Miracle-dz/UniFAR-FTUs huggingface dataset:
Image Classification: A Survey

The Classification of images is a paramount topic in artificial vision systems which have drawn a notable amount of interest over the past years. This field aims to classify an image, which is an input, based on its visual content. Currently, most people relied on hand-crafted features to describe an image in a particular way. Then, using classifiers that are learnable, such as random forest, and decision tree was applied to the extract features to come to a final decision. The problem arises when large numbers of photos are concerned. It becomes a too difficult problem to find features from them. This is one of the reasons that the deep neural network model has been introduced. Owing to the existence of Deep learning, it can become feasible to represent the hierarchical nature of features using a various number of layers and corresponding weight with them. The existing image classification methods have been gradually applied in real-world problems, but then there are various problems in its application processes, such as unsatisfactory effect and extremely low classification accuracy or then and weak adaptive ability. Models using deep learning concepts have robust learning ability, which combines the feature extraction and the process of classification into a whole which then completes an image classification task, which can improve the image classification accuracy effectively. Convolutional Neural Networks are a powerful deep neural network technique. These networks preserve the spatial structure of a problem and were built for object recognition tasks such as classifying an image into respective classes. Neural networks are much known because people are getting a state-of-the-art outcome on complex computer vision and natural language processing tasks. Convolutional neural networks have been extensively used.
EXPRESSIVE SHARES VISUAL SECRET SHARING USING DISCRETE COSINE TRANSFORM FOR IMPROVED EXTRACTED SECRET QUALITY

Traditional visual secret sharing (VSS) generates non-expressive and noisy shares which incur problem in share management when dealing with multiple shares, as it converts secret into several shares, keeping track of which shares belong to which secret becomes challenging. Further, while these meaningless shares are transmitted, it provokes qualms to a potential intruder. Expressive share VSS is suggested in this paper to fix the mentioned problem, which generates expressive shares. These expressive shares created by embedding noisy shares into a cover image using transformed based steganography that is easy to manage. Thus, expressive shares are less likely to provoke qualms to the intruder as these are of high quality, i.e., very much identical to cover image.
Analysis of Ethnic Exploration Ethnic Commitment and Other Group Orientations in Javanese College Students

The study of interpersonal relationships is an aspect that cannot be separated from the discussion of social justice. The phenomenon is that social groups that are formed because of the affiliation of ethnic/racial similarities are very prone to conflict if group members are unable to adapt to all differences with other group members. This study aims to analyze the ethnic exploration and ethnic commitment in relation to the other group orientation of Javanese college students. The Multigroup Ethnic Identity Measure-Revised (MEIM) and Other Group Orientation inventories were distributed among 297 students and analyzed using a bias-corrected bootsrap method N = 5000 with a 95% confididential interval, indicating that other group orientations have a significant role as moderator (β = -0.03; p <0.05). These findings have implications for guidance and counseling services in universities, especially for developing attitudes towards other groups of students.
OPTIMIZATION OF THE PERFORMANCE OF GaAs SOLAR CELLS: EFFECT OF THE WINDOW LAYER

Improving the photovoltaic conversion efficiency requires a good choice for the structure of the cell. The front surfaces of the thin-film cell could improve these performances. In our work we have studied thin-film GaAs cells with thin ternary InGaP and AlGaAs layers used as a window. The one dimensional program SCAPS 1D has been used to study and optimize the performance of the GaAs cells in particular the window layer effect (the thickness and doping) and to compare between the two cells with In0.5Ga0. 5P and Al0.2Ga0. 8As window layers. The results have shown that the In0.5Ga0. 5P layer window epitaxially grown on GaAs solar cell have the best performance compared with the Al0.2Ga0. 8As layer window. The optimum thickness of the window is 0. 1μm, and the maximum efficiency is about 25.02%.
A Visual Approach to Explainable Clinical Decision Support

Clinical Decision Support Systems (CDSS) provide assistance to physicians in decision making. Based on patient-speciﬁc evidence and expert-modeled or machine-learned representations of clinical knowledge, these systems provide recommendations in ﬁnding the right diagnosis or optimal therapy. The acceptance of and trust in a CDSS are highly dependent on the trans-parency of the recommendation generation. Physicians must know both the key inﬂuences leading to a speciﬁc recommendation as well as any contradictory facts. They must be aware of the certainty of a recommendation and its potential alternatives. We present a glyph-based, coordinated multiple views approach to explainable computerized clinical decision support, inspired by the common decision-making process in clinical routine. Multiple views (1) show the certainty of the computation result, i.e., the recommendation and a set of clinical scores, stagings etc., (2) provide a visual summary of all evidence and their relevance for the computation result, (3) facilitate a guided investigation of the reasoning behind recommendation generation and convey the effect of updated evidence, i.e., due to recent clinical examination results, and (4) present linked textual information, such as clinical guidelines or therapy details. We demonstrate our approach for a CDSS based on a causal Bayesian network representing therapy of laryngeal cancer. Our approach has been developed in close collaboration with physicians and was evaluated by two expert otolaryngologists.
: Explainable Artiﬁcial Intelligence for Sentiment Analysis

Social media analytics have proven valuable in numerous research areas as a pragmatic tool for public opinion mining and analysis [1]. Sentiment analysis addresses the dynamics of complex socio-aﬀective applications that permeate intelligence and decision making in the sentient and solution-savvy Social Web [2]. Having started as simple polarity detection, contemporary sentiment analysis has advanced to a more nuanced analysis of aﬀect and emotion sensing [3]. Detecting ﬁne-grained sentiment in natural language, however, is tricky even for humans, making its automated detection very complicated. More-over, online opinions can be put forth in the form of text reviews or ratings, for a product as a whole, or each of its individual aspects [4]. Multiple and lengthy reviews, usage of casual dialect with microtext (wordplay, neologism and slang), use of ﬁgurative language (sarcasm, irony), multilingual content (code-mixed and code-switched) and opinion spamming add challenges to the task of extracting opinions. Recently memes, GIFs, typo-graphic (artistic way of text representation), info-graphic (text embedded along with an image) visual content and edited videos also dominate social feeds. Consequently, the intra-modal modeling and inter-modal interactions between the textual, visual and acoustic components add to the linguistic challenges [5]. Therefore, conceptualization and development of multi-faceted sentiment analysis models to adequately capture
The Semantics of Sketch: A Visual Query System For Time Series Data

— Sketching allows analysts to specify complex and free-form patterns of interest. Visual query systems can make use of sketches to locate these patterns of interest in large datasets. However, sketching is ambiguous: the same drawing could represent a multitude of potential queries. In this work, we investigate these ambiguities as they apply to visual query systems for time series data. We deﬁne a class of “invariants” — the properties of a time series that the analyst wishes to ignore when performing a sketch-based query. We present the results of a crowd-sourced study, showing that these invariants are key components of how people rate the strength of match between sketch and target. We adapt a number of algorithms for time series matching to support invariants in sketches. Lastly, we present a web-deployed prototype sketch-based visual query system that relies on these invariants. We apply the prototype to example datasets from ﬁnance, the digital humanities, and political science.
Implicit and Explicit Pragmatic Learning Strategies: Their Factorial Structure and Relationship with Speech Act Knowledge

Research into second language (L2) pragmatics has addressed a number of learner variables likely to implicate in speech act knowledge. Subscribing to the same line of research, the present study addressed the development and validation of a pragmatic learning strategy inventory (PRALSI), and the relationship between English as a foreign language (EFL) learners’ pragmatic learning strategy (PLS) use and their speech act knowledge. A total of 245 Iranian English-major undergraduates, ranging in age from 19 to 31, participated in PRALSI’s validation study. Of these, 117 further participated in the second phase of the study to unearth the relationship between PLS use and speech act knowledge, measured through a 24-item written discourse completion test (WDCT). Principal component analysis confirmed the 3factor structure of PRALSI, comprising implicit, inductive explicit, and deductive explicit strategies. The inventory contains 41 strategies for the learning of three main aspects of pragmatic competence: speech acts, implicature, and conversational routines. Two one-way ANOVAs also showed strategy use, particularly the use of explicit strategies, to be positively correlated with speech act knowledge. The findings reveal the implications of the implicit/explicit learning dichotomy for pragmatic development and strategy use, and the potentially greater advantage explicit strategies offer for pragmatic knowledge.
Linseed oil supplemented concentrate fed to Brahman crossbred fattening steers on carcass quality traits and intramuscular fatty acid profiles

The objective of this study was to determine the linseed oil supplemented concentrate fed to Brahman crossbred fattening steers on carcass quality trait and intramuscular fatty acid (FA) profiles. All steers were fed 14% CP concentrate. The treatments included: (1) 7 kg/d concentrate; (2) 4 kg/d concentrate supplemented with 200 g/d palm oil (PO); (3) 4 kg/d concentrate supplemented with 100 g/d PO and 100 g/d linseed oil (LSO); and (4) 4 kg/d concentrate supplemented with 200 g/d LSO. The animals in the treatment 1 were fed ad libitum rice straw (RS), whereas the animals in other treatments were fed ad libitum fresh grass (FG). Dietary treatments had no effect on nutrient intake while oil supplement decreased dry matter intake (DMI). Inclusion of LSO did not negatively affect carcass quality, but increasing amount of LSO supplement increased the n-3 fatty acids and lowered the n-6/n-3 ratio in beef.
Distributed ﬁnite-time practical consensus of second-order multi-agent system by event-triggered strategy

This paper considers the ﬁnite-time and distributed event-triggered consensus control for multi-agent system (MAS) with second-order dynamics under a undirected graph. A novel event-triggered function, which depends only on local information, is adopted. By employing a novel distributed event-triggered controller for each agent, practical ﬁnite-time consensus (FTC) of MAS can be achieved without continuous communication. A sufﬁcient condition is obtained through the analysis by Lyapunov theory and graph theory. In addition, the feasibility of the proposed approach is guaranteed by comprehensive theoretical demonstration of practical FTC and analysis of the Zeno behavior. Finally, a numerical simulation is provided to illustrate the correctness of theoretical results.
Renewable Energy based Water Pumping System Employing Sensorless control of BLDC Motor

This paper proposes a simple, cost-effective, and efficient renewable energy based water pumping system. A zeta converter is utilized as a DC-DC converter which extracts maximum power coming out from solar PV array. The soft starting of BLDC motor can be achieved by the control of zeta converter by incremental conductance maximum power point tracking (INC-MPPT). Sliding mode controller is employed to provide an accurate maximum power point tracking of photovoltaic systems using single control stage. Sensorless control of BLDC motor is employed for high efficiency and high reliability operation. Adaptive commutation error compensation strategy for the sensor less brushless DC (BLDC) motor is employed based on the flux linkage function. The results of the proposed system through simulation results are shown using MATLAB/Simulink software
An Automatic Multiple-Choice Question Generation Scheme for English Adjective Understanding

In this paper, we propose a multiple-choice question generation methodology for understanding evaluation of adjectives in a text. Based on the sense association among adjectives, an adjective being examined can be usually substituted by some other adjectives. In order to discourage learners from answering questions by recalling memorized answers, and to expose learners with more vocabularies, the answer of the questions generated is a substitute of the adjective being examined. The candidates of a substitute are gathered from WordNet and filtered by web corpus searching. Based on the proposed methodology, the choice candidates that are not selected as the answer are still useful. They are more distracting than the dissimilar adjectives and can be used as distractors to improve the distinguishability of a question.
Managing Web Experiments for Psycholinguistics: An Example from Experimental Semantics/Pragmatics

This chapter reports on the organization of an experimental semantics/pragmatics project that investigated the extent to which variability in a content’s projectivity is predicted by that content’s at-issueness (Tonhauser, Beaver, & Degen 2018). The project included four web-based experiments in which participants adjusted sliding scales to provide projectivity and at-issueness ratings for close to three hundred items. The workflow and best practices we report here are generalizable to any sufficiently similar web-based study, as well as to in-lab studies and dependent measures that differ substantially from the reported ones, for example, online measures such as eye movements. The workflow further easily accommodates computational project components including cognitive models and corpus analyses, though the current chapter does not include these components. The chapter also describes best practices within the first author’s interActive Language Processing Lab at Stanford (ALPS) at the time of writing. We close with some reflections on what we would do differently if we were we to start this project again from scratch.
Oxygen, pH, and labile organic carbon as possible mechanisms for algal stimulation of bacterial and fungal production in periphyton

Algal photosynthesis can stimulate production of associated microbial heterotrophs; however, the mechanisms for this stimulation remain unknown. I incubated submerged Typha domingensis leaf litter in greenhouse mesocosms under lowand high-nutrient regimes and conducted microbial production assays after 79/80 (week 11) or 128/129 (week 18) days of colonization. I manipulated environmental factors (oxygen, pH, and labile organic carbon) affected by algal photosynthesis to test the hypothesis that one or more of these factors stimulates heterotrophic microbial production. In the low-nutrient treatments, bacterial production was increased by glucose addition during week 11 (p < 0.001) and by photosynthesis during week 18 (p < 0.05). Fungal production was stimulated by photosynthesis in the high-nutrient treatments during weeks 11 (p < 0.001) and 18 (p < 0.01) and by glucose during week 11 (p < 0.05). These results confirm that algal photosynthesis can increase heterotrophic microbial production and suggest that photosynthetic labile organic carbon (LOC) could be a stimulatory mechanism.
Abdominal Massage to Relieve Constipation among Patients with Heart Failure: A Case Study

Background: Constipation among patients with heart failure can have a serious effect because Valsalva maneuver dur ing defecation process could increase heart workload an d aggravate patient's condition. Therefore, appropr iate treatment is needed to overcome the problem. One of the nurse's independen t actions in constipation management is abdominal m assage. Objective: to relieve constipation among patients with heart failure Methodology: This case study was conducted in a reg ional hospital located at the Eastern part of Indon esia during December 2017. Abdominal massage was given daily 2 hours aft er lunch was given as intervention. Five heart fail ure patients with constipation and hospitalized in cardiac ward were participated in this study. Constipation degree was measured by Constipation Assessment Scale (CAS) and feces consistency was de termined by Bristol Stool Chart (BSC). Patients in this study were selected sequentially during December 2017. Results: Constipation degree were decrease and feces consist ency changed after intervention. Abdominal massage performed by directly pressing the abdominal wall in a sequence can increase gastrocolic reflex and contraction of the intestine and rectum. Conclusions: Abdominal massage is effective in relieving consti pation among heart failure patients. Therefore, we propose abdominal massage is integrated as regular nursing intervention given to heart failure patients with c onstipation.
Network Analysis for Learners’ Concept Maps While Using Mobile Augmented Reality Gaming

Using mobile augmented reality games in education combines situated and active learning with pleasure. The aim of this research is to analyze the responses expressed by young, middle-aged, and elderly adults about the location-based mobile augmented reality (MAR) games using methods of content analysis, concept maps, and social network analysis (SNA). The responses to questions related to MAR game Ingress were collected from 36 adult players, aged 20–60, from Greece, and subsequently analyzed by means of content analysis, concept maps, and social network analysis. Our findings show that for question 1 (How do you feel when you endow the geographical space with personal preferences? ), there was a differentiation of the answers between age groups with age groups agreeing in pairs, the first two and the last two, while for question 2 (Do you think that the game offers opportunities for learning and teaching geography, building on your previous geographical knowledge? ), there was an overlap in responses of participants among age groups. It was also revealed that the MAR games foster a constructivism approach of learning, as their use learning becomes an active, socially supported process of knowledge construction.
Novel Insights on the Sustainable Wet Mode Fractionation of Black Soldier Fly Larvae (Hermetia illucens) into Lipids, Proteins and Chitin

The black soldier fly larvae (BSFL) is a sustainable ingredient for feed applications, biofuels, composite materials and other biobased products. Processing BSFL to obtain lipid and protein fractions with enhanced functional properties as a suitable replacement for conventional feed ingredients has gained considerable momentum. In this regard, a novel and sustainable wet mode fractionation (WMF) scheme for BSFL was explored. Fresh BSFL were steam blanched and pulped to obtain BSFL juice and juice press cake. Subsequent treatment of BSFL juice employing homogenization or enzyme incubation and further centrifugation resulted in the obtention of four different BSFL fractions (Lipid—LF; Cream—CF; Aqueous—AF; and Solid—SF). Total energy consumption for a batch BSFL (500 g) WMF process was 0.321 kWh. Aqueous and solid fractions were the predominant constituents of BSFL juice. Lauric acid (44.52–49.49%) and linoleic acid (19.12–20.12%) were the primary fatty acids present in BSFL lipids. Lipid hydrolysis was observed in lipids belonging to the solid (free fatty acids > triacylglycerides) and cream fractions. Aqueous fraction proteins (ctrl) displayed superior emulsion stability and foam capacity than other treatments. Juice press cake retained 60% of the total chitin content and the rest, 40%, was found in the solid fraction (ctrl). The material distribution of principal constituents in different fractions of the WMF process and amino acid profile was elucidated. Overall, the versatile WMF process proposed in this study involves simple unit operations to obtain functional ingredients from BSFL, which can be further explored by researchers and industry stakeholders.
Effects of trust in human-automation shared control: A human-in-the-loop driving simulation study*

Human-automation shared control is proposed to reduce the risk of driver disengagement in Level-3 autonomous vehicles. Although previous studies have approved shared control strategy is effective to keep a driver in the loop and improve the driver's performance, over- and under-trust may affect the cooperation between the driver and the automation system. This study conducted a human-in-the-loop driving simulation experiment to assess the effects of trust on driver's behavior of shared control. An expert shared control strategy with longitudinal and lateral driving assistance was proposed and implemented in the experiment platform. Based on the experiment (N=24), trust in shared control was evaluated, followed by a correlation analysis of trust and behaviors. Moderating effects of trust on the relationship between gaze focalization and minimum of time to collision were then explored. Results showed that self-reported trust in shared control could be evaluated by three subscales respectively: safety, efficiency and ease of control, which all show stronger correlations with gaze focalization than other behaviors. Besides, with more trust in ease of control, there is a gentle decrease in the human-machine conflicts of mean brake inputs. The moderating effects show trust could enhance the decrease of minimum of time to collision as eyes-off-road time increases. These results indicate over-trust in automation will lead to unsafe behaviors, particularly monitoring behavior. This study contributes to revealing the link between trust and behavior in the context of human-automation shared control. It can be applied in improving the design of shared control and reducing risky behaviors of drivers by further trust calibration.