs by investigating their adjacency matrices by means of linear algebra. Similarly, logic-based graph query languages are commonly used to explore graph properties. In this paper, we bridge these two approaches by regarding linear algebra as a graph query language. More specifically, we consider MATLANG, a matrix query language recently introduced, in which some basic linear algebra functionality is supported. We investigate the problem of characterising the equivalence of graphs, represented by their adjacency matrices, for various fragments of MATLANG. That is, we are interested in understanding when two graphs cannot be distinguished by posing queries in MATLANG on their adjacency matrices. Surprisingly, a complete picture can be painted of the impact of each of the linear algebra operations supported in MATLANG on their ability to distinguish graphs. Interestingly, these characterisations can often be phrased in terms of spectral and combinatorial properties of graphs. Furthermore, we also establish links to logical equivalence of graphs. In particular, we show that MATLANG-equivalence of graphs corresponds to equivalence by means of sentences in the three-variable fragment of first-order logic with counting. Equivalence with regards to a smaller MATLANG fragment is shown to correspond to equivalence by means of sentences in the two-variable fragment of this logic.
Defining and Applying Information Security Goals for Blockchain Technology

This article defines and exemplifies the primary information security goals when organisations use, operate or develop blockchain technology solutions. The proposed goals extend the well-known CIA-triad Confidentiality, integrity, Availability) to account for the increased complexity in securing software solutions and organisations. The understanding of these goals can help information security practitioners design and implement more effective security controls for protecting blockchain solutions and organisations. Non-security professionals in the blockchain field can also benefit from the concepts in this article, as security is a core component in all blockchains.
Learning Disentangled Feature Representations For Anomaly Detection

Anomaly detection is a challenging task that requires identifying anomalous data by observing only normal data during training. Previous works typically approached this problem by assessing data recovery with properly selected thresholds. However, the performance would be affected by content variants or background clutter. Hence, in this paper, we propose a novel deep learning based method, which learns disentangled feature representations for separating semantic and visual appearance information, so that the anomaly of the input data can be determined based on its semantic features. Our qualitative results demonstrate the feasibility of the feature disentanglement, and the quantitative experiments confirm that our method outperforms other methods.
Text Summarization and Classification of Conversation Data between Service Chatbot and Customer

In any business, a humongous amount of data is generated within each fraction of a second by each and every software application. However, processing this voluminous data is a tedious task especially when the data is in textual form. In this scenario, Natural Language Processing has contributed majorly in this area of study. In NLP, keyword extraction plays a pivotal role in text processing which helps the readers to determine whether to read a document or a webpage. The system designed in this paper computed extractive text summarization using Graph-based technique and TextRank algorithm on conversation data between the user and the service chatbots which in fact an offline conversation. This summary is then consumed by a classification module which is trained using Naive Bayes classifier to evaluate in which of the three categories the conversation falls into: 1. Help 2.Complaint 3. FakeCustomer. This system can be utilized by various companies such as online shopping websites, software companies to determine in which aspect immediate attention is required. The system is also experimented using different thresholds for determining the length of summary produced and its corresponding accuracy.
Artificial Intelligence and Virtual Assistant—Working Model

In twenty-first-century virtual assistant is playing a very crucial role in day to day activities of human. According to the survey report of Clutch in 2019, 27% of the people are using the AI-powered virtual assistant such as: Google Assistant, Amazon Alexa, Cortana, Apple Siri, etc., for performing a simple task, people are using virtual assistant designed with natural language processing. In this research paper, we have studied and analyzed the working model and the efficiency of different virtual assistants available in the market. We also designed an intelligent virtual assistant that could be integrated with Google virtual services and work with the Google virtual assistant interface. A comparative analysis of the traffic and message communication with length of conversation for approximately three days is taken as input to calculate the efficiency of the designed virtual assistant.
Content enhancement with augmented reality and machine learning

Content enhancement of real-world environments is demonstrated through the combination of machine learning methods with augmented reality displays. Advances in machine learning methods and neural network architectures have facilitated fast and accurate object and image detection, recognition and classification, as well as providing machine translation, natural language processing and neural network approaches for environmental forecasting and prediction. These methods equip computers with a means of interpreting the natural environment. Augmented reality is the embedding of computer-generated assets within the real-world environment. Here I demonstrate, through the development of four sample mobile applications, how machine learning and augmented reality may be combined to create localised, context aware and user-centric environmental information delivery channels. The sample mobile applications demonstrate augmented reality content enhancement of static real-world objects to deliver additional environmental and contextual information, language translation to facilitate accessibility of forecast information and a location aware rain event augmented reality notification application that leverages a nowcasting neural network.
Modeling Long-Term and Short-Term Interests with Parallel Attentions for Session-Based Recommendation

The aim of session-based recommendation is to predict the users’ next clicked item, which is a challenging task due to the inherent uncertainty in user behaviors and anonymous implicit feedback information. A powerful session-based recommender can typically explore the users’ evolving interests (i.e., a combination of his/her long-term and short-term interests). Recent advances in attention mechanisms have led to state-of-the-art methods for solving this task. However, there are two main drawbacks. First, most of the attention-based methods only simply utilize the last clicked item to represent the user’s short-term interest ignoring the temporal information and behavior context, which may fail to capture the recent preference of users comprehensively. Second, current studies typically think long-term and short-term interests as equally important, but the importance of them should be user-specific. Therefore, we propose a novel Parallel Attention Network model (PAN) for Session-based Recommendation. Specifically, we propose a novel time-aware attention mechanism to learn user’s short-term interest by taking into account the contextual information and temporal signals simultaneously. Besides,we introduce a gated fusion method that adaptively integrates the user’s long-term and short-term preferences to generate the hybrid interest representation. Experiments on the three real-world datasets show that PAN achieves obvious improvements than the state-of-the-art methods.
Optimization learning of hidden Markov model using the bacterial foraging optimization algorithm for speech recognition

Nowadays, the speech recognition applications can be found in several activities, and their existence as a field of study and research lasts for a long time. Although, many studies deal with different problems, in security-related areas, biometric identification, access to the Smartphone… Etc. In automatic speech recognition (ASR) systems, hidden Markov models (HMMs) have widely used for modeling the temporal speech signal. In order to optimize HMM parameters (i.e., observation and transition probabilities), iterative algorithms commonly used such as Forward-Backward or Baum-Welch. In this article, we propose to use the bacterial foraging optimization algorithm (BFOA) to enhance HMM with Gaussian mixture densities. As a global optimization algorithm of current interest, BFOA has proven itself for distributed optimization and control. Our experimental results show that the proposed approach yields a significant improvement of the transcription accuracy at signal/noise ratios greater than 15 dB.