Reviewerly/exHarmony huggingface dataset:
Active Learning under Label Shift

Distribution shift poses a challenge for active data collection in the real world. We address the problem of active learning under label shift and propose ALLS, the first framework for active learning under label shift. ALLS builds on label shift estimation techniques to correct for label shift with a balance of importance weighting and class-balanced sampling. We show a bias-variance trade-off between these two techniques and prove error and sample complexity bounds for a disagreement-based algorithm under ALLS. Experiments across a range of label shift settings demonstrate ALLS consistently improves performance, often reducing sample complexity by more than half an order of magnitude. Ablation studies corroborate the bias-variance trade-off revealed by our theory.
Secure and Verifiable Keyword Search in Multiple Clouds

Searchable encryption enables users to perform a keyword-based search over encrypted data. Most of the existing searchable encryption schemes are based on the single cloud server model, which has some limitations, such as the single point of failure and malicious insiders. In contrast, the multicloud server model has higher data availability and security. However, directly moving the existing keyword search schemes to the multicloud would cause larger storage and search costs, and the data security cannot be distinctly improved. In addition, different from single cloud, a challenge is how to effectively verify query integrity when some cloud servers return incorrect or incomplete results in a multicloud situation. To ensure the data security, we propose an iterative encryption method to ensure the file privacy even if multiple cloud servers collude with each other. We use Bloom filter to map keywords and construct a tree index for each cloud server to achieve secure and efficient distributed keyword search. To verify the query integrity, we propose a fast verification algorithm to embed the file content and index information into the file ID. The analysis and experiments demonstrate the security and effectiveness of our scheme.
A Robust Object Encoding Method.

Object encoding and identification is crucial for many robotic tasks such as autonomous exploration and semantic relocalization. Existing works heavily rely on the tracking of detected objects but difficult to recall revisited objects precisely. In this paper, we propose a novel object encoding method based on a graph of key-points. To be robust to the number of key-points detected, we propose a feature sparse encoding and object dense encoding method to ensure that each key-point can only affect a small part of the object descriptors, leading it robust to viewpoint changes, scaling, occlusion, and even object deformation. In the experiments, we show that it achieves superior performance for object identification than the state-of-the art algorithm and is able to provide reliable semantic relocalization. It is a plug-and-play module and we expect that it will play an important role in the robotic applications.
The Influence of COVID-19 News for Religious Activities in Lampung using Apriori Algorithm

The COVID-19 outbreak in 2020, which affected countries in the world, has become a phenomenon that has shaken social life, especially customs in Indonesia. The information about COVID-19 that people get from various sources, such as mass media, social media or word of mouth. Social distancing and calls for “just at home” are being increasingly informed by the government, along with the increase in COVID-19 sufferers in Indonesia, especially in the province of Lampung. Religious activities, especially Islam, which are full of togetherness, such as congregational prayers in mosques, and recitation are among the things that must be undone, along with the government's call to “worship from home” in order to stop the spread of COVID-19. This study seeks to reveal the relationship between information sources, understanding, and people's attitudes towards religious activities, with data mining techniques. That way, we can conclude which media are the most effective in conveying information. The data mining referred to in this study uses a priori algorithm. The priori algorithm is one of the classic data mining algorithms. A priori algorithms are used so that computers can learn association rules, looking for patterns of relationships between one or more items in a dataset. This research was conducted with survey data which were distributed in various districts and cities in the province of Lampung. The results of this study are that there is a connection, if people are afraid of the Covid-19 outbreak and get Covid-19 information through Instagram, they will be relatively obedient, and worship at home. These results can be used by the government in choosing the most appropriate channels/channels, to maximize the dissemination of information, namely through Instagram, especially for millennials.
Analysis of Energy Consumption on IaaS Cloud Using Simulation Tool

The cloud-computing technique provides services to the customer globally. The large-scale datacenters arrangement is very complex due to demand for optimization of resources utilization and power consumption. Simulation tools provide an easy way to model complex research problems through GUI representation. Cloudreports is a GUI based simulation tool that generates reports and provides an API with extensions and user defined plugins. In the present paper using this simulation tool user may set the simulation scenario for various VM’s and resources consumption, and simulation environment for the provider, customer, SAN, network performance etc. The resource utilization and overall resource utilization reports by various data centers, customers are also generated. PUE and DCIE, is also computed for effective power usage within datacenters.
The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain.

In the univariate setting, using the kernel spectral representation is an appealing approach for generating stationary covariance functions. However, performing the same task for multiple-output Gaussian processes is substantially more challenging. We demonstrate that current approaches to modelling cross-covariances with a spectral mixture kernel possess a critical blind spot. For a given pair of processes, the cross-covariance is not reproducible across the full range of permitted correlations, aside from the special case where their spectral densities are of identical shape. We present a solution to this issue by replacing the conventional Gaussian components of a spectral mixture with block components of finite bandwidth (i.e. rectangular step functions). The proposed family of kernel represents the first multi-output generalisation of the spectral mixture kernel that can approximate any stationary multi-output kernel to arbitrary precision.
AutoTrack: Interactive Visual Object Tracking for Efficient Object Annotations

Efficient video annotation is becoming important with the increasing number of machine learning applications. In this paper, we propose using interactive video object tracking for annotation. Inspired by the VOT short-term tracking challenge, we design an evaluation process to compare the KCF, CSRT, and ATOM short-term trackers. This evaluation process measures the performance and speed of the trackers using these four metrics: accuracy, robustness, initialization time, and processed frames per second. We also modify the open-source CVAT to implement the interactive tracker.
New Generalized Cyclotomic Quaternary Sequences with Large Linear Complexity and a Product of Two Primes Period

Linear complexity is an important criterion to characterize the unpredictability of pseudo-random sequences, and large linear complexity corresponds to high cryptographic strength. Pseudo-random Sequences with a large linear complexity property are of importance in many domains. In this paper, based on the theory of inverse Gray mapping, two classes of new generalized cyclotomic quaternary sequences with period pq are constructed, where pq is a product of two large distinct primes. In addition, we give the linear complexity over the residue class ring Z4 via the Hamming weights of their Fourier spectral sequence. The results show that these two kinds of sequences have large linear complexity.
Entanglement from tensor networks on a trapped-ion QCCD quantum computer

The ability to selectively measure, initialize, and reuse during a quantum circuit enables a mapping of the spatial structure of certain tensor-network states onto the dynamics of quantum circuits, thereby achieving dramatic resource savings when using a quantum computer to simulate many-body systems with limited entanglement. We experimentally demonstrate a significant benefit of this approach to quantum simulation: In addition to all correlation functions, the entanglement structure of an infinite system -- specifically the half-chain entanglement spectrum -- is conveniently encoded within a small register of bond qubits and can be extracted with relative ease. Using a trapped-ion QCCD quantum computer equipped with selective mid-circuit measurement and reset, we quantitatively determine the near-critical entanglement entropy of a correlated spin chain directly in the thermodynamic limit and show that its phase transition becomes quickly resolved upon expanding the bond-qubit register.
Equipment Data Sharing Method Based on Block chain

The effective sharing of data is the key to make full use of data. In order to improve the security of data sharing, this paper proposes a data sharing method based on block chain. The overall framework of the method is presented. As the core of data sharing system, block chain ensures the reliable communication between different data centers and terminals, and realizes the whole process of encrypted transmission. The original data does not leave the hard disk of the data holder. This method not only meets the needs of the full use of data sharing, but also avoids the risk of data leakage of data holders, and effectively improves the security of data sharing.
Anisotropic Convolutional Neural Networks for RGB-D based Semantic Scene Completion

Semantic scene completion (SSC) is a computer vision task aiming to simultaneously infer the occupancy and semantic labels for each voxel in a scene from partial information consisting of a depth image and/or a RGB image. As a voxel-wise labeling task, the key for SSC is how to effectively model the visual and geometrical variations to complete the scene. To this end, we propose the Anisotropic Network (AIC-Net), with novel convolutional modules that can model varying anisotropic receptive fields voxel-wisely in a computationally efficient manner. The basic idea to achieve such anisotropy is to decompose 3D convolution into three consecutive dimensional convolutions, and determine the dimension-wise kernels on the fly. One module, termed kernel-selection anisotropic (KSA) convolution, adaptively selects the optimal kernel sizes for each dimensional convolution from a set of candidate kernels, and the other module, termed kernel-modulation anisotropic (KMA) convolution, directly modulates a single convolutional kernel for each dimension to derive more flexible receptive field. By stacking multiple such anisotropic modules, the 3D context modeling capability and flexibility can be further enhanced. Moreover, we present a new end-to-end trainable framework to approach the SSC task avoiding the expensive TSDF pre-processing as in many existing methods. Extensive experiments on SSC benchmarks show the advantage of the proposed methods.
Deep Variational Auto Encoder for Dimensionality Reduction, Denoising in MNIST Datasets Using TensorFlow and Keras

Auto encoder is a data compression algorithm where the compression and decompression functions are data specific, lossy, learned automatically from examples rather than engineered from human. Variational Auto Encoders (VAE) is a type of auto encoder with added constrains on the encoded representations being learned. Clearly speaking it is an auto encoder that learns a latent variable model for its input data. The main objective of my paper is to provide a good visualization of data by denoising of an image using MNIST dataset and achieve better results.
RESEARCH OF DIGITAL TRANSFORMATION RUSSIAN OF REGIONAL UNIVERSITIES UNDER MODERN CONDITIONS

Many researchers come to the conclusion that universities should become knowledge generators. Building a digital economy is designed to ensure, on the one hand, more efficient and less costly economic functioning, on the other - a radical change in the relationship between society, the state and business. This involves the organization of training aimed at the effective use of modern information and communication technologies related to the introduction of artificial intelligence and robotics, based on the formation of new thinking and working style. At the same time, a serious problem in the implementation of digital economy initiatives is personnel training, which in turn determines the role and importance of education in this process. The purpose of the article is to identify and study the main factors that affect the process of transformation of Russian regional universities, based on the study of Russian literature. The scientific novelty of the article is the definition of digital transformation of the educational process as a creative system of relations between its participants and stakeholders, formed as a result of the development and implementation of modern information technologies and corresponding communication devices in the learning process, the end result of which will be the creation of a model of a “digital university” as a set of regulatory requirements for the digital environment of the University. As a result, the study analyzes the main directions of digital transformation of the educational process in higher education, including the formation of an integrated environment, the development of online education, the use of domestic and foreign digital platforms, innovative educational components, retraining and advanced training of teachers in relation to digital formats of the learning process.
3D Point Cloud Matching Technology Based on Depth Image Based Rendering

According to the rapid improvement of technology, the Internet of Things (IoT), big data, automated systems and artificial intelligence technology have also gradually developed. The study of self-driving vehicles is the closest to life, the biggest benefit is to make transportation more convenient. In addition, reducing traffic accident is also a major purpose, so the system that the autonomous vehicle is equipped with playing a very important role, and the autonomous vehicle will be equipped with many sensor elements. Its main purpose is to collect and build environmental information, which is equivalent to the eyes of autonomous vehicles. Through the obtained environmental information, the system can determine whether there are obstacles around the vehicle, and then perform its corresponding action to avoid danger. Three-dimensional LiDAR (Light Detection and Ranging) plays a very important role in autonomous vehicles. It uses optical laser to project to the surrounding environment of the vehicle and receives different reflectance generated by different objects to obtain data. Therefore, the paper proposes to combine 3D LiDAR point cloud information with depth maps. The two information are matched to obtain high-precision point cloud information, and different objects are distinguished to observe the results. The depth map is also used to generate two-dimensional point cloud information to repair the problems of missing point cloud information due to environmental factors. In addition, this paper uses the digital chip design flow to implement the decoding of 3D LiDAR packet information.
Content Disentanglement for Semantically Consistent Synthetic-to-Real Domain Adaptation

Synthetic data generation is an appealing approach to generate novel traffic scenarios in autonomous driving. However, deep learning perception algorithms trained solely on synthetic data encounter serious performance drops when they are tested on real data. Such performance drops are commonly attributed to the domain gap between real and synthetic data. Domain adaptation methods that have been applied to mitigate the aforementioned domain gap achieve visually appealing results, but usually introduce semantic inconsistencies into the translated samples. In this work, we propose a novel, unsupervised, end-to-end domain adaptation network architecture that enables semantically consistent sim2real image transfer. Our method performs content disentanglement by employing shared content encoder and fixed style code.
Fast-forwarding quantum evolution

We investigate the problem of fast-forwarding quantum evolution, whereby the dynamics of certain quantum systems can be simulated with gate complexity that is sublinear in the evolution time. We provide a definition of fast-forwarding that considers the model of quantum computation, the Hamiltonians that induce the evolution, and the properties of the initial states. Our definition accounts for \emph{any} asymptotic complexity improvement of the general case and we use it to demonstrate fast-forwarding in several quantum systems. In particular, we show that some local spin systems whose Hamiltonians can be taken into block diagonal form using an efficient quantum circuit, such as those that are permutation-invariant, can be exponentially fast-forwarded. We also show that certain classes of positive semidefinite local spin systems, also known as frustration-free, can be polynomially fast-forwarded, provided the initial state is supported on a subspace of sufficiently low energies. Last, we show that all quadratic fermionic systems and number-conserving quadratic bosonic systems can be exponentially fast-forwarded in a model where quantum gates are exponentials of specific fermionic or bosonic operators, respectively. Our results extend the classes of physical Hamiltonians that were previously known to be fast-forwarded, while not necessarily requiring methods that diagonalize the Hamiltonians efficiently. We further develop a connection between fast-forwarding and precise energy measurements that also accounts for polynomial improvements.
Test and trace ... and test again?

Editor – I read with interest the rapid report on contact tracing for SARS-CoV-2.[1][1] The authors refer to the potential for a much larger network of potentially secondarily infected contacts, but don't address how this could be solved other than by prompt contact tracing. One addition to the
Summing Up Smart Transitions

Some of the most significant high-level properties of currencies are the sums of certain account balances. Properties of such sums can ensure the integrity of currencies and transactions. For example, the sum of balances should not be changed by a transfer operation. Currencies manipulated by code present a verification challenge to mathematically prove their integrity by reasoning about computer programs that operate over them, e.g., in Solidity. The ability to reason about sums is essential: even the simplest ERC-20 token standard of the Ethereum community provides a way to access the total supply of balances. Unfortunately, reasoning about code written against this interface is non-trivial: the number of addresses is unbounded, and establishing global invariants like the preservation of the sum of the balances by operations like transfer requires higher-order reasoning. In particular, automated reasoners do not provide ways to specify summations of arbitrary length. In this paper, we present a generalization of first-order logic which can express the unbounded sum of balances. We prove the decidablity of one of our extensions and the undecidability of a slightly richer one. We introduce first-order encodings to automate reasoning over software transitions with summations. We demonstrate the applicability of our results by using SMT solvers and first-order provers for validating the correctness of common transitions in smart contracts.
Dual Lattice Attacks for Closest Vector Problems (with Preprocessing)

The dual attack has long been considered a relevant attack on lattice-based cryptographic schemes relying on the hardness of learning with errors (LWE) and its structured variants. As solving LWE corresponds to finding a nearest point on a lattice, one may naturally wonder how efficient this dual approach is for solving more general closest vector problems, such as the classical closest vector problem (CVP), the variants bounded distance decoding (BDD) and approximate CVP, and preprocessing versions of these problems. While primal, sieving-based solutions to these problems (with preprocessing) were recently studied in a series of works on approximate Voronoi cells [, , , ], for the dual attack no such overview exists, especially for problems with preprocessing. With one of the take-away messages of the approximate Voronoi cell line of work being that primal attacks work well for approximate CVP(P) but scale poorly for BDD(P), one may further wonder if the dual attack suffers the same drawbacks, or if it is perhaps a better solution when trying to solve BDD(P).
Thermal Infrared Colorization Using Deep Learning

Day by day the usage of infrared cameras has been increasing in the world. With the increasing use of thermal infrared cameras and images, especially in military, security and medicine, the need for coloring thermal infrared images to visible spectrum has arisen. In this study, a deep based model has been developed to generate visible spectrum images (RGB - Red Green Blue) from thermal infrared (TIR) images. In the proposed model, an autoencoder architecture with skip connections has been used to generate RGB images. KAIST-MS (Korea Advanced Institute of Science and Technology-Multispectral) dataset used for training and test the developed model. The experimental results extensively tested using Peak Signal-to-Noise Ratio (PSNR), Least Absolute Deviations (L1), Root Mean Squared Error (RMSE) and Structural Similarity Index Measure (SSIM).
SSFENet: Spatial and Semantic Feature Enhancement Network for Object Detection

Current state-of-the-art object detectors generally use pre-trained classification networks to extract features, and then utilize feature pyramids to detect objects of different scales. However, classification networks prefer translation invariance and ignore the location information, so directly using the extracted features for fusion will affect the performance. In this paper, we present a novel network to address this dilemma, denoted as Spatial and Semantic Feature Enhancement Network (SSFENet). First, we introduce Spatial Feature Enhancement Block to utilize dilated convolution and weighted feature fusion to enhance the spatial information in features. Second, in the low-level stage, our Semantic Feature Enhancement Block uses the backbone network of the high-level stage to obtain features with richer semantic information and only introduces little computational cost due to the use of shared convolution layers. Experimental results on the MS-COCO benchmark show that the proposed SSFENet significantly improves the mAP of commonly used object detectors.