Reviewerly/exHarmony huggingface dataset:
Set-Structured Latent Representations.

Incorporating relational reasoning into neural networks has greatly expanded their capabilities and scope. One defining trait of relational reasoning is that it operates on a set of entities, as opposed to standard vector representations. Existing end-to-end approaches typically extract entities from inputs by directly interpreting the latent feature representations as a set. We show that these approaches do not respect set permutational invariance and thus have fundamental representational limitations. To resolve this limitation, we propose a simple and general network module called a Set Refiner Network (SRN). We first use synthetic image experiments to demonstrate how our approach effectively decomposes objects without explicit supervision. Then, we insert our module into existing relational reasoning models and show that respecting set invariance leads to substantial gains in prediction performance and robustness on several relational reasoning tasks.
Web Abuse Using Cross Site Scripting (XSS) Attacks

Abstract In today’s modern world, most of the applications are using World Wide Web (www) for information processing and transaction management. The popularity of web has eased out global outreach and accessibility to different users around the globe. Although web traffic has scaled up, it has also increased the abuse of applications by malicious html-based attacks by users; one such attack being the cross-site scripting (XSS). This attack poses a serious threat to web applications and e-databases that may include sensitive user data. Although other web attacks like SQL Injection, CSRF, phishing and session hijacking are also common, XSS tops the list of preferred technique for hackers to capitalize web resources for malicious activities. In this paper, we draw an overview of XSS attacks and its different types. We also discuss certain code prevention techniques possible including robust defense mechanisms. The paper also explicates discussion over the related work that has been concluded by researchers for mitigation scenario and techniques possible for prevention. Keywords: XSS, SQL, WWW, CSRF Cite this Article Mohd Umar John, Junaid Latief Shah, Gazi Imtiyaz Ahmad. Web Abuse Using Cross Site Scripting (XSS) Attacks. Journal of Artificial Intelligence Research & Advances . 2019; 6(1): 69–75p.
Research on Image Segmentation based on Full Convolutional Neural Network

Abstract Image segmentation technology is an important branch in the field of computer vision. Image segmentation is the basis of image analysis processing, and image segmentation effect directly affects image subsequent processing. Aiming at the problem that the segmentation method based on traditional machine learning is not accurate, the edge information is lost and the robustness needs to be improved, this paper proposes an image segmentation algorithm based on improved full convolution neural network. The algorithm utilizes the better feature extraction ability of the deep learning model and the sensitivity of the cluster segmentation to the edge information, and further assists the segmentation with the Ncut algorithm. The experimental results show that compared with the traditional convolutional neural network image segmentation algorithm, the algorithm finally achieves higher segmentation accuracy. From the overall analysis, the segmentation method proposed in this paper is better than other methods.
A Granular Synthesis Strategy Based on Musical Harmony Theory Through a Fuzzy Logic Approach

This paper proposes a granular synthesis technique constrained to musical harmony theory that uses fuzzy logic to playback small portions of sound called grains. The algorithm distributes the grains in a two-dimensional space according to a musical structure shaped by a chord progression. Grains are playback in a timed sequence adjusted to a rhythmical pattern sorted by a fuzzy logic method that considers the energy and frequency of each grain across a defined harmonic environment. Tests regarding the distribution of grains and the generated sounds are performed to compare the effects of different keynotes in the musical structure, as well as considering the sound sources from where the grains were originally taken.
Research on the Application of Instance Segmentation Algorithm in the Counting of Metro Waiting Population

With the development of deep learning, intelligent security and measurement and control products emerge in endlessly. In order to maintain the operation order of subway station, it is one of the important problems in the field of deep learning to carry out real-time and accurate counting of the number of people queued in front of the subway. In order to solve this problem, we propose a simple and flexible real-time counting method for the number of people queued in front of the subway. The image is collected by the camera in front of the subway door, instance segmentation algorithm accurately divides the target in the image, and completes the counting of the number of people queued in front of the subway by calculating the number of targets segmented. Selected the mainstream Mask R-CNN as the basic algorithm, combine the characteristics of the live picture, feature pyramid network and non-maximum suppression process of Mask R-CNN are improved. The experimental results show that the algorithm can realize accurate and real-time counting of the number of people queued in front of the subway, and the accuracy of counting can reach 96%. Compared with the traditional target detection algorithm, it has stronger adaptability to occlusion problem, and can accurately segment the intersection of targets from less than 30% to less than 60%. And the real-time performance has been greatly improved, the target inventory in a single picture only needs about 0.2 s.
Copyright lessons on Machine Learning: what impact on algorithmic art?

JIPITEC 10 (2020) 3 - Nowadays, Artificial Intelligence (AI) is described as “the new electricity”. Current algorithmic innovation allowed the development of software which enables machines to learn and to achieve autonomous decision making, with limited or no human involvement, in a vast number of applications, such as speech recognition, machine translation and algorithmic creation of works (computer generated art), on the basis of a process widely known as Machine Learning (ML). Within the ML context, machines are repeatedly trained by means of specifically designed learning algorithms that use a corpus of examples in the form of data sets as training material. Very often and, especially in the context of algorithmic creativity, the training material is mainly composed by copyrighted works, such as texts, images, paintings, musical compositions, and others. Machine Learning workflow typically involves the realization of (multiple) reproductions of any protected work used as training material. The present paper aims to assess the extent to which the use of copyrighted works for Machine Learning purposes in the field of algorithmic creativity is controlled by the monopolistic power of the copyright rightholder on that work. The answer to this question will be researched in the context of EU copyright law, by examining the content of reproduction right and exceptions possibly applicable in a typical ML workflow in the field of algorithmic art, before making an overall assessment of the current EU regulatory framework for artistic ML projects, as it is shaped after the DSM Directive 2019/790.
Socio-Philosophical Aspects of the Neuro-Digital Noosphere Formation and Development

At present there is more than half of the world population who is currently using the Internet, due to which it seems relevant to develop not only technical, but also humanitarian and metaphysical views and theories that can serve the basis for the analysis of this phenomenon. The aim of these areas of studies should be rethinking of the role and place of man in the structure of modern society, as well as the role and place of the society in human life. The methodology for these processes comprehension lies in the plane of ontology, social philosophy and anthropology, based on the integration of the latter with cybernetics and mathematics, synergetics and mathematical modeling. Systemic and historical methods are inevitably capable of synthesizing the results of such a methodological approach, leading to the appearance of neologisms, the explication of new notions that most adequately reflect the essence of modern processes. The article illustrates a number of phenomena that should be studied with the aim of forming a person not as an object, but as a subject of global digital communication.
Heuristic evaluation on mobile halal detection application

&lt;span&gt;As a country with the largest Muslim population in the world, the halal status of a food and beverage product is essential for the people of Indonesia. An application was created by the government to answer these concerns with the name "Halal MUI". Unfortunately, this application has not been able to meet the needs of users to get a rating of 3.5 out of 920 users. This study aims to provide an evaluation of the Halal MUI application by using a heuristic evaluation approach. As many as three experts did a personal heuristic evaluation, then summarized it in a group discussion forum. The results showed that the application of Halal MUI, in general, has been able to check the halal product. However, some features still have a high severity rating. This paper presents several suggested improvements to improve system usability in Halal MUI applications.&lt;/span&gt;
Local-HDP : Interactive Open-Ended 3D Object Categorization

We introduce a non-parametric hierarchical Bayesian approach for open-ended 3D object categorization, named the Local Hierarchical Dirichlet Process (Local-HDP). This method allows an agent to learn independent topics for each category incrementally and to adapt to the environment in time. Hierarchical Bayesian approaches like Latent Dirichlet Allocation (LDA) can transform low-level features to high-level conceptual topics for 3D object categorization. However, the efficiency and accuracy of LDA-based approaches depend on the number of topics that is chosen manually. Moreover, fixing the number of topics for all categories can lead to overfitting or underfitting of the model. In contrast, the proposed Local-HDP can autonomously determine the number of topics for each category. Furthermore, an inference method is proposed that results in a fast posterior approximation. Experiments show that Local-HDP outperforms other state-of-the-art approaches in terms of accuracy, scalability, and memory efficiency with a large margin.
Theories and Applications of Enhanced Earthquake Back-projection Imaging

Author(s): Zhang, Ailin | Advisor(s): Meng, Lingsen | Abstract: Earthquake source imaging is an efficient tool to reveal the kinematic rupture process of earthquakes. The accuracy and robustness of source imaging is of vital importance to con- strain source properties and help discriminate among potential physical mechanisms. In this work, I develop array-based approach: enhanced back-projection and coherence method to provide better constraints on kinematic source imaging.Slowness correction is proposed to improve the source imaging of complicated rupture in conjugate fault systems. The correction is capable of mitigating 3D structural effects that commonly exist for M8 earthquakes.For deep earthquakes, I introduce 3D back-projection which provides depth resolution of the rupture front. Resolving ruptures in 3D space helps us to understand the rupture process and physical mechanisms of deep earthquakes. Furthermore, coherence model is established to relate earthquake source size with inter-station cross-correlation. This method creates independent measures to validate and constrain the source sizes of deep earthquakes.Aforementioned array-based approaches have been successfully applied to recently oc- curred earthquakes, and I will take this chance to revisit the rupture mechanisms of the events revealed by enhanced source imaging
A Coverless Approach to Data Hiding Using DNA Sequences

In recent years, image steganography is being considered as one of the methods to secure the confidentiality of sensitive and private data sent over networks. Conventional image steganography techniques use cover images to hide secret messages. These techniques are susceptible to steganalysis algorithms based on anomaly detection. This paper proposes a new approach to image steganography without using cover images. In addition, it utilizes Deoxyribonucleic Acid (DNA) sequences. DNA sequences are used to generate key and stego-image. Experimental results show that the use of DNA sequences in this technique offer very low cracking probability and the coverless approach contributes to its high embedding capacity.
KUNUZ: A Multi-Purpose Reusable Test Collection for Classical Arabic Document Engineering

Corpora are important resources for several applications in Information Retrieval (IR) and Knowledge Extraction (KE). Arabic is a low resourced language characterized by its complex morphology. Furthermore, most existent Arabic language resources focus on Modern Standard Arabic (MSA). This paper describes KUNUZ a multi-purpose test collection composed of voweled and structured classical Arabic documents. Its goal is to provide a unique benchmark for assessing applications in several areas of document engineering including IR, document classification and information extraction. The documents are also translated in English to allow Arabic-English cross-lingual IR and machine translation. As far as IR is concerned, we follow the standard topic development and results sampling used in international campaigns. The paper, describes the process of topic development, results pooling and relevance judgment. It also analyses the results of some processing tools and IR models used in the runs. In order to enhance the results of our experiments, we also proposed to combine the results based on a meta-search approach using Support Vector Machines (SVM) classification.
Reflection-equivariant convolutional neural networks improve segmentation over reflection augmentation

Convolutional neural networks (CNNs) have been successfully applied to human brain segmentation. To in- corporate the left and right symmetry property of the brain into a network architecture, we propose a 3D left-right-reflection equivariant network to segment the anatomical structures of the brain. We extended previous group convolutions to account for left-right paired labels in the delineation. The proposed networks were compared with conventional networks trained with left-right reflection data augmentation in several tasks, showing improved performance. This is also the first work to extend reflection-equivariant CNNs to left-right paired labels in the human brain.
Container-Based Complex Programming Skills Training Platform

In recent years, online programming education and evaluation have developed rapidly. The traditional online education platform focuses on the teaching of knowledge, lacking a good evaluation platform to assess the learning outcomes, while the existing online evaluation platform only focuses on the evaluation of algorithms or languages. Until now, no platform can support the training of engineering-level complex technologies. In this paper, we present our solution – Educoder. Educoder is an one-stop service platform that integrates “teaching”, “learning” and “use”. It not only supports simple programming language training type, but also supports the training of complex technology such as Hadoop, Spring, Machine Learning, Docker, etc. The code environment for student learning and evaluation is managed by Gitlab, and the runtime environment is provided by Kubernetes pod, which guarantees good isolation. For complex types of training, we provide the Docker Web Terminal module for SSH connection to enter the pod for operation and use docker in docker technology to provide support for training that requires multiple services or multiple nodes. In Educoder, we use scripts to serialize the training evaluation steps, and we formed a private evaluation script template library for training teacher to reference, which greatly reduced the time cost of writing a new training course.
The Effect of Accountant Professional Ethic’s Education and Religiosity on Student’s Perception of Accountant’s Ethical Behavior (Study on Indonesia College of Economics Bachelor of Accounting Students)

This study purposed to test, obtain empirical evidence whether there is an influence on the accounting professional ethics of accountants and religiosity on students' perceptions of accountant's unethical behaviour. This research was conducted to observe at the relationship between professional ethics in accountants and religiosity towards students' ethical perceptions, and to see whether professional ethics courses taken by students can form the ethical perceptions of students as prospective accountants. Ethical knowledge studied in college is expected to be able to influence students' perceptions in assessing ethical and unethical behaviour therefore whenever they work, both as corporate accountants and public accountants, they will not create similar mistakes that will doubt accountants' credibility. This research uses a type of causality research with a quantitative approach. The population used in this study is all 2017 Indonesia College of Economics Bachelor of Accounting Students who already passed professional ethics and auditing subject. The sampling technique is based on proportional sampling with a sample size of 170 respondents. The data used in this study are primary data. The data analysis model used in this study is multiple linear regression analysis. The results of the research hypothesis prove that (1) Ethics education of the accountant profession influences students' perceptions of the accountant's unethical behaviour. Students who have extensive knowledge of the ethical principles of the accounting profession will be more prudent and able to respond in the form of disapproval or refuse to ethical scandals that befall the accounting profession. (2) Religiosity influences students' perceptions of the accountant's unethical behaviour. The higher a person's religiosity and obedience to religion teachings, the more ethical his behaviour and attitude will be.
Chaotic Time Series Forecasting using Emotional Learning-Based Neural Networks

This paper has investigated a new model based on the brain emotional learning to forecast chaotic series. The proposed model is based on the Moren model which is a model of the emotional part of the brain of mammals. In this paper, we have extended the Moren model based on the actual inputs to the thalamus of mammalian brain, i.e., the number of entries to the thalamus is considered to be more than one. A better performance in chaotic time series forecasting has been achieved in simulations. The results show that in comparison with other methods such as neural network based models and fuzzy neural models, the proposed model has less MSE in both short-term and long-term forecasting for the chaotic time series.
End-To-End Imitation Learning of Lane Following Policies Using Sum-Product Networks

Recent research has shown the potential of learning lane following policies from annotated video sequences through the use of advanced machine learning techniques. They however require high computational power, prohibiting their use in low-budget projects such as educational robotic kits and embedded devices. Sum-product networks (SPNs) are a class of deep probabilistic models with clear probabilistic semantics and competitive performance. Importantly, SPNs learned from data are usually several times smaller than deep neural networks trained for the same task. In this work, we develop an end-to-end imitation learning solution to lane following using SPNs to classify images into a finite set of actions. Images are obtained from a monocular camera, which is part of the low-cost custom made mobile robot. Our results show that our solution generalizes training conditions with relatively few data. We investigate the trade-off between computational and predictive performance, and conclude that sacrificing accuracy for the benefit of faster inference results in improved performance in the real world, especially in resource constrained environments.
Filtering Adversarial Noise with Double Quantization

Despite deep learning being powerful to solve challenging problems, it is vulnerable towards adversarial examples. To defend these adversarial blind spots in the deep learning, researchers have proposed various approaches. However, conventional adversarial training can reduce the accuracy significantly. In this paper, we propose a method to incorporate quantized images in both training and testing to maintain identical accuracy for both normal and adversarial examples. Specifically, the proposed method utilizes dithering during training and dithering and linear quantization as a mean of adversarial filtering during testing. We evaluated the proposed method with a well-known strong first-order adversary and also conducted experiments in different bit depths. The results suggest that the proposed method achieves 87.14% and 85.28% accuracy for 2-bit and 1-bit dithered models for both normal and adversarial tests on the noise level of 8. In addition, due to having identical accuracy for both adversarial and normal tests, the proposed method can detect adversarial examples if the original test dataset is known. The code for the experiments is released on https://github.com/fugokidi/one-bit-quantization.
Exploiting confident information for weakly supervised prostate segmentation based on image-level labels

Prostate segmentation on magnetic resonance images (MRI) is an important step for prostate cancer diagnosis and therapy. After the birth of deep convolution neural network (DCNN), prostate segmentation has achieved great success in supervised segmentation. However, these works are mostly based on abundant fully labeled pixel-level image data. In this work, we propose a weakly supervised prostate segmentation (WS-PS) method based on image-level labels. Although the image-level label is not sufficient for an exact prostate contour, it contains potential information which is helpful to make sure a coarse contour. This information is referred to confident information in this paper. Our WS-PS method includes two steps which are mask generation and prostate segmentation. First, the mask generation (MG) exploits a class activation maps (CAM) technique to generate a coarse probability map for MRI slices based on image-level label. These elements of the coarse map which have higher probability are considered to contain more confident information. To make use of confident information from coarse probability map, a similarity model (S-Model) is introduced to refine the coarse map. Second, the prostate segmentation (PS) uses a residual U-Net with a size constraint loss to segment prostate based on the refined mask obtained from MG. The proposed method achieves a mean Dice similarity coefficient (DSC) of 83.39% as compared to the manually delineated ground-truth. The experimental results indicate that our weakly supervised method can achieve a satisfactory segmentation on prostate MRI only with image-level labels.