Reviewerly/exHarmony huggingface dataset:
Rebutting Rebuttals

Evaluating the impact of the conference review rebuttal process.
Robust open-set classification for encrypted traffic fingerprinting

Encrypted network traffic has been known to leak information about their underlying content through side-channel information leaks. Traffic fingerprinting attacks exploit this by using machine learning techniques to threaten user privacy by identifying user activities such as website visits, videos streamed, and messenger app activities. Although state-of-the-art traffic fingerprinting attacks have high performances, even undermining the latest defenses, most of them are developed under the closed-set assumption. To deploy them in practical situations, it is important to adapt them to the open-set scenario, which allows the attacker to identify its target content while rejecting other background traffic. At the same time, in practice, these models need to be deployed on in-networking devices such as programmable switches, which have limited memory and computation power. Model weight quantization can reduce the memory footprint of deep learning models while at the same time, allowing inference to be done as integer operations as opposed to floating point operations. Open-set classification in the domain of traffic fingerprinting has not been explored well in prior work and none of them explored the effect of quantization on the open-set performance of such models. In this work, we propose a framework for robust open-set classification of encrypted traffic based on three key ideas. First, we show that a well-regularized deep learning model improves the open-set classification and then we propose a novel open-set classification method with three variants that perform consistently over multiple datasets. Next, we show that traffic fingerprinting models can be quantized without a significant drop in both closed-set and open-set accuracy and therefore, they can be readily deployed on in-network computing devices. Finally, we show that when the above three components are combined, the resulting open-set classifier outperforms all other open-set classification methods evaluated across five datasets with a minimum and maximum increase in F1_Score of 8.9% and 77.3% respectively.
Super-resolution in thin section of lacustrine shale reservoirs and its application in mineral and pore segmentation

Lacustrine shale reservoirs present intricate attributes such as the prevalence of lamination, rapid sedimentary phase transitions, and pronounced heterogeneity. These factors introduce substantial challenges in analyzing and comprehending reservoir characteristics. Thin-section imaging offers a direct medium to observe these traits, yet the intrinsic compromise between image resolution and field of view impedes the concurrent capture of comprehensive details and contextual overview. This study delves into the application of super-resolution (SR) techniques to augment the segmentation of thin-section images from lacustrine shale, an unconventional reservoir. SR application furnishes high-resolution images, facilitating a robust analysis of morphology, texture, edge properties, and target classification. Utilizing data from the lacustrine shale reservoir of the Ordos Basin, we evaluate our methodology and assess the impact of SR enhancement on segmentation. Quantitative results indicate significant improvements, with the Jaccard index for shale increasing from 0.4790 (Low-Resolution) to 0.7803 (ESRGAN) in the Y channel of the YCrCb color space after level set segmentation, exemplifying the efficacy of SR in shale gas and oil reservoirs. This research underscores the necessity to consider lacustrine shale's unique features while formulating and implementing SR techniques for improved information extraction. Furthermore, it highlights SR's potential for propelling future research and industry-specific applications.
Prompt learning for metonymy resolution: Enhancing performance with internal prior knowledge of pre-trained language models

Linguistic metonymy is a common type of figurative language in natural language processing (NLP), where a concept is represented by a closely associated word or phrase, for example "business executives suits". As a result, metonymy resolution has become an important NLP task aimed at correctly identifying metonymic expressions within sentences. Previous approaches to this task have typically relied on pre-trained language models (PLMs) using a fine-tuning process. However, this can be time-consuming and resource-intensive, and may lead to a loss of factual prior knowledge. The emergence of a novel learning paradigm termed "prompt learning" or "prompt-tuning" has recently sparked widespread interest and captured considerable attention, as it has proven to yield remarkable results and surpass previous benchmarks. This approach uses a "pre-train→prompt→predict" paradigm and has been shown to better utilize the internal prior knowledge of a PLM, especially in situations with limited supervised resources. Inspired by this success, we investigated how prompt learning could improve metonymy resolution. We have developed a series of prompt learning approaches, called PromptMR, for metonymy resolution, and applied them to several widely-used metonymy resolution datasets. We also designed additional prompt-tuning augmentation strategies to further enhance the potential of prompt learning. Our experiments demonstrated that our method achieved state-of-the-art performance over multiple competitive baselines in both data-sufficient and data-scarce scenarios. The code implementations for PromptMR are accessible on GitHub via the URL: https://github.com/albert-jin/PromptTuning2MetonymyResolution.
AAE-DSVDD: A one-class classification model for VPN traffic identification

Virtual Private Network(VPN) can provide a concealed transmission channel for communication and protect the privacy of users. However, it also brings hidden dangers to cybersecurity with its wide application. Malicious behavior or harmful information can be transmitted secretly through VPN tunnels to avoid firewall censorship. Therefore, VPN traffic identification is an important part of ensure cybersecurity. Although many efforts have been made for VPN traffic identification, existing methods mainly focus on supervised learning models. In this paper, we propose an one-class classification model called AAE-DSVDD for VPN traffic identification. First, we introduce Adversarial AutoEncoder(AAE) for preliminary modeling of VPN traffic. AAE can match the aggregated posterior distribution of the hidden layer to an arbitrary prior distribution. It associates the samples with a normal distribution in the hidden space. Secondly, We implement representation learning for VPN traffic via Deep Support Vector Data Description(DSVDD). A standardized method is designed to match the output distribution of DSVDD with the aggregated posterior distribution of AAE. It alleviates the hypersphere collapse problem of DSVDD and improves identification performance. Finally, we verify the abilities of the AAE-DSVDD model on the public dataset ISCXVPN. Compared with other one-class models, AAE-DSVDD achieved the best identification ability for VPN traffic identification. It also improves the recognition ability when identifying strange classes that are not included in the training data.
The Significance of IoT and Deep Learning in Activity Recognition

Presently, Activity recognition (AR) performs a vital role in engineering, home, health care, universities, and many more other areas. The main role of activity recognition is to recognize the activities or actions performed by the subject, the subject can either be human or any other device. This article is based on the role of IoT Devices and Deep Learning algorithms which can be mostly used for activity recognition. To carry out this survey three research questions are designed. The answer to those research questions will help current researchers to carry out the research in the field of activity recognition using IoT and Deep Learning.
An Inshore SAR Ship Detection Method Based on Ghost Feature Extraction and Cross-Scale Interaction

Ship detection in synthetic aperture radar (SAR) images using a convolutional neural network (CNN) has become a hotspot. However, dense multi-size ship targets in inshore scenes, proximity of ship targets to man-made targets, and datasets containing only one class limit the performance of ship detection methods. In pursuit of attaining heightened performance, most existing methods endeavor to augment the number of parameters and enhance network complexity. To address these problems, this letter proposes an anchor-free ghost feature extraction and cross-scale interaction network (GFECSI-Net), which improves detection performance through an efficient implementation, thereby avoiding the increase in the number of parameters or network complexity. First, to enhance the capability of feature extraction for ship targets, a multi-scale adaptive feature pyramid network (MSAFPN) is proposed to realize intensive information interaction and cross-scale feature fusion between different feature maps. Meanwhile, a selective efficient channel attention module (SECAM) is designed to enable the network to prioritize channels that better characterize ship targets. Besides, a GPU-efficient backbone for generating ghost feature maps and a task alignment detection head are integrated into GFECSI-Net. Comparison results with nine state-of-the-art CNN methods on a high-resolution SAR image dataset (HRSID) and a large-scale multi-class SAR target dataset (MSAR-1.0) indicate that GFECSI-Net achieves superior detection performance in F1-Score and mAP with a small number of parameters.
Decoupling MaxLogit for Out-of-Distribution Detection

In machine learning, it is often observed that standard training outputs anomalously high confidence for both in-distribution (ID) and out-of-distribution (OOD) data. Thus, the ability to detect OOD samples is critical to the model deployment. An essential step for OOD detection is post-hoc scoring. MaxLogit is one of the simplest scoring functions which uses the maximum logits as OOD score. To provide a new viewpoint to study the logit-based scoring function, we reformulate the logit into cosine similarity and logit norm and propose to use MaxCosine and MaxNorm. We empirically find that MaxCosine is a core factor in the effectiveness of MaxLogit. And the performance of MaxLogit is encumbered by MaxNorm. To tackle the problem, we propose the Decoupling MaxLogit (DML) for flexibility to balance MaxCosine and MaxNorm. To further embody the core of our method, we extend DML to DML+ based on the new insights that fewer hard samples and compact feature space are the key components to make logit-based methods effective. We demonstrate the effectiveness of our logit-based OOD detection methods on CIFAR-10, CIFAR-100 and ImageNet and establish state-of-the-art performance.
OmniAL: A Unified CNN Framework for Unsupervised Anomaly Localization

Unsupervised anomaly localization and detection is crucial for industrial manufacturing processes due to the lack of anomalous samples. Recent unsupervised advances on industrial anomaly detection achieve high performance by training separate models for many different categories. The model storage and training time cost of this paradigm is high. Moreover, the setting of one-model-N-classes leads to fearful degradation of existing methods. In this paper, we propose a unified CNN framework for unsupervised anomaly localization, named OmniAL. This method conquers aforementioned problems by improving anomaly synthesis, reconstruction and localization. To prevent the model learning identical reconstruction, it trains the model with proposed panel-guided synthetic anomaly data rather than directly using normal data. It increases anomaly reconstruction error for multi-class distribution by using a network that is equipped with proposed Dilated Channel and Spatial Attention (DCSA) blocks. To better localize the anomaly regions, it employs proposed DiffNeck between reconstruction and localization sub-networks to explore multi-level differences. Experiments on 15-class MVTecAD and 12-class VisA datasets verify the advantage of proposed OmniAL that surpasses the state-of-the-art of unified models. On 15-class-MVTecAD/12-class-VisA, its single unified model achieves 97.2/87.8 image-AUROC, 98.3/96.6 pixel-AUROC and 73.4/41.7 pixel-AP for anomaly detection and localization respectively. Besides that, we make the first attempt to conduct a comprehensive study on the robustness of unsupervised anomaly localization and detection methods against different level adversarial attacks. Experiential results show OmniAL has good application prospects for its superior performance.
Evaluation of inpatient medication guidance from an artificial intelligence chatbot

To analyze the clinical completeness, correctness, usefulness, and safety of chatbot and medication database responses to everyday inpatient medication-use questions.We evaluated the responses from an artificial intelligence chatbot, a medication database, and clinical pharmacists to 200 real-world medication-use questions. Answer quality was rated by a blinded group of pharmacists, providers, and nurses. Chatbot and medication database responses were deemed "acceptable" if the mean reviewer rating was within 3 points of the mean rating for pharmacists' answers. We used descriptive statistics for reviewer ratings and Kendall's coefficient to evaluate interrater agreement.The medication database generated responses to 194 (97%) questions, with 88% considered acceptable for clinical correctness, 76% considered acceptable for completeness, 83% considered acceptable for safety, and 81% considered acceptable for usefulness compared to pharmacists' answers. The chatbot responded to only 160 (80%) questions, with 85% considered acceptable for clinical correctness, 65% considered acceptable for completeness, 71% considered acceptable for safety, and 68% considered acceptable for usefulness.Traditional search methods using a drug database provide more clinically correct, complete, safe, and useful answers than a chatbot. When the chatbot generated a response, the clinical correctness was similar to that of a drug database; however, it was not rated as favorably for clinical completeness, safety, or usefulness. Our results highlight the need for ongoing training and continued improvements to artificial intelligence chatbots for them to be incorporated reliably into the clinical workflow. With continued improvement in chatbot functionality, chatbots could be a useful pharmacist adjunct, providing healthcare providers with quick and reliable answers to medication-use questions.
A Systematic Literature Review of Online Collaborative Story Writing

Collaboratively writing a narrative is a challenging task. Further complications emerge when the involved contributors do not know each other, as it commonly occurs in online crowdsourcing. The paper presents a systematic literature review of online collaborative story writing with the aim of analysing what are the different forms of user involvement in such approaches and systems. To this end, we propose a reference definition based on essential features that characterise online collaborative story writing and we follow an exploratory approach based on a two-step method characterised by i) identification and selection of relevant publications, and ii) analysis of the selected literature. For the analysis, we consider relevant dimensions from the Taxonomy of Collaborative Writing [30] and we compare approaches according to modes of work, writing roles, and writing activities. As a summary of our results, about the modes of work, we focused on synchronicity and we observed that synchronous communications are mostly adopted by dialogue-based approaches, whereas asynchronous communications frequently characterise scene-based ones. On the writing roles, writer, consultant, and reviewer are commonly included in the writing process of the reviewed approaches. Furthermore, the roles of editor and leader are sometimes employed, while the facilitator role never appears in the considered approaches. As for the activities, collaborators are mostly involved in drafting tasks, but the tasks of brainstorming, reviewing, and revising are very common as well. On the opposite, activities like converging, outlining, and copyediting are rarely employed. In line with the results of our review, we propose four further research directions to study: collaborator recruitment, collaborator awareness, creative guidance, and modalities of contribution. This review should be helpful for researchers and practitioners interested in engaging contributors in effective online collaborative story writing.
Satisficing Paths and Independent Multiagent Reinforcement Learning in Stochastic Games

In multi-agent reinforcement learning (MARL), independent learners are those that do not observe the actions of other agents in the system.Due to the decentralization of information, it is challenging to design independent learners that drive play to equilibrium.This paper investigates the feasibility of using satisficing dynamics to guide independent learners to approximate equilibrium in stochastic games.For ≥ 0, an -satisficing policy update rule is any rule that instructs the agent to not change its policy when it is -best-responding to the policies of the remaining players; -satisficing paths are defined to be sequences of joint policies obtained when each agent uses some -satisficing policy update rule to select its next policy.We establish structural results on the existence of -satisficing paths into -equilibrium in both symmetric N -player games and general stochastic games with two players.We then present an independent learning algorithm for N -player symmetric games and give high probability guarantees of convergence to -equilibrium under self-play.This guarantee is made using symmetry alone, leveraging the previously unexploited structure of -satisficing paths.
Similarity Search in the Blink of an Eye with Compressed Indices

Nowadays, data is represented by vectors. Retrieving those vectors, among millions and billions, that are similar to a given query is a ubiquitous problem, known as similarity search, of relevance for a wide range of applications. Graph-based indices are currently the best performing techniques for billion-scale similarity search. However, their random-access memory pattern presents challenges to realize their full potential. In this work, we present new techniques and systems for creating faster and smaller graph-based indices. To this end, we introduce a novel vector compression method, Locally-adaptive Vector Quantization (LVQ), that uses per-vector scaling and scalar quantization to improve search performance with fast similarity computations and a reduced effective bandwidth, while decreasing memory footprint and barely impacting accuracy. LVQ, when combined with a new high-performance computing system for graph-based similarity search, establishes the new state of the art in terms of performance and memory footprint. For billions of vectors, LVQ outcompetes the second-best alternatives: (1) in the low-memory regime, by up to 20.7x in throughput with up to a 3x memory footprint reduction, and (2) in the high-throughput regime by 5.8x with 1.4x less memory.
Deep Learning Approach for Liver Tumor Diagnosis

Liver cancer is a significant issue in Egypt, as indicated by the most recent statistics from the World Health Organisation in 2020, where liver cancer-related deaths reached 4.57 percent of the total mortality rate. Egypt is ranked second worldwide. The immediate detection and correct diagnosis of liver cancer are essential for effective treatment and improved patient outcomes. In recent years, deep learning techniques have been utilized to enhance the accuracy of liver cancer detection and diagnosis. This paper introduces a significant effort to collect and process a real dataset. The liver tumor dataset was gathered from Egypt’s Ain Shams University, Specialized Hospital (ASUSH). The proposed liver dataset includes 223 patients with both healthy and diseased conditions. The dataset comprises a total of 13,287 computed tomography (CT) images. Images were obtained from the PACS system and converted from the Digital Imaging and Communications in Medicine (DICOM) to Tiff image format. The paper also utilizes and compares the results of various convolution neural networks (CNN)-based deep learning models on the collected data.
A Decentralized Oracle Architecture for a Blockchain-Based IoT Global Market

The Internet of Things (IoT) envisions a global market in which it would be possible to easily get data from IoT devices across the globe. However, the potential of this idea still needs to be unlocked. Centralized architectures fall short due to their lack of transparency and tendency to create silos. On the other hand, blockchain technology enables the creation of distributed and trustworthy systems, but its integration with the IoT is still a matter of research. IoT-based scenarios often employ numerous devices for the same sensing task, which may be heterogeneous and unreliable by purpose. In our vision, IoT applications should rely on data and its quality rather than on single providers. For this purpose, we propose an architecture that enables a decentralized IoT global market in which clients pay for data and device owners are rewarded for providing it. Our solution employs a distributed oracle layer on top of smart contracts powered by a distributed global network of IoT devices. The system supports IoT data source decoupling since the end-user can perform semantic queries bounded to specific locations and data types without specifying the target devices. In addition, it features automatic discovery, interoperability mechanisms, and reputation algorithms for the selection of trustworthy data sources. Our results show that the proposed system is robust and consistently provides quality data, even with multiple malicious data sources.
“Do I Run Away?”: Proximity, Stress and Discomfort in Human-Drone Interaction in Real and Virtual Environments

Social drones are autonomous flying machines designed to operate in inhabited environments. Yet, little is known about how their proximity might impact people's well-being. This knowledge is critical as drones are often perceived as potential threats due to their design (e.g., visible propellers, unpleasant noise) and capabilities (e.g., moving at high speed, surveillance). In parallel, Virtual Reality (VR) is a promising tool to study human–drone interactions. However, important questions remain as to whether VR is ecologically valid for exploring human–drone interactions. Here, we present a between-within subjects user study (N = 42) showing that participants' stress significantly differs between different drone states and locations. They felt more comfortable when the drone retreated from their personal space. Discomfort and stress were strongly correlated with the perceived drone's threat level. Similar findings were found across real and virtual environments. We demonstrate that drones' behaviour and proximity can threaten peoples' well-being and comfort, and propose evidence-based guidelines to mitigate these impacts.