Miracle-dz/UniFAR-FTUs huggingface dataset:
Anti-arthritis Effects of Zingiberaceae Extracts on Models of Inflammatory Joint Disease

Abstract Due to this becoming an aging society, the number of arthritis cases has been increasing. Unfortunately, some currently available medications can cause adverse effects. Using herbal remedies as a form of alternative medicine is becoming increasingly popular. Zingiber officinale (ZO), Curcuma longa (CL), and Kaempferia parviflora (KP) are herbal plants in the Zingiberaceae family that have potent anti-inflammatory effects. This study investigates the anti-inflammatory and chondroprotective effects of ZO, CL, and KP extracts on in vitro and ex vivo inflammatory models. The combinatorial anti-arthritis effect of each extract is also evaluated in an in vivo model. ZO extract preserves cartilaginous proteoglycans in proinflammatory cytokines-induced porcine cartilage explant in a fashion similar to that of CL and KP extracts and suppresses the expression of major inflammatory mediators in SW982 cells, particularly the COX2 gene. CL extract downregulates some inflammatory mediators and genes-associated cartilage degradation. Only KP extract shows a significant reduction in S-GAGs release in a cartilage explant model compared to the positive control, diacerein. In SW982 cells, it strongly suppresses many inflammatory mediators. The active constituents of each extract selectively downregulate inflammatory genes. The combined extracts show a reduction in inflammatory mediators to a similar degree as the combined active constituents. Reductions in paw swelling, synovial vascularity, inflammatory cell infiltration, and synovial hyperplasia are found in the combined extracts-treated arthritic rats. This study demonstrates that a combination of ZO, CL, and KP extracts has an anti-arthritis effect and could potentially be developed into an anti-arthritis cocktail for arthritis treatment.
Methodik und Zuordnungserfolg eines Linkage von Daten klinischer Krebsregister mit Abrechnungsdaten gesetzlicher Krankenkassen

Zusammenfassung Hintergrund Das vom Innovationsfonds geförderte Projekt „Wirksamkeit der Versorgung in onkologischen Zentren“ (WiZen) ist ein breit angelegtes Projekt zur Erforschung der Wirksamkeit von Zertifizierungen in der Onkologie. Im Rahmen des Projektes werden bundesweite Daten der AOKen und Daten Klinischer Krebsregister aus verschiedenen Bundesländern für die Jahre 2006–2017 verwendet. Zur Kombination der Stärken beider Datenquellen werden diese für acht verschiedene Krebsentitäten datenschutzkonform miteinander verknüpft. Methoden Das Datenlinkage erfolgte dabei anhand indirekter Identifikatoren und wurde mittels der Krankenversichertennummer als direktem Identifikator und Goldstandard validiert. Dies ermöglicht die Quantifizierung von Potenzial und Qualität verschiedener Linkage-Varianten. Als Kriterien zur Bewertung der Zuordnungen wurden Sensitivität und Spezifität sowie Treffergenauigkeit und Treffergüte genutzt. Die durch das Linkage resultierenden Verteilungen relevanter Variablen wurden anhand der ursprünglichen Verteilungen in den Einzeldatensätzen validiert. Ergebnisse Je nach Kombination indirekter Identifikatoren ergab sich eine Bandbreite von 22.125 bis 3.092. 401 Linkage-Treffern. Eine nahezu perfekte Verknüpfung der betrachteten Daten konnte durch die Kombination von Informationen zu Entitätsart, Geburtsdatum, Geschlecht und Postleitzahl der Personen erreicht werden. Insgesamt wurden mit den genannten Merkmalen 74.586 eineindeutige Verknüpfungen und für die verschiedenen Entitäten eine mediane Treffergüte von mehr als 98% erreicht. Die Alters- und Geschlechtsverteilungen der verschiedenen Datenquellen sowie die verknüpften Sterbedaten wiesen zudem eine hohe Übereinstimmung auf. Diskussion und Schlussfolgerung GKV- und Krebsregisterdaten lassen sich mit hoher interner und externer Validität auf Individualdatenebene verknüpfen. Die stabile Verknüpfung ermöglicht durch den simultanen Zugang zu Variablen beider Datensätze („das Beste aus beiden Welten“) gänzlich neue Analysemöglichkeiten: Für einzelne Personen stehen nun sowohl Informationen zum UICC-Stadium der Erkrankung aus den Registern als auch Komorbiditäten aus den GKV-Daten zur Verfügung. Durch die Verwendung gut verfügbarer Linkagevariablen und den hohen Verknüpfungserfolg ist das Verfahren vielversprechend für künftige Linkages in der Versorgungsforschung.
eHDL: Turning eBPF/XDP Programs into Hardware Designs for the NIC

Scaling network packet processing performance to meet the increasing speed of network ports requires software programs to carefully leverage the network devices’ hardware features. This is a complex task for network programmers, who need to learn and deal with the heterogeneity of device architectures, and re-think their software to leverage them. In this paper we make first steps to reverse this design process, enabling the automatic generation of tailored hardware designs starting from a network packet processing program. We introduce eHDL, a high-level synthesis tool that automatically generates hardware pipelines from unmodified Linux’s eBPF/XDP programs. eHDL is designed to enable software developers to directly define and implement the hardware functions they need in the NIC. We prototype eHDL targeting a Xilinx Alveo U50 FPGA NIC, and evaluate it with a set of 5 eBPF/XDP programs. Our results show that the generated pipelines are efficient in terms of required hardware resources, using only 6.5%-13.3% of the FPGA, and always achieve the line rate forwarding throughput with about 1 microsecond of per-packet forwarding latency. Compared to other network-specific high-level synthesis tool, eHDL enables software programmers with no hardware expertise to describe stateful functions that operate on the entire packet data. Compared to alternative processor-based solutions that perform eBFP/XDP offloading to a NIC, eHDL provides 10-100x higher throughput.
Quality Conversations and Considerations on Reddit

Whilst individual users seem rather ill-equipped to judge information quality on their own, conversations between users about information quality on sensitive topics are found to be characterized by personal attacks and disputational talk. In this study, we investigate whether users are better able to judge information qualities during conversations on everyday topics instead. We analyze a total of 268 quality and credibility judgements from four discussion threads on Reddit on their positivity and conversational style. Results confirm the occurrence of quality conversations with a constructive, positive style on everyday topics. We conclude that conversations are more constructive when identity concerns are limited.
UrbanKG: An Urban Knowledge Graph System

Every day, our living city produces a tremendous amount of spatial-temporal data, involved with multiple sources from the individual scale to the city scale. Undoubtedly, such massive urban data can be explored for a better city and better life, as what the urban computing community has been dedicating in recent years. Nevertheless, existing studies are still facing the challenges of data fusion for the urban data as well as the knowledge distillation for specific applications. Moreover, there is a lack of full-featured and user-friendly platforms for both researchers and developers in the urban computing scenario. Therefore, in this article, we present UrbanKG, an urban knowledge graph system to incorporate a knowledge graph with urban computing. Specifically, the system introduces a complete scheme to construct a knowledge graph for urban data fusion. Built upon the data layer, the system further develops the multiple layers of construction, storage, algorithm, operation, and applications, which achieve knowledge distillation and support various functions to the users. We perform representative use cases and demonstrate the system capability of boosting performance in various downstream applications, indicating a promising research direction for knowledge-driven urban computing.
The Impact of Electronic Word of Mouth (eWOM) and Perceived Value on Young Customers’ Purchase Intention for Open-Shelf Cosmetic Products Shopping at Physical Drugstores

The cosmetic industry is currently thriving as people gain satisfaction in purchasing beauty-enhancing products. The purpose of this study is to investigate the impact of eWOM and perceived value on young customers’ purchase intention for open-shelf cosmetics products shopping at physical drugstores. The target respondents ranged from 15 to 24 years old, as the young generation contributes to a large portion of the cosmetic market. 243 valid questionnaires were received from people who have purchased cosmetic products once in six months in Taiwan. The confirmatory factor analysis and structural equation model were adopted to analyze the relationship between three constructs: eWOM, perceived value, and purchase intention. The results suggest that perceived value impacts purchase intention positively and directly, while eWOM impacts purchase intention indirectly. This result regarding the consumer behavior of young customers could be utilized in the cosmetic industry's marketing strategy to enhance the sales profit and product competitiveness.
Carbon Trading Price Forecasting with a Hybrid Arima and LSTM Deep Leaning Methodology

China's carbon trading market is an emerging market and is influenced by many factors. Considering the regular characteristics of carbon trading price as an important indicator of market development, it is important to make scientific predictions about the price of carbon trading. Since more and more artificial intelligence models (including machine learning models and neural network models, etc.) have been applied to this field in recent years compared to previous studies that mainly used traditional econometric models. This paper tests and evaluates common artificial intelligence models and econometric models in the field of carbon trading price forecasting, taking the carbon trading price of Shenzhen from 2013 to 2020 as an example. It is found that the ARIMA -LSTM hybrid model has the best prediction effect, and the prediction results indicate that the Shenzhen carbon trading market will experience significant fluctuations in the next three years and the carbon trading price will remain unstable.
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. In this work, we present an analysis of 19 interviews of regular participants on both sides of the sales process finding that the main pain points revolve around document management, request tracking, internal and external collaboration. Based upon this analysis, we describe an early-stage prototype to investigate a possible solution for efficient handling of buyers’ information requests. We recruited 12 participants to test this prototype and found that the main issue was misalignment between language in the tool and participants’ mental models. From these two sets of analyses, we present potential implications and considerations for building tools for infrequent but high-risk and high-reward information tasks.
Infinity Stream: Portable and Programmer-Friendly In-/Near-Memory Fusion

In-memory computing with large last-level caches is promising to dramatically alleviate data movement bottlenecks and expose massive bitline-level parallelization opportunities. However, key challenges from its unique execution model remain unsolved: automated parallelization, transparently orchestrating data transposition/alignment/broadcast for bit-serial logic, and mixing in-/near-memory computing. Most importantly, the solution should be programmer friendly and portable across platforms. Our key innovation is an execution model and intermediate representation (IR) that enables hybrid CPU-core, in-memory, and near-memory processing. Our IR is the tensor dataflow graph (tDFG), which is a unified representation of in-memory and near-memory computation. The tDFG exposes tensor-data structure information so that the hardware and runtime can automatically orchestrate data management for bitserial execution, including runtime data layout transformations. To enable microarchitecture portability, we use a two-phase, JIT-based compilation approach to dynamically lower the the tDFG to in-memory commands. Our design, infinity stream, is evaluated on a cycle-accurate simulator. Across data-processing workloads with fp32, it achieves 2.6× speedup and 75% traffic reduction over a state-of-the-art near-memory computing technique, with 2.4× energy efficiency.
Skybox: Open-Source Graphic Rendering on Programmable RISC-V GPUs

Graphics rendering remains one of the most compute intensive and memory bound applications of GPUs and has been driving their push for performance and energy efficiency since its inception. Early GPU architectures focused only on accelerating graphics rendering and implemented dedicated fixed- function rasterizer hardware to speed-up their rendering pipeline. As GPUs have become more programmable and ubiquitous in other application domains such as scientific computing, machine learning, graph analytics, and crypto-currency, generalizing GPU microarchitectures for area and power efficiency becomes necessary, especially for mobile and IoT devices. In this work, we present Skybox, a full-stack open-source GPU architecture with integrated software, compiler, hardware, and simulation environment, that enables end-to-end GPU research. Using Skybox, we explore the design space of software versus hardware graphics rendering and propose and hybrid micro-architecture that accelerates the state-of-the art Vulkan graphics API. Skybox also introduces novel compiler and system optimizations to support its unique RISC-V ISA baseline. We evaluated Skybox on high- end Altera and also Xilinx FPGAs. We were able to generate and execute a 32 cores (512 threads) Skybox graphics processor on Altera Stratix 10 FPGA, delivering a peak fill rate of 3.7 GPixels at 230 MHz. Skybox is the first open-source full-stack GPU software and hardware implementation that supports the Vulkan API
Therabeats: Business Strategies for the Proposed Psychotherapy Telemedicine using SWOT Analysis and the Six Thinking Hats

TheraBeats, the proposed Psychotherapy telemedicine system includes typical features of telemedicine systems in the market such as booking appointments, payment modules, chats and videoconferencing, and profiling. It's edge to other telemedicine is the integration of audio psychotherapy tools or audio interventions. It also includes a sensory substitution feature for the Deaf community to experience the audio interventions and it is done using audio visualization through the mobile screens and through amplified bass lines and volume of the audios. The study aims to determine marketing strategies utilizing SWOT analysis and TOWS matrix in evaluating gathered data from the deployed Six Thinking Hats questionnaire. It used a quantitative and qualitative approach in analyzing gathered data from the conducted online surveys. The performed SWOT analysis and TOWS matrix were able to generate possible marketing strategies for the proposed telemedicine system.
The Evolution of User Knowledge during Search-as-Learning Sessions: A Benchmark and Baseline

In this paper, we present a new benchmark collection that shows how 404 users’ knowledge changed over the course of a search-as-learning session. We estimate the knowledge a user gains from each visited document and monitor knowledge change on a document-by-document basis. We describe the specifics of how this collection was created and provide a use case that illustrates potential future applications.
Event Studies: The Effect of Leaders’ Summit on Electric Vehicle and Clean Power Market

The leaders' climate summit was held in 2021 at a time of growing concern about global warming. This paper collected the S&P 500 Kensho Electric Vehicle Index and S&P 500 Kensho Clean Power Index within 255 days, and the event research method was adopted. Explores and compares electric vehicles and clean energy on the U.S. stock market during the climate summit. Through the visualization of empirical results, at a 95% confidence level, we found that the area beyond the confidence interval was minimal, which was not enough to indicate that this event had a significant impact on these two parts. Therefore, further research is still needed, especially during which environmentally friendly policies become essential, and lots of those kinds of policies appear among different countries.
Understanding Procedural Search Tasks “in the Wild”

People often search online for procedural (i.e., “how-to”) knowledge. A procedural search task might involve a do-it-yourself project, cooking a dish, fixing a problem, or learning a new skill. Prior research has studied procedural search tasks from different perspectives: estimating the frequency of procedural searches online, understanding how people acquire procedural knowledge in specific contexts, and developing tools to support procedural search. Less research has aimed at deeply understanding procedural search tasks “in the wild”. To bridge this gap, we conducted a survey (N = 128) on Amazon Mechanical Turk. Participants were asked to recall a recent procedural task for which they searched online. Participants were asked open-ended questions about the task itself and their unique situation (e.g., constraints and needs). Additionally, participants provided webpages they found useful in their searches and described the characteristics of the page that made it useful. Finally, they provided useful pieces of information from each selected page and explained what they gained from the information. Using an inductive coding approach, we analyzed participants’ responses to gain insights about: (1) procedural task characteristics, (2) goals, (3) constraints, (4) contextual factors, (5) relevance criteria, and (6) gains obtained from useful information. Based on our results, we discuss important implications for future research and system design.
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. In this paper, we present results from a within-subjects user study to investigate users’ interactions, eye-tracking behaviors, and preferences for four different display arrangements of search results (vertical list, 3x3 grid, 4x4 grid, 4x4 sphere) in a VR HMD across two different task types (find all relevant, pick 3 best). 32 participants completed 5 search trials in 8 experimental conditions (4 displays x 2 task types). Our results show that: (1) participants had a positional bias for the top or top left of SERPs, (2) they perceived the list display as requiring more effort, (3) they perceived a result ordering in the list display but not in the other displays, and (4) they showed a wider variety of navigational patterns in the 4x4 displays that did not require scrolling. We describe implications of the results and insights for presenting search results to users in HMD environments.
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. In this paper, we present the results of a log-based study of the information seeking process of recruiters at one of Scandinavia’s largest job portals and recruitment agencies. We analyze the behavior of recruiters at different search stages according to the model by Vakkari [24] and distinguish between different types of recruitment tasks. In addition, we contextualize the results of our log analysis using the findings from earlier conducted contextual inquiries to help explain our findings. Our results show that both matching and recruiting talent search is a complex task: recruiters usually submit multiple queries during sessions that can last for hours. We also find that the search behaviour of recruiters during a recruitment task changes over time: recruiters tend to use more filters, formulate longer and more diverse queries, and spend more time assessing candidates near the end of a session than in the beginning. We also observe some differences in search behavior between the different recruitment tasks.