Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Trajectory Prediction for Robotic Manipulation Under Unreliable Sensor Data

Recent success of Deep Learning models have provided big advancements in many ﬁelds including robotic manipulation and control. There are cases when the sensors of a robot might suddenly start receiving noisy or corrupted input signals, which directly aﬀect the actions the robot calculates to perform. In scenarios where the robot needs to complete the tasks at all costs, the actions based on this unreliable information might not make the robot reach the goal. Thus, there is a possibility for damaging itself while doing so. Therefore, it is important to predict and utilize the actions in advance based on the reliable sensor readings the robot received in the previous timesteps. This work is based on Guided Policy Search algorithm that provides an end-to-end robotic learning framework, mapping vision and state conﬁguration directly to the actions. However, this algorithm predicts actions for one timestep only, based on previous timestep’s sensor readings. We make this procedure more accurate by detecting disruptive noise and verifying it by computing the probability of the current state belonging to the linearized dynamics. For each of the timesteps the model predicts the future timesteps’ actions and upon detecting signiﬁcant noise it executes the planned actions.
Density Functional Analysis of Fluorite-Structured (Ce, Zr)O2/CeO2 Interfaces

The structures and properties of Ce1–xZrxO2 (x = 0–1) solid solutions, selected Ce1–xZrxO2 surfaces, and Ce1–xZrxO2/CeO2 interfaces were computed within the framework of density functional theory corrected for strong electron correlation (DFT+U). The calculated Debye temperature increases steadily with Zr content in (Ce, Zr)O2 phases, indicating a significant rise in microhardness from CeO2 to ZrO2, without appreciable loss in ductility as the interfacial stoichiometry changes. Surface energy calculations for the low-index CeO2(111) and (110) surfaces show limited sensitivity to strong 4f-electron correlation. The fracture energy of Ce1–xZrxO2(111)/CeO2(111) increases markedly with Zr content, with a significant decrease in energy for thicker Ce1–xZrxO2 films. These findings suggest the crucial role of Zr acting as a binder at the Ce1–xZrxO2/CeO2 interfaces, due to the more covalent character of Zr–O bonds compared to Ce–O. The impact of surface relaxation upon interface cracking was assessed and found to.
Literature Survey On Different Techniques Used For Predicting Diabetes Mellitus

: In today’s world diabetes is the major health challenges in India. It is a group of a syndrome that results in too much sugar in the blood. It is a protracted condition that affects the way the body mechanizes the blood sugar. Prevention and prediction of diabetes mellitus is increasingly gaining interest in medical sciences. The aim of this paper is to conduct a survey on different techniques that are used for predicting diabetes.
Actin filaments altered distribution in wheat (Triticum aestivum) “Bending Root” to respond to enhanced Ultraviolet-B radiation

Plants adjust their shoot growth to acclimate to changing environmental factors, such as to enhanced Ultraviolet-B (UV-B) radiation. However, people have ignored that plant roots can also respond to UV-B light. Here, we find the morphology curled wheat roots under UV-B radiation, that we call, “bending roots. ” The curly region is the transition zone of the root after observed at the cellular level. After exposed to enhanced UV-B radiation for 2 d (10.08 KJ/m2/d), cell size decreased and actin filaments gathered in wheat roots. We also find that H2O2 production increased and that content of the indole-3-acetic acid (IAA) increased remarkably. The pharmacological experiment revealed that actin filaments gathered and polymerized into bundles in the wheat root cells after irrigated H2O2 and IAA. These results indicated that actin filaments changed their distribution and formed the “bending root,” which was related to H2O2 production and increase in IAA. Overall, actin filaments in wheat root cells could be a subcellular target of UV-B radiation, and its disruption determines root morphology.
An Evaluation of the Accuracy of the Machine Translation Systems of Social Media Language

—In this age of information technology, it has become possible for people all over the world to communicate in different languages through social media platforms with the help of machine translation (MT) systems. As far as the Arabic-English language pair is concerned, most studies have been conducted on evaluating the MT output for the standard varieties of Arabic, with fewer studies focusing on the vernacular or colloquial varieties. This study attempts to address this gap through presenting an evaluation of the performance of MT output for vernacular or colloquial Arabic in the social media domain. As it is currently the most widely used MT system, Google Translate (GT) has been chosen for evaluating the reliability of its output in the context of translating the Arabic colloquial language (i.e., Egyptian/Cairene Arabic variety) used in social media into English. With this goal in mind, a corpus consisting of Egyptian dialectal Arabic sentences were collected from social media networks, i.e., Facebook and Twitter, and then fed into GT system. The GT output was then evaluated by three human translators to assess their accuracy of translation in terms of adequacy and fluency. The results of the study show that several translation problems have been spotted for GT output. These problems are mainly concerned with wrong equivalents, inappropriate additions and deletions, and transliteration for out-of-vocabulary (OOV) words, which are mostly due to the literal translation of the Arabic vernacular sentences into English. This can be due to the fact that Arabic vernacular varieties are different from the standard language for which MT systems have been basically developed. This, consequently, necessitates the need to upgrade such MT systems to deal with the vernacular varieties.
A large-scale image dataset of wood surface defects for automated vision-based quality control processes [version 1; peer review: awaiting peer review]

The wood industry is facing many challenges. The high variability of raw material and the complexity of manufacturing processes results in a wide range of visible structure defects, which have to be controlled by trained specialists. These manual processes are not only tedious and biased, but also less effective. To overcome the drawbacks of the manual quality control processes, several automated vision-based systems have been proposed. Even though some conducted studies achieved a higher recognition rate than trained experts, researchers have to deal with a lack of large-scale databases and authentic data in this field. To address this issue, we performed a data acquisition experiment set in the industrial environment, where we were able to acquire an extensive set of authentic data from a production line. For this purpose, we designed and implemented a complex technical solution suitable for high-speed acquisition during harsh manufacturing conditions. In this data note, we present a large-scale dataset of high-resolution sawn timber surface images containing more than 43 000 labelled surface defects and covering 10 types of the most common wood defects. Moreover, with each image record, we provide two types of labels allowing researchers to perform semantic segmentation, as well as defect classification, and localization.
Concerned but Ineffective: User Perceptions, Methods, and Challenges when Sanitizing Old Devices for Disposal

Consumers are upgrading their devices more often due to continuous advances in hardware. Old devices need to be sanitized (i.e., personal data removed with low recovery probability) before selling, donating, throwing away, or recycling the device ( “disposal” ), but previous works have shown that users frequently fail to do that. We aim to understand the sources of misconceptions that result in risks to personal data. Through a survey (n=131), we measure where the old devices end up and how they are sanitized. Our survey shows that while most users dispose of their devices, a large proportion of participants (73%) kept at least one old device, often due to data leakage concerns. Among disposed-of devices, 25% of participants reported using methods to erase their data that are insecure. To further explore the processes that were undertaken to sanitize devices and sources of misconception, we invite a subset of respondents (n=35) for interviews. Our interviews uncover the reasons for poor device sanitizing practices—misleading data deletion interfaces and prompts, lack of knowledge, and complex and slow disk wiping procedures. We provide suggestions for device manufacturers and retailers on how to improve privacy, trust, and convenience when sanitizing old devices.
Pinostrobin Derivatives from PrenylationReaction and their Antibacterial Activity against Clinical Bacteria

Kaempferia pandurata (syn. Boesenbergia rotunda, B. pandurata (Roxb.)Schltr), locally known as "TemuKunci"in Indonesia, is one of the medicinal plants of the family Zingiberaceae. Phytochemical studies on the rhizome of K. pandurata showed the presence of flavonoid derivative, namely flavanones, which constitute as the main components of this plant. Bioactivity studies on this species exhibited various biological activities, such as antibacteria, anti-inflammatory, antitumor, antidiarrhea, antidisentri, anti-HIV, antioxidant, antipyretic, analgesic and insecticides. Among the biological activities, the antibacterial activity results are important as an attempt to answer the emergence of resistance of some bacteria against existing drugs, as well as the emergence of a number of outbreaks of disease caused by bacteria. Therefore, a search to find new compounds that are potential as an antibacterial is an urgent matter. The present study was aimed at the chemical transformation of pinostrobin (1) from K. pandurata rhizome and an antibacterial activity.The chemical transformation was performed through a prenylation reaction of pinostrobin (1) which is the main component of K. pandurata rhizome. The prenylation reaction was carried out by reacting pinostrobin (1) with prenyl bromide and potassium carbonat (K2CO3). The purification of product was done using the radial chromatography with mix solvent n-hexane and ethyl acetate (97.5:2.5; 9.5:0.5; 9.0:1.0.; 8.0:2.0). The purity test of isolated compound was analysedby TLC using different types of eluent. The identification of compounds was determined based on NMR data and mass spectra analysis. Five compounds were obtained from the prenylation reaction, i.e. monooxyprenylated pinostrobin (2), monooxyprenylated chalcone (3), diprenylated chalcone (4), triprenylated chalcone (5), and triprenylated cyclohexene chalcone (6). These compounds were tested for antibacterial activities against four clinical bacteria, namely Bacillus subtilis, Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa. The antibacterial test was conducted using microdilution method. The five compounds showed moderate antibacterial activity against clinical bacteria with MIC values in the range of 25 to 50 µg/mL.
GRAYSCALE IMAGE COLORIZATION USING A CONVOLUTIONAL NEURAL NETWORK

A BSTRACT . Image coloration refers to adding plausible colors to a grayscale image or video. Image coloration has been used in many modern ﬁelds, including restoring old photographs, as well as reducing the time spent painting cartoons. In this paper, a method is proposed for colorizing grayscale images using a convolutional neural network. We propose an encoder-decoder model, adapting FusionNet to our purpose. A proper loss function is deﬁned instead of the MSE loss function to suit the purpose of coloring. The proposed model was veriﬁed using the ImageNet dataset. We quantitatively compared several colorization models with ours, using the peak signal-to-noise ratio (PSNR) metric. In addition, to qualitatively evaluate the results, our model was applied to images in the test dataset and compared to images applied to various other models. Finally, we applied our model to a selection of old black and white photographs.
Kualitas Ekstrak Karaginan Dari Rumput Laut “Kappaphycus alvarezii” Hasil Budidaya Di Perairan Pantai Kartini Dan Pulau Kemojan Karimunjawa Kabupaten Jepara

Karaginan merupakan senyawa hidrokoloid yang diekstraksi dari rumput laut merah jenis Kappaphycus alvarezii . Karaginan dapat digunakan untuk meningkatkan kestabilan bahan pangan baik yang berbentuk suspensi (dispersi padatan dalam cairan), emulsi (dispersi gas dalam cairan). Tujuan dari penelitian ini adalah mengetahui Kualitas ekstrak karaginan hasil ekstraksi rumput laut K. alvarezii dari dua lokasi sampling yang berbeda. Penelitian ini dilaksanakan pada bulan Agustus 2015 sampai bulan Januari 2016, di perairan Pantai Kartini, Jepara dan peraian Kemojan Karimunjawa, Jepara. Hasil penelitian menunjukkan analisis spektra FTIR produk telah memenuhi spesifikasi karaginan standar karena gugus-gugus fungsi yang terdapat pada spektrum sampel yang dihasilkan sama seperti gugus pada kappa karaginan yaitu gugus ester sulfat, OH, ikatan glikosidik. Hasil analisis sifat fisik-kimia karaginan (rendemen, kadar air, kadar abu, kadar sulfat, viskositas, dan kekuatan gel) yang paling baik yaitu karaginan dari perairan Kemojan Karimunjawa dibandingkan karaginan Pantai Kartini. Kadar sulfat Pantai Kartini dan Kemojan Karimunjawa masih dibawah kadar mutu yaitu 15-40 %, Sedangkan kekuatan gel kedua perairan yaitu Kemojan Karimunjawa 78,57 g.cm -2 dan Pantai kartini 61,86 g.cm -2 masih jauh di bawah baku mutu yaitu 685,50 g.cm 2 . Carrageenan is a hydrocolloid compounds extracted from red seaweed types Kappaphycus alvarezii. Carrageenan can be used to improve the stability of food in the form of suspension or emulsions. The purpose of this study was to determine the quality of the extract of carrageenan extracted from K. alvarezii at two different sampling locations. This research was conducted in August, 2015 and January, 2016. Samples of seaweed obtained from two different waters, namely Kartini Beach and Kemojan Karimunjawa Island, bolt at jepara regency . The results showed the FTIR spectra analysis of the product met the specifications for a standard carrageenan functional groups contained in the sample spectrum as expressed in clusters of kappa carrageenan namely sulphate ester group , OH , glycosidic bond. The results of analysis of the physical-chemical properties of carrageen an ( yield, moisture content , ash content , sulphate content , viscosity and gel strength ) the best quality of carragenan was obtained from carrageenophyte taken from Kemojan Karimunjawa Island, although sulphate content from two different waters was under quality level at 15 – 40%, while the gel strength kartini 61.86 g . cm -2 and Kemojan 78.57 g . cm -2 both were still far below the of carrageenophyte quality at 685.50 g . cm -2 .
Aristotle University of Thessaloniki

With the continuous advance of the technology and the rather growing demands in ﬁelds such as the industrial, or the medical concerning the automatization of processes and the support of the professional workers, the use of Artiﬁcial Intelligence is vastly deployed. Professional workers have to co-exist and/or collaborate with robotic agents such as Automated Guided Vehicles and robotic arms, to enhance their eﬀectiveness and productivity. Those robotic agents have machine learning algorithms embodied to them, for the proper support of the humans. This task is also the main goal of this thesis. A gesture recognition engine is developed, using the method of a Multilayer perceptron (MLP), for the recognition of the gestures of two diﬀerent datasets with a similar structure. The results of this method are also compared to those, with the use of the methods of Hidden Markov Models (HMMs) and Long-Short Term Memory (LSTM), in order to the reach to the optimal classiﬁcation architecture. Apart from the methods themselves, diﬀerent ways for data handling were also tested. The aim is to examine whether in the speciﬁc case and with the used data, the resampling method was more useful in terms of training or testing an algorithm, compared to training or testing with the whole amount of data. This led to observations concerning the computational cost of some experiments and the robustness of the used algorithms. As such, the scientiﬁc contribution of this thesis relies on the implementation of a robust, accurate, as well as fast computationally gesture recognition engine.
Nonparametric Estimation of the Random Coefficients Model in Python

We present $\textbf{PyRMLE}$, a Python module that implements Regularized Maximum Likelihood Estimation for the analysis of Random Coefficient models. $\textbf{PyRMLE}$ is simple to use and readily works with data formats that are typical to Random Coefficient problems. The module makes use of Python's scientific libraries $\textbf{NumPy}$ and $\textbf{SciPy}$ for computational efficiency. The main implementation of the algorithm is executed purely in Python code which takes advantage of Python's high-level features.
Toxoplasma bradyzoites exhibit physiological plasticity of calcium and energy stores controlling motility and egress

Toxoplasma gondii has evolved different developmental stages for disseminating during acute infection (i.e. tachyzoites) and for establishing chronic infection (i.e. bradyzoites). Calcium ion (Ca2+) signaling tightly regulates the lytic cycle of tachyzoites by controlling microneme secretion and motility to drive egress and cell invasion. However, the roles of Ca2+ signaling pathways in bradyzoites remain largely unexplored. Here we show that Ca2+ responses are highly restricted in bradyzoites and that they fail to egress in response to agonists. Development of dual-reporter parasites revealed dampened calcium responses and minimal microneme secretion by bradyzoites induced in vitro or harvested from infected mice and tested ex vivo. Ratiometric Ca2+ imaging demonstrated lower Ca2+ basal levels, reduced magnitude, and slower Ca2+ kinetics in bradyzoites compared with tachyzoites stimulated with agonists. Diminished responses in bradyzoites were associated with down-regulation of calcium ATPases involved in intracellular Ca2+ storage in the endoplasmic reticulum (ER) and acidocalcisomes. Once liberated from cysts by trypsin digestion, bradyzoites incubated in glucose plus calcium rapidly restored their intracellular Ca2+ and ATP stores leading to enhanced gliding. Collectively, our findings indicate that intracellular bradyzoites exhibit dampened Ca2+ signaling and lower energy levels that restrict egress, and yet upon release they rapidly respond to changes in the environment to regain motility.
On Conditionals

We discuss the evaluation of conditionals. Under classical logic a conditional of the form A implies B is semantically equivalent to not A or B. However, psychological experiments have repeatedly shown that this is not how humans understand and use conditionals. We introduce an innovative abstract reduction system under the three-valued Lukasiewicz logic and the weak completion semantics, that allows us to reason abductively and by revision with respect to conditionals, in three values. We discuss the strategy of minimal revision followed by abduction and discuss two notions of relevance. Psychological experiments will need to ascertain if these strategies and notions, or a variant of them, correspond to how humans reason with conditionals.
Improved Prediction of Deep Temporal Neural Networks with Trend Filtering

Forecasting with multivariate time series that has been studied for a long time has goal to predict future values using previous and current values. Since it is diﬃcult to know how many noise is intrinsic within fast changing time series data, training the model having high predictive performance is diﬃcult. Recently, many researchers have been interested in many deep neural network such as recurrent neural networks, encoder-decoder structure, and neural networks with attention mechanism to design them to forecast future ﬁnancial time series data. Many attempts have been to use those methods for highlighting more important features for capturing the long period of temporal dependencies in the task of forecasting multivariate time series data to make predictive performance better. In this paper, a new framework is introduced, which is utilized deep temporal neural networks with trend ﬁlter techniques that make original time sequence data mixed with the noise into the trend. We show that the performance of each deep neural network using a feature temporally processed by trend ﬁlter skills. To verify this proposed method, three state-of-the-art models for forecasting future values in ﬁnance stock market sequence, are used for comparing the predictive performance with the original method. Expanded experiments for conducting the t-test between this proposed method and original one show that the new framework is useful and has statistical signiﬁcance of the fact that proposed method achieves better performance.
Anti-leukemic Effect of β-Cryptoxanthin against Leukemia Cell Line Via Attenuating Inflammatory Pathway

1Department of Hematology, Dezhou People’s Hospital,1751 Xinhu Road, Dezhou, Shandong Province, 253014, P.R. China 2Department of out-patient operating room, Dezhou People’s Hospital, Dezhou, Shandong Province, 253014, P.R. China 3Department of Hematology, People’s Hospital of Zhangqiu, Jinan, Shandong Province, 250200, China Article Information Received 05 December 2019 Revised 22 February 2020 Accepted 04 March 2020 Available online 12 August 2021
Analysis by GC (Ir), GC/MS and mosquito repellent effect of essential oils against Anopheles gambiae: Case of stem bark of Sterculia tragacantha Lindl (Sterculiaceae) from Cte dIvoire

The objectives of this study were to determine the chemical composition and to evaluate the mosquito repellent property of essential oils extracted from the bark of the stem of Sterculia tragacantha Lindl on Anopheles gambiae, vector of malaria. The essential oil of the stem bark of S. tragacantha Lindl is extracted by hydrodistillation and their chemical compositions were identified by GC (Ir) and GC-MS. This oil has been tested on sensitive « kisumu » strains of A. gambiae adults, according to the World Health Organization (WHO) guidelines for laboratory testing of the repellency of chemicals. The extraction yield obtained from this oil is 0.12%. By means of GC (Ir) and GC-MS, 29 compounds representing 88.4% of the HE were identified. β-elemol (40.54%) is the major compound of this oil. It is rich in sesquiterpenes (79.47%) with a predominance of oxygenated sesquiterpenes (55.86%). As for the oxygenated monoterpenes, they represent 8.93%. The repellency test against adult female Anopheles, revealed that the EO of S. tragacantha (10%) has repellent properties against mosquitoes. Indeed, it induces an average reduction of 98.2% of the blood meal (TIRS) of mosquitoes on guinea pigs with a complete protection time (CPT) of 5 h compared to 3 h 30 min for the natural reference substance (Cymbopogon citratus). In view of its extractive value and prolonged repellent properties, S. tragacantha could be a good alternative in the vector borne diseases control if used as natural repellents for skin application.