Reviewerly/exHarmony huggingface dataset:
Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation

Large-scale text-to-image generative models have been a revolutionary breakthrough in the evolution of generative AI, synthesizing diverse images with highly complex visual concepts. However, a pivotal challenge in leveraging such models for real-world content creation is providing users with control over the generated content. In this paper, we present a new framework that takes text-to- image synthesis to the realm of image-to-image translation - given a guidance image and a target text prompt as input, our method harnesses the power of a pre-trained text-to-image diffusion model to generate a new image that complies with the target text, while preserving the semantic layout of the guidance image. Specifically, we observe and empirically demonstrate that fine-grained control over the generated structure can be achieved by manipulating spatial features and their self-attention inside the model. This results in a simple and effective approach, where features extracted from the guidance image are directly injected into the generation process of the translated image, requiring no training or fine-tuning. We demonstrate high-quality results on versatile text-guided image translation tasks, including translating sketches, rough drawings and animations into realistic images, changing the class and appearance of objects in a given image, and modifying global qualities such as lighting and color.
Counterattacking Cyber Threats: A Framework for the Future of Cybersecurity

Amidst the rapid advancements in the digital landscape, the convergence of digitization and cyber threats presents new challenges for organizational security. This article presents a comprehensive framework that aims to shape the future of cyber security. This framework responds to the complexities of modern cyber threats and provides guidance to organizations to enhance their resilience. The primary focus lies in the integration of capabilities with resilience. By combining these elements into cyber security practices, organizations can improve their ability to predict, mitigate, respond to, and recover from cyber disasters. This article emphasizes the importance of organizational leadership, accountability, and innovation in achieving cyber resilience. As cyber threat challenges continue to evolve, this framework offers strategic guidance to address the intricate dynamics between digitization and cyber security, moving towards a safer and more robust digital environment in the future.
TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation

Large Language Models (LLMs) have demonstrated remarkable performance across diverse domains, thereby prompting researchers to explore their potential for use in recommendation systems. Initial attempts have leveraged the exceptional capabilities of LLMs, such as rich knowledge and strong generalization through In-context Learning, which involves phrasing the recommendation task as prompts. Nevertheless, the performance of LLMs in recommendation tasks remains suboptimal due to a substantial disparity between the training tasks for LLMs and recommendation tasks, as well as inadequate recommendation data during pre-training. To bridge the gap, we consider building a Large Recommendation Language Model by tunning LLMs with recommendation data. To this end, we propose an efficient and effective Tuning framework for Aligning LLMs with Recommendations, namely TALLRec. We have demonstrated that the proposed TALLRec framework can significantly enhance the recommendation capabilities of LLMs in the movie and book domains, even with a limited dataset of fewer than 100 samples. Additionally, the proposed framework is highly efficient and can be executed on a single RTX 3090 with LLaMA-7B. Furthermore, the fine-tuned LLM exhibits robust cross-domain generalization. Our code and data are available at https://github.com/SAI990323/TALLRec.
Channel Attention for Sensor-Based Activity Recognition: Embedding Features into all Frequencies in DCT Domain

During recent years, channel attention has attracted great interest in deep learning community. Despite significant success, it has been rarely exploited in ubiquitous human activity recognition (HAR) scenario. To decrease computational overhead, the channel attention often uses global averaging pooling (GAP) to compress each channel into a simple scalar. It is well known that GAP is equal to the lowest frequency component. Despite obvious lightweight advantage, such compression process inevitably causes severe information loss. In this paper, we propose a novel multi-frequency channel attention framework for activity recognition tasks. Considering various sensing frequencies of human activities, an intuition solution is to convert the time series from time domain to frequency domain. Instead of GAP, the discrete cosine transform (DCT) is used to compress channels. We prove that GAP can be seen as a special case of DCT, which uses the lowest frequency component only and leaves out all other frequency components unused. DCT is able to better compress channels by fully exploiting other frequency components discarded by GAP. Despite multiple frequency components used, each channel will still be represented by a scalar in order to maintain the same computational overhead. Using two frequency screening criteria, our method is able to achieve state-of-the-art results on four benchmark HAR datasets. Extensive ablation studies are conducted, which provides a better interpretability of deep model behaviors. Finally, actual inference is evaluated on an embedded platform.
Role of AI chatbots in education: systematic literature review

Abstract AI chatbots shook the world not long ago with their potential to revolutionize education systems in a myriad of ways. AI chatbots can provide immediate support by answering questions, offering explanations, and providing additional resources. Chatbots can also act as virtual teaching assistants, supporting educators through various means. In this paper, we try to understand the full benefits of AI chatbots in education, their opportunities, challenges, potential limitations, concerns, and prospects of using AI chatbots in educational settings. We conducted an extensive search across various academic databases, and after applying specific predefined criteria, we selected a final set of 67 relevant studies for review. The research findings emphasize the numerous benefits of integrating AI chatbots in education, as seen from both students' and educators' perspectives. We found that students primarily gain from AI-powered chatbots in three key areas: homework and study assistance, a personalized learning experience, and the development of various skills. For educators, the main advantages are the time-saving assistance and improved pedagogy. However, our research also emphasizes significant challenges and critical factors that educators need to handle diligently. These include concerns related to AI applications such as reliability, accuracy, and ethical considerations.
Role play with large language models

As dialogue agents become increasingly human-like in their performance, we must develop effective ways to describe their behaviour in high-level terms without falling into the trap of anthropomorphism. Here we foreground the concept of role play. Casting dialogue-agent behaviour in terms of role play allows us to draw on familiar folk psychological terms, without ascribing human characteristics to language models that they in fact lack. Two important cases of dialogue-agent behaviour are addressed this way, namely, (apparent) deception and (apparent) self-awareness.
Integration of Government Services using Semantic Technologies

The paper describes an approach to semantic interoperability of eGovernment services applied within the 027020 FP6 IST Access-eGov project. The goal of the project was to improve accessibility and connectivity of governmental services for citizens and businesses by means of creating integrated scenarios and providing guidance to users while following this scenario. The scenario helps the user to identify and fulfil any needed electronic or real governmental services in a selected life situation. The Access-eGov project has developed software tools enabling service integration using semantic technologies. In addition to that, a methodology providing guidance to the user-driven process of creating ontologies was developed. Sample ontologies were prepared for trial applications. The developed tools support browsing, discovery, and execution of government services according to a selected life event or goal. The project successfully developed and tested the proposed solutions. The software developed within the project is available as open source software.
Unexpected conflict signals loom larger in a positive context: Evidence from context specific control adjustments.

One prominent feature of adaptive cognition in humans is the ability to flexibly adjust to changing task demands. In this respect, context-specific proportion congruency (CSPC) effects describe the phenomenon that participants learn to adapt to contexts of frequently occurring conflicts even when the upcoming context cannot be anticipated. Here, we aim to provide evidence that such CSPC effects strongly depend on the affective valence of the context. Participants had to categorize pictures as animals versus humans (Experiment 1), words as male versus female (Experiment 2), and food pictures as depicting cold or hot dishes/beverages (Experiment 3). Stimuli were laterally presented to the left or right, creating Simon-like response interference. Half of the stimuli were of positive valence, half of negative valence with valence always being task-irrelevant. Positive or negative stimuli were associated with a high proportion of incongruent trials and a low proportion of congruent trials, respectively. In all three experiments, a CSPC effect was only significant when the high proportion of incongruent trials was associated with positive stimuli but reduced or even absent when the high proportion of incongruent trials was associated with negative stimuli. The findings further speak to the idea that it is the aversive nature of conflict stimuli that functions as a trigger for control adaptation. And such an unexpected aversive conflict signal looms larger when presented in a positive context than in a negative context. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Shinjuku: preemptive scheduling for µsecond-scale tail latency

The recently proposed dataplanes for microsecond scale applications, such as IX and ZygOS, use nonpreemptive policies to schedule requests to cores. For the many real-world scenarios where request service times follow distributions with high dispersion or a heavy tail, they allow short requests to be blocked behind long requests, which leads to poor tail latency.Shinjuku is a single-address space operating system that uses hardware support for virtualization to make preemption practical at the microsecond scale. This allows Shinjuku to implement centralized scheduling policies that preempt requests as often as every 5µsec and work well for both light and heavy tailed request service time distributions. We demonstrate that Shinjuku provides significant tail latency and throughput improvements over IX and ZygOS for a wide range of workload scenarios. For the case of a RocksDB server processing both point and range queries, Shinjuku achieves up to 6.6× higher throughput and 88% lower tail latency.
Time-lapse optical flow regularization for geophysical complex phenomena monitoring

In this paper, we introduce a framework for the tracking of geophysical complex phenomena via time-lapse images. It includes the regularization of the derived surface motion maps time series. The proposed processing chain addresses five main challenges: undesired camera movement, missing frames, important photometric changes, weak/repetitive texture and model-free dense spatial transformations. In the proposed framework the motion maps time series are obtained via robust pre-processing steps and optical flow computing. The contribution consists of regularizing the resulting velocity and position time series to minimize a temporal closure error in a subsequent stage. This step serves to alleviate the limitations of existing methods in the context of geophysical monitoring. The temporal closure errors are formulated as linear mappings to inverse using signal priors and two formulations are defined along with illustrative cases. Related methods are discussed and extensive experimentation on simulated datasets is carried out to validate the approach and compare between the different proposed formulations and resolution schemes. Experimental results are presented on time series acquired by ground-based cameras used for the monitoring of Alpine glaciers. The algorithm is computationally efficient, even considering the quantity of processed and generated data, and is run in parallel on multiple cores for speed-up.
An effective and scalable VM migration strategy to mitigate cross-VM side-channel attacks in cloud

Co-residency of virtual machines (VMs) of different tenants on the same physical platform would possibly lead to cross-VM side-channel attacks in the cloud. While most of current countermeasures fail for real or immediate deployment due to their requirement for modification of virtualization structure, we adopt dynamic migration, an inherent mechanism of the cloud platform, as a general defense against this kind of threats. To this end, we first set up a unified practical information leakage model which shows the factors affecting side channels and describes the way they influence the damage due to side-channel attacks. Since migration is adopted to limit the time duration of co-residency, we envision this defense as an optimization problem by setting up an Integer Linear Programming (ILP) to calculate optimal migration strategy, which is intractable due to high computational complexity. Therefore, we approximate the ILP with a baseline genetic algorithm, which is further improved for its optimality and scalability. Experimental results show that our migration-based defense can not only provide excellent security guarantees and affordable performance cost in both theoretical simulation and practical cloud environment, but also achieve better optimality and scalability than previous countermeasures.
lingvis.io - A Linguistic Visual Analytics Framework

We present a modular framework for the rapidprototyping of linguistic, web-based, visual analytics applications.Our framework gives developers access to a rich set of machine learning and natural language processing steps, through encapsulating them into microservices and combining them into a computational pipeline.This processing pipeline is auto-configured based on the requirements of the visualization front-end, making the linguistic processing and visualization design detached, independent development tasks.This paper describes the constellation and modality of our framework, which continues to support the efficient development of various humanin-the-loop, linguistic visual analytics research techniques and applications.
Learning Words by Drawing Images

We propose a framework for learning through drawing. Our goal is to learn the correspondence between spoken words and abstract visual attributes, from a dataset of spoken descriptions of images. Building upon recent findings that GAN representations can be manipulated to edit semantic concepts in the generated output, we propose a new method to use such GAN-generated images to train a model using a triplet loss. To apply the method, we develop Audio CLEVRGAN, a new dataset of audio descriptions of GAN-generated CLEVR images, and we describe a training procedure that creates a curriculum of GAN-generated images that focuses training on image pairs that differ in a specific, informative way. Training is done without additional supervision beyond the spoken captions and the GAN. We find that training that takes advantage of GAN-generated edited examples results in improvements in the model's ability to learn attributes compared to previous results. Our proposed learning framework also results in models that can associate spoken words with some abstract visual concepts such as color and size.
Semi-Automated Data Labeling for Activity Recognition in Pervasive Healthcare

Activity recognition, a key component in pervasive healthcare monitoring, relies on classification algorithms that require labeled data of individuals performing the activity of interest to train accurate models. Labeling data can be performed in a lab setting where an individual enacts the activity under controlled conditions. The ubiquity of mobile and wearable sensors allows the collection of large datasets from individuals performing activities in naturalistic conditions. Gathering accurate data labels for activity recognition is typically an expensive and time-consuming process. In this paper we present two novel approaches for semi-automated online data labeling performed by the individual executing the activity of interest. The approaches have been designed to address two of the limitations of self-annotation: (i) The burden on the user performing and annotating the activity, and (ii) the lack of accuracy due to the user labeling the data minutes or hours after the completion of an activity. The first approach is based on the recognition of subtle finger gestures performed in response to a data-labeling query. The second approach focuses on labeling activities that have an auditory manifestation and uses a classifier to have an initial estimation of the activity, and a conversational agent to ask the participant for clarification or for additional data. Both approaches are described, evaluated in controlled experiments to assess their feasibility and their advantages and limitations are discussed. Results show that while both studies have limitations, they achieve 80% to 90% precision.
Master of Web Puppets: Abusing Web Browsers for Persistent and Stealthy Computation

The proliferation of web applications has essentially transformed modern browsers into small but powerful operating systems.Upon visiting a website, user devices run implicitly trusted script code, the execution of which is confined within the browser to prevent any interference with the user's system.Recent JavaScript APIs, however, provide advanced capabilities that not only enable feature-rich web applications, but also allow attackers to perform malicious operations despite the confined nature of JavaScript code execution.In this paper, we demonstrate the powerful capabilities that modern browser APIs provide to attackers by presenting MarioNet: a framework that allows a remote malicious entity to control a visitor's browser and abuse its resources for unwanted computation or harmful operations, such as cryptocurrency mining, password-cracking, and DDoS.MarioNet relies solely on already available HTML5 APIs, without requiring the installation of any additional software.In contrast to previous browserbased botnets, the persistence and stealthiness characteristics of MarioNet allow the malicious computations to continue in the background of the browser even after the user closes the window or tab of the initially visited malicious website.We present the design, implementation, and evaluation of our prototype system, which is compatible with all major browsers, and discuss potential defense strategies to counter the threat of such persistent inbrowser attacks.Our main goal is to raise awareness about this new class of attacks, and inform the design of future browser APIs so that they provide a more secure client-side environment for web applications.
Best Arm Identification for Contaminated Bandits

This paper studies active learning in the context of robust statistics. Specifically, we propose a variant of the Best Arm Identification problem for \emph{contaminated bandits}, where each arm pull has probability $\varepsilon$ of generating a sample from an arbitrary contamination distribution instead of the true underlying distribution. The goal is to identify the best (or approximately best) true distribution with high probability, with a secondary goal of providing guarantees on the quality of this distribution. The primary challenge of the contaminated bandit setting is that the true distributions are only partially identifiable, even with infinite samples. To address this, we develop tight, non-asymptotic sample complexity bounds for high-probability estimation of the first two robust moments (median and median absolute deviation) from contaminated samples. These concentration inequalities are the main technical contributions of the paper and may be of independent interest. Using these results, we adapt several classical Best Arm Identification algorithms to the contaminated bandit setting and derive sample complexity upper bounds for our problem. Finally, we provide matching information-theoretic lower bounds on the sample complexity (up to a small logarithmic factor).
Equivariant Transformer Networks.

How can prior knowledge on the transformation invariances of a domain be incorporated into the architecture of a neural network? We propose Equivariant Transformers (ETs), a family of differentiable image-to-image mappings that improve the robustness of models towards pre-defined continuous transformation groups. Through the use of specially-derived canonical coordinate systems, ETs incorporate functions that are equivariant by construction with respect to these transformations. We show empirically that ETs can be flexibly composed to improve model robustness towards more complicated transformation groups in several parameters. On a real-world image classification task, ETs improve the sample efficiency of ResNet classifiers, achieving relative improvements in error rate of up to 15% in the limited data regime while increasing model parameter count by less than 1%.
Interactive Reinforcement Learning with Dynamic Reuse of Prior Knowledge from Human and Agent Demonstrations

Reinforcement learning has enjoyed multiple impressive successes in recent years. However, these successes typically require very large amounts of data before an agent achieves acceptable performance. This paper focuses on a novel way of combating such requirements by leveraging existing (human or agent) knowledge. In particular, this paper leverages demonstrations, allowing an agent to quickly achieve high performance. This paper introduces the Dynamic Reuse of Prior (DRoP) algorithm, which combines the offline knowledge (demonstrations recorded before learning) with online confidence-based performance analysis. DRoP leverages the demonstrator's knowledge by automatically balancing between reusing the prior knowledge and the current learned policy, allowing the agent to outperform the original demonstrations. We compare with multiple state-of-the-art learning algorithms and empirically show that DRoP can achieve superior performance in two domains. Additionally, we show that this confidence measure can be used to selectively request additional demonstrations, significantly improving the learning performance of the agent.