Miracle-dz/UniFAR-FTUs huggingface dataset:
Secure Distributed Estimation Over Wireless Sensor Networks Under Attacks

The problem of distributed estimation over wireless sensor networks in an adversarial environment with the presence of attacks on sensed and communicated information is considered. To tackle with this problem, a secure diffusion least-mean squares (S-dLMS) algorithm is proposed. The proposed S-dLMS can be considered as a hybrid system, which consists of a noncooperative LMS (nc-LMS) subsystem and a diffusion LMS (dLMS) subsystem. The nc-LMS subsystem is used to provide a reliable reference estimate, which is further used for constructing the threshold test to detect the trust neighbors of each node. Then, based on the detected secure network topology, the dLMS subsystem is performed by combining the received information from the trust neighbors. The performance of the proposed S-dLMS algorithm in the mean and mean-square senses is analyzed, and then an adaptive rule is suggested to select the threshold for detection. Finally, some simulations are performed to show the effectiveness of the proposed S-dLMS algorithm under fixed and time-varying attacks, respectively.
Maximum Persistency via Iterative Relaxed Inference in Graphical Models

We consider the NP-hard problem of MAP-inference for undirected discrete graphical models. We propose a polynomial time and practically efficient algorithm for finding a part of its optimal solution. Specifically, our algorithm marks some labels of the considered graphical model either as (i) optimal, meaning that they belong to all optimal solutions of the inference problem; (ii) non-optimal if they provably do not belong to any solution. With access to an exact solver of a linear programming relaxation to the MAP-inference problem, our algorithm marks the maximal possible (in a specified sense) number of labels. We also present a version of the algorithm, which has access to a suboptimal dual solver only and still can ensure the (non-)optimality for the marked labels, although the overall number of the marked labels may decrease. We propose an efficient implementation, which runs in time comparable to a single run of a suboptimal dual solver. Our method is well-scalable and shows state-of-the-art results on computational benchmarks from machine learning and computer vision.
Fuzzy Logic Based FOGI-FLL Algorithm for Optimal Operation of Single-Stage Three-Phase Grid Interfaced Multifunctional SECS

This paper presents a fuzzy-logic-based fourth-order generalized integrator (FOGI) frequency locked loop (FLL) based control for optimal operation of solar energy conversion system (SECS) having distribution static compensator (DSTATCOM) capabilities along with supplying active power to the distribution network. The proposed SECS is multifunctional having capabilities of power factor correction, load balancing, and harmonics mitigation in a three-phase distribution system. The FOGI-FLL has higher order filtering capabilities compared to conventional algorithms. The frequency tracking capabilities of proposed control technique exhibit better performance as compared to a conventional algorithm. The comparative performance of harmonics filtering and frequency tracking capabilities is demonstrated between FOGI-FLL and various conventional algorithms. Simulation results demonstrate the behavior of the system at various conditions. To validate the proposed algorithm, a prototype is developed and test results demonstrate the reliable performance of the system at various conditions, such as load unbalancing, variable insolation, solar photovoltaic to DSTATCOM mode at abnormal grid conditions, such as distorted grid voltages, unbalanced grid voltages, voltage sag, and swell. The total harmonics distortions of grid voltages and currents are found well within limit of the IEEE 519 standard.
Intelligent Impulsive Synchronization of Nonlinear Interconnected Neural Networks for Image Protection

Inspired by security applications in the industrial Internet of things, this paper focuses on the usage of impulsive neural network (NN) synchronization technique for intelligent image protection against illegal swiping and abuse. A class of nonlinear interconnected NNs with transmission delay and random impulse effect is first formulated and analyzed in this paper. In order to make network protocols more flexible, a randomized broadcast impulsive coupling scheme is integrated into the protocol design. Impulsive synchronization criteria are then derived for the chaotic NNs in the presence of nonlinear protocol and random broadcast impulse, with the impulse effect discussed. Illustrative examples are provided to verify the developed impulsive synchronization results and to show its potential application in image encryption and decryption.
Phylogeography of the wild Lager‐brewing ancestor (Saccharomyces eubayanus) in Patagonia

Saccharomyces eubayanus is the close relative of the Lager-brewing yeast and was firstly found in North Patagonia associated with Nothofagus trees. In recent years additional strains were found in North America, Asia and New Zealand, and genomic analyses showed the existence of two main populations of this yeast, both of them present in Patagonia. Here, we performed the most comprehensive study of S. eubayanus in Patagonia natural environments (400 samples) and confirmed that this region has the highest isolation success rate for this species described worldwide (more than 10-fold). The genetic characterization of 200 isolates (COX2, DCR1, intFR) revealed five geographically structured subpopulations. We hypothesized that marine ingressions and glaciations, which shaped the Patagonian landscape, contributed on population differentiation. The first large screening of fermentation performance of 60 wild S. eubayanus strains indicated which subpopulations would be more suitable for beer production.
On The Security of AoA Estimation

Angle of Arrival (AoA) estimation has found its way to a wide range of applications. Much attention have been paid to study different techniques for AoA estimation and its applications for jamming suppression, however, security vulnerability issues of AoA estimation itself under hostile activity have not been paid the same attention. In this paper, the problem of AoA estimation in Rician flat fading channel under jamming condition is investigated. We consider the scenario in which a receiver with multiple antenna is trying to estimate the AoA of the specular line of sight (LOS) component of signal received from a given single antenna transmitter using a predefined training sequence. A jammer equipped with multiple antennas is trying to interrupt the AoA estimation phase by sending an arbitrary signal. We derive the optimal jammer and receiver strategies in various scenarios based on the knowledge of the opponent strategies and the available information about the communication channel. In all scenarios, we derive the optimal jammer signal design as well as its optimal power allocation policy. The results show the optimality of the training based Maximum Likelihood (ML) AoA estimator in case of randomly generated jamming signal. We also show that, the optimal jammer strategy is to emit a signal identical to the predefined training sequence turning the estimation process into a highest power competition scenario in which the detected AoA is the one for the transmitting entity of higher power. The obtained results are supported by the provided computer simulation.
Visual Sensor Network Reconfiguration with Deep Reinforcement Learning

We present an approach for reconfiguration of dynamic visual sensor networks with deep reinforcement learning (RL). Our RL agent uses a modified asynchronous advantage actor-critic framework and the recently proposed Relational Network module at the foundation of its network architecture. To address the issue of sample inefficiency in current approaches to model-free reinforcement learning, we train our system in an abstract simulation environment that represents inputs from a dynamic scene. Our system is validated using inputs from a real-world scenario and preexisting object detection and tracking algorithms.
Computational-Based Study of QuEChERS Extraction of Cyclohexanedione Herbicide Residues in Soil by Chemometric Modeling

Assessment of two buffered QuEChERS (quick, easy, cheap, effective, rugged, and safe) versions (i.e., citrate and acetate) modified by including methanol to recover the residues of three cyclohexanedione oxime (CHD) herbicides and three of their byproducts from agricultural soil was performed. In this context, a full second-order face-centered factorial experimental design was developed to quantify the influences of the main five variables (i.e., extraction time, water content, soil weight, and extraction solvent volume and composition) on the target compound recoveries. The fitting equations satisfactorily described the extraction process behavior. The mathematical models also showed the most influencing independent variables (i.e., extraction solvent composition and soil weight). Handling simpler expressions was possible with the acetate QuEChERS but not with the citrate QuEChERS. The recoveries of the CHD residues were close to 100% after performing the extraction under suitable conditions. Furthermore, dispersive solid-phase extraction (dSPE) clean-up steps were assessed to reduce the matrix effect in mass spectrometry. In this sense, the citrate QuEChERS in combination with the PSA + C18 clean-up step was the best option for the extraction of CHD residues.
Evaluating answer quality across knowledge domains: Using textual and non‐textual features in social Q&A

As an increasing important source of information and knowledge, social questioning and answering sites (social Q&A) have attracted significant attention from industry and academia, as they address the challenge of evaluating and predicting the quality of answers on such sites. However, few previous studies examined the answer quality by considering knowledge domains or topics as a potential factor. To fill this gap, a model consisting of 24 textual and non‐textual features of answers was developed in this study to evaluate and predict answer quality for social Q&A, and the model was applied to identify and compare useful features for predicting high‐quality answers across four knowledge domains, including science, technology, art, and recreation. The findings indicate that review and user features are the most powerful indicators of high‐quality answers regardless of knowledge domains, while the usefulness of textual features (length, structure, and writing style) varies across different knowledge domains. In the future, the findings could be applied to automatically assessing answer quality and quality control in social Q&A.
A Test Bench for Evaluating Communication Delays in LoRaWAnapplications

The Internet of Things (IoT) promises to deeply change the way we live and work thanks to the availability of ubiquitous smart objects. When flexibility and mobility are a must, wireless connections have to be implemented, introducing additional requirements: large coverage, low complexity and low power consumption. Low Power Wide Area Networks (LPWAN) have been proposed as a possible solution, thus several standards appeared in the recent past. Among others, LoRaWAnis emerging as a reference technology, thanks to its open nature and the many allowed modules. The available relevant literature includes several works about theoretical models of obtainable performance; however, no one discusses infrastructure-related delays. In this work, a methodology for the experimental evaluation of LoRaWAninfrastructure performance in realworld scenarios is proposed. When a reference implementation is adopted, results show that the Network Server task is one of the most demanding process (on the order of 0.5s for the considered setup).
Advantages and Limitations of Current Techniques for Analyzing the Biodistribution of Nanoparticles

Nanomedicines are typically submicrometer-sized carrier materials (nanoparticles) encapsulating therapeutic and/or imaging compounds that are used for the prevention, diagnosis and treatment of diseases. They are increasingly being used to overcome biological barriers in the body to improve the way we deliver compounds to specific tissues and organs. Nanomedicine technology aims to improve the balance between the efficacy and the toxicity of therapeutic compounds. Nanoparticles, one of the key technologies of nanomedicine, can exhibit a combination of physical, chemical and biological characteristics that determine their in vivo behavior. A key component in the translational assessment of nanomedicines is determining the biodistribution of the nanoparticles following in vivo administration in animals and humans. There are a range of techniques available for evaluating nanoparticle biodistribution, including histology, electron microscopy, liquid scintillation counting (LSC), indirectly measuring drug concentrations, in vivo optical imaging, computed tomography (CT), magnetic resonance imaging (MRI), and nuclear medicine imaging. Each technique has its own advantages and limitations, as well as capabilities for assessing real-time, whole-organ and cellular accumulation. This review will address the principles and methodology of each technique and their advantages and limitations for evaluating in vivo biodistribution of nanoparticles.
Scanned bi-orthogonal radiographs as a source for 3D cephalometric data

We have developed a visualization environment, CEPH, for the analysis of plain film, biothogonally registered, head x-rays, i.e., cephalograms. Most importantly this program facilitates the collection of 3D landmark data from biorthogonal cephalogram pairs, i.e., correlated frontal and lateral images. To this end we have implemented tools for: contrast enhancement, image compression, morphological feature extraction, and grayscale shape recognition for automatic landmark detection. These data are useful for clinical craniofacial: (1) diagnosis, (2) treatment planning, (3) computed-assisted surgery, and (4) post-procedure follow-up. We are currently using the CEPH visualization environment to produce average landmark sets from the nearly 2000 'Bolton Standards' images. These images were collected for the Bolton-Brush Growth Study at Case Western Reserve University. Two dimensional landmark data captured from these images are universally recognized and used as diagnostic and treatment 'norms. ' We are planning to make new three dimensional Bolton Standards data available this year. To this end we have scanned all of the Bolton Standards cephalograms at 2400 by 2400 resolution with 12 bits of grayscale information on a TDI scanner.
High Density TSV-Free Interposer (TFI) Packaging with Submicron Cu Damascene RDLs for Integration of CPU/GPU and HBM

TSV-Free Interposer (TFI) packaging technology was developed for central/graphics processing unit (CPU/GPU) and stacked memory system-in-package (SiP) applications. TFI package is targeting to accommodate thick module as High Bandwidth Memory (HBM) and wide-bus interconnections between CPU/GPU and HBM in a package with cost-effective process. The redistribution layer structure was studied based on inter-chip connectivity between CPU/GPU and HBM as well as signal integrity and power integrity. This study demonstrated capability of TFI package in view of electrical performance, cost effectiveness and process challenges. The process challenges were overcome by co-design modeling and process optimizations.
An Enhanced 64-Element Dual-Polarization Antenna Array Package for W-Band Communication and Imaging Applications

An organic-based multi-layered phased-array antenna package for 94-GHz radar imaging and communication applications is hereby introduced. The package incorporates 64 dual-polarized antenna elements. Direct antenna probing measurements on a fabricated package and full-wave electromagnetic (EM) simulation of the complete design show over 8 GHz bandwidth and 2 dBi gain at 94 GHz. 64-element beam forming and beam steering are evaluated through EM simulations showing 12-degree half-power beam width and lower than -20dB side levels. A novel flip-chip assembly process is introduced to improve module-level yield and scalability by attaching the package with one die that consists of four joint ICs.
The Study of the Electrocardiography Monitoring for the Elderly Based on Smart Clothes

Aging society leads more demands on health care system. Study shows that cardiovascular diseases are the most common and threatening diseases to the elderly. Moreover, more and more elderly live alone recently. Therefore, a total solution for elderly homecare leads the way. In this study, we develop smart clothes to record 3 lead electrocardiography (ECG). Our system consists of (1) the conductive fiber clothes with four electrodes to acquire physiological signals, (2) a gateway to digitize, process and upload raw data to the server, and (3) the service server to analyze data and make a health profile. The system had been applied to the elderly community to evaluate performance. The experiment results show the average accuracy of ECG data is 86.82%. Thirty-five volunteers (age > 65, 15 male and 20 female) feel the smart clothes comfortable and easy to use than the traditional medical device.
Automatic Reference-Based Evaluation of Pronoun Translation Misses the Point

We compare the performance of the APT and AutoPRF metrics for pronoun translation against a manually annotated dataset comprising human judgements as to the correctness of translations of the PROTEST test suite. Although there is some correlation with the human judgements, a range of issues limit the performance of the automated metrics. Instead, we recommend the use of semi-automatic metrics and test suites in place of fully automatic metrics.
Increasing Motor Noise Impairs Reinforcement Learning in Healthy Individuals

Abstract Motor variability from exploration is crucial for reinforcement learning as it allows the nervous system to find new task solutions. However, motor variability from noise can be detrimental to learning and may underlie slowed reinforcement learning performance observed in individuals with cerebellar damage. Here we examine whether artificially increasing noise in healthy individuals slows reinforcement learning in a manner similar to that seen in patients with cerebellar damage. Participants used binary reinforcement to learn to rotate their reach angle in a series of directions. By comparing task performance between conditions with different levels of added noise, we show that adding a high level of noise—matched to a group of patients with cerebellar damage—slows learning. In additional experiments, we show that the detrimental effect of noise may lie in reinforcing incorrect behavior, rather than not reinforcing correct behavior. By comparing performance between healthy participants with added noise and a group of patients with cerebellar damage, we found that added noise does not slow the learning of the control group to the same degree observed in the patient group. Using a mechanistic model, we show that added noise in the present study matched patients’ motor noise and total learning. However, increased exploration in the control group relative to the group with cerebellar damage supports faster learning. Our results suggest that motor noise slows reinforcement learning by impairing the mapping of reward to the correct action and that this may underlie deficits induced by cerebellar damage.
Knowledge Graph Representation via Similarity-Based Embedding

Knowledge graph, a typical multi-relational structure, includes large-scale facts of the world, yet it is still far away from completeness. Knowledge graph embedding, as a representation method, constructs a low-dimensional and continuous space to describe the latent semantic information and predict the missing facts. Among various solutions, almost all embedding models have high time and memory-space complexities and, hence, are difficult to apply to large-scale knowledge graphs. Some other embedding models, such as TransE and DistMult, although with lower complexity, ignore inherent features and only use correlations between different entities to represent the features of each entity. To overcome these shortcomings, we present a novel low-complexity embedding model, namely, SimE-ER, to calculate the similarity of entities in independent and associated spaces. In SimE-ER, each entity (relation) is described as two parts. The entity (relation) features in independent space are represented by the features entity (relation) intrinsically owns and, in associated space, the entity (relation) features are expressed by the entity (relation) features they connect. And the similarity between the embeddings of the same entities in different representation spaces is high. In experiments, we evaluate our model with two typical tasks: entity prediction and relation prediction. Compared with the state-of-the-art models, our experimental results demonstrate that SimE-ER outperforms existing competitors and has low time and memory-space complexities.
Confirming TDP2 mutation in spinocerebellar ataxia autosomal recessive 23 (SCAR23)

Objective To address the relationship between mutations in the DNA strand break repair protein tyrosyl DNA phosphodiesterase 2 (TDP2) and spinocerebellar ataxia autosomal recessive 23 (SCAR23) and to characterize the cellular phenotype of primary fibroblasts from this disease. Methods We have used exome sequencing, Sanger sequencing, gene editing and cell biology, biochemistry, and subcellular mitochondrial analyses for this study. Results We have identified a patient in the United States with SCAR23 harboring the same homozygous TDP2 mutation as previously reported in 3 Irish siblings (c.425+1G>A). The current and Irish patients share the same disease haplotype, but the current patient lacks a homozygous variant present in the Irish siblings in the closely linked gene ZNF193, eliminating this as a contributor to the disease. The current patient also displays symptoms consistent with mitochondrial dysfunction, although levels of mitochondrial function in patient primary skin fibroblasts are normal. However, we demonstrate an inability in patient primary fibroblasts to rapidly repair topoisomerase-induced DNA double-strand breaks (DSBs) in the nucleus and profound hypersensitivity to this type of DNA damage. Conclusions These data confirm the TDP2 mutation as causative for SCAR23 and highlight the link between defects in nuclear DNA DSB repair, developmental delay, epilepsy, and ataxia.
Building Minimal Classification Rules for Breast Cancer Diagnosis

A rule based classifier is widely applied in breast cancer diagnosis. The classifier with a good performance of disease classification have been developed and highly required over the past decades. Since classification rules are derived from previous diagnosis with a large amount of features, it challenges to build a minimal number of rules with high performance while retaining all diagnosis information. The Principal Component Analysis (PCA) is known as a lossless data reduction technique with good classification performance. Therefore, this paper aims at finding the best performance classifier giving minimal classification rules by employing PCA. Based on experiment result on Wisconsin Breast Cancer data set, the J48 decision tree classifier is found to be the best among the three classifiers: J48 decision tree, Reduced Error Pruning Tree, and Random Tree.