OpenRaiser/Intern-Atlas huggingface dataset:
Abnormal Local Clustering in Federated Learning
Venue: arXiv cs.LG 2022
Year: 2022

Federated learning is a model for privacy without revealing private data by transfer models instead of personal and private data from local client devices. While, in the global model, it's crucial to recognize each local data is normal. This paper suggests one method to separate normal locals and abnormal locals by Euclidean similarity clustering of vectors extracted by inputting dummy data in local models. In a federated classification model, this method divided locals into normal and abnormal.
Living-off-the-Land Abuse Detection Using Natural Language Processing and Supervised Learning
Venue: arXiv cs.CR 2022
Year: 2022

Living-off-the-Land is an evasion technique used by attackers where native binaries are abused to achieve malicious intent. Since these binaries are often legitimate system files, detecting such abuse is difficult and often missed by modern anti-virus software. This paper proposes a novel abuse detection algorithm using raw command strings. First, natural language processing techniques such as regular expressions and one-hot encoding are utilized for encoding the command strings as numerical token vectors. Next, supervised learning techniques are employed to learn the malicious patterns in the token vectors and ultimately predict the command's label. Finally, the model is evaluated using statistics from the training phase and in a virtual environment to compare its effectiveness at detecting new commands to existing anti-virus products such as Windows Defender.
Factors Influencing the Organizational Decision to Outsource IT Security: A Review and Research Agenda
Venue: arXiv cs.CR 2022
Year: 2022

IT security outsourcing is the process of contracting a third-party security service provider to perform, the full or partial IT security functions of an organization. Little is known about the factors influencing organizational decisions in outsourcing such a critical function. Our review of the research and practice literature identified several managerial factors and legal factors. We found research in IT security outsourcing to be immature and the focus areas not addressing the critical issues facing industry practice. We therefore present a research agenda consisting of fifteen questions to address five key gaps relating to knowledge of IT security outsourcing, specifically effectiveness of the outcome, lived experience of the practice, the temporal dimension, multi-stakeholder perspectives, and the impact on IT security practices, particularly agility in incident response.
A Comprehensive Review of Digital Twin -- Part 2: Roles of Uncertainty Quantification and Optimization, a Battery Digital Twin, and Perspectives
Venue: arXiv cs.LG 2022
Year: 2022

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This second paper presents a literature review of key enabling technologies of digital twins, with an emphasis on uncertainty quantification, optimization methods, open source datasets and tools, major findings, challenges, and future directions. Discussions focus on current methods of uncertainty quantification and optimization and how they are applied in different dimensions of a digital twin. Additionally, this paper presents a case study where a battery digital twin is constructed and tested to illustrate some of the modeling and twinning methods reviewed in this two-part review. Code and preprocessed data for generating all the results and figures presented in the case study are available on GitHub.
Robots Still Outnumber Humans in Web Archives, But Less Than Before
Venue: arXiv cs.DL 2022
Year: 2022

To identify robots and humans and analyze their respective access patterns, we used the Internet Archive's (IA) Wayback Machine access logs from 2012 and 2019, as well as Arquivo.pt's (Portuguese Web Archive) access logs from 2019. We identified user sessions in the access logs and classified those sessions as human or robot based on their browsing behavior. To better understand how users navigate through the web archives, we evaluated these sessions to discover user access patterns. Based on the two archives and between the two years of IA access logs (2012 vs. 2019), we present a comparison of detected robots vs. humans and their user access patterns and temporal preferences. The total number of robots detected in IA 2012 is greater than in IA 2019 (21% more in requests and 18% more in sessions). Robots account for 98% of requests (97% of sessions) in Arquivo.pt (2019). We found that the robots are almost entirely limited to "Dip" and "Skim" access patterns in IA 2012, but exhibit all the patterns and their combinations in IA 2019. Both humans and robots show a preference for web pages archived in the near past.
CodeToon: Story Ideation, Auto Comic Generation, and Structure Mapping for Code-Driven Storytelling
Venue: arXiv cs.HC 2022
Year: 2022

Recent work demonstrated how we can design and use coding strips, a form of comic strips with corresponding code, to enhance teaching and learning in programming. However, creating coding strips is a creative, time-consuming process. Creators have to generate stories from code (code->story) and design comics from stories (story->comic). We contribute CodeToon, a comic authoring tool that facilitates this code-driven storytelling process with two mechanisms: (1) story ideation from code using metaphor and (2) automatic comic generation from the story. We conducted a two-part user study that evaluates the tool and the comics generated by participants to test whether CodeToon facilitates the authoring process and helps generate quality comics. Our results show that CodeToon helps users create accurate, informative, and useful coding strips in a significantly shorter time. Overall, this work contributes methods and design guidelines for code-driven storytelling and opens up opportunities for using art to support computer science education.
Domain-Specific NER via Retrieving Correlated Samples
Venue: arXiv cs.CL 2022
Year: 2022

Successful Machine Learning based Named Entity Recognition models could fail on texts from some special domains, for instance, Chinese addresses and e-commerce titles, where requires adequate background knowledge. Such texts are also difficult for human annotators. In fact, we can obtain some potentially helpful information from correlated texts, which have some common entities, to help the text understanding. Then, one can easily reason out the correct answer by referencing correlated samples. In this paper, we suggest enhancing NER models with correlated samples. We draw correlated samples by the sparse BM25 retriever from large-scale in-domain unlabeled data. To explicitly simulate the human reasoning process, we perform a training-free entity type calibrating by majority voting. To capture correlation features in the training stage, we suggest to model correlated samples by the transformer-based multi-instance cross-encoder. Empirical results on datasets of the above two domains show the efficacy of our methods.
Learning to SLAM on the Fly in Unknown Environments: A Continual Learning Approach for Drones in Visually Ambiguous Scenes
Venue: arXiv cs.RO 2022
Year: 2022

Learning to safely navigate in unknown environments is an important task for autonomous drones used in surveillance and rescue operations. In recent years, a number of learning-based Simultaneous Localisation and Mapping (SLAM) systems relying on deep neural networks (DNNs) have been proposed for applications where conventional feature descriptors do not perform well. However, such learning-based SLAM systems rely on DNN feature encoders trained offline in typical deep learning settings. This makes them less suited for drones deployed in environments unseen during training, where continual adaptation is paramount. In this paper, we present a new method for learning to SLAM on the fly in unknown environments, by modulating a low-complexity Dictionary Learning and Sparse Coding (DLSC) pipeline with a newly proposed Quadratic Bayesian Surprise (QBS) factor. We experimentally validate our approach with data collected by a drone in a challenging warehouse scenario, where the high number of ambiguous scenes makes visual disambiguation hard.
YouTube COVID-19 Vaccine Misinformation on Twitter: Platform Interactions and Moderation Blind Spots
Venue: arXiv cs.SI 2022
Year: 2022

While most social media companies have attempted to address the challenge of COVID-19 misinformation, the success of those policies is difficult to assess, especially when focusing on individual platforms. This study explores the relationship between Twitter and YouTube in spreading COVID-19 vaccine-related misinformation through a mixed-methods approach to analyzing a collection of tweets in 2021 sharing YouTube videos where those Twitter accounts had also linked to deleted YouTube videos. Principal components, cluster and network analyses are used to group the videos and tweets into interpretable groups by shared tweet dates, terms and sharing patterns; content analysis is employed to assess the orientation of tweets and videos to COVID-19 messages. From this we observe that a preponderance of anti-vaccine messaging remains among users who previously shared suspect information, in which a dissident political framing dominates, and which suggests moderation policy inefficacy where the platforms interact.
Multi-level Contrastive Learning Framework for Sequential Recommendation
Venue: arXiv cs.IR 2022
Year: 2022

Sequential recommendation (SR) aims to predict the subsequent behaviors of users by understanding their successive historical behaviors. Recently, some methods for SR are devoted to alleviating the data sparsity problem (i.e., limited supervised signals for training), which take account of contrastive learning to incorporate self-supervised signals into SR. Despite their achievements, it is far from enough to learn informative user/item embeddings due to the inadequacy modeling of complex collaborative information and co-action information, such as user-item relation, user-user relation, and item-item relation. In this paper, we study the problem of SR and propose a novel multi-level contrastive learning framework for sequential recommendation, named MCLSR. Different from the previous contrastive learning-based methods for SR, MCLSR learns the representations of users and items through a cross-view contrastive learning paradigm from four specific views at two different levels (i.e., interest- and feature-level). Specifically, the interest-level contrastive mechanism jointly learns the collaborative information with the sequential transition patterns, and the feature-level contrastive mechanism re-observes the relation between users and items via capturing the co-action information (i.e., co-occurrence). Extensive experiments on four real-world datasets show that the proposed MCLSR outperforms the state-of-the-art methods consistently.
Improving debris flow evacuation alerts in Taiwan using machine learning
Venue: arXiv cs.LG 2022
Year: 2022

Taiwan has the highest susceptibility to and fatalities from debris flows worldwide. The existing debris flow warning system in Taiwan, which uses a time-weighted measure of rainfall, leads to alerts when the measure exceeds a predefined threshold. However, this system generates many false alarms and misses a substantial fraction of the actual debris flows. Towards improving this system, we implemented five machine learning models that input historical rainfall data and predict whether a debris flow will occur within a selected time. We found that a random forest model performed the best among the five models and outperformed the existing system in Taiwan. Furthermore, we identified the rainfall trajectories strongly related to debris flow occurrences and explored trade-offs between the risks of missing debris flows versus frequent false alerts. These results suggest the potential for machine learning models trained on hourly rainfall data alone to save lives while reducing false alerts.
Power Delivery for Ultra-Large-Scale Applications on Si-IF
Venue: arXiv cs.AR 2022
Year: 2022

In recent years, with the rise of artificial intelligence and big data, there is an even greater demand for scaling out computing and memory capacity. Silicon interconnect fabric (Si-IF), a wafer-scale integration platform, promotes a paradigm shift in packaging features and enables ultra-large-scale systems, while significantly improving communication bandwidth and latency. Such systems are expected to dissipate tens of kilowatts of power. Designing an efficient and robust power delivery methodology for these high power applications is a key challenge in the enablement of the Si-IF platform. Based on several figure-of-merit parameters, an efficient power delivery methodology is matched with each of three candidate applications on the Si-IF, namely, artificial intelligence accelerators, high-performance computing, and neuromorphic computing. The proposed power delivery approaches were simulated and exhibit compatibility with the relevant ultra-large-scale application on Si-IF. The simulation results confirm that the dedicated power delivery topologies can support ultra-large-scale applications on the SI-IF.
An event detection technique using social media data
Venue: arXiv cs.SI 2022
Year: 2022

People post information about different topics which are in their active vocabulary over social media platforms (like Twitter, Facebook, PInterest and Google+). They follow each other and it is more likely that the person who posts information about current happenings will receive better response. Manual analysis of huge amount of data on social media platforms is difficult. This has opened new research directions for automatic analysis of usercontributed social media documents. Automatic social media data analysis is difficult due to abundant information shared by users. Many researchers use Twitter data for Social Media Analysis (SMA) as the Twitter data is freely available in the public domain. One of the most this research work. Event Detection from social media data is used for different applications like traffic congestion detection, disaster and emergency management, and live news detection. Nature of the information which is shared on twitter platform is short-text, noisy, and ambiguous. Thus, event detection and extraction of event phrases from user-generated and illformed data becomes challenging. To address these challenges, events are extracted from streaming social media data in the form of keyphrases using different cognitive properties. The motivation behind this research work is to provide substantial improvements in the lexical variation of event phrases while detecting events and sub-events from twitter data. In this research work, the approach towards event detection from social media data is divided into three phases namely: Identifying sub-graphs in Microblog Word Co-occurrence Network (WCN) which provides important information about keyphrases; Identifying multiple events from social media data; and Ranking contextual information of event phrases.
A Reformulation of Gaussian Completely Monotone Conjecture: A Hodge Structure on the Fisher Information along Heat Flow
Venue: arXiv cs.IT 2022
Year: 2022

In the past decade, J. Huh solved several long-standing open problems on log-concave sequences in combinatorics. The ground-breaking techniques developed in those work are from algebraic geometry: "We believe that behind any log-concave sequence that appears in nature there is such a Hodge structure responsible for the log-concavity". A function is called completely monotone if its derivatives alternate in signs; e.g., $e^{-t}$. A fundamental conjecture in mathematical physics and Shannon information theory is on the complete monotonicity of Gaussian distribution (GCMC), which states that $I(X+Z_t)$\footnote{The probability density function of $X+Z_t$ is called "heat flow" in mathematical physics.} is completely monotone in $t$, where $I$ is Fisher information, random variables $X$ and $Z_t$ are independent and $Z_t\sim\mathcal{N}(0,t)$ is Gaussian. Inspired by the algebraic geometry method introduced by J. Huh, GCMC is reformulated in the form of a log-convex sequence. In general, a completely monotone function can admit a log-convex sequence and a log-convex sequence can further induce a log-concave sequence. The new formulation may guide GCMC to the marvelous temple of algebraic geometry. Moreover, to make GCMC more accessible to researchers from both information theory and mathematics\footnote{The author was not familiar with algebraic geometry. The paper is also aimed at providing people outside information theory of necessary background on the history of GCMC in theory and application.}, together with some new findings, a thorough summary of the origin, the implication and further study on GCMC is presented.
Asynchronous Training Schemes in Distributed Learning with Time Delay
Venue: arXiv cs.LG 2022
Year: 2022

In the context of distributed deep learning, the issue of stale weights or gradients could result in poor algorithmic performance. This issue is usually tackled by delay tolerant algorithms with some mild assumptions on the objective functions and step sizes. In this paper, we propose a different approach to develop a new algorithm, called $\textbf{P}$redicting $\textbf{C}$lipping $\textbf{A}$synchronous $\textbf{S}$tochastic $\textbf{G}$radient $\textbf{D}$escent (aka, PC-ASGD). Specifically, PC-ASGD has two steps - the $\textit{predicting step}$ leverages the gradient prediction using Taylor expansion to reduce the staleness of the outdated weights while the $\textit{clipping step}$ selectively drops the outdated weights to alleviate their negative effects. A tradeoff parameter is introduced to balance the effects between these two steps. Theoretically, we present the convergence rate considering the effects of delay of the proposed algorithm with constant step size when the smooth objective functions are weakly strongly-convex and nonconvex. One practical variant of PC-ASGD is also proposed by adopting a condition to help with the determination of the tradeoff parameter. For empirical validation, we demonstrate the performance of the algorithm with two deep neural network architectures on two benchmark datasets.
Face Anti-Spoofing from the Perspective of Data Sampling
Venue: arXiv cs.CV 2022
Year: 2022

Without deploying face anti-spoofing countermeasures, face recognition systems can be spoofed by presenting a printed photo, a video, or a silicon mask of a genuine user. Thus, face presentation attack detection (PAD) plays a vital role in providing secure facial access to digital devices. Most existing video-based PAD countermeasures lack the ability to cope with long-range temporal variations in videos. Moreover, the key-frame sampling prior to the feature extraction step has not been widely studied in the face anti-spoofing domain. To mitigate these issues, this paper provides a data sampling approach by proposing a video processing scheme that models the long-range temporal variations based on Gaussian Weighting Function. Specifically, the proposed scheme encodes the consecutive t frames of video sequences into a single RGB image based on a Gaussian-weighted summation of the t frames. Using simply the data sampling scheme alone, we demonstrate that state-of-the-art performance can be achieved without any bells and whistles in both intra-database and inter-database testing scenarios for the three public benchmark datasets; namely, Replay-Attack, MSU-MFSD, and CASIA-FASD. In particular, the proposed scheme provides a much lower error (from 15.2% to 6.7% on CASIA-FASD and 5.9% to 4.9% on Replay-Attack) compared to baselines in cross-database scenarios.
Cross-domain Cross-architecture Black-box Attacks on Fine-tuned Models with Transferred Evolutionary Strategies
Venue: arXiv cs.LG 2022
Year: 2022

Fine-tuning can be vulnerable to adversarial attacks. Existing works about black-box attacks on fine-tuned models (BAFT) are limited by strong assumptions. To fill the gap, we propose two novel BAFT settings, cross-domain and cross-domain cross-architecture BAFT, which only assume that (1) the target model for attacking is a fine-tuned model, and (2) the source domain data is known and accessible. To successfully attack fine-tuned models under both settings, we propose to first train an adversarial generator against the source model, which adopts an encoder-decoder architecture and maps a clean input to an adversarial example. Then we search in the low-dimensional latent space produced by the encoder of the adversarial generator. The search is conducted under the guidance of the surrogate gradient obtained from the source model. Experimental results on different domains and different network architectures demonstrate that the proposed attack method can effectively and efficiently attack the fine-tuned models.
Incremental Semantic Localization using Hierarchical Clustering of Object Association Sets
Venue: arXiv cs.RO 2022
Year: 2022

We present a novel approach for relocalization or place recognition, a fundamental problem to be solved in many robotics, automation, and AR applications. Rather than relying on often unstable appearance information, we consider a situation in which the reference map is given in the form of localized objects. Our localization framework relies on 3D semantic object detections, which are then associated to objects in the map. Possible pair-wise association sets are grown based on hierarchical clustering using a merge metric that evaluates spatial compatibility. The latter notably uses information about relative object configurations, which is invariant with respect to global transformations. Association sets are furthermore updated and expanded as the camera incrementally explores the environment and detects further objects. We test our algorithm in several challenging situations including dynamic scenes, large view-point changes, and scenes with repeated instances. Our experiments demonstrate that our approach outperforms prior art in terms of both robustness and accuracy.
Intensity-Adjustable Non-contact Cold Sensation Presentation Based on the Vortex Effect
Venue: arXiv cs.HC 2022
Year: 2022

Cold sensations of varying intensities are perceived when human skin is subject to diverse environments. The accurate presentation of temperature changes is important to elicit immersive sensations in applications such as virtual reality. We developed a method to elicit intensity-adjustable non-contact cold sensations based on the vortex effect. We applied this effect to generate cold air at approximately 0 °C and varied the skin temperature over a wide range. The perception of different temperatures can be elicited by adjusting the volume flow rate of the cold air. Additionally, we introduced a cooling model to relate the changes in skin temperature to various parameters such as the cold air volume flow rate and distance from the cold air outlet to the skin. For validation, we conducted measurement experiments and found that our model can estimate the change in skin temperature with a root mean-square error of 0.16 °C. Furthermore, we evaluated the performance of a prototype in psychophysical cold discrimination experiments based on the discrimination threshold. Thus, cold sensations of varying intensities can be generated by varying the parameters. These cold sensations can be combined with images, sounds, and other stimuli to create an immersive and realistic artificial environment.