Miracle-dz/UniFAR-FTUs huggingface dataset:
A Compression-based BiLSTM for Treating Teenagers’ Depression Chatbot

Anxiety is a psychological condition that often occurs during adolescence. Due to lack of relief and counseling, teenager’s psychological anxiety may gradually develop into anxiety. Chabot can be used as a new tool to relieve anxiety among teenagers. However, the natural language understanding techniques currently applied to Chabot still have problems, such as lack of effective data, high training complexity, and lack of interpretability of the network. This paper proposes a compression-based bidirectional Long Short-Term Memory depth neural network structure. The main objective is to reduce the complexity of the parameters further, and to make the network of each layer have certain interpretability by means of the reduction of sparsity. Under our own collection of teen depression text data, this structure shows a better performance than traditional networks.
Metal-Organic Frameworks in Green Analytical Chemistry

Metal-organic frameworks (MOFs) are porous hybrid materials composed of metal ions and organic linkers, characterized by their crystallinity and by the highest known surface areas. MOFs structures present accessible cages, tunnels and modifiable pores, together with adequate mechanical and thermal stability. Their outstanding properties have led to their recognition as revolutionary materials in recent years. Analytical chemistry has also benefited from the potential of MOF applications. MOFs succeed as sorbent materials in extraction and microextraction procedures, as sensors, and as stationary or pseudo-stationary phases in chromatographic systems. To date, around 100 different MOFs form part of those analytical applications. This review intends to give an overview on the use of MOFs in analytical chemistry in recent years (2017–2019) within the framework of green analytical chemistry requirements, with a particular emphasis on possible toxicity issues of neat MOFs and trends to ensure green approaches in their preparation.
Build an Intelligent Online Marketing System: An Overview

With the rapid growth of Internet users, online marketing has become one of the most effective channels to reach new customers. In this paper, an intelligent online marketing system architecture is presented to better facilitate online marketing. We first provide an overview of the customer conversion challenges faced when marketing online. Then, we outline an architectural design of the productionized intelligent marketing system, which consists of tracking, metrics, attribution, bidding, keyword generation and many other core functionalities, and discuss the interactions between these components. Based on the internal results, this design can help advertisers reduce operational costs, increase operational efficiency, optimize return on investment, and increase customer engagement. We hope that the proposed architecture can be beneficial to software engineers, marketers, and researchers, and it has the potential to motivate the research on online marketing system design and accelerate the advent of fast, smart, and scalable marketing systems.
A Study on Evaluation Model on Stroke Order Similarity

Since sketch is one of the most important creation by human, analysis of sketch drawing process by artificial intelligence is the essential topic. In this paper, we consider a computational model to evaluate the similarity of stroke order. We have proposed CASOOK in order to realize communication between computers and humans. In this research, we are trying to develop CASOOK-SR which is extended CASOOK adopting sketch-rnn. Since evaluating stroke order similarity is a difficult problem, and there was only a method to evaluate the similarity of stroke order by persons. In this paper, we define an objective comparable evaluation function on the similarity of stroke order. In our experiment, the correlation coefficient between the function and the similarity evaluation values by a person were 0.344 and 0.446. It is found that our calculation model is the same as human evaluation when an image made from a stroke order matrix is similar to another.
Robot Fish Tracking Control using an Optical Flow Object-detecting Algorithm

This paper realizes control of the motion of a swimming robot fish in order to implement an underwater robot fish aquarium. And it implements positional control of a two-axis trajectory path of the robot fish in the aquarium. The performance of the robot was verified though certified field tests. It provided excellent performance in driving force, durability, and water resistance in experimental results. It can control robot motion, that is, it recognizes an object by using an optical flow object-detecting algorithm, which uses a video camera rather than image-detecting sensors inside the robot fish. It is possible to find the robot’s position and control the motion of the robot fish using a radio frequency (RF) modem controlled via personal computer. This paper proposes realization of robot fish motion-tracking control using the optical flow object-detecting algorithm. It was verified via performance tests of lead-lag action control of robot fish in the aquarium.
Power Delivery Exploration Methodology Based on Constrained Optimization

The conventional power network design process requires iterative modifications to the existing power network to eliminate hot spots and to converge to target impedance parameters. At later stages in the IC design process, this procedure may require significant time and human resources due to the limited flexibility to accommodate necessary changes. Power delivery exploration during early stages of the design process may bring considerable savings to the system development effort. The number of iterations may be greatly reduced by choosing the initial parameters sufficiently close to the optimum. This paper presents a power delivery exploration framework based on constrained global optimization. The power network parameters are estimated at early stages of the development process, while considering both electrical and nonelectrical factors, such as area and cost. A Laplace transform-based circuit simulator is described that is well suited for optimization purposes due to the high computational efficiency when a large number of iterations is required. The proposed framework has been applied to the distribution of voltage domains in a large scale complex integrated system, while minimizing the cost of the decoupling capacitor placement. The optimal number of voltage rails are determined, demonstrating an approximately 40% lower on-chip area than alternative solutions.
Main path analysis on cyclic citation networks

Main path analysis is a famous network‐based method for understanding the evolution of a scientific domain. Most existing methods have two steps, weighting citation arcs based on search path counting and exploring main paths in a greedy fashion, with the assumption that citation networks are acyclic. The only available proposal that avoids manual cycle removal is to preprint transform a cyclic network to an acyclic counterpart. Through a detailed discussion about the issues concerning this approach, especially deriving the “de‐preprinted” main paths for the original network, this article proposes an alternative solution with two‐fold contributions. Based on the argument that a publication cannot influence itself through a citation cycle, the SimSPC algorithm is proposed to weight citation arcs by counting simple search paths. A set of algorithms are further proposed for main path exploration and extraction directly from cyclic networks based on a novel data structure main path tree. The experiments on two cyclic citation networks demonstrate the usefulness of the alternative solution. In the meanwhile, experiments show that publications in strongly connected components may sit on the turning points of main path networks, which signifies the necessity of a systematic way of dealing with citation cycles.
Phylogenetic characterization and quantification by Most Probable Number of the microbial communities of biomass from the Upflow Anaerobic Sludge Blanket Reactor under sulfidogenic conditions

Granulated sludge from anaerobic reactors is constituted by the microbial consortia responsible for the degradation of different substrate present in wastewaters. This study characterized anaerobic microorganisms in a granular sludge from a Uasb reactor (Upflow Anaerobic Sludge Blanket) by Most Probable Number (MPN) technique and method of cloning and sequencing the 16S rDNA gene. The main objective of this study was to quantify and to identify the microorganisms in two different culture media containing sulfate (Posgate C [6.0 sodium lactate and 3.13 g L -1 sodium sulfate] and Zinder [2.24 sodium lactate and 0.96 g L -1 sodium sulfate]). Microorganisms quantified by Postgate C and Zinder media were: 9.30x10 10 and 7.50x10 11 MPN mL -1 for general anaerobic bacteria; 4.30x10 8 and 2.10x10 8 MPN mL -1 for sulfate reducing bacteria; and 1.20x10 8 and 1.5x10 8 MPN mL -1 for methane producing archaea, respectively. Bacteria identified by 16S rDNA gene sequencing from the MPN in both culture media were related to the phyla Firmicutes and Proteobacteria. The conditions of Postgate C culture medium favored the sulfate-reducing bacteria and the Zinder culture medium favored the general anaerobic bacteria.
mwJFS: A Multiwrite-Mode Journaling File System for MLC NVRAM Storages

At present, nonvolatile random access memory (NVRAM) is widely considered as a promising candidate for the next-generation storage medium due to its appealing characteristics, including short read/write latency, byte addressability, and low idle energy consumption. In addition, to provide a higher bit density, multilevel-cell (MLC) NVRAM has also been proposed. Nevertheless, when compared with conventional single-level-cell (SLC) NVRAM, MLC NVRAM has longer write latency and higher energy consumption. Hence, the performance of MLC NVRAM-based storage systems could be degraded due to the lengthened write latency. The performance degradation is further magnified by existing journaling file systems (JFS) on MLC NVRAM-based storage devices due to the JFS’s fail-safe policy of writing the same data twice. Such observations motivate us to propose multiwrite-mode JFSs (mwJFSs) to alleviate the drawbacks of MLC NVRAM and boost the performance of MLC NVRAM-based JFS. The proposed mwJFS differentiates the data retention requirement of journaled data and applies different write modes to enhance the access performance with lower energy consumption. A series of experiments was conducted to demonstrate the capability of mwJFS on MLC NVRAM-based storage systems.
Explainable Neural Attention Recommender Systems

Recommender systems, predictive models that provide lists of personalized suggestions, have become increasingly popular in many web-based businesses. By presenting potential items that may interest a user, these systems are able to better monetize and improve users’ satisfaction. In recent years, the most successful approaches rely on capturing what best define users and items in the form of latent vectors, a numeric representation that assumes all instances can be described by their respective affiliation towards a set of hidden features. However, recommendation methods based on latent features still face some realworld limitations. The data sparsity problem originates from the unprecedented variety of available items, making generated suggestions irrelevant to many users. Furthermore, many systems have been recently expected to accompany their suggestions with corresponding reasoning. Users who receive unjustified recommendations they do not agree with are susceptible to stop using the system or ignore its suggestions. In this work we investigate the current trends in the field of recommender systems and focus on two rising areas, deep recommendation and explainable recommender systems. First we present Textual and Contextual Embedding-based Neural Recommender (TCENR), a model that mitigates the data sparsity problem in the area of point-of-interest (POI) recommendation. This method employs different types of deep neural networks to learn varied perspectives of the same user-location interaction, using textual reviews, geographical data and social networks.
Sub-threshold Drain Current model of Double Gate RingFET (DG-RingFET) Architecture: An Analog and Linearity Performance Investigation for RFIC Design

ABSTRACT In the present work, impact of Double Gate RingFET (DG-RingFET) has been investigated for better gate control and suppressed Short Channel Effects (SCEs) using ATLAS 3D device simulator. The analog performance metrics explored in this paper are drain current (Ids − Vgs), trans-conductance (gm), device efficiency (gm/Ids), and early voltage (Vea). In addition to this linearity behaviour of DG-RingFET has been investigated in terms of third-order voltage intercept point (VIP3), third-order current intercept point (IIP3), third-order inter modulation distortion (IMD3) and results are also compared with the single gate architecture. The two-dimensional analytical model for DG-RingFET architecture has also been developed in this paper using parabolic approach. Moreover, the impact of technology variations like drain radii and position of drain, i.e. inside or outside the channel ring on the performance of DG-RingFET architecture has also been assessed.
Toxic Potential ofBacillus thuringiensis: An Overview

The toxins of Bacillus thuringiensis (Bt) have shown great potential in the control of harmful insects affecting human health and agriculture, used as the main biological agent for the formulation of bioinsecticides due to its specificity to target different insects’ orders. This has led Bt-based products to become the best-selling biological insecticides in the world since the genes encoding insecticidal proteins have been successfully used in novel insecticidal formulation, genetically engineered (GE) crops, and development of transgenic rice that produce insecticidal toxins derived from Bacillus thuringiensis. It has been proven that insecticidal activity of Bt protein crystals can prolong their toxicity in shelf life or field under specific conditions, and this can improve the use of special strains and formulations to control insect vectors of diseases. Bt toxins have shown well-documented toxicity against lepidopterans, coleopterans, hemipterans, dipterans, nematodes, Rhabditida and human cancer cells of various origins. These crystal toxins may be responsible for other novel biological properties suggesting a pluripotential nature with different specificities.
On the Modeling of Multi-Point RTT Passive Measurements for Network Delay Monitoring

Many network management actions need a simultaneous consideration of several elements’ state. This is becoming an even more complex matter with the advent of reconfigurable deployments, where scaling functions up can prevent performance bottlenecks. Therefore, fine-grained detection of significant burdens arises as a cornerstone to optimize their monitoring and operation. We present advanced distributed passive retrieval of information, and statistical multi-point analysis (AdPRISMA), a passive monitoring system intended to fit models for network delay measurements with clustering elements to improve representation of central and extreme behaviors. As distinguishing features, it relies on cost-effective multi-point round-trip time (RTT) passive network measurements, and is able to select a suitable parametric model optimizing the trade-off between fitting and complexity. AdPRISMA can correlate records collected from several vantage points and detect where performance issues are most likely to appear; adjust alarms in terms of the probability of events; and adapt its behavior to dynamic network conditions while presenting a fair identification of anomalous situations. We evaluate AdPRISMA with experiments both in virtual environments and with real-world data to provide evidences of its applicability and capabilities to represent network elements’ delay.
A Computer Vision-Based Banknote Recognition System for the Blind with an Accuracy of 98% on Smartphone Videos

This paper proposes a computer vision-based banknote recognition system intended to assist the blind. This system is robust and fast in recognizing banknotes on videos recorded with a smartphone on real-life scenarios. To reduce the computation time and enable a robust recognition in cluttered environments, this study segments the banknote candidate area from the background utilizing a technique called Pixel-Based Adaptive Segmenter (PBAS). The Speeded-Up Robust Features (SURF) interest point detector is used, and SURF feature vectors are computed only when sufficient interest points are found. The proposed algorithm achieves a recognition accuracy of 98%, a 100% true recognition rate and a 0% false recognition rate. Although Korean banknotes are used as a working example, the proposed system can be applied to recognize other countries’ banknotes.
Installation of capacitive touch sensors at water place and implementation of them for user interfaces

We try to install capacitive touch sensors into edges of bathtub, and to implement a bathtub as various controllers. Its functionality is an ambient user interface for everyday life. In this paper, some fundamental experiments, for instance, senser area, probe size, influences of water, are described. We made sure that the capacitive sensors equipped in a bathtub are conditionally available. This paper also describes their conditions and discussions for some implementation of user interface in a water place. 1 はじめに
A Survey of Measures for Network Motifs

Network motifs provide an enlightening insight into uncovering the structural design principles of complex networks across multifarious disciplines, such as physics, biology, social science, engineering, and military science. Measures for network motifs play an indispensable role in the procedures of motif measurement and evaluation which are crucial steps in motif detection, counting, and clustering. However, there is a relatively small body of literature concerned with measures for network motifs. In this paper, we review the measures for network motifs in two categories: structural measures and statistical measures. The application scenarios for each measure and the distinctions of measures in similar scenarios are also summarized. We also conclude the challenges for using these measures and put forward some future directions on this topic. Overall, the objective of this survey is to provide an overview of motif measures, which is anticipated to shed light on the theory and practice of complex networks.
A Clustering Routing Method Based on Semi-Markov Process and Path-Finding Strategy in DTN

In delay tolerant network,we have proposed a clustering routing method based on semi-Markov process and path-finding strategy(CRSMP),which considers not only the nodes' social properties but also the time and maximum probability of node contacts.This method firstly clusters nodes according to path similarity,then predicts node contact probabilities at some point in the future by applying semi-Markov process and determines a collection of nodes used in the routing on the basis of cluster or clusters containing the source node and the destination node.Lastly,dynamic routing tables related to the current time are obtained by executing path-finding algorithm.Simulation results show that the delivery rate of CRSMP is much higher than DirectDeliveryRouter,FirstContactRouter and SimBetRouter which are also single-copy routing schemes under the condition that the size of caches is small,and the delivery rate of CRSMP is also higher than Spray and Wait,Epidemic and Prophet which are multi-copy routing schemes,andCRSMP with 10M caches is similar to Epidemic with 500M caches in routing performance.Furthermore,experiments with the real data set are done,and CRSMP still keeps better routing performance.
A Robust and Lightweight Key Management Protocol for WSNs in Distributed IoT Applications

To maintain the reliable connectivity and the accessibility of distributed IoT, it is vital to establish secure links for end-to-end communication with a robust pervasive communication mechanism. However, due to the resource constraints and heterogeneous characteristics of the sensor devices, traditional authentication and key management schemes are not effective for such applications. Here, we propose a pervasive lightweight authentication and keying mechanism for WSNs in distributed IoT applications in which the sensor nodes can establish secure links with peer sensor nodes and end-users. The established authentication scheme is based on implicit certificates, and it provides application-level end-to-end security. A comprehensive description of the scenario based behaviour of the protocol is presented. With the performance evaluation and the security analysis, it is justified that the proposed scheme is viable to deploy in the resource constrained WSNs.
Finite Set Model Predictive Control of PWM AC/DC Converter With Virtual-Flux Estimation Under Grid imbalance

The aim of this research is to improve the quality of input current for AC/DC PWM converter, in case of grid imbalance with high dynamic transient. This can be achieved by joining compensating power with finite set model predictive control (FSMPC) based on voltage sensor-less technique. The control scheme proposed in this paper is divided into two steps. First, a double second order generalized integrator is used to estimate the virtual-flux and separate sequences of grid current and then the compensating power necessary to obtain sinusoidal input current is calculated. In the second step, the current is regulated via FSMPC, where, the switching state is determined directly. MATLAB/Simulink is selected to simulate and verify the effectiveness of the proposed control scheme; with and without virtual flux estimation. Simulation results are compared to confirm the validity of the algorithm.
Implications of Web 2.0 Technology on Healthcare: A Biomedical Semantic Blog Case Study

Recently, a new wave of web technology and interactive tools in medicine and health care has been called Health 2.0. Unlike traditional eHealth technologies, that only allow web users to accept information passively, Health 2.0 provides web users with the ability to actively modify web information. Health 2.0 services, applications, and tools are defined as “participatory health care characterized by the ability to rapidly share, classify and summarize individual ABSTRACT
Using Deep Learning To Predict Customer Churn In A Mobile Telecomunication Network

Turun naiknya jumlah pelanggan merupakan salah satu masalah besar pada perusahaan jaringan telekomunikasi selular. Pada tugas akhir ini, akan dibangun sebuah sistem prediksi terhadap turun naiknya pelanggan pada layanan jaringan telekomunikasi selular menggunakan arsitektur jaringan Multilayer Perceptron. Selain menggunakan arsitektur jaringan Multilayer Perceptron dilakukan juga implementasi sistem dengan menggunakan Autoencoder untuk mendapatkan bobot yang optimal. Dari metode dan sistem yang digunakan dilakukan perhitungan menggunakan F-Measure didapatkan nilai precision sebesar 70,2 % dan Recall sebesar 70,27 %. Untuk nilai akurasi sebesar 81,35 % pada data training. Didapatkan nilai Precision sebesar 80,4 % dan Recall sebesar 63,07 %. Pada nilai akurasi sebesar 83,12 % untuk data testing. Dengan nilai akurasi yang cukup besar maka metode Deep Learning layak untuk digunakan dalam prediksi Churn. Kata Kunci : Prediksi Churn, Deep Learning, Deep Neural Network, Multilayer Perceptron,Autoencoders, F- Measure