Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced bone regeneration by Aloe vera gel conjugated barnacle cement protein composite hyaluronic acid hydrogel based hydroxyapatite derived from cuttlefish bone

Injectable Aloe vera gel W (AGW®)-conjugated Barnacle Cement Protein (BCP) composite Hyaluronic Acid (HA) hydrogels provide local periodontal tissue for bone filling in periodontal surgery. We developed a novel type of injectable self-supported hydrogel (2 mg/ml of AGW®-BCP/HA) based cuttlefish bone derived hydroxyapatite (CBH) for dental graft, which could good handling property, biodegradation or biocompatibility with the hydrogel disassembly and provided efficient cell adhesion activity and no inflammatory responses. Herein, the aim of this work was to evaluate bone formation following implantation of CBH and collagen membrane in rabbit calvarias defects. Eight male New Zealand rabbits were used and four circular calvarias defects were created on each animal. Defects were filled with different graft materials: 1) collagen membrane, 2) collagen membrane with CBH, 3) collagen membrane with bovine bone hydroxyapatite (BBH), and 4) control. The animals were sacrificed after 2 and 8 weeks of healing periods for histologic analysis. Both sites receiving CBH and BBH showed statistically increased augmented volume and new bone formation (p<0.05). However, there was no statistical difference in new bone formation between the CBH, BBH and collagen membrane group at all healing periods. Within the limits of this study, collagen membrane with CBH was an effective material for bone formation and space maintaining in rabbit calvarias defects.
Understanding the Factors Affecting the Small and Medium Enterprises Adoption of Cloud computing: A Literature Review

Cloud computing is increasingly popular and brings many benefits to organizations. However, the adoption of cloud computing (CC) is influenced by many different factors. The current study is based on the Technology-Environment-Organization (TOE) framework to discover the influencing factors in the decision to adopt CC in SMEs. Quantitative methods have been used to analyze relevant articles. The findings of this study suggest that 28 factors influence the decision to adopt CC in SMEs. Moreover, top management support, technology readiness, security concern, and relative advantage have a stronger impact on CC adoption in SMEs. The findings of this study add to the present CC literature and help practitioners better comprehend the elements that drive CC adoption. As a result, they can develop a clear strategy for enhancing CC adoption in their enterprises
Physical or On the Cloud: Play with IoTgo and Design Smart Things

Smart things, such as smart watches, are popular. Designing them requires technical skills and critical reflections in design, e.g., concerning safety risks due to their physical nature or data they exchange. Engaging end-users in design and reflections is thus complicated and yet beneficial, e.g., to make them aware of such risks. Play can help engage different end users, and especially teens. This paper reports on the latest evolution of the IoTgo playful toolkit for engaging different end users, and especially teens, in design and critical reflections. It presents a case-study across a pandemic with IoTgo used by teens and adults.
Hubungan Penerimaan Teknologi Pembelajaran Daring dengan Kinerja Guru Selama Masa Pandemi Covid-19

Abstract. During the current Covid-19 pandemic, there have been changes in education in schools. The government has made several efforts so that education can run as usual and reduce the spread of the Covid-19 virus by implementing an online learning system. The course of education for junior high school students is very important to prepare lessons for high school or vocational school. The results of interviews with several teachers at SMP N 45 and SMPN 49 in Bandung, there are obstacles because many of the taecers are technology savvy so that they have difficulty adapting to distance learning (PJJ) which focuses in using the online learning application. The research method used is Pearson correlation test, with a sample of 100 people. The correlation result has a value of R = 0.566. It can be said that there is a moderate correlation between the variable acceptance of online learning technology and the performance of junior high school teachers in Antapani District, Bandung City. Abstrak. Pada masa pandemi Covid-19 saat ini telah terjadi perubahan dalam pendidikan di sekolah. Pemerintah melakukan beberapa upaya agar pendidikan berjalan seperti biasa dan mengurangi penyebaran virus Covid-19 dengan diterapkan sistem belajar secara daring. Jalannya pendidikan bagi siswa SMP sangat penting untuk mempersiapkan pelajaran ke SMA atau ke sekolah kejuruan. Hasil wawancara kepada beberapa tenaga didik (guru) di SMP N 45 dan 49 kota Bandung, terdapat kendala dikarenakan banyak dari tenaga pendidik yang gagap teknologi sehingga mengalami kesulitan beradaptasi dengan pembelajaran jarak jauh (PJJ) yang fokus pada penggunaan aplikasi belajar daring tersebut. Metode penelitian yang digunakan yaitu Uji korelasi pearson, dengan sampel sebanyak 100 orang. Hasil korelasi memiliki nilai R= 0,566. Dapat dikatakan bahwa terdapat korelasi yang sedang antara variabel penerimaan teknologi pembelajaran daring dengan kinerja guru SMP Kecamatan Antapani Kota Bandung.
NeuroBE: Escalating NN Approximations to Bucket Elimination

A major limiting factor in graphical model inference is the complexity of computing the partition function. Exact message-passing algorithms such as Bucket Elimination (BE) require exponentially high levels of memory to compute the partition function, therefore approximations are necessary. In this paper, we build upon a recently introduced methodology called Deep Bucket Elimination (DBE) that uses classical Neural Networks (NNs) to approximate messages generated by BE when buckets have large memory requirements. The main feature of our new scheme called NeuroBE is that it customizes the architecture and learning of the NNs to the message size and its distribution. We also explore a new loss function for training taking into account the estimated message cost distribution. Our experiments demonstrate that these enhancements provide significant improvements over DBE in both time and accuracy. We also study the impact of the messages local errors on the global accuracy of the estimate of the partition function.
Design of Digital Workplace Stress-Reduction Intervention Systems: Effects of Intervention Type and Timing

Workplace stress-reduction interventions have produced mixed results due to engagement and adherence barriers. Leveraging technology to integrate such interventions into the workday may address these barriers and help mitigate the mental, physical, and monetary effects of workplace stress. To inform the design of a workplace stress-reduction intervention system, we conducted a four-week longitudinal study with 86 participants, examining the effects of intervention type and timing on usage, stress reduction impact, and user preferences. We compared three intervention types and two delivery timing conditions: Pre-scheduled (PS) by users and Just-in-time (JIT) prompted by the system-identified user stress-levels. We found JIT participants completed significantly more interventions than PS participants, but post-intervention and study-long stress reduction was not significantly different between conditions. Participants rated low-effort interventions highest, but high-effort interventions reduced the most stress. Participants felt JIT provided accountability but desired partial agency over timing. We present type and timing implications.
Evaluation of Antimicrobial Activity, Cytotoxicity, and Phytochemical Composition of Ocimum americanum

Background. Herbal plants are a natural source of novel biomolecules used widely in ethnomedicine. )e present study was intended to examine the antimicrobial properties, cytotoxicity, and phytoconstituents of Ocimum americanum L. , an herb traditionally used by the people of Swahili (Kenya) against microbial infections.Methods. )e aerial parts ofOcimum americanum L. were sourced, dried, milled, and extracted using three solvents: aqueous, acetonic, and 70% hydroethanolic. Additionally, fractions of chloroform and ethyl acetate were obtained from all crude extracts of the plant. )e antimicrobial property was evaluated using agar well diffusion and microdilution techniques against human opportunistic pathogens including S. aureus, E. coli, and C. albicans. )e brine shrimp cytotoxicity test was used to analyze the lethality of the extracts and fractions. Phytochemical screening was used to qualitatively assay the presence of phytoconstituents. Results. )e phytochemical assay confirmed the presence of alkaloids, phenols, flavonoids, tannins, saponins, terpenoids, reducing sugars, anthraquinones, and glycosides. )e lethality test demonstrated that all the extracts and fractions were toxic against Artemia salina nauplii with LC50 values ranging from 0.59 to 559.71 μg/ml. Chloroformic fraction of the hydroethanolic extract had the highest lethality with an LC50 value of 0.59 μg/ml. Two of the extracts and their fractions displayed antimicrobial activity against the Gram-positive bacteria (B. cereus and S. aureus) and fungus (C. albicans), while the same extracts had no activity against the Gram-negative bacteria (E. coli and K. pneumoniae). )e highest antimicrobial activity was seen in the ethyl acetate fraction of the hydroethanolic extract at 250mg/ml against Bacillus cereuswhich had an inhibition zone of 26.00± 0.00 andMIC value of 62.5mg/ml. Conclusion. In the current study, we report that Ocimum americanum L. demonstrated moderate antimicrobial activity, contains numerous phytocompounds, and is highly cytotoxic; thus, further research is needed for bioprospecting a novel compound.
Comparison of Models for Detecting Off-Putting Speaking Styles

In human-human interaction, speaking styles variation is perva-sive. Modeling such variation has seen increasing interest, but there has been relatively little work on how best to discriminate among styles, and apparently none on how to exploit pretrained models for this. Moreover, little computational work has ad-dressed questions of how styles are perceived, although this is often the most important aspect in terms of social and interper-sonal relevance. Here we develop models of whether an utterance is likely to be perceived as off-putting. We explore different ways to leverage state-of-the-art pretrained representations, namely those for TRILL, COLA, and TRILLsson. We obtain reasonably good performance in detecting off-putting styles, and find that architectures and learned representations designed to capture multi-second temporal information perform better.
State-Funded International Voluntary Service in Developing Countries. A Case Study of the German Weltwärts program

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Exploring the Learning Situation of Students with Cultural Probes

Exploring learning environments and planning curricula and courses at universities is an ongoing design process. Furthermore, needs and achievements of selfstudy phases are considered specifically in Bachelor and Master programmes. A group of reserachers, teachers, and curriculum designers at the ChristianAlbrechts-Universitat zu Kiel implemented a co-evolutionary process to explore the learning situation of students as well as respective contextual conditions. Furthermore, it is the basis for gaining ideas and impulse for the development of learning environments. This article describes the use of cultural probes as a design research method applied in a pedagogical field. The focus is on the methodical conception of the study.
Temporomandibular Joint Disorders in Females with Adolescent Idiopathic Scoliosis: Long-Term Effects of Milwaukee Brace Treatment

Patients with adolescent idiopathic scoliosis (AIS) more frequently suffer dysfunctions of dento-skeletal complex. To our knowledge, no study has ever evaluated the temporomandibular joint disorders (TMD) of AIS patients at least 23 years after the completion of Milwaukee brace treatment. We aimed to provide a complex assessment of TMD and AIS patients treated with a Milwaukee brace, in a minimum 23-year follow-up, using radiological, clinical, and socio-demographical data, and to adapt the TMD Disability Index Questionnaire (TMDQ) and Fonseca’s questionnaire (FQ) to Polish conditions. In total, 42 healthy females and 30 AIS patients with a minimum of 23 years after a completed Milwaukee brace treatment were asked to complete the Polish version of (TMDQ-PL) and (FQ-PL). AIS patients present higher TMD levels than healthy controls. Significant differences exist between TMDQ-PL and FQ-PL (both in total scores and particular sub-sections), and AIS patients. Clinical and radiological factors affected the TMDQ-PL and FQ-PL results. Adult patients with scoliosis treated conservatively present limitations in everyday activities connected with the temporomandibular joint (TMJ). The variety of curve-related factors in a long-term follow-up of wearing the Milwaukee brace influence TMJ.
Appendix for “Self-Interpretable Model with Transformation Equivariant Interpretation”

During experiments, the model configurations can be divided into two different settings according to the complexity of the dataset. For MNIST [6], as it is simple, and the intrinsic structures are distinct by pixels among classes, the structure of SITE degenerates from G ◦ F1 to G by setting F1 to be the identical operator. That is, z = F1(x) = x. Correspondingly, the generatorG instead maps input x to its prototypes Gi(x), i = 1, · · · , c. Hence the structure of SITE is built to be an autoencoder-based structure, where there are c parallel decoders. As for CIFAR-10 [5], due to the need for upsampling in visualization, the image data are resized to 128× 128. The feature extractor F1 is built based on ResNet-18 [3]. Here F1 : R3×128×128 → R10×16×16. And for the generator G, it consists of c = 10 (number of categories) parallel autoencoders, such that Gi : R10×16×16 → R10×16×16. Both MNIST and CIFAR-10 datasets are split into the training and validation sets by default. And all presented examples are from the validation sets. We also test on more complex datasets like Food-101 [1] to demonstrate the scalability of SITE. Please refer to the Appendix 5 for details. Besides, in order to balance the classification loss and the transformation loss we set the scalar factor to be λ = 5 throughout the training phase.
Islamic Banking and Finance Review

This paper is aimed to assess the overall perception of the users and stakeholders of Islamic banking towards Islamic banking and finance (IBF). It also intends to find out the crucial points which make the current practices of Islamic banking doubtful for the people and to suggest the appropriate remedial measures to bring about the practices of Islamic banking closer to the teachings of Islam and the Shari’ah. Direct survey method was used to assess and evaluate the perception of the users of Islamic banking and financé. However, the study was circumscribed by the opinions of the survey participants, who were limited in number and scattered across a few major cities of Pakistan. The findings revealed that a negative perception about Islamic banking persists due to the various similarities in the procedures, practices, products, and legal framework of Islamic and conventional banks. Moreover, the negative perception is also due to the fact that the same regulator regulates both the banking systems.
A Brief Review of Biomedical Optical Imaging towards Clinical Applications

Biomedical optical imaging, recently, has been developed to be useful tools for clinical applications in diagnostics. A number of works based on uses of optical and laser techniques to solve the difficult problems in medical and biological studies were proposed. Under investigation of light propagation in biological tissues, various optical techniques have been demonstrated their capabilities in clinical applications such as diagnostics. The model of light propagation in biological tissues or conversion of light energy to acoustics is beneficial to the imaging reconstruction. These techniques currently employed and replaced conventional techniques in hospital gradually and will be the essential tools for biomedical researches.
Escaping Saddle-Point Faster under Interpolation-like Conditions

In this paper, we show that under over-parametrization several standard stochastic optimization algorithms escape saddle-points and converge to local-minimizers much faster. One of the fundamental aspects of over-parametrized models is that they are capable of interpolating the training data. We show that, under interpolation-like assumptions satisﬁed by the stochastic gradients in an over-parametrization setting, the ﬁrst-order oracle complexity of Perturbed Stochastic Gradient Descent (PSGD) algorithm to reach an (cid:15) -local-minimizer, matches the corresponding deterministic rate of ˜ O (1 /(cid:15) 2 ) . We next analyze Stochastic Cubic-Regularized Newton (SCRN) algorithm under interpolation-like conditions, and show that the oracle complexity to reach an (cid:15) -local-minimizer under interpolation-like conditions, is ˜ O (1 /(cid:15) 2 . 5 ) . While this obtained complexity is better than the corresponding complexity of either PSGD, or SCRN without interpolation-like assumptions, it does not match the rate of ˜ O (1 /(cid:15) 1 . 5 ) corresponding to deterministic Cubic-Regularized Newton method. It seems further Hessian-based interpolation-like assumptions are necessary to bridge this gap. We also discuss the corresponding improved complexities in the zeroth-order settings.
Stress Detection through Speech Analysis using Machine Learning

: Voice stress analysis (VSA) is collectively a pseudoscientific technology that aims to infer deception from stress measured in the voice. The technology aims to differentiate between stressed and non-stressed outputs in response to stimuli (e.g., questions posed), with high stress seen as an indication of deception. In this work, we propose a deep learning-based psychological stress detection model using speech signals. With increasing demands for communication between humans and intelligent systems, automatic stress detection is becoming an interesting research topic. Stress can be reliably detected by measuring the level of specific hormones (e.g., cortisol), but this is not a convenient method for the detection of stress in human-machine interactions. The proposed algorithm first extracts Mel-filter bank coefficients using pre-processed speech data and then predicts the status of stress output using a binary decision criterion (i.e., stressed or unstressed) using CNN (Convolutional Neural Network) and dense fully connected layer networks.
A New Nonlinear Compound Forecasting Method Based on ANN

In this paper the compound forecasting method is discussed. The compound forecasting method is one of the hotspots in the current predication. Firstly, the compound forecasting method is introduced and various existing compound forecasting methods are discussed. Secondly, the Artificial Neural Network( ANN ) is brought in compound prediction research and a nonlinear compound prediction model based on ANN is presented. Finally, in order to avoid irregular weight, a new method is presented which uses principal component analyses to increase the availability of compound forecasting information. Higher forecasting precision is achieved in practice.
The Combination Effect Analysis of Catharanthus roseus, Abelmoschus manihot and Dysphania ambrosioides on Rattus norvegicus Blood Triglyceride Content

In the Pandemic Covid-19 era, the search for herbal medicines for community use became the hope of researchers. Triglycerides in the blood are needed by the body at certain levels, but it is a problem at excessive levels or called hypertriglyceridemia. The study aims to analyse the combination effect of the Tapak dara (Catharanthus roseus), Gedi (Abelmoschus manihot) and Pasote (Dysphania ambrosioides) on the blood triglyceride levels of white rats (Rattus norvegicus). The research method used is experimental. This method is carried out gradually starting with the maintenance of rats and administering fats, making extracts, administering treatment, and blood trafficking mice. The research uses samples of Tapak dara, Gedi and Pasote. The dried sample is then extracted by brewing simplisia in the form of tea using boiling water 100 mL, then administered to rats by way of Sonde/Cekok for 30 days. Rat blood after treatment is taken from the heart and then analyzed its triglyceride levels with an enzymatic spectrometer. Results of analysis of the influence of the combination of Tapak dara, Gedi and Pasote show distinct results. After further testing with Duncan's test that the Gemfibrozil treatment resulted in 17.2 mg/dL triglyceride levels and the highest was the third combination treatment of 112.7 mg/dL. The visible effect is the decrease and increase in triglyceride levels. The conclusion is that there are different influences from the combination administration of Tapak dara, Gedi and Pasote against the blood triglyceride levels of white rats with uncombined treatment. Triglyceride levels given the combination of the third leaf extract (PTG) have increased, while in Gedi treatment only and the combination of Tapak dara Pasote is decreased. The combination of several medicinal plants produces a new effect which is the interaction of the effect of a single active compound.
Modeling procrastination as rational metareasoning about task effort

Current theories of procrastination argue that people put things off into the future with the expectation that they will be better able to do them later. In this paper, we rationalize such expec- tations within the framework of evidence accumulation models of the choice process. Speciﬁcally, we show that it is rational for observers to adopt lower decision thresholds for choices with weak evidence for any alternative, and that observers learning to estimate optimal decision thresholds for tasks that involve decisions will ﬁnd it reasonable to put the tasks off un- til the threshold has been sufﬁciently lowered by time-varying urgency. We designed a computational model and an experi- ment to differentiate our theory from more general expectancy based temporal motivation accounts. Both simulation and ex- perimental results support our proposal, indicating a large role for choice difﬁculty in people’s self-assessed estimates for how likely they are to procrastinate any given task.