Reviewerly/exHarmony huggingface dataset:
LatentKeypointGAN: Controlling GANs via Latent Keypoints

Generative adversarial networks (GANs) have attained photo-realistic quality in image generation. However, how to best control the image content remains an open challenge. We introduce LatentKeypointGAN, a two-stage GAN which is trained end-to-end on the classical GAN objective with internal conditioning on a set of space keypoints. These keypoints have associated appearance embeddings that respectively control the position and style of the generated objects and their parts. A major difficulty that we address with suitable network architectures and training schemes is disentangling the image into spatial and appearance factors without domain knowledge and supervision signals. We demonstrate that LatentKeypointGAN provides an interpretable latent space that can be used to re-arrange the generated images by re-positioning and exchanging keypoint embeddings, such as generating portraits by combining the eyes, nose, and mouth from different images. In addition, the explicit generation of keypoints and matching images enables a new, GAN-based method for unsupervised keypoint detection.
Application of Artificial Intelligence in User Interfaces Design for Cyber Security Threat Modeling

In recent years, Cyber Security threat modeling has been discovered to have the capacity of combatting and mitigating against online threats. In order to minimize the associated risk, these threats need to be modelled with appropriate Intelligent User Interface (IUI) design and consequently the development and evaluation of threat metrics. Artificial Intelligence (AI) has revolutionized every facet of our daily lives and building a responsive Cyber Security Threat Model requires an IUI. The current threat models lack IUI, hence they cannot deliver convenience and efficiency. However, as the User Interface (UI) functionalities and User Experience (UX) continue to increase and deliver more astonishing possibilities, the present threat models lack the predictability capacity thus Machine Learning paradigms must be incorporated. Meanwhile, this deficiency can only be handled through AI-enabled UI that utilizes baseline principles in the design of interfaces for effective Human-Machine Interaction (HMI) with lasting UX. IUI helps developers or designers enhance flexibility, usability, and the relevance of the interaction to improving communication between computer and human. Baseline principles must be applied for developing threat models that will ensure fascinating UI-UX. Application of AI in UI design for Cyber Security Threat Modeling brings about reduction in critical design time and ensures the development of better threat modeling applications and solutions.
Shamelessly cute: Understanding gender ambiguous identity performances via “The Desi Bombshell” Snapchat video selfies

Indigenous Pakistani transgenders — khwaja sira — employ gender ambiguous identity performances as a resistance tactic against politics of shame. This article explores how a tactical performance of a gender ambiguous identity portrayed via Snapchat’s cute animal lenses can subvert the culture of gendered shaming. Drawing on two feminist resistance tactics: performative shamelessness and weaponized/agentic cuteness, I investigate how Snapchat’s animal lenses can be used to achieve a subversive effect as identified in the case of “The Desi Bombshell” — a fictive online persona. Through close reading and content analysis of “Desi Bombshell” video selfies, I propose the concept of shamelessly cute. I argue that a shamelessly cute, gender ambiguous performance is a novel resistance tactic on social media as it explicitly displays a clumsy, Snapchat enabled identity, while implicitly it challenges the Pakistani politics of shame from within its culture by reworking indigenous practices and gestures.
Gi and Pal Scores: Deep Neural Network Generalization Statistics

The field of Deep Learning is rich with empirical evidence of human-like performance on a variety of regression, classification, and control tasks. However, despite these successes, the field lacks strong theoretical error bounds and consistent measures of network generalization and learned invariances. In this work, we introduce two new measures, the Gi-score and Pal-score, that capture a deep neural network's generalization capabilities. Inspired by the Gini coefficient and Palma ratio, measures of income inequality, our statistics are robust measures of a network's invariance to perturbations that accurately predict generalization gaps, i.e., the difference between accuracy on training and test sets.
Irreducible Components of the Global Nilpotent Cone

Abstract This paper gives a combinatorial description of the set of irreducible components of the semistable locus of the global nilpotent cone, in genus $\ge 2$.
Antimicrobial Solutions for Touchscreens in Times of Pandemic

Abstract The COVID‐19 pandemic has caused people to rethink touch‐enabled displays’ use in publicly accessible locations because of germs. This article discusses the touchscreen industry's challenges, and how antimicrobial coatings may be one potential solution.
MOE: Multiplication Operated Encryption with Trojan Resilience

In order to lower costs, the fabrication of Integrated Circuits (ICs) is increasingly delegated to offshore contract foundries, making them exposed to malicious modifications, known as hardware Trojans. Recent works have demonstrated that a strong form of Trojan-resilience can be obtained from untrusted chips by exploiting secret sharing and Multi-Party Computation (MPC), yet with significant cost overheads. In this paper, we study the possibility of building a symmetric cipher enabling similar guarantees in a more efficient manner. To reach this goal, we exploit a simple round structure mixing a modular multiplication and a multiplication with a binary matrix. Besides being motivated as a new block cipher design for Trojan resilience, our research also exposes the cryptographic properties of the modular multiplication, which is of independent interest.
PENERAPAN METODE SAS (STRUKTURAL ANALITIK SINTETIK) UNTUK MENINGKATKAN KETERAMPILAN MENULIS TEGAK BERSAMBUNG KELAS 1 SD DI BANDUNG

This research driven by the low level of student writing skills. This is because the results of the students' upright writing are still not systematic between the letters, the arrangement is not perfect, the writing fluency still not good and the use of capital letters is not right. The causal factor is that the teacher does not familiarize students to write with upright letters. This study aims to describe the application of the SAS method to improve the upright writing skills of first grade elementary school students in Bandung city. The research subjects were 19 people. The method used in this research was PTK with Kemmis and Mc Taggart models which were implemented in two cycles with quantitative and qualitative approaches. The research instruments used were observation sheets, field notes and documentation. Qualitative data about RPP and learning analyzed by applying the data saving procedure that has been attained. While quantitative data analyzed using percentages. The results of the study showed an increase from cycle I to cycle II with student graduation rates naturally, which was 89%. This indicates that the application of the SAS method is able to improve the upright writing skills of grade I elementary school students.
Children's Literature: Reflection of Society, Culture and Ethnic Groups

This paper looks at the working definition of literature in general and gives an overview of children's literature. It also highlights the peculiar problems of Nigerian children's literature, dwelling on the dearth of children's books. This, inevitably, leads to a discussion of foreign literature and its effects on Nigerian children. Recommendations on how to remedy the current situation are offered, covering the enrichment of the curriculum, encouraging writers, provision and improvement of public and school libraries, parental involvement in the child's development watching out for abnormal behavior in children, and cultivating good habits.
The Preliminary Elementary School Teachers’ Perceptions and Attitudes on the Future English Education Using AI Technology: Focusing on the Students Majoring in English Education

Purpose: The purpose of this study is to investigate preliminary elementary school teachers’ perceptions and attitudes about the upcoming future English education, namely the fourth industrial revolution era. Method: For the research, 91 preliminary elementary school teachers were surveyed. As for a questionnaire form, this study defines preliminary elementary school teachers’ attitudes in the following categories; 1)interests in English education using AI technology, 2)innovation resistance and anxiety about English educa-tion using AI technology. Results: The results of the survey data were as follows. First of all, the preliminary elementary school teach-ers’ interest in the impact of AI technology on English education was high, but their interest remain at the ‘per-sonal’(1-2 stages) level. Second, the level of the preliminary elementary school teachers’ innovation resistance was not high, but the anxiety level was above average. They showed willingness to take AI technologies as in-structional tools and positive attitudes toward AI technology-embedded future classroom. On the one hand, they were anxious about a lack of knowledge about new teaching method and skills related with utilizing AI technology into their classroom. Conclusion: The curriculum should be the tailored curriculum to meet their level and needs, which was re-quired in the fourth industrial revolution through the use of AI technologies. It is to show the possibility of using AI technology to learn English so that we can maintain the current positive perception of the English education using AI that the preliminary school teacher showed. Also, in order to be open to any paradigm changes related to English education in the era of the 4th industrial revolution, concerns and proposals for a new method of Eng-lish education should be actively discussed. Finally, the university of education, which is a teacher training insti-tution, need to introduce to preliminary elementary school teachers what AI technologies can be combined with English education and what are their characteristics, and to guide how AI technology is being applied to English education
Improved Meta-Learning Training for Speaker Verification

Meta-learning has recently become a research hotspot in speaker verification (SV). We introduce two methods to improve the meta-learning training for SV in this paper. For the first method, a backbone embedding network is first jointly trained with the conventional cross entropy loss and prototypical networks (PN) loss. Then, inspired by speaker adaptive training in speech recognition, additional transformation coefficients are trained with only the PN loss. The transformation coefficients are used to modify the original backbone embedding network in the x-vector extraction process. Furthermore, the random erasing data augmentation technique is applied to all support samples in each episode to construct positive pairs, and a contrastive loss between the augmented and the original support samples is added to the objective in model training. Experiments are carried out on the SITW and VOiCES databases. Both of the methods can obtain consistent improvements over existing meta-learning training frameworks. By combining these two methods, we can observe further improvements on these two databases.
Disentangling Parallelism and Interference in Game Semantics

Game semantics is a denotational semantics presenting compositionally the computational behaviour of various kinds of effectful programs. One of its celebrated achievement is to have obtained full abstraction results for programming languages with a variety of computational effects, in a single framework. This is known as the semantic cube or Abramsky's cube, which for sequential deterministic programs establishes a correspondence between certain conditions on strategies (''innocence'', ''well-bracketing'', ''visibility'') and the absence of matching computational effects. Outside of the sequential deterministic realm, there are still a wealth of game semantics-based full abstraction results; but they no longer fit in a unified canvas. In particular, Ghica and Murawski's fully abstract model for shared state concurrency (IA) does not have a matching notion of pure parallel program-we say that parallelism and interference (i.e. state plus semaphores) are entangled. In this paper we construct a causal version of Ghica and Murawski's model, also fully abstract for IA. We provide compositional conditions parallel innocence and sequentiality, respectively banning interference and parallelism, and leading to four full abstraction results. To our knowledge, this is the first extension of Abramsky's semantic cube programme beyond the sequential deterministic world.
Zimbabwean School Libraries as Social Spaces

The success of students in the future is dependent upon the foundation laid down during years spent at school. Libraries play a critical role in shaping the future of students by preparing them for life beyond the school. The school library as a key component of the school curriculum has great potential to shape students through providing opportunities for independent learning. The paper will seek to find out the strategies that school libraries employ to develop student’s interpersonal skills in the advent of ICT’s. It will also examine the role of school libraries in promoting interpersonal skills development amongst students to promote responsible behaviour or citizenship. The paper will explore the extent to which school libraries can utilize technology to enhance interpersonal communication thus enhancing social inclusion. The writer will use a phenomenological paradigm to highlight the challenges and opportunities facing school libraries in urban Harare to provide effective library services to students so that they can be adequately prepared for the dynamic technology driven life premised on such skills like information literacy.
TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption.

Machine learning algorithms have achieved remarkable results and are widely applied in a variety of domains. These algorithms often rely on sensitive and private data such as medical and financial records. Therefore, it is vital to draw further attention regarding privacy threats and corresponding defensive techniques applied to machine learning models. In this paper, we present TenSEAL, an open-source library for Privacy-Preserving Machine Learning using Homomorphic Encryption that can be easily integrated within popular machine learning frameworks. We benchmark our implementation using MNIST and show that an encrypted convolutional neural network can be evaluated in less than a second, using less than half a megabyte of communication.
Presenting and Exploring Challenges in Human-Robot Interaction Design Through Bodystorming

In the coming era of ubiquitous robotics we envision the need for the effortless design of contextually-aware interactions with robots. Ubiquitous robots create a number of challenges for designers. Firstly, due to their dynamic nature, prototyping requires skillful programming and is often time consuming. Moreover, these devices are often context-aware and their behavior is affected by people, objects, and their environment. Existing tools for human-robot interaction designers require programming expertise, do not leverage design methodologies such as iterative design, and do not support in-situ user testing. We propose that bodystorming can be used as an effective method in this process, to communicate needfinding results and to explore the design of situated interactions with robots. As a case study, we first conduct a series of interviews and observe the workflow of human-robot interaction designers, to better understand the challenges they face. We summarize the insights gathered from our needfinding, including challenges around data capture and information overload. We then describe how we used a mystery-game-style role-playing activity in an interactive workshop to communicate our findings, induce empathy, and initiate an effective ideation phase. Finally, we summarize the learnings from this workshop and how such bodystorming techniques can be used to communicate needfinding results at early stages of the human-robot interaction design process.
A Scalable Method of Maintaining Order Statistics for Big Data Stream

Recently, there are some online quantile algorithms that work on how to analyze the order statistics about the high-volume and high-velocity data stream, but the drawback of these algorithms is not scalable because they take ... | Find, read and cite all the research you need on Tech Science Press
Web-based Image Steganography Application to Hide Secret Messages

The Internet has become a very popular medium for communicating and exchanging information data. However, with the development of technology, information data theft is becoming more frequent so it is very necessary to maintain the security of information data. To maintain the security of information data, steganography can be used, namely hiding data or information to a medium. Least Significant Bit (LSB) is a method that can be used to perform the insertion. The Least Significant Bit (LSB) replaces the insignificant or smallest bit value so that when an insertion is done, the difference is not clear. So, this research aims to make a steganography application using the Least Significant Bit (LSB) method.
Analysis of Activation Functions for Convolutional Neural Network based MNIST Handwritten Character Recognition

This research work has deployed a promising approach to recognize handwritten English alphabets and neural digits. In this research work, the deep neural network based Convolution Neural Network model is analyzed and studied for its performance on the MNIST dataset. This standardized dataset comprises handwritten text including numeric and alphabets. During various experimentations, parameters of CNN were configured at multiple instances to analyze its performance in different environments. Though the prime configuration set included the error function, activation function, number of hidden layers, number of epochs, various optimization techniques to resolve the convex and non-convex optimizable objective function. The experimental outcome proves its promising worth comparable to existing arts. The concluded performances of the discussed mode achieve the highest classification rate of 99.65 at sigmoid activation function with cross-entropy error function and foremost the latency in performance is evaluated as the measure of different number of hidden layers.
GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection

Modern 3D object detectors have immensely benefited from the end-to-end learning idea. However, most of them use a post-processing algorithm called Non-Maximal Suppression (NMS) only during inference. While there were attempts to include NMS in the training pipeline for tasks such as 2D object detection, they have been less widely adopted due to a non-mathematical expression of the NMS. In this paper, we present and integrate GrooMeD-NMS -- a novel Grouped Mathematically Differentiable NMS for monocular 3D object detection, such that the network is trained end-to-end with a loss on the boxes after NMS. We first formulate NMS as a matrix operation and then group and mask the boxes in an unsupervised manner to obtain a simple closed-form expression of the NMS. GrooMeD-NMS addresses the mismatch between training and inference pipelines and, therefore, forces the network to select the best 3D box in a differentiable manner. As a result, GrooMeD-NMS achieves state-of-the-art monocular 3D object detection results on the KITTI benchmark dataset performing comparably to monocular video-based methods. Code and models at https://github.com/abhi1kumar/groomed_nms
Literature Circles Online: Practical Strategies for Creating a Reading Culture using Technology

Providing opportunities and learning environments on-line where students are actively engaged and feel in control of their own learning experiences, encourages lifelong learning skills development such as independence, problem-solving and higher order thinking. Discrete on-line environments can also use the functionality of the technology to facilitate different learning styles and provide a 'safe' environment where students can explore and experiment before presenting their ideas to a larger audience. This paper considers current research and the importance of the fiction collection in the development of literacy outcomes for all students. It also examines an interactive on-line module that: incorporates the concept of literature or reading circles to promote reading; the development of higher order literacy skills and encourages the development of a reading culture. The on-line module was developed using a model that utilizes an holistic approach to learning to create a seamless learning environment where students work within a structured framework that is designed to foster independent learning and teamwork.
A Multistakeholder Approach Towards Evaluating AI Transparency Mechanisms

Given that there are a variety of stakeholders involved in, and affected by, decisions from machine learning (ML) models, it is important to consider that different stakeholders have different transparency needs. Previous work found that the majority of deployed transparency mechanisms primarily serve technical stakeholders. In our work, we want to investigate how well transparency mechanisms might work in practice for a more diverse set of stakeholders by conducting a large-scale, mixed-methods user study across a range of organizations, within a particular industry such as health care, criminal justice, or content moderation. In this paper, we outline the setup for our study.
Attitude towards aging among pedagogues due to socio-psychological characteristics of personality

This article explores the phenomenon of successful aging and the substantiating factors, based on studying the attitude towards aging among pedagogues. The dichotomous attitude towards senescence is demonstrated in both, foreign and Russian literature. On the one hand, old age is considered the final stage in life of a person; while on the other hand, despite the increasing decline in physical strength, health and mental processes, it is also a sensitive time for the accumulation of knowledge, experience and intellect, which allows adapting to age peculiarities. Analysis was conducted on the attitude towards aging among pedagogues and the factors that determine it. The sample was comprised of the pedagogues from higher educational institutions, specialized secondary educational establishment, and general education institutions. The acquired results demonstrate that the pedagogues have a positive image of aging, which helps them to better adapt to the aging process.