Miracle-dz/UniFAR-FTUs huggingface dataset:
Who Takes the Cake: Rethinking the Using of Student Teams-Achievement Division in Electronics Course

Many studies show that the Student Teams-Achievement Divisions(STAD) of cooperative learning helps improve the students’ learning achievement. The objective of this current study is to investigate whether STAD actually benefits the students with lower academic achievement, meanwhile, to understand the corresponding factors influencing on the learning achievement, and to show the course’s conducting process. The study invited 47 sophomores at one University of Science and Technology to participate in a three weeks’ STAD’s teaching project. According to the scores of mid-term exam, they were sorted into three groups. The groups were divided base on high, medium, and low academic achievement and then were evenly divided into 6 groups. The individual participant who had more progress and a higher score was awarded in time every week. Finally, each group got the rewards depending on the total progressive scores after three weeks. The study results presented that there was (1) a significant difference between pre-test and post-test scores. (2) a significant difference between the participant’s learning achievement and the influence factors, including the learning sheet, the heterogeneous grouping, and the alienation behavior. (3) a significant difference between the attendance and the heterogeneous grouping of the participants (4) a very different look exists in the participant behavior, the group discussion, and the learning achievement for the high-score and low-score participants. This current study’s results support the effect of STAD approach and show the fact that the low-score participants cannot have significant improvement in STAD. There are four parts put forward to discuss, which includes the work-division and cooperative ability for the group, remediating the background knowledge and exciting the inner motivation, the various assessments for learning, and the improvement of the teacher’s teaching competence. This study also suggests that the rate and duration for the STAD’s conduction can increase to understand more about its effectiveness and the corresponding influence in the future.
Development of competences in industrial engineering students inmersed in SME’s through challenge based learning

This paper describes a case study of challenge based learning achieved through interactive experiential learning experiences in collaboration with associated training partners to develop disciplinary and cross-disciplinary competences in industrial engineering students. Accordingly, it was used an Innovation Semester (i-semester) format which is part of the new Educational Model developed by Tecnologico de Monterrey. The i-semester was implemented in three university campuses located in Mexico City with the participation of 34 students and 10 professors. Additionally, six Small Medium Enterprises (SME’s) participated as associated training partners. The results obtained show that students enrolled in the i-semester performed better in respect to disciplinary competences than students who did not articipated in the i-semester. Furthermore, this article describes positive impacts in strategic Key Performance Indicators (KPI’s) of the SME’s involved.
Next Generation Network Coding Technique for IoT

Recoding is the unique feature of Random Linear Network Coding (RLNC). It helps to reduce the number of transmissions. It provides low delay. RLNC is a next-generation network coding technique. Its ability to recover packet at the destination, even if there is a packet loss during transmission without retransmission. The receiver can recover back original packets at the destination. The recovery of lost packets is possible with the help of an encoding vector. The encoding vector provides sufficient information at the destination to know what coding coefficients are being used during the encoding process at the sender's side. Here I have successfully demonstrated 4 different concepts in RLNC. They are (i)Multicast sender-receiver (ii) Encode Decode using Random Coefficients (iii) Recoding (iv) Encode Decode on the fly. It is successfully implemented by using heterogeneous architecture and with the help of the Kodo network coding Library. Also, I contributed a survey on the state of the art techniques in RLNC and pointed out the advantages and disadvantages of it. The sliding window is yet another significant concept used in RLNC.
On the Effects of File-level Information on Method-level Bug Localization

CONTEXT: Automated bug localization is expected to help developers reducing effort and has been studied for years. One of the major approaches for this topic is Information Retrieval (IR) based bug localization. It takes a bug report and gives a ranking list of suspicious software modules based on the text similarity. Method-level bug localization is more desirable than file-level to reduce developers’ cost to examine the ranking list. A challenge is that methods have little information inside. OBJECTIVE: To examine whether method-level bug localization can be improved with file-level information around a method. METHOD: An empirical evaluation was conducted with public data sets. A bug localization method BLUiR was used as a testbed. RESULTS: We found the class name of a method contributed to improving method-level bug localization. The comments outside the method often improved the performance but sometimes degraded it. The other elements hardly improved the performance. CONCLUSIONS: It is worth to add the class name for improving method-level bug localization. The comments are also worth trying.
Automated Theme Allotment to Optimise Learning Outcomes in Robotic Competition

In the e-Yantra Robotics Competition we teach robotics through a competition paradigm. Here, ’Themes’ or problem statements, are allotted to participants who clear a preliminary selection test. Theme allotment to student teams is a challenge and is time-consuming since we need to fit a Theme to a team capable of addressing the challenges denoted by a Theme. This allotment of Themes considers multiple factors such as team members’ experience, expertise, Theme complexities, domain requirements and other constraints set on a Theme by the respective Theme designers. Various machine learning techniques were analyzed and experimented with; however, the problem of project tagging could not be solved by machine learning algorithms alone due to certain randomness in data and human bias involved in previous Theme allotment techniques. We thus devised a new metric that projected multiple parameters onto three dimensions, namely Algorithm, Mechanical and Electronics. Further clustering and analysis were done based on these three dimensions. The predicted clusters were subjected to conditional random sampling based on Theme constraints for actual project allocation to ensure an unbiased and a better allotment of Themes. This led to allotment of Themes such that it provided control to Theme designers, whilst automating the time-consuming process.
Towards Automatic Engagement Recognition of Autistic Children in a Machine Learning Approach

Children suffering from autism have been continue rising globally and the early intervention may significantly improve the social and cognitive capacity of them. The autism treatment in China is still largely based on training courses and interventions, nevertheless, lacking of professional teachers is a severe problem. Traditional individual training classes are mainly based on teachers’ evaluation of students’ emotional feedback, and there remain prejudices. The continuous information technology advance, such as ML, have been inspiring novel diagnosis and treatment for autistic children. Note that the majority of the current research on emotional recognition of autistic children have not been conducted on the autistic children in China, in this paper, we aim to conduct a emotional recognition study for them. We study the one-to-one autism training class, and propose an automatic engagement recognition framework to track the behaviors and affection of autistic children by ML. With the improved spatial temporal graph convolutional neural network, our experiments based on the available datasets show that the behavior recognition accuracy reaches beyond 90% and the affection recognition achieves 99%. The result demonstrates that the proposed system can analyze the relationship among arousal, valence and expression category for the autism training class.
Lessons Learned on Research Co-Creation: Making Industry-Academia Collaboration Work

How to increase the impact of software engineering research in the software industry and the society at large is a critical yet perplexing question to answer. Constrained by curtailed communication between researchers and practitioners, research conducted in a vacuum, or the "publish or perish" mindset, research collaborations between industry and academia often fail to deliver on the promise of creating a meaningful impact for practitioners. In an attempt to address these circumstances, this paper gives insights on applying research value co-creation to industry-academia collaboration in software engineering. We observe that the core of co-creation includes commitment, continuous engagement and alignment, aiming to produce value for both sides. Further we contend that co-creation has the potential to bridge the acknowledged research-practice collaboration gap through participative knowledge generation. Our experience stems from an eight-year long large collaborative project between a research organization and Norwegian software industry and public sector services. We suggest that for achieving research impact one needs to rethink the way industry and academia engage in and run collaborative projects. Traditional technology-transfer workflows where research is created in a lab and then pushed to industry practice do not stand the best chance of success. Instead, co-creating research with all stakeholders is likely to bring about the best research impact.
Lewat: A Lightweight, Efficient, and Wear-Aware Transactional Persistent Memory System

Emerging non-volatile memory (also termed as persistent memory, PM) technologies promise persistence, byte-addressability, and DRAM-like read/write latency. A proliferation of persistent memory systems have been proposed to leverage PM for fast data persistence and expose malloc-like persistent APIs. By eliminating disk I/Os, these systems gain low-latency and high-throughput access performance for persistent data. However, there still exist non-negligible limitations in these systems, such as frequent context switches, inefficient allocation, heavy logging overhead, and lack of wear-leveling techniques. To solve these problems, we develop Lewat, a lightweight, efficient, and wear-aware transactional persistent memory system. Lewat is built in user-layer to avoid kernel/user layer context switches and enables lightweight persistent data access. We decouple the data space into slot zone and page zone. Based on this, we design different allocators in these two zones to achieve efficient allocation performance for both small-sized data and large-sized data. To minimize logging overhead, we propose an efficient adaptive logging framework. The main idea is to utilize different logging techniques for different workloads. We also propose a suite of system-coupled wear-leveling techniques that contain wear-aware allocation, wear-aware update, and write reduction. We evaluate Lewat on a real non-volatile memory platform and the experimental results show that compared with state-of-the-art persistent memory systems, Lewat has much lower latency and higher throughput.
Quantification of seepage in a multi-layered disconnected river-aquifer system

Quantification of seepage in disconnected river-aquifer systems is significant for local water management and groundwater pollution control, especially in areas with water shortage or contamination. The vadose zone under riverbeds usually exhibits a multi-layered structure, particularly when paved with low permeability liners. To evaluate the impact of engineering solutions to seepage under such conditions, we proposed an approach by combining GIS and the minimum flux in saturation layer (MFSL) method. MFSL can calculate the stable seepage rate by assessing the dominant low permeability layers (including but not limited to the liners) in multilayered disconnected river-aquifer systems. We used MFSL to calculate local seepage rate, and used GIS to extend the results to a regional scale. The reliability of MFSL is discussed by comparing the results with the double ring infiltration test, the numerical simulation by HYDRUS, and the methods of stream package in MODFLOW, including its improved form. A case study was conducted in the Yongding River with river-aquifer seepage calculated under various conditions, including different river water levels (i.e., under the designated water level, drought stage level, flood stage level and flood inundation level) and with/without low permeability liners (i.e., ecological membranes or geomembrane). Results showed that low permeability liners could greatly reduce the seepage rate. However, if an unlined inundation area exists, the seepage rate may increase greatly. The results indicated that the proposed method was reliable and convenient for calculating long-term, large area seepage in disconnected river-aquifer systems especially those paved with liners.
A Novel Feeder Protection System Using Fast Switching PhotoMOS Relay

The feeder system, being an intermediary element for the distribution of power to the regional transformer, the protection against the occurrence of any fault, becomes a requirement in the power systems. The power system protection devices are used to avoid such developing fault conditions. The relay circuits serve as the fundamental part of feeder protection systems wherein they are responsible for controlling the overload voltage and current to ensure the safety of the devices. In this paper, a novel model for feeder protection systems has been proposed incorporating the enhanced switching characteristics of PhotoMOS solid-state relay. The significant switching time for the open and closed-circuit operations have been obtained using the PhotoMOS device that resulted in switching time ratios (TOFF/TON) 0.03 and 0.06, respectively. The result shows the considerable gain in the operational switching time parameter using the PhotoMOS device over the conventional Electromechanical (EM) relays. The obtained results also assure that the PhotoMOS is a useful relay circuit element and makes it suitable for future Internet of Things (IoT) applications.
Dynamic Deformation Analysis of Power Transformer Windings under Multiple Short-Circuit Impacts

Generally speaking, the transformer will suffer from various short-circuit faults during its service period. Under the action of multiple short-circuit impacts, the winding deformation expands gradually until it threatens the safe and stable operation of the transformer. In order to reveal the mechanism of winding cumulative effect, based on Maxwell's equations and elastoplastic mechanics theory, this paper uses the 2-D finite element method (FEM) to simulate a 220kV oil-immersed power transformer. The simulation results indicate that the transformer windings will produce irreversible deformation under the action of large short-circuit current. And with the increase of impact times, the transformer winding deformation will increase gradually at first and then tend to be stable. Therefore, each time the transformer is subjected to the short-circuit fault, it is necessary to detect the winding deformation again to avoid insufficient winding stability.
Life Automation: A Blessing of IoT

Internet of things (IoT) is turning into a burning exploration topic due to its wide range of application fields and popularity. It comprises of a huge network of things that has interoperability and intercommunication capabilities. This paper points out the IoT application fields that are closely related to our daily activities. IoT application in our life provides automation in our every task. It is making our life more convenient, attractive, secured, entertained and speedy. In this paper, we've broadly analyzed each application fields of IoT and carry out the impact of IoT in practical life. We've then addressed the challenges in IoT implementations. With the invention of newer wireless standards, IoT is evolving newer features to make our life smarter. We've concluded with a discussion on the strategies for further research scopes to present the scenario of potential future expansion of IoT applications.
A Framework for Land Use Scenes Classification Based on Landscape Photos

Space-earth integrated stereoscopic mapping promotes the progress of earth observation technologies. The method which combined remote sensing images with zenith perspectives and ground-level landscape photos with slanted viewing angles improves the efficiency and accuracy of land surveys. Recently, numerous efforts have been devoted to combining deep learning and remote sensing images for the classification of land use scenes. However, improvement of classification accuracy has been limited because of the lack of sectional representation. Landscape photos can describe the cross-sections in detail. For this reason, this study constructed a land-use semantic photo dataset (LSPD) and proposed a land-use classification framework for photos (LUCFP) based on Inception-v4. LSPD was constructed through semantic planning, scene segmentation, supervised iteration transfer learning, and augmentation of photos. LSPD has 1.4 million photos collected from seven geographic regions of China, and covers 13 land-use categories and 44 semantic categories. LUCFP adapts scene segmentation based on depth of field, multisemantic block labeling, and weighting of semantic joint spatial ranges to determine the land use category. To validate LUCFP, nine semantic samples (9×3×2000 photos) were chosen from LSPD, obtaining an overall accuracy of 97.64%. The best photo cropping method was masking, which crops the boundary of the scene labeled by the photo, leading to an accuracy of 90.32%. The optimal pixel size that balances speed and accuracy is 675×675, with speed reaching 30 photos per second with an average accuracy of 93.80%. LUCFP has been successfully applied to the automatic verification of land surveys in China.
Biomechanical Effects of a Passive Hip Structure in a Knee Exoskeleton for People with Spinal Cord Injury: A Comparative Case Study

The inability to stand and walk is one of the major consequences of spinal cord injury (SCI). Wearable lower limb exoskeletons are emerging as a promising solution to restore mobility after SCI. The ABLE Exoskeleton is a powered-knee exoskeleton intended for patients with low SCI, who preserve motor function at the hip. This paper presents a comparative study to investigate the biomechanical effects of adding a hip structure that connects two individual powered-knee exoskeletons, and constrains hip abduction-adduction and internal-external rotation. We carried out a series of tests with an adult man with a motor complete SCI (injury level T11) walking with two independent knee-exoskeletons one in each leg (ABLE Knee) and with the knee-exoskeletons connected to the hip structure (ABLE Hip). The participant was asked to walk with both configurations while we recorded body motion, actuator current, and foot contact pressure. We observed significantly larger deviations in terms of hip abduction-adduction, internal-external rotation and pelvic obliquity with the ABLE Knee compared to the ABLE Hip. In addition, walking with the ABLE Hip significantly increased the range of motion of knee and hip flexion-extension. However, these improvements in joint kinematics did not translate to significant differences in step length or walking speed, when comparing the two configurations. In conclusion, these results suggest that adding a hip structure that limits undesirable hip rotations can improve walking performance for people with SCI.
A preliminary Study of Knowledge Sharing related to Covid-19 Pandemic in Stack Overflow

The Covid-19 outbreak has changed to an unprecedented extent almost every aspect of human activity. At the same time, the pandemic has stimulated enormous amount of research by scientists across various disciplines, seeking to study the phenomenon itself, its epidemiological characteristics and ways to confront its consequences. Information Technology, and particularly Data Science, drive innovation in all related to Covid-19 biomedical fields. Acknowledging that software developers routinely resort to open ‘question & answer’ communities like Stack Overflow to seek advice on solving technical issues, we have performed an empirical study to investigate the extent, evolution and characteristics of Covid-19 related posts. Through the study of 464 Stack Overflow questions posted in February and March 2020 and leveraging the power of text mining, we attempt to shed light into the interest of developers in Covid-19 related topics and the most popular problems for which the users seek information. The findings reveal that indeed this global crisis sparked off an intense activity in Stack Overflow with most post topics reflecting a strong interest on the analysis of Covid- 19 data, primarily using Python technologies.
Detection of plasmid DNA profile in bacterial and fungal isolates from catheterized patients and its relation with antibiotic susceptibility

Study the resistance of bacterial and fungal isolates which identified previously towards different antibiotics that have been recommended for treating those patients. It was found that higher percentage of resistance to penicillin, gentamycin, tetracyclin, chlormphenicol, and trimethoprimsulfmethazxol then moderate resistance to ampcillin, nalidixic acid, cephotaxime, streptomycin, cephalexin, rifampicin and less resistance to ciprofloxacin that represent the most effective antibiotic against uropathogenic bacteria. Ketazole antibiotic was used against yeasts, which the minimum inhibitory concentration of ketazole against 83% of Candida. albicans isolates was 128 μg/ml, the rest of the isolates was 64 μg/ml. Isolation of bacterial and yeasts DNA (chromosome + plasmids), results of DNA isolation showed that bacterial isolates (E.coli (RS1), proteus. mirabilis (RS2), klebsiella. pneumonia (RS3) and Pseudomonas. aeruginosa (RS4) have chromosome and plasmids vary in numbers and size according to isolate. While yeast (C13) isolate have only chromosome without plasmid. A result of curring shows that: -Resistance to ampicillin, tetracycline, gentamycin, pencillin for all bacterial isolates carried on plasmid. Resistance to cephalexin by RS1 isolate, resistance to cephotaxim by RS3 isolate and resistance to chlormphnicol by RS4 isolate carried on plasmid, while resistance to other antibiotics were carried on chromosome. صلختسملا فلس ةصخشملا ةيرطفلاو ةيريتكبلا تلازعلا ةمواقم تسرد ا نم نوناعي نيذلا و ةرطثقلا بيبانا ىضرم نم يف ةلمعتسملا هيويحلا تاداضملا هاجت هيلوبلا يراجملا تاباھتلا ىضرملا ءلاؤھ جلاع روھظ جئاتنلا تنيب دقو هاجت ةيلاع ةمواقم : pincillin gentamycin, tetracyclin, , chlormphenicol و trimethoprimsulfmethaxazol ةاجت ةطسوتم ةمواقمو Ampicillin , nalidixic acid , cephalexin cefotaxime, , streptomycin و rifampicin و هاجت ةمواقم لقا ciprofloxacin داضملا لثمي يذلا زاھجلل ةيريتكبلا تاضرمملا هاجت ةيلاعف رثكلاا لوبلا ي . Ketazol رئامخلا نم صلختلل مدختسملا داضملا لثمي دض لوزاتيكلا نم طبثم زيكرت لقا ناك دق و تايرطفلاو 83 % لا نم ـ Candida albicans وھ 128 مغوركيام / لم وھ طبثم زيكرت لقا ناك دقف تلازعلا هيقب اما 64 مغوركيام / لم . جئاتن ترھظاو لزع اندلا ةيسيئرلا تاضرمملل ) بلا ايرتك – رئامخلا ( جئاتن تطعا ثيح هيريتكبلا تلازعلا هنا اندلا E.coli (RS1), Proteus. mirabilis (RS2), Klebsiella.