Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficiency-Complexity Controllable Rate Adaptive Lossy Source Coding—Hybrid Majority Voting Code

This letter proposes a practical source coding scheme, so-called hybrid majority voting (HMV) code, for lossy compression with a discrete memoryless source. Inspired by the coding scheme used in the classic rate–distortion theorem, we find a series of basic MV codes and analyze their rate–distortion performance. We then present an algorithm to find two component MV codes and apply them to lossy compression, group by group, to construct HMV codes. Finally, we show an example of joint source–channel coding based on the HMV code. The performance evaluation indicates that the HMV code makes it possible to easily control efficiency and complexity.
Generating Handwritten Character Clones from an Incomplete Seed Character Set using Collaborative Filtering

In this paper, we propose a method for generating clones of a target writer's handwritten character images (called handwritten character clones or HCCs) using an incomplete seed character set, which consists of at most one or no example of his/her actual handwriting per character. In HCC generation, not a single HCC but its distribution should be created for each character because humans' actual handwriting images differ from each other even if the same writer writes the same character. However, it is difficult to achieve this from the incomplete seed character set. To solve the problem, in the proposed method, we first create a number of HCC distributions for each character by clustering a set of handwritten character images offered by other writers. Next, for each character contained in the seed character set, we choose the distribution best fit to its example. Finally, for the other characters, we estimate the best distribution for them employing collaborative filtering. We conducted pilot experiments focusing on Japanese character images, in which the proposed method successfully generated various HCCs with a certain level of quality for each character.
A Lightweight and Flexible Tool for Distinguishing Between Hardware Malfunctions and Program Bugs in Debugging Large-Scale Programs

In this paper, we propose a new technique to distinguish the reason for program failure between hardware malfunctions and program bugs, which mitigates the impact of shorter mean time between failures to the debugging process on the future exa-scale supercomputers and improves the productivity of debugging large-scale parallel programs. Our technique detects program failures by observing the abnormal message passing behaviors with distributed monitors and leverages event-driven mechanism to trigger global status checking among different node groups concurrently. Besides, both coarse-grained execution snapshots and fine-grained failure events can be provided for further failure diagnosis and bug analysis. We implement this technique as a user-space library named failure cause resolver (FCR). Experimental results on the Tianhe-2 supercomputer demonstrate that the latency of FCR for failure detection is acceptable with negligible overhead. In addition, FCR does not require administrative privilege and can be easily integrated into existing large-scale parallel programs.
Automatic Jordanian License Plate Recognition System Using Multistage Detection

License Plate Recognition (LPR) system is a key to many traffic related applications such as road traffic monitoring or parking lots access control. This paper proposes an automatic license plate recognition system for Jordanian license plates. The system presents an algorithm for the detection of license plate and segmentation of characters, and then recognition is done using back propagation neural network. The gain contribution of this work is the improvement of the license plate detection subsystem, which in turn improves the whole LPR system. The performance of the proposed system has been investigated on real images of 240 vehicles captured under various illumination conditions. The results show a robust algorithm with average success rate of 90% for Jordanian license plates.
Age and Gender Prediction Using Convolutional Neural Networks

Age has always been an important feature of our identity. It is also an important factor in our social life. Predictions of age made with artificial intelligence can be applied to many areas such as intelligent human-machine interface development, security, cosmetics, electronic commerce. In this study, face images of persons were trained using convolutional neural networks, and age and gender were tried to be predicted with high success rate. Inception V1 convolutional neural network model developed by Google was used for training. Inception V1 model is already trained in the VGGFace2 dataset. Transfer learning provided by Deep Learning, the dataset is trained on this model. The IMDB dataset with face images with gender and age tags was selected as the dataset. There are 460.723 images in this dataset. 260.428 pictures in the IMDB dataset are used for training. As a result of the study, achievement rate of 70.3% in age prediction and 97% in gender prediction has been achieved.
LONG-TERM OUTCOMES OF LIGATION OF INTERSPHINCTERIC FISTULA TRACT FOR COMPLEX FISTULA-IN-ANO: MODIFIED OPERATIVE PROCEDURE EXPERIENCE

ABSTRACT Background: It is important but difficult to treat complex fistula-in-ano due to the high recurrent rate and following incontinence. Ligation of the intersphincteric fistula tract (LIFT), a novel surgical procedure with the advantage of avoiding anal incontinence, has a variable success rate of 57-94.4 %. Aim: To evaluate the long-term outcomes of modified LIFT operative procedure - ligation of intersphincteric fistula tract - to treat complex fistula-in-ano. Methods: Retrospective analysis of 62 cases of complex fistula-in-ano. The group was treated with the modified approach of LIFT (curved incision was made in the anal canal skin; purse-string suture was performed around the fistula; the residual fistulas were removed in a tunnel-based way) and had a follow-up time of more than one year. Patient´s preoperative general condition, postoperative efficacy and their anal function were compared. Results: The median age of the participants was 34, and 43 (69.4%) cases were male. Forty-one (66.1%) cases were of high transsphincteric fistula, four (6.5%) cases of high intrasphincter fistula, and 17 (27.4%) cases of anterior anal fistula in female. The median follow-up duration was 24.5 (range, 12-51) months. The success rate in the end of follow-up was 83.9% (52/62). The anorectal pressure and Cleveland Clinic Florida Fecal Incontinence (CCF-FI) evaluated three months before and after the operation did not find apparent changes. Conclusions: Compared with LIFT, the modified LIFT remarkably reduces postoperative failure and the recurrence rate of complex fistula with acceptable long-term outcomes.
A new Micro-Macro METANET model for platoon control in freeway traffic networks

This paper introduces a new hybrid traffic flow model in which the presence of truck platoons is explicitly taken into account. The coordination of fleets of trucks is a relevant issue in the field of freight transport thanks to the benefits that vehicles platooning yield in the reduction of fuel consumption. However, it is recognised that truck platoons behave as moving bottlenecks causing a detrimental impact on the surrounding flows. The definition of an appropriate modelling framework is therefore needed to represent such traffic scenarios. Furthermore, this work proposes a control scheme in which the speed of each platoon is controlled according to a feedback controller that takes into account the traffic conditions detected in the freeway in which the platoon travels in order to reduce the time spent by truck platoons in the traffic jam, and to avoid abrupt decelerations.
Quality of Experience From the Viewpoint of Electronic Logistics Network

The term quality of experience (QoE) encompasses the concepts of overall level of customers pleasure on a particular service or a product. The term most commonly seen in marketing discipline and tourism discipline. This paper is a positive attempt to create consciousness on customer experience. Customer experience is not only for the hotels, stores whom welcomes customers directly but it is also important for the companies, industries who welcome and serve customers directly or indirectly. This study evaluates the perception of customer experience with taking a special project ETA (in Turkish: Elektronik Tasimacilik Agi / in English: E-Logistics Network), a subsidiary of Borusan Logistics Group, as a case study. ETA is a e-logistics platform that bring SME customers and truckers together. Since ETA has users who aren’t familiar with computers, a customer experience research on ETA gave significant opportunuties in terms of ETA’s improvement. The research took place with ETA’s four old customers, four potential customers, four old truckers and four truckers that were uncooperated. Sixteen participants were allocated into two focus groups: customers and truckers. Twenty questions were answered by sixteen participants. It was concluded that QoE practices can provide remarkable improvements for companies which wants to hear the voice of customers more clear and rescue its current systems from complexity. In this study, a literature review, customer and trucker interviews and direct observations were performed to define the opportunities for e-logistics services, mobile application and website of ETA.
Evaluation of Experimental GNSS and 10-DOF MEMS IMU Measurements for Train Positioning

Integrating new candidate sensors, such as Global Navigation Satellite System (GNSS) and inertial measurement unit (IMU), into fail-safe train positioning systems have recently become a prominent area of research. Although there are a number of contributions related to the design of data fusion algorithms, the lack of details in raw measurements analysis has directly motivated this paper. This paper aims to record data from a variety of sensors (such as GNSS, IMU, magnetometer, barometer, tachometers, and Doppler radars) to evaluate train velocity and railway features (track slope, curve cant, and radius) extending previous works in the instrumentation and measurement field. The field test designed and concisely described in this paper presents several challenging environments, such as a tunnel, which can be used to analyze the candidate sensors limitations. In addition, a demonstration of a data fusion algorithm is presented to calculate train velocity based on measurements from the candidate sensors. The results obtained by an extended Kalman filter using GNSS and IMU are compared with velocity recorded by tachometers and Doppler radars, which is considered to be the reference value. The calculated velocity by IMU and GNSS when both sensors measurements are available presents an absolute error in velocity lower than 2 km/h in more than 90% of test duration. Finally, railway features (curve radius, cant, and slope) are calculated and analyzed according to train and railway dynamics.
Performance Analysis of Direct Current Control and Direct Torque Control Strategies for Three-Level Inverter Fed Brushless DC Motor Drives

In variable speed applications based on brushless direct-current motor (BLDCM) drives, two types of popular control strategies are distinguished: direct current control (DCC) and direct torque control (DTC). The paper investigates a simulation evaluation of three control strategies for three-level three-phase inverter fed BLDCM drives, namely: (i) DCC, (ii) conventional strategy inspired from the earlier Takahashi DTC strategy implemented in induction motor drives DTC1, and (iii) a proposed DTC2 strategy leads to suppress the torque ripples penalizing the DTC1 strategy. The control strategies efficiencies under consideration are analyzed and compared based on several simulation results. The comparative study has highlighted the superiority of the the proposed DTC2 strategy which exhibits damped torque ripples.
A Hardware-Oriented Contrast Enhancement Algorithm for Real-Time Applications

Recently, intelligent automobile electronics, advanced driver assistance system (ADAS) and intelligent transport system (ITS) have become to make great strides towards to the goal of automatic driving and smart cities. Therefore, how to enhance the visibility no matter at night or excessive exposure turns into an important issue of the research. In this paper, a novel effective contrast enhancement and hardware-oriented algorithm is proposed. The proposed contrast enhancement algorithm includes a RGB to HSV module, a mirror expansion module, a weighted filter and a Gamma correction module. A new technique called weighted filter was used to pick up the best brightness factor for the proposed algorithm. To be suitable for VLSI implementation, the proposed algorithm was developed based on low-complexity and low-memory-requirement. Compared with previous studies, this work proposed a high quality and low complexity hardware-oriented contrast enhancement algorithm for VLSI implementation.
Implementation of a Serial AC/DC Converter With Modular Control Technology

This paper is to develop a high-voltage isolated ACDC converter, which is based on modular technology and capable to utilized for charger or DC power supply in electric vehicle charging station. The proposed architecture is similar to the design of high-voltage solid-state transformers, it can be significantly reduce the size and weight of the charging station framework, and enhance the ability to adapt the performance for future smart grid requirement. This architecture is constituted by a cascade structure input stage and followed by independent high-frequency transformer based DC-DC converters. As the cascade input with the same input current, therefore need to consider the voltage and power balance control, particularly in the case of the rear stage output variations; as the output side is parallel, so to consider the appropriate current sharing control is necessary. Finally, a prototype of two modules cascaded system is settled to verify the control idea, each module is about 400W and consisted of a single phase rectifier sourced by AC 110V input and DC 200V output, whereas second stage is a 200V/48V high-frequency transformer.
Electroacupuncture Treatment Alleviates the Remifentanil-Induced Hyperalgesia by Regulating the Activities of the Ventral Posterior Lateral Nucleus of the Thalamus Neurons in Rats

Mechanisms underlying remifentanil- (RF-) induced hyperalgesia, a phenomenon that is generally named as opioid-induced hyperalgesia (OIH), still remain elusive. The ventral posterior lateral nucleus (VPL) of the thalamus, a key relay station for the transmission of nociceptive information to the cerebral cortex, is activated by RF infusion. Electroacupuncture (EA) is an effective method for the treatment of pain. This study aimed to explore the role of VPL in the development of OIH and the effect of EA treatment on OIH in rats. RF was administered to rats via the tail vein for OIH induction. Paw withdrawal threshold (PWT) in response to mechanical stimuli and paw withdrawal latency (PWL) to thermal stimulation were tested in rats for the assessment of mechanical allodynia and thermal hyperalgesia, respectively. Spontaneous neuronal activity and local field potential (LFP) in VPL were recorded in freely moving rats using the in vivo multichannel recording technique. EA at 2 Hz frequency (pulse width 0.6 ms, 1–3 mA) was applied to the bilateral acupoints “Zusanli” (ST.36) and “Sanyinjiao” (SP.6) in rats. The results showed that both the PWT and PWL were significantly decreased after RF infusion to rats. Meanwhile, both the spontaneous neuronal firing rate and the theta band oscillation in VPL LFP were increased on day 3 post-RF infusion, indicating that the VPL may promote the development of RF-induced hyperalgesia by regulating the pain-related cortical activity. Moreover, 2 Hz-EA reversed the RF-induced decrease both in PWT and PWL of rats and also abrogated the RF-induced augmentation of the spontaneous neuronal activity and the power spectral density (PSD) of the theta band oscillation in VPL LFP. These results suggested that 2 Hz-EA attenuates the remifentanil-induced hyperalgesia via reducing the excitability of VPL neurons and the low-frequency (theta band) oscillation in VPL LFP.
Digital Rights Management for Paper-Based Microfluidic Biochips

Paper-based digital microfluidic biochips (PB-DMFBs) provide a promising solution for microfluidic bioassays. Due to the low-cost substrate material and low demand for complicated manufacturing equipment, PB-DMFBs can be fabricated without foundry. On the flip side, convenience of fabrication allows PB-DMFBs to be fabricated everywhere, which makes it is difficult to manage production and distribution of IP (bioassays). As a result, PB-DMFBs are vulnerable to security threats. IP and its creator, the biocoders, may suffer from infringement. To ensure IP protection, in this paper, we proposed the first Digital Rights Management (DRM) scheme to protect IPs of PB-DMFBs from security threats. A chip-level synthesis algorithm is also presented to realize not only complex biochemical operations but also the demand of DRM.
On the Enumeration Complexity of Unions of Conjunctive Queries

We study the enumeration complexity of Unions of Conjunctive Queries (UCQs). We aim to identify the UCQs that are tractable in the sense that the answer tuples can be enumerated with a linear preprocessing phase and a constant delay between every successive tuples. It has been established that, in the absence of self joins and under conventional complexity assumptions, the CQs that admit such an evaluation are precisely the free-connex ones. A union of tractable CQs is always tractable. We generalize the notion of free-connexity from CQs to UCQs, thus showing that some unions containing intractable CQs are, in fact, tractable. Interestingly, some unions consisting of only intractable CQs are tractable too. The question of a finding a full characterization of the tractability of UCQs remains open. Nevertheless, we prove that for several classes of queries, free-connexity fully captures the tractable UCQs.
Training an End-to-End Model for Offline Handwritten Japanese Text Recognition by Generated Synthetic Patterns

This paper presents an end-to-end model of Deep Convolutional Recurrent Network (DCRN) for recognizing offline handwritten Japanese text lines. The end-to-end DCRN model has three parts: a convolutional feature extractor using Deep Convolutional Neural Network (DCNN) to extract a feature sequence from a text line image; recurrent layers employing a Deep Bidirectional LSTM to predict pre-frame from the feature sequence; and a transcription layer using Connectionist Temporal Classification (CTC) to convert the pre-frame predictions into the label sequence. Since our end-to-end model requires a large data for training, we synthesize handwritten text line images from sentences in corpora and handwritten character patterns in the Nakayosi and Kuchibue database with elastic distortions. In the experiment, we evaluate the performance of the end-to-end model and the effectiveness of the synthetic data generation method on the test set of the TUAT Kondate database. The results of the experiments show that our end-to-end model achieves higher than the state-of-the-art recognition accuracy on the test set of TUAT Kondate with 96.35% and 98.05% character level recognition accuracies without and with the generated synthetic data, respectively.
Using Passive Data Collection Methods to Learn Complex Mobility Patterns: An Exploratory Analysis

Collecting detailed information about individual mobility behaviour is a fundamental step in order to implement responsive solutions to mobility issues. One of the biggest challenges is the data collection and extraction of detailed personal travel data, like the type of activity performed on each visited location. As traditional survey methods under represent complex mobility patterns, the current state of the practice is to supplement or eventually replace traditional travel surveys with digital travel surveys and passive data collection methods. Although digital travel surveys can capture complex and heterogeneous user behaviour, they usually require significant inputs from the user, which is supposed to manually complete or confirm part of the survey, hence increasing the respondent's burden when prompted to recall, annotate and classify the activities performed in different locations. The core contribution of this paper is related to the extraction of locations' daily and weekly activity-travel pattern, based on the historical visit patterns. Using raw GPS data, special indexing techniques, and a set of aggregate statistics about activity scheduling and preferences, the proposed methodology provides the probability to classify the activity performed in each location. Results of this exploratory study support the idea that the proposed approach can reconstruct complex mobility patterns while minimizing the number of inputs from the respondent.
Selecting the Most Suitable Material for Punch Dies Using CES EduPack

The purpose of this paper is to perform a material's selection for punch dies using CES EduPack. This study is an initial statement of a problem showing how designers can select a small subset of most promising materials from a huge list of options. It identifies the objectives and constraints related to the given functional component and clarifies how that can be used to make the material's selection. The results revealed that high-speed tool steel is the most suitable material that meets the performance requirements of punch dies.
Power Factor Control of Solar Photovoltaic Inverter as a Solution to Overvoltage

Renewable energy system has become one of the main solutions to overcome the greenhouse effect. Due to its availability, reliability and safety, solar photovoltaic (PV) system gets the attention from people around the world. Apart from reducing electricity bills, this system is also maintenance-free. In this paper, a simulation was performed using DIgSILENT software in order to compare the voltage profile with and without the solar PV system. The voltage profile of the system can be improvised by installing a predetermined capacity of solar PV system. However, the distribution system will experience overvoltage when the solar PV capacity installed is more than the local loads. In this paper, the power factor control of solar PV inverter is shown to improve the voltage profile across the feeder in a distribution system.
A Proposal of CS-index Approach for SPARQL Queries Considering Chain and Star Shaped Subgraphs

The interlinking nature of web-scale RDF data makes a challenge on storage and retrieving of these data efficiently. Even though different storage and query processing techniques have been proposed, query processing on complex linking structured (RDF) data requires many join operations when the input query is large (with respect to number of triple patterns). One solution to this problem is to reduce the number of joins by indexing. Indexing is an effective technique to reduce data searching space and retrieve data as fast as possible. In this paper, we propose an indexing scheme of chain and star (CS-index) and querying approach for SPARQL queries. The proposed approach could support both chain and star shaped queries. It employs graph pattern based technology: the RDF data graph is firstly decomposed into chain and star shaped subgraphs based on the structural information of each vertex. These subgraphs are stored as index, called CS-index. When a SPARQL query is given, it is decomposed into query subgraphs based on common join variable among all triple patterns. And the query results are retrieved by matching these query subgraphs against with CS-index. The proposed approach tends to minimize the query execution time by reducing the number of join operations as well as reduce memory usage for storing data.