Reviewerly/exHarmony huggingface dataset:
Learning Affordance Landscapes for Interaction Exploration in 3D Environments

Embodied agents operating in human spaces must be able to master how their environment works: what objects can the agent use, and how can it use them? We introduce a reinforcement learning approach for exploration for interaction, whereby an embodied agent autonomously discovers the affordance landscape of a new unmapped 3D environment (such as an unfamiliar kitchen). Given an egocentric RGB-D camera and a high-level action space, the agent is rewarded for maximizing successful interactions while simultaneously training an image-based affordance segmentation model. The former yields a policy for acting efficiently in new environments to prepare for downstream interaction tasks, while the latter yields a convolutional neural network that maps image regions to the likelihood they permit each action, densifying the rewards for exploration. We demonstrate our idea with AI2-iTHOR. The results show agents can learn how to use new home environments intelligently and that it prepares them to rapidly address various downstream tasks like find a knife and put it in the drawer. Project page: this http URL
Second Order PAC-Bayesian Bounds for the Weighted Majority Vote

We present a novel analysis of the expected risk of weighted majority vote in multiclass classification. The analysis takes correlation of predictions by ensemble members into account and provides a bound that is amenable to efficient minimization, which yields improved weighting for the majority vote. We also provide a specialized version of our bound for binary classification, which allows to exploit additional unlabeled data for tighter risk estimation. In experiments, we apply the bound to improve weighting of trees in random forests and show that, in contrast to the commonly used first order bound, minimization of the new bound typically does not lead to degradation of the test error of the ensemble.
Ruler: Data Programming by Demonstration for Document Labeling

Data programming aims to reduce the cost of curating training data by encoding domain knowledge as labeling functions over source data. As such it not only requires domain expertise but also programming experience, a skill that many subject matter experts lack. Additionally, generating functions by enumerating rules is not only time consuming but also inherently difficult, even for people with programming experience. In this paper we introduce Ruler, an interactive system that synthesizes labeling rules using span-level interactive demonstrations over document examples. Ruler is a first-of-a-kind implementation of data programming by demonstration (DPBD). This new framework aims to relieve users from the burden of writing labeling functions, enabling them to focus on higher-level semantic analysis, such as identifying relevant signals for the labeling task. We compare Ruler with conventional data programming through a user study conducted with 10 data scientists who were asked to create labeling functions for sentiment and spam classification tasks. Results show Ruler is easier to learn and to use, and that it offers higher overall user-satisfaction while providing model performances comparable to those achieved by conventional data programming.
Software Defect Prediction Based on Fuzzy Weighted Extreme Learning Machine with Relative Density Information

To identify software modules that are more likely to be defective, machine learning has been used to construct software defect prediction (SDP) models. However, several previous works have found that the imbalanced nature of software defective data can decrease the model performance. In this paper, we discussed the issue of how to improve imbalanced data distribution in the context of SDP, which can benefit software defect prediction with the aim of finding better methods. Firstly, a relative density was introduced to reflect the significance of each instance within its class, which is irrelevant to the scale of data distribution in feature space; hence, it can be more robust than the absolute distance information. Secondly, a K-nearest-neighbors-based probability density estimation (KNN-PDE) alike strategy was utilised to calculate the relative density of each training instance. Furthermore, the fuzzy memberships of sample were designed based on relative density in order to eliminate classification error coming from noise and outlier samples. Finally, two algorithms were proposed to train software defect prediction models based on the weighted extreme learning machine. This paper compared the proposed algorithms with traditional SDP methods on the benchmark data sets. It was proved that the proposed methods have much better overall performance in terms of the measures including G-mean, AUC, and Balance. The proposed algorithms are more robust and adaptive for SDP data distribution types and can more accurately estimate the significance of each instance and assign the identical total fuzzy coefficients for two different classes without considering the impact of data scale.
Unsupervised Deep Metric Learning with Transformed Attention Consistency and Contrastive Clustering Loss

Existing approaches for unsupervised metric learning focus on exploring self-supervision information within the input image itself. We observe that, when analyzing images, human eyes often compare images against each other instead of examining images individually. In addition, they often pay attention to certain keypoints, image regions, or objects which are discriminative between image classes but highly consistent within classes. Even if the image is being transformed, the attention pattern will be consistent. Motivated by this observation, we develop a new approach to unsupervised deep metric learning where the network is learned based on self-supervision information across images instead of within one single image. To characterize the consistent pattern of human attention during image comparisons, we introduce the idea of transformed attention consistency. It assumes that visually similar images, even undergoing different image transforms, should share the same consistent visual attention map. This consistency leads to a pairwise self-supervision loss, allowing us to learn a Siamese deep neural network to encode and compare images against their transformed or matched pairs. To further enhance the inter-class discriminative power of the feature generated by this network, we adapt the concept of triplet loss from supervised metric learning to our unsupervised case and introduce the contrastive clustering loss. Our extensive experimental results on benchmark datasets demonstrate that our proposed method outperforms current state-of-the-art methods for unsupervised metric learning by a large margin.
Towards the Uzbek Language Endings as a Language Resource

The Uzbek language belongs to low-resource languages. It is very important to increase the number of language resources such as dictionaries, corpora (monolingual and bilingual) for the Uzbek language. Dictionaries may be different kinds: monolingual, orthographical, bilingual, grammar special dictionaries: stems dictionaries, affixes dictionaries, etc. For different NLP tasks of agglutinative languages, such as morphological analysis, information retrieval, machine translation (segmentation preprocessing) in some cases needs a dictionary of words’ endings. In this paper, we proposed the first electronic dictionary of Uzbek words’ endings in variants for morphological segmentation preprocessing useful for neural machine translation. The resource analysed by the initial version of the Lexicon free stemming tool [3] created by authors. For creation of Uzbek words’ endings’ electronic dictionary, it was used a combinatorial approach inferring apply for part of speech of the Uzbek language: nouns, adjectives, numerals, verbs, participles, moods, voices.
Lattice Reduction Over Imaginary Quadratic Fields

Complex bases, along with direct-sums defined by rings of imaginary quadratic integers, induce algebraic lattices. In this work, we study such lattices and their reduction algorithms. Firstly, when the lattice is spanned over a two dimensional basis, we show that the algebraic variant of Gauss's algorithm returns a basis that corresponds to the successive minima of the lattice if the chosen ring is Euclidean. Secondly, we extend the celebrated Lenstra-Lenstra-Lovász (LLL) reduction from over real bases to over complex bases. Properties and implementations of the algorithm are examined. In particular, satisfying Lovász's condition requires the ring to be Euclidean. Lastly, we numerically show the time-advantage of using algebraic LLL by considering lattice bases generated from wireless communications and cryptography.
Prevention of the Man-in-the-Middle Attack on Diffie–Hellman Key Exchange Algorithm: A Review

In this report, we demonstrate the pros and cons of Diffie–Hellman key exchange algorithm that is commonly used for the exchange of keys in private-key cryptosystem. A very frequently occurring problem of Diffie–Hellman key exchange algorithm during transmission of the keys through some channel is the Man-In-The-Middle Attack. Security of the message gets affected due to this attack because the attacker who is in the middle of the sender and receiver of the message tampers the message and modifies it according to his/her need. There are some well-known solutions available for the prevention of this attack. Our aim is to review those available techniques.
Innovative Research on the Teaching Mode of Piano Group Lessons in Higher Vocational Preschool Education Based on AI

Abstract The current AI technology has penetrated into many aspects of the education field (as shown in figure 1), and has made innovations in teaching models, courses and systems. Based on the background of AI, the preschool piano group teaching of higher vocational education has gradually become a mode of personalized learning, adaptive learning, deep learning, and human-computer collaborative learning. This article will analyze the current situation of piano group lesson teaching and propose innovative strategies to improve piano group lesson teaching in higher vocational preschool education.
Deep Learning for Resource-Limited Devices

In recent years, deep neural networks have revolutionized the development of intelligent systems and applications in many areas. Despite their numerous advantages and potentials, these intelligent models still suffer from several issues. Among them, the fact that they became very complex with millions of parameters. That is, requiring more resources and time, and being unsuitable for small restricted devices. To contribute in this direction, this paper presents (1) some state-of-the-art lightweight architectures that were specifically designed for small-sized devices, and (2) some recent solutions that have been proposed to optimize/compress classical deep neural networks to allow their deployment on embedded systems. In this paper, we also present our four-staged approach that aims to further enhance the performance of lightweight object-detection models. The conducted performance evaluations have demonstrated that the proposed four layers (basic model initialization, pruning, quantization, and adaptation) considerably reduce the redundancy of filters and model size with negligible performance degradation.
MessyTable: Instance Association in Multiple Camera Views

We present an interesting and challenging dataset that features a large number of scenes with messy tables captured from multiple camera views. Each scene in this dataset is highly complex, containing multiple object instances that could be identical, stacked and occluded by other instances. The key challenge is to associate all instances given the RGB image of all views. The seemingly simple task surprisingly fails many popular methods or heuristics that we assume good performance in object association. The dataset challenges existing methods in mining subtle appearance differences, reasoning based on contexts, and fusing appearance with geometric cues for establishing an association. We report interesting findings with some popular baselines, and discuss how this dataset could help inspire new problems and catalyse more robust formulations to tackle real-world instance association problems. (Project page: https://caizhongang.github.io/projects/MessyTable/ .)
Introduction to Hyperparameters

Artificial intelligence (AI) is suddenly everywhere, transforming everything from business analytics, the healthcare sector, and the automobile industry to various platforms that you may enjoy in your day-to-day life, such as social media, gaming, and the wide spectrum of the entertainment industry. Planning to watch a movie on a video-streaming app but can’t decide which movie to watch? With the assistance of AI, you might end up watching one of the recommendations that are based on your past movie selections.
Vulnerabilities perceived by men in the context of the Covid-19 pandemic

Objective: to understand the vulnerabilities perceived by men in the context of the Covid-19 pandemic. Methods: socio-historical, qualitative study, carried out from the results of online research in all regions of Brazil. The sample consisted of 200 men. The data were processed and analyzed methodologically by the Collective Subject Discourse, supported by the framework of epidemic disease. Results: men perceived the vulnerabilities due to the existence of chronic diseases in them and in the family, the need to maintain a work routine that limits the adoption of social distance, the uncertainties generated by the pandemic, which threatens the maintenance of employment and the professional achievement projects, in addition to the discomfort caused by the interruption of sexual interactions. Conclusion: men’s perceptions of vulnerabilities in the pandemic revolved around health, professionalization, work and sexuality.
Engineering Education: Key Features of the Digital Transformation

The paper deals with the digital transformation of engineering education. Such transformation is justified in terms of an engineering education paradigm change. Based on a comprehensive analysis of the didactic system of engineering education, the authors specify the requirements for such important engineering education components as goal setting, content, educational technologies, assessment tools and methods in current educational environment. They provides examples of a practical digital transformation of a number of engineering curricula and syllabi by integrating blended learning based on a Small Private Online Course (SPOC). The authors share their best practices in designing e-learning taking into account the features of the learning-forgetting process, they demonstrate how to make use of the saved time for students’ practical work, how to implement gamification techniques based on such new forms as the Hackathon, Internet of things (IoT) for engineering education. They also provide examples of creating a digital educational environment with specialized software modules to visualize complex mathematical concepts as well as examples of applying principles of artificial intelligence and machine learning to shape a student’s adapted educational trajectory. They discuss how to identify student’s problems with learning material assimilation and suggest a specially developed course including propaedeutic unit for additional study.
The psychological impacts of quarantine on international students’ life satisfaction in Russia during coronavirus COVID-19

As coronavirus quarantine period is increasing, concerns are growing about its possible impact on international migrants and students. The article discusses the basics of quarantine concept and its impact during COVID-19 pandemic. The purpose of study is to develop scale to determine relationship between such factors as financial stability, living condition, educational condition, social life and physiological reactions with psychological stability and life satisfaction of international students in Russia. The focus is done on international students of Russian universities. The questionnaires (n=300) were distributed among target respondents using the convenience random sampling technique. The analysis of survey results established relationship between the factors mentioned above and international students' psychological stability and life satisfaction. The study results proved that the original scale applied in this study is reliable and valid to assess level of life satisfaction. Therefore, policy makers should resolve related problems, including financial incentives, availability of such products as food and hygiene products through applying proper and sufficient information about the situation. using distance learning technologies, communication tools to insure positive impact on life satisfaction of international students. Moreover, the study results can provide to prospect researchers in the field of life satisfaction and psychological stability reliable and useful information.
MPC with Synchronous Security and Asynchronous Responsiveness

Two paradigms for secure MPC are synchronous and asynchronous protocols. While synchronous protocols tolerate more corruptions and allow every party to give its input, they are very slow because the speed depends on the conservatively assumed worst-case delay $$\varDelta $$ of the network. In contrast, asynchronous protocols allow parties to obtain output as fast as the actual network allows, a property called responsiveness, but unavoidably have lower resilience and parties with slow network connections cannot give input. It is natural to wonder whether it is possible to leverage synchronous MPC protocols to achieve responsiveness, hence obtaining the advantages of both paradigms: full security with responsiveness up to t corruptions, and extended security (full security or security with unanimous abort) with no responsiveness up to $$T \ge t$$ corruptions. We settle the question by providing matching feasibility and impossibility results:
Convolutional Neural Network for Human Activity Recognition and Identification

The widespread use of smartphones has triggered active research and various applications of Human Activity Recognition. On the other hand, the role of smartphones in our daily life also makes it a pivotal portal to personal information, various digitalized services, and the digital systems which the smartphone owner has access to. Therefore, the security and user authentication of smartphones has become a problem of paramount importance. This paper presents a deep learning approach to Human Activity Recognition and Identification (HARI) from an entity’s “behavioral signature” represented by the features extracted from the signals of the accelerometer and gyroscope of the smartphone an entity carries, by using onedimensional convolutional neural network (1D-CNN) model. Our primary research question is whether it is possible to identify not only the activity being performed but also the identity of the entity performing the activity based on signals from the gyroscope and accelerometer of the smartphone an entity carries. Based on the initial experiments we conducted on MotionSense dataset, the 1D-CNN model for classifying activity was able to achieve an accuracy of 96.77%, while the model for classifying identity achieved an accuracy of 82.37% in just a one-second time window. The accuracies achieved were based on design decisions that may have limited the success of the models, particularly, the design decision of using a sample length of one second. This design decision is discussed along with additional research topics that may help improve the accuracy in future iterations of this research.
Real-Time Contrast Enhancement for 3D Medical Images Using Histogram Equalization

Medical professionals rely on medical imaging to help diagnose and treat patients. It is therefore important for them to be able to see all the details captured in the images. Often the use of contrast enhancement or noise reduction techniques are used to help improve the image quality. This paper introduces a real-time implementation of 3D Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance 3D medical image stacks, or volumes. This algorithm can be used interactively by medical doctors to help visualize the 3D medical volumes and prepare for surgery. It also introduces two novel extensions to the algorithm to allow a user to interactively decide on what region to focus the enhancement: Focused CLAHE and Masked CLAHE. Focused CLAHE applies the 3D CLAHE algorithm to a specified block of the entire medical volume and Masked CLAHE applies the algorithm to a selected organ or organs. These three contributions can be used, to not only help improve the visualization of 3D medical image stacks, but also to provide that contrast enhancement in real-time.
The Resiliency of MPC with Low Interaction: The Benefit of Making Errors (Extended Abstract)

We study information-theoretic secure multiparty protocols that achieve full security, including guaranteed output delivery, at the presence of an active adversary that corrupts a constant fraction of the parties. It is known that 2 rounds are insufficient for such protocols even when the adversary corrupts only two parties (Gennaro, Ishai, Kushilevitz, and Rabin; Crypto 2002), and that perfect protocols can be implemented in 3 rounds as long as the adversary corrupts less than a quarter of the parties (Applebaum, Brakerski, and Tsabary; Eurocrypt, 2019). Furthermore, it was recently shown that the quarter threshold is tight for any 3-round perfectly-secure protocol (Applebaum, Kachlon, and Patra; FOCS 2020). Nevertheless, one may still hope to achieve a better-than-quarter threshold at the expense of allowing some negligible correctness errors and/or statistical deviations in the security. Our main results show that this is indeed the case. Every function can be computed by 3-round protocols with statistical security as long as the adversary corrupts less than third of the parties. Moreover, we show that any better resiliency threshold requires 4 rounds. Our protocol is computationally inefficient and has an exponential dependency in the circuit’s depth d and in the number of parties n. We show that this overhead can be avoided by relaxing security to computational, assuming the existence of a non-interactive commitment (NICOM). Previous 3-round computational protocols were based on stronger public-key assumptions. When instantiated with statistically-hiding NICOM, our protocol provides everlasting statistical security, i.e., it is secure against adversaries that are computationally unlimited after the protocol execution. To prove these results, we introduce a new hybrid model that allows for 2-round protocols with linear resiliency threshold. Here too we prove that, for perfect protocols, the best achievable resiliency is n/4, whereas statistical protocols can achieve a threshold of n/3. In the plain model, we also construct the first 2-round n/3-statistical verifiable secret sharing that supports second-level sharing and prove a matching lower-bound, extending the results of Patra, Choudhary, Rabin, and Rangan (Crypto 2009). Overall, our results refine the differences between statistical and perfect models of security, and show that there are efficiency gaps even for thresholds that are realizable in both models.