Miracle-dz/UniFAR-FTUs huggingface dataset:
Selection of the best features for leukocytes classification in blood smear microscopic images

Automatic differential counting of leukocytes provides invaluable information to pathologist for diagnosis and treatment of many diseases. The main objective of this paper is to detect leukocytes from a blood smear microscopic image and classify them into their types: Neutrophil, Eosinophil, Basophil, Lymphocyte and Monocyte using features that pathologists consider to differentiate leukocytes. Features contain color, geometric and texture features. Colors of nucleus and cytoplasm vary among the leukocytes. Lymphocytes have single, large, round or oval and Monocytes have singular convoluted shape nucleus. Nucleus of Eosinophils is divided into 2 segments and nucleus of Neutrophils into 2 to 5 segments. Lymphocytes often have no granules, Monocytes have tiny granules, Neutrophils have fine granules and Eosinophils have large granules in cytoplasm. Six color features is extracted from both nucleus and cytoplasm, 6 geometric features only from nucleus and 6 statistical features and 7 moment invariants features only from cytoplasm of leukocytes. These features are fed to support vector machine (SVM) classifiers with one to one architecture. The results obtained by applying the proposed method on blood smear microscopic image of 10 patients including 149 white blood cells (WBCs) indicate that correct rate for all classifiers are above 93% which is in a higher level in comparison with previous literatures.
Automated detection of kinks from blood vessels for optic cup segmentation in retinal images

The accurate localization of the optic cup in retinal images is important to assess the cup to disc ratio (CDR) for glaucoma screening and management. Glaucoma is physiologically assessed by the increased excavation of the optic cup within the optic nerve head, also known as the optic disc. The CDR is thus an important indicator of risk and severity of glaucoma. In this paper, we propose a method of determining the cup boundary using non-stereographic retinal images by the automatic detection of a morphological feature within the optic disc known as kinks. Kinks are defined as the bendings of small vessels as they traverse from the disc to the cup, providing physiological validation for the cup boundary. To detect kinks, localized patches are first generated from a preliminary cup boundary obtained via level set. Features obtained using edge detection and wavelet transform are combined using a statistical approach rule to identify likely vessel edges. The kinks are then obtained automatically by analyzing the detected vessel edges for angular changes, and these kinks are subsequently used to obtain the cup boundary. A set of retinal images from the Singapore Eye Research Institute was obtained to assess the performance of the method, with each image being clinically graded for the CDR. From experiments, when kinks were used, the error on the CDR was reduced to less than 0.1 CDR units relative to the clinical CDR, which is within the intra-observer variability of 0.2 CDR units.
Handling Rare Word Problem using Synthetic Training Data for Sinhala and Tamil Neural Machine Translation

Lack of parallel training data influences the rare word problem in Neural Machine Translation (NMT) systems, particularly for under-resourced languages. Using synthetic parallel training data (data augmentation) is a promising approach to handle the rare word problem. Previously proposed methods for data augmentation do not consider language syntax when generating synthetic training data. This leads to generation of sentences that lower the overall quality of parallel training data. In this paper, we discuss the suitability of using Parts of Speech (POS) tagging and morphological analysis as syntactic features to prune the generated synthetic sentence pairs that do not adhere to language syntax. Our models show an overall 2.16 and 5.00 BLEU score gains over our benchmark Sinhala to Tamil and Tamil to Sinhala translation systems, respectively. Although we focus on Sinhala and Tamil NMT for the domain of official government documents, we believe that these synthetic data pruning techniques can be generalized to any language pair.
Serrate/Notch Signaling Regulates the Size of the Progenitor Cell Pool in Drosophila Imaginal Rings

Drosophila imaginal rings are larval precursors for adult guts and salivary glands. Yang and Deng show that the proliferation of these cells is regulated by the canonical Notch pathway and that both cis- and trans. Drosophila imaginal rings are larval tissues composed of progenitor cells that are essential for the formation of adult foreguts, hindguts, and salivary glands. Specified from subsets of ectoderm in the embryo, imaginal ring cells are kept quiescent until midsecond larval instar, and undergo rapid proliferation during the third instar to attain adequate numbers of cells that will replace apoptotic larval tissues for adult organ formation. Here, we show that Notch signaling is activated in all three imaginal rings from middle embryonic stage to early pupal stage, and that Notch signaling positively controls cell proliferation in all three imaginal rings during the third larval instar. Our mutant clonal analysis, knockdown, and gain-of-function studies indicate that canonical Notch pathway components are involved in regulating the proliferation of these progenitor cells. Both trans-activation and cis-inhibition between the ligand and receptor control Notch activation in the imaginal ring. Serrate (Ser) is the ligand provided from neighboring imaginal ring cells that trans-activates Notch signaling, whereas both Ser and Delta (Dl) could cis-inhibit Notch activity when the ligand and the receptor are in the same cell. In addition, we show that Notch signaling expressed in middle embryonic and first larval stages is required for the initial size of imaginal rings. Taken together, these findings indicate that imaginal rings are excellent in vivo models to decipher how progenitor cell number and proliferation are developmentally regulated, and that Notch signaling in these imaginal tissues is the primary growth-promoting signal that controls the size of the progenitor cell pool.
Building Parallel Monolingual Gan Chinese Dialects Corpus

Automatic language identification of an input sentence or a text written in similar languages, varieties or dialects is an important task in natural language processing. In this paper, we propose a scheme to represent Gan (Jiangxi province of China) Chinese dialects. In particular, it is a two-level and fine-grained representation using Chinese character, Chinese Pinyin and Chinese audio forms. Guided by the scheme, we manually annotate a Gan Chinese Dialects Corpus (GCDC) including 131.5 hours and 310 documents with 6 different genres, containing news, official document, story, prose, poet, letter and speech, from 19 different Gan regions. In addition, the preliminary evaluation on 2-way, 7-way and 20-way sentence-level Gan Chinese Dialects Identification (GCDI) justifies the appropriateness of the scheme to Gan Chinese dialects analysis and the usefulness of our manually annotated GCDC.
Using Adversarial Examples in Natural Language Processing

Machine learning models have been providing promising results in many ﬁelds including natural language processing. These models are, nevertheless, prone to adversarial examples . These are artiﬁcially constructed examples which evince two main features: they resemble the real training data but they deceive already trained model. This paper investigates the effect of using adversarial examples during the training of recurrent neural networks whose text input is in the form of a sequence of word/character embeddings. The effects are studied on a compilation of eight NLP datasets whose interface was uniﬁed for quick experimenting. Based on the experiments and the dataset characteristics, we conclude that using the adversarial examples for NLP tasks that are modeled by recurrent neural networks provides a regularization effect and enables the training of models with greater number of parameters without overﬁtting. In addition, we discuss which combinations of datasets and model settings might beneﬁt from the adversarial training the most.
Classifying Group Emotions for Socially-Aware Autonomous Vehicle Navigation

We present a real-time, data-driven algorithm to enhance the social-invisibility of autonomous robot navigation within crowds. Our approach is based on prior psychological research, which reveals that people notice and-importantly-react negatively to groups of social actors when they have negative group emotions or entitativity, moving in a tight group with similar appearances and trajectories. In order to evaluate that behavior, we performed a user study to develop navigational algorithms that minimize emotional reactions. This study establishes a mapping between emotional reactions and multi-robot trajectories and appearances and further generalizes the finding across various environmental conditions. We demonstrate the applicability of our approach for trajectory computation for active navigation and dynamic intervention in simulated autonomous robot-human interaction scenarios. Our approach empirically shows that various levels of emotional autonomous robots can be used to both avoid and influence pedestrians while not eliciting strong emotional reactions, giving multi-robot systems socially-invisibility.
VMGuards:A Novel Virtual Machine Based Code Protection System with VM Security as the First Class Design Concern

Process-level virtual machine (PVM) based code obfuscation is a viable means for protecting software against runtime code tampering and unauthorized code reverse engineering. PVM-based approaches rely on a VM to determine how instructions of the protected code region are scheduled and executed. Therefore, it is crucial to protect the VM against runtime code tampering that alters the instructions and behavior of the VM. This paper presents VMGuards, a novel PVM-based code protection system that puts the security of VM as the first class design concern. Our approach advances prior work by promoting security of the VM as the first class design constraint. We achieve this by introducing two new instruction sets to protect the internal implementations of critical code segments and the host runtime environment where the VM runs in. Our new instruction sets not only have an identical code structure as standard virtual instructions, but also provide additional information to allow the VM to check whether the critical internal implementation or the runtime environment is affected. We evaluate our approach by using a set of real-life applications. Experimental results show that our approach provides stronger and more fine-grained protection when compared to the state-of-the-art with little extra overhead.
Tough, bendable and stretchable tactile sensors array for covering robot surfaces

This study proposes a bendable and stretchable tactile sensors array and its data acquisition circuit with the aim of realizing a tough tactile skin for robotic surface. Its pressure sensitive part consists of conductive fabrics and pressure-conductive rubber sheets arranged in a matrix form. The entire pressure sensitive part is covered with silicone rubber, which makes up for not only the weakness to mechanical damage and water wetness but also the lack of restoring force. The data acquisition circuit consists of a small number of electronic components. The experimental result shows that each tactile cell of the sensor can detect normal force of 0.7N ∼ 3N with small hysteresis and high repeatability, the sensor can detect force distribution without inaccurate sensing due to a wraparound current, and the mechanical properties of the sensor are suitable for practical use in tough conditions.
Facial Expression Analysis while Using Video Phone

We have been developing a method for analyzing the facial expressions of a person using a video phone system (Skype) to talk to another person. The video is recorded and analyzed by image processing software (OpenCV) and the newly proposed feature vector of facial expression. The newly proposed facial expression intensity can be used to analyze a change of facial expression. The judgment of speaking is performed in our study. The experimental results show the usefulness of the proposed method.
Fuzzy deception game using ant-inspired meta-heuristics

Deception plays a significant role in many intelligent systems whether natural (insect colonies, animal colonies etc. to human beings) or artificial ones. In order to make artificial deception near the one existing in the real world, fuzzy theory seems a reasonable tool since it is the theory of mind through which the uncertainties can be modeled. In this work a hide and seek problem is considered in a maze imaginable in any form between two robots one of which is trying to deceive the other. Fulfilling the deception, the deceiver is supposed to use two signals (track and smell), the second of which is inspired from natural ants. The deceiver is free to choose whether or not to leave a track on the way and also it is the one who decides on the amount of smell deposited in order to confuse the robot under deception (the mark). After the deceiver decides where to go, the robot under deception (the mark) (on each cross-road) is to decide which path to choose based on a utility function calculated within a fuzzy inference system whose inputs are the value of deception signals. The results show that the robot under deception faces high utilities when coming across the roads the deceiver wants him to enter (less-beneficial roads to the deceiver) and consequently is convinced to do the thing desired by the deceiver.
Electromechanical coupling in nonpiezoelectric materials due to nanoscale nonlocal size effects: Green's function solutions and embedded inclusions

In a piezoelectric material, an applied uniform strain can induce an electric polarization (or vice versa). Crystallographic considerations restrict this technologically important property to noncentrosymmetric systems. It has been shown both mathematically and physically that a nonuniform strain can potentially break the inversion symmetry and induce polarization in nonpiezoelectric materials. The coupling between strain gradients and polarization, and conversely between strain and polarization gradients, is investigated in this work. While the conventional piezoelectric property is nonzero only for certain select materials, the nonlocal coupling of strain and electric field gradients is (in principle) nonzero for all dielectrics, albeit manifesting noticeably only at the nanoscale, around interfaces or in general in the vicinity of high field gradients. Based on a field theoretic framework accounting for this phenomena, we (i) develop the fundamental solutions (Green's functions) for the governing equations, and (ii) motivated by eventual applications for quantum dots, solve the general embedded mismatched inclusion problem with explicit results for the spherical and cylindrical shape. Expectedly, our results for the aforementioned problems are size dependent and indicate generation of high electric fields reaching values of approximately hundreds of kV/m in selected sizes and locations---even in isotropic centrosymmetric nonpiezoelectric materials.
Multi-lingual Argumentative Corpora in English, Turkish, Greek, Albanian, Croatian, Serbian, Macedonian, Bulgarian, Romanian and Arabic

Argumentative corpora are costly to create and are available in only few languages with English dominating the area. In this paper we release the ﬁrst publicly available corpora in all Balkan languages and Arabic. The corpora are obtained by using parallel corpora where the source language is English and target language is either a Balkan language or Arabic. We use 8 different argument mining classiﬁers trained for English, apply them all on the source language and project the decision made by the classiﬁers to the target language. We assess the performance of the classiﬁers on a manually annotated news corpus. Our results show when at least 3 to 6 classiﬁers are used to judge a piece of text as argumentative an F1-score above 90% is obtained.
Annotating Modality Expressions and Event Factuality for a Japanese Chess Commentary Corpus

In recent years, there has been a surge of interest in the natural language processing related to the real world, such as symbol grounding, language generation, and nonlinguistic data search by natural language queries. We argue that shogi (Japanese chess) commentaries, which are accompanied by game states, are an interesting testbed for these tasks. A commentator refers not only to the current board state but to past and future moves, and yet such references can be grounded in the game tree, possibly with the help of modern game-tree search algorithms. For this reason, we previously collected shogi commentaries together with board states and have been developing a game commentary generator. In this paper, we augment the corpus with manual annotation of modality expressions and event factuality. The annotated corpus includes 1,622 modality expressions, 5,014 event class tags and 3,092 factuality tags. It can be used to train a computer to identify words and phrases that signal factuality and to determine events with the said factuality, paving the way for grounding possible and counterfactual states.
End-to-end speech emotion recognition based on neural network

Speech has lots of emotional information and speech emotional recognition is one of the top topic in AI field. Traditional research extracted speech feature first and then to classified the emotions by sorting algorithms. However, all kinds of feature extracting algorithms lost some original speech information to some extent, so the accuracy rate is reduced. In this paper, we present an end-to-end speech emotion recognition system which based on neural network without feature extraction. Experimental testing of the proposed scheme was performed using the DMO-DB (German) and CASIA (Chinese) emotional speech datasets, recognition rates reached 74.12% and 44.5% respectively.
Carnosine Inhibits the Proliferation of Human Cervical Gland Carcinoma Cells Through Inhibiting Both Mitochondrial Bioenergetics and Glycolysis Pathways and Retarding Cell Cycle Progression

Carnosine has been demonstrated to play an antitumorigenic role in certain types of cancer. However, its underlying mechanism is unclear. In this study, the roles of carnosine in cell proliferation and its underlying mechanism were investigated in the cultured human cervical gland carcinoma cells HeLa and cervical squamous carcinoma cells SiHa. The results showed that carnosine exerted a significant inhibitory effect on the proliferation of HeLa cells, whereas its inhibitory action on the proliferation of SiHa cells was much weaker. Carnosine decreased the ATP content through inhibiting both mitochondrial respiration and glycolysis pathways in cultured HeLa cells but not SiHa cells. Carnosine reduced the activities of isocitrate dehydrogenase and malate dehydrogenase in TCA (tricarboxylic acid) cycle and the activities of mitochondrial electron transport chain complex I, II, III, and IV in HeLa cells but not SiHa cells. Carnosine also decreased the mRNA and protein expression levels of ClpP, which plays a key role in maintaining the mitochondrial function in HeLa cells. In addition, carnosine induced G1 arrest by inhibiting the G1-S phase transition in both HeLa and SiHa cells. Taken together, these findings suggest that carnosine has a strong inhibitory action on the proliferation of human cervical gland carcinoma cells rather than cervical squamous carcinoma cells. Mitochondrial bioenergetics and glycolysis pathways and cell cycle may be involved in the carnosine action on the cell proliferation in cultured human cervical gland carcinoma cells HeLa.
Source Apportionment and Risk Assessment of Atmospheric Polycyclic Aromatic Hydrocarbons in Lhasa, Tibet, China

Much attention has been given to the distributions, sources, and health risks of atmospheric polycyclic aromatic hydrocarbons (PAHs) in cities. In this study, a total of 62 suspended particle samples were collected from April 2013 till March 2014 in the city of Lhasa. Positive matrix factorization (PMF) was applied to investigate the source apportionment of 15 priority PAHs, the lifetime carcinogenic risk (LCR) levels of which were assessed. The average annual particle phase PAH concentration was 43.9 ± 60.4 ng m–3. Evident seasonal variations of PAHs were observed, with the highest concentration observed in winter, followed by autumn, spring, and summer. Four- and five-ring PAHs accounted for the predominant proportion (63.3%–84.4%) throughout the year. Correspondingly, gas phase PAHs showed the opposite variations, with the highest and lowest concentrations observed in summer and winter, respectively; also, three-ring PAHs, especially Ace, Acel, and Flu, were the largest contributors. Compositions of particle phase PAHs varied seasonally, with four-ring PAHs contributing more in winter than in summer and five-ring PAHs exhibiting the opposite trend, thereby reflecting the variety of emission sources. PMF analysis showed that biomass combustion (48.4%) and vehicle emissions (27.9%) were the two main sources, followed by coal combustion and the air–surface exchange. These results were consistent with the diagnostic molecular ratios. The benzo(a)pyrene equivalent (BaPeq) concentration of particle phase PAHs ranged from 1.48 to 24.5 ng m–3, which exceeds were higher than the new limit in China (1 ng m–3). The average BaPeq of gas phase PAHs was 6.43 ± 4.15 ng m–3, which was similar to that of particle phase PAHs. The LCR of the total PAHs (9.08 × 10–6) was one time higher than that of the particle phase; however, it was slightly lower than the acceptable level, thereby indicating that atmospheric PAHs in Lhasa pose little or no carcinogenic risk to the local population.