Miracle-dz/UniFAR-FTUs huggingface dataset:
Empirical Research on the Impact of Personalized Recommendation Diversity

Personalized recommendation has important implications in raising online shopping efficiency and increasing product sales. There has been wide interest in finding ways to provide more efficient personalized recommendations. Most existing studies focus on how to improve the accuracy of the recommendation algorithms, or are more concerned on ways to increase consumer satisfaction. Unlike these studies, our study focuses on the process of decision-making, using long tail theory as a basis, to reveal the mechanisms involved in consumers’ adoption of recommendations. This paper analyzes the effect of personalized recommendations from two angles: product sales and ratings, and tries to point out differences in consumer preferences between mainstream products and niche products, high rating products and low rating products, search products and experience products. The study verifies that consumers demand diversity in the recommended content, and also provides suggestions on how to better plan and operate a personalized recommendation system.
Image Reconstruction: From Sparsity to Data-Adaptive Methods and Machine Learning

The field of medical image reconstruction has seen roughly four types of methods. The first type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray computed tomography (CT) and the inverse Fourier transform for magnetic resonance imaging (MRI), based on simple mathematical models for the imaging systems. These methods are typically fast, but have suboptimal properties such as poor resolution-noise tradeoff for CT. A second type is iterative reconstruction methods based on more complete models for the imaging system physics and, where appropriate, models for the sensor statistics. These iterative methods improved image quality by reducing noise and artifacts. The U.S. Food and Drug Administration (FDA)-approved methods among these have been based on relatively simple regularization models. A third type of methods has been designed to accommodate modified data acquisition methods, such as reduced sampling in MRI and CT to reduce scan time or radiation dose. These methods typically involve mathematical image models involving assumptions such as sparsity or low rank. A fourth type of methods replaces mathematically designed models of signals and systems with data-driven or adaptive models inspired by the field of machine learning. This article focuses on the two most recent trends in medical image reconstruction: methods based on sparsity or low-rank models and data-driven methods based on machine learning techniques.
Pseudodifferential calculus on noncommutative tori, I. Oscillating integrals

This paper is the first part of a two-paper series whose aim is to give a thorough account on Connes’ pseudodifferential calculus on noncommutative tori. This pseudodifferential calculus has been used in numerous recent papers, but a detailed description is still missing. In this paper, we focus on constructing an oscillating integral for noncommutative tori and laying down the main functional analysis ground for understanding Connes’ pseudodifferential calculus. In particular, this allows us to give a precise explanation of the definition of pseudodifferential operators on noncommutative tori. More generally, this paper introduces the main technical tools that are used in the second part of the series to derive the main properties of these operators.
Investing in Cyber Defense: A Value-Focused Analysis of Investment Decisions for Microgrids

To mitigate disruptions to commercial power grids, and to achieve operational efficiencies by managing energy use, many organizations are fielding smaller, local, self-contained microgrids. The computer control systems that operate the microgrids create new vulnerabilities to a rapidly-escalating array of cyber attacks. This creates a tension between the need to improve energy assurance and efficiency through microgrids, and the need to protect against cyber attacks that can disrupt and damage the organization’s energy systems. Through a series of interviews with subject matter experts and end-users, this exploratory study surfaces the decision-makers’ important values in this decision space and develops a network of those values to guide decision-makers to make better decisions in balancing these competing needs.
EXPLORING THE POTENTIALS OF TECHNOLOGY INTEGRATION FOR TEACHING LANGUAGE SKILLS: A LITERATURE REVIEW

The use of technology has become an essential element in teaching and learning process. Usually, each language class uses some type of technology tools that are used to support and enhance language learning. They are tools to help teachers facilitate their students' language learning. This research focuses on the role of technology in learning English as a foreign and second language. It addresses the significance of technology that allows English language learners to develop their learning abilities. In this paper, the researchers describe the term technology integration, of technology, explain the use of technology in the language classroom, discuss the advantages and challenges of technology integration, and provide some suggestions for better use of these technologies to help learners develop their learning abilities.
TRACE: A Stigmergic Crowdsourcing Platform for Intelligence Analysis

Crowdsourcing has become a frequently adopted approach to solving various tasks from conducting surveys to designing products. In the field of reasoning-support, however, crowdsourcing-related research and application have not been extensively implemented. Reasoning-support is essential in intelligence analysis to help analysts mitigate various cognitive biases, enhance deliberation, and improve report writing. In this paper, we propose a novel approach to designing a crowdsourcing platform that facilitates stigmergic coordination, awareness, and communication for intelligence analysis. We have partly materialized our proposal in the form of a crowdsourcing system which supports intelligence analysis: TRACE (Trackable Reasoning and Analysis for Collaboration and Evaluation). We introduce several stigmergic approaches integrated into TRACE and discuss the potential experimentation of these approaches. We also explain the design implications for further development of TRACE and similar crowdsourcing systems to support reasoning.
The Usefulness of the Recommendations Regarding the Information System Development Method Selection during the Era of Digitalization

The business criticality of information systems (IS) and their development (ISD) appear to have increased recently. Backsourcing, cosourcing and multisourcing of ISD are some of the consequences. They, in turn, extend the need for understanding how to select information systems development methods (ISDM). In this research, we first condensed the knowledge base of ISDM selection research into nine recommendations. We then interviewed 28 ISDM experts and asked them to evaluate how useful the extant ISDM selection recommendations of prior research are to IS user organizations. We discovered that most recommendations were perceived outdated and only limitedly useful. We finally contemplated that paying more attention to how ISDMs are used in business development contexts is a means to increase the usefulness of ISDM selection recommendations.
Weakly Supervised Action Segmentation Using Mutual Consistency

Action segmentation is the task of predicting the actions in each frame of a video. Because of the high cost of preparing training videos with full supervision for action segmentation, weakly supervised approaches which are able to learn only from transcripts are very appealing. In this paper, we propose a new approach for weakly supervised action segmentation based on a two branch network. The two branches of our network predict two redundant but different representations for action segmentation. During training we introduce a new mutual consistency loss (MuCon) that enforces that these two representations are consistent. Using MuCon and a transcript prediction loss, our network achieves state-of-the-art results for action segmentation and action alignment while being fully differentiable and faster to train since it does not require a costly alignment step during training.
Assessing Quality Quickly: Validation of the Responsive Interactions for Learning - Educator (RIFL-Ed.) Measure

ABSTRACT Research Findings: In this study, we tested whether it is possible to reliably evaluate the quality of interactions between early childhood educators and children using a thin-slice coding approach. Ninety-seven early childhood educators were videotaped for two five-minute intervals: one mealtime observation and one standardized activity. Videos were scored using the shortened and revised 15-item Responsive Interactions for Learning – Educator (RIFL-Ed.) measure, an open-access measure that takes less than ten minutes to administer and score. The RIFL-Ed. demonstrated good psychometric properties when used for mealtime observations and scores were associated with the Emotional and Behavioural Support (b = 0.19, p = .02) – and to some extent the Engaged Support for Learning (b = 0.15, p = 0.07) – domains of the widely used Classroom Assessment Scoring System (CLASS): Toddler Version. Less than half of the variance in scores was shared across educators in the same classrooms. Practice or Policy: If these preliminary results can be confirmed in larger studies, the RIFL-Ed.—an open-access measure for which fast and free online training is available– can be used to affordably scale-up targeted quality assessment and improvement efforts in early childhood education and care settings, efforts which have been shown to positively impact children’s developmental outcomes.
Racial Discrimination in Social Media Customer Service: Evidence from a Popular Microblogging Platform

The concept of racial inequality has existed from the early days of service provision, with evidence dating back to ancient civilizations. While the emergence of the Internet and social media has drastically transformed almost every aspect of everyday life, including the intrinsic values of social relationships, the impact of racial disparities on receiving services on online platforms is not so evident. Although many consumer brands provide customer service on social media today, little is known regarding the prevalence and magnitude of racial discrimination in the context of social media customer service. Thus, in this study, we examine the existence and the extent of racial discrimination against African-Americans in social media customer service. We analyzed all complaints to seven major U.S. airlines on Twitter for a period of nine months. Interestingly, our empirical analysis finds that African-American customers are less likely to receive brand responses to their complaints on social media. To the best of our knowledge, this is the first study to empirically analyze the racial discrimination phenomenon in the context of social media customer service.
BriskStream: Scaling Data Stream Processing on Shared-Memory Multicore Architectures

We introduce BriskStream, an in-memory data stream processing system (DSPSs) specifically designed for modern shared-memory multicore architectures. BriskStream's key contribution is an execution plan optimization paradigm, namely RLAS, which takes relative-location (i.e., NUMA distance) of each pair of producer-consumer operators into consideration. We propose a branch and bound based approach with three heuristics to resolve the resulting nontrivial optimization problem. The experimental evaluations demonstrate that BriskStream yields much higher throughput and better scalability than existing DSPSs on multi-core architectures when processing different types of workloads.
A Physical Basis for the H-band Blue-edge Velocity and Light-curve Shape Correlation in Context of Type Ia Supernova Explosion Physics

Our recent work demonstrates a correlation between the high-velocity blue edge, vedge, of the iron-peak Fe/Co/Ni H-band emission feature and the optical light-curve (LC) shape of normal, transitional and subluminous SNe Ia. We explain this correlation in terms of SN Ia physics. vedge corresponds to the sharp transition between the complete and incomplete silicon burning regions in the ejecta. It measures the point in velocity space where the outer 56Ni mass fraction, XNi, falls to the order of 0.03–0.10. For a given 56Ni mass, M(56Ni), vedge is sensitive to the specific kinetic energy Ekin(M(56Ni)/MWD) of the corresponding region. Combining vedge with LC parameters (i.e., sBV, in B and V) allows us to distinguish between explosion scenarios. The correlation between vedge and light-curve shape is consistent with explosion models near the Chandrasekhar limit. However, the available sub-MCh WD explosion model based on SN 1999by exhibits velocities that are too large to explain the observations. Finally, the subluminous SN 2015bo exhibits signatures of a dynamical merger of two WDs demonstrating diversity among explosion scenarios at the faint end of the SNe Ia population.
Spectral Analysis of Matrix Scaling and Operator Scaling

We present a spectral analysis of a continuous scaling algorithm for matrix scaling and operator scaling. The main result is that if the input matrix or operator has a spectral gap, then a natural gradient flow has linear convergence. This implies that a simple gradient descent algorithm also has linear convergence under the same assumption. The spectral gap condition for operator scaling is closely related to the notion of quantum expander studied in quantum information theory. The spectral analysis also provides bounds on some important quantities of the scaling problems, such as the condition number of the scaling solution and the capacity of the matrix and operator. These results can be used in various applications of scaling problems, including matrix scaling on expander graphs, permanent lower bounds on random matrices, the Paulsen problem on random frames, and Brascamp-Lieb constants on random operators. In some applications, the inputs of interest satisfy the spectral condition and we prove significantly stronger bounds than the worst case bounds.
An Initial Overview of the Extent and Structure of Recent Star Formation within the Serpens Molecular Cloud Using Gaia Data Release 2

The dense clusters within the Serpens Molecular Cloud are among the most active regions of nearby star formation. In this paper, we use Gaia DR2 parallaxes and proper motions to statistically measure ∼1167 kinematic members of Serpens, few of which have been previously identified, to evaluate the star formation history of the complex. The optical members of Serpens are concentrated in three distinct groups located at 380–480 pc; the densest clusters are still highly obscured by optically thick dust and have few optical members. The total population of young stars and protostars in Serpens is at least 2000 stars, including past surveys that were most sensitive to protostars and disks, and may be much higher. Distances to dark clouds measured from deficits in star counts are consistent with the distances to the optical star clusters. The Serpens Molecular Cloud is seen in the foreground of the Aquila Rift, dark clouds located at 600–700 pc, and behind patchy extinction, here called the Serpens Cirrus, located at ∼250 pc. Based on the lack of a distributed population of older stars, the star formation rate throughout the Serpens Molecular Cloud increased by at least a factor of 20 within the past ∼5 Myr. The optically bright stars in Serpens Northeast are visible because their natal molecular cloud has been eroded, not because they were flung outwards from a central factory of star formation. The separation between subclusters of 20–100 pc and the absence of an older population together lead to speculation that an external forcing was needed to trigger the active star formation.
Twist-bend coupling and the statistical mechanics of DNA: perturbation theory and beyond

The simplest model of DNA mechanics describes the double helix as a continuous rod with twist and bend elasticity. Recent work has discussed the relevance of a little-studied coupling G between twisting and bending, known to arise from the groove asymmetry of the DNA double helix. Here, the effect of G on the statistical mechanics of long DNA molecules subject to applied forces and torques is investigated. We present a perturbative calculation of the effective torsional stiffness Ceff for small twist-bend coupling. We find that the “bare” G is “screened” by thermal fluctuations, in the sense that the low-force, long-molecule effective free energy is that of a model with G = 0, but with long-wavelength bending and twisting rigidities that are shifted by G-dependent amounts. Using results for torsional and bending rigidities for freely-fluctuating DNA, we show how our perturbative results can be extended to a nonperturbative regime. These results are in excellent agreement with numerical calculations for Monte Carlo “triad” and molecular dynamics “oxDNA” models, characterized by different degrees of coarse-graining, validating the perturbative and non-perturbative analyses. While our theory is in generally-good quantitative agreement with experiment, the predicted torsional stiffness does systematically deviate from experimental data, suggesting that there are as-yet-uncharacterized aspects of DNA twisting-stretching mechanics relevant to low-force, long-molecule mechanical response, which are not captured by widely-used coarse-grained models.
Photocatalytic Degradation of Sulfolane Using a LED-Based Photocatalytic Treatment System

Sulfolane is an emerging industrial pollutant detected in the environments near many oil and gas plants in North America. So far, numerous advanced oxidation processes have been investigated to treat sulfolane in aqueous media. However, there is only a few papers that discuss the degradation of sulfolane using photocatalysis. In this study, photocatalytic degradation of sulfolane using titanium dioxide (TiO2) and reduced graphene oxide TiO2 composite (RGO-TiO2) in a light-emitting diode (LED) photoreactor was investigated. The impact of different waters (ultrapure water, tap water, and groundwater) and type of irradiation (UVA-LED and mercury lamp) on photocatalytic degradation of sulfolane were also studied. In addition, a reusability test was conducted for the photocatalyst to examine the degradation of sulfolane in three consecutive cycles with new batches of sulfolane-contaminated water. The results show that LED-based photocatalysis was effective in degrading sulfolane in waters even after three photocatalytic cycles. UVA-LEDs displayed more efficient use of photon energy when compared with the mercury lamps as they have a narrow emission spectrum coinciding with the absorption of TiO2. The combination of UVA-LED and TiO2 yielded better performance than UVA-LED and RGO-TiO2 for the degradation of sulfolane. Much lower sulfolane degradation rates were observed in tap water and groundwater than ultrapure water.
Organizing Robotic Process Automation: Balancing Loose and Tight Coupling

Robotic Process Automation (RPA) is penetrating organizations at an accelerating rate. This trend is challenging the existing IT governance structures, because RPA usually is acquired and implemented by local business units, outside the control of the IT function. Consequently, how to organize and govern RPA initiatives is a topical issue. The recommendations from prior research are unclear, and there is a call for more research on this area. In this paper, we report from a study on RPA usage in three firms. In particular, we investigate the organizational consequences of having local business units manage the RPA initiatives. We make use of lightweight IT research as our analytical lens, contributing to research by unveiling the consequences and considerations of decentralized management of RPA.
Surface Functionalization of PLGA Nanoparticles to Increase Transport across the BBB for Alzheimer’s Disease

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder that accounts for about 60% of all diagnosed cases of dementia worldwide. Although there are currently several drugs marketed for its treatment, none are capable of slowing down or stopping the progression of AD. The role of the blood-brain barrier (BBB) plays a key role in the design of a successful treatment for this neurodegenerative disease. Nanosized particles have been proposed as suitable drug delivery systems to overcome BBB with the purpose of increasing bioavailability of drugs in the brain. Biodegradable poly (lactic-co-glycolic acid) nanoparticles (PLGA-NPs) have been particularly regarded as promising drug delivery systems as they can be surface-tailored with functionalized molecules for site-specific targeting. In this review, a thorough discussion about the most recent functionalization strategies based on PLGA-NPs for AD and their mechanisms of action is provided, together with a description of AD pathogenesis and the role of the BBB in brain targeting.
Diamond-Like Carbon (DLC) Coatings: Classification, Properties, and Applications

DLC coatings have attracted an enormous amount of interest for science and engineering applications. DLC occurs in several different kinds of amorphous carbon materials. Owing to the extensive diversity in their properties, DLC coatings find applications in mechanical, civil, aerospace, automobile, biomedical, marine, and several other manufacturing industries. The coating life of DLC is predominately influenced by its constituent elements and manufacturing techniques. Numerous researchers have performed multiple experiments to achieve a robust understanding of DLC coatings and their inherent capabilities to enhance the life of components. In this review, a wide range of DLC coatings and their classification, properties, and applications are presented. Their remarkable performance in various applications has made DLC coatings a promising alternative over traditional solitary-coating approaches.
On the classification of rational K-matrices

This paper presents a derivation of the possible residual symmetries of rational K-matrices which are invertible in the ‘classical limit’ (the spectral parameter goes to infinity). This derivation uses only the boundary Yang–Baxter equation and the asymptotic expansions of the R-matrices. The result proves the previous assumption of the literature: if the original and the residual symmetry algebras are and then there exists a Lie-algebra involution of for which the invariant sub-algebra is . In addition, we study a K-matrix which is not invertible in the ‘classical limit’. It is shown that its symmetry algebra is not reductive but a semi-direct sum of reductive and solvable Lie-algebras.