Miracle-dz/UniFAR-FTUs huggingface dataset:
A Heuristic Strategy for Identifying Misclassiﬁed Data Using Classiﬁcation Labels

Cognitive architectures have increasingly incorporated statistical classiﬁers as components for long-lived agents. As these machine learning algorithms receive more training data over the agent’s lifetime, however, prior results stored in long-term memory may become incorrect, leading to false positives and negatives during memory retrieval. This paper proposes a heuristic strategy that can efﬁciently identify such misclassiﬁed data, by leveraging semantic information inherent in the classiﬁcation labels. The key insight is that a classiﬁer-independent distance function can be de-ﬁned on the labels, that correlates with false positive classiﬁcations. We show that this strategy can be applied to color naming, to image recognition, and to text classiﬁcation, in all cases out-performing baselines. Implemented in a cognitive architecture, the heuristic halves the number of re-classiﬁcations required to retrieve the desired knowledge, thus building on and complementing the existing memory mechanisms.
Searching for Semantic Knowledge: A Vector Space Semantic Analysis of the Feature Generation Task

A recent neuropsychological study found that amnesic patients with hippocampal damage (HP) and severe declarative memory impairment produce markedly fewer responses than healthy comparison (CO) participants in a semantic feature generation task (Klooster and Duff, 2015), consistent with the idea that hippocampal damage is associated with semantic cognitive deficits. Participants were presented with a target word and asked to produce as many features of that word as possible (e.g., for target word “book,” “read words on a page”). Here, we use the response sequences collected by Klooster and Duff (2015) to develop a vector space model of semantic search. We use this model to characterize the dynamics of semantic feature generation and consider the role of the hippocampus in this search process. Both HP and CO groups tended to initiate the search process with features close in semantic space to the target word, with a gradual decline in similarity to the target word over the first several responses. Adjacent features in the response sequence showed stronger similarity to each other than to non-adjacent features, suggesting that the search process follows a local trajectory in semantic space. Overall, HP patients generated features that were closer in semantic space to the representation of the target word, as compared to the features generated by the CO group, which ranged more widely in semantic space. These results are consistent with a model in which a compound retrieval cue (containing a representation of the target word and a representation of the previous response) is used to probe semantic memory. The model suggests that the HP group's search process is restricted from ranging as far in semantic space from the target word, relative to the CO group. These results place strong constraints on the structure of models of semantic memory search, and on the role of hippocampus in probing semantic memory.
STUDIES ON PHARMACOGNOSY , MICROMORPHOLOGY AND HISTOCHEMICAL LOCALIZATION OF FEW PHYTOCHEMICALS IN MEDICINAL PLANTS IN THE LATERITIC BELT OF WEST BENGAL

The present study was focused to analyze some phytochemicals along with its Pharmacognosy, micromorphology and histochemical localization of those phytochemicals in different economically important parts in relation to drug production in three important medicinal plants in lateritic belt of West Bengal, India. Among the plants, Holarrhena antidysenterica Wall. (Apocynaceae) was found an adjunctive therapy for Inflammatory Bowel Disease (IBM); Paederia foetida Linn. (Rubiaceae) was shown useful for diarrhoea, piles, gout etc. and Rauwolfia serpentina (L) Benth ex Kurz. (Apocynaceae) was noticed as very important traditional medicine to cure Rheumatic pain, snake and scorpion bite and treated a number of diseases like hypertension and mental disorder. The presence of different phytochemicals like alkaloid, starch, tannin, reducing sugar, protein, flavonoids and amino acids were localized through colourization using different reagents. These were further confirmed through colour development due to the specific chemical reaction tests. The phytochemicals were identified prominently in different locations of the stem, leaf and root of H. antidysenterica, P. foetida and R. serpentina respectively. Observation on micromorphological features revealed the standardization of these traditional drug sources.
Visual Drone Detection and Tracking for Autonomous Operation from Maritime Vessel

To allow incorporation of autonomous Unmanned Aerial Vehicles (UAV's/drones) into maritime military operations, it is critical to be able to accurately localize the UAV with respect to the moving maritime vessel during the take-off and landing phases. This work addresses the study and implementation of a visual detection, tracking and three-dimensional positioning method for a specific drone from a moving maritime vessel. The YOLOv5 detector and the OceanPlus tracker have been trained on a custom dataset with good performance in accuracy and processing time. The drone's position with respect to the vessel is estimated by applying stereo triangulation to the centres of the bounding boxes returned by the object detectors and trackers. The performance of the proposed positioning method was evaluated in a realistic simulated environment in the Unreal Game Engine. The proposed method allows detection, tracking, and positioning of a target drone at ranges exceeding 100m while achieving positioning errors below 10cm during landing phases.
A New Approach for Classification of Prices in the Electricity Market using Core Vector Machine

In this paper, an accurate and efficient classification of electricity prices using Core Vector Machine (CVM) has been presented. CVM is used to classify the prices into low and high classes efficiently. One of the most important aspects for an effective electricity price classification is the proper selection of training features. This paper investigates the use of a filter method for feature selection process. The CVM along with feature selection algorithm is tested for classification of electricity prices on Austria market and test results have been obtained. Simulation results obtained using CVM not only offer a very reliable price classification, but also provide a quantitative level of confidence for price classification.
Fire Prediction Technique using DNN-based Supervised Manifold Learning and Hierarchical Clustering Analysis

Various studies on the prevention of fire accidents have recently been conducted, but they remain in the form of summary analysis or statistical analysis by region. If we could know whether there was a fire accident at the level of each building, we would be able to prevent fire accidents more precisely by targeting buildings in the fire inspection area. This paper proposes a method of manifold learning for predicting a particular group of fire-hazard buildings. However, when unmodified raw data is used for clustering, it is not possible to know which feature is related to fire accidents, and moreover we have the curse-of-dimensionality problem. To solve these problems, we use the deep neural network (DNN) for supervised manifold learning to transform this raw data into low-dimensional space. The DNN has a simple structure consisting of only a fully-connected layer. Our proposed method shows better performance than conventional clustering methods that use the supervised dimensionality reduction. In addition, we investigated the characteristics of sub-clusters within a target cluster through hierarchical cluster analysis.
Robust Regression Procedure for Model Fitting with Application to Image Analysis

The computer vision communities often employ robust methods for model fitting such tasks are undertaken by regression analysis (Rousseeuw and Leroy (1987)). Robust methods with high break down points have been used in situations where the data contains outliers. The outliers namely, uniform outliers and clustered outliers are affect the model fitting, especially clustered outliers often bring negative effects to the results. Most established theories are assuming that outliers are uniformly distributed. A good robust estimator should be able to resist the influence of all types of outliers. Many robust methods deal with uniform outliers and fails to produce the reliable results while data with clustered outliers (Wang and Suter (2003)). In this regard, the author has reviewed existing procedures (Huber (1964), Rousseeuw (1984), Gander et al. (1994)) and proposed a procedure in order to tolerate the clustered outliers while performing computer vision tasks especially in model fitting. The performance of the proposed procedure is demonstrated in the context of model fitting with application to image analysis.
LINEAR INVERTED PENDULUM MODEL AND SWING LEG DYNAMICS IN BIPED ROBOT WALKING TRAJECTORY GENERATION by UTKU SEVEN

Expectations of people and researchers from robotics have changed in the last four decades. Although robots are used to play their roles in the industrial environment, they are anticipated to meet social demands of people in daily life. Therefore, the interest in humanoid robotics has been increasing day by day. Their use for elderly care, human assistance, rescue, hospital attendance and many other purposes is suggested due to their adaptability and human like structure. Biped reference trajectory generation is a challenging task as well as control owing to the instability trend, non-linear robot dynamics and high number of degrees of freedom. Hence, the generated reference trajectories have to be followed with minimum control interference. Linear Inverted Pendulum Model (LIPM) is used to meet this demand which assumes the body as a falling point mass connected to the ground with a massless rod. The Zero Moment Point (ZMP) is a stability criterion for legged robots which provides a more powerful, stable reference generation. With the assistance of this methodology, advanced Linear Inverted Pendulum Models are implemented. This thesis aims to improve the applicability of the versatile and computationally effective LIPM based reference generation approach for the robots with heavy legs. It proposes a swing-leg gravity compensation technique based on a two-mass linear inverted pendulum model which is simulated on a discrete state space model. LIPM modeling is implemented by switching between one-mass and two-mass models during double support and single support phases, respectively. The joint trajectories are then obtained by inverse kinematics and PID controllers are employed independently at joint level for locomotion. The effectiveness of the generated reference trajectories is verified by simulation. The reference generation and control algorithm is tested with a 3-D full dynamic simulator on the model of a 12 DOF biped robot. Results indicate better performance of the one-mass-twomass switching LIPM over the one-mass LIPM.
Data Mining Application for Exploring the Relationship between Addiction and Depression

In recent years, increasing the number of people with addiction and depression has become one of the major challenges in our country. This has a very important role in increasing the number of deaths, suicides, and murders caused by drug use in the society, in addition to the high economic burden due to the rehabilitation and treatment costs, which are imposed to the economic system of the community. Therefore, creating a method or methods for identifying those at risk of developing any of the listed diseases (addiction or depression) and considering preventive measures is one of the important requirements, which prevents the waste of medical expenses and financial or life loss damages in the community. In this paper, the data related to the patients including the results of clinical and para-clinical tests, personal information (such as gender, age, education level, place of residence, economic status, etc.) were collected. Then, data mining techniques such as clustering, discovering forum relationships between various variables (properties) were applied on these data. Finally, a model will be presented to any of the diseases (addiction or depression) with a high degree of reliability and accuracy to help clinicians to identify individuals at high risk. In addition, the relationship between the two diseases can be identified by creating forum rules. This model can be used as a medical assistant or worker in remote or disadvantaged areas in the case of implementing in the form of a smart system.
Understanding Lateral Marsh Edge Erosion with Terrestrial Laser Scanning (TLS)

Coastal wetlands are a crucial buffer zone between land and sea but lateral erosion threatens their long-term sustainability. Better understanding of the forces leading to lateral marsh retreat will benefit the assessment of management options applied to mitigate the erosion. Terrestrial Laser Scanning (TLS), Light Detection and Ranging (lidar), and associated technologies are increasingly being used to assess this erosion. The central objective of this study was to identify a methodology for measuring marsh edge erosion with a TLS and correlate that erosion with exposed roots and incident wave energy. We quantified edge erosion across multiple temporal and spatial scales using a TLS, showing greater than one meter of lateral erosion over a 318-day period. We then evaluated the relationships between the erosion and incident wave energy along with erosion and vegetation roots. Wave height and erosion was strongly related (r2 = 0.99), while vegetation roots did not show an apparent effect. We discuss the challenges that arise from using TLS equipment, TLS data sets, and the use of voxels to measure marsh edge erosion.
1 Proof-theoretic Foundations of Normal Logic Programs

ere are several semantics based in logic programming for negation as failure. ese semantics can be realized with a combination of induction and coinduction, and this realization can be used to develop a goal-directed method of computing models. In essence, the dierence between these semantics is how they resolve the unstratied portions of a program. In this paper, while restricting ourselves to the propositional case, we show how a semantics is a mixture of induction and coinduction, and how we can use coinduction to resolve the cycles formed by the rules in a program. We present denotational semantics based on a xed point of a function, and show its equivalence to the use of induction and coinduction. We take a look at the dierent ways a semantics may resolve cycles, and show how to implement two popular semantics, well-founded and stable models, as well as costable model semantics. Finally, we present operational semantics as a parametrized goal-directed algorithm that allows us to determine how cycles are resolved.
Mobile Microscope supported by whirling Centrifuge for the effective detection of Pathogenic cells

One of the most basic and powerful tools in all of science and medicine is the light microscope, the fundamental device for laboratory as well as research purposes. With the improving technology, the need for portable, economic and user-friendly instruments is on high demand. The light microscope though an effective device, fails to live up to the emerging trend. And adequate access to healthcare, is not widely available, especially in developing countries. The most basic step towards the curing of a malady is the diagnosis of the disease itself. This challenge is solved with the most common device, Cell Phones, which prove to be the immediate solution for most of the modern day needs with the development of wireless infrastructure allowing us to compute and communicate on the move. This opened up the opportunity to develop novel imaging, sensing, and diagnostics platforms using mobile phones as an underlying platform to address the global demand for accurate, sensitive, cost-effective, and field-portable measurement devices for use in remote and resource-limited settings around the world. A new approach is proposed wherein the camera of a mobile phone would be sufficient to study the blood samples and check for the presence of malarial parasites.
Phyllanthus amarus possesses malarial curative and pancreatic tonic potentials in experimental mice

Phyllanthus amarus has been observed to be protecti ve against malarial-induced free radical generation a d oxidative damage to the RBCs, liver and brain by in hibiting plasmodial growth. In this study, the cura tive and pancreatic tonic potentials of P. amarus in experim ntal mice were undertaken by assessing antiplasmod ial activity and associated mice survival rate (curative), and p ancreatic antioxidant capacity, oxidative damage, b ta-cell function and microstructural changes (tonic). Resul ts show that P.berghei malarial parasite infection caused oxidative damage to the pancreas. P. amarus malaria l curative ability compared well with the standard chloroquine treatment. It significantly (P<0.05) enhanced pancr eatic antioxidant defense capacity and beta-cell fu nction but reduced oxidative damage when compared with the con trol, malarial infected and chloroquine-treated gro ups. P. amarus also invigorated the pancreas of the malaria infected mice as evidenced by the microstructural analysis. P. amarus malarial curative ability is associated with pancreatic cell replenishment.
Optimizing Precision Genome Editing through Machine Learning

References Chawla, N. V. , Bowyer, K. W. , Hall, L. O. , & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321-357. Sharon, E. , Chen, S. A. A. , Khosla, N. M. , Smith, J. D. , Pritchard, J. K. , & Fraser, H. B. (2018). Functional genetic variants revealed by massively parallel precise genome editing. Cell, 175(2), 544-557 1Department of Biology, 2Department of Biomedical Informatics, 3 Department of Psychiatry and Behavioral Science
The mesolayer of attached eddies in wall-bounded turbulent flows

It has recently been reported that the outer peak in the secondorder statistics of the streamwise velocity depends on the Reynolds number. Starting from this puzzling observation, here I propose that the streamwise velocity component of each of the energy-containing motions in the form of Towsnend’s attached eddies exhibit innerscaling nature in the region close to the wall (see figure 1). Some compelling evidence on this proposition has been presented with a careful inspection of scaling of velocity spectra from direct numerical simulations and a linear analysis with an eddy viscosity. It is shown that this behavior can emerge due to inhomogeneous turbulent dissipation in the wall-normal direction, and also enables one to explain the Reynolds-number-dependent behavior of the outer peak as well as the near-wall penetration of the large-scale outer structures in a consistent manner. Extension of this concept to Townsend’s attached eddy hypothesis further reveals that the selfsimilarity in the streamwise velocity of the attached eddies would be theoretically broken in the region close to the wall.
CHARACTERIZATION OF POLY-Si 70 % Ge 30 % FOR SURFACE MICROMACHINED THERMOPILES

The paper presents the material characterization methods and results of poly-Si70%Ge30% from the perspective of its thermoelectric applications. In view of this application, relevant properties, namely electrical resistivity, Seebeck coefficient, thermal conductivity and specific contact resistance, have been measured on both p-type and n-type poly-Si70%Ge30% prepared by Low Pressure Chemical Vapor Deposition (LPCVD) and Rapid Thermal Chemical Vapor Deposition (RTCVD). The overall thermoelectric properties, as characterized by the figure-of-merit, are reported. Further optimization can be approached by fine tuning the doping concentration and introducing an additional heavily doped thin layer on top of poly-Si70%Ge30%.
FORMAL GROUNDED THEORY OF THE STRATEGY-MAKING PROCESS !

The “unplugged” section seeks to experience new forms of book reviews. We regularly grant a wild card to a world-class scholar to review his/her own Classic. In “My own book review”, authors will tell us the story of "what I was trying to do" with sometimes some auto-ethnographic considerations. By recounting the building process of one seminal research with a contemporary lens, they may give some insights for the current craft of research and also share with us renunciations, doubts and joys in their intimate writing experience.
Autonomous vehicle control: Exploring driver modelling conventions by implementation of neuroevolutionary and knowledge-based algorithms

In this paper an investigation of driver modelling conventions is presented. The goal was to compare traditional driver modelling with machine learning, to nd indications of when one approach could be preferred over the other. This was done by implementing some representatives of the di erent approaches and evaluating them in the same conditions. The traditional approach was represented with one well established model by Sharp et al., as well as one self made aim point model. Both of these required a path planner and velocity control, that were also designed by the authors themselves. The machine learning approach was represented by neuroevolution, an alternative technique for solving reinforcement learning problems, and speci cally the method called NEAT. The results showed that all implemented methods were able to solve the task, but in the speci c scenario and with the current amount of training the two traditional models were superior to the evolved neural network. Similarities and potential reasons for di erences between the models are discussed, as well as some identi ed advantages and disadvantages to both approaches.
Variational Encoder-Decoder for Joint Modality Completion and Segmentation p . In Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

We propose a new deep learning method for tumour segmentation when dealing with missing imaging modalities. Instead of producing one network for each possible subset of observed modalities or using arithmetic operations to combine feature maps, our hetero-modal variational 3D encoder-decoder independently embeds all observed modalities into a shared latent representation. Missing data and tumour segmentation can be then generated from this embedding. In our scenario, the input is a random subset of modalities. We demonstrate that the optimisation problem can be seen as a mixture sampling. In addition to this, we introduce a new network architecture building upon both the 3D UNet and the Multi-Modal Variational Auto-Encoder (MVAE). Finally, we evaluate our method on BraTS2018 using subsets of the imaging modalities as input. Our model outperforms the current state-of-the-art method for dealing with missing modalities and achieves similar performance to the subset-specific equivalent networks.
Modelling Legal Cases and Legal Rules in conjunction with JURIX 2010 December 15 , 2010 University of Liverpool Liverpool , United Kingdom

This paper reports on a case study in which the use of the Rationale software was investigated to analyse the argumentation structure of a Dutch expert witness report in a criminal case. The underlying motivation of the case study was to explore the usefulness of argumentation visualisation software for increasing a judge’s understanding of expert reports and for assisting him or her in asking the proper critical questions to the expert. By way of an initial exploration of this usefulness, an expert report was analysed with the Rationale software. The visualisation was informally discussed with a legal expert, who was generally positive but also expressed some concerns and expected that the main usefulness of the tool is in training and education of judges.