Reviewerly/exHarmony huggingface dataset:
Beyond pixel-wise supervision for segmentation: A few global shape descriptors might be surprisingly good!

Standard losses for training deep segmentation networks could be seen as individual classifications of pixels, instead of supervising the global shape of the predicted segmentations. While effective, they require exact knowledge of the label of each pixel in an image. This study investigates how effective global geometric shape descriptors could be, when used on their own as segmentation losses for training deep networks. Not only interesting theoretically, there exist deeper motivations to posing segmentation problems as a reconstruction of shape descriptors: Annotations to obtain approximations of low-order shape moments could be much less cumbersome than their full-mask counterparts, and anatomical priors could be readily encoded into invariant shape descriptions, which might alleviate the annotation burden. Also, and most importantly, we hypothesize that, given a task, certain shape descriptions might be invariant across image acquisition protocols/modalities and subject populations, which might open interesting research avenues for generalization in medical image segmentation. We introduce and formulate a few shape descriptors in the context of deep segmentation, and evaluate their potential as standalone losses on two different challenging tasks. Inspired by recent works in constrained optimization for deep networks, we propose a way to use those descriptors to supervise segmentation, without any pixel-level label. Very surprisingly, as little as 4 descriptors values per class can approach the performance of a segmentation mask with 65k individual discrete labels. We also found that shape descriptors can be a valid way to encode anatomical priors about the task, enabling to leverage expert knowledge without additional annotations. Our implementation is publicly available and can be easily extended to other tasks and descriptors: this https URL
Securing IoT Data in the Cloud with Blockchain Technology

Preserving privacy and integrity is one of the important needs of any internet based platform especially if it is based on cloud. Block chain has been a recent technology key player in securing cloud based platforms. In the proposed work, we have used Blockchain technologies to provide encryption for cloud IoT results. Blockchain has recently become a promising software for cloud cluster integration and enhancement of cloud transaction protection and access to data and application codes. The main objective is to use blockchain technology to encrypt heterogeneous and enormous data. Authorizing the data obtained from different sources. Furthermore, more stable and better hash functions, such as SHA-256, SHA-384, and MD5 are now available, a future attacker would require more time to produce all available SHA512 hashes to brute force a hashed password from your database from a security standpoint. As a result, we consider SHA512 to be more robust and reliable in terms of the time it takes to compute a single hash when opposed to all other hash functions. As a result, we have introduced modified SHA512 in the proposed work for added reliability
Cryptocurrencies and Blockchain: Potential Applications in Government and Business

Cryptocurrencies and distributed registers (blockchains) have recently attracted increased interest among specialists from the widest variety of fields. In turn, the public has generated a pool of regularly asked questions, which have not yet been answered thoroughly. This article provides lines of reasoning with regard to several popular questions linked to this subject. It also addresses issues such as the creation of national cryptocurrencies and use of blockchain technology by businesses and governments. In addition, it analyzes the opinion that cryptocurrencies are a financial pyramid. Finally, it briefly examines the configuration of Bitcoin’s distributed register and looks at how this register could be affected by the hypothetical creation of a quantum computer.
Residual Attention: A Simple but Effective Method for Multi-Label Recognition

Multi-label image recognition is a challenging computer vision task of practical use. Progresses in this area, however, are often characterized by complicated methods, heavy computations, and lack of intuitive explanations. To effectively capture different spatial regions occupied by objects from different categories, we propose an embarrassingly simple module, named class-specific residual attention (CSRA). CSRA generates class-specific features for every category by proposing a simple spatial attention score, and then combines it with the class-agnostic average pooling feature. CSRA achieves state-of-the-art results on multilabel recognition, and at the same time is much simpler than them. Furthermore, with only 4 lines of code, CSRA also leads to consistent improvement across many diverse pretrained models and datasets without any extra training. CSRA is both easy to implement and light in computations, which also enjoys intuitive explanations and visualizations.
Synthesis of musical instrument directivities with a single common loudspeaker (L)

This letter synthesizes musical instrument directivities by sequentially rotating a loudspeaker through multiple orientations. The synthesis is conducted in a spherical harmonic domain using the actual measured directivity of a commercial loudspeaker and an open database of musical instrument directivities. The letter further proposes a practically useful set of 26 loudspeaker orientations named Step45, which can be used to reproduce the target directivity as well as if the orientations were determined adopting mathematical methods that give equilibrium configurations.
Adaptive sampling quasi-Newton methods for zeroth-order stochastic optimization

We consider unconstrained stochastic optimization problems with no available gradient information. Such problems arise in settings from derivative-free simulation optimization to reinforcement learning. We propose an adaptive sampling quasi-Newton method where we estimate the gradients using finite differences of stochastic function evaluations within a common random number framework. We develop modified versions of a norm test and an inner product quasi-Newton test to control the sample sizes used in the stochastic approximations and provide global convergence results to the neighborhood of a locally optimal solution. We present numerical experiments on simulation optimization problems to illustrate the performance of the proposed algorithm. When compared with classical zeroth-order stochastic gradient methods, we observe that our strategies of adapting the sample sizes significantly improve performance in terms of the number of stochastic function evaluations required.
Secure Byzantine-Robust Distributed Learning via Clustering.

Federated learning systems that jointly preserve Byzantine robustness and privacy have remained an open problem. Robust aggregation, the standard defense for Byzantine attacks, generally requires server access to individual updates or nonlinear computation -- thus is incompatible with privacy-preserving methods such as secure aggregation via multiparty computation. To this end, we propose SHARE (Secure Hierarchical Robust Aggregation), a distributed learning framework designed to cryptographically preserve client update privacy and robustness to Byzantine adversaries simultaneously. The key idea is to incorporate secure averaging among randomly clustered clients before filtering malicious updates through robust aggregation. Experiments show that SHARE has similar robustness guarantees as existing techniques while enhancing privacy.
Learning of Visual Relations: The Devil Is in the Tails

Significant effort has been recently devoted to modeling visual relations. This has mostly addressed the design of architectures, typically by adding parameters and increasing model complexity. However, visual relation learning is a long-tailed problem, due to the combinatorial nature of joint reasoning about groups of objects. Increasing model complexity is, in general, ill-suited for long-tailed problems due to their tendency to overfit. In this paper, we explore an alternative hypothesis, denoted the Devil is in the Tails. Under this hypothesis, better performance is achieved by keeping the model simple but improving its ability to cope with long-tailed distributions. To test this hypothesis, we devise a new approach for training visual relationships models, which is inspired by state-of-the-art long-tailed recognition literature. This is based on an iterative decoupled training scheme, denoted Decoupled Training for Devil in the Tails (DT2). DT2 employs a novel sampling approach, Alternating Class-Balanced Sampling (ACBS), to capture the interplay between the long-tailed entity and predicate distributions of visual relations. Results show that, with an extremely simple architecture, DT2-ACBS significantly outperforms much more complex state-of-the-art methods on scene graph generation tasks. This suggests that the development of sophisticated models must be considered in tandem with the long-tailed nature of the problem.
Development and Study of a Post-editing Model for Russian-Kazakh and English-Kazakh Translation Based on Machine Learning

This work presents research in the field of machine translation for the Kazakh language. A comparative analysis of translation works of open online machine translation systems (Google translate, Yandex translate, sozdik.kz, webtran.ru.) for English-Kazakh and Russian-Kazakh translation is presented. To improve the quality of translation for the Kazakh language a model of post-editing of the Kazakh language in machine translation has been developed, based on the neural network training approach. For machine learning parallel corpuses for the English-Kazakh and Russian-Kazakh language pairs were collected and processed. Experimental testing has been carried out. The results obtained were evaluated using the BLEU metric.
Protocols Design and Area Division for Privacy-Preserving Delay-Aware Authentication in Vehicular Networks

The problem of security and privacy in vehicular networks is a vital issue, and it attracts increasing attention to address the security vulnerability of vehicular networks.Authentication solutions are introduced for vehicular networks to ensure that network access is only given to authorized users.Among authentication solutions for vehicular networks, group signature not only offers authentication services, but also provides conditional privacy preservation.However, the current group signature approach for authentication in vehicular networks exhibits time-consuming signature verification and poor scalability.To overcome these shortcomings, we propose a flexible and efficient delay-aware authentication scheme (FEDAS) by utilizing edge computing paradigm.In the proposed architecture, we design the authentication group maintaining mechanism and develop the collaborative CRL management method.Moreover, we propose transition zone to solve the reliable authentication problem in border area of the group.To implement the proposed architecture, we propose a model for calculating the length of local CRL, which establishes the relationship between the size of a sub-area and the length of local CRL.And we also design a method for area division based on the length of local CRL, which provides division principle for our authentication scheme.We conduct extensive simulations to verify the effectiveness of our proposed scheme.
Hyperparameter Tuning with Renyi Differential Privacy.

For many differentially private algorithms, such as the prominent noisy stochastic gradient descent (DP-SGD), the analysis needed to bound the privacy leakage of a single training run is well understood. However, few studies have reasoned about the privacy leakage resulting from the multiple training runs needed to fine tune the value of the training algorithm's hyperparameters. In this work, we first illustrate how simply setting hyperparameters based on non-private training runs can leak private information. Motivated by this observation, we then provide privacy guarantees for hyperparameter search procedures within the framework of Renyi Differential Privacy. Our results improve and extend the work of Liu and Talwar (STOC 2019). Our analysis supports our previous observation that tuning hyperparameters does indeed leak private information, but we prove that, under certain assumptions, this leakage is modest, as long as each candidate training run needed to select hyperparameters is itself differentially private.
Determinants of cyber-safety behaviors in a developing economy

In a world increasingly driven by digital technology, cyber-safety is becoming a pressing concern for Internet users. This article contributes to sociological, psychological, and criminological literature on digital risks by investigating the determinants of individuals’ cyber-safety behaviors. Our study adds new insights to digital inequalities studies, cognitive health behavior models, and fear of crime literature by developing and testing a comprehensive theoretical model in a developing economy (Uruguay). To validate our model, we fit structural equations with data from the 2017 WIP+DiSTO Uy survey, a representative sample of Uruguayans (N=653). We found that operational digital skills were the strongest predictor of cyber-safety, while also providing support for the sequentiality of the digital divide hypothesis, as education and age-based disparities affect cyber-safety through their effect on Internet use, which in turn affects digital skills. Additionally, findings contribute both to cognitive behaviors and fear of crime literature, by attesting that gender and age-based disparities only have an indirect effect on cyber-safety, which is mediated by user beliefs regarding the severity of cyber-victimization. The study also provides evidence for the generalization of cyber-safety behavior theories — originally formulated based on data from developed economies — to developing ones, by stressing the role of digital skills and perceptions of victimization severity as the main direct antecedents of cyber-safety.
Lower Bounds for XOR of Forrelations.

The Forrelation problem, first introduced by Aaronson [Scott Aaronson, 2010] and Aaronson and Ambainis [Scott Aaronson and Andris Ambainis, 2015], is a well studied computational problem in the context of separating quantum and classical computational models. Variants of this problem were used to give tight separations between quantum and classical query complexity [Scott Aaronson and Andris Ambainis, 2015]; the first separation between poly-logarithmic quantum query complexity and bounded-depth circuits of super-polynomial size, a result that also implied an oracle separation of the classes BQP and PH [Ran Raz and Avishay Tal, 2019]; and improved separations between quantum and classical communication complexity [Uma Girish et al., 2021]. In all these separations, the lower bound for the classical model only holds when the advantage of the protocol (over a random guess) is more than ≈ 1/√N, that is, the success probability is larger than ≈ 1/2 + 1/√N. This is unavoidable as ≈ 1/√N is the correlation between two coordinates of an input that is sampled from the Forrelation distribution, and hence there are simple classical protocols that achieve advantage ≈ 1/√N, in all these models.To achieve separations when the classical protocol has smaller advantage, we study in this work the xor of k independent copies of (a variant of) the Forrelation function (where k≪ N). We prove a very general result that shows that any family of Boolean functions that is closed under restrictions, whose Fourier mass at level 2k is bounded by α^k (that is, the sum of the absolute values of all Fourier coefficients at level 2k is bounded by α^k), cannot compute the xor of k independent copies of the Forrelation function with advantage better than O((α^k)/(N^{k/2})). This is a strengthening of a result of [Eshan Chattopadhyay et al., 2019], that gave a similar statement for k = 1, using the technique of [Ran Raz and Avishay Tal, 2019]. We give several applications of our result. In particular, we obtain the following separations:Quantum versus Classical Communication Complexity. We give the first example of a partial Boolean function that can be computed by a simultaneous-message quantum protocol with communication complexity polylog(N) (where Alice and Bob also share polylog(N) EPR pairs), and such that, any classical randomized protocol of communication complexity at most o(N^{1/4}), with any number of rounds, has quasipolynomially small advantage over a random guess. Previously, only separations where the classical protocol has polynomially small advantage were known between these models [Dmitry Gavinsky, 2016; Uma Girish et al., 2021].Quantum Query Complexity versus Bounded Depth Circuits. We give the first example of a partial Boolean function that has a quantum query algorithm with query complexity polylog(N), and such that, any constant-depth circuit of quasipolynomial size has quasipolynomially small advantage over a random guess. Previously, only separations where the constant-depth circuit has polynomially small advantage were known [Ran Raz and Avishay Tal, 2019].
Jammify: Interactive Multi-sensory System for Digital Art Jamming

As social distancing is becoming the new normal, technology holds the potential to bridge this societal gap through novel interaction modalities that allow multiple users to collaborate and create content together. We present Jammify, an interactive multi-sensory system that focuses on providing a unique digital art-jamming experience with a visual display and a wearable arm-sleeve. The ‘jamming-canvas’ visual display is a two-sided LED light wall (2 m $$\times $$ 6 m) where users can draw free-hand gestures on either side and switch between two view modes: own-view and shared-view. The arm-sleeve uses shape-memory-alloy integrated fabric to sense and re-create a subtle and natural touch sensation on each other’s hands. We describe the details of the design and interaction possibilities based on the diverse combinations of both input and output modalities of the system, as well as findings from a user study with ten participants.
Spatial-Temporal Feature-Based Sports Video Classification

Video classification has been an active research field of computer vision in last few years. Its main purpose is to produce a label that is relevant to the video given its frames. Unlike image classification, which takes still pictures as input, the input of video classification is a sequence of images. The complex spatial and temporal structures of video sequence incur understanding and computation difficulties, which should be modeled to improve the video classification performance. This work focuses on sports video classification but can be expanded into other applications. In this paper, the authors propose a novel sports video classification method by processing the video data using convolutional neural network (CNN) with spatial attention mechanism and deep bidirectional long short-term memory (BiLSTM) network with temporal attention mechanism. The method first extracts 28 frames from each input video and uses the classical pre-trained CNN to extract deep features, and the spatial attention mechanism is applied to CNN features to decide ‘where' to look. Then the BiLSTM is utilized to model the long-term temporal dependence between video frame sequences, and the temporal attention mechasim is employed to decide ‘when' to look. Finally, the label of the input video is given by the classification network. In order to evaluate the feasibility and effectiveness of the proposed method, an extensive experimental investigation was conducted on the open challenging sports video datasets of Sports8 and Olympic16; the results show that the proposed CNN-BiLSTM network with spatial temporal attention mechanism can effectively model the spatial-temporal characteristics of video sequences. The average classification accuracy of the Sports8 is 98.8%, which is 6.8% higher than the existing method. The average classification accuracy of 90.46% is achieved on Olympic16, which is about 18% higher than the existing methods. The performance of the proposed approach outperforms the state-of-the-art methods, and the experimental results demonstrate the effectiveness of the proposed approach.
CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning.

While Reinforcement Learning has made great strides towards solving ever more complicated tasks, many algorithms are still brittle to even slight changes in their environment. This is a limiting factor for real-world applications of RL. Although the research community continuously aims at improving both robustness and generalization of RL algorithms, unfortunately it still lacks an open-source set of well-defined benchmark problems based on a consistent theoretical framework, which allows comparing different approaches in a fair, reliable and reproducibleway. To fill this gap, we propose CARL, a collection of well-known RL environments extended to contextual RL problems to study generalization. We show the urgent need of such benchmarks by demonstrating that even simple toy environments become challenging for commonly used approaches if different contextual instances of this task have to be considered. Furthermore, CARL allows us to provide first evidence that disentangling representation learning of the states from the policy learning with the context facilitates better generalization. By providing variations of diverse benchmarks from classic control, physical simulations, games and a real-world application of RNA design, CARL will allow the community to derive many more such insights on a solid empirical foundation.
THE EFFECT OF INSTAGRAM IN LEARNING ENGLISH VOCABULARY

The aims of this research are to find out the effect of Instagram in learning vocabulary of third semester students at English Tadris Study Program in IAIN Palu. This research was quantitative method and used total sampling. The subject of this research was the students’ batch 2019 semester 3 academic year 2020-2021 in IAIN Palu. The total numbers of students of in this research were 102 students. After selected the sample of this research were 35 students. Research instrument is questionnaire.The technique of collection data was observation, and reduction.The result showed that Effect of Instagram in Learning Vocabulary of Third Semester Students’ at English Tadris Study Program in IAIN Palu Academic year 2020/2021, used online media namely google form. The data revealed that the level of the mean score 62,48% from 35 students which was categorized as high effect. Then the research concluded that it is evident that Instagram have an effect on learning English (vocabulary) of student. It was revealed by the mean score 62,48%. The research was classified as high category of the students’ questionnaire.