Reviewerly/exHarmony huggingface dataset:
Assessing ‘beyond a reasonable doubt’ without probability: an info-gap perspective

The concept ‘beyond a reasonable doubt’ is fundamental, procedurally and normatively, in legal proceedings. It generates operational criteria for triers’ decisions, and reflects the high social value of protecting innocent individuals. Uncertainty in judicial evidence is prevalent, motivating the concept of ‘beyond a reasonable doubt’. ‘Beyond a reasonable doubt’ is traditionally operationalized with ethical definitions. Recently, qualitative and quantitative probabilistic concepts have been advocated and disputed for defining ‘beyond a reasonable doubt’. Our first claim is that much judicial uncertainty is consistent with Knight’s concept of ‘true uncertainty’, and inconsistent with the normalization and additivity axioms of probability. The prevalence of Knightian uncertainty in judicial evidence results from indeterminism in human affairs as developed by Shackle and Popper. Our second claim is that ‘beyond a reasonable doubt’ can be operationalized with the concept of robustness as developed qualitatively in info-gap decision theory. A trier’s decision is highly robust to uncertainty in the evidence if only very great change in the evidence would undermine the decision. When facing Knightian uncertainty, a decision is acceptable beyond a reasonable doubt if it is highly robust to uncertainty in the evidence. We illustrate the qualitative implementation of this criterion with several examples.
Edge-Semantic Learning Strategy for Layout Estimation in Indoor Environment

Visual cognition of the indoor environment can benefit from the spatial layout estimation, which is to represent an indoor scene with a 2D box on a monocular image. In this paper, we propose to fully exploit the edge and semantic information of a room image for layout estimation. More specifically, we present an encoder-decoder network with shared encoder and two separate decoders, which are composed of multiple deconvolution (transposed convolution) layers, to jointly learn the edge maps and semantic labels of a room image. We combine these two network predictions in a scoring function to evaluate the quality of the layouts, which are generated by ray sampling and from a predefined layout pool. Guided by the scoring function, we apply a novel refinement strategy to further optimize the layout hypotheses. Experimental results show that the proposed network can yield accurate estimates of edge maps and semantic labels. By fully utilizing the two different types of labels, the proposed method achieves state-of-the-art layout estimation performance on benchmark datasets.
A Study on Firefly Algorithm for Breast Cancer Classification

One of the most serious and prominent cancers which affect more women in this world is breast cancer. Various kinds of breast cancer have been reported in medical literature that significantly differ in their capability to spread to other tissues in the human body. Plenty of risk factors have been traced through research though the exact reasons of breast cancer are not yet fully understood. Advances in medicine and technology help to improve the quality of life for breast cancer patients. A lot of contribution especially in the area of artificial intelligence and data mining is done to aid the diagnosis and classification of breast cancer. In order to provide assistance to the doctors, oncologists, and clinicians, neural networks and other optimization techniques serves as a great boon. Here, in this paper, firefly algorithm is utilized effectively as a powerful tool for the analysis and classification of breast cancer. Results show that an average classification accuracy of 98.52% is obtained when firefly algorithm is used for the classification of breast cancer.
Gaussian Membership Function and Type II Fuzzy Sets Based Approach for Edge Enhancement of Malaria Parasites in Microscopic Blood Images

This research presents a three-stage approach. In the first stage, the original image transformed into grayscale image, then normalizes grayscale image using min-max normalization, which performs a linear conversion on the original image data. The second stage calculates the Gaussian membership function on the normalized grayscale image then measure lower membership values and upper membership values using a threshold value. In addition, computed a novel membership function with Hamacher t-conorm using lower and upper membership values on given images. Finally, the median filter applied on these images to obtain edge enhanced microscopic images. The current study is conducted on the microscopic blood images of the malaria parasites. The experimental results compared with Prewitt filter, Sobel edge filter, and rank-ordered filter. The proposed approach is consistent and coherent in all microscopic malaria parasite images with four stages, with average entropy 0.90215 and 59.69% PSNR values, respectively.
Using VR Technologies to Investigate the Flexibility of Human Self-Conception

This chapter will focus on the claim that our self-conception is flexible, such that our thoughts about ourselves do not ultimately determine what kind of thing we are. We will review virtual reality research that seems promising as a means of investigating our self-conception. But we will raise certain key methodological issues with this research in so far as it might serve to demonstrate flexibility. Specifically, these issues concern (1) the possibility that any flexibility demonstrated in the use of the self-concept might simply reflect the flexibility of the imagination; (2) the fact that when we think about our bodies as ourselves, we do not necessarily identify with the body simpliciter, rather our thinking is structured around particular parts.
Motion Detection Algorithm for Surveillance Videos

Locality Sensitive Hashing (LSH) is an approach which is extensively used for comparing document similarity. In our work, this technique is incorporated in a video environment for finding dissimilarity between the frames in the video so as to detect motion. This has been implemented for a single point camera archiving, wherein the images are converted into pixel file using a rasterization procedure. Pixels are then tokenized and hashed using minhashing procedure which employs a randomized algorithm to quickly estimate the Jaccard similarity. LSH finds the dissimilarity among the frames in the video by breaking the minhashes into a series of band comprising of rows. The proposed procedure is implemented on multiple datasets, and from the experimental analysis, we infer that it is capable of isolating the motions in a video file.
Dembowski–Ostrom polynomials and reversed Dickson polynomials

We discuss the problem of classifying Dembowski-Ostrom polynomials from the composition of reversed Dickson polynomials of arbitrary kind and monomials over finite fields of odd characteristic. Moreover, by using a variant of the Weil bound for the number of points of affine algebraic curves over finite fields, we discuss the planarity of all such Dembowski-Ostrom polynomials. Planar Dembowski-Ostrom polynomials have applications in many areas including cryptography and coding theory.
Relation Extraction Using Convolutional Neural Networks

Identifying the relationship between the entities plays a key role in understanding any natural language. The relation extraction is a task, which finds the relationship between entities in a sentence. The relation extraction and named entity recognition are the subtasks of information extraction. In this paper, we have experimented and analyzed the closed-domain relation extraction using three variants of temporal convolutional neural network on SemEval-2018 and SemEval-2010 relation extraction corpus. In this approach, the word-level features are formed from the distributed representation of text and the position information of entity are used as the feature for the model.
Intelligent Intersection: Two-Stream Convolutional Networks for Real-time Near Accident Detection in Traffic Video

In Intelligent Transportation System, real-time systems that monitor and analyze road users become increasingly critical as we march toward the smart city era. Vision-based frameworks for Object Detection, Multiple Object Tracking, and Traffic Near Accident Detection are important applications of Intelligent Transportation System, particularly in video surveillance and etc. Although deep neural networks have recently achieved great success in many computer vision tasks, a uniformed framework for all the three tasks is still challenging where the challenges multiply from demand for real-time performance, complex urban setting, highly dynamic traffic event, and many traffic movements. In this paper, we propose a two-stream Convolutional Network architecture that performs real-time detection, tracking, and near accident detection of road users in traffic video data. The two-stream model consists of a spatial stream network for Object Detection and a temporal stream network to leverage motion features for Multiple Object Tracking. We detect near accidents by incorporating appearance features and motion features from two-stream networks. Using aerial videos, we propose a Traffic Near Accident Dataset (TNAD) covering various types of traffic interactions that is suitable for vision-based traffic analysis tasks. Our experiments demonstrate the advantage of our framework with an overall competitive qualitative and quantitative performance at high frame rates on the TNAD dataset.
Modern lessons’ construction based on the taxonomy of pedagogical objectives and the multiple intelligences theory

Purpose Currently, teachers meet difficulties when design the modern classes so that they respond to the requirements of new educational standards. Thus, the purpose of this paper is to justify the algorithm for applying the “foundations” for designing modern classes of different types and varied subject areas. Design/methodology/approach The author coordinates the “foundations” of constructing modern occupations in various subject areas on the example of lessons for children mastering the basic level of preparation and higher one. As the basis for building classes in modern conditions, the author considers the taxonomy of the pedagogical goals of B. Bloom and G. Gardner’s theory of multiple intelligences (MI). Findings The taxonomy of pedagogical goals can be one of the key foundations in the design of modern occupations of various types; the theory of MI – in the construction of classes of different subject areas. As examples for the argumentation, the author used the educational technologies of “problem education” and “project education.” Practical implications This study is a modest contribution to didactics (the science of theories and learning technologies). The results of the research actualize the work of B. Bloom and G. Gardner in the context of modern requirements for the results and content of education in general and vocational schools. The main provisions of the paper should supplement a number of such works where the solution of modern problems in education is built on the traditions and innovations balance. Originality/value The algorithm of applying the “foundations” in the modern studies’ planning can be used for lessons of various types and different subject areas. Further, research can be built in the context of interpreting the algorithm and testing its effectiveness using examples of other educational technologies.
Detection of Static and Dynamic Abnormal Activities in Crowded Areas Using Hybrid Clustering

In Computer Vision, to monitor the activities, behavior, and other changing information, surveillance is used. Surveillance is used by government organizations, private companies for security purposes. In video processing, anomaly is generally considered as a rarely occurring event. In a crowded area, it is impossible to monitor the occasionally moving objects and each person’s behavior. The main objective is to design a framework that detects the occasionally moving objects and abnormal human activities in the video. Histogram of Oriented Gradient feature extraction is used for the detection of an occasionally moving object and abnormality detection involves in computing the motion map by the flow of motion vectors in a scene that detects the change in movement. The experimental analysis demonstrates the effectiveness of this approach which is efficient to run in real time achieves 96% performance, however, for effective validation of the system is tested with standard UMN datasets and own datasets.
Cryptanalysis of an ElGamal-Like Cryptosystem Based on Matrices Over Group Rings

ElGamal cryptography is one of the most important Public Key Cryptography (PKC) since Diffie-Hellman exchanges was proposed, however these PKCs which are based on the hard problems that discrete logarithm problem and integer factorization problem are weak with advances in quantum computers. So some alternatives should be proposed. Majid Khan et al. proposed two ElGamal-like public-key encryption schemes based on large abelian subgroup of general linear group over a residue ring, however the two schemes were not long before it was proved unsafe by us. Then, Saba Inam and Rashid (2016) proposed an improved cryptosystem which can resist my attack on “NEURAL COMPUTING & APPLICATIONS”. By analyzing the security of the public key cryptography, we propose an improved method of algebraic key-recovery attack in the polynomial computational complexity despiteing the designers’ claim the cryptosystem is optimal security. Besides, we provide corresponding practical attack example to illustrate the attack method in our cryptanalysis, which breaks instances claiming 80 bits of security less than one minute on a single desktop computer.
Adaptive Guidance with Reinforcement Meta-Learning

This paper proposes a novel adaptive guidance system developed using reinforcement meta-learning with a recurrent policy and value function approximator. The use of recurrent network layers allows the deployed policy to adapt real time to environmental forces acting on the agent. We compare the performance of the DR/DV guidance law, an RL agent with a non-recurrent policy, and an RL agent with a recurrent policy in four difficult tasks with unknown but highly variable dynamics. These tasks include a safe Mars landing with random engine failure and a landing on an asteroid with unknown environmental dynamics. We also demonstrate the ability of a recurrent policy to navigate using only Doppler radar altimeter returns, thus integrating guidance and navigation.
How ( Δ&lt;i&gt;t&lt;/i&gt; )&lt;sup&gt;5&lt;/sup&gt; + &lt;i&gt;A&lt;/i&gt;&lt;sub&gt;1&lt;/sub&gt; &amp;#8901; ( Δ&lt;i&gt;t&lt;/i&gt; )&lt;sup&gt;2&lt;/sup&gt; + &lt;i&gt;A&lt;/i&gt;&lt;sub&gt;2&lt;/sub&gt; = 0 Is Generally, in the Galois Sense Solvable for a Kerr-Newman Black Hole Affect Questions on the Opening and Closing of a Wormhole Throat and the Simplification of the Problem, Dramatically Speaking, If &lt;i&gt;d&lt;/i&gt; = 1 (Kaluza Klein Theory) and Explaining the Lack of Overlap …

and Explaining the Lack of Overlap with the Results When Applying the Gauss-Lucas Theorem
The Development of Supervised Motion Learning and Vision System for Humanoid Robot

With the development of the concept of Industry 4.0, research relating to robots is being paid more and more attention, among which the humanoid robot is a very important research topic. The humanoid robot is a robot with a bipedal mechanism. Due to the physical mechanism, humanoid robots can maneuver more easily in complex terrains, such as going up and down the stairs. However, humanoid robots often fall from imbalance. Whether or not the robot can stand up on its own after a fall is a key research issue. However, the often used method of hand tuning to allow robots to stand on its own is very inefficient. In order to solve the above problems, this paper proposes an automatic learning system based on Particle Swarm Optimization (PSO). This system allows the robot to learn how to achieve the motion of rebalancing after a fall. To allow the robot to have the capability of object recognition, this paper also applies the Convolutional Neural Network (CNN) to let the robot perform image recognition and successfully distinguish between 10 types of objects. The effectiveness and feasibility of the motion learning algorithm and the CNN based image classification for vision system proposed in this paper has been confirmed in the experimental results.
The Effects of Explicit/Implicit Instructions on the Development of Advanced EFL Learners’ Pragmatic Knowledge of English: Apology Speech Act

The current study explored the effects of explicit and implicit instructions on the development of advanced EFL learners’ pragmatic knowledge in terms of apology speech act. A total of 10 English native speakers and 40 advanced EFL students participated in this study. The Iraqi Kurdish EFL learners were selected from grade 12 in British International School in Erbil/ Iraq and their level was determined by Oxford Quick Placement Test (OQPT) proficiency test. These 40 participants were divided randomly into two; explicit group (EG) and implicit group (IG) Later, they were provided a Multiple-choice Discourse Completion Test (MDCT) as a pre-test, which consisted of 10 different situations of apology and were based on the participants’ real-life situation, each situation included 3 options and only one answer could be selected which considered to be the most appropriate answer to them. The aim was to show if there were any significant differences between EG and IG. The English native speakers were given the same (MDCT) and were asked to choose the most appropriate answers. After the treatment, (EG) and (IG) were provided the same MDCT as a post-test. The findings revealed that the results of EG and IG were significant and they showed improvements after the treatment, and the EG outperformed the IG in post-test and delayed-test.
Lexical Disambiguation (CKBD): A Tool to Identify and Resolve Semantic Conflicts Using Context Knowledge

The schema matching process is a fundamental step in a schema integration system, and its quality impacts the overall performance of the system. Recently, a large number of schema matching approaches have been developed. Until today, the performance of schema matching is inherently uncertain and requires improvement. The most difficult task is inferring the real-world semantics of data from the information provided by schema labels in their representations. Usually, schemas with identical semantics are represented by different vocabularies and only their own designers can completely understand. A schema may contain synonyms and homonyms words. Therefore, it is necessary to understand how the schema elements are presented; it is often hard to get aware meaning associated with elements names, due to the semantic ambiguity of human language. Semantic ambiguity problem means the capability of being understood in two or more possible senses. Having more than one meaning for an individual schema element would cause confusion in interpretation of schema name. This may affect negatively on the matching result. Therefore, this paper aims to resolve this problem of semantic ambiguity and represent the intended meaning of the schema labels name, by introducing the CKBD (Context Knowledge-Based Disambiguation) approach. The CKBD is obtained by integrating two pieces of context knowledge:  semantic domain and more frequency used into a disambiguation processor.  Finally, the CKBD is implemented and is tested in a real dataset.  The result is deeply grounded in the ability to detect schema name intended meaning.
Two-Wheeled Wheelchair Stabilization Using Interval Type-2 Fuzzy Logic Controller

In this paper, an Interval Type-2 Fuzzy Logic Control (IT2FLC) is proposed to control a two-wheeled wheelchair system which mimics double-links inverted pendulum and known as highly nonlinear, unstable and complex system. The control structures of the two-wheeled wheelchair is based on IT2FLC for balancing and maintaining stability of two-wheeled wheelchair system in the upright position. This paper is aimed to develop a 3-Dimensional (3D) model of two-wheeled wheelchair using a SimWise 4D (SW4D) software, which replace a complex mathematical representation that is obtained using long equation and derivation. The movement of the system is visualized using the SW4D as it is integrated with Matlab Simulink. Simulation results show that the IT2FLC give a good performance in term of tilt angle at zero degree in the upright position.
The information-theoretic value of unlabeled data in semi-supervised learning

We quantify the separation between the numbers of labeled examples required to learn in two settings: Settings with and without the knowledge of the distribution of the unlabeled data. More specifically, we prove a separation by $Θ(\log n)$ multiplicative factor for the class of projections over the Boolean hypercube of dimension $n$. We prove that there is no separation for the class of all functions on domain of any size. Learning with the knowledge of the distribution (a.k.a. fixed-distribution learning) can be viewed as an idealized scenario of semi-supervised learning where the number of unlabeled data points is so great that the unlabeled distribution is known exactly. For this reason, we call the separation the value of unlabeled data.
Framed! Reproducing and Revisiting 150-Year-Old Charts

The Statistical Atlases published by the Census Bureau in the late 1800s utilized a number of novel methods for displaying data. In this paper, we examine the use of framed spine and mosaic plots used in two plates of the Statistical Atlas of 1870. We use forensic statistics to recreate the data using available census information, and then use that data to create framed charts using modern plotting methods. We then examine the effectiveness of the framed charts compared to other alternatives with a user study. The data and code for this study are available online.
Design of Location-Based Waste Collecting on Enviro Andalas Waste Bank

EnviroAndalas Waste Bank is engaged in worth selling dry waste management at Andalas University. Waste bank management activities include customer registration, waste bank transactions and picking-up of customerâ€™s waste. The high number of customers and the transactions result in high workloads for the operators. During picking up customerâ€™s waste, oftentlythe operator has difficulty in finding the pick-up location because the customer verbally gives the location instructions. Therefore the purpose of this study is to analyze, design, build, and test information systems for waste bank management at EnviroAndalasWaste Bank. The system development method used in this study is the waterfall method. In this study, the steps of the waterfall method used are requirements definitions, systems, and software design, implementation, and system testing. Application testing is carried out with black box testing and User Acceptance Testing (UAT) by the user. Blackbox testing of the functions has been in line with the initial system design and user requirements. This study resulted in web and mobile-based application on waste-bank management information system with location features that can be applied to the Enviro Andalas Waste Bank
A Novel Deep Learning Method for Obtaining Bilingual Corpus from Multilingual Website

Machine translation needs a large number of parallel sentence pairs to make sure of having a good translation performance. However, the lack of parallel corpus heavily limits machine translation for low-resources language pairs. We propose a novel method that combines the continuous word embeddings with deep learning to obtain parallel sentences. Since parallel sentences are very invaluable for low-resources language pair, we introduce cross-lingual semantic representation to induce bilingual signals. Our experiments show that we can achieve promising results under lacking external resources for low-resource languages. Finally, we construct a state-of-the-art machine translation system in low-resources language pair.