Miracle-dz/UniFAR-FTUs huggingface dataset:
SUFAT - An Analytics Tool for Gaining Insights from Student Feedback Comments

Teacher evaluation is a vital element in improving student learning outcomes. Course and instructor feedback given by students, provides insights that can help improve student learning outcomes and teaching quality. Teaching and course evaluation systems help to collect quantitative and qualitative feedback from students. Since manually analysing the qualitative feedback is painstaking and a tedious process, usually, only the quantitative feedback is often used for evaluating the course and the instructor. However, useful knowledge is hidden in the qualitative comments, in the form of sentiments and suggestions that can provide valuable insights to help plan improvements in the course content and delivery. In order to efficiently gather, analyse and provide deeper insights from student feedback by topics, we developed a user-friendly application, Student Feedback Analysis Tool (SUFAT). The tool is an independent desktop application that can be installed and used by any nontechnical user. The tool takes an excel sheet with comments as an input and generates an excel sheet with visual reports of summaries that include sentiments and suggestions as an output. The tool can benefit instructors to quickly analyse the termly qualitative feedback and take appropriate actions. We intend to release the tool for public use so that instructors can download and install the tool on their computer system.
A Survey on CAN Bus Protocol: Attacks, Challenges, and Potential Solutions

The vehicles are equipped with electronic control units that control their functions. These units communicate with each other via in-vehicle communication protocols like CAN bus. Although CAN is the most common in-vehicle communication protocol, its lack of encryption and authentication can cause serious security shortcomings. In the literature, many attacks are reported related to CAN bus and the number increases with rising connectivity in the cars. In this paper, we present CAN protocol and analyze its security vulnerabilities. Then we survey the implemented attacks and proposed solutions in the literature.
Creativity and job tension in experiential learning

This work-in-progress research paper presents a study on employability skills. Employability or soft skills refer to personality traits, attitudes and behavior that are complementary to professional knowledge. These skills are part of a series of competencies that are intertwined with the engineer’s technical work [1]. A high percentage of job success depends on employability skills [2]. A gap exists between the attributes of engineering graduates and company requirements [3]. Experiential learning can develop a myriad of skills required by the workplace. Universidad EAFIT, located in Medellin (Colombia), has developed an experiential learning program called KRATOS. KRATOS was sent an invitation to participate in an international competition that implied designing and building a solar/electric powered vehicle. Think creatively is a competency that is important for engineering practice across areas, disciplines and countries [1]. Although student competitions that include design activities may enhance a passion for engineering, they can also have negative emotional consequences [4] (i.e. job tension). Using the structural equation modeling technique, the authors of this study analyzed the responses of 334 undergraduate students. The results of the study indicate that job tension significantly decreased over time, whereas no significant change was detected in terms of creativity.
Site fidelity, residency and habitat use of the Risso’s dolphin Grampus griseus in the Gulf of Taranto (Northern Ionian Sea, Central-eastern Mediterranean Sea) by photo-identification

A photo-identification tool was applied to identify and create a catalogue of photo from Risso’s dolphins providing evidence of its site fidelity, residency and habitat use in the Gulf of Taranto (Northern Ionian Sea, Central-eastern Mediterranean Sea). Sightings carried out from 2013 to 2016, occurred in a depth range from 436 to 1000 m with a mean depth value of 762±217 m. A total of 215 specimens of G. griseus were observed including 206 adults and juveniles, 7 calves and 2 new-borns. The evidence of calving and nursering females was inferred from calf association without any other sex information. Group size varied between 2 and 30 dolphin specimens, with a mean value of 13±7 specimens. A total of 54 Risso’s dolphins were identified as distinctly marked. Among these, 34 specimens (63%) were sighted only once and 20 specimens (37%) were re-sighted up to 4 times. Rates of re-sighting within single year were relatively high, reaching 23, 18 and 43% during 2013 (3 re-sighted/13 dolphins), 2014 (4 re-sighted/22 dolphins) and 2016 (12 re-sighted/28 dolphins), respectively. The site fidelity reached 0.50, with a mean value of 0.13 ± 0.18. The monthly residency rate varied between 0.11 and 0.44, with a mean value of 0.17 ± 0.09. Among a total of 9 Risso’s dolphins considered as "multi-year residents", 7 specimens were re-sighted in two consecutive years and 2 were re-sighted in a period of two non-consecutive years. Although results suggest the need for further studies implementing the photo-identification catalogue for the Risso’s dolphin and allowing possible reconstruction of its migration patterns on the Mediterranean and global scale, the Taranto Valley canyon system seems to be a critical habitat to enforce specific conservation measures for the effective protection of the species.
Persian Language Modeling Using Recurrent Neural Networks

In this paper, recurrent neural networks are applied to language modeling of Persian, using word embedding as word representation. To this aim, unidirectional and bidirectional Long Short-Term Memory (LSTM) networks are used, and the perplexity of Persian language models on a 100-million-word data set is evaluated. The effect of various parameters, including number of hidden layers and size of LSTM units, on the performance of the networks in reducing the perplexity of the models are investigated. Among different LSTM language models, the best perplexity, which is equal to 59.05, is achieved from a 2-layer bidirectional LSTM model. Comparing this value with the perplexity of the classical Trigram model, which is equal to 138, an improvement in the modeling is noticeable, which is due to the ability of neural networks to make a higher generalization in comparison with the well-known N-gram model.
Compensating Utterance Information in Fixed Phrase Speaker Verification

This work investigates on explicitly utilizing utterance information for fixed phrase speaker verification (SV). In this scenario, the same phrase is spoken by the speakers during the training and testing sessions. In other words, the speaker model possesses both speaker as well as utterance information. Therefore, there is a potential to improve the speaker characterization by compensating the utterance information. In this work, we propose a framework to compensate the utterance information, which is used to normalize the lexical content. A hidden Markov model (HMM) based triphone model is considered as a universal background model (UBM). It is used for adapting the speaker-utterance model and the background utterance model in the proposed utterance compensation framework. Given a test utterance and a claimed speaker-utterance model, the UBM as well as background utterance model is utilized for compensating the average speaker information and the lexical content information, respectively. The studies are conducted on RSR2015 database, which reveal the importance of the proposed utterance compensation framework as compared to the framework without utterance compensation.
Towards Scene Understanding Implementing the Stixel World

In this paper, we present our work towards scene understanding based on modeling the scene prior to understanding its content. We describe the environment representation model used, the Stixel World, and its benefits for compact scene representation. We show our preliminary results of its application in a diverse environment and the limitations reached in our experiments using imaging systems. We argue that this method has been developed in an ideal scenario and does not generalise well to uncommon changes in the environment. We also found that this method is sensitive to the quality of the stereo rectification and the calibration of the optics, among other parameters, which makes it time-consuming and delicate to prepare in real-time applications. We think that pixel-wise semantic segmentation techniques can address some of the shortcomings of the concept presented in a theoretical discussion.
Steady-State Thermal Model of a Synchronous Reluctance Motor

This paper presents an analytical thermal model of a synchronous reluctance motor (SynRMs) to predict the temperature of its different parts. For the analytical calculation, a lumped parameter thermal network (LPTN) is developed and analyzed. To validate the developed model, measurements are carried out on an experimental setup including the analyzed machine. Finally, the test results are compared with the analytical results to assess the accuracy of the model.
Feature-Based Learning Hidden Unit Contributions for Domain Adaptation of RNN-LMs

In recent years, many approaches have been proposed for domain adaptation of neural network language models. These methods can be separated into two categories. The first is model-based adaptation, which creates a domain specific language model by re-training the weights in the network on the in-domain data. This requires domain annotation in the training and test data. The second is feature-based adaptation, which uses topic features to perform mainly bias adaptation of network input or output layers in an unsupervised manner. Recently, a scheme called learning hidden unit contributions was proposed for acoustic model adaptation. We propose applying this scheme to feature-based domain adaptation of recurrent neural network language model. In addition, we also investigate the combination of this approach with bias-based domain adaptation. For the experiments, we use a corpus based on TED talks and the CSJ lecture corpus to show perplexity and speech recognition results. Our proposed method consistently outperforms a pure non-adapted baseline and the combined approach can improve on pure bias adaptation.
Polycystin-1 Regulates Skeletogenesis through Stimulation of the Osteoblast-specific Transcription Factor RUNX2-II*

Polycystin-1 (PC1) may play an important role in skeletogenesis through regulation of the bone-specific transcription factor Runx2-II. In the current study we found that PC1 co-localizes with the calcium channel polycystin-2 (PC2) in primary cilia of MC3T3-E1 osteoblasts. To establish the role of Runx2-II in mediating PC1 effects on bone, we crossed heterozygous Pkd1m1Bei and Runx2-II mice to create double heterozygous mice (Pkd1+/m1Bei/Runx2-II+/-) deficient in both PC1 and Runx2-II. Pkd1+/m1Bei/Runx2-II+/- mice exhibited additive reductions in Runx2-II expression that was associated with impaired endochondral bone development, defective osteoblast-mediated bone formation, and osteopenia. In addition, we found that basal intracellular calcium levels were reduced in homozygous Pkd1m1Bei osteoblasts. In contrast, overexpression of a PC1 C-tail construct increased intracellular calcium and selectively stimulated Runx2-II P1 promoter activity in osteoblasts through a calcium-dependent mechanism. Site-directed mutagenesis of critical amino acids in the coiled-coil domain of PC1 required for coupling to PC2 abolished PC1-mediated Runx2-II P1 promoter activity. Additional promoter analysis mapped the PC1-responsive region to the “osteoblast-specific” enhancer element between -420 and -350 bp that contains NFI and AP-1 binding sites. Chromatin immunoprecipitation assays confirmed the calcium-dependent binding of NFI to this region. These findings indicate that PC1 regulates osteoblast function through intracellular calcium-dependent control of Runx2-II expression. The overall function of the primary cilium-polycystin complex may be to sense and transduce environmental clues into signals regulating osteoblast differentiation and bone development.
A Review of Driver Fatigue Detection: Progress and Prospect

Fatigue driving has become one of the main causes of road traffic accidents which threatens people's life and property safety seriously. This paper mainly reviews the research status and development trends of driver fatigue detection technology where the fatigue detection based on driver’s physiological signals and behavior characteristics, vehicle’s running status and information fusion are respectively studied and compared. In addition, we also study two new trends in this field in recent years: fatigue detection based on RGB-D camera and deep learning. Then, the idea of using both RGB-D camera and deep learning for driver fatigue detection is also discussed.
Dominant Melody Enhancement in Cochlear Implants

The ability of cochlear implant (CI) users to accurately perceive and enjoy listening to music has remained unsatisfactory in a significant subset of users. Basic elements of music such as pitch, melody, and timbre that are easily discerned by normal-hearing (NH) listeners are difficult for CI users to perceive, owing to factors such as limited resolution of the devices' electrode array, audio processing that relies on coarse separation of the frequency spectrum into a limited number of overlapping bands, and temporal envelope extraction that discards the temporal fine structure. Alternative signal processing methods have been explored to enhance music enjoyment in CI users, with varying results, and most are computationally complex or require reprogramming of the audio processing device. This paper explores a new pre-processing technique to enhance music enjoyment in CI listeners through extraction and enhancement of the music's dominant melody, a technique we refer to as DoME (Dominant Melody Enhancement). In the described pilot studies, we employed a DoME technique of adding a frequency-modulated sine wave at the fundamental frequency (F0) of the music's dominant melody, and conducted perceptual experiments on nine CI users to gauge the effect of DoME on their music enjoyment. Initial results suggest DoME could increase some CI users' enjoyment of music listening.
Internet of Things for Greenhouse Monitoring System Using Deep Learning and Bot Notification Services

Internet of things (IoT) plays a big important role in agricultural industry recently in order to provide a support to farmers such as growth monitoring system of temperature, humidity and water supply, and also early disease monitoring and detection system. To provide a smart farming solutions, this paper proposed an IoT system with a bot notification on tomato growing stages. The tomato dataset was obtained from Shinchi Agri-Green, the tomato greenhouse in Fukushima, Japan. We trained and tested the deep learning model to detect the fruit proposal region. Then, the detected regions were classified into 6 stages of fruit growth using the visible wavelength as a feature in SVM classification with the weight accuracy of 91.5%.
Wideband Slotted Array Antenna Based on Substrate Integrated Waveguide with Circular Aperture at Ka-Band

In this paper, a large wide band double layer substrate integrated waveguide (SIW) slotted array antenna is designed. The array is consisted of 12 radiating waveguides with the different number of longitudinal slots to form the circular aperture. The feed waveguide is placed perpendicularly underneath the branch waveguides for feeding them through the inclined coupling slots. To have accurate design and good agreement between design specification and simulated results not only external mutual coupling between slots but also internal mutual coupling between progressive mode and evanescent modes in the waveguide are taken into account in the design procedure. Also to increase the bandwidth, the overloading technique is used. In this technique, the admittance of each radiating waveguides is tuned correctly to design a wideband antenna. To have low side lobe level in radiation patterns, 25dB Taylor distribution has been chosen for slots voltage amplitudes. Radiation pattern and return loss are in very good agreement with design specifications. The sidelobe level at the center frequency is 23.9dB in the E-plane and 20.5dB in the H-plane. In addition, the gain of 25.7dB and 630MHz bandwidth for 10dB return loss is obtained. The relative bandwidth is 1.8% which is considered wideband for this large antenna by the size of about 6λ×6 λ where λ is the operating wavelength.
Kermanshah Earthquake Event Tracking Through Persian Tweets

By expanding online social networks during the last decade, online event tracking has become one of the vital issues among researchers. In this study, Persian tweets which exchange among Iranian users have been considered in order to track hidden patterns of evolutionary Kermanshah earthquake event. A well-known dynamic community detection algorithm has been applied over key-graphs which are generated from extracted keywords of tweets. The key-graphs are generated from KNTUPT dataset which consists of entire Persian tweets between November 24th, 2017 and December 8th, 2017. The experimental study reveals the dynamic community detection algorithm had a superior result in terms of modularity as community quality and execution time as performance.
Particle Swarm Optimization for Sizing Hybrid Power Systems Incorporating Demand Response

This paper presents an optimal sizing method for a hybrid wind, solar, battery storage and diesel generation units designed to meet a specific demand. The energy output and load demand used in the model is structured in one hour intervals over the course of a year. In addition, demand response is implemented showing a direct relationship with total system price. Based on generating unit capital and variable costs, the particle swarm optimization (PSO) algorithm is implemented. The yearly normalized total system cost is taken as the objective function which is based on the generating technologies life cycle. PSO exhibits an optimum generating unit sizing assuming 5 percent of the average load as demand response power capacity.
Efficient mm Wave Link Establishment and Maintaining Using Wi- Fi/mm Wave Interworking

Millimeter wave (mmWave) communication is considered as a promising candidate for the access link of the next 5G cellular networks. However, directional transmission via narrow beams must be used to mitigate the severe isotropic losses in this band. Hence, establishing and maintaining the mm Wave link especially in dynamic environments (i.e. under user mobility) turns to be a big challenge. Where, each deployed mm Wave access point (AP) in the target environment needs to perform an intensive and frequent beamforming training (BT) to track the channel of a moving user to preserve the link. This huge training overhead significantly deteriorates the system performance. This paper proposes a novel location-based BT scheme for mmWave link establishment and maintaining based on an interworking between mmWave and Wi-Fi bands. Where, an accurate UE positioning supported by the legacy Wi-Fi APs is used for reducing mmWave BT complexity and enables the network to fast explore the channel quality of the surrounding APs. Thus, a rapid path switching in case of the link quality degradation can be done. Simulations show that the proposed scheme provides a robust mmWave communication links with higher throughput and energy efficiency than the conventional scheme with the great overhead exhaustive BT protocol.
EEG Dipole Source Localization in Hemispherical Harmonics Domain

Dipole source localization problem is addressed in hemispherical harmonics (HSH) domain. Human head is approximated by spherical head model. Hence, spherical harmonics (SH) basis functions have been natural choice for EEG source reconstruction and localization. However, sensor placed over scalp to acquire EEG signal, assume the shape of a hemisphere. Hemispherical harmonics basis functions will more appropriately represent the data sampled over hemisphere. In this paper, the forward data model is formulated in HSH domain. Optimal array processing methods for source localization such as MUltiple SIgnal Classification (MUSIC) and minimum variance distortion-less response (MVDR) are proposed in HSH domain. Various experiments have been presented to illustrate the theory.
7.1 An 11.5TOPS/W 1024-MAC Butterfly Structure Dual-Core Sparsity-Aware Neural Processing Unit in 8nm Flagship Mobile SoC

Deep learning has been widely applied for image and speech recognition. Response time, connectivity, privacy and security drive applications towards mobile platforms rather than cloud. For mobile systems-on-a-chip (SoCs), energy-efficient neural processing units (NPU) have been studied for performing the convolutional layers (CLs) and fully-connected layers (FCLs) [2–5] in deep neural networks. Moreover, considering that neural networks are getting deeper, the NPU needs to integrate 1K or even more multiply/accumulate (MAC) units. For energy efficiency, compression of neural networks has been studied by pruning neural connections and quantizing weights and features with 8b or even lower fixed-point precision without accuracy loss [1]. A hardware accelerator exploited network sparsity for high utilization of MAC units [3]. However, since it is challenging to predict where pruning is possible, the accelerator needed complex circuitry for selecting an array of features corresponding to an array of non-zero weights. For reducing the power of MAC operations, bit-serial multipliers have been applied [5]. Generally, extremely low- or variable-bit-precision neural networks need to be carefully trained.
Versican 3′‐untranslated region (3′‐UTR) functions as a ceRNA in inducing the development of hepatocellular carcinoma by regulating miRNA activity

This study was designed to explore the role of versican in the development of hepatocellular carcinoma (HCC). Ectopic expression of the versican 3′‐untranslated region (3′‐UTR) was studied as a competitive endogenous RNA for regulating miRNA functions. We used this approach to modulate the expression of versican and its related proteins in 3′‐UTR transgenic mice and in the liver cancer cell line HepG2, stably transfected with the 3′‐UTR or a control vector. We demonstrated that transgenic mice expressing the versican 3′‐UTR developed HCC and increased expression of versican isoforms V0 and V1. HepG2 cells transfected with versican 3′‐UTR displayed increased proliferation, survival, migration, invasion, colony formation, and enhanced endothelial cell growth, but decreased apoptosis. We found that versican 3′‐UTR could bind to miRNAs miR‐133a, miR‐199a*, miR‐144, and miR‐431 and also interacted with CD34 and fibronectin. As a consequence, expression of versican, CD34, and fibronectin was up‐regulated by ectopic transfection of the versican 3′‐UTR, which was confirmed in HepG2 cells and in transgenic mice as compared with wild‐type controls. Transfection with siRNAs targeting the versican 3′‐UTR abolished the effects of the 3′‐UTR. Taken together, these results demonstrate that versican V0 and V1 isoforms play important roles in HCC development and that versican mRNAs compete with endogenous RNAs in regulating miRNA functions. —Fang, L. , Du, W. W. , Yang, X. , Chen, K. , Ghanekar, A. , Levy, G. , Yang, W. , Yee, A. J., Lu, W. ‐Y. , Xuan, J. W. , Gao, Z. , Xie, F. , He, C. , Deng, Z. , Yang, B. B. Versican 3′‐untranslated region (3′‐UTR) functions as a ceRNA in inducing the development of hepatocellular carcinoma by regulating miRNA activity. FASEB J. 27, 907–919 (2013). www.fasebj. org