Reviewerly/exHarmony huggingface dataset:
HYPE: Human-eYe Perceptual Evaluation of Generative Models.

Generative models often use human evaluations to measure the perceived quality of their outputs. Automated metrics are noisy indirect proxies, because they rely on heuristics or pretrained embeddings. However, up until now, direct human evaluation strategies have been ad-hoc, neither standardized nor validated. Our work establishes a gold standard human benchmark for generative realism. We construct Human eYe Perceptual Evaluation (HYPE) a human benchmark that is (1) grounded in psychophysics research in perception, (2) reliable across different sets of randomly sampled outputs from a model, (3) able to produce separable model performances, and (4) efficient in cost and time. We introduce two variants: one that measures visual perception under adaptive time constraints to determine the threshold at which a model's outputs appear real (e.g. 250ms), and the other a less expensive variant that measures human error rate on fake and real images sans time constraints. We test HYPE across six state-of-the-art generative adversarial networks and two sampling techniques on conditional and unconditional image generation using four datasets: CelebA, FFHQ, CIFAR-10, and ImageNet. We find that HYPE can track model improvements across training epochs, and we confirm via bootstrap sampling that HYPE rankings are consistent and replicable.
KARAKTER KERJA KERAS DAN KARAKTERISASINYA DALAM NOVEL PARA PENJAHAT DAN KESUNYIANNYA MASING-MASING

ABSTRACTThis study aims to obtain the character and characterization of the main characters in the novels of the villains and their silence by Eko Triono. This research is a qualitative descriptive study. Data comes from the novels of the villains and their silence by Eko Triono. The data collection technique used is to read literary works in the form of novels of the villains and their silence and mark the descriptions that are in accordance with the context under study. The results and discussion about the character of hard work in accordance with the novel and its characterization. The study of the novels of the Criminals and the Silence of Each of these uses a Psychological Study that leads to the human nature. Results and discussion contain motivating, economic factors, and national development issues that require leaders to do hard work. The hard work character is shown by the author through characterization in the form of indirect characterization and direct characterization. Keyword: Literary, Character, Characterization
Uncertainty in Model-Agnostic Meta-Learning using Variational Inference

We introduce a new, rigorously-formulated Bayesian meta-learning algorithm that learns a probability distribution of model parameter prior for few-shot learning. The proposed algorithm employs a gradient-based variational inference to infer the posterior of model parameters to a new task. Our algorithm can be applied to any model architecture and can be implemented in various machine learning paradigms, including regression and classification. We show that the models trained with our proposed meta-learning algorithm are well calibrated and accurate, with state-of-the-art calibration and classification results on two few-shot classification benchmarks (Omniglot and Mini-ImageNet), and competitive results in a multi-modal task-distribution regression.
Bringing CP, SAT and SMT together: Next Challenges in Constraint Solving (Dagstuhl Seminar 19062)

This report documents the program and the outcomes of Dagstuhl Seminar 19062 Bringing CP, SAT and SMT together: Next Challenges in Constraint Solving, whose main goals were to bring together leading researchers in the different subfields of automated reasoning and constraint solving, foster greater communication betweenthese communities and exchange ideas about new research directions. Constraint solving is at the heart of several key technologies, including program analysis, testing, formal methods, compilers, security analysis, optimization, and AI. During the last two decades, constraint solving has been highly successful and transformative: on the one hand, SAT/SMT solvers have seen a significant performance improvement with a concomitant impact on software engineering, formal methods and security; on the other hand, CP solvers have also seen a dramatic performance improvement, with deep impact in AI and optimization. These successes bring new applications together with new challenges, not yet met by any current technology. The seminar brought together researchers from SAT, SMT and CP along with application researchers in order to foster cross-fertilization of ideas, deepen interactions, identify the best ways to serve the application fields and in turn help improve the solvers for specific domains.
Habits of Mind and Concept Mastery of Cell in Multimedia Virtual Class Environment: A Case of Biology Students in Lampung University

The ability of students to use Information and Communication Technology (ICT) is still not optimally integrated with learning. Even though most students already have gadgets, internet connections are available via campus hotspots. Based on this, learning in higher education cannot be separated from the use of computerized and online-based technology. The integration of ICT into vclass-based multimedia learning is a potential thing to develop. This vclass-based multimedia application is expected to improve habits of mind and conceptual understanding of biology cell of students. Improved learning outcomes can be demonstrated through conceptual understanding which are the ability to understand the concepts that exist in the learning material after the learning process takes place. Keywords: habits of mind, mastery of concepts, cells, virtual class. Abstrak: Kemampuan mahasiswa dalam menggunakan Teknologi Informasi dan Komunikasi (TIK) masih belum optimal diintegrasikan dengan pembelajaran. Padahal sebagian besar mahasiswa sudah memiliki gadget, tersedia koneksi internet melalui hotspot kampus. Berdasarkan hal tersebut, pembelajaran di perguruan tinggi tidak bisa terlepas dari pemanfaatan teknologi yang terkomputerisasi dan berbasis online. Integrasi TIK ke dalam pembelajaran melalui multimedia berbasis vclass merupakan hal yang potensial untuk dikembangkan. Penerapan multimedia berbasis vclass ini dapat meningkatkan habits of mind dan penguasaan konsep sel mahasiswa pendidikan biologi. Peningkatan hasil belajar dapat ditunjukkan melalui penguasaan konsep yang merupakan kemampuan memahami konsep-konsep yang ada dalam materi pembelajaran setelah proses pembelajaran berlangsung. Kata kunci: habits of mind, penguasaan konsep, sel, kelas virtual. DOI: http://dx.doi.org/10.23960/jpmipa/v20i1.pp17-22
The Software for Authorship and Style Attribution

A new program has been developed for style and authorship attribution. Differentiation of styles by transcription symbols has proved to be efficient The novel approach involves a combination of two ways of transforming texts into their transcription variants. The java programming language makes it possible to improve efficiency of style and authorship attribution.
Pelatihan dan Pendampingan Penulisan Karya Ilmiah Remaja bagi Siswa/i SMA/Sederajat di Kecamatan Muara Jawa, Kabupaten Kutai Kartanegara

Many schools only provide intensive mentoring or guidance if there is an agenda to take part in the competition to write scientific papers. But not a few who did not hold these activities, so students did not participate in the competition writing scientific papers. During this time the assistance in facing scientific writing competition was only focused on the problem of writing patterns and content of students' scientific writing. The accompanying teacher mostly corrects the writing and then agrees to be included in the competition. In addition, students' interest in participating in these competitions is also very minimal. For this reason, the need for the provision of Training and Mentoring for Youth Scientific Writing for Students of the High School / Equivalent in Muara Jawa District, Kutai Kartanegara Regency. The results obtained were from 9 groups of students formed during the activity, only 1 group was still conducting consultation and assistance online via email. This shows that students' interest is still lacking and this is a service evaluation so that in the future they can provide more time in activities like this.&#x0D; Keywords: youth scientific writing, training
Principles of Digital Quantum Coprocessor Based on a FPGA, which Operates under the Control of a Classical Computer

Classical quantum computer is analog probabilistic computer. The digital quantum computer that can be implemented in FPGA is described in the article. Digital quantum coprocessor is designed to implement algorithms for execution on analog quantum computers. A digital quantum coprocessor operates under the control of a classical computer and together they are digital quantum computer. The digital quantum coprocessor is a set of digital units called digital qubits that have multibit data input and one bit data output each. The digital qubit is a wave function calculator. There is pseudo random number generator (PRNG) in described digital qubit to generate the probabilistic output bit. One bit qubit output qb is formed by the probable reduction of its multibit result x to one bit (0 or 1) according to result value x and pseudo random code k (x is an angle which determines the position of normalized vector with length 1 in polar grid, it is a result of multibit input data calculation). The decision about output state is made after the functional conversion of the qubit multibit result x to result probability p - sin.x and subsequent comparison p with the pseudo random code k. Qb -1 when k <; p (as in classical quantum computer). In article the alternative variant of decision option with p - arcsin(sqrt(k)) and qb - 1 when x > p is described. This variant allows use one PRNG for all digital qubits. Qubit presentation as vector in Bloch's sphere and vector in polar grid is discussed in the work. Also two types of coprocessor data formats (Cartesian and polar) are described. Instruction set of digital quantum coprocessor based in vector presentation in polar coordinates are discussed.
Learning Goal-Oriented Visual Dialog Agents: Imitating and Surpassing Analytic Experts

This paper tackles the problem of learning a questioner in the goal-oriented visual dialog task. Several previous works adopt model-free reinforcement learning. Most pretrain the model from a finite set of human-generated data. We argue that using limited demonstrations to kick-start the questioner is insufficient due to the large policy search space. Inspired by a recently proposed information theoretic approach, we develop two analytic experts to serve as a source of high-quality demonstrations for imitation learning. We then take advantage of reinforcement learning to refine the model towards the goal-oriented objective. Experimental results on the GuessWhat?! dataset show that our method has the combined merits of imitation and reinforcement learning, achieving the state-of-the-art performance.
Diagnosis study of carcinoma mammae (breast cancer) disease using fuzzy logic method

Breast cancer (Carcinoma mammae) is the second most common disease experienced by women in Indonesia. Lack of awareness or knowledge of this disease in young women is the main reason why there is a lot of case when doctor late to diagnose it and the patients were beyond help. The purpose of this study is to build a program that could show the risk of Carcicona mammae based on the fuzzy logic method. Inputs of this program are parameters that related to this disease taken from study literature and interviewing a doctor. Methods that used in this study were is literature study that related to the diagnosis of Carcinoma mammae disease by using the fuzzy logic method, interviewing a doctor who expert on Carcinoma mammae. Result of this study is a program that connects the fuzzy logic system to determine someone who has Carcinoma mammae or not. The risk is also measured by analyzing the symptoms of Carcinoma mammae such as lump, size, shape, papilla discharge and retraction, and change of skin of mammae. The program is quite good enough to detect an early stage of this disease from young women in early twenty.
A Novel Memetic Algorithm with Explicit Control of Diversity for the Menu Planning Problem

Menu planning is a complex task that involves finding a combination of menu items by taking into account several kinds of features, such as nutritional and economical, among others. In order to deal with the menu planning as an optimization problem, these features are transformed into constraints and objectives. Several variants of this problem have been defined and metaheuristics have been significantly successful solving them. In the last years, Memetic Algorithms (MAs) with explicit control of diversity have lead the attainment of high-quality solutions in several combinatorial problems. The main aim of this paper is to show that these types of methods are also viable for the menu planning problem. Specifically, a simple problem formulation based on transforming the menu planning into a single-objective constrained optimization problem is used. An MA that incorporates the use of iterated local search and a novel crossover operator is designed. The importance of incorporating an explicit control of diversity is studied. This is performed by using several well-known strategies to control the diversity, as well as a recently devised proposal. Results show that, for solving this problem in a robust way, the incorporation of explicit control of diversity and ad-hoc operators is mandatory.
The Development of an Autonomous Maze Robot

This paper presents the design and development of a robot capable to navigate through a simple maze, based on algorithms for maintaining the trajectory, making turns and finding the exit from the maze. Once we have been able to create such a robot, expanding the robot by adding new functionalities leads to its transformation, for example into a room vacuum cleaner, or why not, if the environment is a labyrinth, in a tool capable of determining the shortest route from any two points of the labyrinth. Its dimensions allow exploration of spaces inaccessible to humans. Robots of this kind are based on decision making algorithms, of which we mention the wall tracking algorithm and the flood fill algorithm. The robot presented is based on the wall tracking algorithm running on a microcontroller. This robot is made up of a printed circuit board that also has the role of chassis, two DC gear motors with incremental encoders, six distance sensors and a battery.
Rebooting Virtualization Research (Again)

Visible or hidden, virtualization platforms remain the cornerstone of the cloud and the performance overheads of the latest generations have shrunk. Is hypervisor research dead? We argue that the upcoming trends of hardware disaggregation in the data center motivate a new chapter of virtualization research. We explain why the guest virtual machine abstraction is still relevant in such a new hardware environment and we discuss challenges and ideas for hypervisor and guest OS design in this context. Finally, we propose the architecture of a research platform to explore these questions.
Exploring the Variability of Interconnected Product Families with Relational Concept Analysis

Among the various directions that SPLE promotes, extractive adoption of complex product lines is especially valuable, provided that appropriate approaches are made available. Complex variability can be encoded in different ways, including the feature model (FM) formalism extended with multivalued attributes, UML-like cardinalities, and references connecting separate FMs. In this paper, we address the extraction of variability relationships depicting connections between systems from separate families. Because Formal Concept Analysis provides suitable knowledge structures to represent the variability of a given system family, we explore the relevance of Relational Concept Analysis, an FCA extension to take into account relationships between different families, to tackle this issue. We investigate a method to extract variability information from descriptions representing several inter-connected product families. It aims to be used to assist the design of inter-connected FMs, and to provide recommendations during product selection.
Real-Time Reconfigurable Processor to Detect Similarities in Compressed Video Using Generalized Hough Transformation

In this work, we present a FPGA-based Generalized Hough Transform custom processor to calculate similarities between arbitrary shapes. Raw data are 44 × 36 DC images extracted directly from low-resolution compressed video (352 × 288). The outputs are two numbers per frame that quantify the image similitude in terms of scale and rotation. The proposed architecture efficiently resolves the detection of pixel pairs, and the voting of distances and rotations, without memory access conflicts. These operations are inherent to Hough transformation. The paper condenses some circuit solutions suitable to hardwiring video processing. They take full advantage of using small embedded memories as look-up tables. The complete processor is validated with benchmark video samples that cover different scenarios and problems: sport, drama, and news. The final version internally operates at 100 MHz and fits inside a small FPGA chip. The highly concurrent architecture employs both pipelining and parallelism using hardware replication. The final performance is over 40 Giga fixed-point operations per second.
Research on the Chinese Language and Literature Teaching Assisted by Computer Multimedia Technology

Abstract The computer multimedia technology has changed the traditional teaching form, especially providing the special form to the Chinese language and literature teaching. By using multimedia technology, Chinese language and literature can make characters more vivid and three-dimensional, which promotes the improvement of teaching efficiency and effect. With the application of multimedia technology in modern teaching, its disadvantages are increasingly exposed, such as distracting students’ attention and weakening the teaching pattern. Through the "multimedia hot" cold thinking, this paper analysis the countermeasures on negative impact. We should develop multimedia teaching in a healthy, effective and sustainable way.
A Crowd Science framework to support the construction of a Gold Standard Corpus for Plagiarism Detection

The construction of a Gold Standard Corpus for Plagiarism Detection (GSCPD) is a challenging task for reproducible research in computer science, given that there is a trade off between the time expended by the experts and the size, quality, and reliability of a GSCPD. In such a challenging scenario, this paper describes a framework to support the construction of a GSCPD in any language. Aiming for reproducibility and scalability, the framework involves a data acquisition process and a Crowd Science project that employs human processing power to identify plagiarism in pairs of textual data extracted via the data acquisition process. This papers also presents the application of this framework in Portuguese language and the preliminary results of a feasibility study about the use of a tool that composes the framework.
Using Culturally Responsive Narratives in Virtual Reality To Influence Cognition and Self Efficacy

NeuroSpeculative AfroFeminism (NSAF) is a transformative Virtual Reality (VR) experience that gives women of color an entry point into the tech narrative at the intersection of neuroscience and speculative design [1]. As the experience begins, the user arrives in a speculative world where they explore a futuristic hair salon. The salon is a Neuro Cosmetology lab owned and occupied by women of color who are the lead scientists in the space. NSAF was created in response to the lack of narratives that include women of color at the center of New Media such as VR. This project is the focus of ongoing research to inform the design of culturally responsive VR experiences (tailored to minorities in Computing) in hopes of influencing self-efficacy within these groups. Research has shown that VR can be used to increase functional activity and influence brain reorganization [2]. However, there is little research that explores how VR could be used to influence self-efficacy through its affordance of embodied cognition [3]. The goal of this study is to show how VR-compared to traditional storytelling methods-could be used to tailor learning experiences. This video briefly describes the experience and introduces the project methodology, goals, and expected outcomes.
Using Honeypots in a Decentralized Framework to Defend Against Adversarial Machine-Learning Attacks

The market demand for online machine-learning services is increasing, and so have the threats against them. Adversarial inputs represent a new threat to Machine-Learning-as-a-Services (MLaaSs). Meticulously crafted malicious inputs can be used to mislead and confuse the learning model, even in cases where the adversary only has limited access to input and output labels. As a result, there has been an increased interest in defence techniques to combat these types of attacks. In this paper, we propose a network of High-Interaction Honeypots (HIHP) as a decentralized defence framework that prevents an adversary from corrupting the learning model. We accomplish our aim by (1) preventing the attacker from correctly learning the labels and approximating the architecture of the black-box system; (2) luring the attacker away, towards a decoy model, using Adversarial HoneyTokens; and finally (3) creating infeasible computational work for the adversary.
Crowdsourcing Salt Model Building: Kaggle-TGS Salt Identification Challenge

Summary Can a data scientist without domain knowledge build a machine learning algorithm to identify salt? Can a deep learning network identify salt by training purely on images without any knowledge in geology? There are the fundamental questions at the core of of the Kaggle-TGS Salt Identification Challenge. These are the fundamental questions at the core of the Kaggle-TGS Salt Identification Challenge. In this study, we present an overview of our approach towards constructing the dataset and some results from the Gulf of Mexico showing the performance of salt segmentation algorithms that help us generate useful prior models for seismic interpreters. We also show the comparison of top results from Kaggle competition for TGS Salt Identification Challenge.