Reviewerly/exHarmony huggingface dataset:
Design of a Quantum One-Way Trapdoor Function

Of late security has become a key concern of data transmission mechanism over a communications channel. In an asymmetric cryptographic system, a public key is shared across an insecure medium. This makes the data exchange vulnerable to potential threat from various attackers. This paper proposes the design of a one-way trapdoor function built upon a quantum public key and a classical private key-based encryption–decryption technique of the secret message. The mapping between numbers used in the classical paradigm and their corresponding quantum states is established through the proposed quantum one-way trapdoor function.
Estimation of Missing Human Body Parts Via Bidirectional LSTM

In this paper, a bi-directional long-short term memory (LSTM) based approach is proposed for the estimation of missing body parts in a human pose estimation context. Accurate human pose estimation is often a key component for accurate human action and activity recognition. The key idea of our algorithm is to learn the temporal consistencies of the human body poses between previous and subsequent frames. This helps in estimating missing body parts and improves the general smoothness of the pose detection results. The approach acts as a post-processing step after the application of any off-the-shelf body part detector and has been evaluated on the PoseTrack dataset for both validation and testing sequences. The results show consistent improvement in the detection across all body parts.
Preliminary Results and Analysis Independent Core Observer Model (ICOM) Cognitive Architecture in a Mediated Artificial Super Intelligence (mASI) System

This paper is focused on some preliminary cognitive and consciousness test results of using an Independent Core Observer Model Cognitive Architecture (ICOM) in a Mediated Artificial Super Intelligence (mASI) System. These preliminary test results including objective and subjective analyses are designed to determine if further research is warranted along these lines. The comparative analysis includes comparisons to humans and human groups as measured for direct comparison. The overall study includes a mediation client application optimization in helping preform tests, AI context-based input (building context tree or graph data models), intelligence comparative testing (such as an IQ test), and other tests (i.e. Turing, Qualia, and Porter method tests) designed to look for early signs of consciousness or the lack thereof in the mASI system. Together, they are designed to determine whether this modified version of ICOM is a) in fact, a form of AGI and/or ASI, b) conscious and c) at least sufficiently interesting that further research is called for. This study is not conclusive but offers evidence to justify further research along these lines.
A Protocol for Preventing Transaction Commitment without Recipient's Authorization on Blockchain

In recent years, blockchain is known as a decentralized secure digital ledger of economic transactions. In this paper, we point out a new issue by abusing published information with relation to recipients on blockchain. Then, we propose a solution to our discovered issue.
An Experimental Study of a Modified Version of Quicksort

In the present work, a certain modified version of quicksort, Quicksort_wmb [1], has been taken into consideration. The authors of this new algorithm have claimed that the worst-case complexity of this algorithm is θ(nlogn) which is, in fact, the best-case time complexity for ordinary quicksort. The author has also given examples of its application on some random arrays. Our aim is to study the performance of Quicksort_wmb on input elements drawn from various continuous and discrete probability distributions. The performance is measured in terms of the number of comparisons the algorithm makes to sort the whole array. Here, we have assumed that comparisons are the dominant computer operations, i.e., the most time-taking and therefore performance determining operation in the algorithm. A number of common probability distributions—both continuous and discrete—were simulated to constitute the elements of a random unsorted list of numbers. Next, the aforementioned modified version of quicksort was applied on these arrays to sort the numbers. The number of comparisons required to sort the list of numbers was recorded for each distribution. The results obtained were very interesting. The continuous distributions were sorted faster than the discrete ones by the algorithm, the reason for which, after further investigation, was found to be the existence of ties in discrete distributions, thus providing evidence that this modified version of quicksort is sensitive to ties. The sensitivity of quicksort to ties is not new. What is interesting is that the sensitivity to ties remains irrespective of the improvement.
Intrusion Detection Using Growing Hierarchical Self-Organizing Maps and Comparison with other Intrusion Detection Techniques

Intrusion detection systems (IDS) based on machine learning (ML) can be used to detect anomalies in data traffic. Common challenges for IDSs are low detection rates, high false alarm rates, and the need to process large amount of data. In order to overcome these challenges various types of supervised, semi-supervised and unsupervised ML methods are being widely researched. However, the need for high-performance processing capabilities in order to perform the calculations restricts the use in industrial control systems (ICS) with usually small embedded processing elements. In this paper, a comparison of key ML methods is carried out that focuses on data traffic and processing requirements for the application to ICS. Furthermore, a variant of growing hierarchical self-organizing maps (GHSOM) is described that proposes a new method to detect anomalies based on the GHSOM behaviour. The influence of the GHSOM parameters on the detection ratio of different kinds of attacks on a production line for the proposed method is examined. To detect attacks on the ICS, growth criteria of the GHSOM have been applied as alarm generating conditions. The comparison of the performance of GHSOM, Feedforward Artificial Neural Network (ANN), Support Vector Machine (SVM) and Local Outlier Factor (LOF) took place based on receiver operating characteristics of the classification between normal and anomalous data captured from a real industrial line testbed. The proposed method has shown comparable classification results with one-class SVM and LOF as novelty detection method in the studied case scenario.
Assessing information interaction to improve library ebook collections and services

That university students encounter issues and experience frustration when using ebooks is well documented, but more research into the specific issues they encounter is needed. This study focused on how successfully undergraduate and graduate students use ebooks and whether there are measurable differences between the two groups. Researchers employed user testing methodologies, including task analysis and talk aloud protocol, to research student interaction with the content and structure of ebooks on two platforms: ProQuest Ebook Central and EBSCO eBooks. Findings include suggested improvements for ebook platforms, considerations for collection development librarians, and areas of focus for library instructors.
Image processing for quantitative measurement of e/m in the undergraduate laboratory

Abstract In conventional e / m experiments performed in undergraduate laboratories, generally a simple mechanical ruler is used to measure the radius of the circular trajectory of the electrons. But, the measurement error in this technique is mostly big and varies from one student to another. As an alternative to a mechanical ruler, we proposed a more accurate and a more precise technique for measuring the radius. This technique is based on image processing and it is well known that the image processing is one of the more powerful techniques for precise measurements in physical experiments. ImageJ software was used for image processing and for taking photographs of the circular trajectory of the electrons in the e / m experiment. ImageJ is a java-based image processing program which was developed by the National Institute of Health of USA. The photographs of the trajectory were taken by using a generic webcam connected to the computer and it was observed that the trajectory was not visible for a one shot photograph which is due to the low intensity of the light coming from the trajectory. By taking multiple photographs of the trajectory at the same time and adding these photographs to each other, the trajectory on the photograph became visible. It was concluded that the accuracy of the measuring the radius of the circular trajectory could be increased by using the image processing and it was found that the relative error made in this measurement is about 0.5% which is 10 times smaller than the relative errors made in conventional measuring techniques performed by a simple mechanical ruler (around 5%). This result directly increases the precision and the accuracy of measuring the e / m ratio of the electrons in undergraduate physics laboratories.
Using Metrics in the Analysis and Synthesis of Reliable Graphs

The interaction of intelligent agents implies the existence of an environment to support it. The usual representations of this environment are graphs with certain properties. Reliability is one of the most important characteristics of such graphs. Traditional metrics, i.e. the usual shortest paths and minimal cuts, form the basis of the traditional measures of reliability. The analyzing and synthesizing of reliable graphs using the Euclidian metric are described. The Euclidian metric allows us to achieve better results in doing this compared to the cases of using traditional metrics. The described approach can be used in the analysis and synthesis of the environment supporting the intercommunication of intelligent agents in conditions of limited resources to organize the structure of this interaction.
Location Estimation for Light Field Microscopy based on Convolutional Sparse Coding

In this work, we propose an algorithm to estimate the depth location of objects from lightfield microscopy data by leveraging the sparsity of Epipolar Plane Images (EPIs) and convolutional sparse coding.
The Application of Student Participation in the Design of Virtual Reality Educational Products

With the development of computer hardware and human interface devices, Virtual Reality (VR) has become a commonly adopted educational tool. In recent years, changes have taken place in technological innovation, with principles of user-centered design and human-computer interaction behavior receiving more attention. This paper presents the application of student participation in the design of VR educational products. By analyzing the data on students who use VR educational products, this paper investigates the relationship between educational objectives and the audiovisual experience, usability, exploratory nature, direction and feedback, and enjoyability. A method for involving students in the design of VR educational products is proposed and subsequently adopted in the development of VR teaching on Beijing Subway maintenance. Hence, the significance of VR educational products is demonstrated.
Fidelity: A Property of Deep Neural Networks to Measure the Trustworthiness of Prediction Results

With the increasing performance of deep learning on many security-critical tasks, such as face recognition and malware detection, the security issues of machine learning (ML) have become increasingly prominent. Recent studies have shown that deep learning is vulnerable to adversarial examples: carefully crafted inputs with negligible perturbations on legitimate samples could mislead a deep neural network (DNN) to produce adversary-selected outputs while humans can still correctly classify them. Therefore, we need an additional measurement on the trustworthiness of the results of a machine learning model, especially in adversarial settings. In this paper, we analyse the root cause of adversarial examples, and propose a new property, namely fidelity, of machine learning models to describe the gap between what a model learns and the ground truth learned by humans. One of its benefits is detecting adversarial attacks. We formally define fidelity, and propose a novel approach to quantify it. We evaluate the quantification of fidelity of DNNs in adversarial settings on two neural networks. Our preliminary results show that involving the fidelity enables a DNN system to detect adversarial examples with true positive rate 97.7%, and false positive rate 1.67% on a studied DNN model.
On the Average Case of MergeInsertion

Abstract MergeInsertion, also known as the Ford-Johnson algorithm, is a sorting algorithm which, up to today, for many input sizes achieves the best known upper bound on the number of comparisons. Indeed, it gets extremely close to the information-theoretic lower bound. While the worst-case behavior is well understood, only little is known about the average case. This work takes a closer look at the average case behavior. In particular, we establish an upper bound of $n \log n - 1.4005n + o(n)$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> <mml:mi>log</mml:mi> <mml:mi>n</mml:mi> <mml:mo>−</mml:mo> <mml:mn>1.4005</mml:mn> <mml:mi>n</mml:mi> <mml:mo>+</mml:mo> <mml:mi>o</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:math> comparisons. We also give an exact description of the probability distribution of the length of the chain a given element is inserted into and use it to approximate the average number of comparisons numerically. Moreover, we compute the exact average number of comparisons for n up to 148. Furthermore, we experimentally explore the impact of different decision trees for binary insertion. To conclude, we conduct experiments showing that a slightly different insertion order leads to a better average case and we compare the algorithm to Manacher’s combination of merging and MergeInsertion as well as to the recent combined algorithm with (1,2)-Insertionsort by Iwama and Teruyama.
A Hierarchical Characterization of Knowledge for Cognition

A modestly formal systematic representation for "knowledge" is presented in the context of structured cognition. This representation places knowledge artifacts ("data", "facts", "rules", etc. to be defined later) into a hierarchy. This hierarchy aligns naturally with "stages" or "levels" often observed in intelligent biological and mechanical systems engaged in structured cognition. Each level within the knowledge hierarchy consists of knowledge artifacts that arise as specific relations on the level prior to it. This establishes a recursive relational algebra by which knowledge at all levels can be specified in terms of percepts ("data") at the bottom. This is quintessential essentialism as a ground for cognition [1]. "The thing is its form." For the purposes of this work, an object of thought is regarded as equal to the assemblage of attributes it manifests. From the standpoint of cognition, nothing is sacrificed here, since percepts arising from this assemblage constitute the entirety of material available for structured cognition [2]. This formalizes a structured context for the analysis of cognition and the knowledge artifacts it uses. The U.S. Intelligence Community and the Department of Defense use similar but looser formalisms to support data fusion processes [3]. These are briefly described. A brief case study is presented applying this knowledge representation to the analysis of an important pattern processing problem: the classification of distant military vehicles from their Doppler RADAR phase history.
Matrix Factorization Model with Dual Preferences for Rating Prediction

Most matrix factorization methods for rating prediction in recommender system seek to model the preference of each user by a latent feature vector with small dimension. Moreover, these methods assume that user's preferences over all items may be captured by the same vector. However, a given user may have different preferences on a given item when he/she plays different roles in different scenarios. In this paper, we propose a new matrix factorization model named PSVD, which allows us to capture user's different preferences over different items flexibly in rating prediction. Specially, we use a pair of preferences to represent the whole preference of user over items. Then the dual preferences are considered simultaneously in building the latent feature vector of user. Moreover, PSVD model allows users to adjust their own feature vector when selecting different products. A comprehensive set of experiments on three public datasets show that PSVD model can outperform several models in terms of different evaluation metrics.
Compression And Intensity Modules For Brain MRI Segmentation

The methods using attention module have been studied recently on image processing using Convolution Neural Network (CNN). The main purpose of CNN, where the method is applied, is to emphasize important features and weaken less important ones. From this perspective, we propose Compression and Intensity modules in order to boost the representation of feature map, by focusing on pixel-wise spatial attention. For each pixel, the importance of the spatial information which the feature possesses is identified and enhanced, so that an efficient segmentation task can be performed. The performance of the proposed module with state-of-the-art CNN models outperformed other recent attention modules for the brain MRI segmentation evaluation on MRBrainS18.
Segregating Offline and Online Handwriting for Conditional Classification Analysis

Abstract Offline and online handwriting patterns vary when exposed to external factors. Recent researches on both type handwriting analysis were focused on the image feature extraction, pattern recognition, and classification approach. However, no studies have considered segregating the conditional effects of handwriting patterns on two categories: offline vs. online and normal vs. vibration. Hence, the main goal of the study was to investigate the effects of classifying induced vibration on the offline and online handwriting patterns. There were 25 experimental handwriting samples retrieved under four pre-defined class: FLN, FLV, NLN, and NLV. Extracted data attributes consisted of seven handwriting size metrics and two demographic data. JRip algorithm and Decision Stump algorithm of WEKA tool were employed on tenfold cross-validation mode for handwriting classification and a further segregation by offline and online handwriting. JRip and Decision Stump merely resulted in 54% and 44% classification accuracy respectively. On data categorization between the offline and online handwriting, both algorithms achieved classification accuracies of 56% and 76% respectively. Findings showed that the misclassifications between offline and online were reduced to 5 and 7 instances for JRip and Decision Stump algorithms respectively, whereas the total misclassification between normal and vibration were reduced to 22 and 13 instances.
A Distributed Access Control with Outsourced Computation in Fog Computing

With the rapid development of information technology and the Internet of Things Technology (IOT), data security and healthy privacy are getting a lot of attention. In order to store, access, and share electronic health records, storage of this data is transferred to a third-party-cloud server. The security and privacy of electronic health records stored at date center or cloud server are not guaranteed. Before being sent to date center or cloud server, this data should be encrypted. Designing an efficient and secure fine-grained access control strategy for personal health records is facing enormous challenges. Security and privacy for electronic health records are very important because the electronic health data which plays an important role in medical server and treatment is directly associated with a particular patient. Attribute-based encryption (ABE) can effectively achieve fine-grained access control. However, the computation of bilinear pairings requires a large amount of computation overhead in ABE scheme. In order to decrease the computational overhead and ensure the confidentiality of electronic health records, a distributed fine-grained access control scheme with outsourced computation for IOT is proposed in this paper. Little calculation is executed by the receiver and sender in our proposed scheme. Outsourcing computing reduces the computing burden. The analyses of safety and performance show that our proposed scheme is safe and effective compared with previous schemes.
Motion Estimation of Plush Toys Through Detachable Acceleration Sensor Module and Machine Learning

We propose a system that estimates motion in a plush toy by means of an attached sensor device and gives the user a sound feedback corresponding to the predicted motion. We have created several different types of detachable acceleration sensor modules as an accessory for the toy. This module can be attached at any position on a commercially available plush toy. The user can create original motions by teaching through demonstration, and the captured sensor data is converted into 2D image data. We extracted the histograms of oriented gradients (HOG) features and performed learning with a support vector machine (SVM). In an evaluation, we decided the attaching parts and motions in advance, and participants moved a plush toy in accordance with these. Results showed that it was possible to estimate the plush toy's motion with high accuracy, and the system was able to register a sound for each motion.
Feature Boosting in Natural Image Classification

Computer Vision has become the poster child for Deep Learning. The image classification accuracy of convolutional neural nets on benchmark data sets has increased every year since their inception. This has been aided with advances in feature fusion. The increase in the availability of imagetext occurrence has lead to text augmented feature spaces that have lead to higher accuracy in in image classification tasks. However, these works are limited to instances where text is readily available. This study presents an approach to featurize text within natural images with the goal of augmenting image features for image classification tasks. Text extraction and featurization in natural images is a challenging task due to challenges in reliable text localization and OCR results, both being impeded by the variability in image text and errors in OCR. We overcome these challenges by implementing a novel bounding box concatenation algorithm and a novel feature boosting algorithm. The result is a pipeline that encodes an image into a text feature space. Classifiers trained on the text based feature space have comparable accuracy to the state of the art Convolutional Neural Nets (CNN's) while being significantly inexpensive computationally. Moreover, the augmentation of text features to image features generates a hybrid feature space with a higher information content for a classification problem when compared to a feature space comprised exclusively of image features. Thus, we see a rise in classification accuracy across all state of the art machine learning algorithms.