Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensors-based treatment system of the organic waste with RFID identification and on-cloud traceability

Aim of the present manuscript is to summarize the multi-disciplinary activities related to the research project called POIROT that proposes to design a platform for transforming the organic waste into an inert, odorless and sanitized material, employable for building applications or as thermal acoustic insulator. The platform prototype is equipped with Arduino-based electronic sensing stations for characterizing the material during the different steps of the transformation process. Further innovation introduced by the proposed system is the integration of the platform with a RFID (radio frequency identification)-based user-recognition and products traceability system, allowing both to identify the enabled users and to track the produced panels/bricks along the whole supply chain. Therefore, a proper software on-cloud application was developed for supporting the products’ traceability functionality.
Ants for Identifying and Optimizing the Core Set K-Covering Problem

For large set k-covering problems, finding the set of core columns (CC), which includes columns for which it is hard to decide their status in the optimal solution, and searching CC only can improve heuristic and meta-heuristic algorithms. In this paper, we introduce an independent Max-Min ant system with information exchange about core columns (IMMAS-CC). In this algorithm, ants start with different CC sets. Ants exchange information about their CC sets, with a limited number of their neighbours; consequently, neighbouring ants will include and exclude columns from CC. Each ant has its own pheromone matrix, and a novel pheromone updating mechanism is used by ants, which depends on local information obtained from their neighbourhoods, giving ants more independence compared to traditional ACO algorithms where a single pheromone matrix is used. Using three sets of instances having different values of K, better results were obtained compared to a previous MMAS that uses a single pheromone matrix; moreover, competitive results were obtained using IMASS-CC with respect to the state-of-the-art algorithm.
Comparative Study of State-Of-Charge Estimation with Recurrent Neural Networks

The key to the safe and reliable operation of an electric vehicle is the precise knowledge of the state-of-charge (SOC)of its energy storage system. Since this state variable is not directly observable, it is derived from other quantities using model-based techniques such as Kalman filtering. More recently, data-based approaches using machine-learning have become feasible, promising the potential of reduced modeling effort and therefore reduced implementation time under similar or even higher accuracy. Many studies in this field, however, utilize single cell battery data and an extract of operating conditions that limits the reliability of the deduced conclusions for real-world applications. This paper provides an evaluation of recurrent neural networks with long short-term memory cells for SOC estimation in comparison to extended Kalman filtering based on equivalent circuit models. The analysis is conducted using the measurements of a battery module with 60 parallel connected cylindrical 18650 lithium-ion cells, a wide ambient temperature range between 0°C and 40°C and the deployment of a battery management system.
Investigating the use of sequencing and infeasibility driven strategies for constrained optimization

Real-world optimization problems involve constraints that must be satisfied for the design to be viable. The constraints are a manifestation of statutory physical limitations such as allowable strength, geometric compatibility or other practical considerations such as cost and time required for manufacturing. Constraint handling is thus an important area in the domain of optimization and there exists rich literature on the subject. Within population based stochastic optimization methods, constraint handling is typically implemented through a ranking process, where feasible solutions are considered better then infeasible ones. Recent studies have suggested that preserving infeasible solutions can be advantageous to the evolutionary search. Such studies have typically considered a paradigm where all objectives and constraints are evaluated simultaneously. In many practical scenarios however, it is possible to evaluate them independently. This opens up opportunities to gain computational benefits through sequencing constraint evaluation and using partial evaluation (i.e. only evaluate some of the constraints). In this paper, we systematically construct and study the performance of these strategies by combining them in different ways (total 8 variants). The numerical experiments compare the performance of these strategies under different evaluation and ranking scenarios. The study offers understanding of the advantages that can be gained by using appropriate combinations of these strategies for the cases where objective and constraint(s) are associated with individual cost and can be computed independently.
DC Offset Removal in Power Systems Using Deep Neural Network

In this paper, we have studied a removing method of exponentially decaying DC offset by deep learning approach. Since conventional FIR filter is ineffective when time constant is predicted imprecisely, the main goal of this research was to make a deep neural network (DNN) that has adaptability to imprecise time constants. For the implementation of DNN, we used Tensorflow deep learning library with python language. Stochastic gradient descent (SGD) method was adopted for training algorithm, and the training dataset used for training was generated by the theoretical equation of the exponentially decaying DC offset. After training process, the performance test was conducted using the PSCAD/EMTDC output as test data. The results are compared with the conventional FIR filter, and it was confirmed that DNN has better characteristics for imprecise time constants compared to the FIR filter.
OCM-PUF: organic current mirror PUF with enhanced resilience to device degradation

Physically unclonable functions (PUFs) are the important primitives in the field of hardware security [1] . Their applications would be further broadened if PUFs are fabricated using organic thin lm transistors (oTFTs) [2] , [3] . However, the rapid temporal degradation of the oTFTs deteriorates their performance. This paper proposes an organic current mirror PUF (OCM-PUF) that is fabricated on exible substrates [4] . The basic structure of the proposed circuit is a current mirror in which oTFTs share a gate electrode to self-compensate temporal degradation. The OCM-PUF is fabricated using bottom gate oTFTs to evaluate the metrics as a PUF. It achieved the randomness of 85.8%, the diffuseness of 98.4%, and the reliability of 76.4%. The response of the proposed PUF presented a high tolerance against the response change due to aging.
Transfer Learning in Genetic Programming Hyper-heuristic for Solving Uncertain Capacitated Arc Routing Problem

Uncertain Capacitated Arc Routing Problem (UCARP) is a combinatorial optimization problem that has many important real-world applications. Genetic programming (GP) is a powerful machine learning technique that has been successfully used to automatically evolve routing policies for UCARP. Generalisation is an open issue in the field of UCARP and in this direction, an open challenge is the case of changes in number of vehicles which currently leads to new training procedures to be initiated. Considering the expensive training cost of evolving routing policies for UCARP, a promising strategy is to learn and reuse knowledge from a previous problem solving process to improve the effectiveness and efficiency of solving a new related problem, i.e. transfer learning. Since none of the existing GP transfer methods have been used as a hyper-heuristic in solving UCARP, we conduct a comprehensive study to investigate the behaviour of the existing GP transfer methods for evolving routing policy in UCARP, and identify the potentials of existing methods. The results suggest that the existing methods applying subtree transfer cannot scale well to environment changes and cannot be adapted for this purpose. However, applying GP transfer methods is a good option for creating a better initial populations on target domain and though this effect does not last, we can obtain comparable results in the target domain in a much shorter time. Overall, we conclude that UCARP needs stronger and more effective transfer learning methods.
A Novel Binary Negatively Correlated Search for Wind Farm Layout Optimization

Wind farm layout optimization(WFLO) is a largescale binary optimization problem which is difficult to solve by conventional optimization methods. On the other hand, due to the wake effect, the power generation efficiency of the wind farm can be greatly reduced, which increases the complexity of the WFLO problem. This paper proposes a binary negatively correlated search (BNCS), which extends the evolutionary logic of negatively correlated search and generates binary variables using a rounding transfer function. Numerical results confirm that the proposed BNCS can achieve good performance for both Knapsack and WFLO problem.
Coherence Analysis of Wafer Process Measurements from Chamber and Optical Emission Spectrum : APC: Advanced Process Control

Coherency analysis is adopted in this paper to examine the linear dependence between measurements of three processing parameters during wafer fabrication process. They are power, pressure and voltage measurements and three channels from optical emission spectrum data. The measurement data were divided into consecutive short data segments to provide time-stamped coherence signatures. Our results observe significant linear dependence appeared in multiple harmonic frequencies. The linear dependence appears in the beginning of the processing step and continues toward the end. The verified linear relationship between process parameters sheds the light for redundancy reduction and more compact data representation.
Empirical Comparison between Autoencoders and Traditional Dimensionality Reduction Methods

In order to process efficiently ever-higher dimensional data such as images, sentences, or audio recordings, one needs to find a proper way to reduce the dimensionality of such data. In this regard, SVD-based methods including PCA and Isomap have been extensively used. Recently, a neural network alternative called autoencoder has been proposed and is often preferred for its higher flexibility. This work aims to show that PCA is still a relevant technique for dimensionality reduction in the context of classification. To this purpose, we evaluated the performance of PCA compared to Isomap, a deep autoencoder, and a variational autoencoder. Experiments were conducted on three commonly used image datasets: MNIST, Fashion-MNIST, and CIFAR-10. The four different dimensionality reduction techniques were separately employed on each dataset to project data into a low-dimensional space. Then a k-NN classifier was trained on each projection with a cross-validated random search over the number of neighbours. Interestingly, our experiments revealed that k-NN achieved comparable accuracy on PCA and both autoencoders projections provided a big enough dimension. However, PCA computation time was two orders of magnitude faster than its neural network counterparts.
A single-cell Systems Biology approach for disease-specific subpathway extraction

Single-cell sequencing technologies offer a platform to explore deeper the complex diseases and biological processes. On parallel, Systems Biology approaches can tackle part of this complexity, since they are based on biological networks elucidating genotype to phenotype relationships in a more comprehensive framework. Hence, research community is shifting towards singlecell systems biology approaches to identify and validate disease-specific biomarkers. Under this perspective, we propose a methodological framework aimed to infer disease-specific subpathway activities, using cell signaling pathway topology and transcriptomics data derived from single-cell RNA-seq technologies. More specifically, we concatenated and integrated pathway network refining with single-cell expression profiles. Linear subpathways were extracted and a subpathway profile score was calculated taking into account the pathway information flow. The core of our method is XGBoost supervised classification based on the aforementioned score in order to identify important subpathways that control the rest of the network and cause major changes from a control to a disease state under study. The proposed methodology was applied to a real experimental study with single-cell RNA-seq expression profiles revealing changes in type 2 diabetes. Exported subpathways showed promising results, suggesting that the proposed approach can reliably identify subpathways, acting as potential disease-specific biomarkers.
A CMOS Low-noise Active Mixer with Capacitive Neutralization Technique

a broadband CMOS active down-conversion mixer is presented. Specifically, a capacitor cross-coupled transconductor serves as the input stage to reduce noise figure of the mixer while providing wideband input matching. Moreover, a capacitive neutralization technique is used to compensate the source-drain parasitic of input stage and boost loop gain of the transconductor, resulting in improved isolation and linearity. The current-reuse technique applied to the developed transconductor by stacked nMOS/pMOS architecture efficiently saves power consumption of the circuit. Implemented in the TSMC 28-nm CMOS process, post-simulations show that, the proposed mixer provides a maximal conversion gain of 11.4 dB and a NF of 3.9~4.7 dB across RF input frequency range of 2~9.6 GHz. The average IIP3 of 5 dBm are obtained while the mixer core consumes 6.2 mW.
A Detection Method for Android Repackaged Applications with Malicious Features Similarity of Family Homology

There are widespread repackaging problems in android applications. Most of studies focus on the detection of malicious applications themselves with the lack of family homology research. In face of this problem, this paper proposes a detection method based on sensitive behavior sequences for detecting malicious repackaged android applications with malicious feature similarity of family homology. The method improves the ability against various malwares by extracting malicious code part from malicious applications. The paper tested the samples of 1009 applications, with accuracy as high as 87.52%. The solution is better than that of Kirin and Androguard. Moreover, the proposed solution tested some malicious family codes. Experimental studies show that the solution can be used for detecting android malicious repackaged malwares and providing a reference for some researchers in static analysis.
Evolution of Biological Eye in Computer Simulation

This research demonstrates an evolutionary process of a biological eye in a computer model. This demonstrates using a model, how the biological eye could have evolved, starting from a sheet of light sensitive cells towards a concave pinhole camera eye without a lens. The advantages of each iterative improvement lead to increased chance of being selected for subsequent generations because improved eye clarity helps animals to detect prey or predators. The fitness of the evolving eyes are evaluated on a range of 5 varied datasets with a range of classification algorithm tasks. The developed simulated eye model demonstrates that the requirement to detect the direction of an approaching predator does produce a gradual evolutionary transition from a flat light sensitive surface to the pinhole camera eye which models how eyes evolved in biological creatures.
A Testbed for Adaptive Microphones in Ultra-Low-Power Systems

Smart cities bring new technological advances that help improve everyday life. One such improvement is the ability to map out a city based on a characteristic. The amount of acoustic noise in an environment has many impacts on human life and has the potential to be collected wirelessly. Unfortunately, systems made today would not have the battery life capabilities to handle such a high demand if continuous transmission was used. In this paper, the design of a testbed for a smart microphone system is presented. In order to promote power savings, an analog-to-digital converter (ADC) which dynamically switches between high and low power modes in response to environmental noise is presented. Specifically, the high power ADC mode is triggered from a spike in the acoustic noise level. Ideally, this would be configurable and allow for detection of different types of sound such as human voice or music. A framework for basic environmental sound collection is presented along with preliminary results of the testbed.
Drug-Drug Interaction Extraction Based on Transfer Weight Matrix and Memory Network

Extracting drug–drug interaction (DDI) in the text is the process of identifying how two target drugs in a given sentence interact. Previous methods, which were limited to conventional machine learning techniques, we are susceptible to issues such as “vocabulary gap” and unattainable automation processes in feature extraction. Inspired by deep learning in natural language preprocessing, we addressed the aforementioned problems based on dynamic transfer matrix and memory networks. A TM-RNN method is proposed by adding the transfer weight matrix in multilayer bidirectional LSTM to improve robustness and introduce a memory network for feature fusion. We evaluated the TM-RNN model on the DDIExtraction 2013 Task. The proposed model achieved an overall F-score of 72.43, which outperforms the latest methods based on support vector machine and other neural networks. Meanwhile, the experimental results also indicated that the proposed model is more stable and less affected by negative samples.
Automatic Cardiac Arrhythmia Classification Using Combination of Deep Residual Network and Bidirectional LSTM

Cardiac arrhythmia is associated with abnormal electrical activities of the heart, which can be reflected by altered characteristics of electrocardiogram (ECG). Due to the simplicity and non-invasive nature, the ECG has been widely used for detecting arrhythmias and there is an urgent need for automatic ECG detection. Up to date, some algorithms have been proposed for automatic classification of cardiac arrhythmias based on the features of the ECG; however, their stratification rate is still poor due to unreliable features of signal characteristics or limited generalization capability of the classifier, and therefore, it remains a challenge for automatic diagnosis of arrhythmias. In this paper, we propose a new method for automatic classification of arrhythmias based on deep neural networks (DNNs). The two DNN models constitutive of residual convolutional modules and bidirectional long short-term memory (LSTM) layers are trained to extract features from raw ECG signals. The extracted features are concatenated to form a feature vector which is trained to do the final classification. The algorithm is evaluated based on the test set of China Physiological Signal Challenge (CPSC) dataset with F<sub>1</sub> measure regarded as the harmonic mean between the precision and recall. The resulting overall F<sub>1</sub> score is 0.806, <inline-formula> <tex-math notation="LaTeX">$F_{AF}$ </tex-math></inline-formula> score is 0.914 for atrial fibrillation (AF), <inline-formula> <tex-math notation="LaTeX">$F_{Block}$ </tex-math></inline-formula> score is 0.879 for block, <inline-formula> <tex-math notation="LaTeX">$F_{PC}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$F_{ST}$ </tex-math></inline-formula> scores are 0.801 and 0.742 for premature contraction and ST-segment change, which demonstrates a good performance that may have potential practical applications.
Hierarchical Adversarial Network for Human Pose Estimation

This paper presents a novel adversarial deep neural network to estimate human poses from still images, such as those obtained from CCTV and the Internet-of-Things (IoT) devices. Specifically, the proposed adversarial deep neural network exhibits the spatial hierarchy of human body parts considering the fact that predicting the position of some parts is more challenging than others. The generative and the discriminative portions of the proposed adversarial deep neural network are designed to encode the spatial relationship between the parts in the first stage of the hierarchy (parents) and the parts in the second stage of the hierarchy (children). Each of the generator and the discriminator networks is designed as two components, which are sequentially connected together to infer rich appearance potentials and to encode not only the likelihood of the part’s existence but also the relationships between each body part and its parent. The method is evaluated on three different datasets, whose findings suggest that the proposed network achieves comparable results with other competing state-of-the-art approaches.
Comparative Analysis of Two Asynchronous Parallelization Variants for a Multi-Objective Coevolutionary Solver

We describe and compare two steady state asynchronous parallelization variants for DECMO2++, a recently proposed multi-objective coevolutionary solver that generally displays a robust run-time convergence behavior. The two asynchronous variants were designed as trade-offs that maintain only two of the three important synchronized interactions / constraints that underpin the (generation-based) DECMO2++ coevolutionary model. A thorough performance evaluation on a test set that aggregates 31 standard benchmark problems shows that while both parallelization options are able to generally preserve the competitive convergence behavior of the baseline coevolutionary solver, the better parallelization choice is to prioritize accurate run-time search adaptation decisions over the ability to perform equidistant fitness sharing.
A Refined Bilateral Filtering Algorithm Based on Adaptively-Trimmed-Statistics for Speckle Reduction in SAR Imagery

This paper proposes a refined bilateral filtering algorithm based on adaptively trimmed-statistics (ATS-RBF) for speckle reduction in SAR imagery. The new de-speckling method is based on the bilateral filtering method, where the similarities of gray levels and the spatial location of the neighboring pixels are exploited. However, the traditional bilateral filter is not effective to reduce the strong speckle, which is often presented as impulse noise. The ATS-RBF designs an adaptive sample trimming method to properly select the samples in the local reference window and the trimming depth used for sample trimming is automatically derived according to the homogeneity of the local reference window. Furthermore, an alterable window size-based scheme is proposed to enhance the speckle noise smoothing strength in homogeneous backgrounds. Finally, bilateral filtering is applied using the adaptively trimmed samples. The ATS-RBF has an excellent speckle noise smoothing performance while preserving the edges and the texture information of the SAR images. The experiments validate the effectiveness of the proposed method using TerraSAR-X images.