Reviewerly/exHarmony huggingface dataset:
Training Accelerator for Two Means Decision Tree

Decision trees (DTs) are profusely used in machine learning (ML) applications on account of their fast execution and high interpretability. As DT training is time-consuming, in this brief, we proposed a hardware training accelerator to speedup the training process. The proposed training accelerator is implemented on the field-programmable gate array (FPGA) having a maximum operating frequency of 62 MHz. The proposed architecture uses a combination of parallel execution for training time reduction and pipelined execution to minimize resource consumption. For a given design, the proposed hardware implementation is found to be at least 14× faster than the C-based software implementation. Moreover, the proposed architecture can be easily retrained for the next set of data using a single RESET signal. This on-the-go training makes the hardware versatile for any kind of application.
Deep learning based vehicle violation detection system

Due to the limited road resources and the ever-increasing number of vehicles and persons, more frequent traffic violations and higher management costs have resulted. It is crucial to propose a more intelligent and less cost scheme to solve the traffic management problem. In this paper, we design and implement a vehicle violation detection system based on deep learning, which includes the detection, tracking and recognition of vehicles. On this basis, the detection and real-time alarm of common violations, such as red light running and impolite pedestrian, are also supported. Compared with the traditional detection and monitoring based on physical equipment, our system is completely based on computer vision, where the cutting-edge achievements of deep learning have been improved and applied. The system is not only more intelligent, but also can reduce the cost to a greater extent. Experiments illustrate that the system can meet the needs of the intelligent management of urban traffic through real-time monitoring and data analysis of the traffic scenes.
Automatic generation of algorithms for robust optimisation problems using Grammar-Guided Genetic Programming

We develop algorithms capable of tackling robust black-box optimisation problems, where the number of model runs is limited. When a desired solution cannot be implemented exactly the aim is to find a robust one, where the worst case in an uncertainty neighbourhood around a solution still performs well. To investigate improved methods we employ an automatic generation of algorithms approach: Grammar-Guided Genetic Programming. We develop algorithmic building blocks in a Particle Swarm Optimisation framework, define the rules for constructing heuristics from these components, and evolve populations of search algorithms for robust problems. Our algorithmic building blocks combine elements of existing techniques and new features, resulting in the investigation of a novel heuristic solution space. We obtain algorithms which improve upon the current state of the art. We also analyse the component level breakdowns of the populations of algorithms developed against their performance, to identify high-performing heuristic components for robust problems.
Research on Artificial Intelligence Algorithm and Its Application in Games

With the in-depth development of intelligent technology, game artificial intelligence (AI) has become the technical core of improving the playability of a game and the main selling point of game promotion, deepening the game experience realm. Modern computer games achieve the realism of games by integrating graphics, physics and artificial intelligence. It is difficult to define the meaning of realistic game experience, but generally speaking, it usually refers to the immersion of the game and the intelligence of non-player characters in the game. As the technical core of improving game playability and the selling point of many commercial games, game artificial intelligence gives players a way to interact with non-player characters in the game, and promotes the realm of game experience to a higher level. Based on this, this paper analyzes the history and present situation of artificial intelligence in game development, and puts forward the possible changes and impacts of artificial intelligence technology based on machine learning on game development in the future.
RobusterNet: Improving Copy-Move Forgery Detection with Volterra-based Convolutions

Convolutional Neural Networks (CNNs) have recently been introduced for addressing copy-move forgery detection (CMFD). However, current CMFD CNN-based approaches have insufficient performance commitment regarding the localization of the positive class. In this paper, this issue is explored by considering both linear and nonlinear interactions between pixels. A nonlinear Inception module based on second-order Volterra kernels is proposed, in order to ameliorate the results of a state-of-the-art CMFD architecture. The outcome of this work shows that a combination of linear and nonlinear convolution kernels can make the input foreground and background pixels more separable. The proposed approach is evaluated on CASIA and CoMoFoD, two publicly available CMFD datasets, and results to an improved positive class localization performance. Moreover, the findings of the proposed method imply that the nonlinear Inception module stimulates immense robustness against miscellaneous post processing attacks.
Self-Supervised Depth Estimation Via Implicit Cues from Videos

In self-supervised monocular depth estimation, the depth discontinuity and motion objects' artifacts are still challenging problems. Existing self-supervised methods usually utilize two views to train the depth estimation network and use one single view to make predictions. Compared with static views, abundant dynamic properties between video frames are beneficial to refining depth estimation, especially for dynamic objects. In this work, we improve the self-supervised learning framework for depth estimation using consecutive frames from monocular and stereo videos. The main idea is to exploit an implicit depth cue extractor which leverages dynamic and static cues to generate useful depth proposals. These cues can predict distinguishable motion contours and geometric scene structures. Moreover, a new high-dimensional attention module is proposed to extract a clear global transformation, which effectively suppresses the uncertainty of local descriptors in high-dimensional space, resulting in a more reliable optimization in the learning framework. Experiments demonstrate that the proposed framework outperforms the state-of-the-art on KITTI and Make3D datasets.
Adaptive Image Compression Using GAN based Semantic-perceptual Residual Compensation

Image compression is a basic task in image processing. The existing methods always have problems such as the loss of image details and the reconstructed image does not conform to human vision. This paper presents an adaptive image compression algorithm that relies on GAN based semantic-perceptual residual compensation, which is available to offer visually pleasing reconstruction at a low bitrate. Our method derive from a U-shaped encoder-decoder structure accompanied by a well-designed dense residual connection with a strip pooling module to improve the original auto-encoder. Besides, we utilize the idea of adversarial learning by introducing a discriminator, thus constructed a complete GAN. To improve the coding efficiency, we creatively designed an adaptive semantic-perception residual compensation block based on the Grad-CAM algorithm. Through the strategy of adversarial learning, the reconstructed image is more towards the distribution of the real image, and further semantic perception can achieve higher quality compression of the region of interest from the human attention. Besides, we combine multiple existing quantitative methods, including the latest FLIF lossless compression algorithm, BPG vector compression algorithm and soft-quantization to perform deeper compression on the image. Experimental results, including PSNR, MS-SSIM demonstrate that the proposed approach outperforms the current state-of-the-art image compression methods.
Neuromancer Workshop: Towards Designing Experiential Entanglement with Science Fiction

The use of design fiction to speculate an imaginative and critical future has increasingly been recognized in the design research community. Instead of focusing on speculation with a critical position, this paper reports an experiential approach to entangling everyday experiences in the process of speculating. As science fiction has successfully provided fictional-world-building as entanglement material for speculation, we held a workshop to conduct an entanglement experiment of personal photographs with cyberpunk literature, Neuromancer. We built a card deck consisting of 206 quotes from the novel and invited 15 participants to shuffle, draw, and re-compose sentences that best matched their personal photographs. Purposefully selected everyday anchors and sci-fi features in the quotes allowed us to investigate the moment when an everyday photograph encountered a fictional world. We describe the phenomena of imagination and entanglement, explain experiential entanglement, propose a conceptual model for entangled status, and present interpretations and implications in HCI.
Unit Selection Synthesis Based Data Augmentation for Fixed Phrase Speaker Verification

Data augmentation is commonly used to help build a robust speaker verification system, especially in limited-resource case. However, conventional data augmentation methods usually focus on the diversity of acoustic environment, leaving the lexicon variation neglected. For text dependent speaker verification tasks, it’s well-known that preparing training data with the target transcript is the most effectual approach to build a well-performing system, however collecting such data is time-consuming and expensive. In this work, we propose a unit selection synthesis based data augmentation method to leverage the abundant text-independent data resources. In this approach text-independent speeches of each speaker are firstly broke up to speech segments each contains one phone unit. Then segments that contain phonetics in the target transcript are selected to produce a speech with the target transcript by concatenating them in turn. Experiments are carried out on the AISHELL Speaker Verification Challenge 2019 database, the results and analysis shows that our proposed method can boost the system performance significantly.
AdaGUM: An Adaptive Graph Updating Model-Based Anomaly Detection Method for Edge Computing Environment

With the rapid development of Internet of Things (IoT), massive sensor data are being generated by the sensors deployed everywhere at an unprecedented rate. As the number of Internet of Things devices is estimated to grow to 25 billion by 2021, when facing the explicit or implicit anomalies in the real-time sensor data collected from Internet of Things devices, it is necessary to develop an effective and efficient anomaly detection method for IoT devices. Recent advances in the edge computing have significant impacts on the solution of anomaly detection in IoT. In this study, an adaptive graph updating model is first presented, based on which a novel anomaly detection method for edge computing environment is then proposed. At the cloud center, the unknown patterns are classified by a deep leaning model, based on the classification results, the feature graphs are updated periodically, and the classification results are constantly transmitted to each edge node where a cache is employed to keep the newly emerging anomalies or normal patterns temporarily until the edge node receives a newly updated feature graph. Finally, a series of comparison experiments are conducted to demonstrate the effectiveness of the proposed anomaly detection method for edge computing. And the results show that the proposed method can detect the anomalies in the real-time sensor data efficiently and accurately. More than that, the proposed method performs well when there exist newly emerging patterns, no matter they are anomalous or normal.
Quaternion Capsule Networks

Capsules are grouping of neurons that allow to represent sophisticated information of a visual entity such as pose and features. In the view of this property, Capsule Networks outperform CNNs in challenging tasks like object recognition in unseen viewpoints, and this is achieved by learning the transformations between the object and its parts with the help of high dimensional representation of pose information. In this paper, we present Quaternion Capsules (QCN) where pose information of capsules and their transformations are represented by quaternions. Quaternions are immune to the gimbal lock, have straightforward regularization of the rotation representation for capsules, and require less number of parameters than matrices. The experimental results show that QCNs generalize better to novel viewpoints with fewer parameters, and also achieve on-par or better performances with the state-of-the-art Capsule architectures on well-known benchmarking datasets. Our code is available11https://github.com/Boazrciasn/Quaternion-Capsule-Networks.git.
A Joint Convolutional and Spatial Quad-Directional LSTM Network for Phase Unwrapping

Phase unwrapping is a classical ill-posed problem which aims to recover the true phase from wrapped phase. In this paper, we introduce a novel Convolutional Neural Network (CNN) that incorporates a Spatial Quad-Directional Long Short Term Memory (SQD-LSTM) for phase unwrapping, by formulating it as a regression problem. Incorporating SQD-LSTM can circumvent the typical CNNs’ inherent difficulty of learning global spatial dependencies which are vital when recovering the true phase. Furthermore, we employ a problem specific composite loss function to train this network. The proposed network is found to be performing better than the existing methods under severe noise conditions (Normalized Root Mean Square Error of 1.3% at SNR = 0 dB) while spending a significantly less computational time (0.054s). The network also does not require a large scale dataset during training, thus making it ideal for applications with limited data that require fast and accurate phase unwrapping.
Measurement, selection, and visualization of association rules: A compositional data perspective

Abstract Association rule mining is a powerful data analytic technique used for extracting information from transaction databases with a collection of itemsets. The aim is to indicate what item goes with what item (ie, an association rule) in a set of collected transactions. It is extensively used in text analytics of text records or social media. Here we use Compositional Data analysis (CoDa) techniques to generate new visualizations and insights from association rule mining. These CoDa methods show the relationship between itemsets, their strength, and direction of dependency. Moreover, after expressing each association rule as a contingency table, we discuss two statistical tests to guide identification of the relevant rules by analyzing the relative importance of the elements of the table. As an example, we use these visualizations and statistical tests for investigating the association of negative mood emotions to various types of headache/migraine events. Data for those analysis comes from N1‐Headache TM , a digital platform where individual users record attacks and symptoms as well as their daily exposure to a list of potential factors.
An Epistemic Probabilistic Logic with Conditional Probabilities

We present a proof-theoretical and model-theoretical approach to reasoning about knowledge and conditional probability. We extend both the language of epistemic logic and the language of linear weight formulas, allowing statements like “Agent Ag knows that the probability of A given B is at least a half”. We axiomatize this logic, provide corresponding semantics and prove that the axiomatization is sound and strongly complete. We also show that the logic is decidable.
Focus on the Present: A Regularization Method for the ASR Source-Target Attention Layer

This paper introduces a novel method to diagnose the source-target attention in state-of-the-art end-to-end speech recognition models with joint connectionist temporal classification (CTC) and attention training. Our method is based on the fact that both, CTC and source-target attention, are acting on the same encoder representations. To understand the functionality of the attention, CTC is applied to compute the token posteriors given the attention outputs. We found that the source-target attention heads are able to predict several tokens ahead of the current one. Inspired by the observation, a new regularization method is proposed which leverages CTC to make source-target attention more focused on the frames corresponding to the output token being predicted by the decoder. Experiments reveal stable improvements up to 7% and 13% relatively with the proposed regularization on TED-LIUM 2 and Librispeech.
Design and implementation of an intelligent single axis automatic solar tracking system

Sun-oriented solar power production with sunlight-based vitality plays an important role. In reality, untimely or innovative countries gracefully take a move forward. When oil is used for power era, it contributes to a rise in poisoning and greenhouse gases. This creates natural complications. Another human need for existence can be filled by using numerous sun- oriented developments in vitality, the planet can still prevent worldwide problems which can also serve to decrease pollution at the same time. In far-off areas where communities of citizens do not approach electricity photovoltaic frameworks are added many any administration offices have taken the amounts of action to allow creativity in sun-driven vitality to be used for power production. Single axis monitoring devices are discussed in this article. This analysis supports the findings. In comparison with the static systems, the single axis monitoring system is more accurate. In either scenario, a single axis monitoring system is simpler to accomplish in cost and adaptability viewpoints than a static one.
Multi-Models Fusion for Light Field Angular Super-Resolution

Light field (LF) imaging has received increasing attention due to its richer interpretation of the scene. However, an inherent spatial-angular trade-off exists in LF that prevents LF from practical applications. Consequently, how to break such a trade-off has become one of the main challenges in sparsely sampled LF reconstruction. LF super-resolution (SR) can provide an opportunity to solve this issue, but most methods exploit only one form of LF, thereby leading to much loss of information. We believe that different LF forms can compensate each other to obtain higher gains via fusion strategy. In this paper, therefore, we propose a multi-models fusion for LF SR in angular domain. Cascading models which are trained by different LF forms can fully exploit rich LF information. Experimental results demonstrate that our method is effective and achieves a comparable result against state-of-the-art techniques.
Click Ad Fraud Detection Using XGBoost Gradient Boosting Algorithm

The growth of the online advertising industry has created new business opportunities on the Internet. Companies and advertisers are turning to digital ad platforms like never before to compete for the attention of their audience. In this environment, actions such as clicking an ad result in financial transactions among advertisers, advertising networks and publishers. Since these new opportunities have financial impact, fraudsters have been trying to gain illegal advantages and profit through them. Mitigating the negative effects of illegal traffic is extremely important to the success of any marketing endeavor. Today, false clicks that waste budgets and don't generate any meaningful value or revenue are costing advertisers billions of dollars. These are the biggest challenge PPC (Pay Per Click) marketers face, although there are efforts made by the advertisers to block fake traffic, they still try to find leading security strategy to identify click fraud. This paper analyzes the click fraud mechanism, focusing on its detection and methods of solution used in recent cases, we try and explain various fundamentals related to online advertising. The objective of this research is to propose solution for click ad fraud present in online advertising using the XGBoost Gradient Boosting algorithm and this model provides the accuracy of 96% with a set of hyperparameters along with features that can be implemented on datasets related to click frauds.
Clothes-Changing Image Generation Based on Attention for Person Re-identification

Person re-identification is an important issue in the field of video surveillance security. However, the differences of pedestrians under different cameras bring challenges. The existing person re-identification methods mainly focus on the person's posture and feature extraction, but do not pay much attention to the high-frequency clothes changing for one person. Therefore, the study of person clothes-changing images is of great help to person re-identification. In this paper, we introduce a network which fuses feature separation and self-attention jointly in a generative adversarial network for image generation of person clothes. We evaluate this model based on self-attention on a benchmark dataset: Market-1501. The experiment results validate the advances of attention mechanism by comparing with state-of-the-art baselines. Based on the generated datasets, we introduce a network containing channel attention and spatial attention which focus on local regions of clothes changes in a convolutional neural network for image recognition. We evaluate this network on our generative dataset. The experiment results show that joint attention network outperforms the baseline.
Secure Automatic Question Paper with Reconfigurable Constraints

Examination plays a vital role in all over the world. To test the knowledge and ability of a student, examination is the elementary method. For examination, a teacher has to create sets of question paper as per the institutions or university. It is challenging for the teacher to prepares the question paper, and it takes lots of time for educator. Due to huge efforts, sometimes it leads to human fault. The proposed of this project is easy to prepare the question paper process using the bloom's taxonomy and random package. To create the high quality of question paper, it covers six hierarchical levels of bloom's taxonomy. Question will separate in the database based on section wise and user can easily add the question in the database. After clicking generate button the question paper will automatically generate in PDF format and send to the teacher.
Special Interest Group on Visualization Grammars

Visualization grammars, often based on the Grammar of Graphics, are popular choices for specifying expressive visualizations and supporting visualization systems. However, there are still open questions about grammar design and evaluation not well-answered in visualization research. In this SIG, we propose to discuss what makes a grammar "good" and explore evaluation methodologies best suited for visualization grammars.