Reviewerly/exHarmony huggingface dataset:
Universal Adversarial Perturbation Attack on the Inception-Resnet-v1 model and the Effectiveness of Adversarial Retraining as a Suitable Defense Mechanism

In this study, we analyse the impact of the Universal Adversarial Perturbation Attack on the Inception-ResNet-v1 model using the lung CT scan dataset for COVID-19 classification and the retinal OCT scan dataset for Diabetic Macular Edema (DME) classification. The effectiveness of adversarial retraining as a suitable defense mechanism against this attack is examined. This study is categorised into three sections - the implementation of the Inception-ResNet-v1 model, the effect of the attack and the adversarial retraining.
Understanding Recruiters’ Information Seeking Behavior in Talent Search

While the rise of online job portals and corporate websites have allowed for easier collection of digital candidate CVs, much of the candidate identification and assessment process—also known as talent search—still requires manual work from recruiters. Recruitment is a professional search domain that has been largely overlooked in IR research, even though better support of recruiters in finding more high-quality candidates could have a big impact on job seekers, companies and society as a whole. Such recruiter support can only be built on top of a more thorough understanding of the information seeking behavior of recruiters when trying to identify the most relevant candidates for open job postings.
Untangling Cognitive Processes Underlying Knowledge Work

In a post-industrial society, the workplace is dominated primarily by Knowledge Work, which is achieved mostly through human cognitive processing, such as analysis, comprehension, evaluation, and decision-making. Many of these processes have limited support from technology in the same way that physical tasks have been enabled through a host of tools from hammers to shovels and hydraulic lifts. To develop a suite of cognitive tools, we first need to understand which processes humans use to complete work tasks. In the past century several classifications (e.g., Blooms) of cognitive processes have emerged, and we assessed their viability as the basis for designing tools that support cognitive work. This study re-used an existing data set composed of interviews of environmental scientists about their core work. While the classification uncovered many instances of cognitive process, the results showed that the existing cognitive process classifications do not provide a sufficiently comprehensive deconstruction of the human cognitive processes; the work is quite simply too abstract to be operational.
Investigating Online Browsing Practices

It has been long acknowledged that browsing is an important information seeking strategy that should be well-supported by digital systems. However, a recent evaluation showed that existing digital systems do not meet the basic browsing requirements of users. That motivates this research to improve the existing digital support for browsing. To support browsing in the digital context, we need to know what browsing practices need to be supported. Previous literature illustrated fundamental browsing practices from multiple perspectives. Many empirical studies provided a detailed description of how these fundamental browsing practices happened in the physical context. This research will extend the existing understanding of browsing to the digital context and provide an enhanced foundation for future browsing designs.
Using Data-Prompted Interviews in Interactive Information Retrieval Research

Capturing authentic information behaviors, feelings, and attitudes in natural settings is a challenge in interactive information retrieval research (IIR). Quantitative data collection is useful for understanding IIR at scale. Yet common data collection techniques, such as surveys, lack participants' reasoning behind their choices. Qualitative data collection, such as think aloud and after protocols, support understanding how people behave, think, and feel immediately following an experience, but may be subject to cognitive biases and are challenging to deliver longitudinally. IIR studies have an opportunity to enhance ecological validity by using mixed and multi-method studies. This paper applies learnings from a longitudinal mixed method study that combines the 'in-situ' benefits of collecting real-time data over time with the benefits of retrospectives. This approach has potential to advance SAL research by providing a contextualised approach to longitudinal data collection and can be used to gain deeper insights into subjective experiences.
Building a Better Mousetrap: Tools and Processes for Selling A Company

It is a fact of life for many start-ups that they must sell part of their company (i.e., fund raising) in order to have enough capital to grow the company to one day successfully exit the market. The unfortunate side effect of this necessity is that it places a large burden on start-ups to respond to information requests from potential buyers which then forces employees to step away from their day jobs to formulate responses. While it has been the norm to respond to such requests using manual review of contracts and other information sources, the increasingly competitive funding market has resulted in growing time pressure for all participants of start-up purchasing endeavours. Furthermore, current technological offerings often fall short of providing optimal support to the start-up and the buyer which continues to reinforce a process that is often cumbersome and chaotic.
Beyond Accurate Answers: Evaluating Open-Domain Question Answering in Enterprise Search

Open-domain question answering (OpenQA) research has grown rapidly in recent years. However, OpenQA usability evaluation in its real world applications is largely left under studied. In this paper, we evaluated the actual user experience of OpenQA model deployed in a large tech company's production enterprise search portal. From qualitative query log analysis and user interviews, our preliminary findings are: 1) There exists a large number of "contingency answers" that cannot be simply evaluated against their face textual values, due to noisy source passages and ambiguous query intents from short keywords queries. 2) Contingency answers contribute to positive search experience for providing "information scents". 3) Click-through-rate (CTR) is a good user-behavior metric to measure OpenQA result quality, despite the rare existence of "good abandonment". This exploratory study reveals an often neglected gap between existing OpenQA research and its search engine applications that disconnects the offline research effort with online user experience. We call for reformulating OpenQA model objective beyond answer face value and developing new dataset and metrics for better evaluation protocols.
Eyes on Immersive Search: Eye-Tracking Study of Search Engine Result Pages in Immersive Virtual Environments

User interactions with search engine result pages (SERPs) are well researched in desktop and mobile computing environments. However, relatively little work has focused on how users interact with SERPs presented in 3D immersive virtual environments (IVEs) using virtual reality head-mounted displays (VR HMDs). While 2D displays have well-understood methods to present search results (e.g., ranked lists of 10 blue links), 3D IVEs do not yet have established paradigms for presenting search results.
Understanding the Cognitive Influences of Interpretability Features on How Users Scrutinize Machine-Predicted Categories

The goal of interpretable machine learning (ML) is to design tools and visualizations to help users scrutinize a system's predictions. Prior studies have mostly employed quantitative methods to investigate the effects of specific tools/visualizations on outcomes related to objective performance—a human's ability to correctly agree or disagree with the system—and subjective perceptions of the system. Few studies have employed qualitative methods to investigate how and why specific tools/visualizations influence performance, perceptions, and behaviors. We report on a lab study (N = 30) that investigated the influences of two interpretability features: confidence values and sentence highlighting. Participants judged whether medical articles belong to a predicted medical topic and were exposed to two interface conditions—one with and one without interpretability features. We investigate the effects of our interpretability features on participants' performance and perceptions. Additionally, we report on a qualitative analysis of participants' responses during an exit interview. Specifically, we report on how our interpretability features impacted different cognitive activities that participants engaged with during the task—reading, learning, and decision making. We also describe ways in which the interpretability features introduced challenges and sometimes led participants to make mistakes. Insights gained from our results point to future directions for interpretable ML research.
Exit-Less, Isolated, and Shared Access for Virtual Machines

This paper explores Exit-Less, Isolated, and Shared Access (ELISA), a novel in-memory object sharing scheme for Virtual Machines (VMs). ELISA has the isolation advantage over the shared memory directly exposed to guest VMs while its overhead is smaller than that of host-interposition relying on the costly exit from the VM context. In a nutshell, ELISA isolates shared in-memory objects by Extended Page Table (EPT) separation, and a guest VM accesses them by switching the EPT context using VMFUNC, a low-overhead CPU instruction of Intel CPUs. Our experiment shows that the overhead of ELISA is 3.5 times smaller than that of VMCALL-oriented host-interposition. We demonstrate the benefits of ELISA through two use cases; by replacing VMCALL with ELISA, a VM networking system and an in-memory key-value store exhibit 163% and 64% higher performance respectively.
Vidi: Record Replay for Reconfigurable Hardware

Developers are turning to heterogeneous computing devices, such as Field Programmable Gate Arrays (FPGAs), to accelerate data center and cloud computing workloads. FPGAs enable rapid prototyping and should facilitate an agile software-like development workflow to fix correctness bugs, performance issues, and security vulnerabilities. Unfortunately, hardware development still does not have a vast ecosystem of tools needed to support the agile hardware development vision. The capability to record and replay FPGA executions would constitute a key building block that will inspire the development of many tools, similar to what record/replay did for software. However, building a practical record/replay tool for FPGA is challenging; existing approaches either record too much or too little information and cannot support real-world executions.
Untangle: A Principled Framework to Design Low-Leakage, High-Performance Dynamic Partitioning Schemes

Partitioning a hardware structure dynamically among multiple security domains leaks some information but can deliver high performance. To understand the performance-security tradeoff of dynamic partitioning, it would be useful to formally quantify the leakage of these schemes. Unfortunately, this is hard, as what partition resizing decisions are made and when they are made are entangled.
Efficient Scheduler Live Update for Linux Kernel with Modularization

The scheduler is a critical component of the operating system (OS)and is tightly coupled with Linux. Production-level clouds often host various workloads, and these workloads require different schedulers to achieve high performance. Thus the capability of updating the scheduler lively without rebooting the OS is crucial for the production environments. However, emerging live update techniques only apply for the fine-grained function-level updates or require extra constraints such as microkernel. It fails to update the entire heavy process scheduler subsystem lively. We therefore propose Plugsched to enable scheduler live update, and there are two key novelties. First of all, with the idea of modularization, Plugsched decouples the scheduler from the Linux kernel to be an independent module; Secondly, Plugsched uses the data rebuild technique to migrate the state from the old scheduler to the new one. This scheme can be directly applied to the Linux kernel scheduler in production environments without modifying kernel code. Unlike current function-level live update solutions, Plugsched allows developers to update the entire scheduler subsystem and modify internal scheduler data via the rebuilding technique. Moreover, an optimized stack inspection method is introduced to further effectively reduce the downtime due to the update. Experimental and production results show that Plugsched can effectively update kernel scheduler lively and the downtime is less than tens of milliseconds.
irchiver: A Full-Resolution Personal Web Archive for Users and Researchers

irchiver is a personal web archive tool for users, which also provided an opportunity for information retrieval researchers to access naturalistic long-term browsing histories. It offers users the ability to capture and search their web archives, which are stored in full-resolution images on their local filesystem. Researchers can request these files, or develop a plugin that triggers when a new image is captured. irchiver runs as a Windows background process using a hook and capture technique that works on all 5 major desktop browsers. The first author has been using irchiver for 12 months, encountering unexpected benefits in archive retrieval, change detection, search functionality, and content recovery.
Speakerbox: Few-Shot Learning for Speaker Identification with Transformers

Automated speaker identification is a modeling challenge for research when large-scale corpora, such as audio recordings or transcripts, are relied upon for evidence (e.g.Journalism, Qualitative Research, Law, etc.).To address current difficulties in training speaker identification models, we propose Speakerbox: a method for few-shot fine-tuning of an audio transformer.Specifically, Speakerbox makes multi-recording, multi-speaker identification model fine-tuning as simple as possible while still fitting an accurate, useful model for application.Speakerbox works by ensuring data are safely stratified by speaker id and held-out by recording id prior to fine-tuning of a pretrained speaker identification Transformer on a small number of audio examples.We show that with less than an hour of audio-recorded input, Speakerbox can fine-tune a multi-speaker identification model for use in assisting researchers in audio and transcript annotation.
A lossless compression method for random integer based on three-dimensional product- triangular chaotic iterative sequences

Abstract Using the ergodicity of chaotic iterative sequences to realize data compression is a new research perspective. We find that, under suitable initial conditions, one or more local segments that are numerically identical to random integer sequences appear on the iterative sequence, which is a prerequisite for compression. Based on this, this paper designs a random integer lossless compression method based on three-dimensional product-triangular chaotic iterative sequences. The method proposed only needs to input a small amount of iterative initial information to compress a large amount of data through an iterative sequence of limited length. The key lies in three aspects. First, according to the characteristics of the data to be compressed, the iterative initial conditions suitable for compression are obtained by screening. Secondly, map the traversal results into a sequence of binary integers to complete the recording of key information. Finally, this binary integer sequence is rapidly compressed using a designed parity symmetric transformation algorithm, and decompression is achieved in its reverse process. As a new way to achieve compression, this approach is not only simple, but also requires less computation time. The experimental results show that the compression effect achieved by this method has obvious advantages in terms of compression ratio, data reconstruction quality, and compression and decompression speed.
Signal modulation based on software radio platforms

With the rapid development of communication technology, due to the advantages of high flexibility, good compatibility, wide openness and other advantages of software radio, and it is easy to upgrade and expand the system in the later stage, software radio technology has been applied in many radio engineering occasions, showing broad application prospects. This paper applies the GNU Radio experimental platform, and selects eight modulation methods of AM, DSB, SSB, 2ASK, MASK, 2FSK, 2PSK, MPSK, etc., through the setting of basic parameters such as frequency, phase, FFT points, window function and the setting of channel parameters such as additive white Gaussian noise, frequency offset, epsilon and signal-to-noise ratio range, to obtain different simulated time domain charts, frequency domain charts, waterfall charts, constellation charts, and compare and analyze different signal modulation techniques, understand and master the modulation principles and modulation methods of signals. At the same time, modeled on a number of open source datasets such as RML2016.10A and RML2018.01A, a small sample electromagnetic dataset is formed in the GNU Radio software platform, providing a reliable data set for technical research in the field of electromagnetic space and providing more general options for researchers who design related systems.
Research on satellite operation control system based on cloud platform

The ground satellite operation and control system are complex and involves many elements. In the future, the demand for on orbit satellite telemetry, operation and control services will increase significantly, the operation control system is difficult to deploy and expand rapidly, and the staff work intensity is high. In order to meet "one station and multiple satellites" parallel operation control, automation and scalability requirements. With the study of cloud computing and automation technology technologies, the satellite operation control system architecture based on cloud platform is proposed with the idea of service oriented. Integrate the hardware and software resources of the ground operation control system using the computing power, storage capacity and application service capacity allocated on-demand by the cloud platform.
We Built It and No One Came: Visualization Lab Redesign, Take 2, in a Pandemic

While visualization labs draw initial enthusiasm, they are difficult to maintain, support, and refresh to sustain and grow usage. In 2016, University of Oregon (UO) Libraries opened a renovated science library equipped with a makerspace and high-definition visualization lab. After substantial investment of time and technology, by 2020, the lab was not living up to expectations. Complicated, vendor-dependent system design and support coupled with restrictive use policies resulted in low use and unreliable technology. At the time, the need for an exclusive visualization screen for research was oversold, resulting in a space that was not flexible to accommodate the diverse needs of faculty across campus. Lockdown of the science library during the pandemic provided time to re-envision the lab as an innovative space to encourage collaboration between students and faculty to compile, render, and visualize research. It also allowed time to develop a training program for student employees to support users and secure campus IT partners to redesign the space to enhance performance.
Analyzing web descriptions of cybersecurity breaches in the healthcare provider sector: A content analytics research method

Though the ever-increasing data breach incidents in healthcare providers are exposing their clients' personal, financial, and medical data to cybercriminals, there is limited empirical research on the dynamics of data breaches. These dynamics include relationships between data breach types and locations, vulnerabilities, and mitigation efforts. To address the research gap, a four-step content analytics-based research method is developed. Firstly, the types of data breaches that take place at various data breach locations are analysed. Secondly, co-occurring keywords are identified from web descriptions of data breach incidents. Thirdly, multi-level themes related to keywords are derived. Lastly, themes and contextual information are translated into vulnerabilities, impacts, and mitigation efforts. The identified vulnerabilities, impacts, and mitigation efforts of the different breach type/location combinations aim to assist practitioners in the prevention and mitigation of cybersecurity breaches.
Collaborative knowledge-aware recommendation based on neighborhood negative sampling

Knowledge graph and negative sampling, as the sources of auxiliary information, have been playing a vital role since they were included in the recommendation system. Creating an end-to-end model based on a graph neural network is the current technical trend. In order to generate samples with negative signals for the recommended model, negative sampling is also done from the unobserved data. However, the current end-to-end model based on graph neural network is unable to effectively capture the high-order collaboration signals of items, making it unable to learn the high-quality user and item representation. Additionally, existing negative sampling technique is insufficient for producing high-quality negative samples that can both reflect users’ true preferences and offer information for model training. Therefore, this paper proposes a new model, namely, collaborative knowledge-aware network based on neighborhood negative sampling (CKNNS). Users and items are technically modeled based on the collaboration-aware and knowledge-aware. To help the model better capture the users’ preferences and the personality traits, a gated aggregation strategy is used to adaptively capture collaboration-aware signals and knowledge-aware information. Meanwhile, a negative sampling method based on user’s neighborhood has been proposed. Specifically, the similarity of users is judged according to their collaboration information, so as to build a negative sampling domain for a specific user and improve the quality of negative sampling. The experimental outcomes on four benchmark datasets reveal that CKNNS outperforms sophisticated approaches like LightGCN, CKAN, KGIN and KGCL.