Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of dependency parser for Amharic sentences

Dependency parsing provides information regarding word relationships and has many applications in natural language processing. Several methods of dependency parsing have been proposed in the literature for English and European languages. No sufficient dependency parsing system is available for Amharic, which is a Semitic and a national language of Ethiopia. Due to its morphological structure and low-resource availability, customizing available dependency parser systems is not efficient for Amharic language. In this paper, a novel dependency parser system is proposed for the Amharic language based on a long-short-term memory (LSTM) classifier in two steps, unlabeled dependency parsing, and relation label assignment. First, an arc-eager transition-action classifier was designed and trained on transition configurations generated from Amharic treebank to predict. Then, the output of the classifier is used by the arc-eager transition algorithm to produce an unlabeled dependency tree. Second, a relation-label classifier was designed and trained on pairs of parts of speech tags of the head and the dependent words from the treebank to assign an appropriate label for the dependency relation. Experiments were conducted on 1574 annotated sentences collected from universal-dependency Amharic treebank (1074) and a treebank that was prepared during this study (500). Both classifiers were tested on 30% of the dataset, and 92% and 81% accuracies were found for the transition-action classifier and relation-label classifier, respectively. The proposed system was also evaluated using an unlabeled and labeled attachment score on 30% of the dataset, and 91.54% unlabeled and 86% labeled attachment scores were found. Our experimental results demonstrate that the proposed system can be used for parsing Amharic sentences and as a preprocessing tool during the development of natural language processing tools.
FABRICATION OF AN ARTIFICIAL DNA NANOMOTOR OF EXTREME RUN LENGTH

Motor proteins are a class of biomolecules which are capable of converting chemical energy to mechanical work. Their functions are not only essential in biological processes, but also have huge potential in nanotechnology applications. However, the study of bio-systems still faces many technical difficulties, and the understanding of bio-motors is very limited. Therefore, fabrication of artificial nano-motors is attracting attention for two purposes: to produce a simplified system for further physical analysis and; to produce more effective motors for specific applications. Prof. Wang Zhisong and his lab members have been successful in fabricating several nano-motors using DNA origami technique. These motors mimic kinasins and other bio-motors, which adopt a hand-over-hand mechanism. Nevertheless, the current products are still incomparable to bio-motors in many aspects, among which is the limited run length. This project aims to produce a new motor with super run length. This was achieved by adopting a new mechanism: the inchworm motor. The design comprised a bridge with two G-quadruplex engines, and two legs which bind to a DNA duplex track to form triple helices. The motor was fully light driven by tethering of azo-benzenes. Azo-benzenes were tethered to the G-quadruplex engines to control the driving force. One of the legs was treated similarly to adjust the relative stability between the two legs. The combination
Facial Expression Recognition by Regional Attention and Multi-task Learning

— Facial expression recognition(FER) is an significant branch of artificial intelligence and plays an indispensable role in optimizing and improving the experience of HCI. Although much progress has been made in this field, there is a need to optimize the accuracy rate of facial expression recognition and the network ’ s generalization ability. This Paper brings forward a new end-to-end region attention and multitask learning network (RAMN) for FER. The method learns the importance of the facial features and combines different numbers of regional features obtained from the neural network for FER. A region loss function (R-loss) and collaborative loss function (C-loss) are used to jointly optimize RAMN. The R-loss can learn the most discriminative region in the facial images, and the C-loss substantially reduces the intra-class differences and increases the intra-class differences in the facial features. Finally, experiments on two uncontrolled datasets (FER2013, RAF-DB) and two controlled datasets (CK +, Oulu-CASIA) to verify the superior performance ability of the proposed method. A contrast with existing methods demonstrates the superiority of the RAMN method for FER.
SUITABILITY OF LORA, SIGFOX AND NB-IOT FOR DIFFERENT INTERNET-OF-THINGS APPLICATIONS

The large-scale implementation of the internet of things (IoT) technologies is becoming a re-ality. IoT technologies benefit from low-power wide area network (LPWAN) systems. These technologies include Long Range (LoRa), Sigfox, and Narrowband IoT (NB-IoT). Numerous networks have already been deployed around the world, which is expected to accelerate the growth of IoT. This thesis discusses the performance of these three prominent LPWAN technologies in the market that have been specifically designed for IoT use. The main idea of LPWAN technologies is to provide wide coverage area using only small amount of base stations and to serve large amount of low-power and low-cost IoT devices. The main purpose of this thesis work is to compare LoRa, Sigfox, and NB-IoT and evaluate their suitability to various IoT applications. The appropriate technology selection is possible through in-depth analysis and technological comparison of LPWAN systems. There are many technological differences among these LPWAN technologies. A single technology may not be able to meet all requirements of all IoT applications. Therefore, some IoT applications can benefit from one technology more than others. The right selection helps in fulfilling the need of IoT application to save cost, time and improve efficiency. In addition to the literature-based suitability evaluation of the aforementioned technologies some practical measurements are performed using commercial off-the-shelf hardware. These measurements consider LoRa and Sigfox user devices in both outdoor and indoor locations. The key performance indicators obtained from the measurements are signal-to-noise ratio (SNR) and received signal strength indicator (RSSI). In addition, also penetration loss from outdoor to indoor is derived. The obtained measurement results were in line with the ones found from the literature. power consumption, coverage, sustainability for real time applications, data rates and quality of services.
Content based Image Retrieval using Deep Learning Technique with Distance Measures

-The use of Convolution neural networks (CNN) with deep learning performed an excellent performance in many applications of image processing. The use of CNN based techniques to extract image features from the final layer and the use of a single CNN structure may be used for identifying similar images. Learning feature extraction and effective similarity comparison comprises the Content-Based Image Retrieval (CBIR). In CBIR feature extraction, as well as similarity measures, play a vital role. The experiments are carried out in two datasets such as UC Merced Land Use Dataset By using a pre-trained model that is trained on millions of images and is fine-tuned for the retrieval task. Pre-trained CNN models are used for generating feature descriptors of images for the retrieval process. This method deals with the feature extraction from the two fully connected layers, which is present in the VGG-16 and VGG19 network by using transfer learning and retrieval of feature vectors using various similarity measures. The proposed architecture demonstrates an outstanding performance in extracting the features as well as learning features without a prior knowledge about the images. By using various performance metrics.
Research on Blind Watermarking Embedding Algorithms of Permutation DCT Domain Medium Frequency Coefficient Based on Image Acquisition

: A blind watermarking algorithm based on the replacement of intermediate frequency coefficients in discrete cosine transform domain (DCT) is introduced. The adaptive embedding method of digital watermarking is studied with this algorithm. The proposed blind watermarking algorithm for replacing IF coefficients in DCT domain not only breaks through the traditional watermarking embedding formulas, such as additive algorithm, but also solves the problem that the extracted watermarking contains DCT coefficients of the original image, thus affecting the detection accuracy, and gives the verification. The algorithm combines the masking property of HVS, so that the embedding energy of watermark information has adaptive characteristics, which ensures that the embedded watermark is robust to some common image processing operations under the premise of invisibility. The experimental results verify the effectiveness of the proposed algorithm.
Anaphora Resolution in Thai EDU Segmentation

: Human knowledge is mostly in the form of unstructured text. Text can be transcribed into various languages such as the Thai language. To extract knowledge from Thai text, natural language tasks such as word segmentation, Elementary Discourse Unit (EDU) segmentation, and anaphora resolution is the needed tasks. Some interesting phenomena such as non-referential anaphora and the ellipsis of the owner are the significant problems that are necessary to resolve before constructing the complete semantic in the Natural Language Processing (NLP) application. The non-referential anaphora must be detected before identifying the referential anaphora to improve the precision of the anaphora resolution. The ellipsis of the owner is also a crucial problem that needs to be resolved to find the complete semantics. This study presents the methodology to resolve the anaphora from Thai EDU segmentation. The methodology is divided into 2 parts: Thai morphological analysis and the anaphora resolution. The ranking model is applied to resolve the reference of anaphora with the features from the surface word, surround word, syntactic information, and ontology. The results show that precision is 0.77, recall is 0.84 and the F1 score is 0.81.
PSA: A Family of Probabilistic Surrogate-Assisted Algorithms for Single-Objective Optimization

In the last two decades, significant effort has been made to solve computationally expensive optimization problems using surrogate models. Regardless of whether surrogates are the primary drivers of an algorithm or improve the convergence of an existing method, most proposed concepts are rather specific and not very generalizable. Some important considerations are the selection of a base-line optimization algorithm, a suitable surrogate methodology, and the surrogate’s involvement in the overall algorithm design. This paper proposes a family of probabilistic surrogate-assisted algorithms (PSA), demonstrating its applicability to a broad category of single-objective optimization algorithms. The concept introduces knowledge from a surrogate into an existing algorithm through a tournament-based procedure and continuing optimization on the surrogate’s predictions. The surrogate’s involvement is determined by updating a replacement probability based on the accuracy from past iterations. A study of four well-known population-based optimization algorithms with and without the proposed probabilistic surrogate-assistance indicates its usefulness in achieving a better convergence. The concept should provide a generic and comprehensive use of surrogates within an optimization algorithm and pave the way for new surrogate-assisted algorithms dealing with challenges in less frequently addressed computationally expensive functions, such as different variable types, large dimensional problems, multiple objectives, and constraints.
MAGNETIC RESONANCE IMAGING (MRI) APPLICATION OF Fe 3 O 4 BASED FERROFLUID SYNTHESIZED BY THERMAL DECOMPOSITION USING POLY (MALEIC ANHYDRIDE -ALT-1-OCTADECENE) (PMAO)

The Fe 3 O 4 fluid synthesis by thermal decomposition method carried out in organic solvents with high boiling temperatures disposes a possibility of creating high-quality nanoparticles with uniform particle size and high degree of crystallization. In this paper, Fe 3 O 4 fluid was prepared by thermal decomposition using poly (maleic anhydride-alt-1-octadecene) (PMAO) as a phase transfer ligand. The crystalline structure, morphology and magnetic property of the as-prepared samples were thoroughly characterized. The results demonstrated that the magnetic Fe 3 O 4 nanomaterial was formed in liquid phase with spinel single phase structure, average size of 13-16 nm, and high saturation magnetization (up to 70 emu/g). Iron oxide (Fe 3 O 4 ) nanoparticles coated with biocompatible poly (maleic anhydride-alt-1-octadecene) (PMAO) were synthesized for use as an MRI (magnetic resonance imaging) contrast agent. The spin-lattice (T1) and the spin-spin (T2) relaxation times of the nuclear spins (hydrogen protons) in aqueous solutions of various concentrations of coated ferrite nanoparticles were determined using a nuclear magnetic resonance (NMR) spectrometer. The MRI image was detected with higher contrast in comparison with that before injecting. By comparing with the MRI images taken in T1 weighted the T2 weighted images are clearer. The MRI images of a rabbit taken by the T2 weighted which shows that our coated ferrite nanoparticles can be used as a T2 MRI contrast agent.
Do Individual Differences Predict Attitudes Towards Recreational Cannabis Legalisation and are these Relationships Mediated by Political Ideology?

We investigate the psychological predictors of support or opposition to recreational cannabis legalisation (RCL), of which little is known. However, support for RCL co-varies with liberalism while opposition co-varies with conservatism. Furthermore, Openness of the Five Factor Model (FFM) of personality and cognitive ability are known to predict liberalism, while Conscientiousness predicts conservatism. We hypothesise that cognitive ability and personality will predict attitudes towards RCL and this relationship will be mediated by Political Ideology. Using an online survey, we measured personality, cognitive ability, Political Ideology, and presented participants with social and economic consequences of prohibition and legalisation models before asking them to endorse their preference. Undertaking mediation analysis, we find that Political Ideology mediates the relationship between Openness, cognitive ability, Agreeableness, and endorsement of RCL. Although Conscientiousness was associated with a lower probability of endorsing RCL, this relationship was not mediated by Political Ideology. Nearly three quarters of participants endorsed RCL, while Past Use of cannabis was the second strongest predictor of endorsement of RCL after liberal Political Ideology. Political Ideology partially accounts for the relationship between individual differences and endorsement of RCL. We speculate that widespread use of cannabis, the further normalisation of use through RCL in other countries, and the wider recognition of the social and economic benefits could lead to RCL becoming a bigger issue in Australia. abstract analytic reasoning problems. Each problem is presented as a 3x3 matrix in which images transform vertically and horizontally. In each problem, the lower right-hand corner of the matrix is blank, and it is the participant’s task to pick which one of eight possible solutions is correct according to the rules of the problem, which are determined by the horizontal and vertical transformations. Participants attempt two practice problems before undertaking the 12 test problems. Participants are scored out of 12, with higher scores indicating higher cognitive ability. Bors and Stokes (1998) report that the APM short-form is less time consuming than the full-length version but correlates highly ( r = .92, p < .001) and has high consistency with α analytic problem-solving &
Comparison of Lingshan Island Formation in Qingdao, Shandong Province and Shuinan Formation in Laiyang Sag

: Constrained by geochronological and paleontological data, this paper conducts a comparative study on the Shuinan Formation and the Qingdao Lingshan Island Formation in the Laiyang Sag from the aspects of paleoenvironment, paleoclimate characteristics, and soft sediment deformation structures. The research results are as follows: (1) The minimum detrital zircon age values of the lower and upper strata of the Lingshan Island Formation are consistent with the minimum detrital zircon age values of the lower and upper strata of the Shuinan Formation within the error range, both belonging to the Early Cretaceous Aptian period; (2) The paleo-salinity of the Shuinan Formation and the Lingshandao Formation gradually decreased as the strata changed from old to new, while the paleo-salinity of the Wawukuang Formation and Zhifengzhuang Formation changed from old to new as the strata changed. (3) Both sets of strata were affected by seismic activity in the early stage of deposition, resulting in the development of soft sediment deformation structures; (4) The types of paleontology in the two sets of strata are the same. Therefore, the authors believe that the layers of the Lingshandao Formation and the Shuinan Formation are comparable, and the special structure, paleoenvironment and paleoclimate characteristics can be used as the basis for stratigraphic comparison and division.
Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost Wearables

Detecting stress when performing physical activities is an interesting field that has received relatively little research interest to date. In this paper, we took a first step towards redressing this, through a comprehensive review and the design of a low-cost body area network (BAN) made of a set of wearables that allow physiological signals and human movements to be captured simultaneously. We used four different wearables: OpenBCI and three other open-hardware custom-made designs that communicate via bluetooth low energy (BLE) to an external computer—following the edge-computingconcept—hosting applications for data synchronization and storage. We obtained a large number of physiological signals (electroencephalography (EEG), electrocardiography (ECG), breathing rate (BR), electrodermal activity (EDA), and skin temperature (ST)) with which we analyzed internal states in general, but with a focus on stress. The findings show the reliability and feasibility of the proposed body area network (BAN) according to battery lifetime (greater than 15 h), packet loss rate (0% for our custom-made designs), and signal quality (signal-noise ratio (SNR) of 9.8 dB for the ECG circuit, and 61.6 dB for the EDA). Moreover, we conducted a preliminary experiment to gauge the main ECG features for stress detection during rest.
Decision trees for COVID-19 prognosis learned from patient data: Desaturating the ER with Artificial Intelligence

Objectives: To present a model that enhances the accuracy of clinicians when presented with a possibly critical Covid-19 patient. Methods: A retrospective study was performed with information of 5,745 SARS-CoV2 infected patients admitted to the Emergency room of 4 public Hospitals in Madrid belonging to Quiron Salud Health Group (QS) from March 2020 to February 2021. Demographics, clinical variables on admission, laboratory markers and therapeutic interventions were extracted from Electronic Clinical Records. Traits related to mortality were found through difference in means testing and through feature selection by learning multiple classification trees with random initialization and selecting the onesthat were used the most. We validated the model through cross-validation and tested generalization with an external dataset from 4 hospitals belonging to Sanitas Hospitals Health Group. The usefulness of two different models in real cases was tested by measuring the effect of exposure to the model decision on the accuracy of medical professionals. Results: Of the 5,745 admitted patients, 1,173 died. Of the 110 variables in the dataset, 34 were found to be related with our definition of criticality (death in <72 hours) or all-cause mortality. The models had an accuracy of 85% and a sensitivity of 50% averaged through 5-fold cross validation. Similar results were found when validating with data from the 4 hospitals from Sanitas. The models were found to have 11% better accuracy than doctors at classifying critical cases and improved accuracy of doctors by 12% for non-critical patients, reducing the cost of mistakes made by 17%.
Encryption Schemes for Securing Cloud-based PHR Systems

— Presently, Personal Health Records are widely used by people not only to store their health details, but also to share their health information among doctors, friends, relatives, medical organizations etc. Health records are now stored and shared with the help of cloud services. The sensitive nature of these health records makes it vulnerable to attacks. To ensure privacy of health information, the best method is to encrypt it. In this paper, Attribute Based Encryption and its variants are studied so that they can be used for developing an efficient health record sharing scheme which is more flexible and scalable.
Combatting Salinity: Evaluation of Tomato Rootstocks Under Mild and Severe Salt Stress

Thanks to its multiple uses; its nutritional characteristics; and pleasing attributes related to touch, taste, smell or feel, tomatoes are the second most important vegetable crop after potatoes in the United States for area planted and total production (USDA, 2020). Being a warmseason crop, tomatoes are grown by Nevada farmers during the summer in open fields, gardens and in protected environments (e.g., hoop houses). Growing vegetables in Nevada’s climate can be challenging because of high evapotranspiration levels resulting from high summer daytime temperatures and low air relative humidity. In addition, salts tend to accumulate in soils from lowquality irrigation water sources and overfertilization, resulting in impaired vegetable production and yields. Groundwater irrigation sources, in particular, can be high in salts in areas where geothermal waters and earthquake faults are common, such as in Nevada (Ayers & Westcot, 1985). Soil electrical conductivity (EC) is a measure of the salts accumulated in soil (USDA, 1999) and affects the productivity of saltsensitive crops when it is higher than 2.0 decisiemens per meter (dS m-1) (Shannon and Grieve, 1999). Most of Nevada’s soils are characterized by an EC that is above this threshold (i.e., between 2.3 and 9.3 dS m-1, see Figure 1). High soil EC tends to increase concentrations of sodium and chloride in the soil even over other salinizing ions such as calcium, and sodium is especially toxic to many plants (Grattan and Grieve, 1999). Moreover, soil salinity lowers the soil water potential, making it more difficult for the plant to take up water and impairing the availability of other nutrients.
Simulation study of different sensing directions in OPM and SQUID MEG

Magnetoencephalography (MEG) measures magnetic fields in the vicinity of the subject’s head. Due to the nature of the magnetic field, it is not the same when measured in different directions. This raises the question, in which direction to place the sensors to measure the largest possible signals. Now that optically pumped magnetometers (OPMs) are being used in MEG, this question is even more relevant since they can be placed arbitrarily on the subject’s head. In this work we made a numerical simulation to check how the noise of spontaneous brain activity affects signal-to-noise ratio of different SQUID MEG sensor configurations and different components of OPM MEG system.