Reviewerly/exHarmony huggingface dataset:
Three-dimensional multiscale discrete Radon and John transforms

Two algorithms are introduced for the computation of discrete integral transforms with a multiscale approach operating in discrete three-dimensional (3-D) volumes while considering its real-time implementation. The first algorithm, referred to as 3-D discrete Radon transform of planes, will compute the summation set of values lying in discrete planes in a cube that imitates, in discrete data, the integrals on two-dimensional planes in a 3-D volume similar to the continuous Radon transform. The normals of these planes, equispaced in ascents, cover a quadrilateralized hemisphere and comprise 12 dodecants. The second proposed algorithm, referred to as the 3-D discrete John transform of lines, will sum elements lying on discrete 3-D lines while imitating the behavior of the John or x-ray continuous transform on 3-D volumes. These discrete integral transforms do not perform interpolation on input or intermediate data, and they can be computed using only integer arithmetic with linearithmic complexity, thus outperforming the methods based on the Fourier slice-projection theorem for real-time applications. We briefly prove that these transforms have fast inversion algorithms that are exact for discrete inputs.
Multi-Agent Logic’s Modelling Non-Monotonic Information and Reasoning

We study multi-agent logic modelling non-monotonic reasoning and taking decisions. Non-monotonicity is reflected by means of non-transitive agents’ accessibility relations. The semantics is based at relational models for modelling computational processes and analysis of databases with incomplete information, for instance, with information forgotten in the past. Besides, the agents’ accessibility relations may have lacunas; agents may have no access to some potentially known and stored information. Satisfiability and decidability issues are discussed. We find algorithms solving satisfiability problem. Illustrating examples are provided.
Hand Gesture Recognition for Non-Contact Control of a Technical System

The article proposes a method and algorithm for determining the hand gesture on a video sequence from only one optical camera. An example of gesture recognition is considered: palm clenching into a fist followed by unclenching of a fist working in real time on Intel Atom and ARM architectures. The method features are low requirements for the resources of a computing device and the possibility of using several gestures for analysis at the same time. At the same time, the method reliably detects gestures against a complex background, including when the camera vibrates, the presence of moving objects in the frame, when the palm covers part of the face, and also when the gesture is performed with a glove. The gesture determination algorithm is based on an analysis of the statistical characteristics of objects in frames. Objects are clusters consisting of closely spaced image areas in several consecutive frames of a video sequence in which motion was detected. The analysis of changes in the characteristics of the cluster reveals events related to the behavior of an object (hand) recorded by the camera. The approach based on the analysis of cluster characteristics allows implementing the embeded algorithm on real-time ARM computers. To increase productivity, the authors propose their own clustering algorithm. And also, an example of applying the implementation algorithm for a gesture of squeezing the palm of a hand into a fist followed by unclenching of a fist.
How to Keep an Online Learning Chatbot From Being Corrupted

Online learning can improve chatbots' conversational abilities. Although the online learning method has enhanced the diversity of chatbots' statements, it also brings opportunities for corruption. The chatbot may be corrupted to generate offensive responses such as racist and hate speech. The key component to keeping chatbots from being corrupted is offensive-response detection. Until now, the training datasets for offensive detection have focused only on individual response sentences, disregarding user input sentences. In this paper, we introduce a dialogue-based offensive-response dataset, which consists of 110K input-response chat records. The dataset fills the gap in response detection for chatbots. Then, we build two challenging tasks based on the dataset: an offensive-response detection task and a corrupted chatbot purification task. In addition, we propose a strong benchmark method for the tasks: an encoder-classifier model to detect input-response pairs and a one-shot reinforcement learning (RL) method to reduce rapidly the probability of generating offensive responses.
A situation-modulated minimal change account for causal inferences about causal networks

Although causal Bayes networks are applicable to examining causal inferences about different static objects and about a changing object with different states, previous studies investigated the former, but not the latter. We propose a situation-modulated minimal change account for causal inferences. It predicts that dynamic situations are more likely to elicit minimal revisions on causal networks and adherence to the Markov assumption than static situations. Two experiments were conducted to investigate qualitative causal inferences about causal networks with binary and numerical variables, respectively. It was found that qualitative causal inferences were more likely to adhere to the Markov assumption in dynamic situations than in static situations. This finding supports the situation-modulated minimal change account rather than the other alternative accounts. We conclude that dynamic situations are more likely to elicit minimal revisions on causal networks and adherence to the Markov assumption than static situations. This conclusion is beyond the previous predominant view that causal inferences are apt to violate the Markov assumption.
Double Attention for Pathology Image Diagnosis Network with Visual Interpretability

In recent years, cervical cancer has been one of the most common diseases in women's cancer. The advanced diagnosis of cervical precancerous lesions is essential for preventing cervical cancer. Its effectiveness and efficiency can be greatly improved by computer aided diagnosis, while challenged by the imprecise conclusions and uninterpretable process of diagnosis. To solve this problem, we propose a novel deep learning-based interpretable diagnosis system for pathology images, consisting of three interrelated models: an image model, an attention model and a conclusion model.Computer aided diagnosis improves the effectiveness and efficiency of the proposed image model uses a convolutional neural network (CNN) to ex-tract semantic features. Combining the model with the semantic attribute attention model, it aims to capture the discriminant relationship between se-mantic attributes by predicting the conclusion label through long-term and short-term memory (LSTM). The network is trained in an end-to-end manner, with different weights for each model. Experimental results on cervical intraepithelial neoplasia images, diagnostic reports and label datasets show that the proposed method achieves a significant improvement over traditional methods with a better interpretability.
Total Solar Irradiance Forecasting with Keras Recurrent Neural Networks

The prediction of solar irradiance at the top of the atmosphere is useful for research that analyzes the behavior and response of the different layers of the Earth’s atmosphere to variations in solar activity. It would also be useful for the reconstruction of the measurement history (time series) of different instruments that suffered from time failures and discrepancies in scales due to the calibration of equipment. In this work we compare three Keras recurrent neural network architectures to perform forecast of the total solar irradiance. The experiments are part of a larger proposal for modularization of the prediction workflow, which uses digital images of the Sun as input, and aims to make the process modular, accessible and reproducible.
TeMoCo-Doc

A common task in a number of application areas is to create textual documents based on recorded audio data. Visualizations designed to support such tasks require linking temporal audio data with contextual data contained in the resulting documents. In this paper, we present a tool for the visualization of temporal and contextual links between recorded dialogues and their summary documents.
A Calculation Method of the Similarity Between Trained Model and New Sample by using Gaussian Distribution

In many Human Robot Interaction scenarios, social robots are expected to communicate with human naturally. Especially, the skill of predicting human internal state is a field of great interest in many applications, including video surveillance, behavior analysis, human robot interaction, life-logging. While accurately predicting these technologies (e.g. emotion, confident for talking a topic) could have benefits for many fields, generic machine learning systems still yield low performance in some situation. We hypothesize that these sophisticated models suffer from individual differences of human's personality. Therefore, we proposed a multi characteristic model architecture which combines the personalized machine learning models and utilize each model's prediction score in the inference. This architecture formed with reference to ensemble machine learning architecture. In this research, we focus on a similarity between new user and trained user model by using the idea of applicability domain of machine learning models. In the empirical result, we confirmed that data distribution (one way of checking applicability domain) of each user model correspond to the performance of models and we estimated confidence during communication as a human internal state.
Latent space decomposition into task-specific and domain-specific subspaces for domain adaptation

In this study, we propose a novel method of acquiring the latent space (composed of task-specific and domain-specific subspaces) of data. The layered neural networks acquire the effective features for a given task in its internal layers as a latent space. If the acquired latent space is relevant only to the task and invariant to the data domain, it can be adapted to a new domain. Therefore, our goal is to acquire the latent space in which all the features relevant to the task are contained in a task-specific subspace, whereas all the features relevant to the data domain are contained in a domain-specific subspace. The method iterates two simple training steps. First, a classifier acquires a task-specific space in its final layer through a conventional supervised learning. Whereas a domain-specific space is obtained as its complementary subspace in the complete latent space. The latter is realized by an autoencoder training using both subspaces. In the second step, two input data are used, and two task-specific representations and two domain-specific representations are exchanged to generate two composite data. Further training of the classifier using the composite data forces each subspace to be more specific to either the task or the domain. Computer experiments are conducted on three famous datasets of handwritten digits as three different domains. The visualization of the obtained task-specific space shows the clusters corresponding to the digit classes, whereas the domain-specific space shows the areas corresponding to the datasets. The domain adaptation to an unlearned dataset using the task-specific representation obtained by our method demonstrates a higher performance than an alternative method.
Tailoring TOGAF Architectural Development Method to Cloud Adoption Strategy

Actual results from Information Technology (IT) implementations do not always meet business expectations. Cloud computing, as one of the continuously growing IT technology, is also not an anomaly. It is perceived that flexibility and scalability offered by cloud services will easily address business demands. However, the suitability of cloud services to certain use cases requires a holistic cloud strategy. This motivates this literature research to propose a framework to facilitate a successful implementation of cloud solutions and to indicate which potential areas are for future research. There are two main perspectives served as the basis of this framework, i.e. technical perspective on Enterprise Architecture (EA) using TOGAF, and business perspective for cloud investment decision making. To develop this framework, we followed a design science approach and conducted a literature review on respective theories.
Mathematical Model for Prediction the Dynamics of Organic Traffic at E-commerce Web-site in the Process of its Search Engine optimization

The problem of prediction dynamics of organic traffic at e-commerce web-site located on Prom.ua marketplace is considered. To do this, in the paper the mathematical model in the form of a difference equation is proposed. This model provides the ability to predict web-site's organic traffic in the process of its search engine optimization (SEO). In the future, this will allow to predict and to plan the budgets needed for site development and to estimate the traffic growth.
GAN-based classifier protection against adversarial attacks

In recent years, deep neural networks have made significant progress in image classification, object detection and face recognition. However, they still have the problem of misclassification when facing adversarial examples. In order to address security issue and improve the robustness of the neural network, we propose a novel defense network based on generative adversarial network (GAN). The distribution of clean - and adversarial examples are matched to solve the mentioned problem. This guides the network to remove invisible noise accurately, and restore the adversarial example to a clean example to achieve the effect of defense. In addition, in order to maintain the classification accuracy of clean examples and improve the fidelity of neural network, we input clean examples into proposed network for denoising. Our method can effectively remove the noise of the adversarial examples, so that the denoised adversarial examples can be correctly classified. In this paper, extensive experiments are conducted on five benchmark datasets, namely MNIST, Fashion-MNIST, CIFAR10, CIFAR100 and ImageNet. Moreover, six mainstream attack methods are adopted to test the robustness of our defense method including FGSM, PGD, MIM, JSMA, CW and Deep-Fool. Results show that our method has strong defensive capabilities against the tested attack methods, which confirms the effectiveness of the proposed method.
A Performance Prediction Model for Spark Applications

Apache Spark is a popular open-source distributed processing framework that enables efficient processing of massive amounts of data. It has a large number of configuration parameters that are strongly related to performance. Spark performance, for a given application, can significantly vary because of input data type and size, design & implementation of algorithm, computational resources and parameter configuration. So, involvement of all these variables makes performance prediction very difficult. In this paper, we take into account all the variables and try to learn machine learning based performance prediction model. We ran extensive experiments on a selected set of Spark applications that cover the most common workloads to generate a representative dataset of execution time. In addition, we extracted application and data features to build a machine learning based performance model to predict Spark applications execution time. The experiments show that boosting algorithms achieved better results compared to the other algorithms.
Computer-Assisted Audit Techniques (CAATs) for Financial Fraud Detection: A Qualitative Approach

Fraud can harm financial statement, yet it is difficult to detect. The emergence of technological advancements is believed can help company to detect fraud. This research aimed to identify external auditor's perception of CAATs adoption for fraud detection and types of fraud which can be detected with CAATs. The data in this research was primary data collected by doing semi-structured interview to six external auditors from the Big Four and Non-Big Four public accounting firms. Interview data analyzed by conducting open and axial coding using NVIVO 11. The results indicated that adoption of CAATs improve the auditor's performance in identification of the transactions associated with fraud. In conclusion, the implementation of CAATs provides benefits in the audit process and plays an important role for fraud detection.
Signal characters and non-classical properties of quadratically amplified squeezed vacuum

Based on the squeezed vacuum (SV) and the quadratic function of the photon number operator, we introduce the quadratically amplified squeezed vacuum (QASV) in this paper. We study the intensity, noise, squeezing effect, antibunching effect, and Wigner function of the QASVs. Compared with the SV, the QASVs have distinctive signal characters and possess peculiar non-classical properties in the proper range of interaction parameters.
Learning User Preferences from Corrections on State Lattices

Enabling a broader range of users to efficiently deploy autonomous mobile robots requires intuitive frameworks for specifying a robot's task and behaviour. We present a novel approach using learning from corrections (LfC), where a user is iteratively presented with a solution to a motion planning problem. Users might have preferences about parts of a robot's environment that are suitable for robot traffic or that should be avoided as well as preferences on the control actions a robot can take. The robot is initially unaware of these preferences; thus, we ask the user to provide a correction to the presented path. We assume that the user evaluates paths based on environment and motion features. From a sequence of corrections we learn weights for these features, which are then considered by the motion planner, resulting in future paths that better fit the user's preferences. We prove completeness of our algorithm and demonstrate its performance in simulations. Thereby, we show that the learned preferences yield good results not only for a set of training tasks but also for test tasks, as well as for different types of user behaviour.
A*-Based Path Planning Algorithm for Swarm Robotics

Currently path planning for a swarm of mobile robots is a relevant problem in the domain of robotics. Various approaches to its solution exist. One of such approaches comprises different methods of informed sampling, which boost the search process through direction of search frontier towards the target. This paper presents combination of such method with the simplified representation of the operational environment, specifically, cellular decomposition. The proposed method ensures transition of a robot swarm into the predefined formation in such manner, that during robot motion along the planned paths no collisions occurred. The experimentation was performed with groups of 5, 10, 15, 20, 25 and 30 robots in three different scenes. Upon experimentation it was revealed, that for a swarm of 30 robots in a complex scene the path computing time does not exceed 7 s.
Flexible Multi-Precision Accelerator Design for Deep Convolutional Neural Networks Considering Both Data Computation and Communication

Due to the quick advance in deep convolutional neural networks (CNN), hardware acceleration of convolution computations for edge devices is crucial for many artificial intelligence applications. This paper presents a CNN accelerator design that supports various CNN filter sizes/strides and different bit precisions. In particular, we analyze the latency of data communication and computation and determine the proper precision that maximizes the utilization efficiency of available hardware resource. The proposed design supports data precision of 8-bit and 16-bit, and weight precision of 2-bit, 4-bit, 8-bit, and 16-bit for popular CNN models. It can effectively increase speed performance of low-precision computation by exploiting the additional parallelism.
DRAB-LOCUS: An Area-Efficient AES Architecture for Hardware Accelerator Co-Location on FPGAs

Advanced Encryption Standard (AES) implementations on Field Programmable Gate Arrays (FPGA) commonly focus on maximizing throughput at the cost of utilizing high volumes of FPGA slice logic. High resource usage limits systems' abilities to implement other functions (such as video processing or machine learning) that may want to share the same FPGA resources. In this paper, we address the shared resource challenge by proposing and evaluating a low-area, but high-throughput, AES architecture. In contrast to existing work, our DSP/RAM-Based Low-CLB Usage (DRAB-LOCUS) architecture leverages block RAM tiles and Digital Signal Processing (DSP) slices to implement the AES Sub Bytes, Mix Columns, and Add Round Key sub-round transformations, reducing resource usage by a factor of 3 over traditional approaches. To achieve area-efficiency, we built an inner-pipelined architecture using the internal registers of block RAM tiles and DSP slices. Our DRAB-LOCUS architecture features a 12-stage pipeline capable of producing 7.055 Gbps of interleaved encrypted or decrypted data, and only uses 909 Look Up tables, 593 Flip Flops, 16 block RAMs, and 18 DSP slices in the target device.
Projected Improved Fista And Application To Image Deblurring

The analysis-sparse model has been shown to be efficient as compared to the synthesis-sparse model when the sparsifying transform is redundant particularly for image restoration applications. We pose the image deblurring problem as an optimization problem based on the analysis-sparse model considering the sparsifying basis to be a tight frame, more specifically, the shift-invariant discrete wavelet transform (SIDWT). We propose two algorithms, namely, projected improved fast iterative soft-thresholding algorithm (piFISTA) and projected improved fast iterative soft-thresholding algorithm beyond Nesterov's momentum (piFISTA-BN). The proposed algorithms are the analysis counterparts of the improved fast iterative soft-thresholding algorithm (iFISTA) and improved fast iterative soft-thresholding algorithm beyond Nesterov's momentum (iFISTA-BN), respectively, both of which consider the synthesis-sparse model. We demonstrate that piFISTA and piFISTA-BN significantly outperform FISTA, pFISTA, iFISTA, and iFISTA-BN considering standard objective metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM). Further, we demonstrate empirically that the proposed algorithms converge faster than the state-of-the-art techniques.
Binarized Weight Neural-Network Inspired Ultra-Low Power Speech Recognition Processor with Time-Domain Based Digital-Analog Mixed Approximate Computing

In this paper, an ultra-low power speech recognition processor is implemented based on an optimized binarized weight neural-network (BWN). To accelerate the BWN and make it energy efficient, we proposed an approximate computing architecture for the quantized BWN based on time-domain digital-analog mixed addition unit and precision optimization with fault-tolerant training method. Experimental results show that the proposed digital-analog mixed approximate computing architecture can significantly reduce the power consumption while maintaining the recognition accuracy. Implemented under TSMC 28nm, the proposed processor can support 10 keywords real time recognition under different noise types and SNRs, while the power consumption is 56μW.