Reviewerly/exHarmony huggingface dataset:
Hybrid cryptosystem based on plaintext related computational ghost imaging encryption and elliptic curve algorithm

A hybrid cryptosystem based on plaintext-related ghost imaging and elliptic curve public-key cryptographic algorithm (ECC) is proposed in this paper. Thereinto, the ECC algorithm is used to protect and distribute session keys and the optical encryption system with session keys is used to encrypt and decrypt images. Thousands of phase-only mask keys can be replaced by parameter keys in this method, which successfully reduces the pressure of key storage. With dynamic updating session keys, the attacks are effectively prevented and the security resilience is enhanced. In addition, the utilization of an ECC cryptosystem solves the inevitable problem of key management and dispatch in conventional ghost imaging encryption system. The feasibility, security and robustness of the method are further analyzed through computer simulations.
Analysis of Detecting Compensation for Robotic Stroke Rehabilitation Therapy using Imbalanced Learning and Outlier Detection

Stroke therapy is essential to reduce impairments and improve the motor movements of stroke survivors, however sessions can be expensive, time consuming, and geographically limited. Robotic stroke therapy seeks to remedy the limitations of traditional stroke therapy, but it is hampered by incorrect movements during the session. Incorrect usage of muscles, called as compensatory movements, can cause problems that can hamper the recovery of the patients. Thus, there is a need to develop tools to automatically detect compensatory movements to assist patients doing autonomous therapy sessions. Previous studies on automatic detection using depth sensors did not yield satisfactory results. This study explores class imbalance as a possible reason for the low F1-score results on machine learning classifiers. Different methods to address class imbalance were employed to improve and to analyze the performance of the classifiers. The methods employed allowed the classifiers to sometimes detect compensatory movements however this degraded the performance of detecting the correct movements. Adjusting the decision thresholds of outlier detection algorithms shows this explicitly. Since addressing class imbalance only marginally improves the performance of the classifiers, other possible methods can be explored in conjunction with it. This study shows the possibility of detecting compensations in stroke patients.
Revisiting Habitability in Conversational Systems

Conversational systems are inherently disadvantaged when indicating either what capabilities they have or the state they are in. The notion of habitability, the appropriate balancing in design between the language people use and the language a system can accept, emerged out of these early difficulties with conversational systems. This literature review aims to summarize progress in habitability research and explore implications for the design of current AI-enabled conversational systems. We found that i) the definitions of habitability focus mostly on matching between user expectations and system capabilities by employing well-balanced restrictions on language use; ii) there are two comprehensive design perspectives on different domains of habitability; iii) there is one standardized questionnaire with a sub-scale to measure habitability in a limited way. The review has allowed us to propose a working definition of habitability and some design implications that may prove useful for guiding future research and practice in this field.
SCNet: Scale-aware coupling-structure network for efficient video object detection

In recent years, outstanding image-based object detectors were extended for video object detection. Such extension requires to address two challenging problems in videos, namely, scale variation and deformation anomaly. Most image-based works deal with scale variation by resizing the input image for multi-scale training and testing. Such way incurs very high computational cost, which becomes even higher in videos. For deformation anomaly, low-quality RoI feature can be caused by motion blur, video defocus and rare poses, resulting to poor detection accuracy in videos. In this paper, an end-to-end scale-aware coupling-structure network (SCNet) focusing on the two issues is proposed for video object detection with high accuracy and affordable computation. In SCNet, a lightweight scale-aware module is structured to flexibly model the object scale variation, which mainly consists of a set of dilated convolutional layers with parameter constraint. Furthermore, a coupling-structure RoI (region of interest) module is designed to extract robust RoI feature with position-sensitive and context-sensitive information for accuracy improvement. Besides, the feature aggregation strategy is simplified for efficiency. Experiments are conducted on the ImageNet VID dataset. SCNet achieves the state-of-the-art detection performance, exactly 79.5% mAP, with 5.9 points improvement compared to the strong single-frame baseline.
Chinese News Text Classification Algorithm Based on Online Knowledge Extension and Convolutional Neural Network

Chinese text classification is an important task in data mining, which extracts category features from unstructured contents. Conventional Chinese text classification models only leverage the surface features in the original text, which omits the potential extensional knowledge of each word. To capture the semantic features of each word more comprehensive, this paper proposed a Chinese news text classification algorithm based on an online knowledge extension and convolutional neural network (OKE-CNN), which leverages both knowledge graph to extend latent semantic information and CNN to obtain the category. Compared with other baseline methods, OKE-CNN can utilize the surface and latent features, simultaneously, which can be adapted to complex scenes, e.g., sparse data and unclear topics. In our experiment, OKE-CNN exhibits superior performance and achieves 97.94% and 87.03% on THUCNews and TouTiao datasets, separately, over SOTA competitors.
Prediction of Intentions Behind a Single Human Action: An Application of Convolutional Neural Network

Due to the rapidly increasing need of human robot interaction (HRI), more intelligent robots are in demand. However, the vast majority of robots can only follow strict instructions, which seriously restricts their flexibility and versatility. A critical fact that strongly negates the experience of HRI is that robots cannot understand human intentions. This study aims at improving the robotic intelligence by training it to understand human intentions. Different from previous studies that estimated human intentions from distinctive actions, this paper introduces a method, CNN-vote, that applies convolutional neural networks (CNN) to predict human intentions behind a single action. Experiments of predicting the intention of human throwing a ball towards designated targets are conducted to verify the effectiveness of the method. The proposed deep learning based method proves the feasibility of applying CNN under a novel circumstance. Experiment results show that the CNN-vote method requires less tuning work and out competes three traditional machine learning techniques. In current context, the CNN-vote predictor achieves the highest testing accuracy with relatively less data needed.
Automatic Approach for Cervical Cancer Detection Based on Deep Belief Network (DBN) Using Colposcopy Data

Cervical cancer is one of the diseases with the highest mortality rate. In the world, cervical cancer is ranked as the fourth most dangerous disease. Based on these problems, this paper can be an alternative to help medical authorities in detecting cervical cancer with the help of the Computer-Aided Diagnosis (CAD) System. CAD System used has two processes, such as preprocessing and classification. Preprocessing is useful to improve the image so that it is easier to do the process of identifying features. Preprocessing used is greyscale, histogram equalization, and median filter. Preprocessing results will be formed into a vector matrix using the reshaping process. The final step is the process of classifying data using the Deep Belief Network method. The best accuracy results obtained from the identification process of cervical cancer using the DBN method is 84%. Based on the results of accuracy, is expected to help reduce the number of deaths from cervical cancer with early detection.
Behind the Comments Section: The Ethics of Digital Native News Discussions

Initially offered as a digital public sphere forum, comments sections became the preferred democratic arena for gatekeepers to encourage their readers to engage in constructive dialogue about relevant issues. However, news sites require commenters to remain civil in their interactions, which led users to seek alternative ways of commenting on the news. This article explores in-depth the contents of a sample of 98,426 user-comments collected between February–March 2019 from three major Spanish digital native newspapers: &lt;em&gt;ElDiario.es&lt;/em&gt;, &lt;em&gt;ElEspañol.com&lt;/em&gt;, and &lt;em&gt;ElConfidencial.com&lt;/em&gt;. The main goals were to analyze whether comments in news outlets are deliberative, to assess the quality of the debate that takes place in them, and to describe their specific features. Discourse ethics were explored to determine the discussions’ impact, the language used, the acceptance of arguments, and the recognition and civility of participants. Findings reveal that comments sections in news outlets do not have a dialogic nature and that the debates have a low-quality profile. Nonetheless, the degree of mutual respect in interaction is acceptable, with slightly observed levels of incivility. Finally, the data suggest that the focused comments are higher on social media and that memes and emojis represent a new form of digital discourse.
Quantum Key Distribution Based on Random Grouping Bell State Measurement

We propose a four-qubit quantum key distribution protocol via two Bell states which constitute a transmitted unit from the sender to the receiver in each communication. An encryption here is designed by randomly grouping four qubits of a unit into two new couples, which is a way to increase the possibility of detecting the eavesdropper. Ultimately, the receiver randomly measures this grouped unit with two Bell state measurements. From the comparison of grouping information of these four qubits, we find that the two sides in a valid communication can discover the illegal party in the channel. In the proposed protocol, the receiver measures the unit when he receives it instantly, which is an efficient way to overcome the ultrashort storage time of quantum state.
Linear Approximation of F-Measure for the Performance Evaluation of Classification Algorithms on Imbalanced Data Sets

Accuracy is a popular measure for evaluating the performance of classification algorithms tested on ordinary data sets. When a data set is imbalanced, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> -measure will be a better choice than accuracy for this purpose. Since <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> -measure is calculated as the harmonic mean of recall and precision, it is difficult to find the sampling distribution of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> -measure for evaluating classification algorithms. Since the values of recall and precision are dependent, their joint distribution is assumed to follow a bivariate normal distribution in this study. When the evaluation method is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -fold cross validation, a linear approximation approach is proposed to derive the sampling distribution of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> -measure. This approach is used to design methods for comparing the performance of two classification algorithms tested on single or multiple imbalanced data sets. The methods are tested on ten imbalanced data sets to demonstrate their effectiveness. The weight of recall provided by this linear approximation approach can help us to analyze the characteristics of classification algorithms.
Design and Implementation of Mobile Manipulator System

In this paper, we develop and implement a kind of mobile manipulator named Jupiter, which consists of a six-degree-of-freedom(DOF) manipulator and an omnidirectional mobile platform with four actively steerable wheels. In contrast to the omnidirectional mobile manipulator with Mecanum wheels, the proposed mobile manipulator can be adopted in rough terrains. Two laser scanners and a stereo camera are mounted in the system to perceive the environment. With the IMU and odometry data, Jupiter is capable of doing SLAM by Hector SLAM method and path planning. A manipulation frameworks with a single shot multibox detector (SSD) approach is used to detect and grasp the objects. Combining the two function, both the manipulator and the platform are integrated into a system which can execute mobile manipulation tasks autonomously. Our contribution includes a complete hardware construction and software pipeline of the mobile manipulator which is capable of autonomously doing SLAM, navigation, detecting and grasping objects. The performance of the system are tested and proved by autonomously executing an mobile manipulation mission including autonomous navigation with multi-sensors and apples grasping and placing.
A Recurrent Neural Network Approach to Image Captioning in Braille for Blind-Deaf People

Limitation of resources in braille is one of the biggest obstacles faced by the blind community trying to learn and integrate themselves better in our societies. The fact that not only text but also images are used for communicating information does not help their cause. In this paper, we intend to help not only the visually impaired but also people with deaf-blindness, by converting images into captions which then can be read in braille. Being able to automatically translate the content of an image using properly formed English sentences is a challenging task in itself, but it could have great impact by helping a blind person better understand his/her surroundings and even in mundane tasks like browsing the web for example. While text-to-speech methods can be then implemented using voice synthesizers for blind only people, the text needs to be converted into braille for people who have both hearing and sight loss. Our paper has been centered on the concept of transforming images into grade 1 braille. Here we present a deep recurrent neural network architecture that automatically generates brief descriptions of images directly in braille. Our model achieves a BLEU-4 score of 0.24 on the Flickr 8K Dataset, which is comparable to current text based state-of-the-art deep learning models. Moreover, the generated captions are translated into haptic feedbacks readable for the blind by a microcontroller-based system on a 2 × 3 arrangement of push-pull solenoids.
Vehicle Tracking and Monitoring in Surveillance Video

A video surveillance system has become an integral part of the smart city infrastructure. However, it still lacks its utilization to the full potential. In this work, we present a case study in which a storage center surveillance data has been used to extract useful information automatically. A surveillance camera records the movement of trucks passing through a storage center gate where a clerk registers the truck-related data. This process is prone to suffer from inaccuracy, fraud, and loss. The challenge is to automate the data entry process through video such that the warehouse achieves a seamless and errorfree record-keeping. In this paper, we present a framework to use the surveillance video to extract useful information such as detection of trucks, their registration number/ownership identification, count of incoming and outgoing trucks, and count of loaded or empty trucks. We tested the work presented in this paper at the paddy storage centers in Chhattisgarh, India, and the results were very encouraging.
Parallel computation of alpha complexes for biomolecules

The alpha complex, a subset of the Delaunay triangulation, has been extensively used as the underlying representation for biomolecular structures. We propose a GPU-based parallel algorithm for the computation of the alpha complex, which exploits the knowledge of typical spatial distribution and sizes of atoms in a biomolecule. Unlike existing methods, this algorithm does not require prior construction of the Delaunay triangulation. The algorithm computes the alpha complex in two stages. The first stage proceeds in a bottom-up fashion and computes a superset of the edges, triangles, and tetrahedra belonging to the alpha complex. The false positives from this estimation stage are removed in a subsequent pruning stage to obtain the correct alpha complex. Computational experiments on several biomolecules demonstrate the superior performance of the algorithm, up to a factor of 50 when compared to existing methods that are optimized for biomolecules.
MATT: A Mobile Assisted Tense Tool for Flexible m-Grammar Learning Based on Cloud-Fog-Edge Collaborative Networking

The proliferation of modern mobile technologies on grammar learning (i.e., m-grammar learning) has generated a multitude of challenges in developing effective pedagogically-informed learning tools. The existing systems have mostly suffered from low motivation and poor learning effectiveness because of the three key reasons, namely: i) a weak tie to motivational theoretical principles, ii) a lack of proper instructional design, and iii) a lack of proper infrastructural design for data sharing between students and instructors. To deal with this issue, this paper presents MATT: a Mobile-Assisted Tense Tool that encapsulates an m-grammar instructional design leveraging upon cloud-fog-edge collaborative networking. Central to MATT is the incorporation of the Cognitive Theory of Multimedia Learning principles to minimize the extraneous cognitive load and a motivational model to increase motivation and learning effectiveness. To ensure effective instructional design, we exploit adaptive and dynamic approaches embodied in a flexible instructional paradigm that takes advantage of collective learning data exchange across cloud (central unit), fog (regional units) and edge (end devices/learners). To demonstrate the overall effectiveness of this system, we describe our findings in the evaluation of both the learning aspect (using a quantitative research design) and collaborative network performance (using numerical simulation). With an appropriate condition of delay-tolerant network-enabled learning data exchange, the results suggest that the students' cognitive load is low and motivational nature is high after using this system, which led them to perform more positively in the post-test evaluation.
A Lightweight Hash Function Based on Cellular Automata for Mobile Network

Since the classical hash function widely used are costly for resource constrained devices, in this paper we propose a new lightweight hash function LNHASH through Cellular Automata(CA), in which sponge construction is applied as the mode of operation. Both linear and non-linear CA rules are employed to construct the internal permutation to achieve high diffusion and confusion. LNHASH allows making trade-offs among security, speed, energy consumption and implementation costs by adjusting the corresponding parameters. The hardware-friendly Sbox and linear layer bring low area implementation. Security analysis results show the resistance of the LNHASH scheme against the known attacks.
Detection of Human Bodies in Lying Position based on Aggregate Channel Features

In recent years, detection of human body has drawn a lot of attention from researchers in the field of image recognition, with most work focused on pedestrian detection. The detection of human bodies in lying position also received numerous attention in applications such as elderly fall detection, sleep studies as well as in search and rescue (SAR) operations. Thus, in this paper, feature extraction performed by the Aggregate Channel Features (ACF) algorithm is explored for detection of human bodies in lying positions. ACF makes use of a Boosted Decision Tree (BDT) classifier that has resulted in increase in speed of detection. The classification was carried out using a dataset developed from aerial images of human bodies obtained from the internet. Initial result showed that the accuracy of ACF using the given dataset is 88% and the value of F-measure obtained was 0.9231. This proposed method will be further explored on a more advanced dataset.
2D Morphable Feature Space for Handwritten Character Recognition

Handwriting style of an individual will be usually varying from other individuals. The degree of complexity involved in recognition of a handwritten character of south Indian languages is high due to the presence of compound characters. Furthermore the complexity of interpreting handwritten text aggravates due to irregular handwriting styles. Building of datasets for training the database in such environments is tremendous and requires a lot of manual data processing and other preliminary tasks. The major contribution focuses on creation of morphable feature space for addressing handwritten recognition problem. In this paper, techniques useful for simplifying dataset preparation procedures along with a hybrid recognition approach for improvising recognition accuracies of Telugu handwritten characters are proposed. The techniques include text document segmentation, automatic cropping and pincushion and distance transformation based recognition approach. A feature vector is generated from the normalized Gabor features are extracted from pincushion and distance transform models of a character image and classified using Ada boost classifier with a recognition accuracy of 89%.
Adversarial Attack on Deep Learning-Based Splice Localization

Regarding image forensics, researchers have proposed various approaches to detect and/or localize manipulations, such as splices. Recent best performing image-forensics algorithms greatly benefit from the application of deep learning, but such tools can be vulnerable to adversarial attacks. Due to the fact that most of the proposed adversarial example generation techniques can be used only on end-to-end classifiers, the adversarial robustness of image-forensics methods that utilize deep learning only for feature extraction has not been studied yet. Using a novel algorithm capable of directly adjusting the underlying representations of patches we demonstrate on three non end-to-end deep learning-based splice localization tools that hiding manipulations of images is feasible via adversarial attacks. While the tested image-forensics methods, EXIF-SC, SpliceRadar, and Noiseprint, rely on feature extractors that were trained on different surrogate tasks, we find that the formed ad- versarialperturbations can be transferable among them regarding the deterioration of their localization performance.