Miracle-dz/UniFAR-FTUs huggingface dataset:
Trust-aware Recommendations

When forming a work team, the choice of its members is a key step. We believe that a recommender system could help to select the members of a team by taking into account individual competences criteria, as well as the relationships between people. One important aspect of this relationship is the trust that members have with each other. However, this concept of trust remains difficult to be used because it is subject to many definitions and is complex to grasp. We try in this research to shed some light on the notion of trust. In particular, we study if there is a correspondence between a previously declared trust (off-line) and an actual trust put into practice during an experiment. To use the trust to recommend profiles of competences that stem from a wider and more reliable social network, we also investigate if the use of transitivity and reciprocity for trust makes sense.
Unsupervised Subspace Extraction via Deep Kernelized Clustering

Feature extraction has been widely studied to find informative latent features and reduce the dimensionality of data. In particular, due to the difficulty in obtaining labeled data, unsupervised feature extraction has received much attention in data mining. However, widely used unsupervised feature extraction methods require side information about data or rigid assumptions on the latent feature space. Furthermore, most feature extraction methods require predefined dimensionality of the latent feature space,which should be manually tuned as a hyperparameter. In this article, we propose a new unsupervised feature extraction method called Unsupervised Subspace Extractor (USE), which does not require any side information and rigid assumptions on data. Furthermore, USE can find a subspace generated by a nonlinear combination of the input feature and automatically determine the optimal dimensionality of the subspace for the given nonlinear combination. The feature extraction process of USE is well justified mathematically, and we also empirically demonstrate the effectiveness of USE for several benchmark datasets.
An Improved Hybrid Method Combining Backtracking with Pencil and Paper for Solving Sudoku Puzzles

Sudoku is a puzzle which can be solved by putting digits in a two-dimensional grid/matrix so that no row, column or sub-grid contains any digit more than once. Many methods as well as their hybrids are available to solve Sudoku puzzles. However, these methods face some shortcomings such as not being able to solve puzzles of different difficulty levels and being time-consuming. This is because of overlooking some constraints, leading to violations of Sudoku rules, and/or executing fruitless steps as well as missing potential opportunities to find a solution faster. In this paper, we propose an improved hybrid of backtracking algorithm and pencil and paper method to overcome the limitations of the existing methods. Experimental results show that the proposed method is more effective as well as efficient in solving puzzles of different difficulty levels than the other available methods.
ROS2 Extension of Functionally and Temporally Correct Real-Time Simulation of Cyber Systems for Automotive Systems

For developing automotive systems, it is important to validate the system performance at the design phase. For this, the simulation methodology is commonly used for the validation of the automotive systems. However, if there is a gap between real performance and simulation result, developers need to go through the process of redesigning and implementing the system which increases the developing cost. For this, we have a novel simuation approach that gurantees functional and temporal correctness while enjoying the freedom of job scheduling. However we cannot apply this approach directly to the ROS2 cyber system which has become popular by its open source community and supporting real-time features because of different task execution behaviors. Therefore, we propose an extended approach which defines new system model for the cyber system based on ROS2 framework to keep the functional and temporal correct simulation approach on the ROS2 cyber system.
Multi-Class Primary Morphology Lesions Classification Using Deep Convolutional Neural Network

Skin diseases are becoming the most prevalent health concern among all nations worldwide. Recognition of skin lesion, abnormal change usually caused by disease or infection in the skin is the first step in diagnosing skin diseases. In dermatology, morphology skin lesions occur due to the disease process's direct result and indicate categorizing a skin lesions' structure and appearance. In this work, we focus on primary skin lesion classification, particularly early-stage detection, and present a deep learning approach to classify images containing skin lesions, macule, nodule, papule, plaque pustule, wheal, and bulla. We applied deep learning techniques for classifying such images into seven classes covering the aforementioned types of lesion. In particular, we performed experiments on pre-trained deep convolutional neural network models to find the most accuracy one. The result shows that the pre-trained model ResNet-50 after the training and testing can achieve satisfactory accuracy of 85.95%.
Lost in Transduction: Transductive Transfer Learning in Text Classification

Obtaining high-quality labelled data for training a classifier in a new application domain is often costly. Transfer Learning (a.k.a. “Inductive Transfer”) tries to alleviate these costs by transferring, to the “target” domain of interest, knowledge available from a different “source” domain. In transfer learning the lack of labelled information from the target domain is compensated by the availability at training time of a set of unlabelled examples from the target distribution. Transductive Transfer Learning denotes the transfer learning setting in which the only set of target documents that we are interested in classifying is known and available at training time. Although this definition is indeed in line with Vapnik’s original definition of “transduction”, current terminology in the field is confused. In this article, we discuss how the term “transduction” has been misused in the transfer learning literature, and propose a clarification consistent with the original characterization of this term given by Vapnik. We go on to observe that the above terminology misuse has brought about misleading experimental comparisons, with inductive transfer learning methods that have been incorrectly compared with transductive transfer learning methods. We then, give empirical evidence that the difference in performance between the inductive version and the transductive version of a transfer learning method can indeed be statistically significant (i.e., that knowing at training time the only data one needs to classify indeed gives an advantage). Our clarification allows a reassessment of the field, and of the relative merits of the major, state-of-the-art algorithms for transfer learning in text classification.
Efficient regulation of emotion by positive music based on EEG valence-arousal model

Emotional regulation plays an important role in the process of affective computing and interactive system. Although studies have confirmed that music can be used as an effective medium for regulating emotions, it does not explain the effects of regulation from different dimensions of emotions. In this study, we collected electroencephalogram (EEG) data of 40 participants while regulating negative emotions by positive music. A valence-arousal model was trained on EEG features through machine learning. The model provided a binary prediction of valence (high or low) of 78.759. 48% and 73.985. 54% for arousal. Our results confirmed the function of positive music in emotion improvement. More attention should be paid to the value of music in emotional regulation arousal in clinical practice.
Cross-domain Recommendation with Bridge-Item Embeddings

Web systems that provide the same functionality usually share a certain amount of items. This makes it possible to combine data from different websites to improve recommendation quality, known as the cross-domain recommendation task. Despite many research efforts on this task, the main drawback is that they largely assume the data of different systems can be fully shared. Such an assumption is unrealistic different systems are typically operated by different companies, and it may violate business privacy policy to directly share user behavior data since it is highly sensitive. In this work, we consider a more practical scenario to perform cross-domain recommendation. To avoid the leak of user privacy during the data sharing process, we consider sharing only the information of the item side, rather than user behavior data. Specifically, we transfer the item embeddings across domains, making it easier for two companies to reach a consensus (e.g., legal policy) on data sharing since the data to be shared is user-irrelevant and has no explicit semantics. To distill useful signals from transferred item embeddings, we rely on the strong representation power of neural networks and develop a new method named as NATR (short for Neural Attentive Transfer Recommendation). We perform extensive experiments on two real-world datasets, demonstrating that NATR achieves similar or even better performance than traditional cross-domain recommendation methods that directly share user-relevant data. Further insights are provided on the efficacy of NATR in using the transferred item embeddings to alleviate the data sparsity issue.
A framework for the specification and validation of dynamic reconfigurable systems

We study a framework for the specification and validation of dynamic reconfigurable systems. The framework is based on configuration logic for the description of architecture styles which are families of architectures sharing common connectivity features. We express specifications in the Temporal Configuration Logic (TCL), a linear time temporal logic built from atomic formulas characterizing system configurations and temporal modalities. Two non-trivial benchmarks are introduced to show the adequacy of TCL for the specification of dynamic reconfigurable systems. We study an effective model-checking procedure based on SMT techniques for a non-trivial fragment of TCL which has been implemented in a prototype runtime verification tool. We provide preliminary experimental results illustrating the capabilities of the tool on the considered benchmark systems.
Organosolv pretreatment of plant biomass for enhanced enzymatic saccharification

The combination of dwindling petroleum reserves and population growth make the development of renewable energy and chemical resources more pressing than ever before. Plant biomass is the most abundant renewable source of energy and chemicals. Enzymes can selectively convert the polysaccharides in plant biomass into simple sugars which can then be upgraded to liquid fuels and platform chemicals using biological and/or chemical processes. Pretreatment is essential for efficient enzymatic saccharification of plant biomass and this article provides an overview of how organic solvent (organosolv) pretreatments affect the structure and chemistry of plant biomass, and how these changes enhance enzymatic saccharification. A comparison between organosolv pretreatments utilizing broadly different classes of solvents (i.e., low boiling point, high boiling point, and biphasic) is presented, with a focus on solvent recovery and formation of by-products. The reaction mechanisms that give rise to these by-products are investigated and strategies to minimize by-product formation are suggested. Finally, process simulations of organosolv pretreatments are compared and contrasted, and discussed in the context of an industrial-scale plant biomass to fermentable sugar process.
On the evolution of internet flow characteristics

The ongoing evolution of technologies and network services on the Internet indicates ongoing changes in traffic and flow characteristics. Since the analysis of flow characteristics, like duration, size, and rate, has been a frequently studied topic before, results on the evolution of flow characteristics are rare. This paper surveys how flow characteristics have changed over time and whether there are significant trends in such characteristics. We present a long-term study of TCP flow characteristics based on traffic captures taken between 2008 and 2019. We apply different methods to analyze the distribution of characteristics, the relevance of heavy hitters, and correlations between characteristics. Our analysis shows significant trends in the 99th percentiles of flow characteristics, persistent dominance by heavy hitters regarding transmitted data, and increasing relevance of so-called big-fast flows.
Effect of Extraction Solvent on the Phytoconstituents of Aegle marmelos (L.) Correa

The objective of the present study was to evaluate the effect of extraction solvents such as absolute ethanol, absolute methanol, aqueous ethanol (ethanol: water, 80:20 v/v), aqueous methanol (methanol: water, 80:20 v/v), hydro alcohol (ethanol: water 50:50 v/v) and water on phytochemicals of different parts of Aegle marmelos. The chemical comparison was done using thin layer chromatography. Major class of compounds such as phenolics and flavonoids were quantitatively estimated spectrophotometrically. Among the six solvents used for the extraction, hydro alcohol showed the maximum yield of extract. In the case of root, the yield of hydro alcoholic extract was 42.05 % higher than that of aqueous extract. The increase of yield for stem and fruit were found as 27.5 % and 46.35 % respectively. Due to the higher yield of extract, use of hydro alcohol instead of water for the preparation of herbal formulations can be considered and it may lead to the judicious use of raw materials.
Prediction of Calibration Parameters of the Oxygen Saturation Estimation Equation by Optical Recording on Smartphones: Prediction Parameters  by Optical Recording

This paper demostrate the calculate for calibration parameters of the Percent Oxygen Saturation estimation equation by means of a multiple linear regression type supervised machine learning algorithm with the objective of being able to use a smartphone as an oximeter, for this a training and test data set composed of predictor variables representing the characteristics of the smartphones used in this development is used, in addition to the statistical parameters extracted from two thousand videos, predicting values of with an error of less than with respect to a standard oximeter.
kNN-join Query Processing Algorithm on Mapreduce for Large Amounts of Data

Recently, the amount of data is rapidly increasing with the continuous development of computation and communication capabilities. So, it has been actively studied for the effective data analysis schemes of the large amounts of data on MapReduce which supports efficient parallel data processing for large-scale data. Among various queries for analysing data, k nearest neighbour (kNN) join query, which aims to combine the k nearest neighbours of each point of dataset R with those from another dataset S, has been considered typical. However, existing kNN join schemes on MapReduce require high computation cost for constructing and managing index structures. To solve the problems, we propose a kNN-join query processing algorithm on MapReduce for analysing large-scale data. First, our algorithm can reduce the overhead for constructing the index structure by using the seed-based dynamic partitioning. Second, it can reduce the computational overhead to find candidate partitions by using the average distance between a pair of neighbouring seeds. We show that our algorithm outperforms the existing scheme in terms of the query processing time.
Improved Stacked Hourglass Network for Robust 6D Object Pose Estimation

In this article, we introduce an accurate yet robust method to recover the 6D pose of the object from an RGB image. The core of our method is using the farthest point sampling algorithm to design a set of representative keypoints on the object model surface, and then use the improved stacked hourglass network (ISHN) with multi-scale aggregation module to localize them in the 2D image by predicting the keypoints heatmaps. Finally, the PnP algorithm can recover the 6D pose according to the 3D-2D relationship of keypoints. Besides, when the object is partially occluded, we can successfully recover the pose of the object by selecting the most confident keypoints. Our method can simultaneously detect and recover the 6D pose of the instance object in the RGB image without additional post-processing steps. Experimental results show that compared with the state-of-the-art RGB-based pose estimation methods, our method can achieve competitive or more superior performance on two benchmark datasets.
Automatic I/O stream management based on file characteristics

Data fragmentation exists widely in SSD NAND flash, which greatly increases garbage collection overhead. Dividing data of different lifetimes into different I/O streams can effectively solve the problem of data fragmentation. Therefore, the management of I/O streams is the key to reduce data fragmentation and thereby improve the lifetime and performance of SSD. In this paper, we propose FileStream, a file-based automatic stream management scheme with machine learning, which can be directly applied to multiple workloads. We find that file characteristics are effective in distinguishing data lifetimes, and thus FileStream divides data into different I/O streams based on the file characteristics. The results of six benchmarks on real SSD show that FileStream reduces the write amplification factor by 34.5% and 21.6% on average, compared to the baseline and two advanced automatic stream management methods, respectively.
Proposal of the Aesthetic Experience-Oriented Evaluation Framework for Field-recording Sound Retrieval System: Experiments using Acoustic Feature Signatures Based on Multiscale Fractal Dimension

Sound designers and musicians often need to retrieve sound materials based on their similarity to aesthetic hearing experiences from sound databases such as Freesound. This study proposes an aesthetic experience-oriented evaluation framework for a field-recording sound retrieval system, using the sound clips extracted from Freesound. Furthermore, we discuss the features of the framework by analyzing the performance of the similarity search system for field-recording sound material using acoustic feature signatures that are based on the multiscale fractal dimension.
A Preliminary Study on the Design of Compliant Surgical Gripper Jaw

The study evaluates on the mechanical design and dimensional synthesis for pseudo-rigid-body model (PRBM) and finite element analysis (FEA) on a compliant constant-force surgical gripper. Polyethylene Terephthalate (PET), Polylactide (PLA) and Polypropylene Co-polymer (PPC) were selected for the analysis of output force, flexural pivot stress and deflection for both methods. The study found that the output force computed from PRBM and FEA method has the difference of about 30% for PET and PLA material, while the flexural pivot stress computed between PRBM and FEA method is less than 15 %. It was also revealed that maximum deflection occurred at the flexure hinge between the slider and crank of the compliant surgical gripper jaw.
On the Impact of Emotions on the Detection of False Information

A great amount of fake news are propagated in online social media, with the aim, usually, to deceive users and formulate specific opinions. The threat is even greater when the purpose is political or ideological and they are used during electoral campaigns. Bots play a key role in disseminating these false claims. False information is intentionally written to trigger emotions to the readers in an attempt to be believed and be disseminated in social media. Therefore, in order to discriminate credible from non credible information, we believe that it is important to take into account these emotional signals. In this paper we describe the way that emotional features have been integrated in deep learning models in order to detect if and when emotions are evoked in fake news.
Effect of n-3 long-chain polyunsaturated fatty acid intake on the eicosanoid profile in individuals with obesity and overweight: a systematic review and meta-analysis of clinical trials

Abstract Dietary n-3 polyunsaturated fatty acids (PUFAs) present beneficial effects on counteracting inflammation status, displaying a critical anti-inflammatory role and maintaining physiological homeostasis in obesity. The primary objective of this systematic review was to evaluate the effect of n-3 PUFAs intake on the eicosanoid profile of people with obesity and overweight. The search strategy on Embase, Scopus, PubMed, Web of Science, Cochrane Library, Google Scholar and ProQuest was undertaken until November 2019 and updated January 2021. The effect size of n-3 PUFAs on prostaglandins was estimated by Glass's, type 1 in a random-effect model for the meta-analysis. Seven clinical trials met the eligible criteria and a total of 610 subjects were included in this systematic review, and four of seven studies were included in meta-analysis. The intake of n-3 PUFAs promoted an overall reduction in serum pro-inflammatory eicosanoids. Additionally, n-3 PUFAs intake significantly decreased the arachidonic acid COX-derived PG eicosanoid group levels (Glass's Δ −0⋅35; CI −0⋅62, −0⋅07, I2 31⋅48). Subgroup analyses showed a higher effect on periods up to 8 weeks (Glass's Δ −0⋅51; CI −0⋅76, −0⋅27) and doses higher than 0⋅5 g of n-3 PUFAs (Glass's Δ −0⋅46; CI −0⋅72, −0⋅27). Dietary n-3 PUFAs intake contributes to reduce pro-inflammatory eicosanoids of people with obesity and overweight. Subgroup's analysis showed that n-3 PUFAs can reduce the overall arachidonic acid COX-derived PG when adequate dose and period are matched.
Why are women in the Caribbean so much more likely than men to be unemployed

Caribbean women are more likely than men to be unemployed, as evidenced by the economies studied here—Barbados, Jamaica, and Trinidad and Tobago. This paper uses aggregate data to explore macroeconomic factors that contribute to gender differentials in unemployment. National economic conditions and job segregation explain a portion of gender differences in unemployment, with men more likely to find employment during an economic upturn. Even within job categories, though, women’s unemployment rates are higher than men’s, suggesting employment discrimination. The results imply that economic growth is not sufficient to ensure equitable job access, and more targeted efforts are therefore necessary to ensure gender equity.