Miracle-dz/UniFAR-FTUs huggingface dataset:
FAST RECOGNITION ALGORITHM OF PAVEMENT CRACKS BASED ON DEEP CONVOLUTION AND UNSUPERVISED CLUSTERING

With the large-scale operation of highways in China, the daily maintenance and management of highways has restricted the road operation. If the pavement cracks can be repaired in time before being deteriorated into pits, the maintenance cost can be greatly saved. This work proposed a new fast recognition algorithm for pavement cracks based on unsupervised clustering. First, the normalized image of pavement cracks was sliced into two-scale image blocks using the sliding window algorithm. Then a deep convolutional self-encoder was constructed to extract and reconstruct the image data. Finally, the identification model with the encoder and the cluster layer detected the pavement cracks. The results showed that compared with the current mainstream algorithms, the proposed method has better recognition with faster speed.
Corpus n -Grams and Electronic Dictionaries of Serbian

In this paper we present an experiment of interacting the two language resources for Serbian, the Corpus of contemporary Serbian (SrpKor) and the system of electronic morphological dictionaries of Ser-bian (SrpMD-DELA). Both resources are developed by the Human Language Technology Group at the University of Belgrade. The objective of their interaction is to improve the characteristics of both resources iteratively, i.e. to incorporate all the information present in SrpMD-DELA into the SrpKor data and to extend SrpMD-DELA in order to reduce morphological ambiguity. An idea behind the experiment is to identify high frequent SrpKor n -grams which can be tagged manually in an un-ambiguous manner and to generate the corresponding system of graphs (SrpNg) for their actual tagging. Combining the preprocessing of Serbian texts with SrpNg before applying the SrpMD-DELA with the complementary manual tagging can produce a dataset with the full extent of information present in SrpMD-DELA for training the Unitex machine learning-based tagger for Serbian.
Simulation of Production of Anhydrous Bioethanol using Glycerol and 1-Butyl-3-Methylimidazolyl-Tetrafluoroborate in the Aspen Plus Simulator

Corresponding Author: José Izaquiel Santos da Silva Institute of Science and Technology, Federal University of Jequitinhonha and Mucuri Valleys (ICT/UFVJM), Diamantina MG, Brazil Email: izaquiel@ict.ufvjm. edu.br Abstract: Biofuels are present in the global scenario as an energy source derived from organic biomass, representing an economic and environmental alternative. They are a renewable source of energy with low rates of pollutants emissions and, consequently, less carbon dioxide is released into the atmosphere. The obtaining of bioethanol is originated from a fermentation process, in which a multi-component mix is generated and the anhydrous bioethanol is separated. To obtain such compounds, some operations are required, such as extractive distillation, where solvents are added in order to “break” the ethanol-water azeotrope. In the present work two solvents were used: Glycerol and the ionic liquid [BMIM][BF4]. Starting from a multi-component mixture composed by ethanol, water, acetic acid and isoamyl alcohol, the bioethanol purification process was simulated using the computational tool Aspen Plus® simulator. Through a comparative analysis, it was possible to determine which solvent presented the best performance, where operational parameters such as the reflux ratio, distillate rate and the solvent flow were analyzed. The purity degree of 99.7% in mass and an approximate production of 2764 kg/h of anhydrous bioethanol were fixed and the results showed that glycerol was the solvent that presented greater economic and environmental viability for the process, considering the operational parameters mentioned above.
L EARNING FROM D EMONSTRATIONS WITH E NERGY BASED G ENERATIVE A DVERSARIAL I MITATION

Traditional reinforcement learning methods usually deal with the tasks with explicit reward signals. However, for vast majority of cases, the environment wouldn’t feedback a reward signal immediately. It turns out to be a bottleneck for modern reinforcement learning approaches to be applied into more realistic scenarios. Recently, inverse reinforcement learning (IRL) has made great progress in making full use of the expert demonstrations to recover the reward signal for reinforcement learning. And generative adversarial imitation learning is one promising approach. In this paper, we propose a new architecture for training generative adversarial imitation learning which is so called energy based generative adversarial imitation learning (EB-GAIL). It views the discriminator as an energy function that attributes low energies to the regions near the expert demonstrations and high energies to other regions. Therefore, a generator can be seen as a reinforcement learning procedure to sample trajectories with minimal energies (cost), while the discriminator is trained to assign high energies to these generated trajectories. In detail, EB-GAIL uses an auto-encoder architecture in place of the discriminator, with the energy being the reconstruction error. Theoretical analysis shows our EB-GAIL could match the occupancy measure with expert policy during the training process. Meanwhile, the experiments depict that EB-GAIL outperforms other SoTA methods while the training process for EB-GAIL can be more stable.
Motion Planning via Bayesian Learning in the Dark

—Motion planning is a core problem in many applications spanning from robotic manipulation to autonomous driving. Given its importance, several schools of methods have been proposed to address the motion planning problem. However, most existing solutions require complete knowledge of the robot’s environment; an assumption that might not be valid in many real-world applications due to occlusions and inherent limitations of robots’ sensors. Indeed, relatively little emphasis has been placed on developing safe motion planning algorithms that work in partially unknown environments. In this work, we investigate how a human who can observe the robot’s workspace can enable motion planning for a robot with incomplete knowledge of its workspace. We propose a framework that combines machine learning and motion planning to address the challenges of planning motions for high-dimensional robots that learn from human interaction. Our preliminary results indicate that the proposed framework can successfully guide a robot in a partially unknown environment quickly discovering feasible paths.
AI Based Healthcare Chatbot System by Using Natural Language Processing

: Artificial Intelligence has core branches like, Machine Learning which takes in data, searches patterns, improves itself using the data, and displays the outcome. To lead healthy lifestyle healthcare is very much important. In few unsocialized areas, it is quite hard to find a consultation with a doctor that easily regarding health issues. The main idea here is to make a healthcare chatbot based on Artificial Intelligence using NLP that can diagnose the disease and provide required details about the specific disease before consulting or visiting a doctor. Reduces the healthcare costs and improves accessibility to this medical chatbot. Specific chatbots act as virtual medical assistance, which helps the patient know more about their disease and helps to improve their health. The user can achieve the real benefit of a chatbot only when it can diagnose all kinds of diseases and provide the necessary information. A text-to-text medical chatbot involves patients in online conversation considering their health problems which provides a set of personalized diagnoses based on their provided symptoms. These bots connect with the potential patients visiting the site, helping them discover specialists, booking appointments, and getting them access to correct treatment. This chatbot uses Natural language processing techniques to process and analyze the data and give the output inappropriate manner. It brings up the disease-related problems about whether the task mentioned above should be assigned to human staff. This healthcare chatbot system will provide patients healthcare support online at all times. It helps to generate health data and automatically delivers the information of reports to medical management. By asking the questions in series it helps the patient by guiding what exactly the user is looking for queries.
Cultural and Linguistic Variables in Usability Testing: A Canadian Evaluation of International Students

More than 1 in 5 Canadians are immigrants. While industries and public services in the country interact with users of diverse backgrounds, it is unclear how the cultural and linguistic backgrounds of users influence their perceived usability of services. We conducted a usability test of the website of Immigration, Refugee and Citizenship Canada (IRCC) to explore cultural — Individualism/Collectivism, Power Distance, and Uncertainty Avoidance — and linguistic variables with English-speaking international students from China, India, and Nigeria and French-speaking international students. We found that second-language participants were more comfortable with the website’s language , and Chinese and Nigerian participants criticized the interface more than Indian participants. Our work suggests that researchers should recruit and understand participants from multiple cultural and linguistic backgrounds to ensure the reliability and reproducibility of usability test results and protocols.
Microscopic Timetable Optimization for a Moving Block System

We present an optimization model which is capable of routing and ordering trains on a microscopic level under a moving block regime. Based on a general timetabling deﬁnition ( GTTP ) that allows the plug in of arbitrarily detailed methods to compute running and headway times, we describe a layered graph approach using velocity expansion, and develop a mixed integer linear programming formulation. Finally, we present promising results for a German corridor scenario with mixed trafﬁc, indicating that applying branch-and-cut to our model is able to solve reasonably sized instances with up to hundred trains to optimality.
Waste Classification using Convolutional Neural Network on Edge Devices

:- Waste management is an important issue in the current scenario. Sorting of waste into different categorizes is one of the most important and tedious step in waste management. Normally this is a manual (hand-picking) process which has its own cons and hence the need for an automated and efficient system to manage waste arises. Through this paper we propose an intelligent waste classification system, developed using a deep pre-trained (Xception) Convolutional Neural Network model that can classify solid wastes such as glass, metal, paper and plastic etc. on stand-alone edge devices. This system can be further deployed into a real time embedded system by adding a mechanism to physically separate wastes. The proposed system is trained on an open source dataset available online and is able to achieve a test accuracy of 92% on the dataset. Thus the system could make the separation process faster and intelligent without or reducing human involvement.
A Hybrid Indoor Positioning System for the Blind and Visually Impaired Using Bluetooth and Google Tango

Blind & visually impaired individuals often face challenges in wayfinding in unfamiliar environments. Thus, an accessible indoor positioning and navigation system that safely and accurately positions and guides such individuals would be welcome. In indoor positioning, both Bluetooth Low Energy (BLE) beacons and Google Tango have their individual strengths but also have weaknesses that can affect the overall usability of a system that solely relies on either component. We propose a hybrid positioning and navigation system that combines both BLE beacons and Google Tango in order to tap into their strengths while minimizing their individual weaknesses. In this paper, we will discuss the approach and implementation of a BLE- and Tango-based hybrid system. The results of pilot tests on the individual components and a human subject test on the full BLE and hybrid systems are also presented. In addition, we have explored the use of vibrotactile devices to provide additional information to a user about their surroundings.
Peripheral Arterial Disease of the Lower Limbs: Epidemiological and Ultrasound Profile of Patients with Stenosis at the Conakry University Hospital

Introduction: Peripheral arterial disease of the lower limbs (PAD) is a partial or total obliteration of one or more arteries intended for the lower limbs by atheroma. Its prevalence increases with age, but also with other major cardiovascular risk factors such as diabetes, smoking, high blood pressure and dyslipidemia. Its diagnosis is essentially based on arterial Doppler ultrasound, which studies the topography and extension of the lesions, thus constituting the essential tool for therapeutic decision-making. The objective was to describe the epidemiological and ultrasound profile of patients with atheromatous arterial stenosis of the lower limbs. Methodology: This was a cross-sectional descriptive study running from 1 January 2019 to 31 March 2020, we took a duration of 15 months. This study was carried out at the Conakry University Hospital (Donka and Ignace Deen Hospitals). Results: In our series, the average age was 64.7 ± 13 years, with a sex ratio of 1.04. The average age was 64.7 ± 13 years, with a sex ratio of 1.04. The average age of the women in our series was 64.7 ± 13 years. Risk factors were dominated by advanced age (78.67%), diabetes (58%), hypertension (57%) and physical inactivity (47.66%). Among the 300 patients, 145 were carriers of atheromatous arterial stenosis, or a frequency of 48.33%. The leg arteries were the most affected, followed by the common femoral arteries. Concerning the degree of stenosis, patients with stenosis greater than 70% were the most represented. Conclusion: Peripheral arterial disease of the lower limbs (AOMI) is a pathology not to be neglected in Guinea. Patients have several risk factors : stenosis is an important part of the ultrasound lesions of this disease.
Preoperative Evaluation of The Patients with Cardiovascular Disease Undergoing Noncardiac Surgery

Objective: Preoperative evaluation of the patients with cardiac problems who will undergo noncardiac surgery is crucial. The aim of our study is to investigate the relationship between cardiac risk determined by the American Society of Anesthesiologists (ASA) classification, Goldman scoring system, and the cardiology consultation, and the perioperative mortality and morbidity. Material and Method: Five hundred patients undergoing noncardiac surgery for whom cardiology consultation requested were studied prospectively. The relationship between the patients’ ASA classes, Goldman scores, cardiac risks, comorbidities, operation types, preoperative cardiac symptoms, and perioperative morbidity and mortality was assessed. Results: The mean age in the group with morbidity (69.3±10.6 years) was significantly higher than that in the group without morbidity (64.0±12.1 years) (p<0.001). There was no perioperative mortality while in 354 (70.8%) of 500 patients cardiovascular complications developed.While a significant difference between operation type of both groups was observed (p=0.001), preoperative hypertension was more frequently seen in the morbidity group (p=0.007). Preoperative ASA classes (p=0.016), Goldman scores (p<0.001), and cardiac risks of patients (p=0.039) were significantly different between the groups. Logistic regression analysis was applied, and only age, hypertension, and operation type were found to be risk factors for perioperative morbidity. Conclusion: We believe that ASA classification, Goldman Cardiac Risk Index, and cardiac risk determined by the cardiologists can affect the patients’ perioperative management . Besides use of risk indices and algorithms can reduce the requirement for consultation and request for unnecessary laboratory or imaging tests and can prevent unnecessary cancellation or delaying of the surgery.
POPL: G: Data-Parallel Shell Scripting

This paper introduces PaSh, a new shell that exposes data parallelism in POSIX shell scripts. To achieve that, PaSh proposes: (i) an order-aware dataflow model that captures a fragment of the shell, (ii) a set of dataflow transformations that extract parallelism and have been proven to be correct, (iii) a lightweight framework that captures the correspondence of shell commands and order-aware dataflow nodes, and (iv) a just-in-time compilation framework that allows for effective compilation despite the dynamic nature of the shell. A preliminary evaluation of PaSh on 50 unmodified Unix scripts on a 16-core machine shows significant speedups (up to 14 . 93 × , avg: 4 . 15 × ).
Textile education during the 2020 pandemic: experiences in US, South Africa and Germany

The Covid-19 pandemic has had a major impact on higher education across the world. In this paper we consider how textile education has been impacted and what approaches have been employed to maintain quality education and laboratory experience when traditional methods are not appropriate. This paper considers three different countries – United States, South Africa and Germany. Each has been affected in a different way, has a different sociological makeup, and has developed distinct solutions to the challenge. Methods related to HyFlex, flipped classrooms, and blending learning have been applied by all three institutions. Lectures have been presented as pre-recorded videos, synchronous video conferencing, and hybrid. Similarly, laboratory and studio experiences have been handled through pre-recorded video, guided “at home” experiments, and modified in-person experiences. This paper gives an overview of the laboratory and studio experiences, time spent in preparation, and reaction of the students to remedies. It also addresses best practices from each country in the three continents.
Solid Pseudopapillary Neoplasm of the Pancreas: A Good Prognostic Tumor of the Bad Lucky Organ

Introduction Solid pseudopapillary neoplasm (SPN) of the pancreas is a rare clinical entity with low malignant potential and good clinical prognosis. It was first defined by Frantz Virginia in 1959 and was renamed solid pseudopapillary tumor by the World Health Organization (WHO) in 2010. SPN is more common in young women with an average age of 28-32 years and a female: male ratio of 3-10:1 in different publications. Material & Method The data were retrospectively analyzed for 26 patients whose pathology was compatible with SPN and operated on between 2003 and 2019 in the General Surgery Clinic at XXX. Results Of the patients, 25 (96.2%) were female and 1 (3.8%) was male; the mean age at diagnosis was 37.1 (range: 18-69, eight patients <30 years); mean body mass index was 28.8 kg/m2. The most common reason for referral was abdominal pain (n=13, 50%); three patients (11%) had nausea and vomiting, and one patient (3.8%) had jaundice with mass at the head of the pancreas. Ten patients (38%) were asymptomatic and were diagnosed incidentally during examinations performed for other reasons. The most frequent tumor localization was the head and neck part of the pancreas (n=10, 38%); eight patients (31%) had body, and eight patients (31%) had tail localization. Ten patients (38%) had Whipple procedure (pancreaticoduodenectomy), 15 patients (53%) had distal pancreatectomy, and one patient had intra-abdominal mass excision and segmental small bowel resection operation in addition to distal pancreatectomy. Six (37%) of the 16 patients who underwent distal pancreatectomy had splenectomy too. One of the patients had laparoscopic distal pancreatectomy and splenectomy. The mean tumor diameter was 7.2 cm (range: 2-23 cm). Conclusion SPN is a rare tumor, and even though it is diagnosed late and in large sizes, it has prolonged survival when appropriate surgical resection is applied. The ability to perform surgery even in cases with relapse or meta stasis during the SPN follow-up reveals the importance of accurate diagnosis.
A Literature Review of Foreign Studies on the Impact of CALL on Second Language Acquisition from 2015

With the rapid development of economy and technology, more and more fields are combined with electronic intelligence and artificial intelligence, and language teaching is no exception. As a research hot issue, computer-assisted language learning (CALL) has attracted more and more people's attention. Based on computer-aided technology, teachers can not only provide students with rich language input, but also improve classroom efficiency. However, there are still some problems in this technology. This paper reviews some empirical studies on the impact of computer-assisted language teaching from 2015 and reveals the relevant progress and shortcomings of foreign researches in terms of six aspects from the perspective of language teaching, in order to provide some enlightenment for future research.
Multi-Agent Mission Planning with Reinforcement Learning

State of the art mission planning software packages such as AFSIM use traditional AI approaches including allocation algorithms and scripted state machines to control the simulated behavior of military aircraft, ships, and ground units. We have developed a novel AI system that uses reinforcement learning to produce more effective high-level strategies for military engagements. However, instead of learning a policy from scratch with initially random behavior, it also leverages existing traditional AI approaches for automation of simple low-level behaviors, to simplify the cooperative multi-agent aspect of the problem, and to bootstrap learning with available prior knowledge to achieve order of magnitude faster training.
Learning Object Detectors from Weakly-Labeled Internet Images ∗

Learning visual object detectors typically requires a large amount of labeled data, which is hard to obtain. To overcome this limitation, we propose a system that avoids any human labeling and autonomously learns an object detector from unlabeled Internet images. Without using any visual information! we obtain them by just typing the name of an object class. First, we determine the presence of the target object in a number of images and then, estimate its localization. Since we have to cope with ambiguously/wrongly labeled data a multiple instance learning (MIL) in both stages. In the experimental results, we demonstrate the beneﬁts of this approach on publicly available benchmark datasets. In fact we show that we can train competitive object detectors without using visually labeled data.
Literary Style Transfer with Content Preservation

Successful style transfer in images has led to fruitful human-AI collaborations, as both novice and expert imaginations are sparked by high-fidelity and intuitive image outputs. But style transfer for text is still an open challenge. Drawing on computational studies of literature, we present a neural encoder-decoder model that transfers sentences from one literary style to another. We model literary style as a suite of low-level linguistic controls, such as frequency of pronouns, prepositions, and subordinate clause constructions. We perform style transfer by keeping the content words fixed while adjusting the controls to be indicative of another style. In this demo, users are able to type in their own sentences and transfer them into one of three literary styles: philosophical, gothic horror, or science fiction. This demo is presented as a web interface, and allows users to transfer any sentence or paragraph into a desired style.