Reviewerly/exHarmony huggingface dataset:
Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data

We propose a novel unsupervised generative model that learns to disentangle object identity from other low-level aspects in class-imbalanced data. We first investigate the issues surrounding the assumptions about uniformity made by InfoGAN, and demonstrate its ineffectiveness to properly disentangle object identity in imbalanced data. Our key idea is to make the discovery of the discrete latent factor of variation invariant to identity-preserving transformations in real images, and use that as a signal to learn the appropriate latent distribution representing object identity. Experiments on both artificial (MNIST, 3D cars, 3D chairs, ShapeNet) and real-world (YouTube-Faces) imbalanced datasets demonstrate the effectiveness of our method in disentangling object identity as a latent factor of variation.
Generalized List Decoding

This paper concerns itself with the question of list decoding for general adversarial channels, e.g., bit-flip (XOR) channels, erasure channels, AND (Z-) channels, OR channels, real adder channels, noisy typewriter channels, etc. We precisely characterize when exponential-sized (or positive rate) (L-1)-list decodable codes (where the list size L is a universal constant) exist for such channels. Our criterion essentially asserts that:For any given general adversarial channel, it is possible to construct positive rate (L-1)-list decodable codes if and only if the set of completely positive tensors of order-L with admissible marginals is not entirely contained in the order-L confusability set associated to the channel.The sufficiency is shown via random code construction (combined with expurgation or time-sharing). The necessity is shown by 1) extracting approximately equicoupled subcodes (generalization of equidistant codes) from any using hypergraph Ramsey’s theorem, and 2) significantly extending the classic Plotkin bound in coding theory to list decoding for general channels using duality between the completely positive tensor cone and the copositive tensor cone. In the proof, we also obtain a new fact regarding asymmetry of joint distributions, which may be of independent interest.Other results include 1) List decoding capacity with asymptotically large L for general adversarial channels; 2) A tight list size bound for most constant composition codes (generalization of constant weight codes); 3) Rederivation and demystification of Blinovsky’s [Blinovsky, 1986] characterization of the list decoding Plotkin points (threshold at which large codes are impossible) for bit-flip channels; 4) Evaluation of general bounds [Wang et al., 2019] for unique decoding in the error correction code setting.
Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models

Humans can learn structural properties about a word from minimal experience, and deploy their learned syntactic representations uniformly in different grammatical contexts. We assess the ability of modern neural language models to reproduce this behavior in English and evaluate the effect of structural supervision on learning outcomes. First, we assess few-shot learning capabilities by developing controlled experiments that probe models’ syntactic nominal number and verbal argument structure generalizations for tokens seen as few as two times during training. Second, we assess invariance properties of learned representation: the ability of a model to transfer syntactic generalizations from a base context (e.g., a simple declarative active-voice sentence) to a transformed context (e.g., an interrogative sentence). We test four models trained on the same dataset: an n-gram baseline, an LSTM, and two LSTM-variants trained with explicit structural supervision. We find that in most cases, the neural models are able to induce the proper syntactic generalizations after minimal exposure, often from just two examples during training, and that the two structurally supervised models generalize more accurately than the LSTM model. All neural models are able to leverage information learned in base contexts to drive expectations in transformed contexts, indicating that they have learned some invariance properties of syntax.
Information Ecology as a “Survival Strategy” of a Person in the Digital Environment

The digital environment has already become a lifeworld of modern man. In this new lifeworld, the fundamental parameters of human existence, such as space and time, are being transformed. The forms of interpersonal and linguistic com­munication, types of personal identity and ways of understanding reality are changing. The main target of the impact of the digital environment is the human brain as a biological substrate of the psyche. In particular, under the influence of the digital environment, the nature of the adaptation of information by the human brain is changing. As a result, transformations (often negative) occur in various fields: emotional and behavioral (changes in needs and motivation, impaired em­pathy and attachment), cognitive (clip thinking, weakening of the synthesizing capabilities of the mind). These transformations entail changes in the human mind, in the ways of his self-identification, in his worldview and culture. The author traces how all these processes are studied in the recently developed new scien­tific field – information ecology. An attempt is also being made to philosophi­cally reflect on how information ecology can help in a positive change in the di­gital environment that best meets human needs and requirements. Information ecology is a kind of culture of interaction with the digital environment. Its devel­opment is a manifestation of environmentalism – an “environmental change” in&nbsp;the study of the digital environment
Display that bend and stretch: Some smartphones can now fold like a wallet. In a few years, you may wear one on your skin

Motorola demonstrated the very first handheld mobile phone almost a half century ago. It was the size of a brick and weighed half as much. That prototype spawned the first commercial mobile phone a decade later. It, too, was ungainly, but it allowed a person to walk around while sending and receiving phone calls, which at that point was a great novelty. Since then, mobile phones have acquired many other functions. They now have the ability to handle text messages, browse the Web, play music, take and display photos and videos, locate the owner on a map, and serve countless other uses-applications well beyond what anybody could have imagined when mobile phones were first introduced.
Enhancing The Competitiveness Of College Graduates Through The Digital Educational Environment

Almost all ongoing reforms and transformations that take place in the sphere of the secondary vocational education system in one way or another happen due to the necessity to improve the training of a future specialist - a competitive graduate of a professional educational organization, who is able to meet the requirements of the labor market and employers, and therefore participate in social economic development of the country. To create a competitive specialist it is necessary to use the potential of the digital educational environment of a professional educational organization, since such educational bodies have a special mission – to prepare a graduate with the necessary set of professional and extraprofessional skills. Such specialist should be ready to continue education in a high-tech information society because they know how to form new necessary competencies, able to grow their new professional skills and qualities in changing conditions of professional activity, and know how to take an advantage of a digital educational environment. In the course of a theoretical analysis of scientific and pedagogical literature and positive pedagogical experience we analyzed the essence and structure of competitiveness and the digital educational environment, necessary for conducting diagnostic procedures and confirming the correctness of the hypothesis. The article substantiates the pedagogical conditions for increasing the graduate’s competitiveness by means of the digital educational environment: the interaction of subjects of educational relations is mediated by high-tech means of informatization that meet the modern needs of the training system for future specialists.
ColloQL: Robust Text-to-SQL Over Search Queries

Translating natural language utterances to executable queries is a helpful technique in making the vast amount of data stored in relational databases accessible to a wider range of nontech-savvy end users.Prior work in this area has largely focused on textual input that is linguistically correct and semantically unambiguous.However, real-world user queries are often succinct, colloquial, and noisy, resembling the input of a search engine.In this work, we introduce data augmentation techniques and a sampling-based content-aware BERT model (COLLOQL) to achieve robust text-to-SQL modeling over natural language search (NLS) questions.Due to the lack of evaluation data, we curate a new dataset of NLS questions and demonstrate the efficacy of our approach.COLLOQL's superior performance extends to well-formed text, achieving 84.9% (logical) and 90.7% (execution) accuracy on the WikiSQL dataset, making it, to the best of our knowledge, the highest performing model that does not use execution guided decoding.
A Novel Color Image Encryption Scheme Based on Controlled Alternate Quantum Walks and DNA Sequence Operations

The powerful storage function and convenient service function of cloud computing attract more and more users to save their digit image on the cloud server. However, the insecure data transmission process of cloud computing may lead to the disclosure of user privacy. At the same time, the existing machine learning algorithms do not consider the privacy and security of digit image in the application process. To solve the above problems, a novel color image encryption scheme based on controlled alternate quantum walks (CAQW) and DNA sequence operations is proposed. CAQW is used as a pseudo-random number generator (PRNG) to scramble pixel positions, and DNA encoding rule is used to replace the pixel value, and then the sequence of pseudo-random numbers generated by PRNG and DNA operation rules are used in two rounds of encryption of plaintext images. Our anlaysis and simulation show that this scheme can resist statistical analysis attack, differential attack and other attacks. In conclusion, the color image encryption scheme is robustness and efficient for protection of privacy image security.
Knowledge Grounded Conversational Symptom Detection with Graph Memory Networks

In this work, we propose a novel goal-oriented dialog task, automatic symptom detection.We build a system that can interact with patients through dialog to detect and collect clinical symptoms automatically, which can save a doctor's time interviewing the patient.Given a set of explicit symptoms provided by the patient to initiate a dialog for diagnosing, the system is trained to collect implicit symptoms by asking questions, in order to collect more information for making an accurate diagnosis.After getting the reply from the patient for each question, the system also decides whether current information is enough for a human doctor to make a diagnosis.To achieve this goal, we propose two neural models and a training pipeline for the multi-step reasoning task.We also build a knowledge graph as additional inputs to further improve model performance.Experiments show that our model significantly outperforms the baseline by 4%, discovering 67% of implicit symptoms on average with a limited number of questions.
f-Divergence Variational Inference

This paper introduces the $f$-divergence variational inference ($f$-VI) that generalizes variational inference to all $f$-divergences. Initiated from minimizing a crafty surrogate $f$-divergence that shares the statistical consistency with the $f$-divergence, the $f$-VI framework not only unifies a number of existing VI methods, e.g. Kullback-Leibler VI, Renyi's $\alpha$-VI, and $\chi$-VI, but offers a standardized toolkit for VI subject to arbitrary divergences from $f$-divergence family. A general $f$-variational bound is derived and provides a sandwich estimate of marginal likelihood (or evidence). The development of the $f$-VI unfolds with a stochastic optimization scheme that utilizes the reparameterization trick, importance weighting and Monte Carlo approximation; a mean-field approximation scheme that generalizes the well-known coordinate ascent variational inference (CAVI) is also proposed for $f$-VI. Empirical examples, including variational autoencoders and Bayesian neural networks, are provided to demonstrate the effectiveness and the wide applicability of $f$-VI.
Refactoring Policy for Compositional Generalizability using Self-Supervised Object Proposals

We study how to learn a policy with compositional generalizability. We propose a two-stage framework, which refactorizes a high-reward teacher policy into a generalizable student policy with strong inductive bias. Particularly, we implement an object-centric GNN-based student policy, whose input objects are learned from images through self-supervised learning. Empirically, we evaluate our approach on four difficult tasks that require compositional generalizability, and achieve superior performance compared to baselines.
RETRACTED ARTICLE: A smart position optimization scheme for badminton doubles based on human–computer interactive training in wireless sensor networks

Abstract In the continuous development process, robot technology based on multimedia interactions has been widely used in aerospace, medical, education and service industries. The relationship between robots and humans is getting closer. The improvement of robot intelligence is a process of continuously learning the outside world. Since 1959, multimedia human–computer interaction technology has provided more and more technical support for human research robots. In this paper, we firstly study the characteristics of badminton competition, including the technical characteristics, tactics characteristics, and the collection requirements about on-site training. The technical description of the badminton doubles station is also completed. Furthermore, in terms of the digitalization of the gait-based sensation, the automated acquisition of the gait technology, the simultaneously with the video, and the high-speed imaging system, the system implement information is integrated. After that, badminton multimedia techniques and tactics are designed based on the extensive statistical analysis. The skills and tactics of athletes are expressed in the form of video, graphics and text. Finally, the operators can carry out the technical and tactical analysis processes through good human–computer interactions. This increases the end user’s game information. The related attention and understanding can strengthen the strength and depth of technical and tactical ability analysis. Thus, we propose the station optimization plan for badminton doubles.
Call for Papers—<i>Management Science</i> Special Issue on Blockchains and Crypto Economics

Free AccessAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to SectionFree Access HomeManagement ScienceVol. 67, No. 1 Call for Papers—Management Science Special Issue on Blockchains and Crypto EconomicsBruno Biais, Agostino Capponi , Lin William Cong , Vishal Gaur , Kay Giesecke Bruno Biais, Agostino Capponi , Lin William Cong , Vishal Gaur , Kay Giesecke Published Online:20 Nov 2020https://doi.org/10.1287/mnsc.2020.3888"Call for Papers—Management Science Special Issue on Blockchains and Crypto Economics." Management Science, 67(1), pp. 6–7 Previous Back to Top Next FiguresReferencesRelatedInformation Volume 67, Issue 1January 2021Pages v, 1-659, iii-iv Article Information Metrics Downloaded 861 times in the past 12 months Information Published Online:November 20, 2020 Copyright © 2020, INFORMSCite asBruno Biais, Agostino Capponi, Lin William Cong, Vishal Gaur, Kay Giesecke (2020) Call for Papers—Management Science Special Issue on Blockchains and Crypto Economics. Management Science 67(1):6-7. https://doi.org/10.1287/mnsc.2020.3888PDF download
Adoption of Web 2.0 Technologies in Driving Reading Habits of Secondary School Learners in Bulawayo Metropolitan Province, Zimbabwe

Reading in the 21st century networked society is no longer confined to print. The scope of reading has extended to Web 2.0 technologies that have changed the traditional reading culture of readers. The problem is that secondary school learners in Zimbabwe are not fully adopting Web 2.0 technologies to cultivate their reading habits. This qualitative study examined the adoption of Web 2.0 technologies in driving the reading habits of secondary school learners in the Bulawayo Metropolitan Province in Zimbabwe. A multiple-case study design was employed, which collected data from 210 learners. The learners were divided into 14 focus groups as a data collection tool. Data were augmented through interviews with school librarians and then analysed thematically. Findings reveal that some learners spend five hours per day using different types of Web 2.0 technologies, which include WhatsApp, Facebook, WattPad, Pinterest and YouTube for the purposes of entertainment, and this has a negative effect on their reading. The study revealed potential positive effects of using Web 2.0 technologies, such as having up-to-date reading materials. It is concluded that Web 2.0 technologies can be adopted in driving the reading habits of secondary school learners. Some learners are already using Web 2.0 technologies for downloading novels. Especially girls (and some boys) are reading newspapers by using Web 2.0 technologies; however, other learners are using them for the wrong reasons. This paper contends that if guided well, young people can use these technologies to cultivate their reading habits. A further study is recommended on how parents/guardians can adopt the use of Web 2.0 technologies as a drive to improve the reading habits of learners.
From RE Cares to SE Cares: software engineering for social good, one venue at a time

As software practitioners, we can help society by using our communities of experts to address a software need of a socially-conscious organization. Doing so can benefit society in the locale of a software engineering conference, and provides access to international experts for local organizations which may otherwise not have access. Furthermore, established Software Engineering (SE) researchers as well as practitioners and students have the opportunity for a unique learning experience. In this paper, we argue that the SE community should use SE conferences as the focal points for activities that benefit society at the locations of the conferences, and make such activities an integral, valued, and recognized part of the conference programs. The proposed series of events, termed SE Cares, can follow and learn from the model of Requirements Engineering (RE) Cares events that took place in 2018 and 2019, and can be a co-located event at all interested SE-related conferences.
Using CloudWatch with SageMaker

In this chapter, we will explore CloudWatch functionality in AWS in detail. Specifically, we will look at two components of CloudWatch, CloudWatch Logs and CloudWatch Metrics, that we will use a lot while using SageMaker.
The Role of Social Networks in the Educational Process

The purpose of the research is a comparative study of the use of social networks in the educational and sociocultural context. The comparison criteria include the following indicators: social status (student-teacher), demographic characteristics (gender and age), and sociocultural indicators (the country of residence – Russia, Kazakhstan and Azerbaijan). A questionnaire based on closed and open questions was used to collect data. The data were analyzed in SPSS. Various strategies (constructively individualistic and value-normative strategies) were identified for obtaining and using information through social networks that are mediated by ex-isting cultural and social practices. There is a qualitative difference in the use of social networks by teachers and students. Students are primarily attracted by the ability to communicate on social networks. The use of social networks by teach-ers is mediated by their professional activities. The results obtained allow us to build other forms of interaction between the teacher and students A teacher coor-dinates students' independent intellectual activities rather than transmits knowledge; students become not just consumers of educational information, but active participants in the creation of an intellectual product.
Requirements Engineering Out of the Classroom: Anticipating Challenges Experienced in Practice

Requirements Engineering (RE) education makes use of different methods to motivate and prepare students to perform well this activity in the practical scenario. Such educational methods, however, are typically based on some simulation, be it in the scope of the scenario, the problem, or the stakeholders. A change in the traditional teaching paradigm becomes necessary, bringing the student to the center of the RE activities. This helps them realize the importance of properly identifying and organizing the demands from the target audience, so that a quality requirements specification document can be produced. This paper describes a method applied during the Requirements Engineering course using a real scenario, in which students sought to identify the diverse stakeholders' needs with respect to the university institute website. Through this work, students achieved effective engagement in practical RE activities, whose results has benefits not only to the university community, but also to the external public interested in institute information. The conduction of this work revealed - and reinforced - some relevant challenges and lessons learned.
Ultrasound image segmentation through deep learning based improvised U-Net

&lt;p&gt;&lt;span id="docs-internal-guid-cea63826-7fff-8080-83de-ad2ba4604953"&gt;&lt;span&gt;Thyroid nodule are fluid or solid lump that are formed within human’s gland and most thyroid nodule doesn’t show any symptom or any sign; moreover there are certain percentage of thyroid gland are cancerous and which could lead human into critical situation up to death. Hence, it is one of the important type of cancer and also it is important for detection of cancer. Ultrasound imaging is widely popular and frequently used tool for diagnosing thyroid cancer, however considering the wide application in clinical area such estimating size, shape and position of thyroid cancer. Further, it is important to design automatic and absolute segmentation for better detection and efficient diagnosis based on US-image. Segmentation of thyroid gland from the ultrasound image is quiet challenging task due to inhomogeneous structure and similar existence of intestine. Thyroid nodule can appear anywhere and have any kind of contrast, shape and size, hence segmentation process needs to designed carefully; several researcher have worked in designing the segmentation mechanism, however most of them were either semi-automatic or lack with performance metric, however it was suggested that U-Net possesses great accuracy. Hence, in this paper, we proposed improvised U-Net which focuses on shortcoming of U-Net, the main aim of this research work is to find the probable Region of interest and segment further. Furthermore, we develop High level and low-level feature map to avoid the low-resolution problem and information; later we develop dropout layer for further optimization. Moreover proposed model is evaluated considering the important metrics such as accuracy, Dice Coefficient, AUC, F1-measure and true positive; our proposed model performs better than the existing model. &lt;/span&gt;&lt;/span&gt;&lt;/p&gt;