Miracle-dz/UniFAR-FTUs huggingface dataset:
Aggregating classifiers with ordinal response structure

In recent years, the introduction of aggregation methods led to many new techniques within the field of prediction and classification. The most important developments, bagging and boosting, have been extensively analyzed for two- and multiclass problems. While the proposed methods treat the class indicator as a nominal response without any structure, in many applications the class may be considered as an ordered categorical variable. In this article, variants of bagging and boosting are proposed, which make use of the ordinal structure. It is demonstrated how the predictive power is improved by the use of appropriate aggregation methods. Comparisons between the methods are based on misclassification rates as well as criteria that take ordinality into account, like absolute or squared distance measures.
A Risk Assessment Methodology for Supply Chain Tracking Services

The supply chain (SC) encompasses various assets and stakeholders covering the production, storage, transportation, and sales processes. SC tracking services are essential for maintaining and verifying the proper state of transferred goods by gathering, exchanging, and maintaining state information such as the temperature or humidity level of products throughout their supply chain lifecycle. Supply chain tracking involves various stakeholders and systems like IoT, cloud services, and typical IT systems, and it is subject to various security threats and risks. In this paper, we propose a risk assessment methodology specifically focusing on supply chain tracking services. The proposed methodology relies on eliciting the strategic business and technical requirements of SC tracking systems, the assessment of targeted threat agents and threats, and their mapping to key operational components. To demonstrate the applicability and effectiveness of the proposed methodology, a risk assessment was performed based on functional and security requirements in a real-case scenario. Finally, we discuss various challenges relevant to the development and implementation of smart SC tracking systems, as well as the disadvantages associated with their costs and the subsequent stages in the SC ecosystem.
Optimization of Distribution Network Considering Distributed Renewable Energy and V2G of Electric Vehicles

With the increasing number of electric vehicles (EVs), the connection of large-scale electric vehicles will affect the stability of the power system. Based on the analysis of electric vehicles travel characteristics, this paper proposes an optimal scheduling model for vehicle to grid (V2G) with the cost of power system operation as the objective function. Then, simulation calculations are carried out through the IEEE 33-node distribution network by MATLAB. Through the results, the scheduling model for vehicles to grid attaches great significance to reducing system cost, flattening the load curve and reducing the wind power curtailment. It is concluded that the optimization of distribution network is beneficial to improve the reliability of the power grid.
SMC for Discrete-Time Nonlinear Semi-Markovian Switching Systems With Partly Unknown Semi-Markov Kernel

This article is devoted to the discrete-time sliding mode control (DSMC) for nonlinear semi-Markovian switching systems (S-MSSs). Motivated by the fact that the complete information of the semi-Markov Kernel is difficult to be obtained in practical applications, it is recognized to be partly unknown as the most common mean. By utilizing the prior information of the sojourn-time upper bound for each switching mode, sufficient conditions under the equivalent DSMC law are proposed for the mean square stability. Moreover, the designed DSMC law realizes the finite-time reachability of the sliding region, and makes the sliding dynamics converge to the predesignated sliding region in a finite time. In the end, a numerical example and an electronic throttle model are given to validate the proposed control strategy.
TripAdvisor Restaurant Customer Reviews Analysis in Bangkok, Thailand

There are currently a plethora of customer reviews online through websites such as TripAdvisor that can be utilized to satisfy customer needs. This study examined TripAdvisor restaurant reviews in Bangkok during 2007-2021 using the VADER model. Customer reviews were classified into three categories: positive, neutral, and negative. A total of 5,728 restaurants and 105,205 reviews were found from TripAdvisor during the study period. In 2019, it obtained the most reviews with 17,969 while 2016 recorded the most negative reviews. Meanwhile, the COVID19 pandemic has caused a significant decline in the number of reviews. The findings showed that both quantitative and qualitative dimensions of consumer feedbacks on restaurants could be used to improve the restaurant business and adapt to changes in customer preferences.
Review of Data Mining Techniques in Performance Prediction for Medical Schools

Decision-makers in the higher education sector are very concerned with predicting attrition, which has an impact on how Medical Schools select the most qualified candidates for admission. The purpose of the study is to review the literature on Data Mining (DM) techniques to minimize attrition rates in Medical Schools by enhancing the capabilities of the selection process. The major finding of this research is that applicants for medical school could be accepted using DM techniques, which offer an acceptable level of prediction accuracy. By doing so, attrition and its detrimental effects on educational institutions and students would be avoided.
A Product Review-Based Case Study Approach with Influencer Youth in an Urban Indonesian Context

Instagram users with very active engagement in the online environment conduct their everyday lives under the influences of their social media timelines. The aim of this study was to explore the business application of Instagram to promote products and services, with the conduct of commercial product reviews. Based on qualitative methodology involving a young Indonesian Instagrammer from January 2021 to June 2022, this article shows how product reviews became part of Instagram users’ and influencers’ everyday lives and the prioritization of time and resources for basic activities. The article argues that the products showcased in the product review and endorsement should fit the niche which the influencers interact with.
On a Theorem of Childs on Normal Bases of Rings of Integers: Addendum

Let p ⩾ 3 be a prime number, F be a number field with ζp ∉ F×, and K = F(ζp). In a previous paper, the author proved, under some assumption on p and F, that an unramified cyclic extension N/F of degree p has a normal integral basis if and only if the pushed‐up extension NK/K has a normal integral basis. This addendum shows that the assertion holds without the above‐mentioned assumption.
Research on Multi-port AC/DC Distribution Network Power Flow Control Strategy Based on V2G Time-Sharing Tariff for Electric Vehicles

In this paper, a two-layer control strategy is proposed to realize the power flow control between Electric Vehicles (EVs) and a multi-port hybrid AC/DC distribution network In the lower layer control scheme, an adaptive droop control can dynamically change the power supplied by the AC/DC converters according to the transformers’ remaining capacity to avoid the transformer overload. In the upper layer control scheme, a Vehicle to Grid (V2G) control based on a time-sharing tariff strategy is proposed considering the economical benefits of both grid operators and EV users at the same time. The proposed strategy can realize the power flow control between the EVs and multiple distribution transformers through the common DC bus to greatly alleviate the stress of distribution grid and increase revenue for EV users. The benefits of the proposed strategy are verified by the simulation.
MSCP Based Stator Fault Identification in Induction Motor Using Power Quality Analyzer

Most of the machines are driven by Induction motor nowadays. Induction motor gets failure due to various reasons. This fault mostly occurs in the stator. By measuring the current of a motor and comparing it to a fixed value, any fault can be detected. Different kind of faults exhibits different types of electrical current profile. The nature of this current is measured by a Power Quality Analyzer and converted into waveforms and spectrums. By looking closely at these three-phase current readings, one can predict when a machine is about to fail. Motor Stator Current Profile (MSCP) based method is proposed to identify the different stator faults.
Divide and Distill: New Outlooks on Knowledge Distillation for Environmental Sound Classification

Environmental sound classification (ESC) is an important research problem with a broad range of applications including audio-based surveillance, audio-visual systems, smart homes, and robotics, among others. The recently proposed vision multi-layer perceptron-mixer (MLP-mixer) has outperformed traditional deep models (CNN or ResNet) and attained new state-of-the-art performances for several computer vision applications (image/video classification and image segmentation). Following the success of MLP-mixer, in this paper, we propose a novel audio MLP-mixer (AMM) network that classifies the different types of environmental sounds. Despite the higher performance, the high computational cost (number of trainable parameters and floating point operations) prohibits deployment of the AMM model on edge for designing real-life applications. To alleviate the aforementioned issue, in this work, we present three different knowledge distillation (KD) strategies to train a compact deep network for ESC. The proposed strategies divide the input Mel-spectrogram into patches and a lightweight deep ESC model is trained in the presence of three teacher networks under the offline KD training framework. Additionally, we have designed two novel loss functions for KD that are free from a temperature parameter that need to be set manually by a user as in the case of the traditional vanilla KD technique. We conducted our experiments on three benchmark ESC datasets namely ESC-10, Urbansound8k (US8K), and DCASE-2019 Task-1(A). The obtained results demonstrate the significance of utilization of proposed methods over other existing KD methods in terms of classification accuracy.
Deep Natural Language Processing in unstructured big data analysis and insights extraction - A quantitative study

In today’s digital age, businesses create tremendous data as part of their regular operations. On legacy or cloud platforms, this data is stored mainly in structured, semi-structured, and unstructured formats, and most of the data kept in the cloud are amorphous, containing sensitive information. With the evolution of AI, organizations are using deep learning and natural language processing to extract the meaning of these big data through unstructured data analysis and insights (UDAI). This study aims to investigate the influence of these unstructured big data analyses and insights on the organization’s decision-making system (DMS), financial sustainability, customer lifetime value (CLV), and organization’s long-term growth prospects while encouraging a culture of self-service analytics. This study uses a validated survey instrument to collect the responses from Fortune-500 organizations to find the adaptability and influence of UDAI in current data-driven decision making and how it impacts organizational DMS, financial sustainability and CLV.
CIMAX-Compiler: An End-to-End ANN Compiler for Heterogeneous Computing-in-Memory Platform

As artificial neural network (ANN) being widely adopted in edge applications, such as voice recognition, and face detection, etc. Computing-In-Memory (CIM) accelerators have received much attention because they are faster and more energy efficient. However, existing compilers are mainly applicable for traditional backend devices rather than emerging hardware architectures like CIM accelerator. In this paper, we propose an end-to-end neural network compiler, named CIMAX-Compiler. It can convert an ANN model in Open Neural Network Exchange (ONNX) format to executable codes, which runs on a heterogeneous embedded system composed of an MCU and a CIM accelerator. CIMAX-Compiler greatly reduces model deployment effort from tens of engineer-hours to less than a second. In addition, we applied several optimization techniques such as operator fusion and convolution compression to further improve compiled code performance. Experimental results show that the optimized code can speed up ANN model inference performance by more than 2× compared with the base-line layer to layer implementation.
High-Speed and Pinpoint Liquid Exchange on Microfluidic Chip using 3d Printed Double-Barreled Microprobe with Dual Pumps

We propose a method to exchange the ultra-small volume of liquid on the microfluidic chip in high-speed by using a 3D printed double-barreled microprobe with dual pumps. The probe tip is designed double-barreled and assembled with two piezoelectric stacks to realize the simultaneous liquid injection and suction. We evaluated the performances of the developed liquid exchange system. The results showed that high-speed and pinpoint liquid exchange can be achieved within 10 ms. The highest flow speed is approximately 18 mm/s. The sensitivity and resolution are 22 pL/v and 0.54 pL respectively. This system will contribute to revealing the detailed cell deformation process under rapid osmolarity change, and characterizing the physiological function of mechanosensitive channels without destroying cells.
A Three-Dimensional Artificial Intestinal Tissue With A Crypt-Like Inner Surface

Disruption of the microbiome in humans caused several diseases. To infer causality, the intestinal models are essential, but the commonly used model has still room for improvements such as biomimicry, productivity, and ability to co-culture with bacteria. This paper describes the artificial intestinal tissue by culturing intestinal cells (Caco-2) in the collagen gel tube with crypt-like cavities created by electrolysis-triggered microbubbles. This tube device has high biomimicry and productivity and also consists of glass/silicone tubes connector at both ends, which allows for connecting external pumps easily and running two types of flows: the inside of the tube (internal flow) and permeation from the outside of the tube to the inside (external flow). In order to show this high extensibility, we demonstrated the co-culture of Caco-2 cells and bacteria (Bifidobacteria). We believe that our device will be a platform for investigating complex intestinal diseases due to its high biomimetic properties.
On the $p$-adic cohomology of some $p$-adically uniformized varieties

Let $K$ be a finite extension of ${\mathbb Q}_p$ and let $X$ be Drinfel'd's symmetric space of dimension $d$ over $K$. Let $\Gamma\subset {\rm SL}_{d+1}(K)$ be a cocompact discrete (torsionfree) subgroup and let ${{X}}_{\Gamma}=\Gamma\backslash {X}$, a smooth projective ${{K}}$-variety. In this paper we investigate the de Rham and log crystalline (log convergent) cohomology of local systems on $X_{\Gamma}$ arising from $K[\Gamma]$-modules. (I) We prove the monodromy weight conjecture in this context. To do so we work out, for a general strictly semistable proper scheme of pure relative dimension $d$ over a cdvr of mixed characteristic, a rigid analytic description of the $d$-fold iterate of the monodromy operator acting on de Rham cohomology. (II) In cases of arithmetical interest we prove the (weak) admissibility of this cohomology (as a filtered $(\phi,N)$-module) and the degeneration of the relevant Hodge spectral sequence.
Drought Stress Segmentation on Drone captured Maize using Ensemble U-Net framework

Water is essential for any crop production. Lack of sufficient supply of water supply causes abiotic stress in crops. Accurate identification of the crops affected by drought is required for achieving sustainable agricultural yield. The image data plays a crucial role in studying the crop's response. Recent developments in aerial-based imaging methods allow us to capture RGB maize data by integrating an RGB camera with the drone. In this work, we propose a pipeline to collect data rapidly, pre-process the data and apply deep learning based models to segment drought affected/stressed and unaffected/healthy RGB maize crop grown in controlled water conditions. We develop an ensemble-based framework based on U-Net and U-Net++ architectures for the drought stress segmentation task. The ensemble framework is based on the stacking approach by averaging the predictions of fine-tuned U-Net and U-Net++ models to generate the output mask. The experimental results showed that the ensemble framework performed better than individual U-Net and U-Net++ models on the test set with a mean IoU of 0.71 and a dice coefficient of 0.74.
A New Finding on Nonlinear Damping and Stiffness of Flexural Mode Capacitive MEMS Resonators

This work reports a new resonant phenomenon of flexural mode capacitive MEMS resonators induced by nonlinear damping and nonlinear stiffness. The measured frequency response clearly demonstrates a variety of interesting resonant behaviors of a capacitive resonator when operated from its linear into nonlinear regime. As the input ac voltage increases (i.e., drive level dependency), the resonant frequency shifts with 6000 ppm while transmission magnitude also varies greater than 200%. Such strong nonlinear phenomenon mainly comes from the flexural mode capacitive resonator featuring excellent electromechanical coupling strength contributed by its narrow transduction gap spacing and large transduction area [1]. A behavior model for predicting nonlinear electrostatic force is built based on the measured linear response under different DC bias. By comparing the simulated result from the behavior model and the actual measurement data, both nonlinear stiffness term and nonlinear damping term can be successfully extracted.
A Systematic Review on Personal Route Prediction Techniques based on Trajectory data

With the advancement of location-acquisition and mobile computing devices in today’s life, the trajectory data obtained from Global Positioning System (GPS) is becoming large which makes route prediction a very challenging task. The route prediction can be done in two modes-offline route prediction and online route prediction in terms of acquiring performance and accuracy. There are many different techniques of route prediction which mainly consists of three consecutive tasks- i) Route abstraction ii) Frequent route pattern mining iii) Route Prediction applied in the context of different application domains. Route prediction plays an important role in many location-based applications including vehicular ad-hoc networks, route navigation, traffic control and congestion estimation, Place recommendation and many more. Trajectory data have different useful information hiding into it like background data and contextual information. So, Privacy is a big concern while sharing this personal trajectory data with the server predicting the best optimal route. Many techniques have been proposed by researchers for ensuring privacy of user data along with maintaining availability and usability of the data. Rather than doing all pre-processing computations on a single machine, many algorithms have been proposed to make system scalable. In this paper, a comparative analysis of various existing techniques of route prediction along with privacy preservation and scalability issues is discussed with pros and cons. The main aim of this paper is to give insights of the different algorithms of the route prediction with the directions of future research.
Effective Diffusion Coefficient in Porous Media

Recent calculations by Prager of upper bounds for the effective diffusion coefficient (or conductivity) in porous media, in terms of certain statistical parameters of the random geometry, are reformulated so as to apply specifically to a bed of spherical particles. The calculations are simplified by considering an idealized bed in which centers are randomly situated without restricting the spheres to nonoverlapping locations. The result, applicable to randomly overlapping spheres of either uniform or nonuniform sizes, gives the upper bound for the effective diffusion coefficient as φD0/[1−½lnφ], where D0 is the actual diffusion coefficient in a fluid which fills the void regions of the bed and φ is the void fraction. This result is compared with experimental results of various investigators for nonoverlapping spheres and also with Hashin and Shtrikman's expression for the best upper bound that can be calculated without taking the statistics of a particular random geometry into account.
Bespoke Weighting Schema and Sequence of Transformations for Enhanced Insight into Prospective False Command Injection Attacks

Malicious False Command Injection Attacks (FCIA) can induce power outages, particularly in high automation Smart Grids (SGs). Establishing defensive mitigation bulwarks against FCIA remains a challenging task. Perhaps even more daunting, particularly for those charged with mitigation, is the task of positing likely FCIA Attack Areas of Interest (AAOIs) (FCIA-AAOIopt). This paper introduces a bespoke Adaptive Weighting Schema (AWS) for the derivation of the involved Convex Constrained Optimization Problems (CCOPs), via a Sequence of Transformations (SOT), for better insight into the prospective FCIA-AAOIopt. An Advanced Persistent Threat (APT) Insider Threat Paradigm (ITP) was considered, and an Optimized Contextualization Schema or OCS — comprised of an enhanced Constriction Factor Particle Swarm Optimization-based Robust Convex Relaxation platform — is utilized for the examination. Given the highly significant and impactful arena of SG-related FCIA, the bespoke AWS-SOT-OCS construct should be meaningful, as its posits will have the tightest possible relaxations.
Distributed Interval Consensus of Multiagent Systems With a Pulse Width Modulation Protocol

Rather than the conventional pure consensus, this article proposes a pulsewidth modulation (PWM) protocol for an improved interval consensus of multiagent systems from the distributed perspective. In particular, the consensus of all the agents is achieved within a prescribed interval, which is merely available to partial agents, though. The PWM protocol effectively relieves the running burden of the agents without the additional analogue-to-digital conversion. The modulation periods of different agents are allowed to be heterogeneous. By nominating the agents that have access to the given interval as leaders and others as followers, the proposed distributed PWM protocol introduces a projection operator for the leaders in order for the collective aggregation. In terms of the graph connectivity condition of containing a spanning tree, it is shown that the concerned interval consensus objective is realized by the proposed distributed PWM protocol. Simulations are eventually performed to validate the established theoretical results.
Resonant Confiners for Acoustic Loss Mitigation in Bulk Acoustic Wave Resonators

This paper presents a new method to mitigate the lateral acoustic energy leakage and improve the parallel Q factor (Qp) of thickness-extensional BAW resonators. The proposed technique consists in surrounding the top electrode with concentric λ/2 metal rings (a.k.a. resonant confiners) that vibrate at the A0-mode present in the substrate outskirts at the parallel resonance frequency (fp). Experimental results from dual-Bragg acoustic resonators (DBARs) of different electrode shapes (circle, square and pentagon) and normalized area ratios (100%, 75%, 50% and 25%) show double-digit percentage boosts, validating our technique to improve Qp and setting a precedent for its implementation in other types of BAW technologies.