Reviewerly/exHarmony huggingface dataset:
Harnessing Disagreement to Create AI-Powered Systems That Reflect Our Values

How do we build artificial intelligence systems that reflect our values? Competing potential values we may want to choose from are, at their core, made up of disagreements between individual people. But while the raw datasets that most ML systems rely on are made up of individuals, today’s approaches to building AI typically abstract the individuals out of the pipeline. My thesis contributes a set of algorithms and interactive systems that re-imagine the pipeline for designing and evaluating AI systems, requiring that we deal with competing values in an informed and intentional way. I start from the insight that at each stage of the pipeline, we need to treat individuals as the key unit of operation, rather than the abstractions or aggregated pseudo-humans in use by today’s approaches. I instantiate this insight to address two problems that arise from today’s AI pipeline. First, evaluation metrics produce actively misleading scores about the extent to which some people’s values are being reflected. I introduce a mathematical transformation that more closely aligns metrics with the values and methods of user-facing performance measures. Second, the resulting AI systems either surreptitiously choose which values to listen to without input from users, or simply present several different outcomes to users without mechanisms to help them select an outcome grounded in their values. I propose a new interaction paradigm for deploying classifiers, asking users to compose a jury consisting of the individual people they’d like their classifiers’ decisions to come from.
Attentive Autoencoders For Improving Visual Anomaly Detection

Understanding the notion of normality in visual data is a complex issue in computer vision with plenty of potential applications in several sectors. The immense effort required for optimal design for real-world application of existing methods warrants the need for a generic framework that is efficient, automated and can be momentarily deployed for the operation, reducing the effort expended on model design and hyper-parameter tuning. Hence, we propose a novel, modular and model-agnostic improvement to the conventional AutoEncoder architecture, based on visual soft-attention for the inputs to make them robust and readily improve their performance in automated semi-supervised visual anomaly detection tasks, without any extra effort in terms of hyperparameter tuning. Besides, we discuss the role of attention in AutoEncoders (AE) that can significantly improve learning and the efficacy of the models with detailed experimental results on diverse visual anomaly detection datasets.
ANN Based Security Analysis of Block Ciphers with Focus on Non-linear Component

In today’s world with billions of people on the internet, and data being one of the most valuable assets, cryptography plays a very important role. However, we notice that the usage of deep learning has not been explored much in the field of cryptography particularly in analyzing block ciphers. In this paper, we explore the significance of non-linear components (such as s-box) in the case of AES and PICO block ciphers using the deep learning method. Our aim is to predict the individual bits of the plaintext one by one from the available ciphertext. We perform our experiment in two scenarios; firstly when the non-linear component (S-box) is not used in the crypto algorithm and secondly when the non-linear component (S-box) is present in the crypto algorithm. We validate our study using the accuracy metrics of our deep learning model.
Gaussian Mixture Estimation from Weighted Samples

We consider estimating the parameters of a Gaussian mixture density with a given number of components best representing a given set of weighted samples. We adopt a density interpretation of the samples by viewing them as a discrete Dirac mixture density over a continuous domain with weighted components. Hence, Gaussian mixture fitting is viewed as density re-approximation. In order to speed up computation, an expectation-maximization method is proposed that properly considers not only the sample locations, but also the corresponding weights. It is shown that methods from literature do not treat the weights correctly, resulting in wrong estimates. This is demonstrated with simple counterexamples. The proposed method works in any number of dimensions with the same computational load as standard Gaussian mixture estimators for unweighted samples.
Using Generative Adversarial Networks to Create Graphical User Interfaces for Video Games

Designing and creating a Graphical User Interface (GUI) is a difficult and slow process. It requires a number of professions to all contribute to its development and it can be heavily detrimental to a product if implemented poorly. This research aims to investigate a method of using Generative Adversarial Networks (GANs) to generate new and usable designs for GUIs. GANs are a relatively new architecture for adversarial learning and have been used to good effect in replicating instances of a real dataset. The primary aim is to develop a GAN that is capable of processing a collection of existing GUIs and learn how to replicate these to allow for creation of further designs. These GUI designs need to be formatted in a manner that enables modification, allowing for them to be used by a development team to enhance their production process. Completed work demonstrates numerous approaches at using GANs to create text files that contain the component elements of a GUI. Their results and the release of a similar research paper (GUIGAN) has led to a new approach focusing on more abstract data representation, with a quality control system for ensuring the output data is properly formatted. It is hypothesised that the approach will develop a model capable of creating new, editable GUI designs.
Face Detection Model for Thermal Images

Face detection is one of hot issue in image processing combined with Deep Learning methods. The application have influenced in many area such as security, and medical. Mostly face detection only applied in RGB images to locate the face area and continued with face recognition. Thermal camera function is not only to capture the thermal images, but it also applied in security system avoiding the spoofing faces. Nowadays, the temperature measurement is important to do but without any contact with the subject. In this research, we proposed a method to create a face model based on the thermal images. This model will apply in the multi object temperature measurement as real time measurement. YOLO is one of the Deep Learning methods which have best performance in object detection. The main YOLO architecture was formed by Convolutional Neural Network. The YOLO method was applied to create the face model with some modification from previous YOLO architectures. The dataset was built from direct measurement combined with online dataset. FLIR Lepton thermal 3.5 camera was applied in this research to capture subject. The dataset size was extended by using data augmentation to prevent over-fitting during training. By using 1600 images, the face model was successfully created with the average accuracy around 72.7%.
On the use of CNNs with patterned stride for medical image analysis

The use of deep learning techniques for early and accurate medical image diagnosis has grown significantly in recent years, with some encouraging results across many medical specialties, pathologies, and image types. One of the most popular deep neural network architectures is the convolutional neural network (CNN), widely used for medical image classification and segmentation, among other tasks. One of the configuration parameters of a CNN is called stride and it regulates how sparsely the image is sampled during the convolutional process. This paper explores the idea of applying a patterned stride strategy: pixels closer to the center are processed with a smaller stride concentrating the amount of information sampled, and pixels away from the center are processed with larger strides consequently making those areas to be sampled more sparsely. We apply this method to different medical image classification tasks and demonstrate experimentally how the proposed patterned stride mechanism outperforms a baseline solution with the same computational cost (processing and memory). We also discuss the relevance and potential future extensions of the proposed method.
Towards an Operationalization of AI acceptance among Pre-service Teachers

Artificial Intelligence (AI) has found its popularity in many fields of our life, ranging from song recommendation to lung cancer identification. CSE can impart AI knowledge to all people (including teachers) so that they can make an informed decision about AI. Besides, AI technologies can support tailoring the education to the students' needs or unload teachers by doing routine work for them and leaving more space for creativity. However, despite the growing potential of AI applications in education, some skepticism or even rejection can be observed. It could be especially critical for teacher education: by being a role model for pupils, teachers could multiply their attitude towards AI while transferring it to the pupils.
Text Style Transfer With Decorative Elements

The text rendered by special effects can give a rich visual experience. Text stylization can help users migrate their favorite styles to specified texts, improving production efficiency and saving design cost. This paper proposes a novel text stylization framework, which can transfer mixed text styles, including font glyph and fine decorations, to user-specified texts. The transfer of decorative elements is difficult due to the text is obscured by decorative elements to a certain extent. Our method is divided into three stages: firstly, the position of decorative elements in the image is extracted and retained; secondly, the effects of font glyph and textures other than decorative elements are migrated; finally, a structure-aware strategy is used to reorganize the decorative elements to complete the entire stylization process. Experiments on open source text data sets demonstrated the advantages of our approach over other state- of-the-art style migration methods.
From Multi-Target Sensory Coverage to Complete Sensory Coverage: An Optimization-Based Robotic Sensory Coverage Approach

This paper considers progressively more demanding off-line shortest path sensory coverage problems in an optimization framework. In the first problem, a robot finds the shortest path to cover a set of target nodes with its sensors. Because this mixed integer nonlinear optimization problem (MINLP) is NP-hard, we develop a polynomial-time approximation algorithm with a bounded approximation ratio. The next problem shortens the coverage path when possible by viewing multiple targets from a single pose. Its polynomial-time approximation simplifies the coverage path geometry. Finally, we show how the complete sensory coverage problem can be formulated as a MINLP over a decomposition of a given region into arbitrary convex polygons. Extensions of the previously introduced algorithms provides a polynomial time solution with bounded approximation. Examples illustrate the methods.
A Practical Approach into Some Software to Create Videos about Historical Figures to Enhance the Teaching Quality of Topic “Counter-Offensive on the Southwestern Border (1975-1979)” At FPT Education

In the 21st century, the problem of innovating teaching and learning methods and applying information technology in the process of organizing training is an objective and urgent requirement both in the short and long term. This is a decisive factor in improving the quality of education and training in general and teaching national history in particular. Fortunately, the available video creation software (Biteable, ProShow Gold, Camtasia) are effective support tools in preparing electronic lesson plans. The application of the mentioned software to create documentaries about historical figures is an appropriate choice in the 4.0 era. Particularly, the application of these software in teaching about “Counter-offensive on the Southwestern border (1975-1979)” (also known as Cambodian-Vietnamese war) will create a new step for the problem of teaching and learning history. History teaching and learning activities at FPT educational institution are improved compared to traditional methods.
Programming as architecture, design, and urban planning

Our thinking about software is shaped by basic assumptions and metaphors that we rarely question. Computer science has the term science in its very name; we think of programming languages as formal mathematical objects and we hope to make better software by treating it as an engineering discipline. Those perspectives enabled a wide range of useful developments, but I believe they have outlived their usefulness. We need new ways of thinking about software that are able to cope with ill-defined problems and the increasing complexity of software. In this essay, I draw a parallel between the world of software and the world of architecture, design and urban planning. I hope to convince the reader that this is a well-justified parallel and I point to a number of discussions in architecture, design and urban planning from which the software world could learn. What kind of software may we be able to build if we think of programming as a design problem and aim to create navigable and habitable software for all its users?
Toward a 3D Velocity Interferometer Testbed: Early Results

We present preliminary results from a multi-plane camera, exploring 3D image reconstruction of a high speed velocimetry target beyond a single depth of field.
Detection of Pediatric Femur Configuration on X-ray Images

In this paper, we present a fully automatic solution for denoting bone configuration on two-dimensional images. A dataset of 300 X-ray images of children’s knee joints was collected. The strict experimental protocol established in this study increased the difficulty of post-processing. Therefore, we tackled the problem of obtaining reliable information from medical image data of insufficient quality. We proposed a set of features that unambiguously denoted configuration of the bone on the image, namely the femur. It was crucial to define the features that were independent of age, since age variability of subjects was high. Subsequently, we defined image keypoints directly corresponding to those features. Their positions were used to determine the coordinate system denoting femur configuration. A complex keypoint detector was proposed, composed of two different estimator architectures: gradient-based and based on the convolutional neural network. The positions of the keypoints were used to determine the configuration of the femur on each image frame. The overall performance of both estimators working in parallel was evaluated using X-ray images from the publicly available LERA dataset.
PERANCANGAN ILUSTRASI PADA BUKU “CELIN” SEBAGAI EDUKASI KESEHATAN MENTAL UNTUK PENGGUNA MEDIA SOSIAL USIA 16 – 22 TAHUN PENCIPTAAN KARYA

Illustration in the book “Celin” is created based on the problems of the youth to accept and appreciate yourself (self-acceptance). Teens use social media to must upload the activities in the form of photos or video of the best he had. But not everyone loves what's uploaded. There are individual or group performs an oppressor and do the hate speech that is called abuse. Abuse that is done on social media called cyberbullying be the trigger factors disorders mental health in adolescents. Victims of abuse can feel doubt himself and feel confident. When a teenager is not able to survive in the face of cyberbullying will be bad for their mental health. Through the method of Literature study and interview the speakers an expert on mental health disorders and a teen survivors of mental health disorders. Create an illustration in a book Celin as educating on the importance of mental health. The purpose of this design is to create a book illustration that discuss information about mental health with the contents as simple as possible, as well as draw to be easily understood by adolescents and build confidence.
Impact of Cost of Governance on the Socio-Economic Development in Nigeria: 1999 to 2019

This study is an examination of the implications of the cost of governance on socio-economic development in Nigeria using Secondary data. The study found that of the over 20 years of uniterrupted democracy, there has been high petroleum revenue and borrowing for various deficit budgeting. This is accompanied by high recurrent and less capital budgeting which makes the negligible resources left to be used for capital projects, unable to encourage the desired socio-economic development. Some factors responsible for this high cost of governance are discussed amongst which are; the size and structure of governance, duplication of government functions and corruption. It is the conclusion of this study that the cost of maintaining government institutions is very high without anything to show for it. The study recommends accountability and infrastructural development to move to a better level, Nigeria’s present state of socio-economic development.
Transformation of education in the perspective of secular ethics

The article analyzes the preconditions for the transformation of the higher education system to the trinity of social, emotional, and ethical learning. The author considers the formation of education system with the use of secular Buddhist ethics on the example of Emory University (Emory, USA). The article pays great attention to the speeches of Dalai Lama and the influence of Buddhist philosophy of education on the system of socio-emotional and ethical education. The theoretical analysis of the curriculum of social-emotional-ethical training presents the methodology and the methods of the study. The object of the research is the Buddhist philosophy of education. The subject of the study is SEE Learning program (social-emotional-ethical learning) as a way of reception of the Buddhist philosophy of education in the higher education system. The author considers the implementation of SEE Learning program – social-emotional-ethical learning at Emory University (Atlanta, Georgia). This program is the result of the university’s collaboration with Dalai Lama XVI and the Tibetan Exile Community. The author concludes that the change of value orientations and the decolonization of education are factors that influence the development of educational systems. Social-emotional-ethical learning can solve the problem of lack of ethical education at universities. The introduction of ethics education will enable higher education institutions to produce not only practically and theoretically competent professionals, but also individuals who are able to understand the consequences of their decisions.
Exploit Vague Relation: An Augmented Temporal Relation Corpus and Evaluation

Temporal relation classification is a challenging task in Natural Language Processing (NLP) that faces many difficulties, such as imbalanced distribution of instances, and ambiguity of the vague instances. To address the above issues, this paper proposes a novel data augmentation method on the TimeBank-Dense (TBD) corpus to distinguish those vague instances, which can provide more evidence to identify temporal relation clearly. Specifically, we additionally annotate those VAGUE instances in the form of multiple labels, to further distinguish varied semantic phenomena in VAGUE circumstances. Experimental results show that the models trained on our augmented corpus VATBD significantly outperform those trained on the original TBD corpus, and these results verify the effectiveness of our augmented corpus on temporal relation classification.
An improved distortion feature information restoration method

Accurate acquisition of image features is particularly important for environmental perception of robots. It is difficult to obtain the image features accurately and effectively because of the interference of high-light reflection of the parts’ surface. To solve this problem an improved distortion feature information restoration method is proposed in this paper. First, an image acquisition method is proposed, which can successfully reduce the effect of high-light reflection. Then, an image quality evaluation method based on cosine similarity is proposed to realize the accurate evaluation image quality of highlight reflection image. Finally, an image quality compensation method is proposed to effectively reduce the effect of high light distortion on feature extraction. Contrastive experiments fully prove that the method proposed in this paper can effectively improve the problem of feature recognition caused by local strong light interference, and the accuracy of image surface feature extraction after compensation is significantly improved.
Performance Evaluation of Radar against Complicated E.M. Environment Based on AHP

The performance of current radars is degraded in complicated E.M. environment and there is no uniform method to make evaluation to the performance degradation till now. Aiming at certain 3D radar in specific combat scenario, the paper provided the specification system for performance evaluation of radar against complicated E.M. environment, established the evaluation model and conducted normalization processing to specifications through AHP and made quantitative evaluation. The simulation results indicate that the established model by that method is effective and feasible and the evaluation result can provide reference for performance evaluation of radar against complicated E.M. environment.
Character Flow Detection and Rectification for Scene Text Spotting

Text can be widely found in natural scenes. However, it is considerably difficult to detect and recognize the scene text due to its variations and distortions. In this paper, we propose a three-stage bottom-up scene text spotter, including text segmentation, text rectification and text recognition. The text segmentation part adopts a feature pyramid network (FPN) to extract character instances by combining local and global information, then a joint network of FPN and bidirectional long short-term memory is developed to explore the affinity among the isolated characters, which are grouped into character flows. The text rectification part utilizes a spatial transformer network to deal with the complex deformation of the character flows, thus enhancing their readability. Finally, the rectified text is recognized through an attention-based sequence recognition network. Extensive experiments are conducted on several benchmarks, showing that our approach achieves the state-of-the-art performance.
Applying an Iterative Filtering Method for Optical Fringe Patterns Preprocessing

We adapt the Iterative Filtering signal processing technique for fringe pattern preprocessing and modify it with a novel fringe extension algorithm. We compare its performance with state-of-the-art empirical mode decomposition and variational image decomposition methods.