Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Inquisitiveness of Chatbots Through NER Integration

The need and use of virtual assistance are ever increasing. They have wide range of applications in the fields of entertainment systems, scientific research and commercial applications and can bring a drastic change in the way human-computer interaction takes place. Virtual assistance can be implemented by means of chatbot. Chatbots are software frameworks that can respond to natural language input and it converse in such a way that it imitates a real person. Chatbots are expects to give a proper response by understanding and analysing the input query. Chatbots can be used for entertainment, business and commercial purposes. Chatbot implementation can be considered successful only when it correctly analyses user query and returns an appropriate response to the user. In many of the cases, the input provided by the user is ambiguous for the chatbot to provide an accurate response. Such a situation demands for inquisitive chatbot as it becomes more interactive and communicate just like humans. This paper presents the utilization of Natural Language Processing (NLP) techniques to improve the inquisitiveness and interaction of chatbots by adapting Stanford CoreNLP framework for integrating the NLP techniques of Named Entity Recognition (NER) to chatbots.
Contention-Aware Fair Scheduling for Asymmetric Single-ISA Multicore Systems

Asymmetric single-ISA multicore processors (AMPs), which integrate high-performance big cores and low-power small cores, were shown to deliver higher performance per watt than symmetric multicores. Previous work has demonstrated that the OS scheduler plays an important role in realizing the potential of AMP systems. While throughput optimization on AMPs has been extensively studied, delivering fairness on these platforms still constitutes an important challenge to the OS. To this end, the scheduler must be equipped with a mechanism enabling to accurately track the progress that each application in the workload makes as it runs on the various core types throughout the execution. In turn, this progress largely depends on the benefit (or speedup) that an application derives on a big core relative to a small one, which may differ greatly across applications. While existing fairness-aware schedulers take application relative speedup into consideration when tracking progress, they do not cater to the performance degradation that may occur naturally due to contention on shared resources among cores, such as the last-level cache or the memory bus. In this paper, we propose CAMPS, a contention-aware fair scheduler for AMPs that primarily targets long-running compute-intensive workloads. Unlike other schemes, CAMPS does not require special hardware extensions or platform-specific speedup-prediction models to function. Our experimental evaluation, which leverages real asymmetric hardware and scheduler implementations in the Linux kernel, demonstrates that CAMPS improves fairness by up to 11 percent with respect to a state-of-the-art fairness-aware OS-level scheme, while delivering better system throughput.
RAB7L1-Mediated Relocalization of LRRK2 to the Golgi Complex Causes Centrosomal Deficits via RAB8A

Mutations in the LRRK2 gene cause autosomal-dominant Parkinson’s disease (PD), and both LRRK2 as well as RAB7L1 have been implicated in increased susceptibility to idiopathic PD. RAB7L1 has been shown to increase membrane-association and kinase activity of LRRK2, and both seem to be mechanistically implicated in the same pathway. Another RAB protein, RAB8A, has been identified as a prominent LRRK2 kinase substrate, and our recent work demonstrates that aberrant LRRK2-mediated phosphorylation of RAB8A leads to centrosomal alterations. Here, we show that RAB7L1 recruits LRRK2 to the Golgi complex, which causes accumulation of phosphorylated RAB8A in a pericentrosomal/centrosomal location as well as centrosomal deficits identical to those observed with pathogenic LRRK2. The centrosomal alterations induced by wildtype LRRK2 in the presence of RAB7L1 depend on Golgi integrity. This is in contrast to pathogenic LRRK2 mutants, which cause centrosomal deficits independent of Golgi integrity or largely independent on RAB7L1 expression. Furthermore, centrosomal alterations in the presence of wildtype LRRK2 and RAB7L1 are at least in part mediated by aberrant LRRK2-mediated RAB8A phosphorylation, as abolished by kinase inhibitors and reduced upon knockdown of RAB8A. These results indicate that pathogenic LRRK2, as well as increased levels of RAB7L1, cause centrosomal deficits in a manner dependent on aberrant RAB8A phosphorylation and centrosomal/pericentrosomal accumulation, suggesting that centrosomal cohesion deficits may comprise a useful cellular readout for a broader spectrum of the disease.
Automatic Modulation Classification: A Deep Learning Enabled Approach

Automatic modulation classification (AMC), which plays critical roles in both civilian and military applications, is investigated in this paper through a deep learning approach. Conventional AMCs can be categorized into maximum likelihood (ML) based (ML-AMC) and feature-based AMC. However, the practical deployment of ML-AMCs is difficult due to its high computational complexity, and the manually extracted features require expert knowledge. Therefore, an end-to-end convolution neural network (CNN) based AMC (CNN-AMC) is proposed, which automatically extracts features from the long symbol-rate observation sequence along with the estimated signal-to-noise ratio (SNR). With CNN-AMC, a unit classifier is adopted to accommodate the varying input dimensions. The direct training of CNN-AMC is challenging with the complicated model and complex tasks, so a novel two-step training is proposed, and the transfer learning is also introduced to improve the efficiency of retraining. Different digital modulation schemes have been considered in distinct scenarios, and the simulation results show that the CNN-AMC can outperform the feature-based method, and obtain a closer approximation to the optimal ML-AMC. Besides, CNN-AMCs have the certain robustness to estimation error on carrier phase offset and SNR. With parallel computation, the deep-learning-based approach is about $ 40$ to $ 1700$ times faster than the ML-AMC regarding inference speed.
Investigation of Agel Leaf Fiber/Unsaturated Polyester Composite Cutting Parameters Using CO2 Laser

Composite is a material with some advantages such as corrosion-resistant, strong, and lightweight. The development of composite materials moves toward sustainable issues by using natural fibers as reinforcement. One of natural fiber type that can be used as reinforcement is agel leaf fiber. The result of composite forming using agel leaf fiber can be applied as the main structure of unmanned aerial vehicle (UAV). The process of making the main structure of UAV is done by cutting the composite sheet. Cutting process using conventional machining results various kinds of damage to composite such as rupture, crack, delamination and tool wear. Laser cutting presents capability to eliminate the potential damage due to no contact between the cutting tools and the workpiece. Another advantage from laser cutting is capability to repeat the same process with a high level of precision. This study aims to determine the effect of various cutting factors of agel leaf fiber/unsaturated polyester with CO2laser in order to obtain best results in the form of smallest kerf width/depth (L/D). Laser power varies at 30, 50, and 70 W. Cutting speed varies at 2, 4 and 6 mm/s. Nozzle distance varied at 24, 26, and 28 mm. Gas pressure varied at 2.5; 5; and 7.5 kPa. Taguchi Method's Orthogonal Arrays L9 is used to combine multiple factor. To ensure data accuracy, each number of tests are done by 3 times repetition. Based on Signal to Noise (S/N) Ratio and ANOVA analysis, gas pressure rank first as the most influencing factor in cutting process of agel leaf fiber/unsaturated polyester composite using CO2laser. Cutting speed, nozzle distance and laser power rank second, third and fourth, respectively.
Detecting and tracking objects in HRI: YOLO networks for the NAO “I See You” function *

Object detection and tracking is a basic but a key feature in many robotics tasks, including for human-robots interactions. Handing over objects for instance rely almost exclusively on such a capability: robots should be able to detect the object of interest, localize and track its movements in order to synchronize temporally and spatially the transition phase. In the past few years, a variety of architectures based on convolutional neural networks, especially that of the R-CNN and YOLO (You Only Look Once) models, largely contribute to the improvement of detection accuracy and efficiency. In this paper, we design a visual detection system to detect some objects the Nao robot should detect and track based on the YOLO architecture. We adopt the tiny YOLO network as a pretrained model and modify the last fully connected layer for three-class object detection. The model is trained on 4322 training images and achieves the mean average precision (mAP) of 44.3% on our test set. This model is then applied to a well-known function of the Nao vision system: detecting and localizing landmarks. It is also applied to daily objects. The results demonstrate that it could help localizing and tracking accurately objects of interest in HRI (human-robot interaction).
A Dual-Polarized Antenna Array With Enhanced Interport Isolation for Far-Field Wireless Data and Power Transfer

An in-band full-duplex scheme is proposed for simultaneous power transfer and data delivery in far-field wireless sensor systems. To realize two independent links for power and data delivery, a high isolation level between the local transmitter and receiver of the in-band full-duplex system is critical. An RF front-end architecture, here standing for a dual-polarized antenna array with its feeding network, is presented and detailed in this paper. By using the proposed architecture, self-interference can be well canceled within a wide frequency band, leading to a high inter-port isolation level. To demonstrate the performance of such a front-end structure, an on-board prototype has been developed and examined experimentally. Measured results show that the impedance bandwidth of the developed antenna is from 4.5 to 5.8 GHz. The achieved inter-port isolation is higher than 65 dB from 4.4 to over 6.0 GHz, and over 71 dB from 5.36 to 5.82 GHz, indicating a very high self-interference suppression level. Furthermore, RF power and video transferring experiments are carried out, and good consistency with calculated results is observed.
Matrix Product Operator Restricted Boltzmann Machines

A restricted Boltzmann machine (RBM) learns a probability distribution over its input samples and has numerous uses like dimensionality reduction, classification and generative modeling. Conventional RBMs accept vectorized data that dismiss potentially important structural information in the original tensor (multi-way) input. Matrix-variate and tensor-variate RBMs, named MvRBM and TvRBM, have been proposed but are all restrictive by model construction and have weak model expression power. This work presents the matrix product operator RBM (MPORBM) that utilizes a tensor network generalization of Mv/TvRBM, preserves input formats in both the visible and hidden layers, and results in higher expressive power. A novel training algorithm integrating contrastive divergence and an alternating optimization procedure is also developed. Numerical experiments compare the MPORBM with the traditional RBM and MvRBM for data classification and image completion and denoising tasks. The expressive power of the MPORBM as a function of the MPO-rank is also investigated.
A Parametric Study of Partitioning and Density Based Clustering Techniques for Boxplot Generation

The Boxplot uses five point summary of data to display information. K-Means and DBSCAN clustering methods are used to improve the Boxplots. Both the above methods have parameters which affect the clusters formed by the algorithms. The results obtained by varying these parameters i.e. $K$ in K-Means and Eps and MinPts in DBSCAN clustering methods are studied in this paper. The Boxplots obtained by varying the above mentioned parameters are plotted and the effects of varying the parameters have been studied. A comparative figure of traditional Boxplot and Boxplots generated using K-Means and DBSCAN clustering has also been plotted. The comparisons and differences have been discussed between the above mentioned Boxplots.
Emotion Recognition from Human Behaviors Using Attention Model

Human behaviors and the emotional states that they convey have been studied by psychologist and sociologists. The tracking of behaviors and emotions is becoming more pervasive with the advent of the Internet of Things (IoT), where small and always connected sensors can continuously capture information about human gestures, movements and postures. The captured information about readable behaviors conveys signicant information that can be represented as time series. Few studies in emotion recognition and affective computing have explored the connection between the time series sensors data and the emotional behavior they conveys. In this paper, an innovative approach is proposed to study the emotions and behaviors connected to the sensors time series data. A deep learning attention-based bidirectional LSTM is introduced to represent the correlations between data and emotions. The attention-based mechanism is then used to focus on the most significant information. The advantage of this model is that it can represent various human emotions by exploiting the data captured by sensors. The experimental results show that the proposed deep learning method outperforms shallow models and achieves a high degree of accuracy for modelling human behaviors and emotions.
Antenna measurement using Vertical-Cavity Surface-Emitting Laser up to 18 GHz

We have already developed a low-cost type microwave receiving system using a vertical-cavity surfaceemitting laser. Our developed system consists of 850 nm verticalcavity surface-emitting laser, a photo diode with trans-impedance amplifier and OM3 multi-mode optical fiber. Further, the system can be measure the microwave signal up to 18 GHz with 40 dB dynamic range and less than 0.22 dB standard deviation without temperature control and pre-amplifier.
QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery

Virtual screening (VS) has emerged in drug discovery as a powerful computational approach to screen large libraries of small molecules for new hits with desired properties that can then be tested experimentally. Similar to other computational approaches, VS intention is not to replace in vitro or in vivo assays, but to speed up the discovery process, to reduce the number of candidates to be tested experimentally, and to rationalize their choice. Moreover, VS has become very popular in pharmaceutical companies and academic organizations due to its time-, cost-, resources-, and labor-saving. Among the VS approaches, quantitative structure–activity relationship (QSAR) analysis is the most powerful method due to its high and fast throughput and good hit rate. As the first preliminary step of a QSAR model development, relevant chemogenomics data are collected from databases and the literature. Then, chemical descriptors are calculated on different levels of representation of molecular structure, ranging from 1D to nD, and then correlated with the biological property using machine learning techniques. Once developed and validated, QSAR models are applied to predict the biological property of novel compounds. Although the experimental testing of computational hits is not an inherent part of QSAR methodology, it is highly desired and should be performed as an ultimate validation of developed models. In this mini-review, we summarize and critically analyze the recent trends of QSAR-based VS in drug discovery and demonstrate successful applications in identifying perspective compounds with desired properties. Moreover, we provide some recommendations about the best practices for QSAR-based VS along with the future perspectives of this approach.
Micro-Burst in Data Centers: Observations, Analysis, and Mitigations

Micro-burst traffic is not uncommon in data centers. It can cause packet dropping, which results in serious performance degradation (e.g., Incast problem). However, current solutions that attempt to suppress micro-burst traffic are extrinsic and ad hoc, since they lack the comprehensive and essential understanding of micro-burst's root cause and dynamic behavior. On the other hand, traditional studies focus on traffic burstiness in a single flow, while in data centers micro-burst traffic could occur with highly fan-in communication pattern, and its dynamic behavior is still unclear. To this end, in this paper, we re-examine the microburst traffic in typical data center scenarios. We find that evolution of micro-burst is determined by both TCP's self-clocking mechanism and bottleneck link. Besides, dynamic behaviors of micro-burst under various scenarios can all be described by the slope of queue length evolution. Our observations also implicate that conventional solutions like absorbing and pacing are ineffective to mitigate micro-burst traffic. Instead, senders need to slow down as soon as possible. Inspired by the findings and insights from experimental observations, we propose S-ECN policy, which is an ECN marking policy leveraging the slope of queue length evolution. Transport protocols utilizing S-ECN policy can suppress the sharp queue length increment by over 2×, and reduce the average query completion time by ~12-27%.
Clustering-Aided Approach for Predicting Patient Outcomes with Application to Elderly Healthcare in Ireland

Predictive analytics have proved promising capabilities and opportunities to many aspects of healthcare practice. Data-driven insights can provide an important part of the solution for curbing rising costs and improving care quality. The paper implements machine learning techniques in an attempt to support decision making in relation to elderly healthcare in Ireland, with a particular focus on hip fracture care. We adopt a combination of unsupervised and supervised learning for predicting patient outcomes. Initially, elderly patients are grouped based on the similarity of age, length of stay (LOS) and elapsed time to surgery. Using the K-Means algorithm, our clustering experiments suggest the presence of three coherent clusters of patients. Subsequently, the discovered clusters are utilised to train prediction models that address a particular cluster of patients individually. In particular, two machine learning models are trained for every cluster of patients in order to predict the inpatient LOS, and discharge destination. The developed models are claimed to make predictions with relatively high accuracy. Furthermore, the potential usefulness of the clustering-guided approach of prediction is discussed in general.
Resource Endowments Strategy for Sustainable E-Government

Researchers have highlighted that public sectors with strong resources are able to sustain their innovation. E-government is new government innovation to provide electronic-based services. However, most of e-government systems are failed to sustain due resource challenges. Despite many studies have highlighted these challenges, limited studies have focused on how collective resource endowments are able to sustain e-government. Through the use of case study approach, this study investigates how resources were collectively endowed to support sustainable e-government systems within two regencies. This study found that resources for sustainable e-government were endowed through number of Resource channels such as cooperation with central government institutions, other local governments, private companies, and collaboration within local institutions
Neural-based Pinyin-to-Character Conversion with Adaptive Vocabulary

Pinyin-to-character (P2C) conversion is the core component of pinyin-based Chinese input method engine (IME). However, the conversion is seriously compromised by the ambiguities of Chinese characters corresponding to pinyin as well as the predefined fixed vocabularies. To alleviate such inconveniences, we propose a neural P2C conversion model augmented by a large online updating vocabulary with a target vocabulary sampling mechanism. Our experiments show that the proposed approach reduces the decoding time on CPUs up to 50$\%$ on P2C tasks at the same or only negligible change in conversion accuracy, and the online updated vocabulary indeed helps our IME effectively follows user inputting behavior.
Cutaneous Leukemic Infiltrates Successfully Treated With Biomodulatory Therapy in a Rare Case of Therapy-Related High Risk MDS/AML

Cutaneous manifestations in hematologic malignancies, especially in leukemia, are not common and may be very variable. Here we report a very unusual case of a patient (female, 70 years old) who was admitted to the hospital in 2016 because of skin lesions on the face, the trunk of the body and the extremities. She had a history of breast cancer in the year 2004 (pT1b, pN0, cM0, L0, V0, R0) which had been resected and treated with adjuvant radiation and chemotherapy (cyclophosphamide, methotrexate, 5-fluorouracile) as well as psoriasis treated with methotrexate and cyclosporine. Because of mild cytopenia a bone marrow aspirate/biopsy was performed showing myelodysplastic syndrome (MDS) with multilineage dysplasia. Cytogenetic review revealed a complex aberrant karyotype denoting adverse outcome. Simultaneously, a skin biopsy could confirm leukemic skin infiltration. Consequently, a therapy with azacitidine was started. After the first cycle the patient developed severe pancytopenia with a percentage of 13% peripheral blasts (previously 0–2%) as well as fever without evidence for infection which was interpreted as progressive disease. Therefore, the therapeutic regimen was changed to a biomodulatory therapy consisting of low-dose azacitidine 75 mg/day (given sc d1-7 of 28), pioglitazone 45 mg/day per os, and all-trans-retinoic acid (ATRA) 45 mg/m2/day per os. After cycle 1 of this combined biomodulatory therapy the patient showed hematologic recovery; besides a mild anemia (hemoglobin 11.1 g/dl) she developed a normal blood count. Moreover, the cutaneous leukemic infiltrates which had been unaffected by the azacitidine ameliorated tremendously after 2 cycles resulting in a complete remission of the skin lesions after cycle 6. In conclusion, we report a very unusual case with cutaneous infiltrates being the first clinical manifestation of hematologic disease, preceding the development of acute myeloid leukemia. While azacitidine alone was ineffective, a combined biomodulatory approach resulted in a complete remission of the cutaneous manifestation.