Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Test Device for Non-destructive Experimental Characterization of Void Evolution in Microscale Solder Joints subjected to Thermal Aging

There is a trend towards reduction in micro-bump size and pitch in 2.5D/3D packaging driven by increased bandwidth and power efficiency requirements. One of the reliability challenges that arises is the void evolution in the solder joint volume of the micro-bump accompanied by the growth of Cu-Sn IMCs in the Cu pillar after thermal aging for long periods of time. In this study, we describe a methodology to fabricate test devices with multiple sub-30 µm pitch Cu-Sn-Cu junctions, with the capability to observe the growth of voids in the micro-bumps in a non-destructive manner. Typically used material and void characterization methods for microbump interconnects are destructive, requiring cross-sectioning and polishing of the samples before observation of voids in solder joints. The cross-sectioning is cumbersome, results in loss of data, and once cross-sectioned, the samples cannot be used for further thermal aging. A non-destructive method of characterization assures continuity in the experiments during the void evolution.The fabricated test device consists of multiple sub-30 µm pitch Cu-Sn-Cu junctions that are directly observable under a Scanning Electron Microscope(SEM). This setup can be used to do experiments ex-situ (by periodically pausing experiments, to measure void growth under SEM) or in-situ (with measurements of void growth in real-time under SEM). The test devices can also be subjected to current via a 4-wire Kelvin sensing probe setup attached to 3-axis piezo stages with a resolution of 50 nm. Multiple devices with varying sizes of solder bumps are fabricated on a single die. The dies are aged in an environmental chamber at a temperature of 175 ◦C for hundreds of hours. They were periodically taken out of the environmental chamber and the void evolution in the solder joint on the devices measured under both optical microscopes and under an SEM. Finally, the effect of different bump sizes on the reliability of the solder joints is reported in this study.
Validity of Molecular Dynamics for the Simulation of Soft Matter

We examine the validity of molecular dynamics simulations for a simple one-dimensional system having a piecewise continuous linear repulsive potential wall with a constant slope a. The inevitable energy change ΔE that occurs in the collision with the potential wall is shown to be dependent on only two parameters α and μ ≡ aτ/p0, where α is a fraction of the time step τ immediately after the contact with the potential wall and p0 is the momentum just before the collision. The whole space of parameters α and μ can be divided into an infinite number of regions, where each region generates separately a positive or negative energy change ΔE. The envelope of |ΔE| shows a power law behavior |ΔE| ∝ μβ with the exponent β ≈ 1.08. The average of ⟨|ΔE|⟩α over a uniform distribution of α shows a broken power law behavior ⟨|ΔE|⟩α ∝ μβ with β ≈ 1.98 at larger μ values and β ≈ 0.99 at smaller μ values.
Improvements in Brazilian Portuguese Speech Emotion Recognition and its extension to Latin Corpora

Speech emotion recognition (SER) is challenging, language dependent, and, as with any supervised learning task constrained by data availability. An important aspect in SER modeling is the selection of optimal features to have an unbiased approach towards recognizing emotions while retaining accuracy. Brazilian Portuguese (BP) is a dialect of the 6th most spoken language in the world yet BP SER studies are scarce. This work aims to explore the solely available BP SER database, providing better features to increase the recognition rate for all emotions, and then proposing a simple but robust multi-corporal SER model. For all corpora analyzed in this work, an improvement of up to 9% in mean accuracy is achieved and the obtained recognition rate indicates that the proposed feature sets show comparatively less biased behavior towards all emotions. Also, we prescribe a combination of different metrics to be used with two cepstral features to obtain a higher recognition rate. Our proposed state of the art Latin multi-corporal model contains few features, and achieves outperforming results with a classical machine learning classifier, compared to previously exercised complex features, algorithms and architectures, by yielding the best recognition rate.
Prediction of Real Popularity based on Sample Debias

Predicting the popularity of a topic is an important task for modeling and analyzing the trending patterns of social network topics. Deep learning-based methods are one of the mainstream approaches to deal with this task. Among them, the propagation structure is used to assist in predicting topic popularity, which can well model the evolution process of propagation. However, existing methods essentially predict the sampled value of a topic on the sampled data, which is different from the recorded value in the real world. In this paper, we mainly complete the task that uses the sampled data and historical real values to predict the future real forwarding value. We propose a temporal analysis model based on Long Short-Term Memory (LSTM) for historical real values and apply a Graph Convolutional Network (GCN) on the sampled data to extract the spatial features of propagation to assist in final prediction. To reduce the loss caused by using sampled features to predict the true value, we design a sub-module termed debias regressor that corrects bias after feature extraction. Passing the original predicted value through the regression debiasing sub-module can make the predicted value better fit the true records. Experimental results show that our proposed method has a great improvement in reality prediction compared with other popularity prediction methods, and is of great help in practical applications.
Affective Robot Behavior Improves Learning in a Sorting Game

Nonverbal communication in the field of education can allow teachers to emotionally support their students and improve educational experience and performance. Robot nonverbal movements have been shown to improve both subjective experiences and task performance, and this work investigates whether affective robot behavior can improve human learning. This is tested using an online sorting game where players learn easy or difficult rules, aided by robot feedback videos that contain either neutral or affective movements. Results indicate that affective robot behavior improves learning of the sorting rules and reduces the perceived difficulty of the task. Extensions include expanding the features used to determine the robot feedback and increasing the possible robot motions to create a rich set of robot feedback options to personalize the education experience further for the student.
Missing Value Imputation for Diabetes Prediction

Machine learning (ML) models have been widely used to improve the accuracy and efficiency of various types of disease diagnostic tasks. However, it is still challenging to apply ML models to perform diabetes-related prediction tasks mainly because patients' health records are sparse and have a vast amount of missing values. Missing values often break the diabetes prediction pipelines, posing challenges to existing approaches. Such problem deteriorates significantly when critical attribute values (e.g., blood test results on HbAlc, FPG and OGTT2hr) are missing. In this paper, we introduce a large-scale diabetes-related dataset named Chronic Disease Management System (CDMS) dataset, which collects the clinical records of more than 700,000 visits of over 65,000 patients across eight years. CDMS is anonymously collected and has a high percentage of missing values on several critical attributes for diabetes prediction. If not being dealt with carefully, the missing values will cause significant performance degradation of the applied ML models. In this paper, we also investigate the effectiveness of multiple data imputation methods through conducting extensive experiments using CDMS. Experimental results show that k-Nearest Neighbor Imputation (KNNI) performs better than other methods in this diabetes prediction task. Specifically, with KNNI applied, the diabetes prediction accuracy and precision are both over 0.8 using various ML predictive models.
Evaluating Path Selection Strategies with Blockchain-based Routing in Multi-Domain SDNs

Software-Defined Networking (SDN) has emerged as one of the most prolific inventions in the general area of computer communications over the past decade. Among many of the touted benefits, SDN architecture facilitated by OpenFlow has the potential to help network administrators to provide End-to-End (E2E) Quality of Service (QoS)-guaranteed paths for flows among networks while providing finer-granular flow management along with global network view. In our previous work, we have introduced QoSChain to amalgamate the advantages of blockchain and SDN for inter-network QoS provisioning to orchestrate an agile and software-driven traffic management framework in order to facilitate cross-network policy automation, assurance, E2E visibility, control and validation. In this study, we focus on the impact of the path selection strategies on the performance of the overall QoSChain performance. Specifically, we exploit three simple but effective path selection strategies, First Feasible Path Selection (FFPS), Random Feasible Path Selection (RFPS), and Minimum Hop Path Selection (MHPS), to evaluate their performance, impact, and overhead in terms of the flow setup time, message overhead, and network resource consumption metrics. Our preliminary simulations results show that the path selection plays an important role in the overall performance with hop count minimization delivering a superior performance offset by an initial higher setup times.
Optimizing RF-DC Converters Input Impedance Selection

This paper explores the optimization of RF-DC converters, focusing on the impacts of the input power on the design of the impedance matching networks. In order to implement this analysis, a source pull simulation was performed, and two circuits were designed to compare its behaviours. This analysis made it possible to conclude that when using a voltage multiplier as the RF-DC solution, the diode requires high reactance at the source for low input power values but for high input powers, the diode requires an impedance approaching a very low impedance. Both high and low input power circuits achieved approximately 40 % efficiency.
Computer-Aided COVID-19 Screening from Chest CT-Scan using a Fuzzy Ensemble-based Technique

The worldwide breakout of the novel COVID-19 has resulted in one of the worst epidemics in modern times since World War II. Although various vaccinations are being produced, their efficacy remains a considerable hurdle. This is especially true when new virus strains emerge. The main challenge to combating this pandemic is diagnosing and isolating COVID-19 positive cases as early as possible. As a result, COVID-19 needs to be detected early and accurately to prevent its spread. This paper proposes a computer-aided automated COVID-19 detection tool based on Computed Tomography (CT-scan) images of lungs. The proposed approach applies an ensemble technique based on Sugeno Fuzzy Integrals with convolutional neural networks (CNNs) as the base model. The lack of COVID-19 data makes it challenging to train a standard CNN from scratch, so we use a transfer learning approach instead of training the base classifiers, VGG-16, InceptionResnetV2, and Xception. We apply the gained knowledge in the target domain of small CT-scan data, considering ImageNet dataset as the source domain. We have also adapted image pre-processing techniques to remove noises so that the model can only focus on specific features. Our proposed framework achieves 98.99% accuracy on a publicly available dataset and outperforms the existing state-of-the-art methods. Experimental results and comparative analysis with baselines establish the need and effectiveness of our proposed model.
Advanced Topics in Information Dynamics

This work is sequel to the book "A Treatise in Information Geometry", submitted to vixra in late 2009. The aim of this dissertation is to continue the development of fractal geometry initiated in the former volume. This culminates in the construction of first order self-referential geometry, which is a special form of 8-tensor construction on a differential manifold with nice properties. The associated information theory has many powerful and interesting consequences. Additionally within this treatise, various themes in modern mathematics are surveyed- Galois theory, Category theory, K-theory, and Sieve theory, and various connections between these structures and information theory investigated. In particular it is demonstrated that the exotic geometric analogues of these constructions - save for Category theory, which is foundational - form special cases of the self referential calculus.
Heterogeneous Trust-based Social Recommendation via Reliable and Informative Motif-based Attention

Recently, social recommender systems are promising to enhance the quality of recommendations by integrating user-user social networks and user-item bipartite networks. However, there are very little success in that direction. Pessimistic findings are ascribed mainly to three factors. (1) Very few works focus on the identification of implicit neighbors to overcome the sparsity problem of explicit links. Furthermore, these works do not con-sider higher-order and complex patterns of interactivity among users. (2) Very less number of trust-based social recommender systems integrate heterogeneous trust relationships, and this het-erogeneity is considered for explicit social links only. Moreover, these works ignore user-user heterogeneous trust relationships of higher-order network structure and user-item heterogeneous interactivity. (3) Existing works overlook the reliability (or lack of that) problem of links in higher-order and complex patterns of interactivity. To address the above mentioned challenges, we develop, a Graph $\bar{C}$ onvolutional $\bar{N}$ etworks via $\bar{R}$ eliable and $\bar{I}$ nformative $\bar{M}$ otif-based Attention Model (CNRIM). To the best of our knowledge, it is the first work that investigates user-user heterogeneous trust relationships and user-item heterogeneous interactivity via reliable, informative motif-based attention mech-anisms. Varying reliability and informative motifs introduce the heterogeneity. The experiments on publicly available real-world datasets, and empirical analyses present the superiority of our model over popular baselines.
Angular spectra of the intrinsic galaxy ellipticity field, their observability and their impact on lensing in tomographic surveys

Subject of this paper are intrinsic ellipticity correlations between galaxies, their statistical properties, their observability with future surveys and their interference with weak gravitational lensing measurements. Using an angular momentum-based, quadratic intrinsic alignment model we derive correlation functions of the ellipticity components and project them to yield the four non-zero angular ellipticity spectra $C^\epsilon_E(\ell)$, $C^\epsilon_B(\ell)$, $C^\epsilon_C(\ell)$ and $C^\epsilon_S(\ell)$ in their generalisation to tomographic surveys. For a Euclid-like survey, these spectra would have amplitudes smaller than the weak lensing effect on nonlinear structures, but would constitute an important systematic. Computing estimation biases for cosmological parameters derived from an alignment-contaminated survey suggests biases of $+5\sigma_w$ for the dark energy equation of state parameter $w$, $-20\sigma_{\Omega_m}$ for the matter density $\Omega_m$ and $-12\sigma_{\sigma_8}$ for the spectrum normalisation $\sigma_8$. Intrinsic alignments yield a signal which is easily observable with a survey similar to Euclid: While not independent, significances for estimates of each of the four spectra reach values of tens of $\sigma$ if weak lensing and shape noise are considered as noise sources, which suggests relative uncertainties on alignment parameters at the percent level.
A Study on Big Data Utilization Intention of Small and Medium-sized Manufacturing Companies

Big data, which is attracting attention as a game-changer in the manufacturing industry and innovative technology, is an important factor for securing the competitiveness of manufacturing companies and innovative growth. However, introducing and using big data in manufacturing companies requires a lot of effort from the company and the capabilities of its members due to the characteristics of the manufacturing process. To apply big data to manufacturing, developed countries and Korea are building pilot projects and use cases as experimental steps to this end. Through the review of previous studies, it was confirmed that big data utilization by manufacturing companies requires a lot of academic research from a management perspective. Therefore, this study examined the applicable fields, the actual conditions and problems related to big data utilization by manufacturing companies by checking Industry 4.0, changes in the manufacturing industry, and related prior studies for big data utilization in the manufacturing industry. It also aims to examine the intention of big data utilization through empirical research on the previous researches on big data in small and medium-sized manufacturing industries.
Instabilities and topological defects in active nematics

We study a continuum model of an extensile active nematic to show that mesoscale turbulence develops in two stages: i) ordered regions undergo an intrinsic hydrodynamic instability generating walls, lines of strong bend deformations; ii) the walls relax by forming oppositely charged pairs of defects. Both creation and annihilation of defect pairs reinstate nematic regions which undergo further instabilities, leading to a dynamic steady state. We compare this with the development of active turbulence in a contractile active nematic.
Semi-Decentralized File Sharing as a Service

Files are the most widely used format for recording data in the modern world. Files are either exchanged through a storage medium or transported from one computer to another via a file sharing platform. In the latter case, most services are centralised meaning that a file copy is freely available to the platform provider exposing users to the risk of sensitive information leakage. Alternatively, some protocols ensure that a third party will not be able to maintain a copy of the data. This work presents a solution to this problem, providing a Peer-to-Peer, semi-decentralized file sharing system as a service enabling the end-user to share any file in a secure manner and ensuring no usable copy of the file is stored anywhere but the sender’s and recipient’s file system while the recipient can ensure that the file has not been tampered with in any way using digital signatures. A microservice-based novel application has been developed that implements this functionality while abstracting the complexity to a simple browser-based user interface.
I Can’t Believe That Happened! : Exploring Expressivity in Collaborative Storytelling with the Tabletop Robot Haru

Collaborative storytelling has long been a goal of social robotics, however, much of this research is limited in interactivity or assumes that story content is curated. In this paper, we present a working fully-automatic collaborative storytelling robot, which can collaborate with a person to create a unique, improvised story by using a large-scale neural language model to dynamically generate continuations to a story. Because effective storytelling requires engaging the emotions of participants, we explore several modalities of procedurally-generated expressivity: 1. an expressive text-to-speech voice with several delivery styles, 2. physical and verbal reactions performed by the robot, and 3. an external display used to show instructions and graphics during storytelling.To understand the issues associated with improvised collaborative storytelling with a social robot, we conduct an online survey and elicitation study with a group of online observers of collaborative storytelling gameplay, comparing several expressivity strategies in terms of storytelling-related characteristics, expressivity characteristics, and personality traits as measured by RoSAS. This evaluation showed that expressivity strategies using both emotive voice and performed reactions were perceived to be more competent storytellers and more strongly associated with positive personality traits.
Context-Dependent Spatial Representations in the Hippocampus using Place Cell Dendritic Computation

The hippocampus in rodents encodes physical space using place cells that show maximal firing in specific regions of space - their place fields. These place cells are reused across different contexts and environments with uncorrelated place fields. Though place fields are known to depend on distal sensory cues, even identical environments can have completely different place fields if the contexts are different. We propose a novel place cell network model for this feature using two frequently overlooked aspects of neural computation - dendritic morphology and the spatial co-location of spatiotemporally co-active afferent synapses - and show that these enable the reuse of place cells to encode different maps for environments with identical sensory cues.
Two-Stage Deep Learning Approach for Speech Enhancement and Reconstruction in The Frequency and Time Domains

Deep learning has recently shown promising improvement in the speech enhancement field, due to its effectiveness in eliminating noise. However, a drawback of the denoising process is the introduction of speech distortion, which negatively affects speech quality and intelligibility. In this work, we propose a deep convolutional denoising autoencoder-based speech enhancement network that is designed to have an encoder deeper than the decoder, to improve performance and decrease complexity. Furthermore, we present a two-stage learning approach, in which denoising is performed in the first frequency domain stage using magnitude spectrum as a training target; while, in the second stage, further denoising and speech reconstruction are performed in the time domain. Results show that our architecture achieves 0.22 improvement in the overall predicted mean opinion score (Covl) over state of the art speech enhancement architectures, using the Valentini dataset benchmark. Moreover, the architecture was trained using a larger dataset and tested using a mismatched test corpus, to achieve 0.7 and 6.35% improvement in Perceptual Evaluation of Speech Quality (PESQ) and Short Time Objective Intelligibility (STOI) scores, respectively, compared to the noisy speech.
Robot Vulnerability and the Elicitation of User Empathy

This paper describes a between-subjects Amazon Mechanical Turk study (n = 220) that investigated how a robot’s affective narrative influences its ability to elicit empathy in human observers. We first conducted a pilot study to develop and validate the robot’s affective narratives. Then, in the full study, the robot used one of three different affective narrative strategies (funny, sad, neutral) while becoming less functional at its shopping task over the course of the interaction. As the functionality of the robot degraded, participants were repeatedly asked if they were willing to help the robot. The results showed that conveying a sad narrative significantly influenced the participants’ willingness to help the robot throughout the interaction and determined whether participants felt empathetic toward the robot throughout the interaction. Furthermore, a higher amount of past experience with robots also increased the participants’ willingness to help the robot. This work suggests that affective narratives can be useful in short-term interactions that benefit from emotional connections between humans and robots.