Reviewerly/exHarmony huggingface dataset:
Stakeholders in the cloud computing value-chain : A socio-technical review of data breach literature

This paper is about stakeholders in the cloud computing value-chain. Early cloud computing literature focused on the technical aspect of the technology and viewed the provider and customer as essential value-chain stakeholders. The more users that use cloud services, the potential for data breaches increases. The review of the literature was carried out using a social-technical approach. Socio-technical theory encapsulates the social, technical and environmental dimensions of a system. The outcomes of the search indicated that there are two pertinent stakeholder types: operational and non-operational. Operational stakeholders include cloud providers, customers, enablers, resellers and third-party providers. Non-operational stakeholders include regulators, legislators, courts, non-government organisations, law enforcement, industry-standard bodies and end-users. The end-users are critically important in the cloud value-chain in that they rely on online services for everyday activities and have their data compromised. The cloud value-chain presents that cloud services encapsulate more than just technology services. The paper considers the complex stakeholder relationships and data breach issues, indicating the need for a better socio-technical response from the stakeholders within the value-chain.
How Do Adam and Training Strategies Help BNNs Optimization?

The best performing Binary Neural Networks (BNNs) are usually attained using Adam optimization and its multi-step training variants. However, to the best of our knowledge, few studies explore the fundamental reasons why Adam is superior to other optimizers like SGD for BNN optimization or provide analytical explanations that support specific training strategies. To address this, in this paper we first investigate the trajectories of gradients and weights in BNNs during the training process. We show the regularization effect of second-order momentum in Adam is crucial to revitalize the weights that are dead due to the activation saturation in BNNs. We find that Adam, through its adaptive learning rate strategy, is better equipped to handle the rugged loss surface of BNNs and reaches a better optimum with higher generalization ability. Furthermore, we inspect the intriguing role of the real-valued weights in binary networks, and reveal the effect of weight decay on the stability and sluggishness of BNN optimization. Through extensive experiments and analysis, we derive a simple training scheme, building on existing Adam-based optimization, which achieves 70.5% top-1 accuracy on the ImageNet dataset using the same architecture as the state-of-the-art ReActNet while achieving 1.1% higher accuracy. Code and models are available at https://github.com/liuzechun/AdamBNN.
Exploring Semantic Relationships for Unpaired Image Captioning

Recently, image captioning has aroused great interest in both academic and industrial worlds. Most existing systems are built upon large-scale datasets consisting of image-sentence pairs, which, however, are time-consuming to construct. In addition, even for the most advanced image captioning systems, it is still difficult to realize deep image understanding. In this work, we achieve unpaired image captioning by bridging the vision and the language domains with high-level semantic information. The motivation stems from the fact that the semantic concepts with the same modality can be extracted from both images and descriptions. To further improve the quality of captions generated by the model, we propose the Semantic Relationship Explorer, which explores the relationships between semantic concepts for better understanding of the image. Extensive experiments on MSCOCO dataset show that we can generate desirable captions without paired datasets. Furthermore, the proposed approach boosts five strong baselines under the paired setting, where the most significant improvement in CIDEr score reaches 8%, demonstrating that it is effective and generalizes well to a wide range of models.
Affective Image Content Analysis: Two Decades Review and New Perspectives

Images can convey rich semantics and induce various emotions in viewers. Recently, with the rapid advancement of emotional intelligence and the explosive growth of visual data, extensive research efforts have been dedicated to affective image content analysis (AICA). In this survey, we will comprehensively review the development of AICA in the recent two decades, especially focusing on the state-of-the-art methods with respect to three main challenges -- the affective gap, perception subjectivity, and label noise and absence. We begin with an introduction to the key emotion representation models that have been widely employed in AICA and description of available datasets for performing evaluation with quantitative comparison of label noise and dataset bias. We then summarize and compare the representative approaches on (1) emotion feature extraction, including both handcrafted and deep features, (2) learning methods on dominant emotion recognition, personalized emotion prediction, emotion distribution learning, and learning from noisy data or few labels, and (3) AICA based applications. Finally, we discuss some challenges and promising research directions in the future, such as image content and context understanding, group emotion clustering, and viewer-image interaction.
RANCANG BANGUN APLIKASI BELAJAR PEMROGRAMAN DENGAN GAME EDUCATION PADA SMARTPHONE BERBASIS ANDROID

Programming is a vocational subject in the Vocational School majoring in Software Engineering and Electronics. Programming is classified as a subject that is less desirable and difficult for students, because these subjects are not graphically based and students do not understand the basic structure of the program. And less Affective use of conventional learning resources as the main source of the learning. With the existence of smartphones can be used as an additional source in the world of education. One of them is the use of smartphones as learning media. The purpose of this study is to produce learning media that can help teachers and students in the learning process by loading educational games that can attract students' interest in learning. This application is made using the System Development Life Cycle (SDLC) Prototype method, using an Android-based java programming language, by applying game education algorithms and word game genres where players must arrange random letters into coherent sentences, the random letters will be matched with the answers stored in the database to be the correct answer. The content in the application is the 2013 curriculum syllabus. The material consists of RPP and teaching materials for each basic competency. Ebooks, articles and videos that discuss basic programming. Evaluation is a practice question to measure the level of understanding of students and quiz games in the form of stacking words to train students' memory about basic programming.Keywords: Android, Programming, Game education. word game
Tighter Analysis of Alternating Stochastic Gradient Method for Stochastic Nested Problems

Stochastic nested optimization, including stochastic compositional, min-max and bilevel optimization, is gaining popularity in many machine learning applications. While the three problems share the nested structure, existing works often treat them separately, and thus develop problem-specific algorithms and their analyses. Among various exciting developments, simple SGD-type updates (potentially on multiple variables) are still prevalent in solving this class of nested problems, but they are believed to have slower convergence rate compared to that of the non-nested problems. This paper unifies several SGD-type updates for stochastic nested problems into a single SGD approach that we term ALternating Stochastic gradient dEscenT (ALSET) method. By leveraging the hidden smoothness of the problem, this paper presents a tighter analysis of ALSET for stochastic nested problems. Under the new analysis, to achieve an $ε$-stationary point of the nested problem, it requires ${\cal O}(ε^{-2})$ samples. Under certain regularity conditions, applying our results to stochastic compositional, min-max and reinforcement learning problems either improves or matches the best-known sample complexity in the respective cases. Our results explain why simple SGD-type algorithms in stochastic nested problems all work very well in practice without the need for further modifications.
A survey on state-of-the-art of cloud computing: Its challenges and solutions

Cloud computing provides a good services to internet users on low cost basis. Going towards the concept of online services, resources are shifting towards the distributive systems. Security is the main issue of cloud computing. This paper surveys the idea of cloud computing and challenges of security issues in cloud computing and its solutions which effects the performance. We also investigate the security threats and its solutions. We survey three current methods for threat detection and evaluate its performances.
The Patentability of Computer-Implemented Simulations and Implications for Computer-Implemented Inventions (CIIs)

Abstract G 1/19 (Simulations), Enlarged Board of Appeal of the European Patent Office, 10 March 2021 The Enlarged Board of Appeal of the European Patent Office (EPO) held that a computer-implemented simulation of a technical system or process which is claimed as such can, for the purpose of assessing inventive step, solve a technical problem by producing a technical effect going beyond the simulation’s implementation on a computer; and that the established case law on computer-implemented inventions (COMVIK approach) applies to computer-implemented simulations even if these do not have an output having a direct link with physical reality.
Energy-Based Generative Cooperative Saliency Prediction

Conventional saliency prediction models typically learn a deterministic mapping from an image to its saliency map, and thus fail to explain the subjective nature of human attention. In this paper, to model the uncertainty of visual saliency, we study the saliency prediction problem from the perspective of generative models by learning a conditional probability distribution over the saliency map given an input image, and treating the saliency prediction as a sampling process from the learned distribution. Specifically, we propose a generative cooperative saliency prediction framework, where a conditional latent variable model~(LVM) and a conditional energy-based model~(EBM) are jointly trained to predict salient objects in a cooperative manner. The LVM serves as a fast but coarse predictor to efficiently produce an initial saliency map, which is then refined by the iterative Langevin revision of the EBM that serves as a slow but fine predictor. Such a coarse-to-fine cooperative saliency prediction strategy offers the best of both worlds. Moreover, we propose a ``cooperative learning while recovering" strategy and apply it to weakly supervised saliency prediction, where saliency annotations of training images are partially observed. Lastly, we find that the learned energy function in the EBM can serve as a refinement module that can refine the results of other pre-trained saliency prediction models. Experimental results show that our model can produce a set of diverse and plausible saliency maps of an image, and obtain state-of-the-art performance in both fully supervised and weakly supervised saliency prediction tasks.
Influence of Achievement Motivation and Locus of Control Towards Competence of Lecturer Performance in AKUBANK ICM Cantrika Mitra Medan

This research discussed about lecturer competence in Akubank ICM Cantrika Mitra Medan of knowledge to develop increase the competence, namely by increasing education and training for lecturers who are still considered less competence. This study used quantitative methods, with questionnaires over 50 population. The Lecturer's performance is influenced by a number of factors, including (1) internal factors and (2) external factors. From the data found, motivation and locus of control tend to be high (53.33%). The average value of the overall cognitive abilities of lecturers is 81.92. Motivation and locus of control directly influences the performance of permanent lecturers. In other words, the better the support of motivation and locus of control, the higher the performance of permanent lecturers. Motivation and locus of control directly influence the performance of lecturers, all indicators in cognitive abilities are categorized as good, with indicators of the motivation of aspects of experience, skills and evaluations said to be good in constructing the variable motivation and locus of control.
Application of APRIORI correlation algorithm on music education curriculum association rules

Abstract Association rules is a research direction of data mining. It can be used in all walks of life by analyzing the correlation of data items in a large amount of data to mine the internal connections between data items. The various professional courses in colleges and universities are independent of each other, and there is a certain sequence of classes, which will also have a certain influence on each other. The learning situation of one course may also affect the learning of other courses. This article uses the APRIORI correlation algorithm to analyze the performance of music education majors, reveals the connections between and within the various course groups, and provides guidance for students’ choice and study of professional courses.
DEVELOPMENT OF INTEREST IN PROFESSIONAL TRAINING AT THE UNIVERSITY AMONG FUTURE ARCHITECTS AND DESIGNERS AT THE CURRENT STAGE

Despite the difficulties encountered, in connection with the transition to a new system of higher education, and the process of professional training of architects and designers remains the most important tool for preserving the national "architectural school" in Russia. Today, in higher architectural education in Russia, there has been a reduction in the basic educational program in many disciplines in favor of independent work. This and other factors lead to some misunderstanding and loss of interest in the training process among students. The scientific novelty of the study consists in determining a set of pedagogical conditions for the development of interest in professional training at the university among future architects and designers at the current stage; in clarifying the content of the concepts of "professional architectural activity", "emotionally comfortable creative educational environment", "interest in professional architectural and design training", which made it possible to systematize the theoretical basis for the development of interest in professional activity among future architects and designers. The results of the experiment were obtained, during which a set of pedagogical conditions were implemented to increase the level of development of interest in professional training at the university among future architects and designers.
Comparing UML-Based and DSL-Based Modeling from Subjective and Objective Perspectives

In the last two decades, researchers have conducted several empirical evaluations, involving thousands of subjects, to understand the use of models in software development. The results of these evaluations show that most of the subjects make informal use of the models, which is known as ‘modeling as sketch’. In this paper, we present an experiment that compares UML-based and DSL-based modeling when subjects model a part of a commercial video game. In the comparison, we have used objective and subjective measures, in contrast to other works that focus either on objective measures to evaluate modeling performance or on subjective measures to analyze modeling styles. Our results reveal that subjects underestimate the potential of their own models. Our finding is relevant for the design of future evaluations and for the teaching and adoption of modeling. If users correctly assess their models, they might leverage their potential as programs.
Mandoline: Model Evaluation under Distribution Shift

Machine learning models are often deployed in different settings than they were trained and validated on, posing a challenge to practitioners who wish to predict how well the deployed model will perform on a target distribution. If an unlabeled sample from the target distribution is available, along with a labeled sample from a possibly different source distribution, standard approaches such as importance weighting can be applied to estimate performance on the target. However, importance weighting struggles when the source and target distributions have non-overlapping support or are high-dimensional. Taking inspiration from fields such as epidemiology and polling, we develop Mandoline, a new evaluation framework that mitigates these issues. Our key insight is that practitioners may have prior knowledge about the ways in which the distribution shifts, which we can use to better guide the importance weighting procedure. Specifically, users write simple "slicing functions" - noisy, potentially correlated binary functions intended to capture possible axes of distribution shift - to compute reweighted performance estimates. We further describe a density ratio estimation framework for the slices and show how its estimation error scales with slice quality and dataset size. Empirical validation on NLP and vision tasks shows that Mandoline can estimate performance on the target distribution up to 3x more accurately compared to standard baselines.
Development of Motion Graphic as Education Material for Promoting Shipping Industry Using EPIC Model Testing

Education related to problems in the shipping industry to the general public who have no background in the shipping industry and activities when ships dock at the port becomes a challenge to conduct research. This research develops multimedia motion graphics to combine graphic design, animation, text, and sound in visualizing materials and activities in the shipping industry. Stages of making multimedia start from ideas, narration, storyboard, tracing, editing, rendering, and testing. The research uses the EPIC Model testing by paying attention to aspects of empathy, persuasion, impact, and communication. The results of testing the effectiveness of the EPIC model obtained an EPIC rate of 4.15, which means that the resulting motion graph falls within the effective range criteria.&#x0D; &#x0D; Index terms— multimedia; motion graphic; EPIC model; effectiveness
Digital Literacy and its Acquisition by Teachers and Principals at Educational Workplaces

This study investigates the digital literacy skills among workers in the educational workplace in Jordon. A convenience sample of 139 teachers and 73 administrators spread over 173 public schools and 39 private schools was selected. The study followed mixed quantitative and qualitative methods. Data were collected using two instruments: a semi structured interview to investigate and monitor the digital literacy skills; a questionnaire to measure the availability degree of the digital literacy skills and the significant differences in the availability degree due to the job title and the workplace. In this study, four areas represent digital literacy skills: functional skills, information skills, digital competency in the educational process and digital competency in empowering learners. The availability degree of digital skills is medium in general and statistically significantly higher in administrators than in teachers. The results showed that private sector workers have better digital literacy skills than their public sector peers. Finally, the study concluded that functional and information skills increase digital competency in the educational learning process and empower learners.
New results on quasi-subfield polynomials

Quasi-subfield polynomials were introduced by Huang et al. together with a new algorithm to solve the Elliptic Curve Discrete Logarithm Problem (ECDLP) over finite fields of small characteristic. In this paper we provide both new quasi-subfield polynomial families and a new theorem limiting their existence. Our results do not allow to derive any speedup for the new ECDLP algorithm compared to previous approaches.
Importance of Diagonal Gates in Tensor Network Simulations

In this work we present two techniques that tremendously increase the performance of tensor-network based quantum circuit simulations. The techniques are implemented in the QTensor package and benchmarked using Quantum Approximate Optimization Algorithm (QAOA) circuits. The techniques allowed us to increase the depth and size of QAOA circuits that can be simulated. In particular, we increased the QAOA depth from 2 to 5 and the size of a QAOA circuit from 180 to 244 qubits. Moreover, we increased the speed of simulations by up to 10 million times. Our work provides important insights into how various techniques can dramatically speed up the simulations of circuits.
Random Walk and Shared Neighbors-Based Similarity for Patterns in Graph Data

This paper discusses the similarity of patterns in graph data. Here, the so-named graph data is composed by two absolutely different information, one is pattern’s attribute information and the other is the relation between patterns, i.e., topological information. Bearing this in mind, a random walk and shared neighbors-based similarity measurement for patterns is proposed. In this measurement, the reachability of any two patterns is fully studied. For the patterns topological structure is connected, we consider the similarity of shared neighbors and topological information. For patterns, whose topological structure is unconnected, we only consider the attribute-based information of patterns. On this base, a composite and novel measurement similarity for patterns in graph data is constructed. In conclusion, an example is given to verify the effectiveness of the measurement.
A ROS-Gazebo Interface for the Katana Robotic Arm Manipulation

Nowadays, simulators are being used more and more during the development of robotic systems due to the efficiency of the development and testing processes of such applications. Undoubtedly, these simulators save time, resources and costs, as well as enable ease of demonstrations of the system. Specifically, tools like the open source Robotic Operating System (ROS) and Gazebo have gained popularity in building models of robotic systems. ROS is extensively used in robotics due to the pros of hardware abstraction and code reuse. The Gazebo platform is used for visualisation because of its high compatibility with ROS. In this paper, ROS and Gazebo have been integrated to build an interface for the visualisation of the Katana Arm manipulator.
Analytically Tractable Bayesian Deep Q-Learning.

Reinforcement learning (RL) has gained increasing interest since the demonstration it was able to reach human performance on video game benchmarks using deep Q-learning (DQN). The current consensus for training neural networks on such complex environments is to rely on gradient-based optimization. Although alternative Bayesian deep learning methods exist, most of them still rely on gradient-based optimization, and they typically do not scale on benchmarks such as the Atari game environment. Moreover none of these approaches allow performing the analytical inference for the weights and biases defining the neural network. In this paper, we present how we can adapt the temporal difference Q-learning framework to make it compatible with the tractable approximate Gaussian inference (TAGI), which allows learning the parameters of a neural network using a closed-form analytical method. Throughout the experiments with on- and off-policy reinforcement learning approaches, we demonstrate that TAGI can reach a performance comparable to backpropagation-trained networks while using fewer hyperparameters, and without relying on gradient-based optimization.