Miracle-dz/UniFAR-FTUs huggingface dataset:
A Mobile Mental Health Monitoring System: A Smart Glove

This paper proposes a mobile mental health monitoring system for the use during simple daily activities. The primary function of the system is to continuously monitors changes in skin temperature, galvanic skin resistance, and heart rate variability (inter-beat-intervals and cardiac vagal tone index). Changes in these recorded parameters can be later viewed and interpreted by an expert to provide insight about mental health and mental states. The architecture of the proposed system mainly consists of a wearable wireless multi-sensor glove, a permanent data storage unit (cloud database), and a user interface (web page). The wearable glove integrates a skin temperature sensor, a galvanic skin resistance sensor, and a pulse sensor, and it records then transmits the raw signals from the sensors through Wi-Fi to the cloud database. The data existing in the database can be retrieved, processed if needed, then displayed via the user interface. %THE SAMPLING RATE, THE TRANSMISSION RATE, AND THE CLOUD STORAGE CAPACITY The proposed system was tested for two simulated emotional situations: relaxation and annoyance. The recorded signals showed significant variability in the galvanic skin resistance and heart rate variability signals between the two situations. The proposed system can be effectively used in many fields where physiological measures are utilized to evaluate and understand human behavior and mental health. Such fields include psychophysiological research, education, performance enhancement, and biofeedback therapy.
Prp43p Is a DEAH-Box Spliceosome Disassembly Factor Essential for Ribosome Biogenesis

ABSTRACT The known function of the DEXH/D-box protein Prp43p is the removal of the U2, U5, and U6 snRNPs from the postsplicing lariat-intron ribonucleoprotein complex. We demonstrate that affinity-purified Prp43p-associated material includes the expected spliceosomal components; however, we also identify several preribosomal complexes that are specifically purified with Prp43p. Conditional prp43 mutant alleles confer a 35S pre-rRNA processing defect, with subsequent depletion of 27S and 20S precursors. Upon a shift to a nonpermissive temperature, both large and small-ribosomal-subunit proteins accumulate in the nucleolus of prp43 mutants. Pulse-chase analysis demonstrates delayed kinetics of 35S, 27S, and 20S pre-rRNA processing with turnover of these intermediates. Microarray analysis of pre-mRNA splicing defects in prp43 mutants shows a very mild effect, similar to that of nonessential pre-mRNA splicing factors. Prp43p is the first DEXH/D-box protein shown to function in both RNA polymerase I and polymerase II transcript metabolism. Its essential function is in its newly characterized role in ribosome biogenesis of both ribosomal subunits, positioning Prp43p to regulate both pre-mRNA splicing and ribosome biogenesis.
MicroRNA-145, a Novel Smooth Muscle Cell Phenotypic Marker and Modulator, Controls Vascular Neointimal Lesion Formation

Phenotypic modulation of vascular smooth muscle cells (VSMCs) plays a critical role in the pathogenesis of a variety of proliferative vascular diseases. Recently, we have found that microRNA (miRNA) miR-145 is the most abundant miRNA in normal vascular walls and in freshly isolated VSMCs; however, the role of miR-145 in VSMC phenotypic modulation and vascular diseases is currently unknown. Here we find that miR-145 is selectively expressed in VSMCs of the vascular wall and its expression is significantly downregulated in the vascular walls with neointimal lesion formation and in cultured dedifferentiated VSMCs. More importantly, both in cultured rat VSMCs in vitro and in balloon-injured rat carotid arteries in vivo, we demonstrate that the noncoding RNA miR-145 is a novel phenotypic marker and a novel phenotypic modulator of VSMCs. VSMC differentiation marker genes such as SM α-actin, calponin, and SM-MHC are upregulated by premiR-145 or adenovirus expressing miR-145 (Ad-miR-145) but are downregulated by the miR-145 inhibitor 2′OMe-miR-145. We have further identified that miR-145–mediated phenotypic modulation of VSMCs is through its target gene KLF5 and its downstream signaling molecule, myocardin. Finally, restoration of miR-145 in balloon-injured arteries via Ad-miR-145 inhibits neointimal growth. We conclude that miR-145 is a novel VSMC phenotypic marker and modulator that is able of controlling vascular neointimal lesion formation. These novel findings may have extensive implications for the diagnosis and therapy of a variety of proliferative vascular diseases.
Business Intelligence for Evaluating Loan Collection Performance at Bank

Bank XYZ is a bank in Bandung that gets profit from customers who borrow money. According to Bank Indonesia (BI) standars, if a bank has a non performing loan (NPL) or a value above 5% so the bank suffers losses. In this case, the bank must have a strategy to reduce this value to be stable. Bank XYZ concern about performance of collectors because the collector is the core of the bank. The solution for this case study is using business intelligence carried on the data warehouse and OLAP development. So this paper describes how important the selection of data that can help managers to make decision so that performance can be better.
A COBIT-Based Critical Asset Evaluation of Electronic Certificate Management in Central, Urban, and Rural Government Agencies: Study and Analysis

The authority for digital procurement certification in Indonesia, known as Otoritas Sertifikat Digital Pengadaan Secara Elektronik (OSD PSE), is provided by a unit at National Cyber and Crypto Agency. This is an in-demand service for many sectors that require secure electronic procurement. Our study analyzes and identifies the vulnerability of electronic certificate assets using COBIT 5.0 framework. There are four assets that are at high risk when OSD PSE services are interrupted: OSD PSE Private Key Compromise, Spamkodok Auditor Application, EJBCA Application, and Hardware Security Module. We evaluated these assets using COBIT 5’s goals cascade mechanism, which is designed to achieve enterprise and IT goals and to determine domain processes. Based on our evaluation, the EDM03, APO12, APO13, and BAI06 domain processes require deeper study. Additionally, we identified 10 secondary priority processes.
SLAM Implementation For Mobile Robots Using Physical Sensors

An algorithm for simultaneous localization and mapping of the surrounding space (SLAM) using the Microsoft Kinect multifunctional device and the Eken H9 camera has been implemented and investigated. A comparative analysis of the use of Microsoft Kinect and lidars. Describes the process of setting up, calibrating and obtaining a 3D model of the surrounding space using Kinect. Implemented monocular SLAM for use on board an aircraft. The main parameters and problems of the resulting system are investigated. The results obtained are applied on an aircraft of the type hexacopter.
Multilingual Speech Recognition Training and Adaptation with Language-Specific Gate Units

Multi-task learning (MTL) framework is usually adopted to build automatic speech recognition (ASR) system for multiple languages. The shared-hidden-layer multilingual deep neural network (SHL-MDNN), where different languages share the common hidden layers while the output layers are different according to the language-specific context-dependent states sets, brings significant improvement over conventional monolingual baselines. It is hopeful that language-specific adaptation techniques can be applied on the SHL-MDNN to further improve the recognition accuracy of multilingual speech recognizer. In this paper, language-specific gate units (LGU) are added to the specific output layers of different languages. The LGU takes advantage of language identify information, which is obtained from the language identification branch of the multi-task framework. And the language identify information is injected to the output layers of corresponding speech recognition branch. Experiments show that the LGU adaptation on the multilingual neural network provides competitive performance compared to conventional shared-hidden-layer multilingual neural network. And the multilingual speech recognition system achieves a maximum absolute improvement of 7.04% compared to the baseline system.
An End-to-End Approach to Automatic Speech Assessment for People with Aphasia

Conventionally, automatic assessment of pathological speech involves two main steps: (1) extraction of pathology-specific features; (2) classification or regression of extracted features. Given the great variety of speech and language disorders, feature design is never a straightforward task, and yet it is most critical to the performance of assessment. This paper presents an end-to-end approach to automatic speech assessment for Cantonese-speaking people with aphasia (PWA). The assessment is formulated as a binary classification problem to differentiate PWA with high scores of subjective assessment from those with low scores. The sequence-to-one GRU-RNN and CNN models are applied to realize the end-to-end mapping from speech signals to the classification result. The speech features used for assessment are learned implicitly by the neural network model. Preliminary experimental results show that the end-to-end approach could reach a performance level comparable to conventional two-step approach. The experimental results also suggest that CNN performs better than sequence-toone GRU-RNN in this specific task.
Models of fluorescence and photosynthesis for interpreting measurements of solar-induced chlorophyll fluorescence

We have extended a conventional photosynthesis model to simulate field and laboratory measurements of chlorophyll fluorescence at the leaf scale. The fluorescence paramaterization is based on a close nonlinear relationship between the relative light saturation of photosynthesis and nonradiative energy dissipation in plants of different species. This relationship diverged only among examined data sets under stressed (strongly light saturated) conditions, possibly caused by differences in xanthophyll pigment concentrations. The relationship was quantified after analyzing data sets of pulse amplitude modulated measurements of chlorophyll fluorescence and gas exchange of leaves of different species exposed to different levels of light, CO2, temperature, nitrogen fertilization treatments, and drought. We used this relationship in a photosynthesis model. The coupled model enabled us to quantify the relationships between steady state chlorophyll fluorescence yield, electron transport rate, and photosynthesis in leaves under different environmental conditions.
CNN Models Performance Analysis on MRI images of OASIS dataset for distinction between Healthy and Alzheimer's patient

Here in this paper we present the performance result of pretrained model trained on natural Image and its result in medical image classification. Besides, scratch trained model is also trained from available medical MRI images, in order to have a comparative analysis. We have performed shallow tuning and fine tuning of pretrained model (Alexnet, Googlenet, and Resnet50) in a bunch of layers in order to find the impact of each section of layers in classification result. We have used 28 Normal controls (NC) and 28 Alzheimer's disease (AD) patients for classification, selecting 30 important slices from each patient. Once all the slices are collected, each model was trained, validated and tested in ratio of 6:2:2 on random selection basis. The resulting testing results are reported and analyzed. So, that the final CNN model was built with minimal number of layers for optimal performance.
Business Intelligence for Analyzing Department Unit Performance in eProcurement System

Reporting using query script mostly takes the long process. It makes the company is too late to provide the strategy decision. This also results inefficient way because the updating and changing SQL query to be adjusted for matching the requirements of decision makers. So, this paper describes how to implement the data warehouse technology to create the fastest way to make a report based on the requirements without customize the SQL query and just read the required data only. With Kimball methodology, data could be reported in various forms and the decision makers could take the best decision in eProcurement System.
Speech Emotion Recognition using Convolutional Neural Network with Audio Word-based Embedding

A complete emotional expression typically contains a complex temporal course in a natural conversation. Related research on utterance-level, segment-level and multi-level processing lacks understanding of the underlying relation of emotional speech. In this work, a convolutional neural network (CNN) with audio word-based embedding is proposed for emotion modeling. In this study, vector quantization is first applied to convert the low level features of each speech frame into audio words using k-means algorithm. Word2vec is adopted to convert an input speech utterance into the corresponding audio word vector sequence. Finally, the audio word vector sequences of the training emotional speech data with emotion annotation are used to construct the CNN- based emotion model. The NCKU-ES database, containing seven emotion categories: happiness, boredom, anger, anxiety, sadness, surprise and disgust, was collected and five-fold cross validation was used to evaluate the performance of the proposed CNN-based method for speech emotion recognition. Experimental results show that the proposed method achieved an emotion recognition accuracy of 82.34%, improving by 8.7% compared to the Long Short Term Memory (LSTM)- based method, which faced the challenging issue of long input sequence. Comparing with raw features, the audio word-based embedding achieved an improvement of 3.4% for speech emotion recognition.
TaSRD: Task Scheduling Relying on Resource and Dependency in Mobile Edge Computing

Offloading computation-intensive tasks from mobile to nearby resource-rich surrogates, called edge servers, is proposed recently because traditional mobile cloud computing has a bottleneck of bandwidth and resource limitation for devices. The primary performance concern of offloading is how to maximize energy saving under task delay and task dependency limitation. Besides, edge servers that mobile perceived are changeable and heterogeneous in the process of offloading. In this paper, we formalize this problem, reduce it into knapsack problem and propose a task scheduling scheme, named TaSRD, including independent sub-task scheduling for tasks without dependencies and dependent sub-task scheduling for dependent tasks. We implement TaSRD and evaluate it by case study and simulation on CloudSim framework developed by Melbourne University. We use time model and energy model to measure results and recommend suitable parameters for TaSRD. The experimental results demonstrate that TaSRD can effectively save energy and reduce makespan for mobile while offloading tasks to edge servers.
Leveraging Knowledge Graphs of Movies and Their Content for Web-Scale Analysis

The amount of video content available on the Web is constantly growing, especially due to the increasing popularity of Video on Demand (VoD) platforms such as Netflix, Hulu and Youtube. This has made it harder for viewers to discover the right visual content for them. Recommender systems are being offered by VoD services in order to automatically suggest potentially interesting videos to users. However, recommendations are typically based on: (i) limited video metadata fields, such as genre, title and actors; (ii) the content that other users liked; (iii) private or isolated data repositories. In this paper we describe our approach leveraging the richness of semantic technologies to enrich movie metadata, and create meaningful semantically-enriched descriptions of movie scenes using video and audio processing techniques. This approach allows for the creation of a structured and interoperable Knowledge Graph (KG) describing movies and their content. This KG can be easily interlinked with existing Linked Data datasets available on the Web, resulting in a more comprehensive representation of the movies. Our approach enables seamless integration with existing data sources and fine-grained data analysis of movie content at Web scale.
Design and Implementation of Cell Search in NB-IoT Downlink Receiver

Narrow Band Internet of Things (NB-IoT) is built on cellular network. It has the advantages of low power consumption, low cost, super coverage and large connection. Cell search is an essential component of NB-IoT and its main purpose is to obtain the Physical-layer Cell Identity. Since the cells of NB-IoT are not grouped, NB-IoT cell search is more complex than LTE systems. In this paper, we propose an effective method to reduce the complexity of NB-IoT cell search. Simulation results show that the detection accuracy reaches 80% with 10 radio frames, with a signal-to-noise ratio of -12.6dB.
Design of Analog and Digital Hybrid MAC Circuit for Artificial Neural Networks

Demand for high-performance hardware acceleration for machine learning applications is increasing rapidly. This paper presents a low power analog and digital hybrid MAC (Multiply and Accumulation) circuit for artificial neural networks. The proposed MAC circuit consists of an analog synapse unit, digital preprocessing and postprocessing unit for support of the parallel analog synapse cores. As the hybrid MAC circuit supports relatively low power and fast multiple MAC operations, it provides a good advantage in developing hardware accelerator for artificial neural networks.
Simulation of the Sensitivity Distribution of Four- Electrode Impedance Sensing on Radial Artery

Recording of pulsatile blood pressure waveform plays important role in monitoring of functioning of cardiovascular system of patient in medical applications. This study presents simulation of non-invasive sensing to evaluate the physical condition of patient throw measuring continuous cardiac output on radial artery of human. In order to optimize the signal acquisition, the measurement should possess highest sensitivity in the region. Finite modeling and simulation of the arterial network of human forearm is described in this work. The spatial sensitivity of four-electrode system for bioimpedance measurements is estimated for different electrode setups and the sensitivity field is described graphically based on FEM simulation.
Slotline-Based Balun with Wide Bandwidth and High Isolation

A slotline-based balun with wide bandwidth and high isolation is proposed in this paper. The proposed balun integrates Wilkinson power divider, which is devoted to improving the isolation between the differential output ports. By utilizing asymmetrical slotline and symmetrical output microstrip, the implemented balun achieves excellent balance performance and wideband response. The simulation results demonstrate a wideband property with a return loss better than 10 dB from 4.4 GHz to 20.4 GHz, an improved minimum isolation of 10 dB as well as competitive balance performances including 0.5 dB amplitude imbalance and 3. 7 phase imbalance.
AdS Holography and Strings on the Horizon

The microscopic origin of black hole entropy remains one of the more intriguing open questions in theoretical physics. A subplot in this drama is the renowned Cardy-Verlinde formula, which uses two-dimensional conformal formalism to explain the entropy of an arbitrary-dimensional black hole. In this paper, by exploiting the AdS/CFT and black hole-string dualities, we are able to provide a physical picture for this paradoxical behavior. Following a recent study by Haylo (in a dS context), we show that the dual CFT for an asymptotically AdS spacetime actually conforms to a string-like description. Moreover, we demonstrate that this stringy CFT is directly related to a string that lives on the stretched horizon of an AdS-Schwarzschild-like black hole. In fact, after an appropriate renormalization, these two boundary theories are shown to be thermodynamically equivalent.
Syntactic Structure and Communicative Function of Echo Questions in Chinese Dialogues

Echo question is a linguistic phenomenon that exists in verbal interaction. A typical echo question is usually a repetition of others’ speech information as a response to him/her. The “repetition” feature makes it a drilling task in language teaching. Although quite common in linguistic field, there is limited specialized research on it. Existing studies mainly focus on its semantic relations, structural features, pragmatic features, etc., with few exploring the interactive relationships among them. Based on speech corpus Discourse-CASS which contains more than 6 hours records [1], this paper proposes an annotation scheme for echo question and makes a simple statistical analysis and discusses the interaction relations between the syntactic structure and the pragmatic functions. We conclude that echo questions can be expressed by various of interrogative sentences, usually expressing the intention of requesting confirmation and explication. Confirmation request can be divided into requests for affirmation, repetition, and supplement. There are also two kinds of echo questions shown as backchannel or understanding check. Explication request can be requests for details and explanation request. In conversation, echo questions most often express the intention of requesting affirmation, which are always in the form of yesno question and tag question.
Nivolumab in Patients With Relapsed or Refractory Hematologic Malignancy: Preliminary Results of a Phase Ib Study.

PURPOSE Cancer cells can exploit the programmed death-1 (PD-1) immune checkpoint pathway to avoid immune surveillance by modulating T-lymphocyte activity. In part, this may occur through overexpression of PD-1 and PD-1 pathway ligands (PD-L1 and PD-L2) in the tumor microenvironment. PD-1 blockade has produced significant antitumor activity in solid tumors, and similar evidence has emerged in hematologic malignancies. METHODS In this phase I, open-label, dose-escalation, cohort-expansion study, patients with relapsed or refractory B-cell lymphoma, T-cell lymphoma, and multiple myeloma received the anti-PD-1 monoclonal antibody nivolumab at doses of 1 or 3 mg/kg every 2 weeks. This study aimed to evaluate the safety and efficacy of nivolumab and to assess PD-L1/PD-L2 locus integrity and protein expression. RESULTS Eighty-one patients were treated (follicular lymphoma, n = 10; diffuse large B-cell lymphoma, n = 11; other B-cell lymphomas, n = 10; mycosis fungoides, n = 13; peripheral T-cell lymphoma, n = 5; other T-cell lymphomas, n = 5; multiple myeloma, n = 27). Patients had received a median of three (range, one to 12) prior systemic treatments. Drug-related adverse events occurred in 51 (63%) patients, and most were grade 1 or 2. Objective response rates were 40%, 36%, 15%, and 40% among patients with follicular lymphoma, diffuse large B-cell lymphoma, mycosis fungoides, and peripheral T-cell lymphoma, respectively. Median time of follow-up observation was 66.6 weeks (range, 1.6 to 132.0+ weeks). Durations of response in individual patients ranged from 6.0 to 81.6+ weeks. CONCLUSION Nivolumab was well tolerated and exhibited antitumor activity in extensively pretreated patients with relapsed or refractory B- and T-cell lymphomas. Additional studies of nivolumab in these diseases are ongoing.