Reviewerly/exHarmony huggingface dataset:
Privately Querying Privacy: Privacy Estimation with Guaranteed Privacy of User and Database Party

Many fields of science and industry increasingly rely on the availability of individualized data that a given user may or may not be willing to share. Anonymization methods are often employed to improve user acceptance or are even required by law. However, the user has to rely on the technical competence and faithfulness of the database provider in implementing such measures, and even for honest and competent providers, sophisticated attacks on anonymity may still work. Consequently, many measures have been developed to allow users to estimate their anonymity in a given dataset; however, these measures are typically applied after a user has already offered his data to the provider. Here, we demonstrate how protocols based on cryptographic techniques such as secure multiparty computation and oblivious transfer can be employed to allow a user to query anonymization measures before revealing his own data without compromising the database privacy along the way.
PBNet: Position-specific Text-to-image Generation by Boundary

Most existing methods focus on improving the clarity and semantic consistency of the image with a given text, but do not pay attention to the multiple control of generated image content, such as the position of the object in generated image. In this paper, we introduce a novel position-based generative network (PBNet) which can generate fine-grained images with the object at the specified location. PBNet combines iterative structure with generative adversarial network (GAN). A location information embedding module (LIEM) is proposed to combine the location information extracted from the boundary block image with the semantic information extracted from the text. In addition, a silhouette generation module (SGM) is proposed to train the generator to generate object based on location information. The experimental results on CUB dataset demonstrate that PBNet effectively controls the location of the object in the generated image.
A Symbol Rate Estimation Scheme for Non-Cooperative Communication

In non-cooperative communication, symbol rate estimation is a prerequisite for signal demodulation. However, it is particularly difficult to estimate the symbol rate in the scenario with unknown modulation type and root cosine roll-off shaping. In this paper, an estimator without the requirement of communication parameters is proposed. First, the signal is recognized as constant envelope or non-constant envelope signal by square spectrum. Then, for the former, the symbol rate estimation is based on wavelet transform; for the latter, an envelope velocity strategy is adopted. In the proposed scheme, we elaborate some design details, including resampling factor, extraction of discrete spectral lines in the squared spectrum, selection of wavelet transform scale, and compensation of frequency deviation. We further propose a low-complexity implementation to meet the requirements for real-time capability and accuracy. Simulation results show that our proposed scheme is effective and accurate.
1 Measuring the Shapes of Ancient Greek Vases

Although the shapes of ancient Greek vases have been studied for a century, these studies have been nearly entirely qualitative. Here we develop a quantitative method to study their shapes that relies on a rich and largely untapped resource: photographs. We compare profiles of 140 Iron-Age Greek vases derived from photographs to those derived from either manual measurement or 3d scans of their cognate vases, and test their accuracy by means of a machine classifier. We show that photograph-derived profiles are almost as good as manual or 3d-derived profiles for classification purposes. We also show that photograph-derived profiles can be used to detect subtle changes in shape such as those that arise over short time evolution of vase shape, suggesting that they are sufficiently accurate to enable the analysis of large corpora of vase images.
Popularity-Guided Cost Optimization for Live Streaming in Mobile Edge Computing

Live streaming service usually delivers the content in mobile edge computing (MEC) to reduce the network latency and save the backhaul capacity. Considering the limited resources, it is necessary that MEC servers collaborate with each other and form an overlay to realize more efficient delivery. The critical challenge is how to optimize the topology among the servers and allocate the link capacity so that the cost will be lower with delay constraints. Previous approaches rarely consider server collaborations for live streaming service, and the scheduling delay is usually ignored in MEC, leading to suboptimal performances. In this paper, we propose a popularity-guided overlay model which takes the scheduling delay into consideration and utilizes MEC collaboration to achieve efficient live streaming service. The links and servers are shared among all channel streams and each stream is pushed from cloud servers to MEC servers via the trees. Considering the optimization problem is NP-hard, we propose an effective optimization framework called cost optimization for live streaming (COLS) to predict the channel popularity by a LSTM model with multiscale input data. Finally, we compute topology graph by greedy scheme and allocate the capacity with convex programming. Experimental results show that the proposed approach achieves higher prediction accuracy, reducing the capacity cost by more than 40% with an acceptable delay compared with state-of-the-art schemes.
Apotheosis

Other| November 01 2021 Apotheosis Michael Welch Michael Welch Search for other works by this author on: This Site Google the minnesota review (2021) 2021 (97): 25. https://doi.org/10.1215/00265667-9335688 Cite Icon Cite Share Icon Share Twitter Permissions Search Site Citation Michael Welch; Apotheosis. the minnesota review 1 November 2021; 2021 (97): 25. doi: https://doi.org/10.1215/00265667-9335688 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search Books & JournalsAll Journalsthe minnesota review Search Advanced Search The text of this article is only available as a PDF. Copyright © 2021 Michael Welch2021 Article PDF first page preview Close Modal You do not currently have access to this content.
Gobang Game Algorithm Based on Reinforcement Learning

The traditional Gobang game program generally evaluates the chess type, and the game power mostly depends on the developer’s understanding of Gobang. Reinforcement learning combined with Monte Carlo tree search and upper confidence bound algorithm is introduced into the Gobang game in this work. Convolutional Neural Network is used to evaluate the current game board situation. Besides, the article also adjusts the neural network structure, introduces a small amount of calculation kill, artificial knowledge, and other programs to improve reinforcement learning. After 100,000 times of game training, the winning rate of the game program applied proposed methods has increased from 13% to 100% compared with the open-source Gobang game program Pela.
AGRP: A Fused Aspect-Graph Neural Network for Rating Prediction

User reviews have been widely used in rating prediction. Among rating prediction methods, the aspect-based method has proven effective because the aspects are argued as an effective expression of user preferences and item attributes. However, aspect-based methods still suffer from problems. First, the aspects are usually very sparse. Moreover, enhancing the connection between the aspects and using them to build an effective attention mechanism remains a challenge. In this paper, we propose a fused aspect-graph neural network for rating prediction (AGRP). We first extract three types of aspects, namely the user reviews aspect (URA), the item reviews aspect (IRA), and the item description aspect (IDA). We believe that these three types of aspects have different reflections on user preferences and item features. Then we propose a novel aspect graph, where the nodes represent the aspects and the edges represent the co-occurrence connection as well as the similarity connection between the aspects. The enriched aspects and connection help to address the issues of sparsity. Finally, a graph-based attention fusion method is proposed to fuse the aspect graph with the neural network through a graph attention network. The experiments show that AGRP consistently outperforms the state-of-the-art recommendation approaches on four Amazon datasets, which proves the effectiveness of our model.
Surfacing Thematic Universe using Knowledge Mining and Unsupervised Concept Graph

Thematic investing is a financial offering for investors which aims to identify macro-level forward-looking emerging trends of economy and invest capital in themes that best reflect their conviction. The thematic portfolio construction requires flexible creation and analysis of basket of companies that will benefit from the trend/theme in long term. The current sectorial investing approach are predefined, constrained and not granular as it cannot surface companies based on theme of interest dynamically, thus do not account for changing trends. This paper explores text mining techniques that can be leveraged to discover concepts automatically from textual data and utilize the discovered concepts to mine thematic universe. The paper proposes an unsupervised approach to create contextual clusters of concepts (concept graph) from unstructured text where concepts are linked to companies. The paper describes the NLP pipeline utilizing phrases extraction and hierarchical clustering using contextual embeddings (Bert like embeddings) for concept graph creation. We evaluated semantic probing methods of surfacing thematic companies by overlapping it with companies in competitor's thematic exchange traded funds (ETFs). We compared automated concept-cluster approach with keyword search approach on the same corpus and demonstrates that former outperforms the later in recall rate by absolute 16%, 12% and 8% in top 100 ranked companies for three themes namely cybersecurity, cloud computing and clean energy respectively.
COVID-19 Tracking Mobile Application at ITCG

Objectives: Implement a mobile application to control COVID-19 in all its variants, containing or reducing the spread of the virus during the return to face-to-face classes at the ITCG. Methodology: The project was developed in Android Studio using different libraries and technological resources. You have access to a web server with a MySQL database. The application allows scanning the QR code of the visited site from the mobile application, reporting a positive case and vaccine reactions, monitoring any unusual adverse effects, as well as receiving notifications via SMS. Contribution: Contain the advance of the virus, avoiding saturating health systems, monitoring users in the event of any eventuality. The application was distributed to the student community of the Technological Institute of Ciudad Guzmán (ITCG) during the application of the first dose of the AstraZeneca vaccine, to monitor any symptoms, as well as positive cases during the return to face-to-face classes.
Curriculum Meta Learning

Meta-learning is a machine learning paradigm that extracts crosstask knowledge by learning a large number of subtasks, to fast adapt to new tasks. Many meta-learning methods are widely applied in few-shot classification. These methods adopt an episodic training strategy, and the learning subtasks are sampled uniformly from the task distribution. In this paper, we explore the effect of the order of training subtasks on the performance of different meta-learning algorithms and propose a curriculum learning framework to improve the generalization performance. We define the hardness of subtasks at the class level and guide the model to learn training subtasks from easy to hard. We evaluate our curriculum learning framework on two few-shot classification benchmarks (mini-ImageNet and FC100), and it achieves improvements across different meta-learning algorithms and datasets. In the cross-domain scenario, we compare the performance of different meta learning algorithms under three curriculum settings. The results show that our CL approach improves significantly the generalization performance of different meta-learning methods.
Improving Gradient-based DAG Learning by Structural Asymmetry

Directed acyclic graph (DAG) learning plays a fun-damental role in causal inference and other scientific scenes, which aims to uncover the relationships between variables. However, identifying a DAG from observational data has al-ways been a challenging task. Recently, gradient-based DAG learning algorithms that convert a combination-optimization DAG learning problem into a continuous-optimization problem have achieved emerging successes. These algorithms are easy to optimize and able to deal with both parametric and non-parametric data but suffer from many reversed edges learnt by these algorithms. In this paper, we propose a framework named Residual Independence Test (RIT) to correct those reversed edges by leveraging the structural asymmetry reflected in the depen-dence between regression residual and direct cause. We conduct extensive experiments on both synthetic and benchmark datasets, the results show that the RIT framework significantly improve the performance of gradient-based DAG learning algorithms.
A User-manageable Credential System based on Blockchain

Credentials are one of the most important things to prove oneself in modern society. As the world goes digital, credentials change its form from paper to digital. However, digital data are easy to be forged and to be duplicated. To make digital data secure, cryptographic digital signature is the key technology ensuring its authenticity and integrity. And it is an inevitable choice for secure credential management systems. Recently blockchain has been raised as another key technology to guarantee data transparency. It is a decentralized and public ledger of transactions that anyone can participate and execute programs called smart contract. In this paper, we propose a credential management system based on Ethereum blockchain. This system provides users with a means both to manage their credentials by themselves and to prove their credentials without the issuer. We implemented its prototype on Ethereum, and analyzed the proposed system compared to previous systems.
Theoretical foundations of the formation of the readiness of future teachers to work in the regional digital educational environment

The purpose of the article is to characterize the conceptual foundations of the content and structure of the regional system of teacher education in the digital educational environment. The directions of modernization of the teacher training system are based on the substantiation of scientific approaches to the ways of organizing the interaction of the subjects of the educational process in the region with an emphasis on eliminating the professional deficits of teachers, on the formation of a new type of competencies among teachers for the implementation of pedagogical activities at a high level in a regional digital educational environment, taking into account its specifics.
A gentle introduction to theory (for non-theoreticians)

tutorial A gentle introduction to theory (for non-theoreticians) Author: Benjamin Doerr Institut Polytechnique de Paris Institut Polytechnique de ParisView Profile Authors Info & Claims GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2021 Pages 369–398https://doi.org/10.1145/3449726.3461406Published:08 July 2021Publication History 0citation111DownloadsMetricsTotal Citations0Total Downloads111Last 12 Months31Last 6 weeks8 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Trust Mechanism on the Relationship of Community Residents Based on IoT and Video Tracking Algorithms

Trust mechanism on the relationship of the community residents based on IoT and video tracking algorithms is studied in this paper. Decentralized dynamic trust management and safe and transparent use of user sensitive data and cloud services are essential. However, the identity alliance has significant security and tracking risks, and does not protect user privacy well. Hence, the trust model with blochain is essential. The situation of the general platform in this article is that one receiving end corresponds to multiple sending ends and this IoT framework is then applied into the community. The experimental results are simulated with modelling.
INDIVIDUAL EDUCATIONAL TRAJECTORY OF A YOUNG TEACHER: THE FOUNDATIONS OF FORMATION

Ponte Academic JournalJan 2022, Volume 78, Issue 1 INDIVIDUAL EDUCATIONAL TRAJECTORY OF A YOUNG TEACHER: THE FOUNDATIONS OF FORMATIONAuthor(s): Kuznetsova Yu. A. ,Matveeva N. A., Veretennikova L. A., Zabneva E. I.J. Ponte - Jan 2022 - Volume 78 - Issue 1 doi: 10.21506/j.ponte.2022.1.4 Abstract:The formation of a young teacher is a complex process that requires detailed study. Along with the aging of teaching staff, the increase in vacancies in schools, the increasing need for a highly qualified teacher, the task of training and accompanying a young teacher who is able to successfully adapt and gain a foothold in educational organizations is being actualized. The purpose of this work is to analyze the problem space of a young teacher\'s professional activity in order to create complex conditions for his professional development and consolidation in the education system. It is based on a systematic approach that allows the collection of primary information, processing and analysis, generalization and description, followed by the design and testing of system measures of various formats and levels, and also contribute to the formation of system-forming horizontal and vertical interactions between young teachers, pedagogical university and employer. Based on the screening study of professional difficulties of 83 young specialists of the Altai Territory education system in 2014-2016 and 2021, the main directions of designing a system of adaptation and support for the professional development of young teachers in the Altai Territory were determined. The study revealed that from 2014 to 2021, young teachers became more demanding of their knowledge and work skills (32%), more clearly understand the mindset to determine their own trajectory of professional growth (25%), better adapt to the conditions of professional activity in rural areas (16%). In total, 6 directions are identified that form the conceptual basis for the educational space formation of a young teacher: \&amp;amp;amp;amp;amp;quot;The trajectory of professional development\&amp;amp;amp;amp;amp;quot;; \&amp;amp;amp;amp;amp;quot;Development and improvement of professional competence of a small school specialist\&amp;amp;amp;amp;amp;quot;; \&amp;amp;amp;amp;amp;quot;School of responsible parenthood and teacher-parent interaction\&amp;amp;amp;amp;amp;quot;; \&amp;amp;amp;amp;amp;quot;The specifics of teaching and educating gifted and highly motivated children\&amp;amp;amp;amp;amp;quot;; \&amp;amp;amp;amp;amp;quot;Social networks as a tool of a teacher\'s professional communication\&amp;amp;amp;amp;amp;quot;; \&amp;amp;amp;amp;amp;quot;The role of digital technologies and robotechnics in the development of educational space\&amp;amp;amp;amp;amp;quot;. Download full text:Check if you have access through your login credentials or your institution Username Password
Distance Measurement in Industrial Scenarios

Abstract 3D reconstruction technology is one of the important technologies of computer vision. Compared with 2D data, 3D space contains more abundant information, including location information, local / global features and so on. It is of great significance to solve the problem of target distance measurement in a single image scene. It is difficult for neural network to predict hole size without image scale information. In this paper, we focus on the measurement of objects with little change in height in the image. We use the relationship between camera parameters and regional features and the internal relationship between regional features to solve the problems of three-dimensional parameter reconstruction and monocular image distance measurement and use the standard cross entropy loss to optimize the transformer model. This model has achieved good results on the sampled data set.
Tailored Software Design for Remote Communication Skill Development in Software Engineering Curriculums

Communication skill is one of the most crucial skills that Software Engineering (SE) industry demands. According to literature, it is evident that oral communication, feedback, and presentation skills are the most crucial among all. SE curriculums need to be structured to prepare its undergraduates to meet these demands. Due to Covid-19 pandemic, most universities opted for online education. Therefore, communication skill development inevitably needed to be conducted remotely. Current mechanisms used in these remote sessions do not cater to unique needs of SE industry. This paper proposes a design of a plugin to be used with Zoom to analyze and improve student&#x0027;s communication skills real time. The proposed design is inclusive of two phases. In phase one, variables which determine communication skills are identified. In relation to these variables, student communication data will be collected for a period of a semester. The data collection will occur via Zoom sessions, for a selected project-based learning (PBL) module. In phase two, a machine learning model will be created using the gathered data in phase 1, and the plugin will be implemented. The plugin will categorize the students&#x0027; communication skills into 3 categories such as &#x201C;weak&#x201D;, &#x201C;average&#x201D; and &#x201C;good&#x201D;. The proposed plugin generates a comprehensive report on student&#x0027;s communication skills which could be downloaded at the end of each session. The expected accuracy rate of the plugin is 80&#x0025;.
Research on success factors of internet product project management with grounded theory

There are obvious differences between internet product project management (IPPM) and traditional industry project management. Therefore, it is of great value to analyse and find the key success factors of IPPM. According to grounded theory, we interviewed 30 project management engineers, who had rich experience in IPPM from different top internet enterprises (such as NetEase, ByteDance, Taobao and JD, etc. in China), identified 40 success factors of IPPM, and constructed a theoretical model of IPPM success factors based on project external factors, project team factors and project management process factors. We also determined the top ten key success factors with the use of the analytic network process; analysed and verified the success factors of IPPM with practical wisdom (the six abilities of wise leaders, SAWL). The key success factors of IPPM are consistent with the practical wisdom (SAWL) and have practical application.