Reviewerly/exHarmony huggingface dataset:
Snapshot-Oblivious RAMs: Sub-logarithmic Efficiency for Short Transcripts

Oblivious RAM (ORAM) is a powerful technique to prevent harmful data breaches. Despite tremendous progress in improving the concrete performance of ORAM, it remains too slow for use in many practical settings; recent breakthroughs in lower bounds indicate this inefficiency is inherent for ORAM and even some natural relaxations. This work introduces snapshot-oblivious RAMs, a new secure memory access primitive. Snapshot-oblivious RAMs bypass lower bounds by providing security only for transcripts whose length (call it $$c$$ ) is fixed and known ahead of time. Intuitively, snapshot-oblivious RAMs provide strong security for attacks of short duration, such as the snapshot attacks targeted by many encrypted databases. We give an ORAM-style definition of this new primitive, and present several constructions. The underlying design principle of our constructions is to store the history of recent operations in a data structure that can be accessed obliviously. We instantiate this paradigm with data structures that remain on the client, giving a snapshot-oblivious RAM with constant bandwidth overhead. We also show how these data structures can be stored on the server and accessed using oblivious memory primitives. Our most efficient instantiation achieves $$\mathcal{O}(\log c)$$ bandwidth overhead. By extending recent ORAM lower bounds, we show this performance is asymptotically optimal. Along the way, we define a new hash queue data structure—essentially, a dictionary whose elements can be modified in a first-in-first-out fashion—which may be of independent interest.
Software bots in software engineering

Software bots are becoming increasingly popular in software engineering (SE). In this tutorial, we define what a bot is and present several examples. We also discuss the many benefits bots provide to the SE community, including helping in development tasks (such as pull request review and integration) and onboarding newcomers to a project. Finally, we discuss the challenges related to interacting with and developing software bots.
Brightness and Saturation Enhancement by Using Color Vector on Equi-Hue Plane in RGB Color Space

In order to improve the image quality of an image with reduced visibility, it is necessary to perform image processing such as brightness correction and sharpening. As a method for improving the visibility of this image, unsharp masking by local projection brightness that emphasizes contrast by using brightness that reflects local color changes while preserving hue has been proposed. In this method, however, block noises are occurred in the image by the excessive emphasis. In this study, we proposed a novel sharpening and saturation enhancement method using an Equi-Hue Plane that has the same hue as the input pixels. The effectiveness of the proposed method has been verified through the various experiments.
Total Variation Optimization Layers for Computer Vision

Optimization within a layer of a deep-net has emerged as a new direction for deep-net layer design. However, there are two main challenges when applying these layers to computer vision tasks: (a) which optimization problem within a layer is useful?; (b) how to ensure that computation within a layer remains efficient? To study question (a), in this work, we propose total variation (TV) minimization as a layer for computer vision. Motivated by the success of total variation in image processing, we hypothesize that TV as a layer provides useful inductive bias for deep-nets too. We study this hypothesis on five computer vision tasks: image classification, weakly supervised object localization, edge-preserving smoothing, edge detection, and image denoising, improving over existing baselines. To achieve these results we had to address question (b): we developed a GPU-based projected-Newton method which is 37× faster than existing solutions.
BodyGAN: General-purpose Controllable Neural Human Body Generation

Recent advances in generative adversarial networks (GANs) have provided potential solutions for photo-realistic human image synthesis. However, the explicit and individual control of synthesis over multiple factors, such as poses, body shapes, and skin colors, remains difficult for existing methods. This is because current methods mainly rely on a single pose/appearance model, which is limited in dis-entangling various poses and appearance in human images. In addition, such a unimodal strategy is prone to causing severe artifacts in the generated images like color distortions and unrealistic textures. To tackle these issues, this paper proposes a multi-factor conditioned method dubbed BodyGAN. Specifically, given a source image, our Body-GAN aims at capturing the characteristics of the human body from multiple aspects: (i) A pose encoding branch consisting of three hybrid subnetworks is adopted, to generate the semantic segmentation based representation, the 3D surface based representation, and the key point based rep-resentation of the human body, respectively. (ii) Based on the segmentation results, an appearance encoding branch is used to obtain the appearance information of the human body parts. (iii) The outputs of these two branches are represented by user-editable condition maps, which are then processed by a generator to predict the synthesized image. In this way, our BodyGAN can achieve the fine-grained dis-entanglement of pose, body shape, and appearance, and consequently enable the explicit and effective control of syn-thesis with diverse conditions. Extensive experiments on multiple datasets and a comprehensive user study show that our BodyGAN achieves the state-of-the-art performance.
Turbo-charging SPJ query plans with learned physical join operator selections

The optimization of select-project-join (SPJ) queries entails two major challenges: (i) finding a good join order and (ii) selecting the best-fitting physical join operator for each single join within the chosen join order. Previous work mainly focuses on the computation of a good join order, but leaves open to which extent the physical join operator selection accounts for plan quality. Our analysis using different query optimizers indicates that physical join operator selection is crucial and that none of the investigated query optimizers reaches the full potential of optimal operator selections. To unlock this potential, we propose TONIC , a novel cardinality estimation-free extension for generic SPJ query optimizers in this paper. TONIC follows a learning-based approach and revises operator decisions for arbitrary join paths based on learned query feedback. To continuously capture and reuse optimal operator selections, we introduce a lightweight yet powerful Query Execution Plan Synopsis ( QEP-S ). In comparison to related work, TONIC enables transparent planning decisions with consistent performance improvements. Using two real-life benchmarks, we demonstrate that extending existing optimizers with TONIC substantially reduces query response times with a cumulative speedup of up to 2.8x.
Physical Layer-Based IoT Security: An Investigation Into Improving Preamble-Based SEI Performance When Using Multiple Waveform Collections

The Internet of Things (IoT) is a collection of inexpensive, semi-autonomous, Internetconnected devices that sense and interact within the physical world.IoT security is of paramount concern, because most IoT devices use weak or no encryption at all.This concern is exacerbated by the fact that the number of IoT deployments continues grow, IoT devices are being integrated into key infrastructures, and their weak or lack of encryption is being exploited.Specific Emitter Identification (SEI) is being investigated as an effective, cost-saving IoT security approach, because it is a passive technique that exploits inherent, distinct features that are unintentionally imparted to the waveform during its formation and transmission by the IoT device's Radio Frequency (RF) front-end.Despite the amount of research conducted, SEI still faces roadblocks that hinder its integration within operational networks.Our work focuses on the lack of feature permanence across time and environments, which is designated herein as the "multi-day" problem.We present results and analysis for six distinct experiments focused on improving multi-day SEI performance through the use of multiple waveform representations, deeper Convolutional Neural Networks (CNNs), increasing numbers of waveforms, channel model impacts, and two channel mitigation techniques.Our work shows improved multi-day SEI performance using the waveform's frequency-domain representation and a CNN comprised of four convolutional layers.However, the traditional channel model and both channel mitigation techniques fail to sufficiently mitigate or remove real-world channel impacts, which suggests that the channel may not be the dominant effect hindering multi-day SEI performance.
Prior Knowledge Guided Unsupervised Domain Adaptation

The waive of labels in the target domain makes Unsupervised Domain Adaptation (UDA) an attractive technique in many real-world applications, though it also brings great challenges as model adaptation becomes harder without labeled target data. In this paper, we address this issue by seeking compensation from target domain prior knowledge, which is often (partially) available in practice, e.g., from human expertise. This leads to a novel yet practical setting where in addition to the training data, some prior knowledge about the target class distribution are available. We term the setting as Knowledge-guided Unsupervised Domain Adaptation (KUDA). In particular, we consider two specific types of prior knowledge about the class distribution in the target domain: Unary Bound that describes the lower and upper bounds of individual class probabilities, and Binary Relationship that describes the relations between two class probabilities. We propose a general rectification module that uses such prior knowledge to refine model generated pseudo labels. The module is formulated as a Zero-One Programming problem derived from the prior knowledge and a smooth regularizer. It can be easily plugged into self-training based UDA methods, and we combine it with two state-of-the-art methods, SHOT and DINE. Empirical results on four benchmarks confirm that the rectification module clearly improves the quality of pseudo labels, which in turn benefits the self-training stage. With the guidance from prior knowledge, the performances of both methods are substantially boosted. We expect our work to inspire further investigations in integrating prior knowledge in UDA. Code is available at https://github.com/tsun/KUDA .
Unstructured Feature Decoupling for Vehicle Re-identification

TheVan Author, First E. misalignment of features caused by pose and viewpoint variances is a crucial problem in Vehicle Re-Identification (ReID). Previous methods align the features by structuring the vehicles from pre-defined vehicle parts (such as logos, windows, etc.) or attributes, which are inefficient because of additional manual annotation. To align the features without requirements of additional annotation, this paper proposes a Unstructured Feature Decoupling Network (UFDN), which consists of a transformer-based feature decomposing head (TDH) and a novel cluster-based decoupling constraint (CDC). Different from the structured knowledge used in previous decoupling methods, we aim to achieve more flexible unstructured decoupled features with diverse discriminative information as shown in Fig. 1. The self-attention mechanism in the decomposing head helps the model preliminarily learn the discriminative decomposed features in a global scope. To further learn diverse but aligned decoupled features, we introduce a cluster-based decoupling constraint consisting of a diversity constraint and an alignment constraint. Furthermore, we improve the alignment constraint into a modulated one to eliminate the negative impact of the outlier features that cannot align the clusters in semantics. Extensive experiments show the proposed UFDN achieves state-of-the-art performance on three popular Vehicle ReID benchmarks with both CNN and Transformer backbones. Our code is released at: https://github.com/damo-cv/UFDN-Reid .
Self-Organizing Map algorithm as a tool for outlier detection

The research addresses the problem of outlier detection using the (unsupervised) self-organizing map (S OM) algorithm introduced by Kohonen [1]. Since the outliers can indicate something scientifically interesting, we need to analyze these issue deeply. In this research, we do not assume that after outliers are detected, they are removed. We hand them over to domain experts asking for further exploration. We have experience applying the LOF (local outlier factor) algorithm to detect outliers in real data. Now we wanted to check how a new algorithm, an S OM algorithm, will behave. We compare the results of outlier detection process using the LOF algorithm with the results of using the S OM algorithm. We were interested whether a data type significantly influences the efficiency of the process of outlier detection. It is obvious that qualitative data is much more difficult to explore than quantitative data. We chose two various dataset (quantitive and qualitative). We kept changing the learning parameters: a learning rate, a neighborhood radius, an activation function, a distance measure and others and analyzed the accuracy of the outlier detection process. The accuracy of the S OM algorithm for outlier detection is satisfying. This algorithm is faster than the LOF algoritm. The results of the experiments show that there is a strong negative correlation between the learning parameters and the training time of an SOM map and the QE quantization error computing.
Focus and Align: Learning Tube Tokens for Video-Language Pre-Training

Video-language pre-training (VLP) has attracted increasing attention for cross-modality understanding tasks. To enhance visual representations, recent works attempt to adopt transformer-based architectures as video encoders. These works usually focus on the visual representations of the sampled frames. Compared with frame representations, frame patches incorporate more fine-grained spatio-temporal information, which could lead to a better understanding of video contents. However, how to exploit the spatio-temporal information within frame patches for VLP has been less investigated. In this work, we propose a method to learn tube tokens to model the key spatio-temporal information from frame patches. To this end, multiple semantic centers are introduced to focus on the underlying patterns of frame patches. Based on each semantic center, the spatio-temporal information within frame patches is integrated into a unique tube token. Complementary to frame representations, tube tokens provide detailed clues of video contents. Furthermore, to better align the generated tube tokens and the contents of descriptions, a local alignment mechanism is introduced. The experiments based on a variety of downstream tasks demonstrate the effectiveness of the proposed method.
Efficient Special Character Entry on a Virtual Keyboard by Hand Gesture-Based Mode Switching

The need to support efficient text input in virtual reality continues to attract significant research attention. However, much of this research understandably focuses exclusively on core text input tasks involving the entry of standard alphabetic characters. The less common though still critical task of entering special characters is often ignored. In this paper we focus on this niche use case as chiefly encountered when entering passwords. Current commercial virtual keyboards allow users to switch between different layers of the keyboard in order to access capital letters, numerals and special characters by pressing an explicit mode-switch button. We propose a new method of switching between layers of a virtual keyboard using hand gestures. Critically, these hand gestures are seamlessly performed in conjunction with key selections to deliver an efficient and intuitive interaction. We report on a user study with 16 participants entering standard passwords comparing our gesture-based mode switching approach to a conventional button-based baseline. We find that with practice our proposed method results in significantly faster entry rates without any deterioration in accuracy. Feedback from users also indicates that our technique is considered efficient and comfortable to use.
Federated Learning Based Resource Allocation for Wireless Communication Networks

In this paper we introduce federated learning (FL) based resource allocation (RA) for wireless communication networks, where users cooperatively train a RA policy in a distributed scenario. The RA policy for each user is represented by a local deep neural network (DNN), which has the same structure for all users. Each DNN monitors local measurements and outputs a power allocation to the user. The proposed approach is model-free; each user is responsible for training its own DNN to maximize the sum rate (SR) and communicates with the server to aggregate its local DNN with other DNNs. More importantly, each user needs to probe only its own data rate as a distributed reward function and communications with the server once in a while. Simulations show that the proposed approach enables conventional deep learning (DL) based RA methods to not only use their policy in a distributed scenario, but also to (re)train their policy in time-varying environments in a model-free distributed manner without needing a computationally complex server.
Applicative approach to construe a computational model of concepts and individuals

This paper considers the application of conceptual mathematics to construct a computational model of concepts and individuals. An applicative approach is systematically applied to build a concept-as-process design. Since modern computing considers information processes as the main objects of modeling, the developed design is indeed representative of the semantic processing of information. The nature of concepts – what concepts are – and the constraints that govern the theory of concepts have been, and continue to be, the subject of debate. It is especially interesting to discuss the nature of concepts in connection with the recently established fundamental nature of information processes that are attributable to all phenomena and events occurring in the world around us. The current trend elevates information processes to forms of computing, which can also be implemented through practices, for example, in the form of programming. The deep component is computational models, one way or another expressed by means of mathematics and metamathematics. The main meta-operations used are abstraction and application. Of greatest interest is functional abstraction and application in the form of applying a function to an argument. Despite this “conceptual minimalism”, a rich theory of concepts can be developed. Using this theory, it is possible to focus further discussion not only on the nature of concepts, but also to characterize the position on each of the five important issues that are central to many theories of concepts: (1) ontology of concepts, (2) structure of concepts, (3) empiricism, and nativism about concepts, (4) concepts and natural language, and (5) concepts and conceptual analysis.
The Status and Early Results of Hyperspectral Imager Suite (HISUI)

Hyperspectral Imager Suite (HISUI) was launched in December 2019 and started its operation observation from International Space Station in October 2020. HISUI data are being acquired based on data acquisition requests from HISUI project and principal investigators of HISUI Research Announcement. The total area of acquired HISUI data by December 2021 exceeded 110 M km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and the data are being used in various application studies. HISUI will be operated by FY2023. In this presentation, HISUI's instrument and operation status will be presented together with its early results in terms of geology and greenhouse gas emission.
From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution

How to design proper training pairs is critical for super-resolving real-world low-quality (LQ) images, which suffers from the difficulties in either acquiring paired ground-truth high-quality (HQ) images or synthesizing photo-realistic degraded LQ observations. Recent works mainly focus on modeling the degradation with handcrafted or estimated degradation parameters, which are however incapable to model complicated real-world degradation types, resulting in limited quality improvement. Notably, LQ face images, which may have the same degradation process as natural images, can be robustly restored with photo-realistic textures by exploiting their strong structural priors. This motivates us to use the real-world LQ face images and their restored HQ counterparts to model the complex real-world degradation (namely ReDegNet), and then transfer it to HQ natural images to synthesize their realistic LQ counterparts. By taking these paired HQ-LQ face images as inputs to explicitly predict the degradation-aware and content-independent representations, we could control the degraded image generation, and subsequently transfer these degradation representations from face to natural images to synthesize the degraded LQ natural images. Experiments show that our ReDegNet can well learn the real degradation process from face images. The restoration network trained with our synthetic pairs performs favorably against SOTAs. More importantly, our method provides a new way to handle the real-world complex scenarios by learning their degradation representations from the facial portions, which can be used to significantly improve the quality of non-facial areas. The source code is available at https://github.com/csxmli2016/ReDegNet .
HiCoOB: Hierarchical Concurrent Optimistic Blockchain Consensus Protocol for Peer-to-Peer Energy Trading Systems

Hierarchical consensus protocols are one of the most promising approaches considered to improve the performance and scalability of blockchain-based peer-to-peer energy trading (P2P ET) systems. They allow a network to be divided into multiple clusters where each cluster can independently run its own local consensus on local transactions (i.e., transactions that belong to a single cluster) and also cooperate with other clusters for global transactions (i.e., transactions that affect more than one cluster). However, current solutions do not allow the parallel processing of local and global transactions, leading to low throughput and high latency. Moreover, they impose an additional burden on the leader node by assigning to it the responsibility for both local and global consensus. This paper proposes a hierarchical concurrent optimistic blockchain consensus protocol, called HiCoOB, which includes an optimistic local consensus and a deterministic global consensus that enables the concurrent execution of local and global transactions. Additionally, the local and the global consensus are coordinated by two distinct leaders, thereby reducing the overhead on the local leader node. HiCoOB also provides a synchronization method between local and global consensus to avoid the double spending of energy or money that arises from interdependent client transactions. Experimental results indicate that the proposed protocol achieves 87% higher throughput than the existing systems. Furthermore, it reduces the waiting time of the local transactions by a factor of 5x. Finally, HiCoOB has 10 times less overhead on the local leader node in terms of message exchange when compared to state-of-the-art systems.