Miracle-dz/UniFAR-FTUs huggingface dataset:
Combined K-Means and Amplitude Clustering of Impulse Response for 60 GHz Vehicular Channels

Saleh- Valenzuela based clustered propagation concept is widely adopted for modelling UWB channels operating in the 3–11 GHz band. In this paper, we examine whether 60 GHz mmWave channels can be modelled along the same line. First, an automatic clustering algorithm is developed for the purpose which groups multipath components according to their time delays and amplitudes. Next, the algorithm is used to analyze measured impulse responses for 60 GHz channels inside a car. It was found that 60 GHz in-vehicle propagation can be characterized quite satisfactorily with IEEE 802.15. 3a CM1. The out-of-car channel behaviour, however, is significantly distinct.
Adaptive Learning Models Evaluation in Twitter’s Timelines

Current challenges in machine learning include dealing with temporal data streams, drift and non-stationary scenarios, often with text data, whether in social networks or in business systems. This dynamic nature tends to limit the performance of traditional static learning models and dynamic learning strategies must be put forward. However, acquiring the performance of those strategies is not a straightforward issue, as sample’s dependency undermines the use of validation techniques, like crossvalidation. In this paper we propose to use the McNemar’s test to compare two distinct approaches that tackle adaptive learning in dynamic environments, namely DARK (Drift Adaptive Retain Knowledge) and Learn++. NSE (Learn++ for Non-Stationary Environments). The validation is based on a Twitter case study benchmark constructed using the DOTS (Drift Oriented Tool System) dataset generator. The results obtained demonstrate the usefulness and adequacy of using McNemar’s statistical test in dynamic environments where time is crucial for the learning algorithm.
Hybrid neural models for automatic handwritten digits recognition

In this paper a novel Handwritten Character Identification methodology that performs the recognition of the students identification numbers handwritten in classroom maps has been proposed. A dataset of 60.0000 handwritten training images of the MNIST dataset and a proprietary dataset composed of 3.415 images extracted from 12 classroom maps handwritten by 11 different persons is used in this work. These images were obtained through the segmentation process described in this work, and had suffer some image processing before feature extraction and classification in order to be as similar as possible to the MNIST dataset samples. The algorithm is composed by four main steps: pre-processing, segmentation, feature extraction and classification. It was implemented a segmentation strategy designed for the classroom maps, based on morphological operations and connected components. This strategy can be easily adapted to others handwritten character recognition problems. In classification, three different approaches are used: Support Vector Machine, Convolutional Neural Networks and an hybrid approach that use the CNN extracted features as inputs for a Support Vector Machine. The hybrid approach has the advantage of avoiding the feature extraction step. The dataset is divided into train, test and validation sets and the performance of the three different classifiers is compared and evaluated. The results presented were obtained using real classroom maps. The hybrid CNN+SVM classifier achieved a high performance with overall accuracy of 96.5%. In conclusion, this designed system could successfully reduce the time of creating and accessing the digital storage of students examinations, as well as allow the automatic indexing of student grades towards a full digital evaluation system.
Synchronize Inertial Readings From Multiple Mobile Devices in Spatial Dimension

In this paper, we investigate the problem of space synchronization, i.e., synchronizing inertial readings from multiple mobile devices in the spatial dimension, in other words, multiple mobile devices are space synchronized to have the same 3-D coordinates except that each device is the origin of its corresponding coordinate. We propose a scheme called MObile Space Synchronization (MOSS) for devices with two sensors: an accelerometer and a gyroscope, which are available on most mobile devices. Accelerometer readings from multiple mobile devices on a human subject are used to achieve space synchronization when the human subject is moving forward, such as walking and running. Gyroscope readings from multiple mobile devices on a human subject are used to maintain space synchronization when the human subject stops moving forward, which means that we can no longer obtain the consistent acceleration caused by body moving forward. Experiment results show that our MOSS scheme can achieve an average angle deviation of 9.8° and an average measurement similarity of 97%.
Combining Local and Global Measures for DIBR-Synthesized Image Quality Evaluation

Depth-image-based-rendering (DIBR) techniques are significant for 3D video applications, e.g., 3D television and free viewpoint video (FVV). Unfortunately, the DIBR-synthesized image suffers from various distortions, which induce an annoying viewing experience for the entire FVV. Proposing a quality evaluator for DIBR-synthesized images is fundamental for the design of perceptual friendly FVV systems. Since the associated reference image is usually not accessible, full-reference (FR) methods cannot be directly applied for quality evaluation of the synthesized image. In addition, most traditional no-reference (NR) methods fail to effectively measure the specifically DIBR-related distortions. In this paper, we propose a novel NR quality evaluation method accounting for two categories of DIBR-related distortions, i.e., geometric distortions and sharpness. First, the disoccluded regions, as one of the most obvious geometric distortions, are captured by analyzing local similarity. Then, another typical geometric distortion (i.e., stretching) is detected and measured by calculating the similarity between it and its equal-size adjacent region. Second, considering the property of scale invariance, the global sharpness is measured as the distance between the distorted image and its downsampled version. Finally, the perceptual quality is estimated by linearly pooling the scores of two geometric distortions and sharpness together. Experimental results verify the superiority of the proposed method over the prevailing FR and NR metrics. More specifically, it is superior to all competing methods except APT in terms of effectiveness, but greatly outmatches APT in terms of implementation time.
Automated Cell Tracking Using Motion Prediction-Based Matching and Event Handling

Automated cell segmentation and tracking enables the quantification of static and dynamic cell characteristics and is significant for disease diagnosis, treatment, drug development, and other biomedical applications. This paper introduces a method for fully automated cell tracking, lineage construction, and quantification. Cell detection is performed in the joint spatio-temporal domain by a motion diffusion-based Partial Differential Equation (PDE) combined with energy minimizing active contours. In the tracking stage, we adopt a variational joint local-global optical flow technique to determine the motion vector field. We utilize the predicted cell motion jointly with spatial cell features to define a maximum likelihood criterion to find inter-frame cell correspondences assuming Markov dependency. We formulate cell tracking and cell event detection as a graph partitioning problem. We propose a solution obtained by minimization of a global cost function defined over the set of all cell tracks. We construct a cell lineage tree that represents the cell tracks and cell events. Finally, we compute morphological, motility, and diffusivity measures and validate cell tracking against manually generated reference standards. The automated tracking method applied to reference segmentation maps produces an average tracking accuracy score (<inline-formula><tex-math notation="LaTeX">$TRA$</tex-math><alternatives> <mml:math> <mml:mrow> <mml:mi>T</mml:mi> <mml:mi>R</mml:mi> <mml:mi>A</mml:mi> </mml:mrow> </mml:math> <inline-graphic xlink:href="makrogiannis-ieq1-2875684.gif"/></alternatives></inline-formula>) of 99 percent, and the fully automated segmentation and tracking system produces an average <inline-formula><tex-math notation="LaTeX">$TRA$</tex-math><alternatives> <mml:math> <mml:mrow> <mml:mi>T</mml:mi> <mml:mi>R</mml:mi> <mml:mi>A</mml:mi> </mml:mrow> </mml:math> <inline-graphic xlink:href="makrogiannis-ieq2-2875684.gif"/></alternatives></inline-formula> of 89 percent.
A Cortical Thickness Mapping Method for the Coxal Bone Using Morphing

As human body finite element models become more integrated with the design of safety countermeasures and regulations, novel models need to be developed that reflect the variation in the population's anthropometry. However, these new models may be missing information which will need to be translated from existing models. During the development of a 5th percentile female occupant model (F05), cortical thickness information of the coxal bone was unavailable due to resolution limits in the computed tomography (CT) scans. In this study, a method for transferring cortical thickness information from a source to a target model with entirely different geometry and architecture is presented. The source and target models were the Global Human Body Models Consortium (GHBMC) 50th percentile male (M50) and F05 coxal bones, respectively. To project the coxal bone cortical thickness from the M50 to the F05, the M50 model was first morphed using a Kriging method with 132 optimized control points to the F05 anthropometry. This technique was found to be accurate with a mean nodal discrepancy of 1.27 mm between the F05 and morphed M50 (mM50) coxal bones. Cortical thickness at each F05 node was determined by taking the average cortical thickness of every mM50 node, non-linearly weighted by its distance to the F05 nodes. The non-linear weighting coefficient, β, had a large effect on the accuracy and smoothness of the projected cortical bone thickness. The optimal projection had β = 4 and was defined when the tradeoff between projection accuracy and smoothness was equal. Finally, a quasi-static pelvis compression was simulated to examine to effect of β. As β, increased from 0 to 4, the failure force decreased by ~100 N, whereas the failure displacement increased by 0.9 mm. Results from quasi-static compression tests of the F05 pelvis were comparable to experimental results. This method could be applied to other anatomical regions where cortical thickness variation is important, such as the femur and ribs and is not limited to GHBMC-family models. Furthermore, this process will aid the development of subject-specific finite element models where accurate cortical bone thickness measurements cannot be obtained.
Electricity fraud detection using committee semi-supervised learning

Electricity fraud results in significant losses to utilities. This paper proposes the use of a semi-supervised learning framework to derive an electricity fraud detector from data lacking information on the presence of fraud for the majority of samples. Utilities are only able to make a limited number of inspections, resulting in a lack of data representing cases of fraud. Using a co-training by committee semi-supervised learning framework, the detection performance is improved in comparison to the use of supervised models only trained with labeled data. The framework starts by training a random forest classifier on the labeled data. Next, the unlabeled data that the model can classify with the most confidence is added iteratively to the set of labeled samples, augmenting the data available for model training. The electricity fraud detector achieves a classification performance of 84% true positive rate, 11% false positive rate and 0.89 area under the receiver operating characteristic curve under a positive class balance of 5% and 90% unlabeled samples in the training data.
Joint Equalization and Phase Noise Estimation for the Uplink in Massive MIMO SC-FDE Schemes

In this paper we consider the use of massive multiple-input, multiple-output (MIMO) combined with single-carrier with frequency-domain equalization (SC-FDE) modulations for the uplink, where phase noise occurs both at the receiver and the transmitter. To cope with the channel and the phase noise effects, we propose a receiver structure for joint equalization and phase noise estimation using low-complexity iterative frequency-domain equalization based on the equal gain combining (EGC) concept for the channel equalization allied to a particle filter for the phase noise estimation procedure. Since the equalization step decouples the data streams, the complexity of the phase noise estimation step grows linearly with the number of transmit antennas. In fact, phase noise estimation occurs concurrently for each receive antenna phase noise process. Assuming small phase noise levels at the receiver (i.e., the base station) while letting the phase noise levels take larger values at the transmitter the proposed scheme displays excellent performance.
Adversarial Manipulation of Reinforcement Learning Policies in Autonomous Agents

Machine learning classifiers are known to be vul- nerable to intentional perturbation of inputs, namely adversarial examples. There are extensive studies of adversarial machine learning in the context of computer vision with high dimen- sional inputs. In this work, we show that adversarial attacks are also effective in targeting reinforcement learning policies based on low-dimensional sensory inputs in autonomous agent controls. Our results show that the two adversarial example crafting techniques significantly degrade test performance of the trained reinforcement learning policies. In addition, we compare adversarial examples with random noise on the effectiveness of the attacks. Furthermore, we study the importance of the input features, in terms of the impact of performance in adversarial attacks.
A Novel Image Steganography Approach Based on Interval Type-2 Fuzzy Similarity

Image steganography is the art of hiding secret data into an image in such a way that it cannot be detected by any intruder. For steganography, image is a good carrier because it contains a higher redundancy of pixel values, making it less sensitive to the human visual system. Therefore, embedding in those pixels of the image leads to high visual quality, payload capacity and security. This paper proposes an interval type-2 fuzzy logic-based system to detect those pixels of the image which are less sensitive to human eyes. The embedding is performed on the selected pixels using the least significant bit (LSB) method. We have termed this method the interval type-2 fuzzy logic system based LSB (IT2FLS-LSB) steganographic method. The experimental simulations were performed on a collection of image datasets to show the efficacy of IT2FLS-LSB embedding. Quality index metrics such as PSNR (peak signal-to-noise ratio), UQI (universal quality index), and SSIM (structural similarity measure) are used to assure the visual quality of the stego images. We have also evaluated the high payload capacity of our proposed method.
Possibilistic clustering with seeds

Clustering methods assign objects to clusters using only as prior information the characteristics of the objects. However, clustering algorithms performance can be improved when background knowledge is available. Such background knowledge can be incorporated in a clustering method as label constraints which results in a semi-supervised clustering algorithm. We propose to extend two possibilistic clustering algorithms to make use of available a priori information. The goal is twofold: to improve the accuracy of the clustering result by leading the method towards a desired solution and to detect outliers by taking advantage of the generated possibilistic partition. The proposed methods are called semi-supervised repulsive possibilistic c-means (SRPCM) and semi-supervised possibilistic fuzzy c-means (SPFCM). They correspond to possibilistic clustering algorithms that introduce label constraints. Experimental results show that the proposed algorithms using label constraints improve (1) the clustering result and (2) the outliers detection.
Queenless honey bees build infrastructure for direct reproduction until their new queen proves her worth

The terminal investment hypothesis predicts that individuals will alter their reproductive investments based on reproductive prospects. This hypothesis, however, has never been tested at the colony‐level, where reproductive prospects for thousands of individuals can change instantly with the death of a single individual: the queen. A honey bee queen's death also changes the reproductive mechanism; if the queen is not replaced, then workers reproduce directly, by producing males in reproductive comb‐drone comb‐before the colony dies. To test how workers respond to reproductive uncertainty, I made colonies queenless and measured comb building throughout the stages of rearing a replacement queen. Queenless workers built only drone comb throughout queen rearing, even when the colony possessed a virgin or mated queen. Only when the new queen started laying fertilized eggs did workers stop building drone comb. Despite queenless colonies being more likely to eventually rear a replacement queen (88%), than to fail (12%), workers still built drone comb for direct reproduction, even when the colony was provided with ample drone comb. Therefore, workers “pessimistically” invest in drone comb to prepare for direct reproduction, even while rearing a replacement queen to prevent that outcome. When faced with reproductive uncertainty, honey bees may “hope” for the best, but they prepare for the worst.
Domain Wall Motion-based XOR-like Activation Unit With A Programmable Threshold

Spintronic devices promise an excellent opportunity for implementing ultra-low power neuromorphic platforms due to the inherent correspondence between their physical characteristics and the required neuronal, synaptic functionalities. Neuromorphic circuits using domain wall motion-based threshold neurons have been demonstrated in previous studies. However, threshold neurons are unable to realize linearly inseparable functions. Our work addresses this challenge by proposing a new domain wall motion-based neural activation unit with XOR-like activation function. We also develop a new learning algorithm for neurons with this activation unit. Offline training is performed on real-world datasets from the UCI machine learning repository. Neuromorphic circuits corresponding to these datasets are also simulated. The results suggest femto-Joule range energy consumption of a neuron with the proposed activation unit and $ 1.08\times -1.82\times $ lower misclassification rate (MCR) of the proposed algorithm in comparison to the traditional perceptron learning algorithm.
Spying on chaos-based cryptosystems with reservoir computing

Reservoir computing is a machine learning approach to designing artificial neural networks. Despite the significant simplification of the training process, the performance of such systems is comparable to other digital algorithms on a series of benchmark tasks. Recent investigations have demonstrated the possibility of performing long-horizon predictions of chaotic systems using reservoir computing. In this work we show that a trained reservoir computer can reproduce sufficiently well the properties a chaotic system, hence allowing full synchronisation. We illustrate this behaviour on the Mackey-Glass and Lorenz systems. Furthermore, we show that a reservoir computer can be used to crack chaos-based cryptographic protocols and illustrate this on two encryption schemes.
Strain Engineered Band Gaps and Electronic Properties in PbPdO2 and PbPd0.75Co0.25O2 Slabs

Electronic structure and corresponding electrical properties of PbPdO2 and PbPd0.75Co0. 25O2 ultrathin slabs with (002) preferred orientation were systematically investigated using first-principles calculations. The calculated results revealed the strain induced evidently the changes of band structure and carrier concentration in both slabs. It was also found that PbPdO2 and PbPd0.75Co0. 25O2 ultrathin slabs exhibited evident differences in the external strain dependence of the band gap and charge carrier concentration, which was strongly dependent on bond angle and bond length induced by in-plane anisotropy strain. Interestingly, the carrier concentration of the PbPd0.75Co0. 25O2 slab could increase up to 5–6 orders of magnitude with the help of external strain, which could explain the potential mechanism behind the observed colossal strain-induced electrical behaviors. This work demonstrated that the influence of the doping effect in the case of PbPdO2 could be a potentially fruitful approach for the development of promising piezoresistive materials.
Transcriptomic Plasticity in the Arthropod Generalist Tetranychus urticae Upon Long-Term Acclimation to Different Host Plants

The two-spotted spider mite Tetranychus urticae is an important pest with an exceptionally broad host plant range. This generalist rapidly acclimatizes and adapts to a new host, hereby overcoming nutritional challenges and a novel pallet of constitutive and induced plant defenses. Although recent studies reveal that a broad transcriptomic response upon host plant transfer is associated with a generalist life style in arthropod herbivores, it remains uncertain to what extent these transcriptional changes are general stress responses or host-specific. In the present study, we analyzed and compared the transcriptomic changes that occur in a single T. urticae population upon long-term transfer from Phaseolus vulgaris to a similar, but chemically defended, host (cyanogenic Phaseolus lunatus) and to multiple economically important crops (Glycine max, Gossypium hirsutum, Solanum lycopersicum and Zea mays). These long-term host plant transfers were associated with distinct transcriptomic responses with only a limited overlap in both specificity and directionality, suggestive of a fine-tuned transcriptional plasticity. Nonetheless, analysis at the gene family level uncovered overlapping functional processes, recruiting genes from both well-known and newly discovered detoxification families. Of note, our analyses highlighted a possible detoxification role for Tetranychus-specific short-chain dehydrogenases and single PLAT domain proteins, and manual genome annotation showed that both families are expanded in T. urticae. Our results shed new light on the molecular mechanisms underlying the remarkable adaptive potential for host plant use of generalist arthropods and set the stage for functional validation of important players in T. urticae detoxification of plant secondary metabolites.