Reviewerly/exHarmony huggingface dataset:
Specification-Guided Data Aggregation for Semantically Aware Imitation Learning

Advancements in simulation and formal methods-guided environment sampling have enabled the rigorous evaluation of machine learning models in a number of safety-critical scenarios, such as autonomous driving. Application of these environment sampling techniques towards improving the learned models themselves has yet to be fully exploited. In this work, we introduce a novel method for improving imitation-learned models in a semantically aware fashion by leveraging specification-guided sampling techniques as a means of aggregating expert data in new environments. Specifically, we create a set of formal specifications as a means of partitioning the space of possible environments into semantically similar regions, and identify elements of this partition where our learned imitation behaves most differently from the expert. We then aggregate expert data on environments in these identified regions, leading to more accurate imitation of the expert's behavior semantics. We instantiate our approach in a series of experiments in the CARLA driving simulator, and demonstrate that our approach leads to models that are more accurate than those learned with other environment sampling methods.
Supplementary Figures 1-8, Supplementary Tables 1-6 from The Initial Detection and Partial Characterization of Circulating Tumor Cells in Neuroendocrine Prostate Cancer

&lt;p&gt;Figure S1: Examples of a decision boundary that separate class A (red) from class B (blue) in 1 dimension (figure 1A) and 2 dimensions (figure 1B); Figure S2: Schematic of the supervised learning process (A) and leave one out cross validation (B); Figure S3: Single iteration of Leave-One-Out Cross-Validation performed at the blood sample level; Figure S4: An example of concordance of AURKA amplification in tumor and CTCs; Figure S5: (A) Kernel Density Estimate (KDE) curves of the classifier output are plotted for each patient sample colored by their diagnosis: NEPC (red) and CRPC (blue); Figure S6: Cell-level NEPC classifier (A) Receiver-Operating-Characteristic (ROC) curve generated on the classifier's single-cell output after LOOCV; Figure S7: To address whether the CTC classifier is simply stochastic, a reflection of an overall higher CTC count, linearity was assessed with a Pearson's coefficient showing a weak relationship between frequency of NEPC CTCs and total cell count; Table S1: A summary of cell-level features utilized to train Random Forest cell-level classifiers in both the LOOCV and for the classification of CTCs in the test cohort; Table S2: Patient characteristics (discovery cohort) including prior systemic therapies, serum markers including PSA (ng/ml), Chromogranin (ng/ml), NSE (ng/ml) and CTC counts; Table S3: Liver metastases in NEPC vs. CRPC; Table S4: The median concentration of CK-negative and AR-negative CTCs in CRPC, atypical CRPC, and NEPC patients; Table S5: Confusion matrix for the ability of CD56 staining to discriminate patients diagnosed with Small Cell Carcinoma NEPC vs CRPC; Table S6: Example of results from a single tube of blood.&lt;/p&gt;
Image Patch comparison using Convolutional Neural Networks

To encode such a function, we opt for a CNN-based model that is trained to account for a wide variety of changes in image appearance. To that end, we explore and study multiple neural network architectures, which are specifically adapted to this task. We show that such an approach can significantly outperform the state-of-the-art on several problems and benchmark datasets. Comparing patches across images is probably one of the most fundamental tasks in computer vision and image analysis. It is often used as a subroutine that plays an important role in a wide variety of vision tasks. These can range from low-level tasks such as structure from motion, wide baseline matching, building panoramas, and image super-resolution,up to higher-level tasks such as object recognition, image retrieval, and classification of object categories, to mention a few characteristic examples.
Lithological mapping of ophiolitic rocks from southern part of the Sivas Basin (Turkey) using ASTER imagery

The east-west extended Sivas Basin in central-eastern Anatolia is a foreland basin that formed after the obduction of the Tethyan ophiolite during the late Cretaceous and is also a north-verging fold-and-thrust belt. The basement rocks of the basin represent mainly sedimentary (Mesozoic platform-type carbonates) and late Cretaceous Divriği ophiolitic complex. As the ophiolitic rocks are affected by intense tectonic processes, field-based mapping studies require long processes and costs. The study tests to reveal the lithological features of the ophiolitic complex outcropping around the Ulaş district of the Sivas Province using remote sensing methods and techniques. Due to arid climatic conditions and rare vegetation cover in the region, almost all outcrops of the basin rocks can be separated by spectral enhancement methods easily. Band ratio (BR), spectral indices (SI), decorrelation stretch (DS), principal component analysis (PCA), and support vector machine (SVM) on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were used in this research. BR, PCA, DC, and SI techniques clearly distinguish the Divriği Ophiolitic Complex from the basement and cover sedimentary rocks. SVM distinguishes the chromite-bearing dunites from the other ophiolite-related rock units. According to image analysis performances, it has been observed that the rocks of the Divriği ophiolitic complex can be differentiated in more detail compared to a 1/100.000 scaled geological map of the region.
Y-X-Y Encoding for Identifying Types of Sentence Similarity

Determining the semantic similarity of any two arbitrary sentences requires two steps, i.e. sentence encoding and semantic similarity measure. The most important step is to encode a set of sentences into a set of equal-length vectors for similarity measure in forms of classification. Two practical encoding schemes had been proposed, statistical-based direct encoding and pretrained encoding. The first approach lacks considering word correlation and the dimension of encoded vector is very large. For the second approach, it requires an extra training time prior to the classification process. This study compromises the previous approaches by considering shallow neural networks for encoding sentences and classifying entailment relations between two sentences. A set of y-x-y encoder models is proposed where y can be greater or less than x depending on given dataset. Neither encoder models nor their corresponding classifiers are built upon big and complex structure, and hence is suitable for carrying out such task. The encoding scheme is tested with SICK 2014 dataset [1], specially designed for neutral, entailment, and contradiction sentence pairs. Comparison results (neutral 97.1%, entailment 91.1%, contradiction 94.6%) support the possibility of the proposed scheme to sentence similarity measure.
Pathfinding visualizer

Visualizations of algorithms contribute to improving computer science education. The process of teaching and learning of algorithms is sometimes, complex and hard to understand problem. Visualization is a useful technique for learning and analyzing any algorithm easily. Pathfinding refers to computing an optimal route between the specified start and goal nodes. It is an important research topic in the area of Artificial Intelligence with applications in fields such as GPS, Real-Time Strategy Games, Robotics, logistics while implemented in static or dynamic or real-world scenarios. Recent developments in pathfinding lead to more improved, accurate and faster methods and still captivates the researcher's attention for further improvement and developing new methods as more complex problems arise or being developed in AI. A great deal of research work is done in pathfinding for generating new algorithms that are fast and provide optimal path since the publication of the Dijkstra algorithm in 1959.
Facial emotion recognition using hybrid features-novel leaky rectified triangle linear unit activation function based deep convolutional neural network

Facial Expression Recognition (FER) is an important topic that is used in many areas. FER categorizes facial expressions according to human emotions. Most networks are designed for facial emotion recognition but still have some problems, such as performance degradation and the lowest classification accuracy. To achieve greater classification accuracy, this paper proposes a new Leaky Rectified Triangle Linear Unit (LRTLU) activation function based on the Deep Convolutional Neural Network (DCNN). The input images are pre-processed using the new Adaptive Bilateral Filter Contourlet Transform (ABFCT) filtering algorithm. The face is then detected in the filtered image using the Chehra face detector. From the detected face image, facial landmarks are extracted using a cascading regression tree, and important features are extracted based on the detected landmarks. The extracted feature set is then passed as input to the Leaky Rectified Triangle Linear Unit Activation Function Based Deep Convolutional Neural Network (LRTLU-DCNN), which classifies the input image expressions into six emotions, such as happiness, sadness, neutrality, anger, disgust, and surprise. Experimentation of the proposed method is carried out using the Extended Cohn-Kanade (CK+) and Japanese Female Facial Expression (JAFFE) datasets. The proposed work provides a classification accuracy of 99.67347% for the CK+ dataset along with 99.65986% for the JAFFE dataset.
Forensic investigation with digital evidence

To establish the time of death, it focuses on the detection and identification of body fluids at the crime scene, which /is a very important forensic model. There are many methods for estimating the time of death, which in medicine is called Livor Mortis. This paper is mainly focused on forensic medicine by determining the approximate time of death by collecting various bodily fluids. There are number of different approaches that affect the post-mortem appearance of the body. Here, as a first step in the investigation, photographs are taken to ascertain the change in skin color after death caused by the accumulation of blood. It has been noticed that skin color is due to predetermined hemoglobin and red chromospheres present in red blood cells called melanin. They are separated and grouped by an outstanding digital technology called 3D Filter Block Similarity Clustering (3-DFBSC), which is a very good approach for the proposed system where clustering is very easy. With these digital methods, cluster analysis plays an important role in the analysis of image matching methods with which it can establish the time of death with these digital methods.
Now That We Have the Way, Do We Have the Will?

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation SEENA M. SKELTON, KATHLEEN A. KING THORIUS, CRISTINA SANTAMARÍA GRAFF; Now That We Have the Way, Do We Have the Will?. Multiple Voices for Ethnically Diverse Exceptional Learners 1 September 2021; 21 (2): 1–3. doi: https://doi.org/10.56829/2158-396X-21.2.1 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest Search
A Qualitative Study on the Impact of Mobile Technology among Students in Private Higher Education Institutions (PHEIs) in Peninsular Malaysia

The introduction of mobile tools into our classroom spaces ought to be a catalyst for remarkable possibility and genuine change in how we engage learners in the work we pursue together. It should be a moment where we look collectively at what is now possible in how we engage and learn alongside our students and where we do some cultivating that allows us to pull out less timely practices or tools and evolve to what our students need now. It should be a vibrant time. Today?s knowledge-based economy requires that nations equip their citizens with appropriate skills and a demand for university education has continued to soar. In much of Peninsular Malaysia, the rapid increase in university enrollments has been matched with an expansion of the relevant infrastructure, resulting in among other things, adequate classrooms and libraries. There has also been a robust growth in the technology sector globally. This study examines the impact of mobile technology usage among students in PHEIs in Peninsular Malaysia and its implications on quality as well as possible contributions of mobile technology to learning. Challenges aside, the study concludes that mobile technology could be exploited to supplement learning as it enables the student to access academic and research materials from credible sources that are either on free-access or subscribed-for through their home university. The findings also add new understanding as regard to factors influencing students? intention to use mobile technology in higher education.
A Simplified Model of an Interval Type-2 Takagi-Sugeno Fuzzy PID Controller using One-Dimensional Input Space

Deriving analytical structures or mathematical models are quite common to reveal the black-box nature of fuzzy controllers. However, this approach was applied on a two-/three- dimensional input space resulting in complex models of fuzzy controllers. In this paper, we propose a simplified approach to obtain mathematical models of interval type-2 (IT2) Takagi-Sugeno (TS) fuzzy PID controller using one-dimensional (1-D) input space. The fuzzy PID control action is designed by individually modelling and combining fuzzy proportional (F-P), fuzzy integral (F-I), and fuzzy derivative (F-D) control actions on 1-D input space. In this approach, the need of triangular norms and co-norms is eliminated, resulting in a simplified controller model. To show the usefulness of the proposed controller model, we present simulation studies on nonlinear unstable processes such as continuously stirred tank reactor (CSTR). However, as a model free approach, these controllers can also be implemented for other control applications.
Determinants of Sterilization using a Trilemma Policy: A study in Qatar, Kuwait, Indonesia and Japan

Trilemma policy refers to a concept of international economics that states it is impossible to gain all three variables at one time, which is also known as Mundell-Fleming framework. The trilemma policies consist of independent monetary policy, exchange rate stability, and free capital mobility. Pursuing any two of the variables will shadow the other one. Sterilization, on the other hand, reflects the policy set by Central Bank to limit the effects of inflow and outflow of capital money supply. Trilemma policy may influence sterilization, proxy by Central Bank Reserve, as the latter depends on the former. This study determined the relationship between trilemma policy and sterilization by placing focus on four nations, namely Qatar, Kuwait, Indonesia and Japan. Monthly data were gathered from 2009 to 2017. At the end of this research, the findings verified if it is “possible or not possible to maintain all the three variables in the trilemma policy to achieve a stable economic condition by way of sterilization.”
SHEARING STRAIN OF SQUARE CROSS-SECTION SHAFT BY IMAGE ANALYSIS IN LARGE TORSION AFTER AXIAL LOAD

This paper describes the characteristics of the distribution of shearing strain along the cross section when a large torsion after axial load is applied to a test specimen having a square cross section made of natural rubber. As for the measurement of finite strain under large deformation, a method based on image analysis, which can continuously measure a wide range from infinitesimal deformation to large deformation, is effective. Therefore, in the present study, a method by image analysis is adopted as measurement of shearing strain that occurs under large torsion. On taking image data, marking-off lines are preliminarily drawn on the surface by describing equally-spaced parallel lines vertically as well as horizontally, and images of horizontal elements along cross sections at upper position or center position of the test specimen are taken with high-resolution cameras with macro lens. The shearing strain distributions in the transverse section are measured by using the image analysis reflecting the Natural Strain theory. Then, these distributions are compared to the distribution based on the torsion theory of shaft having square cross-section, commonly known as the Saint Venant's torsion theory, and the distribution of modified theory that considers the elongation between gauge points. Moreover, the experimental results in this study are compared with the results of our previous study, which is obtained by applying only large torsion, and the effects of initial axial load on the shearing strain distribution are revealed.
Implementation of haze removal algorithm to enhance low light images

The image is captured in foggy atmospheric conditions, resulting in hazy, visually degraded visibility; it obscures image quality. Instead of producing clear images, pixel-based metrics are not guaranteed. This updated image is used as input in computer vision for low-level tasks like segmentation. To improve this, it introduces a new approach to de-hazing an image, the end-to-end approach, to keep the visual quality of the generated images. So, it takes one step further to explore the possibility of using the network to perform a semantic segmentation method with U-Net. U-Net will be built and used in this model to improve the quality of the output even more.
Specifics of the Application of Cybernetic Methods in Forensic Handwriting Examination

This article discusses the possibilities of applying cybernetic methods in the production of forensic handwriting examinations at the present stage. The authors describe the history, current state and development trends of this direction of mathematical modeling. Analyzes the practical experience of Western and African criminologists in this area of special knowledge application. The peculiarities of the application of methods that are most widely used in Eastern, Western Europe and Africa are described: GLOBALGRAF, CEDAR-FOX, Cybid Graphlog, FLASH ID, WANDA. The key functions and algorithms of the mentioned computer complexes and programs, certain advantages and disadvantages in relation to the traditional qualitative-descriptive method of identification forensic handwriting examination are disclosed. On the basis of the results formed the conclusion about the auxiliary value of cybernetic methods (techniques) and the need for their use in addition to the existing methodological tools through the application of a comprehensive methodology. This format will allow handwriting experts to determine some additional features of handwriting, to automate part of the stages and stages of expert examination, will contribute to its comprehensiveness and objectivization, as well as increase the scientific validity and reliability of the conclusions obtained.
Introducing Refined Agile Model (Ram) in the Context of Bangladesh's Software Development Environment Concentrating on the Improvement of Requirement Engineering Process

The Software Companies of Bangladesh are using different types of agile models for software development. Although theoretically these models are worthy for small and medium projects, in practical case they are not so effective. In doing so, this paper tries to find out why do the agile models not suitable for Bangladesh’s Software Companies and how do the problems that the Software Companies face for using the models can be solved. To reveal the answers, this study is based on survey and interview methods. Findings of this paper show that Bangladesh's Software Companies are facing different problems for implementing traditional agile models, such as, Communicational gap, lack of Documentation, unavailability of Prototype, Customer’s lack of knowledge in the area of IT and many more. The study shows that if the Requirement Engineering Process is perfectly managed and some rules are modified in the traditional agile models, these problems can be solved. In doing so, a new model has been proposed by the study named Refined Agile Model (RAM) which is claimed to be better for Bangladesh rather than the traditional Agile Models. This model proposes a process flow which consists of Prototyping Cycle, Development Iteration Cycle and Additional Development Iteration Cycle. This new model also ensures a Requirement Engineer at Client End, sufficient documentation, preparation of prototype and presentation of frequent Demos. After ensuring these requirements in several real time projects, it was found that those projects were completed more effectively compared to all other old project experiences. Eventually, the paper concludes by mentioning that the Refined Agile Model (RAM) is the best model in the Bangladeshi software environment.
Short-Term Forecast of Photovoltaic Power Generation by Optimized BP Neural Network Based on Meteorological Environment

Taking a 100 MW photovoltaic power station in East China as an instance, this paper analyzes the variation characteristics of power generation and its correlation with meteorology and atmospheric environment. In addition, establishes a short-term power generation forecast method based on improved BP neural network. The research shows that the power generation shows obvious seasonal variation characteristics in a year, with the most power generation in spring and the least in winter. Among the meteorological and atmospheric environmental factors that affect power generation, the total radiation, PM2.5, ambient humidity, PM10, O3, and back temperature, are strongly correlated with power generation. Using BP neural network model and its optimization models (GA-BP) and (PSO-BP) to forecast the power generation at each moment in the future 7 days, which manifests the PSO-BP has higher accuracy than others, and its relative error is reduced by 60.77% less than the traditional means.
Secure multimedia data transfer using crypto and stegano algorithms

Recent developments in Steganalysis have made it difficult to secure personal content, messages, or digital images using steganography. Using Steganalysis can easily reveal the presence of hidden information in storage files. This paper presents a new Steganographic approach to communication between two individuals. The approach presented in this paper uses both Steganographic and cryptographic methods. Cryptography uses Rivest–Shamir–Adleman (RSA) and Steganography use image Steganography to hide data. It also uses the mutual authentication process to satisfy all services in the field of cryptography, i.e., access control, confidentiality, integrity, and authentication. In this way, it can store data more securely. Since it uses the RSA algorithm to protect the data again, it performs Steganography to hide the data in the image. Thus, any other person on the network cannot access the data present on the network. Only the sender and receiver can extract the message from the data.