Miracle-dz/UniFAR-FTUs huggingface dataset:
Business Workflow Optimization Through Process Model Redesign

Business process redesign has garnered much interest because such processes must be continuously improved to realize greater efficiency in the midst of evolving changes in work practices, environment, and technology. This helps a business to stay competitive. A process model may be redesigned by a rearrangement of various activities as per best practices to satisfy predefined business rules and constraints for a specific objective. This objective is stated in terms of functional goals like desired or acceptable process behavior, and nonfunctional goals such as cost, time, quality of service, and flexibility. Thus, many potential improvement possibilities that emerge through restructuring can be explored. When many such alternatives exist, there is a need for a formal methodology for evaluating these designs on various metrics to gain a deeper understanding of the tradeoffs involved. In this article, we describe a novel, formal methodology for optimizing the cost-benefit considerations within a space of possible redesigns to develop a cost vs. time efficient frontier. We also illustrate our approach with two case studies, one based on a large, real dataset, and describe a proposal for implementing it. Several managerial insights emerge from our work.
STICKER-T: An Energy-Efficient Neural Network Processor Using Block-Circulant Algorithm and Unified Frequency-Domain Acceleration

The emerging edge intelligence requires low-cost energy-efficient neural network (NN) processors. Supporting various types of edge NN models leads to extra circuit overhead. Designing a unified NN processor with high energy/area efficiency is challenging. This work presents a frequency-domain-accelerated unified NN processor, named STICKER-T. It combines algorithm, architecture, and circuit-level optimization to achieve high energy/area efficiency. By utilizing the block-circulant NN (CirCNN) algorithm, this work supports frequency-domain acceleration and a unified workflow for convolutional, fully connected, and recurrent NN (CNN/FC/RNN). Three key innovations are proposed. First, a block-circulant-accelerated chip architecture is implemented to support unified CNN/FC/RNN workflow. Second, a multi-bit 8-128-point global-parallel local-bit-serial fast Fourier transform (FFT) module is designed for efficient high-throughput FFT/inverse FFT (IFFT) operation. Third, by utilizing a 6T hierarchical-bitline-switching transpose-SRAM (HBST-TRAM), 2-D data reuse is enabled in the proposed multi-bit frequency-domain multiply–accumulate (MAC) array. STICKER-T was fabricated in a 65-nm CMOS technology. It can operate at 0.54–1.15 V and 25–200 MHz with 13.3–339-mW power consumption. The peak energy efficiency achieves 140.3 TOPS/W. It shows 8.1 $\times $ area efficiency and 4.2 $\times $ energy efficiency at 4-bit precision compared with the state-of-the-art reconfigurable NN processor.
Comparison of demineralization around orthodontic brackets cured by conventional method and transillumination technique-an in vitro evaluation

OBJECTIVE: To compare demineralization around orthodontic brackets cured by conventional method and transillumination method. MATERIALS AND METHOD: Sixty freshly extracted human premolar teeth were divided into four groups. Group 1: Brackets bonded with conventional method of bonding by curing labially for 40 sec. Group 2: Brackets bonded with transillumination method of bonding for 50 sec. Group 3: Brackets bonded with conventional method of bonding by curing labially for 20 sec followed by 30 sec of transillumination. Group 4: Brackets bonded with transillumination method of bonding for 30 sec followed by labial curing for 20 sec. Ground sections were prepared of each tooth and microleakage was evaluated using a binocular microscope at 40× magnification (Olympus BX53) and an image was taken using a digital camera (Olympus EPL3) connected to the microscope. The images were analyzed using Magnus Pro Image software. Scores were assigned to different degrees of microleakage at the demineralization zone around enamel-adhesive-bracket complex at the occlusal, middle, and gingival margins using linear measurement tool. Data obtained was subjected to statistical analysis using SPSS software (Version 20.0). Level of significance was kept at 5%. Intragroup comparison was done using Kruskal-Wallis test followed by Mann-Whitney U-tests for pairwise comparison. RESULTS: Group 4 showed least mean demineralization in occlusal, middle, and cervical areas as compared to other groups and the results were statistically significant (P < 0.05). CONCLUSION: Transillumination can be employed as a method synergistically with conventional curing to achieve minimum amount of demineralization during fixed orthodontic treatment.
A New Convolutional Neural Network-Based System for NILM Applications

Electrical load planning and demand response programs are often based on the analysis of individual load-level measurements obtained from houses or buildings. The identification of individual appliances’ power consumption is essential, since it allows improvements, which can reduce the appliances’ power consumption. In this article, the problem of identifying the electrical loads connected to a house, starting from the total electric current measurement, is investigated. The proposed system is capable of extracting the energy demand of each individual device using a nonintrusive load monitoring (NILM) technique. An NILM algorithm based on a convolutional neural network is proposed. The proposed algorithm allows simultaneous detection and classification of events without having to perform double processing. As a result, the calculation times can be reduced. Another important advantage is that only the acquisition of current is required. The proposed measurement system is also described in this article. Measurements are conducted using a test system, which is capable of generating the electrical loads found on a typical house. The most important experimental results are also included and discussed in the article.
Audit Environment and Internal Audit Effectiveness as Antidote to Risk Management Within State-Owned Enterprises for Emerging Markets

Global demands for improved governance practices have brought about tremendous changes in internal audit function. This study examines the factors associated with internal audit effectiveness playing a pivotal role in addressing the challenges facing the economic and productive state-owned enterprises in Namibia. Using a sample of selected diverse economic and productive state-owned enterprises, a quantitative approach was employed to conduct the research and data analysed via the variance based structural equation modeling, and the results suggest that internal audit effectiveness could positively impact on risk management practices of the economic and productive state-owned enterprises given a conducive internal audit environment. This study will augment the understanding of IA practices in the country and contribute to knowledge enhancement on the continent. The recommendations, therefore, have implications for policymakers, regulators and researchers in assessing the effectiveness of internal audit function in advancing the governance practices of the economic and productive state-owned enterprises.
Effect of Delivery Devices on Nasal Deposition and Mucociliary Clearance in Rhesus Monkeys

Gamma camera scintigraphy studies were carried out to determine the differences in deposition pattern and clearance with the use of four nasal delivery methods: a compressed air nebulizer, a nasal pump sprayer, drops from an instillation catheter, and a dry powder insufflator. Six rhesus monkeys were studied while anesthetized with ketamine. 99m Tc-labeled sulfur colloid particles were used as the test markers and gamma camera scintigraphy was used to measure deposition patterns and clearance rates. Deposition patterns were qualitatively similar for all four devices despite the fact that particle size distributions differed considerably. Clearance, when measured 30 min after initial deposition, was also similar for all four devices with a mean retention value (±SE) for all four methods of 30 ± 3%. Choice of device in these controlled laboratory studies did not markedly affect nasal deposition or clearance in monkeys. The compressed air nebulizer would be the device of choice for delivery of therapeutic ma.
FEM research of internal stresses evolution in the prestressing strand production

The article presents the finite-element modeling results of stranding, reduction, straightening and mechanicalthermal treatment (MTT) of prestressing strand (PSC strand). Computer models took into account the distribution of residual stresses formed at the preliminary stage of wire drawing. During the simulation, the effect of MTT on the internal stresses of the wires was studied: residual stresses after the drawing process and additional stresses after the stranding. All studied methods demonstrated a positive effect not only from the point of eliminating internal stresses, but also from the point of view of their favorable redistribution. The reduction of PSC strand in a monolith tool with strain degree 1-3% allows to minimize tensile stresses on the surface of the wires and save compressive stresses in the center of the wires. The straightening by a group of 5 rolls made it possible to reduce the tension of outer wires twice. The MTT application, combining the effects of various physical nature, made it possible to control over a wide range the redistribution of residual stresses in the wires after high strain degrees and the additional stresses created during the stranding, which affect the preservation of the geometric parameters of the PSC strand. The study showed that to eliminate longitudinal residual stresses, the magnitude of the tension in the MTT is the most important parameter. Therefore, with a minimum value of tension, the residual stresses are not redistributed either in the central or in the surface layers, regardless of the temperature of the MTT. However, with tension above 70 kN and a temperature of 380-400°C, the residual stresses in the center and on the surface of the wires are balanced or almost completely eliminated.
A Khasminskii Type Averaging Principle for Non-autonomous Slow-fast Stochastic Differential Equations and an Application to a Local Stochastic Volatility Model

In this work we study the averaging principle for non-autonomous slow-fast systems of stochastic differential equations. In particular in the first part we generalize Khasminskii's averaging principle to the non-autonomous case, assuming the sublinearity, the Lipschitzianity and the Holder's continuity in time of the coefficients, an ergodic hypothesis and an $\mathcal{L}^2$-bound of the fast component. In this setting we prove the weak convergence of the slow component to the solution of the averaged equation. Moreover we provide a suitable dissipativity condition under which the ergodic hypothesis and the $\mathcal{L}^2$-bound of the fast component, which are implicit conditions, are satisfied. In the second part we propose a financial application of this result: we apply the theory developed to a slow-fast local stochastic volatility model. First we prove the weak convergence of the model to a local volatility one. Then under a risk neutral measure we show that the prices of the derivatives, possibly path-dependent, converge to the ones calculated using the limit model.
Eco-friendly dyeing of raw cotton fibres in an ethanol–water mixture without scouring and bleaching pretreatments

Raw cotton fibres (RCFs) are generally scoured and bleached to remove impurities before the dyeing process, which consumes large amounts of chemicals and water. Therefore, the development of an eco-friendly dyeing technology for RCFs that avoids the need for scouring and bleaching pretreatments is highly important for both the economic and ecological reasons. Herein, we report on a green approach for industrial-scale dyeing involving salt-free coloration of RCFs using an eco-friendly ethanol (EtOH)–water (H2O) mixture. The method involved initially allowing the RCFs to swell in the EtOH–H2O mixture followed by dyeing in another EtOH–H2O mixture with 95% of ethanol by volume. The results showed that the swelling solution could be reused for at least five times, and the investigated dyes exhibited superior exhaustion and total fixation levels, indicating the high rate of dye utilization and low levels of effluent discharge. The research elucidated in detail the swelling and dyeing mechanisms of RCFs in relation to the EtOH–H2O mixture. Additionally, the industrial-scale dyeing of RCFs revealed the successful establishment and verification of the proposed coloration technology. Meanwhile, we designed and established an automatically controllable EtOH recovery system for the reuse of EtOH from the EtOH–H2O mixture, which accelerates its industrialization. Compared with the conventional dyeing method for RCFs, this eco-friendly approach consumes fewer chemicals and energy. Therefore, the eco-friendly dyeing of RCFs in the EtOH–H2O mixture without scouring and bleaching pretreatments is a promising approach for protecting the environment by reducing effluent discharge.
Semiclassical solutions for critical Schr\"odinger-Poisson systems involving multiple competing potentials

In this paper, a class of Schrodinger-Poisson system involving multiple competing potentials and critical Sobolev exponent is considered. Such a problem cannot be studied with the same argument of the nonlinear term with only a positive potential, because the weight potentials set $\{Q_i(x)|1\le i \le m\}$ contains nonpositive, sign-changing, and nonnegative elements. By introducing the ground energy function and subtle analysis, we first prove the existence of ground state solution $v_\varepsilon$ in the semiclassical limit via the Nehari manifold and concentration-compactness principle. Then we show that $v_\varepsilon$ converges to the ground state solution of the associated limiting problem and concentrates at a concrete set characterized by the potentials. At the same time, some properties for the ground state solution are also studied. Moreover, a sufficient condition for the nonexistence of the ground state solution is obtained.
Essential self-adjointness and the $L^2$-Liouville property

We discuss connections between the essential self-adjointness of a symmetric operator and the constancy of functions which are in the kernel of the adjoint of the operator. We then illustrate this relationship in the case of Laplacians on both manifolds and graphs. Furthermore, we discuss the Green's function and when it gives a non-constant harmonic function which is square integrable.
Continuous Positional Payoffs

What payoffs are positionally determined for deterministic two-player antagonistic games on finite directed graphs? In this paper we study this question for payoffs that are continuous. The main reason why continuous positionally determined payoffs are interesting is that they include the multi-discounted payoffs. We show that for continuous payoffs, positional determinacy is equivalent to a simple property called prefix-monotonicity. We provide three proofs of it, using three major techniques of establishing positional determinacy -- inductive technique, fixed point technique and strategy improvement technique. A combination of these approaches provides us with better understanding of the structure of continuous positionally determined payoffs as well as with some algorithmic results.
Classification Algorithm Accuracy Improvement for Student Graduation Prediction Using Ensemble Model

According to National Center for Education Statistics, almost half of the first-time freshmen full time students who began seeking a bachelor’s degree do not graduate. The imbalance between the student enrolment and student graduation can be solved by early predicting and identifying students who are prone of not having graduation on time, so proper remediation and retention policies can be formulated and implemented by institutions. The study focused on the application of the ensemble models in predicting student graduation. Ensemble modeling is the process of running two or more related but different analytical models and then synthesizing the results into a single score or spread in order to improve the accuracy of predictive analytics and data mining applications. The study recorded an increase of classification accuracy in predicting student graduation using ensemble models and combining multiple algorithms.
No Budget? Don't Flex! Cost Consideration when Planning to Adopt NLP for Your Business

Recent advances in Natural Language Processing (NLP) have largely pushed deep transformer-based models as the go-to state-of-the-art technique without much regard to the production and utilization cost. Companies planning to adopt these methods into their business face difficulties because of the lack of machine and human resources to build them. In this work, we compare both the performance and the cost of classical learning algorithms to the latest ones in common sequence and text labeling tasks. We find that classical models often perform on par with deep neural ones despite the lower cost. We argue that under many circumstances the smaller and lighter models fit better for AI-pivoting businesses and that we call for more research into low-cost models, especially for under-resourced languages.
Task Offloading for Large-Scale Asynchronous Mobile Edge Computing: An Index Policy Approach

Mobile-edge computing (MEC) offloads computational tasks from wireless devices to network edge, and enables real-time information transmission and computing. Most existing work concerns a small-scale synchronous MEC system. In this paper, we focus on a large-scale asynchronous MEC system with random task arrivals, distinct workloads, and diverse deadlines. We formulate the offloading policy design as a restless multi-armed bandit (RMAB) to maximize the total discounted reward over the time horizon. However, the formulated RMAB is related to a PSPACE-hard sequential decision-making problem, which is intractable. To address this issue, by exploiting the Whittle index (WI) theory, we rigorously establish the WI indexability and derive a scalable closed-form solution. Consequently, in our WI policy, each user only needs to calculate its WI and report it to the BS, and the users with the highest indices are selected for task offloading. Furthermore, when the task completion ratio becomes the focus, the shorter slack time less remaining workload (STLW) priority rule is introduced into the WI policy for performance improvement. When the knowledge of user offloading energy consumption is not available prior to the offloading, we develop Bayesian learning-enabled WI policies, including maximum likelihood estimation, Bayesian learning with conjugate prior, and prior-swapping techniques. Simulation results show that the proposed policies significantly outperform the other existing policies.
Online Learning Demands in Max-min Fairness

We describe mechanisms for the allocation of a scarce resource among multiple users in a way that is efficient, fair, and strategy-proof, but when users do not know their resource requirements. The mechanism is repeated for multiple rounds and a user's requirements can change on each round. At the end of each round, users provide feedback about the allocation they received, enabling the mechanism to learn user preferences over time. Such situations are common in the shared usage of a compute cluster among many users in an organisation, where all teams may not precisely know the amount of resources needed to execute their jobs. By understating their requirements, users will receive less than they need and consequently not achieve their goals. By overstating them, they may siphon away precious resources that could be useful to others in the organisation. We formalise this task of online learning in fair division via notions of efficiency, fairness, and strategy-proofness applicable to this setting, and study this problem under three types of feedback: when the users' observations are deterministic, when they are stochastic and follow a parametric model, and when they are stochastic and nonparametric. We derive mechanisms inspired by the classical max-min fairness procedure that achieve these requisites, and quantify the extent to which they are achieved via asymptotic rates. We corroborate these insights with an experimental evaluation on synthetic problems and a web-serving task.
Session-based k-NNs with Semantic Suggestions for Next-item Prediction

One of the most critical problems in e-commerce domain is the information overload problem. Usually, an enormous number of products is offered to a user. The characteristics of this domain force researchers to opt for session-based recommendation methods, from which nearest-neighbors-based (SkNN) approaches have been shown to be competitive with and even outperform neural network-based models. Existing SkNN approaches, however, lack the ability to detect sudden interest changes at a micro-level, i.e., during an individual session; and to adapt their recommendations to these changes. In this paper, we propose a conceptual (cSkNN) model extension for the next-item prediction allowing better adaptation to the interest changes via the semantic-level properties. We use an NLP technique to parse salient concepts from the product titles to create linguistically based product generalizations that are used for change detection and a recommendation list post-filtering. We conducted experiments with two versions of our extension that differ in semantics derivation procedure while both showing an improvement over the existing SkNN method on a sparse fashion e-commerce dataset.
Copyspace: Where to Write on Images?

The placement of text over an image is an important part of producing high-quality visual designs. Automating this work by determining appropriate position, orientation, and style for textual elements requires understanding the contents of the background image. We refer to the search for aesthetic parameters of text rendered over images as "copyspace detection", noting that this task is distinct from foreground-background separation. We have developed solutions using one and two stage object detection methodologies trained on an expertly labeled data. This workshop will examine such algorithms for copyspace detection and demonstrate their application in generative design models and pipelines such as Einstein Designer.