Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy and Processing Time Efficiency for an Optimal Offloading in a Mobile Edge Computing Node

This article describes a processing time, energy and computing resources optimization in a Mobile Edge Computing (MEC). We consider a mobile user MEC system, where a smart mobile device (SMD) demand computation offloading to a MEC server. For that, we consider a SMD contains a set of heavy tasks that can be offloadable. The formulated optimization problem takes into account both the dedicated energy capacity and the processing times. We proposed a heuristic solution schema. To evaluate our solution, we realized a range of simulation experiments. The results obtained in terms of treatment time and energy consumption are very.
Learning from partially labeled data

Providing suﬃcient labeled training data in many application domains is a laborious and costly task. Designing models that can learn from partially labeled data, or leveraging labeled data in one domain and unlabeled data in a diﬀerent but related domain is of great interest in many applications. In particular, in this context one can refer to semi-supervised modelling, transfer learning, domain adaptation and multi-view learning among others. There are several possibilities for designing such models ranging from shallow to deep models. These type of models have received increasing interest due to their successful applications in real-life problems. This paper provides a brief overview of recent techniques in learning from partially labeled data.
Cross-Encoded Meta Embedding towards Transfer Learning

In this paper we generate word meta-embeddings from already existing embeddings using cross-encoding. Previous approaches can only work with words that exist in each source embedding, while the architecture presented here drops this requirement. We demonstrate the method using two pre-trained embeddings, namely GloVE and FastText. Furthermore, we propose additional improvements to the training process of the meta-embedding. Results on six standard tests for word similarity show that the meta-embedding trained outperforms the original embeddings. Moreover, this performance can be further increased with the proposed improvements, resulting in a competitive performance with those reported earlier.
Do we need hundreds of classifiers or a good feature selection?

The task of choosing the appropriate classiﬁer for a problem is not an easy-to-solve question due to the high number of algorithms available belonging to different families. Most of these classiﬁcation algorithms exhibit a degradation in the performance when faced with many irrelevant and/or redundant features. Thus, in this work we analyze the impact of feature selection in classiﬁcation. Experimental results over ten synthetic datasets show that the signiﬁcance of selecting a classi-ﬁer decreases after applying an appropriate preprocessing step and, not only this alleviates the choice, but it also improves the results in almost all classiﬁers tested
An agile machine learning project in pharma - developing a Mask R-CNN-based web application for bacterial colony counting

We present a web application to assist lab technicians with the counting of diﬀerent types of bacteria colonies. We use a Mask R-CNN model trained and tuned speciﬁcally to detect the number of BVG+ (virulent) and BVG-(avirulent) colonies. We achieve a mAP IoU = . 5 of 94 %. With these encouraging results, we see opportunities to bring the beneﬁts of improved accuracy and time saved to nearby problems and labs such as generalising to other bacteria types and viral foci counting.
SDOstream: Low-Density Models for Streaming Outlier Detection

Data commonly changes over time. Algorithms for anomaly detection must therefore be adapted to overcome the challenges of evolving data. We present SDOstream, a distance-based outlier detection algorithm for stream data that uses low-density models, therefore operating in linear time and avoiding the limitations of sliding windows and instance-based methods. SDOstream is designed to ensure a good integration in applications, hence the definition of “outlier” is not predetermined, but can be decided by the application based on distances to representative point locations. We describe the algorithm and evaluate algorithm performance with several datasets.
On-edge adaptive acoustic models: an application to acoustic person presence detection

This paper validates a machine learning framework that enables processing on resource limited devices. The discussed framework allows both inference and learning to be executed on the edge. More specifically, a Least-Squares Support Vector Machine (LS-SVM) framework with a time-recursive learning algorithm is evaluated in an application where person presence is estimated based on acoustic signals only. For this purpose, a real-life acoustical dataset of 555 hours was collected in an oﬃce environment for the evaluation of the on-edge machine learning framework.
Automatic Question Generation System for English Exercise for Secondary Students

: Automatic Question Generation (AQG) is a research trend that assists teachers to create efficiency assessments. In this paper, we propose a web-based system as a tool to generate English exercises for secondary school students automatically. The system applies NLTK library tags function words. The source texts are selected based on their grammar. The AQG module can generate exercises in ten topics; 1) Noun, 2) Pronoun, 3) Verb, 4) Adverb, 5) Adjective, 6) Comparison, 7) Conjunction, 8) Article, 9) Preposition, and 10) twelve Verb Tenses. There are four types of generated exercises; 1) complete the missing blank, 2) choose the correct answer from two choices, 3) true or false questions, and 4) error correction. The evaluation results show that our proposed system performs effectively with 97.36% F-measure. From the user experience point of view, they are well satisfied with ease of use of the system, including the capability and accuracy to generate a variety of exercises.
A study of machine learning approaches to cross-language code clone detection

While clone detection across programs written in the same programming language has been studied extensively in the literature, the task of detecting clones across multiple programming languages is not covered as well, and approaches based on comparison cannot be directly applied. In this thesis, we present a clone detection method based on supervised machine learning able to detect clone across programming languages. Our method uses an unsupervised learning approach to learn tokenlevel vector representations and an LSTM-based neural network to predict if two code fragments are clones. To train our network, we present a cross-language code clone dataset — which is to the best of our knowledge the first of its kind — containing more than 50000 code fragments written in Python and Java. We show that our method is able to detect code clones between Python and Java. We also compare our method to state-of-the-art tools in single-language clone detection and show we achieve better F1-score.
Yoga-Preksha-Dhyan Practice As A Cost-Effective Preventive Strategy Against Aggressiveness In Primary School Children

Increasing aggressiveness in adolescents and youth throughout the world has become a global public health issueinvolving huge psychological, social and economic costs. High aggressiveness in children has been observed very often to develop into conduct disorders, anti-social activities, and juvenile delinquency. Therefore, it is important to identify children with tendencies of high aggressiveness at an early age and todevelop cost-effective preventive strategies, which can be implemented on a large-scale. Our previous pilot studies on primary school children in a model municipal school in New Delhi showed a significant reduction in aggressiveness after administration of a yoga-preksha-dhyan ((YPD) intervention module, specially developed for children (Jain et al., 2014). We have, therefore, undertaken further investigations on larger number of subjects from multiple schools to assess reproducibility and generalizability. The present study was conducted on 2080 elementary school students of IIIrd to Vth class (Age; 7-14 years, average 9.3+1.7yrs) from 5 South Delhi Municipal Corporation schools. 330 highly aggressive students (Boys: 181; Girls: 149) were identified on the basis of scores obtained using a validated and reliable Self-Report Questionnaire which also distinguishes proactive aggression (PA) from reactive aggression (RA). Themulti-modal YPD moduleconsisting of asanas, pranayama andpreksha-dhyan components, was administered to students with high Overall aggressiveness Scores (OA-Score) for 36 sessions (45 minutes/session, 3 days/week). Aggressiveness was assessed by the self-report questionnaire pre and post-YPD intervention. The results show that, after the intervention theOA-, PAand RA-Scores of students reduced considerably. The average pre-YPD OA-Scores (44.26+6.5) decreasedto OA-Score (30.40+8.3) post-YPD, which is statistically highly significant (p-value = 0.001). The results are in agreement with our previous pilot study, confirming that YPD practicecould be a cost-effective preventive intervention against the development of conduct disorders, anti-social behaviors and violent crimes. Since it is relatively easy to implement, the introduction of YPD program on large scale in schools, could lead to a more peaceful society in future. Further research is therefore warranted.
An Optimized SVM Model for Accurate Duplicate Bug Report Retrieval

— Bug reporting facility is an integral part of software support system and a critical aspect of beta testing. Managing the incoming bug reports for large software projects is a challenging task. Software testers or end users submit bug reports, as and when potential defects are identified in the system. Sometimes two or more bug reports corresponds to the same defect. The analysis of reports to check whether the incoming report is for the same bug for which a master report already exists in the database is one of the most important task in managing the bug reports. This analysis needs to be done very carefully so that a report corresponding to a bug which is already reported is discarded while at the same time, a textually similar report, but corresponding to a different bug is to be attended properly. To address this problem with duplicate bug reports, a person called a triager needs to manually label these bug reports as duplicates, and link them to their ”master” reports for subsequent maintenance work. However, for large software systems, this process could be highly time consuming. To address this issue, recently, several techniques have be proposed using various similarity based metrics to detect candidate duplicate bug reports for manual verification. Automating triaging has been proved challenging as two reports of the same bug could be written in various ways. There is still much room for improvement in terms of accuracy of duplicate detection process. In this dissertation, an optimized SVM model is proposed to detect duplicate bug reports more accurately. The proposed techniques are validated on three large software bug repositories from Firefox, Mysql, and Apache. Analytical methods are presented over samples and results are provided which are in excellent agreement with the simulation results.
Distributed Intelligence at the Edge on IoT Networks

The Internet of Things (IoT) has revolutionized innovation to collect and store the information received from physical objects or sensors. The smart devices are linked to a repository that stores intelligent information executed by sensors on IoT-based smart objects. Now, the IoT is shifted from knowledge-based technologies to operational-based technologies. The IoT integrates sensors, smart devices, and a smart grid of implementations to deliver smart strategies. Nowadays, the IoT has been pondered to be an essential technology. The transmission of information to or from the cloud has recently been found to cause many network problems to include latency, power usage, security, privacy, etc. The distributed intelligence enables IoT to help the correct communication available at the correct time and correct place. Distributed Intelligence could strengthen the IoT in a variety of ways, including evaluating the integration of different big data or enhancing efficiency and distribution in huge IoT operations. While evaluating distributed intelligence in the IoT paradigm, the implementation of distributed intelligence services should take into consideration the transmission delay and bandwidth requirements of the network. In this article, the distributed intelligence at the Edge on IoT Networks, applications, opportunities, challenges and future scopes have been presented.
Large-angle X-ray diffuse scattering, a new method for investigating changes in the conformation of globular proteins in solutions

A comparative analysis of the sperm-whale myoglobin structure in the crystal and in solution has been carried out with the technique previously formulated by the authors which uses the large-angle X-ray diffuse scattering for investigating the protein structure in solution. A `modified cube method', correctly taking into account the cavities within the protein molecule accessible to the solvent, is proposed for an accurate estimate of the solvent influence and for calculation of the scattering intensities. A comparison of the theoretical myoglobin scattering curves with the experimental scattering curve of this protein obtained by Stuhrmann shows rather noticeable quantitative divergences, which can be eliminated by a small increase in the distance between the `hairpin' GH and the other part of the protein molecule. It is also shown that several other physically reasonable shifts of helices (or groups of helices) do not lead to an agreement between the experimental and theoretical scattering indicatrices.
Ultrasonic Tomography for Nondestructive Evaluation

Ultrasonic inspection of manufactured parts has been widely used in industry for over 30 years (1, 2). Conventional methods, however, are largely limited to determining the presence or absence of defective regions in these parts without characterizing the detailed nature of the defect. As manufacturing methods have progressed and new, high-strength, high­ performance alloys have found widespread use, more detailed information about material parameters is required to assess the failure probability of parts in critical applications. One of the most promising new techniques that has been developed in recent years to deal with this need for quantitative materials information is ultrasonic tomography. This article reviews the current status of ultrasonic tomography in the context of the broader area of ultrasonic nondestructive evaluation (NDE). Tn particular, the role of ultrasonic tomography as one part of a large arsenal of NDE methods is examined in order to determine the area of application in which ultrasonic tomography can make the greatest contribution. Based on an assessment of available results (which are relatively few owing to the earJy stage of development of this field), some likely extensions of current techniques are suggested. Application of ultrasonic tomography to medical imaging, which is much more extensive because of its greater similarity to x-ray computerized tomography (CT), is not discussed here except as it directly impacts the NDE field.
Poly(vinyl alcohol)-based electrospun meshes as potential candidate scaffolds in regenerative medicine

Fibrous meshes based on three different poly(vinyl alcohol) (PVA) polymers, with 12% vinyl acetate monomeric units and molar weights of 37,000, 67,000, and 130,000 were developed as potential scaffolds for regenerative medical applications. The meshes were electrospun and characterized by molecular weight, concentration, applied voltage, and needle—collector distance. The influence of feed rate and the electrodes configuration (needle-to-tip and screen-to-screen system) was determined. Highly porous, 3D structures composed of randomly oriented ultrafine fibers, with an average fiber diameter of a few hundred nanometers were developed. Solutions of PVA and human serum albumin were successfully electrospun and the fibrous mesh was stabilized with glutaraldehyde. The influence of these operations on the mechanical properties was evaluated by uniaxial tensile testing.
Skeletal Muscle Depletion and Nutrition Support Affected Postoperative Complications in Patients Underwent Pancreatoduodenectomy

BackgroundPostoperative complications remain high in patients underwent pancreaticoduodenectomy (PD). Body composition has been shown closely related to the outcome in critical and surgical patients. The aim of the present study was to investigate whether preoperative skeletal muscle condition and postoperative nutrition would affect major complications in patients underwent PD.MethodsThis retrospective study included 265 patients underwent PD with a CT scan of the abdomen within 1 week before surgery. Body compartment data was extracted from the L3 level of the CT scan, which included skeletal muscle area and density, subcutaneous and visceral fat area. Univariable and multivariable regression analyses were performed to investigate correlations between the above variables and postoperative complications. Furthermore, a subgroup analysis was conducted to explore the relationship between postoperative nutrition strategy and the outcome.ResultsOf all the 265 patients, the mean age and BMI was 59.5±13.9 years and 23.1 ±3.6 kg/m2, respectively. Major complications occurred in 81 patients (30.6%). Cutoff values for skeletal muscle depletion were defined by ROC curve analysis from postoperative complications in skeletal muscle index (SMI) (male 47.32 cm2/m2 and female 40.65 cm2/m2). Univariable analysis showed postoperative complications were significantly associated with age, sex, systemic comorbidity, SMI, skeletal muscle density (SMD) and visceral adipose tissue index (VATI). Multivariable regression reveled only age (OR 1.49, 95% CI 1.22-1.83, p=0.026), SMI (OR 0.77, 95%CI 0.51-0.94, p=0.015) and SMD (OR 0.85, 95%CI 0.64-1.03, p=0.029) were independent predictors for major complications. Subgroup analysis showed the initial parenteral nutrition time (IPNT) (OR1.89, 95% CI 1.43-2.49, p=0.032) and average protein delivery (APD) (OR 0.76, 95% CI 0.53-0.89, p=0.021) were significantly associated with major complications in patients with lower SMI. However, in normal SMI patients, such association was not significant enough.ConclusionsPreoperative skeletal muscle index and density were independently associated with major complications in patients underwent PD. In patients with lower SMI, early parenteral nutrition and higher protein delivery were related to better outcome.
Photo and electrically switchable behavior of azobenzene containing pendant bent‐core liquid crystalline polymers

New class of photo and electrically switchable azobenzene containing pendant bent-core liquid crystalline monomers (AZBM 1, 2, and 3) and their polymers (AZBP 1, 2, and 3) are reported. The synthesized precursors, monomers, and polymers were characterized by FT-IR, 1H, and 13C NMR spectroscopy. Thermal stability of polymers was examined by thermogravimetric analysis and revealed stable up to 260 °C. The mesophase transition of monomers and polymers are observed through polarized optical microscopy (POM) and further confirmed by differential scanning calorimetry (DSC). The electrically switching property of monomers and their polymers were studied by electro-optical method. Among the three monomers AZBM 1, 2, and 3, AZBM 1 and 2 exhibit antiferroelectric (AF) switching and AZBM 3 exhibits ferroelectric (F) switching behavior. On the other hand, low molecular weight polymers (AZMP 1, 2, and 3) show weak AF and F switching behavior. The photo-switching properties of bent-core azo polymers are investigated using UV-vis spectroscopy, trans to cis isomerization occurs around 25 s for AZBP-1 and 30 s for AZBP-2 and 3 in chloroform, whereas reverse processes take place around 80 and 90 s. © 2012 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem, 2013
Analysis of pulse‐time modulated high‐density discharges

Time modulation of the input power has been used recently to improve processing rates and etch selectivity in high‐density discharges. Plasma characteristics of a pulse‐time modulated high‐density discharge is investigated here with the aid of a spatially averaged model. The model consists of mass and energy conservation equations for neutral and ionic species. Results are presented for chlorine and CF4 discharges for a range of pulse periods and duty ratios.
Immobilization of Penicillin G Acylase: The Key to Optimum Performance

Penicillin G acylase is a major industrial biocatalyst that is used in the enzymatic production of 20,000 t a−1 of 6-aminopenicillanic acid, the industrial β-lactam intermediate, as well as in the enzymatic production of semi-synthetic β-lactam antibiotics. Because efficient recovery and reuse of the biocatalyst is a prerequisite for a viable process, much attention has been focused on the immobilization of penicillin G acylase. Methods that have been studied and will be discussed in this review include covalent attachment to porous organic and inorganic carriers, inclusion in and attachment to biopolymer gels and carrier-free immobilization techniques. Highly active and stable preparations have been developed; mass transfer limitations in the carrier are now a major barrier to further improvement of the biocatalyst performance.
Determination of Material Constants of Antiferroelectric Liquid Crystal Mixture CS4001

This work outlines a number of experimental techniques that can be used to determine some fundamental material constants in antiferroelectric liquid crystal materials, exemplified by results for the commercial AFLC mixture CS4001. The parameters measured range from the helical pitch of the material to the interlayer interaction coefficients, elastic constants and viscosities. The experimental methods employed include a variety of both optical and dielectric techniques.
CASCADE HEAT PUMP DRYER PERFORMANCE IMPROVEMENT USING A SOLAR COLLECTOR

Heat pump cycles are used in heating, drying, and other applications, and have recently seen greater use in the drying of clothes and food stuffs. Driers using the heat pump cycle are limited in that their performance changes in environments with a low ambient temperature such as winter. Researchers are working to resolve this downside, and the most prevalent solution is to use additional heat sources, such as solar, geothermal, and recovered heat. This paper implemented the cascade cycle with solar radiation as an additional external heat source, and conducted performance analyses of the drier at different ambient temperatures. Results indicated that when a cascade cycle combined with a solar collector is used, operational performance at low temperatures is greatly improved.