Miracle-dz/UniFAR-FTUs huggingface dataset:
Distribution-free and Risk-averse Disassembly Line Balancing Problem

With green and smart manufacturing emergence and development, effective conduct with End of Life (EOL) products is an extremely important and hot research topic. Disassembly line design is a fundamental and complex problem in EOL product recycling and remanufacturing, especially when considering uncertainty. In this paper, we investigate a disassembly line balancing problem (DLBP) with stochastic task processing time to minimize the cycle time. We focus on selecting alternative task processes, determining the number of open workstations and assigning operational tasks to the workstations. A joint chance-constrained programming is proposed and we adopt a distribution-free method, which is based on Markov Inequality, to approximate the chance constraints as the distribution is difficult to fully estimated. Risk-neutral model may provide solutions that perform poorly while considering uncertainty. Therefore, a risk-averse measure called Conditional Value-at-Risk (CVaR), which is rarely used in the DLBP, is considered in this paper to incorporate decision makers’ perspectives. To the best of our knowledge, we are the first to consider risk-averse model in DLBP with minimizing cycle time under uncertain task processing time. Finally, computational experiments are conducted and give the impacts by testing different risk levels and confidence levels.
A Simple Wireless ECG System for Education Practice

This article is focused on the design and the construction of the simple low-cost wireless ECG system for university education practices. The system is split into functional blocks performing particular functions. In separated sections, these blocks are described in detail. The paper presents analog circuit for acquisition and filtration of the ECG signal in detail. ATmega328p embedded in the Arduino Nano board is the primary component of the hardware dealing with signal digitalization. The wireless transmission of the digitalized signal is made by the HC-06 Bluetooth module. The end of the article deals with experimental measurements and the device's capacity to measure the ECG signal. The usage of our tool for educational purposes is also presented.
SplitSecond: Flexible Privilege Separation of Android Apps

Android applications have been attractive targets to attackers due to the large number of users and the sensitive information they possess. After the success of the first step of an attack exploiting a software vulnerability, the consequential damage is primarily determined by the criticality and the amount of Android permissions that a victim application has. As a countermeasure, process separation techniques that isolate potentially vulnerable components - usually native libraries - from the critical data and permissions, have been proposed. However, existing techniques offer little flexibility in the separation, e.g., with all native code being placed into one process without considering its dependency with other (Java) components and the non-empty set of permissions needed. In this paper, we propose a flexible privilege separation system, named SplitSecond, that enables selective permission separation at the granularity of Java components and native methods. SplitSecond provides safety against the attacks by restricting permissions on a user selectable isolation unit. According to our case study and experimental evaluation on a real handset with SplitSecond adopted Android OS and 100 top-ranked Android applications, 59.59% of activities, 66.8% of native methods, and 47.49% of permissions on average are flexibly splittable by SplitSecond with moderate overhead.
A Multi-objective Model for Multi-skill Project Scheduling Problem considering Perform Efficiency

The growing need of responsiveness for enterprises facing market volatility raises a strong demand for flexibility in their human resource management. This paper presents a multi-objective model for Multi-skill Project Scheduling Problem. We propose a new wage distribution method in which different perform efficiencies of human resources are taken into account. The model aims at minimizing project duration and project costs concurrently. An improved NSGA-II algorithm is designed to solve the model. The algorithm introduces a multi-dimensional chromosome coding scheme to identify the priorities and staff allocation of each activity. Special chromosome crossover and mutation operation are employed to address resource conflicts and constraint violations. Eventually, A case study is presented to verify the efficiency of the proposed approach.
Iterative Parallel Test to Detect and Diagnose Multiple Defects for Digital Microfluidic Biochip

Digital microfluidic biochip is a revolutionizing platform to execute complex bioassay operations concurrently. Dependability is an important feature of digital microfluidic biochip which is used for many safety-critical applications, such as point-of-care health assessment, air-quality monitoring, and food-safety testing. Therefore to ensure the proper functionality of the biochip a robust offline and online testing is required. Testing can be done before manufacturing or concurrently with other bioassay operations. In this work, we are presenting an efficient parallel testing and diagnosis scheme to reveal the location of all the faulty electrodes. Most of the algorithms present in the literature are mainly focused on single fault identification. But the diagnosis of biochip with multiple faults is not addressed properly. Even some of the algorithms have incorrectly classified the non faulty electrode as a faulty electrode. Thus in this paper, we are mainly focusing on the detection of multiple faults, correctly and efficiently. Moreover, this testing method is capable to check the given biochip with other concurrently running bioassay operations.
A Lagrangean relaxation heuristic for a two-stage capacitated facility location problem with depot size selection

In this paper, we study a variant of the two-stage capacitated facility location problem, a two-stage capacitated facility location problem with depot size selection. This problem is to open plants and depots, select depots’ size, determine the product flows from the opened plants to the opened depots and the assignments of the customers to the opened depots for satisfying the customers’ demands. The objective is to minimize the sum of the opening costs of the plants and the depots, the handling costs at the depots, the transportation costs and the assignment costs. For the problem, a Lagrangean relaxation heuristic is proposed to find a lower bound and an upper bound. 125 instances are randomly generated and tested to evaluate the performances of the Lagrangean relaxation heuristic. The computational results show that the Lagrangean relaxation heuristic is efficient and effective for our problem.
Verilog-A Model for a Switched-Capacitor DC-DC Converter

This paper presents the development and validation of a Verilog-A behavioral model for a switched capacitor DC-DC converter. For each converter component block, a behavioral model was created and validated, comparing simulation results with the correspondent transistor level implementation. The model was developed to allow the control of the switches ON resistance and dead time between operation phases. Using the behavioral model, the impact on the output voltage ripple amplitude of the switches RON, dead time, capacitors ESR and output track parasitic resistance was evaluated.
Strabismus Classification Using Face Features

Strabismus is one of the most common vision diseases that would cause amblyopia and even permanent vision loss. Timely diagnosis is crucial for well treating strabismus. In contrast to manual diagnosis, automatic recognition can significantly reduce labor cost and increase diagnosis efficiency. This paper present a completely automatic strabismus detection system using face features. It is based on automatically analyze the degree of left and right symmetry of the patient face measurements. To achieve that was created a special model, using Active appearance model (AAM) algorithm that detected face landmarks that were used for calculation strabismus features slope differences.
ECG Biometrics Method Based on Convolutional Neural Network and Transfer Learning

Personal identification based on ECG signals has been a significant challenge. The performance of an ECG authentication system depends significantly on the features extracted and the classifier subsequently applied. Although recently the deep neural networks based approaches featuring adaptive feature extractions and inherent classifications have attracted attention, they usually require a substantial set of training data. Aiming at tackling these issues, this paper presents a convolutional neural network-based transfer learning approach. It includes transferring the big data-trained GoogLeNet model into our identification task, fine-tuning the model using the ‘finetune’ idea, and adding three adaptive layers behind the original feature layer. The proposed approach not only requires a small set of training data, but also obtains great performance.
Controller Placement and Availability Link Upgrade Problem in SDN Networks

In SDN networks, the problem of how many controllers and where to place them, has been extensively studied. This is known as the controller placement problem, and has been addressed mainly considering the delays between the SDN switches and controllers. Although the delays between switches and controllers and the intercontroller delays are key aspects, a less addressed issue is the availability of the control paths (the routing paths between the switches and their controllers). In this paper, the problem regarding controller placements with QoS requirements, both in terms of delays and availability of the control paths, is addressed. To guarantee the availability requirements, a set of links is selected to have upgraded availability. An exact method and a heuristic method are proposed for solving the problem. Computational results show that the heuristic method provides near-optimal solutions within reasonable runtimes, when the exact method becomes computationally expensive.
Dual Intercommunication Network: Enabling Interhemispheric Communications in Hemisphere-Inspired ANNs

The human brain has been a main source of inspiration for designing deep learning models. Recently, inspired by the specialized functions of two cerebral hemispheres in processing low and high spatial frequency information, some dual-path neural networks with global and local branches have been proposed to deal with both coarse- and fine-grained visual tasks simultaneously. However, in existing works, the interhemispheric communication mechanism, which is responded by the corpus callosum, the largest white matter structure in the human brain that connecting the left and right cerebral hemispheres, is still not fully explored and exploited. This paper aims to explore how the corpus callosum can inspire us to enable transfer and integration of information between global and local branches in hemisphere-inspired artificial neural networks, such that one branch can leverage the other’s learned knowledge and benefit each other. To this end, we propose a gated intercommunication unit to selectively transfer useful knowledge between the two branches via attention mechanisms to alleviate the negative transfer. Experiments on sb-MNIST and two pedestrian attribute datasets show that the proposed method outperforms the compared ones in most cases.
K-Adaptability in Robust Container Vessel Sequencing Problem in a Route with Fluctuating Demand

In global container shipping, a liner company usually deploys a fixed sequence of vessels in a shipping route, i.e., a rotation of ports, to provide weekly service. Based on two critical observations that the marketing demands share the same fluctuation pattern in different years and weekly demands can hardly be forecast accurately, we study a robust container vessel sequencing (RCVS) problem in a cyclic fashion. When the available capacity is not sufficient in a week, demand may be outsourced or backlogged to the next week. Under a mild assumption, we establish that the problem can be equivalently formulated as a two-stage stochastic program. The objective is to minimize the worst-case total cost, consisting of container tardy and outsourcing cost. We apply an associated K-adaptability approach to solve the RCVS. The approach pre-selects K candidate demand allocation plans here-and-now and implements the best one when the uncertain demands are observed. A branch-and-bound solution approach is also explored in solving the problem.
Machine Learning for Strategic Urban Planning

Data mining is an important part of strategic planning for the development of modern urban settlement with capacities to accommodate population explosion. Developing countries are fast becoming urbanized giving the developments and opportunities that are lacking in rural areas. Data regarding urban development such as satellite image need to be analysed to ascertain the possibilities for further development or opening up of new settlements. This work presents a binary sub-pixel and feature based method of classification to detect water bodies and vegetation in earth observatory images. In this work, the images were subjected data pre-processing, feature extraction, and analysed the data using machine learning classification method to detection regions that support urban expansion or development of new settlement. The proposed method achieved 88.93% accuracy and 0.06% RMSE.
Performance Evaluation of a Mobile Deice System Using Fuzzy Logic Control with Multi-Hop in a Multi-Radio Opportunistic Network

In this paper, we based on hybrid network of Dual-Radio Opportunistic Networking for Energy Efficiency (DRONEE) method and propose Dual-Radio Opportunistic Networking for Energy Efficiency using fuzzy logic control with multi-hop (DRONEE-FM) to improve original method which is a mixed network method using the cluster concept of a Wireless Sensor Network (WSN). Mobile phone users are divided into clusters and the best mobile phone user signal is selected as a cluster head in each cluster where that device is used to forward data to the base station. Other cluster members pass their transmission data to the cluster head through a Wi-Fi interface and the cluster head of nodes which does not communicate with the base station channels (i.e., 3G / 4G mobile networks, etc.) will be closed. Thus, signal interference from other mobile phone users affecting cluster head mobile phone users can be reduced and the channel quality can be improved.
Does Similarity Matter? The Impact of User Similarity on Online Collaborative Shopping

Online group buying is increasingly recognized as an important model of online commerce; however, research examining the antecedents of group buying remains limited. Drawing on the stimulus-organism-response framework and the social exchange theory, this study proposes an integrated model to explore the impact of user similarity on social exchange and group buying behavior in the social commerce context. Using a survey of 233 respondents with group buying experience, we empirically validate the proposed model. The results largely support the proposed hypotheses, and the findings indicate that user similarity (i.e., internal and external similarity) is positively associated with social exchange (i.e., reciprocity, reputation, and trust), which in turn affects consumer group buying behavior. The three social exchange constructs are found to fully mediate the impact of user similarity on group buying behavior. The present study advances theoretical understanding of online consumer collaborative shopping from the perspective of user characteristics and provides some practical implications for vendors, consumers, and platform operators.
Physics of Organization Dynamics: An AI Framework for opportunity and risk management

The identification of risks and opportunities is generally massively depending on the ability of managers and decision makers to analyze multi-dimensional situations, to mobilize their experience and to infer risks and opportunities. However, in the Big Data era, early warning systems have shown that data science could be an efficient way to automatize risk detection. In this article, a new and original vision of risks and opportunities management is introduced and discussed in the context of a simple example. The main expected benefit is to enable decision makers to manage the trajectory of a considered system with regards to its performance towards its associated objectives, and to also support the definition of these performance objectives. The system could be an enterprise in an economic context but mainly any social system trying to avoid or manage instability, disruptions or crises.
Web-Based Cataract Detection System Using Deep Convolutional Neural Network

The alarming cases of cataract within the last decade and the projection of cataract cases within the next few decades call for urgent intervention by early diagnosis. Formal ways of detecting cataract such as physical examination, tests and diagnosis are clinic and professional bound. Hence the need for automation process. Some works have been done on Computer Aided Diagnosis (CAD) of cataract with tools such as Expert systems, which are limited to their knowledgebase thus inaccurate. Early diagnosis of cataract enables quick intervention and treatment. This paper presents a web-based Computer Aided Diagnostic for cataract detection system using Convolutional Neural Network that can be used by any nonprofessional outside the clinic environment. The systems model trained on a data set of 100 eye images using transfer learning which were retrieved from google image search results of “normal human eyes” and “human eye cataract”. It utilized ImageNet model developed in ILSVRC2012 using the Convolutional Neural Network classifier and transferred its knowledge using Transfer learning to train a new model. The new model gained the ability to classify eye images into “Normal” and “Cataractious”. The system was designed to take images as inputs and achieved a Sensitivity of 69%, a Specificity of 86%, Precision of 86%, F-Score of 56% and AUC of 84.56%. Its accuracy score was 78% which was influenced using the model trained during the ImageNet image classification using deep convolutional neural network
Electric Power Distribution System Fault Recovery Based on Visual Computation

A study case of electric power distribution system fault recovery has been introduced in this article. With proper connections, network reconfiguration should be considered an effective solution to the system fault condition. Considering the radial structure of the distribution system, appropriate observation on visualized outcome of the voltage profile can lead the system operator to obtain the best switching line effectively. Contour plots are applied for visualizing the voltage profiles of a modified IEEE 13-node test feeder model.
Garment Fashion Recommendation System for Customized Garment

Data is the collection of facts and details which leads to create useful information for any organization after performing analysis [1]. The large volume of data generated via internet applications and thus the requirement of retrieving most relevant information from data has become a priority. Recommendation system (RS) provides a path to deal with the large volume of data and to come out with potential insights for the business and end users.This paper explores to propose RS for customized garment using the hybrid filtering technique. Major activities like data collection, data modeling,data pre-processing, and data analysis over past transactions of customers purchase are explored. Collaboratively this paper explores the approach of building a system with implicit feedback.
Binaural Noise Reduction in Hearing Aid Simulations Using a Multichannel Wiener Filter

In audio signal processing, unwanted noise is a very common and serious problem. For people with hearing loss, this problem is magnified. In this project, a two-stage binaural noise reduction algorithm using a Wiener filter is highlighted, analyzed, and viewed as a potential solution to the problem of noise reduction. Compared to other algorithms, this one is very robust and capable at enhancing noisy input signals while still preserving the natural sound spatial information within them. This work implements the algorithm and tests its efficiency in several ways, on simulations involving a listener wearing earpieces in a noisy environment. Evaluation results show that the noise in the input signals is significantly reduced, while the sound spatial information and speech intelligibility is retained to an acceptable degree. Such an effective algorithm could be useful for hearing aid technologies, as well as for future studies and work on audio signal enhancement.
Synthesis of Low-Sidelobe 4-D Heterogeneous Antenna Arrays Including Mutual Coupling Using Iterative Convex Optimization

A novel iterative convex optimization approach is proposed for the synthesis of low-sidelobe 4-D heterogeneous arrays in the presence of mutual coupling effect. To realize wide bandwidth and wide-angle scanning, a tightly coupled dipole element (TCDE) is selected as the basic element. Due to the strong coupling between antenna elements, the active element pattern (AEP) and active reflection coefficient (ARC) are included in the proposed approach in order to evaluate the overall mutual coupling and port matching. Specifically, a nonconvex programming problem in terms of the given maximum sidelobe level (SLL), the ARC or reflected power at the center frequency, and the given maximum sideband level (SBL) at sidebands is established. After the application of some mathematical transformations, the nonconvex programming problem is decomposed into a convex optimization problem at the center frequency and an iterative convex problem (ICP) at sidebands. Owing to the efficiency of the convex optimization, the two problems can be efficiently solved. The proposed approach is applied to synthesize low-sidelobe patterns while minimizing the SBL in a 32-element cone-shaped 4-D heterogeneous array. The simulated and measured results verify the effectiveness of the proposed approach and show the advantages (improved gain) of the heterogeneous array.
Automatic Elevator Button Localization Using a Combined Detecting and Tracking Framework for Multi-Story Navigation

Simultaneous localization and mapping (SLAM) is an important function for service robots to self-navigate modernized buildings. However, only a few existing applications allow them to automatically move between stories through elevator. Some approaches have accomplished with the aid of hardware; however, this study shows that computer vision can be a promising alternative for button localization. In this paper, we proposed a real-time multi-story SLAM system which overcomes the problem of detecting elevator buttons using a localization framework that combines tracking and detecting approaches. A two-stage deep neural network initially locates the original positions of the target buttons, and a part-based tracker follows the target buttons in real-time. A positive-negative classifier and deep learning neural network (particular for button shape detection) modify the tracker’s output in every frame. To allow the robot to self-navigate, a 2D grid mapping approach was used for the localization and mapping. Then, when the robot navigates a floor, the A* algorithm generates the shortest path. In the experiment, two dynamic scenes (which include common elevator button localization challenges) were used to evaluate the efficiency of our approach, and compared it with other state-of-the-art methods. Our approach was also tested on a prototype robot system to assesses how well it can navigate a multi-story building. The results show that our method could overcome the common background challenges that occur inside an elevator, and in doing so, it enables the mobile robot to autonomously navigate a multi-story building.