Reviewerly/exHarmony huggingface dataset:
CompressStreamDB: Fine-Grained Adaptive Stream Processing without Decompression

Stream processing prevails and SQL query on streams has become one of the most popular application scenarios. For example, in 2021, the global number of active IoT endpoints reaches 12.3 billion. Unfortunately, the increasing scale of data and strict user requests place much pressure on existing stream processing systems, requiring high processing throughput with low latency. To further improve the performance of current stream processing systems, we propose a compression-based stream processing engine, called CompressStreamDB, which enables adaptive fine-grained stream processing directly on compressed streams, without decompression. Particularly, CompressStreamDB involves eight compression methods targeting various data types in streams, and it also provides a cost model for dynamically selecting the appropriate compression methods. By exploring data redundancy among streams, CompressStreamDB not only saves space in data transmission between client and server, but also achieves high throughput with low latency in SQL query on stream processing. Our experimental results show that compared to the state-of-the-art stream processing system on uncompressed streams, CompressStreamDB achieves 3.24× throughput improvement and 66.0% lower latency on average. Besides, CompressStreamDB saves 66.8% space.
In-Situ Cross-Database Query Processing

Today’s organizations utilize a plethora of heterogeneous and autonomous DBMSes, many of those being spread across different geo-locations. It is therefore crucial to have effective and efficient cross-database query processing capabilities. We present XDB, an efficient middleware system that runs cross-database analytics over existing DBMSes. In contrast to traditional query processing systems, XDB does not rely on any mediating execution engine to perform cross-database operations (e.g., joining data from two DBMSes). It delegates an entire query execution including cross-database operations to underlying DBMSes. At its core, it comprises an optimizer and a delegation engine: the optimizer rewrites cross-database queries into a delegation plan, which captures the semantics as well as the mechanics of a fully decentralized query execution; the delegation engine then deploys the plan to the underlying DBMSes via their declarative interfaces. Our experimental study based on the TPC-H benchmark data shows that XDB outperforms state-of-the-art systems (Garlic and Presto) by up to 6× in terms of runtime and up to 3 orders of magnitude in terms of data transfer.
Automatic Generation of an Efficient Less-Than Oracle for Quantum Amplitude Amplification

Grover's algorithm is a well-known contribution to quantum computing. It searches one value within an unordered sequence faster than any classical algorithm. A fundamental part of this algorithm is the so-called oracle, a quantum circuit that marks the quantum state corresponding to the desired value. A generalization of it is the oracle for Amplitude Amplification, that marks multiple desired states. In this work we present a classical algorithm that builds a phase-marking oracle for Amplitude Amplification. This oracle performs a less-than operation, marking states representing natural numbers smaller than a given one. Results of both simulations and experiments are shown to prove its functionality. This less-than oracle implementation works on any number of qubits and does not require any ancilla qubits. Regarding depth, the proposed implementation is compared with the one generated by Qiskit automatic method, UnitaryGate. We show that the depth of our less-than oracle implementation is always lower. This difference is significant enough for our method to outperform UnitaryGate on real quantum hardware.
ColorfulCurves: Palette-Aware Lightness Control and Color Editing via Sparse Optimization

Color editing in images often consists of two main tasks: changing hue and saturation, and editing lightness or tone curves. State-of-the-art palette-based recoloring approaches entangle these two tasks. A user's only lightness control is changing the lightness of individual palette colors. This is inferior to state-of-the-art commercial software, where lightness editing is based on flexible tone curves that remap lightness. However, tone curves are only provided globally or per color channel (e.g., RGB). They are unrelated to the image content. Neither tone curves nor palette-based approaches support direct image-space edits---changing a specific pixel to a desired hue, saturation, and lightness. ColorfulCurves solves both of these problems by uniting palette-based and tone curve editing. In ColorfulCurves , users directly edit palette colors' hue and saturation, per-palette tone curves, or image pixels (hue, saturation, and lightness). ColorfulCurves solves an L 2,1 optimization problem in real-time to find a sparse edit that satisfies all user constraints. Our expert study found overwhelming support for ColorfulCurves over experts' preferred tools.
Carbon allowance auction design of China's ETS: A comprehensive hierarchical system based on blockchain

This study proposes a blockchain-based optimization framework for carbon allowance allocation auctions. First, the study assesses the status quo of carbon trading, pinpointing barriers, future development trends, and identifying carbon quota auction standards and methods pertinent to the Chinese context. Second, based on China's carbon market needs and the technical characteristics of blockchain, this study reconfigures the functions and responsibilities of each participant and creates a framework for a carbon allowance auction system to optimize the carbon trading process. Third, an algorithm for the carbon allowance auction system is constructed, and the corresponding smart contract is created and simulated. The results illustrate that our proposed system framework facilitates universal carbon allowance auctions, efficiently addresses potential disputes during the auction process, enhances market transparency and credibility, and offers comprehensive privacy protection with flexible information disclosure options for corporations. Blockchain's role in fostering paid distribution and incentivizing participation in the carbon market is further recognized. A blockchain-based carbon trading system safeguards transaction security and efficiency, maintains precise transaction records, and provides automated settlement. Ultimately, this paper presents policy suggestions for the development of a national carbon market in China.
Blockchain-assisted computation offloading collaboration: A hierarchical game to fortify IoT security and resilience

With the rapid development of the Internet of Things (IoT), computation offloading collaboration has emerged as an important solution to the data processing challenges faced by resource-constrained devices. Nonetheless, the collaboration process introduces intricate, multi-layered security and privacy issues. This paper presents a blockchain-assisted computation offloading collaboration (BACOC) scheme that leverages blockchain technology to reinforce security and resilience during computation offloading. Concurrently, we adopt a hierarchical game model grounded in Stackelberg game and contract theory, which helps ascertain the optimal incentive strategies for the purpose of incentive compatibility. Experimental and simulation results demonstrate the effectiveness and feasibility of the BACOC scheme.
Uncertainty-Aware Graph Reasoning With Global Collaborative Learning for Remote Sensing Salient Object Detection

Recently, fully convolutional networks (FCNs) have contributed significantly to salient object detection in optical remote sensing images (RSIs). However, owing to the limited receptive fields of FCNs, accurate and integral detection of salient objects in RSIs with complex edges and irregular topology is still challenging. Moreover, suffering from the low contrast and complicated background of RSIs, existing models often occur ambiguous or uncertain recognition. To remedy the above problems, we propose a novel hybrid modeling approach, i.e., uncertainty-aware graph reasoning with global collaborative learning (UG2L) framework. Specifically, we propose a graph reasoning pipeline to model the intricate relations among RSI patches instead of pixels, and introduce an efficient graph reasoning block (GRB) to build graph representations. On top of it, a global context block (GCB) with a linear attention mechanism is proposed to explore the multiscale and global context collaboratively. Finally, we design a simple yet effective uncertainty-aware loss (UAL) to enhance the model’s reliability for better prediction of saliency or non-saliency. Experimental and visual results on three datasets show the superiority of the proposed UG2L. Code is available at https://github.com/lyf0801/UG2L.
Graph clustering network with structure embedding enhanced

Recently, deep clustering utilizing Graph Neural Networks has shown good performance in the graph clustering. However, the structure information of graph was underused in existing deep clustering methods. Particularly, the lack of concern on mining different types structure information simultaneously. To tackle with the problem, this paper proposes a Graph Clustering Network with Structure Embedding Enhanced (GC-SEE) which extracts nodes importance-based and attributes importance-based structure information via a feature attention fusion graph convolution module and a graph attention encoder module respectively. Additionally, it captures different orders-based structure information through multi-scale feature fusion. Finally, a self-supervised learning module has been designed to integrate different types structure information and guide the updates of the GC-SEE. The comprehensive experiments on benchmark datasets commonly used demonstrate the superiority of the GC-SEE. The results showcase the effectiveness of the GC-SEE in exploiting multiple types of structure for deep clustering.
A reflectance re-weighted Retinex model for non-uniform and low-light image enhancement

Image enhancement is a fundamental low-level task of significant importance that can directly affect high-level image processing tasks. Although various methods have been proposed to enhance images, the effectiveness of current methods deteriorates significantly under non-uniform lighting. Since the brightness may vary dramatically in different regions of real-world photos, current methods hardly achieve a good balance between enhancing low-light regions and retaining normal-light regions in the same image. Consequently, either the low-light regions are under-enhanced or the normal-light regions are over-enhanced, while at the same time, color distortion and artifacts are frequently found. To overcome this shortcoming, we propose a robust Retinex-based model with reflectance map re-weighting that can improve the brightness level of the low-light image and re-balance the brightness concurrently. We introduce an alternating scheme to solve our proposed model, in which the illumination map, reflectance map, and weighting map are updated iteratively. By utilizing the regularization terms, the noise is well-suppressed during the process. An initialization scheme for the weighting map is also proposed to make our model adaptable to a wide range of light conditions. To the best of our knowledge, we are the first to propose a variational model with an explicitly constructed re-weighting prior and the associated weighing map concept for the reflectance map. It can estimate the reflectance map, suppress noise, and re-balance the brightness simultaneously. A series of experimental results on a variety of popular datasets demonstrate the efficacy of our method and its superiority in enhancing real low-light images when compared to other state-of-the-art methods.
Concordancing for CADS

Abstract Concordance analysis is widely recognised as one of the main techniques in a corpus linguist’s toolkit. However, despite a growing body of work critically exploring previously unquestioned mainstays of corpus methods ( Mautner, 2015 ; Taylor &amp; Marchi, 2018 ), this has not focused on concordance analysis specifically. In this paper, we aim to discuss issues that researchers may encounter when interpreting concordances. We begin in Step One with a cursory examination of 800 concordance lines in order to identify potential issues. In Step Two, we assess the distribution of those issues in a reduced sample of 200. As a result, we identify eight interpretability issues: noise in the corpus, non-standard syntax, unclear referring expressions, unclear quotation source attribution, technical terms/jargon, acronyms/initialisms, unspecific co-text, and lines unrelated to the research question. After reflecting on practical challenges, we discuss the epistemological implications of removing concordance lines uncritically and suggest ten recommendations for future work.
A Demonstration of How ChatGPT Can be Used in the Internal Auditing Process

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Risk assessment of drinking water intake contamination from agricultural activities using a Bayesian network

Agricultural activities can result in the contamination of surface runoff with pathogens, pesticides, and nutrients. These pollutants can enter surface water bodies in two ways: by direct discharge into surface waters or by infiltration and recharge into groundwater, followed by release to surface waters. Lack of financial resources makes risk assessment through analysis of drinking water pollutants challenging for drinking water suppliers. Inability to identify agricultural lands with a high-risk level and implement action measures might lead to public health issues. As a result, it is essential to identify hazards and conduct risk assessments even with limited data. This study proposes a risk assessment model for agricultural activities based on available data and integrating various types of knowledge, including expert and literature knowledge, to estimate the levels of hazard and risk that different agricultural activities could pose to the quality of withdrawal waters. To accomplish this, we built a Bayesian network with continuous and discrete inputs capturing raw water quality and land use upstream of drinking water intakes (DWIs). This probabilistic model integrates the DWI vulnerability, threat exposure, and threats from agricultural activities, including animal and crop production inventoried in drainage basins. The probabilistic dependencies between model nodes are established through a novel adaptation of a mixed aggregation method. The mixed aggregation method, a traditional approach used in ecological assessments following a deterministic framework, involves using fixed assumptions and parameters to estimate ecological outcomes in a specific case without considering inherent randomness and uncertainty within the system. After validation, this probabilistic model was used for four water intakes in a heavily urbanized watershed with agricultural activities in the south of Quebec, Canada. The findings imply that this methodology can assist stakeholders direct their efforts and investments on at-risk locations by identifying agricultural areas that can potentially pose a risk to DWIs.
An Intelligent Chatbot for Negotiation Dialogues

In negotiation dialogue tasks, opponents may use various strategies. Such negotiations are challenging because our chatbot needs to be able to detect the behavior change and then to adapt its own policy accordingly. Moreover, instead of adopting stationary strategies, a more advanced opponent may demonstrate sophisticated behaviors by employing reasoning strategies to predict its opponent behavior. To address these challenges, this work proposes a novel chatbot for negotiation dialogues, which leverages the predictive power of Bayesian policy reuse (BPR) and the recursive reasoning ability of theory of mind (ToM). BPR can help the chatbot efficiently detect strategies of an opponent and select the best response policy, and ToM can efficiently identify whether an opponent is using stationary or higher-level reasoning strategies to improve policy selection. The performance of the proposed chatbot is evaluated on the CRAIGSLISTBARGAIN dataset against state-of-the-art baselines. The experimental results show that it outperforms existing agents on the task, and is also able to make efficient detection and optimal response against state-of-the-art baselines.
Lightweight Hardware Accelerator for Post-Quantum Digital Signature CRYSTALS-Dilithium

The looming threat of an adversary with quantum computing capability led to a worldwide research effort towards identifying and standardizing novel post-quantum cryptographic primitives. Post-standardization, all existing security protocols will need to support efficient implementation of these primitives. In this work, we contribute to these efforts by reporting the smallest implementation of CRYSTALS-Dilithium, one of the chosen post-quantum digital signature scheme for NIST standardization process. By invoking multiple optimizations to leverage parallelism, pre-computation and memory access sharing, we obtain an implementation that could be fit into one of the smallest Zynq FPGA. On Zynq Ultrascale+, our design achieves an improvement of about 36.7%/35.4%/42.3% in Area <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> Time (LUTs <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times \text{s}$ </tex-math></inline-formula> ) trade-off for KeyGen/Sign/Verify respectively over state-of-the-art implementation. We also evaluate our design as a co-processor on three different hardware platforms and compare the results with software implementation, thus presenting a detailed evaluation of CRYSTALS-Dilithium targeted for embedded applications. Further, on ASIC using TSMC 65nm technology, our design requires 0.227mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> area and can operate at a frequency of 1.176 GHz. As a result, it only requires <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$53.7\mu \text{s}/96.9\mu \text{s}/57.7\mu \text{s}$ </tex-math></inline-formula> for KeyGen/Sign/Verify operation for the best-case scenario.
Automated wildlife image classification: An active learning tool for ecological applications

Wildlife camera trap images are being used extensively to investigate animal abundance, habitat associations, and behavior, which is complicated by the fact that experts must first classify the images manually. Artificial intelligence systems can take over this task but usually need a large number of already-labeled training images to achieve sufficient performance. This requirement necessitates human expert labor and poses a particular challenge for projects with few cameras or short durations. We propose a label-efficient learning strategy that enables researchers with small or medium-sized image databases to leverage the potential of modern machine learning, thus freeing crucial resources for subsequent analyses. Our methodological proposal is two-fold: (1) We improve current strategies of combining object detection and image classification by tuning the hyperparameters of both models. (2) We provide an active learning (AL) system that allows training deep learning models very efficiently in terms of required human-labeled training images. We supply a software package that enables researchers to use these methods directly and thereby ensure the broad applicability of the proposed framework in ecological practice. We show that our tuning strategy improves predictive performance. We demonstrate how the AL pipeline reduces the amount of pre-labeled data needed to achieve a specific predictive performance and that it is especially valuable for improving out-of-sample predictive performance. We conclude that the combination of tuning and AL increases predictive performance substantially. Furthermore, we argue that our work can broadly impact the community through the ready-to-use software package provided. Finally, the publication of our models tailored to European wildlife data enriches existing model bases mostly trained on data from Africa and North America.
Trade-offs between coherence and mixedness and their evolution under quantum noise channels

Generally quantum processes tend to mix the pure input states and degrade the original coherence. Such processes display exactly the trade-offs between the coherence and the mixedness. In this paper, we present trade-offs between the coherence measures (l2 norm of coherence, coherence via skew information, geometric measure of coherence) and the mixedness (normalized linear entropy mixedness, geometric measure of mixedness, mixedness via skew information). The evolution of these trade-offs are also investigated under four quantum noise channels: the bit flip, phase flip, depolarizing and amplitude damping channels.
Decode-MOT: How Can We Hurdle Frames to Go Beyond Tracking-by-Detection?

The speed of tracking-by-detection (TBD) greatly depends on the number of running a detector because the detection is the most expensive operation in TBD. In many practical cases, multi-object tracking (MOT) can be, however, achieved based tracking-by-motion (TBM) only. This is a possible solution without much loss of MOT accuracy when the variations of object cardinality and motions are not much within consecutive frames. Therefore, the MOT problem can be transformed to find the best TBD and TBM mechanism. To achieve it, we propose a novel decision coordinator for MOT (Decode-MOT) which can determine the best TBD/TBM mechanism according to scene and tracking contexts. In specific, our Decode-MOT learns tracking and scene contextual similarities between frames. Because the contextual similarities can vary significantly according to the used trackers and tracking scenes, we learn the Decode-MOT via self-supervision. The evaluation results on MOT challenge datasets prove that our method can boost the tracking speed greatly while keeping the state-of-the-art MOT accuracy. Our code will be available at https://github.com/reussite-cv/Decode-MOT.