Miracle-dz/UniFAR-FTUs huggingface dataset:
SURAGH: Syntactic Pattern Matching to Identify Ill-Formed Records

Every day, data are produced from different sources with varying structures for multiple purposes. With increasing heterogeneity, the ability to manage it accurately and according to user requirements has become more challenging for downstream applications. Among other challenges, detecting ill-formed records in files is a difficult problem: ill-formed records occur in raw data due to loosely defined schemata, incorrect formatting of values, record structure discrepancy, etc. They can lead to aborted loading processes, incorrectly parsed data, and can interfere with the training process of machine learning algorithms. We propose Suragh 1 , a method to detect ill-formed records without relying on external information, such as data types, record structure, or schemata. It parses an input file and generates syntax-based patterns for column values using order-dependent abstractions . We combine these into patterns for entire rows and use them to identify ill-formed records.
Opportunities and Challenges for Small and Micro Medical Device Companies under the Pandemic from the Perspective of New-institutionalism -- Taking Company B as an Example

Taking Company B as an example, this paper discusses the problems and influencing factors faced by the small and micro medical device management enterprises under the epidemic situation from the perspective of New-institutionalism. The study has found that there is the problem of substitution of enterprise goals. And the main problems existing in the enterprise structure are the cost aggravation caused by the institutional convergence and the legal punishment brought by the separation of structure and operation. The causes of these problems are composed of elements contained in the institutional and technical environment, such as government policies, cultural norms, efficiency, etc. Furthermore, from analysis, the main rules affecting the survival of small and micro enterprises before and after the epidemic have gradually changed from informal system to formal system.
OPTH_A_360293 973..980

Introduction: Tribology is known as the science of friction, lubrication and the determination of what occurs when two surfaces slide against one another. The required partners in this science are a minimum of two surfaces and relative motion. Tribology could be a key factor in dissecting the issues that surround and confound dry eye in patients as well as contact lens discomfort and intolerance. Specifically, it is this issue that is a potential causative underlying factor that could lead to conditions like lid wiper epitheliopathy (LWE). Methods: Peer-reviewed literature was reviewed as It pertains to ocular tribology and the application to the ocular anatomy. A PubMed review was performed using the keywords: tribology, friction, lid wiper epitheliopathy, contact lens, and dry eye. All manuscripts were reviewed for mentions of tribology and friction. The exact same process was performed with The Association for Research in Vision and Ophthalmology (ARVO) abstracts. Results: In relation to the ocular surface, tribology describes the mechanical interactions between the upper and lower lid wipers and the globe. The Stribeck curve can be applied, as the sliding partners are separated by a complex, lubricating tear film. The surface brush anatomy at the eye to eyelid juncture reduces friction and alters the Stribeck curve in favor of the hydrodynamic regime, allowing for high velocity movement with minimal wear. Changes to the tear film or the introduction of a contact lens can displace the Stribeck curve, increase friction, and induce wear thus leading to patient symptomology. Conclusion: Further studies may provide new insight into contact lens discomfort and ocular surface disease, including LWE; however, adaptation of tribology work performed in vitro to in vivo patient care is challenging.
Does the mandatory adoption of IFRS influence the forecast of growth and profitability? An analysis in emerging countries

This study aims to investigate the impact of mandatory IFRS adoption on growth and profitability forecasts for companies in emerging countries. It is investigated whether IFRS adoption by 324 publicly traded companies in emerging countries (South Africa, Brazil, Chile, Philippines, Malaysia, Mexico, Nigeria, Peru, Russia and Colombia) is associated with an increase in profitability forecast accuracy and growth, through the mean reversion model, considering the pre-IFRS and post-IFRS periods of each country analyzed, totaling 5,149 observations. The results suggest that the mandatory adoption of IFRS reduces the expected growth and profitability errors. This study contributes to the literature on the effect of IFRS on forecast analysis, and fills a gap in the empirical accounting literature.
Effects of Pedestrian Behavior, Time Pressure, and Repeated Exposure on Crossing Decisions in Front of Automated Vehicles Equipped with External Communication

Automated vehicles are expected to substitute driver-pedestrian communication via LED strips or displays. This communication is expected to improve trust and the crossing process in general. However, numerous factors such as other pedestrians’ behavior, perceived time pressure, or previous experience influence crossing decisions. Therefore, we report the results of a triply subdivided Virtual Reality study (N=18) evaluating these. Results show that external communication was perceived as hedonically pleasing, increased perceived safety and trust, and also that pedestrians’ behavior affected participants’ behavior. A timer did not alter crossing behavior, however, repeated exposure increased trust and reduced crossing times, showing a habituation effect. Our work helps better to integrate research on external communication in ecologically valid settings.
The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systems

Conversational agents have come increasingly closer to human competence in open-domain dialogue settings; however, such models can reflect insensitive, hurtful, or entirely incoherent viewpoints that erode a user’s trust in the moral integrity of the system. Moral deviations are difficult to mitigate because moral judgments are not universal, and there may be multiple competing judgments that apply to a situation simultaneously. In this work, we introduce a new resource, not to authoritatively resolve moral ambiguities, but instead to facilitate systematic understanding of the intuitions, values and moral judgments reflected in the utterances of dialogue systems. The Moral Integrity Corpus, MIC, is such a resource, which captures the moral assumptions of 38k prompt-reply pairs, using 99k distinct Rules of Thumb (RoTs). Each RoT reflects a particular moral conviction that can explain why a chatbot’s reply may appear acceptable or problematic. We further organize RoTs with a set of 9 moral and social attributes and benchmark performance for attribute classification. Most importantly, we show that current neural language models can automatically generate new RoTs that reasonably describe previously unseen interactions, but they still struggle with certain scenarios. Our findings suggest that MIC will be a useful resource for understanding and language models’ implicit moral assumptions and flexibly benchmarking the integrity of conversational agents. To download the data, see https://github.com/GT-SALT/mic
Optimal design of experiments for the modelling of food processes

of unknown coefficients in dynamic models. This approach is validated over 4 applications, 2 of which being taken from literature: (A) Reproduction of the pharmacokinetic results from [1]: diffusion of a drug between 2 compartments; (B) Reproduction of the fermentation results from [2], non sequential optimal design of 6 fermentations trials. (C) Order 1 reaction of ascorbic acid in stewed apples; (D) Rice drying, most complex case with no analytical solution: optimal use of experimental device to identify unknown heat and mass transfer coefficients The objective is to diminish the experimental effort needed to make this identification within acceptable confidence ranges. After each experiment, the next experiment is A-, D- or E-optimally designed. Our design of experiments can take into account all experimental constraints and experimental results of al previous experiments to calculate best experimental conditions and to obtain smallest uncertainties on estimated parameters. In all contexts (A-D), this methodology is applied to a simulated noised experiment, and its stability and convergence is shown to be effective. In the last context (D), this methodology is also applied to a drying pilot plant; the identification made with only three real experiments with non-constant drying conditions are shown to be as effective as an identification based on two-factor three-level grid of nine experiments at constant conditions.
VCSR: Mutable CSR Graph Format Using Vertex-Centric Packed Memory Array

The compressed sparse row (CSR) is a widely used graph storage format due to its compact memory layout and high performance on graph analytic tasks. However, the compact design also limits itself from supporting many graph applications that operate on dynamic or temporal graphs as updates on these graph will need to rebuild the entire CSR structure, leading to high costs. Extending CSR to support efficient graph mutations without losing its high performance on graph analysis then becomes critical to these applications. Existing mutable CSR extensions leverage packed memory array (PMA) to store edge list to enable graph mutations. But, such a naive way has fundamental limitations in handling imbalanced graphs, which many real-world graphs belong to. To address such issues, we propose VCSR, a new mutable CSR storage format that leverages the packed memory array (PMA) via a new vertex-centric strategy to efficiently support temporal graphs. Our evaluation results show that compared with the state-of-the-art mutable CSR extensions, VCSR can achieve 1.41x-3.81x better performance in graph insertions and 1.22x-2.05x better performance in running typical graph analytic algorithms. In addition, VCSR can achieve similar performance as the original immutable CSR in running graph analytic tasks, making it a promising storage format for temporal graphs.
A rare case of ovarian Actinomycosis in an 8-year-old girl. A case reports

Anaerobic, nonsporulating, Gram-positive bacteria groups called actinomyces organisms are responsible for the so called actinomycosis. This chronic disease is rare in children and has tendency to mimic many other diseases. It also has wide variety of manifestations and non-specific symptoms. As a result, it is difficult to diagnose before the biopsy and microscopic examination. Although infection may involve any organ in the body, the significant sites of actinomyces infection include cervicofacial, abdominal, pelvic and pulmonary tissues. Case report: Here, we describe one case of unusual presentation; an 8-year-old girl with an ovarian mass which was finally diagnosed actinomycosis based on the pathological findings. Conclusions: Actinomycosis may rarely present with ovarian mass in young girls.
Learning Traffic Encoding Matrices for Delay-Aware Traffic Engineering in SD-WANs

This paper introduces Traffic Encoding Matrices (TEMs) as an alternative to traditional Traffic Matrices (TMs) for representing network traffic demands. While TMs only capture average demand information, TEMs are designed to capture distributional demand information by learning representations whose variations capture most of the structure of the distribution of demands. We present a practical approach based on off-the-shelf neural autoencoders to efficiently construct TEMs at the edge of the network. We then present the design and evaluation of NeuroTE, a DRL-based framework for delay-aware traffic engineering in SD-WANs using TEMs. Using real traffic traces, we present experimental results to demonstrate the advantages of using TEMs instead of TMs in traffic engineering. Our results show that, when traffic demands are dynamic, TEM-based control leads to: 1) improved network performance, and 2) faster convergence to the optimal solution compared to TM-based control using exactly the same DRL control algorithm.
Arbiter: Bridging the Static and Dynamic Divide in Vulnerability Discovery on Binary Programs

In spite of their effectiveness in the context of vulnerability discovery, current state-of-the-art binary program analysis approaches are limited by inherent trade-offs between accuracy and scalability. In this paper, we identify a set of vulnerability properties that can aid both static and dynamic vulnerability detection techniques, improving the precision of the former and the scalability of the latter. By carefully integrating static and dynamic techniques, we detect vulnerabilities that exhibit these properties in real-world programs at a large scale. We implemented our technique, making several advancements in the analysis of binary code, and created a prototype called A RBITER . We demonstrate the effectiveness of our approach with a large-scale evaluation on four common vulnerability classes: CWE-131 (Incorrect Calculation of Buffer Size), CWE-252 (Unchecked Return Value), CWE-134 (Un-controlled Format String), and CWE-337 (Predictable Seed in Pseudo-Random Number Generator). We evaluated our approach on more than 76,516 x86-64 binaries in the Ubuntu repositories and discovered new vulnerabilities, including a flaw inserted into programs during compilation.
Computer-aided methods for furniture decoration with traditional motifs of textile heritage

The present paper introduces computer-aided methods for the transposition of ornaments inspired from the textile heritage into the surface furniture decoration. Using digital technology, the furniture industry may bring into modern life the traditional motifs, contributing thus to the preservation of the cultural heritage from the region Ţara Bârsei, located in Transylvania. Two motifs inspired by the traditional costumes from South-Eastern Transylvania were digitized using CorelDraw software. They were imported in AutoCAD and the information was transferred both to CNC router and laser equipment. The CNC routing of ornaments was simulated with two types of tools and two processing methods (engraving and carving) using CAD-CAM-CAE software and the models were afterward processed on wood panels by CNC router and laser equipment and then visually analysed, concluding which method and tool is suitable for each type of ornament, so to transpose better the original motif on furniture wood surface.
Complex Assembly and Supercomplex Stabilization

Mammalian mitochondria may contain up to 1,500 different proteins, and many of them have neither been confidently identified nor characterized. In this study, we demonstrated that C11orf83, which was lacking experimental characterization, is a mitochondrial inner membrane protein facing the intermembrane space. This protein is specifically associated with the bc1 complex of the electron transport chain and involved in the early stages of its assembly by stabilizing the bc1 core complex. C11orf83 displays some overlapping functions with Cbp4p, a yeast bc1 complex assembly factor. Therefore, we suggest that C11orf83, now called UQCC3, is the functional human equivalent of Cbp4p. In addition, C11orf83 depletion in HeLa cells caused abnormal crista morphology, higher sensitivity to apoptosis, a decreased ATP level due to impaired respiration and subtle, but significant, changes in cardiolipin composition. We showed that C11orf83 binds to cardiolipin by its α-helices 2 and 3 and is involved in the stabilization of bc1 complex-containing supercomplexes, especially the III2/IV supercomplex. We also demonstrated that the OMA1 [. ] DESMURS-ROUSSEAU, Marjorie, et al. C11orf83, a Mitochondrial Cardiolipin-Binding Protein
Periodontitis Affects Skeletal Muscle Metabolism Through an Increase in Proinflammatory Cytokines

Periodontitis is an inflammatory disease of the oral cavity which attacks the gingival and periodontal tissue. One of the results of immune response activity in periodontitis is an increase in proinflammatory cytokines both locally and systematically. An increase in proinflammatory cytokines will affect tissue metabolism, including skeletal muscle tissue. The mechanism of periodontitis in affecting skeletal muscle is still being discussed and studied, this review will discuss the mechanism of periodontitis in affecting skeletal muscle metabolism. The interaction between bacteria and the immune system in periodontal tissues will activate the immune system and can cause an increase in proinflammatory cytokines such as IL-6, IL-1β, and TNF-α both locally and systemically. The presence of bacteria in the body's circulation and an enhancement of proinflammatory cytokines systemically can affect other tissues in the body, one of which is skeletal muscle. Several researchers have proven that periodontitis affects the regeneration process, glucose uptake, and recovery in skeletal muscle. An increase in IL-6, IL-1β, and TNF-α caused by periodontitis can reduce the process of muscle regeneration, reduce glucose uptake in skeletal muscle, and decreases the level of muscle responsiveness to training loads. Review (J Int Dent Med Res 2021; 14(4): 1623-1628)
BIG DATA AND NEURAL NETWORK APPROACH ON ONLINE PRODUCT SALES PREDICTION USING CUSTOMER REVIEWS AND PROMOTION STRATAGEM - A REVIEW

: With the advent of Information Technology (IT) and Data Sciences, businesses now have the opportunity to better understand and forecast consumer demands using quantitative methods. The use of Big Data to better understand business processes and results is an evolving IT trend that has piqued the interest of researchers and practitioners. Big Data systems have the potential to assist businesses in better understanding complicated business relationships by delivering knowledge that was previously unavailable. Previous research has shown that providing an effective supply chain gives a producer a competitive advantage over its competitors, how data from online marketplaces or e-commerce allows manufacturers to better understand product demands is a field that has not been thoroughly researched by previous researchers. This paper provides a comprehensive survey about the ongoing researches done in this field by reviewing the articles of the other computer scientists. This paper paves a better way for the upcoming researchers in this field.
Low-Frequency Electromagnetic Energy Harvesting

The demand for portable permanent sources of electrical energy increases every day to power portable or non-accessible devices. Energy harvesting from vibrations offers a non-traditional source of energy. It is renewable and prevailing, since nature around is rich in kinetic energy that can be harvested. In this work, we have developed two mechanisms to harvest energy from lowfrequency vibrations present in nature using electromagnetic transduction. The harvesting mechanisms use a mass-on-spring mechanical oscillator to capture kinetic energy from a host body. Prototypes embodying the two harvesting mechanisms were fabricated and tested. We identified the system parameters of the harvester prototypes and generated their frequency-response curves. We analyzed the results using and compared them with mathematical models of the system dynamics to characterize the harvesters’ performance including their output power, center frequency, and harvesting bandwidth. We were successful in demonstrating energy harvesters that can harvest lowfrequency vibration with center frequencies in the range of 8-14 Hz, harvesting bandwidth in the range of 8-12Hz, and output power on the order of 1mW. The realized harvesters are relatively small, a few inches in dimension, and light, a few tens of grams in mass. We also introduced a novel electromagnetic transduction mechanism that can be used in harvesting low-frequency vibrations. The novel electromagnetic transduction mechanism we developed induces a current in a coil by disrupting the electromagnetic field around the coil. where we have a coil wound around hollow track, a tube made of non-conducting material, surrounded by source of magnetic field, both coil and magnetic field source are stationary and a ball made of ferromagnetic material is moving freely along the tube cutting the field lines causing the magnetic field to be disrupted and induces current in the coil.
Audit mechanisms and earnings management

In this study we evaluated the influence of audit mechanisms on earnings management based on a sample of 142 non-financial firms listed in the ‘Differentiated Corporate Governance’ segment of the Brazilian stock market (B3) between 2011 and 2017. A descriptive analysis of the adopted metrics of earnings management (discretionary accruals, abnormal real activities, abnormal earnings) suggests that accounting information quality is most severely impacted by discretionary accruals. Multiple regressions on panel data using the quantile regression method revealed that audit mechanisms are protective of accounting information quality and that the existence of internal control and audit bodies―commonly regarded as internal mechanisms of corporate governance―was insufficient to inhibit the adoption of earnings management strategies, such as classification shifting, for the sample and period analyzed.
AVID: GPU-enabled Visual Analytics with GPU-FAST-PROCLUS

GPU-FAST-PROCLUS is a GPU-parallelized algorithm for projected clustering based on the 𝑘 -medoids approach. It speeds up clustering to allow for real-time interaction – even for datasets of millions of items. Interactivity allows users to quickly determine sensible clustering parameters such as the number of clusters 𝑘 , provided a suitable visualization is available. Yet, as clustering and visualization are usually decoupled, cluster results are fun-neled from the GPU back to the CPU, only to be mapped onto appropriate graphics, which are then rendered on the GPU again. This introduces a bottleneck that hinders fluid interaction with clustering. Asasolution to this, we propose AVID (Analysis and Visualization In Device). Following the principle “What happens on the GPU, stays on the GPU”, AVID removes the round trip to the CPU and keeps clustering results on the GPU to render them on the GPU directly. By doing so, users can interactively tune projected clustering parameters and observe the effects without noticeable delay. In our demo system, we showcase the efficiency of our data management strategies for projected clustering as well as the efficacy of data visualization.