Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study of Speech, Speaker and Emotion Recognition Using Mel Frequency Cepstrum Coefficients and Support Vector Machines

With the advent of digitization of every possible avenue, Automatic Speech and Emotion Recognition is a widely researched topic that is a subset of Human Computer Interface (HCI) and has a range of applications. With machines taking over many menial jobs it has become important for the computer to understand us as we understand it. Features such as MFCC, pitch and amplitude are extracted from a given sample and run across the existing and growing database of training samples. MFCC is being used to detected speaker and utterance, while SVM is used to distinguish the emotion of the sample given. An SVM classifier differentiates between anger, happiness, fear, sadness and updates the database as it goes.
Alterations of Gut Microbiota in Cholestatic Infants and Their Correlation With Hepatic Function

Cholestasis is a major hepatic disease in infants, with increasing morbidity in recent years. Accumulating evidence has revealed that the gut microbiota (GM) is associated with liver diseases, such as non-alcoholic steatohepatitis, cirrhosis, and hepatocellular carcinoma. However, GM alterations in cholestatic infants and the correlation between the GM and hepatic functions remain uninvestigated. In this study, 43 cholestatic infants (IC group) and 37 healthy infants (H group) were enrolled to detect GM discrepancies using 16S rDNA analysis. The diversity in the bacterial community was significantly lower in the IC group than that in the H group (P = 0.013). After determining the top 10 abundant genera of microbes in the IC and H groups, we found that 13 of them were differentially enriched, including Bifidobacterium, Bacteroides, Streptococcus, Enterococcus, and Staphylococcus. As compared with the H group, the IC group had a more complex GM co-occurrence network featured by three core nodes: Phyllobacterium, Ruminococcus, and Anaerostipes. In addition, the positive correlation between Faecalibacterium and Erysipelatoclostridium (r = 0.689, P = 0.000, FDR = 0.009) was not observed in the IC patients. Using the GM composition, the cholestatic patients can be distinguished from healthy infants with high accuracy [areas under receiver operating curve (AUC) > 0.97], wherein Rothia, Eggerthella, Phyllobacterium, and Blautia are identified as valuable biomarkers. Using KEGG annotation, we identified 32 functional categories with significant difference in enrichment of the GM of IC patients, including IC-enriched functional categories that were related to lipid metabolism, biodegradation and metabolism of xenobiotics, and various diseases. In contrast, the number of functions associated with amino acid metabolism, nucleotide metabolism, and vitamins metabolism was reduced in the IC patients. We also identified significant correlation between GM composition and indicators of hepatic function. Megasphaera positively correlated with total bilirubin (r = 0.455, P = 0.002) and direct bilirubin (r = 0.441, P = 0.003), whereas γ-glutamyl transpeptidase was positively associated with Parasutterella (r = 0.466, P = 0.002) and negatively related to Streptococcus (r = -0.450, P = 0.003). This study describes the GM characteristics in the cholestatic infants, illustrates the association between the GM components and the hepatic function, and provides a solid theoretical basis for GM intervention for the treatment of infantile cholestasis.
A Channel-level Pruning Strategy for Convolutional Layers in CNNs

A large number of parameters and computing operations of deep convolutional neural networks(CNNs) make it hinder to apply them to real-world scenarios. In this paper, we propose a channel-level pruning strategy for convolutional layers to reduce the number of parameters and computing operations in CNNs with no accuracy loss. First, we use “Squeeze-and-Excitation” block to extract the activation factors of each sample, which can be used to evaluate the importance of each channel. Second, we compute the overall weight of a specific channel by accumulating its activation factors generated by all training samples. Finally, we prune the redundant channels with low weight and thus yield compact network. On a colorectal pathology dataset, we reduce the number of channels and the convolution layer parameters by a factor of 5× and 21×, without any accuracy loss.
The Disappearing Robot: An Analysis of Disengagement and Distraction During Non-Interactive Tasks

Social Assistive Robots are mainly designed for tasks requiring the interaction with the user. However, they could also be involved in other non-interactive tasks which execution may potentially distract the user from his/her current activity. In this direction, we aim at evaluating the disengagement levels caused by a robot's movements in the human's surroundings. In particular, we consider a robotic system approaching an elder person to monitor his/her behavior, while he/she is occupied in carrying out a specific daily activity. We conducted a classic video analysis of human behaviors in order to identify relevant non-verbal disengagement signals such as the human gaze and pose variation when he/she is approached by the robot. Results obtained from ambient cameras are compared with the ones from the robot camera showing a moderate correlation between them. Additionally, the role of other contextual factors, such as the activity posture, approaching distance, and cognitive load are discussed in the direction of an automatic evaluation of the user's distraction to be used by the robot to plan its motion.
Improvement of the Vehicle's Onboard Diagnostic System by Using the Vibro-Diagnostics Method

The article analyzes ways to increase the operational reliability of trucks by monitoring and diagnosing their technical condition. Preventing sudden vehicles failures is possible with the help of built-in diagnostic systems. The article analyzes existing on-board and stationary diagnostic systems. The types of sensors and their signals used to diagnose the condition of vehicles systems are given. Methods of transformation and processing of signals are determined. The connections of structural and diagnostic parameters of the vehicles are established. The possibility of application of vibration diagnostics methods for assessing the technical condition and predicting the remaining life of the vehicle's clutch under operating conditions is explored. The authors proposed a way to improve the on-board diagnostics system with the help of a set of methods, rules and means necessary for measuring the parameters of the system's operation, converting them into diagnostic parameters to assess the technical state of the system under study. The article gives an example of an integrated system for diagnosing vehicle's clutch with vibration sensors.
T2KG: An End-to-End System for Creating Knowledge Graph from Unstructured Text

Knowledge Graph (KG) plays a crucial role in many modern applications. Nevertheless, constructing KG from unstructured text is a challenging problem due to its nature. Consequently, many approaches propose to transform unstructured text to structured text in order to create a KG. Such approaches cannot yet provide reasonable results for mapping an extracted predicate to its identical predicate in another KG. Predicate mapping is an essential procedure because it can reduce the heterogeneity problem and increase searchability over a KG. In this paper, we propose T2KG system, an end-to-end system with keeping such problem into consideration. In the system, a hybrid combination of a rule-based approach and a similarity-based approach is presented for mapping a predicate to its identical predicate in a KG. Based on prelim-inary experimental results, the hybrid approach improves the recall by 10.02% and the F-measure by 6.56% without reducing the precision in the predicate mapping task. Furthermore, although the KG creation is conducted in open domains, the system still achieves approximately 50% of F-measure for generating triples in the KG creation task.
Temporal and Flexible Automation of Machine Tools

Today, loading machine tools in small and middle sized enterprises is mainly done manually by machine operators. Low quantity and high variance in mechanical components production require a high flexible automation process which is usually not achievable for contract manufacturers. This paper describes a concept of a mobile robot platform – consisting of a collaborative robot, gripping technology and an intuitive task-based programming system – which enables small and medium-sized enterprises (SME) to temporary automate the machine loading process.
Classification of Nonalcoholic Fatty Liver Texture Using Convolution Neural Networks

Fatty liver disease is the major cause for the liver dysfunction and is highly prevalent in developed and developing nations. Fatty liver can progress into chronic diseases if left untreated. Depending on the density of accumulated fat in liver, the liver is diagnosed into three classes/grades namely Grade 1, Grade 2, and Grade 3 apart from being Normal. For fatty liver diagnosis, doctors use the texture properties of liver parenchyma to quantify the fat in liver. The texture properties of ultrasonic liver parenchyma changes with the proportion of fat and acts as a useful feature for the doctors in doing the diagnosis. The diagnostic accuracy of fatty liver is less due to minute variations observed in the texture properties. Hence to improve the classification accuracy in diagnosing the fatty liver, we propose a convolution neural network based computer-aided diagnosis algorithm for categorizing the ultrasound liver parenchyma texture into four classes. The proposed algorithm is analyzed using 1000 texture images comprising of 250 images belonging to each class. Performance analysis shows that the proposed framework classifies the texture with an accuracy of 93.5% when 80% and 20% of data used for training and testing respectively.
Preserving Community Feature Extraction And Mrmr Feature Selection For Link Classification In Complex Networks

Links prediction based on supervised learning is a main research topic in the field of complex network analysis. The core process of these methods is that the network is divided into training and target sets, then a classification model is used to learn the training set and forecast the missing links in target set. Such methods have two major challenges: first, we need to dig deep network information to define a set of features; Second, how to incorporate feature selection model to mine discriminative features. To solve the above problem, a model which integrates community features and mRMR feature selection was proposed. Such model first discovered global features associated with the link through the community, then used classical mRMR algorith-m metrics to measure the correlation between features, and filter out the best representative candidates by clearing noisy information. Experimental results show our proposed model can effectively improve the performance of link classification.
Acoustic Feature Extraction from Music Songs to Predict Emotions Using Neural Networks

We describe a scheme of emotion prediction from the music songs based on seven acoustic features from three different categories; timbre, rhythm, and dynamics. 20 full-length songs from different genre were selected. Two-dimensional emotion model (arousal-valence model) is adopted. The ratings of arousal and valence for these songs were calculated by multiple linear regression method. Neural network with two different parameter sets were trained by extracted features using 80% of the dataset. The trained network is tested on the remaining 20% of data. First neural network with 7 input layers, the default number of hidden layers (average input and output layers) and 4 output layers did not perform well (50% accuracy). The second neural network with the number of hidden layers equal to the number of input layers and 4 output layers performed a good job with 75% accuracy in predicting the emotion class. The result of the study indicates, that training a neural network can identify the statistical consistencies across acoustic features of music to predict the emotion.
Playing by the Book: Towards Agent-based Narrative Understanding through Role-playing and Simulation

Understanding procedural text requires tracking entities, actions and effects as the narrative unfolds (often implicitly). We focus on the challenging real-world problem of structured narrative extraction in the materials science domain, where language is highly specialized and suitable annotated data is not publicly available. We propose an approach, Text2Quest, where procedural text is interpreted as instructions for an interactive game. A reinforcement-learning agent completes the game by understanding and executing the procedure correctly, in a text-based simulated lab environment. The framework is intended to be more broadly applicable to other domain-specific and data-scarce settings. We conclude with a discussion of challenges and interesting potential extensions enabled by the agent-based perspective.
A Novel Approach for Churn Prediction Using Deep Learning

Customer retention in telecommunication is one of the prime issue in customer relationship management (CRM). The primary focus of CRM is on existing customer as it is difficult to acquire new customers. The main goal of churn prediction is to classify customers into churner & non-churner. Towards this, deep learning as they are equipped with large increasing data sizes and uncover hidden pattern insights, detects pattern, underlying risks and alert the Telecom Industry about customer behaviour with a better accuracy as compared to the traditional machine learning methods. In this paper, Deep learning by Convolutional Neural Network (CNN) is implemented for churn prediction and it showed good performance in terms of accuracy. The experimental results shows that the predictive model for churn prediction out performs with an accuracy of 86.85%, error rate of 13.15%, precision 91.08, recall 93.08%, F-score 92.06%.
Temperature Correction and Composite Dielectric Layer for Optimization of Plasmon Waveguide Resonance

Plasmon waveguide resonance (PWR) sensor is an extension of surface plasmon resonance (SPR) sensor, where temperature control has significant effect on the measurement reliability. In this article, a PWR sensing structure based on the composite dielectric layer was proposed, and the temperature correction mode was presented for the PWR sensors. The presented PWR sensor could be prepared with conventional film coating techniques, and its feasibility was demonstrated both theoretically and experimentally.
Multi-layer Perceptron Based Video Surveillance System

Conventional video surveillance systems involve continuous human monitoring of video feeds which is laborious and error prone. In this paper, we present a design for automation of video surveillance using behavior analysis and face recognition. Behavior analysis involves extraction of ‘motion vectors’ using multi-object tracking, which are then fed to a multi-layer perceptron. This is shown to be more adaptive than conventional techniques, requires lesser training time and gives improved performance as a result of requiring simpler networks as compared to typical deep network based approaches. Experiments conducted with two real world scenarios gave accuracy levels of above 75% and performance improvement of 7% against contemporary solutions. Face recognition involves facial feature vector extraction and uninformed search in the feature space guided by a Kohonen classifier, eliminating the need for deep-networks while requiring lesser computation than legacy techniques. Face recognition has been tested against the ‘Landmarked Faces in the Wild’ dataset.
Inflammation and pericarditis: Are neutrophils actors behind the scenes?

The morbidity of acute pericarditis is increasing over time impacting on patient quality of life. Recent clinical trials focused especially on clinical aspects, with a modest interest in pathophysiological mechanisms. This narrative review, based on papers in English language obtained via PubMed up to April 2018, aims at focusing on the role of the innate immunity in pericarditis and discussing future potential therapeutic strategies impacting on disease pathophysiology.
Optimizing High Performance Distributed Memory Parallel Hash Tables for DNA k-mer Counting

High-throughput DNA sequencing is the mainstay of modern genomics research. A common operation used in bioinformatic analysis for many applications of high-throughput sequencing is the counting and indexing of fixed length substrings of DNA sequences called k-mers. Counting k-mers is often accomplished via hashing, and distributed memory k-mer counting algorithms for large datasets are memory access and network communication bound. In this work, we present two optimized distributed parallel hash table techniques that utilize cache friendly algorithms for local hashing, overlapped communication and computation to hide communication costs, and vectorized hash functions that are specialized for fc-mer and other short key indices. On 4096 cores of the NERSC Cori supercomputer, our implementation completed index construction and query on an approximately 1 TB human genome dataset in just 11.8 seconds and 5.8 seconds, demonstrating speedups of 2.06× and 3.7×, respectively, over the previous state-of-the-art distributed memory k-mer counter.
Cooperative Task Scheduling for Computation Offloading in Vehicular Cloud

Technological evolutions in the automobile industry, especially the development of connected and autonomous vehicles, have granted vehicles more computing, storage, and sensing resources. The necessity of efficient utilization of these resources leads to the vision of vehicular cloud computing (VCC), which can offload the computing tasks from the edge or remote cloud to enhance the overall efficiency. In this paper, we study the problem of computation offloading through the vehicular cloud (VC), where computing missions from edge cloud can be offloaded and executed cooperatively by vehicles in VC. Specifically, computing missions are further divided into computing tasks with interdependency and executed in different vehicles in the VC to minimize the overall response time. To characterize the instability of computing resources resulting from the high vehicular mobility, a mobility model focusing on vehicular dwell time is utilized. Considering the heterogeneity of vehicular computing capabilities and the interdependency of computing tasks, we formulate an optimization problem for task scheduling, which is NP-hard. For low complexity, a modified genetic algorithm based scheduling scheme is designed where integer coding is used rather than binary coding, and relatives are defined and employed to avoid infeasible solutions. In addition, a task load based stability analysis of the VCC system is presented for the cases where some vehicles within the VC are offline. Numerical results demonstrate that the proposed scheme can significantly improve the utilization of computing resources while guaranteeing low latency and system stability.
The Preliminary Stage of the Algorithm for Detecting Information and Psychological Manipulation in Online Communities

The paper is devoted to the problem of detecting information and psychological manipulation (IPM) in online communities. The preliminary stage of the algorithm of monitoring online communities with the aim of detecting informational and psychologic manipulation is scrutinized. The components utilized at the stage are described. The functions performed by each component are outlined. The functional model of metasearch engine designed for finding relevant to an IPM task discussions is depicted. To devise methods and algorithms for searching for relevant discussions specialized thematic vocabularies, logic expressions, search query patterns, social and demographic characteristics, fusion algorithms are used. Diversity and heterogeneity of online communities and their structural differences are considered.
Multisource Fusion for Robust Road Detection Using Online Estimated Reliabilities

For highly available automated driving, a robust road estimation is indispensable. In order to tackle the challenges of this task, many works employ a fusion of multiple sources, e.g., visually detected lane markings, leading vehicle, digital maps, etc. However, each source has certain advantages and drawbacks depending on the operational scenarios. Hence, the assumption made by many existing approaches that the sources always are equally reliable for the fusion process is inappropriate. Therefore, this work proposes a novel concept by incorporating reliabilities into the multisource fusion so that the road estimation task can alternately select only the most reliable sources. Thereby, the reliability for each source is estimated online using classifiers trained with the sensor measurements, the past performance, and the context. Using real data recordings, experimental results show that the presented reliability-aware fusion increases the availability of automated driving up to 7 percentage points compared to the average fusion.
57–67-GHz Highly Compact Bidirectional 3-Bit Phase Shifter in 28-nm CMOS

This letter describes a highly compact, low-loss, high-bandwidth, bidirectional, passive 3-bit phase shifter (PS) implementation for 5G and WiGig applications at 57–67 GHz, fabricated in Taiwan Semiconductor Manufacturing Company 28-nm CMOS technology. First, a highly compact, differential quadrature hybrid coupler is used for the I/Q generation. Consuming only 0.006 mm2, it achieves 4.5-dB insertion loss (IL) with less than 1-dB amplitude imbalance, and 6° I/Q phase imbalance, for the entire frequency band. The hybrid coupler is followed by a novel compact transistors switching matrix to create the 3-bit, bidirectional, 360° PS, which provides impedance matching for all states. It consumes the smallest ever reported PS chip area of only 0.007 mm2 with state-of-the-art results of the maximum rms phase and gain errors of 6.3° and 0.94 dB, respectively. The measured IL was 10.8 dB between 57 and 67 GHz.
A Protection Method Based on Message Identification and Flow Monitoring for Managing the Congestion Arising From Network Attacks on Smart Substation

The information security of smart substations has become a major issue, as substations become increasingly dependent on communication networks. This letter analyzes the data flows in the communication networks of smart substations and proposes a protection method based on message identification and flow monitoring for managing the congestion arising from network attacks. This method implements the rapid identification and filtering of messages by condensing key information into a message information tag and then examines the tag along with the traffic rates at switches to identify and discard counterfeit messages. OPNET simulation results show that the method successfully identifies and discards counterfeit messages and avoids timeouts and packet drops in the transmission process of authentic messages during periods of congestion.