Reviewerly/exHarmony huggingface dataset:
Autonomous mobile robot navigation between static and dynamic obstacles using multiple ANFIS architecture

Purpose This paper aims to design and implement the multiple adaptive neuro-fuzzy inference system (MANFIS) architecture-based sensor-actuator (motor) control technique for mobile robot navigation in different two-dimensional environments with the presence of static and moving obstacles. Design/methodology/approach The three infrared range sensors have been mounted on the front, left and right side of the robot, which reads the forward, left forward and right forward static and dynamic obstacles in the environment. This sensor data information is fed as inputs into the MANFIS architecture to generate appropriate speed control commands for right and left motors of the robot. In this study, we have taken one assumption for moving obstacle avoidance in different scenarios the speed of the mobile robot is at least greater than or equal to the speed of moving obstacles and goal. Findings Graphical simulations have designed through MATLAB and virtual robot experimentation platform (V-REP) software and experiments have been done on Arduino MEGA 2560 microcontroller-based mobile robot. Simulation and experimental studies demonstrate the effectiveness and efficiency of the proposed MANFIS architecture. Originality/value This paper designs and implements MANFIS architecture for mobile robot navigation between a static and moving obstacle in different simulation and experimental environments. Also, the authors have compared this developed architecture to the other navigational technique and found that our developed architecture provided better results in terms of path length in the same environment.
Abstractive social media text summarization using selective reinforced Seq2Seq attention model

Abstractive text summarization aims to generate a brief version of a given sentence while attempting to express its main meaning. Although some models based on the sequence-to-sequence framework have achieved remarkable results recently, there are still many problems that cannot be ignored. In this paper, we propose a selective reinforced sequence-to-sequence (i.e. Seq2Seq) attention model for abstractive social media text summarization. We add a selective gate after the encoder module for better filtering out invalid information. Specifically, we combine the cross-entropy and reinforcement learning policy for optimizing the ROUGE score directly. Evaluations on a famous social media dataset (i.e. LCSTS) demonstrate that our model outperforms most of famous baseline models, and the proposed model is 2.6%, 2.1% and 2.5% higher than the basic Seq2Seq attention model on the F1 score of ROUGE-1, ROUGE-2, and ROUGE-L, respectively.
Will-They-Won’t-They: A Very Large Dataset for Stance Detection on Twitter

We present a new challenging stance detection dataset, called Will-They-Won't-They 1 (WT-WT), which contains 51,284 tweets in English, making it by far the largest available dataset of the type.All the annotations are carried out by experts; therefore, the dataset constitutes a high-quality and reliable benchmark for future research in stance detection.Our experiments with a wide range of recent state-of-the-art stance detection systems show that the dataset poses a strong challenge to existing models in this domain.The entire dataset is released for future research 2 .
Unauthorized Broadcasting Identification: A Deep LSTM Recurrent Learning Approach

Radio broadcasting plays an important role in our daily life. Meanwhile, with the development of wireless communications, the application of software-defined radio platforms gives rise to cheap and easy design of illegal broadcasting stations. These unauthorized broadcasting stations sometimes illegally occupy licensed frequency band, especially associated with amateur radios and unlicensed personal communication devices and services. These unauthorized broadcasting stations may severely interfere with the authorized broadcasting and further disrupt the management of spectrum resource in civil applications, such as emergency services and air traffic control. However, it still remains a challenging task to automatically and effectively identify the unauthorized broadcasting in complicated electromagnetic environments. Aiming at developing an intelligent and efficient unauthorized broadcasting identification system, in this article, a novel identification approach is proposed based on long short-term memory (LSTM) recurrent neural network (RNN), and LabVIEW software. In our approach, first, a series of LabVIEW applications are developed to drive USRP 2930s for the acquisition of broadcasting signals. Then, the LSTM identification network is proposed to recognize unauthorized broadcasting. Through the special gate structure inside, the proposed LSTM framework can effectively extract the distinguishing features, such as channel state information and RF device fingerprinting. Simulation results show that the proposed LSTM-based approach perform better than other contrastive methods, especially in identification accuracy. Implementation results also demonstrate that the proposed method has an outstanding unauthorized broadcasting identification performance with a high accuracy, i.e., identify the unauthorized broadcasting signals with 99.83% accuracy at the licensed frequency of 107.8 MHz, in realistic electromagnetic environments.
Simple colour image cryptosystem with very high level of security

To obtain a more secure colour image cryptosystem without complex construction, this paper presents a general simple colour image encryption model with a very high level of security, and that is based on two nearby orbits of chaotic systems. First, the initial value of a one-dimensional (1D) chaotic map is obtained using plaintext. Then, we obtain two nearby orbits of 1D chaotic maps to generate three new chaotic signals. Next, the generated systems independently encrypt the red, green and blue components (RGB) of the colour image. Finally, the three encrypted images are combined to obtain the final encrypted image. Simulation results show that our method is simple, effective and passed all NIST tests and part of TestU01 test. Since the proposed method is related to plaintext, it has much higher security level compared with the most recently reported chaos-based image algorithms. More importantly, the 1D logistic cryptosystem based on our method has a large key space with higher security.
The ethical application of biometric facial recognition technology

Biometric facial recognition is an artificial intelligence technology involving the automated comparison of facial features, used by law enforcement to identify unknown suspects from photographs and closed circuit television. Its capability is expanding rapidly in association with artificial intelligence and has great potential to solve crime. However, it also carries significant privacy and other ethical implications that require law and regulation. This article examines the rise of biometric facial recognition, current applications and legal developments, and conducts an ethical analysis of the issues that arise. Ethical principles are applied to mediate the potential conflicts in relation to this information technology that arise between security, on the one hand, and individual privacy and autonomy, and democratic accountability, on the other. These can be used to support appropriate law and regulation for the technology as it continues to develop.
High-Order Correlation Preserved Incomplete Multi-View Subspace Clustering

Incomplete multi-view clustering aims to exploit the information of multiple incomplete views to partition data into their clusters. Existing methods only utilize the pair-wise sample correlation and pair-wise view correlation to improve the clustering performance but neglect the high-order correlation of samples and that of views. To address this issue, we propose a high-order correlation preserved incomplete multi-view subspace clustering (HCP-IMSC) method which effectively recovers the missing views of samples and the subspace structure of incomplete multi-view data. Specifically, multiple affinity matrices constructed from the incomplete multi-view data are treated as a third-order low rank tensor with a tensor factorization regularization which preserves the high-order view correlation and sample correlation. Then, a unified affinity matrix can be obtained by fusing the view-specific affinity matrices in a self-weighted manner. A hypergraph is further constructed from the unified affinity matrix to preserve the high-order geometrical structure of the data with incomplete views. Then, the samples with missing views are restricted to be reconstructed by their neighbor samples under the hypergraph-induced hyper-Laplacian regularization. Furthermore, the learning of view-specific affinity matrices as well as the unified one, tensor factorization, and hyper-Laplacian regularization are integrated into a unified optimization framework. An iterative algorithm is designed to solve the resultant model. Experimental results on various benchmark datasets indicate the superiority of the proposed method. The code is implemented by using MATLAB R2018a and MindSpore library: https://github.com/ChangTang/HCP-IMSC.
Deep Neural Networks Classification over Encrypted Data

Deep Neural Networks (DNNs) have overtaken classic machine learning algorithms due to their superior performance in big data analysis in a broad range of applications. On the other hand, in recent years Machine Learning as a Service (MLaaS) has become more widespread in which a client uses cloud services for analyzing its data. However, the client's data may be sensitive which raises privacy concerns. In this paper, we address the issue of privacy preserving classification in a Machine Learning as a Service (MLaaS) settings and focus on convolutional neural networks (CNN). To achieve this goal, we develop new techniques to run CNNs over encrypted data. First, we design methods to approximate commonly used activation functions in CNNs (i.e. ReLU, Sigmoid, and Tanh) with low degree polynomials which is essential for a practical and efficient solution. Then, we train CNNs with approximation polynomials instead of original activation functions and implement CNNs classification over encrypted data. We evaluate the performance of our modified models at each step. The results of our experiments using several CNNs with a varying number of layers and structures are promising. When applied to the MNIST optical character recognition tasks, our approach achieved 99.25% accuracy which significantly outperforms state-of-the-art solutions and is close to the accuracy of the best non-private version. Furthermore, it can make up to 164000 predictions per hour. These results show that our approach provides accurate, efficient, and scalable privacy-preserving predictions in CNNs.
Secure User Authentication Using Honeywords

The purpose of the password is to protect the user account from unauthorized usage by the hacker. But in the current situation the field of security also realizes lot of threat to the password even in case if it is hashed. With the rise of hacking technology even the hashed password doesn’t provide the required security and also provides the hacker to misuse or exploit the user account without being noticed. The most vulnerable part in this is the misuse of account can be realized only after the user logs and sees the changes in their account usage. And so, the system doesn’t yet been improved in safeguarding or detecting the attacks against the database of password which are hashed. Ari Juels and et al. in 2013 [10] discovered the method using honeywords for detecting the password cracking. Honey words are the imitated passwords which are connected with the account of each user. We intended to produce a safer system by creating an authentication using the honey words in the password database. The newly created database contains a combination of both the imitated ones and the original passwords in order to detect whether the attack is happened or not. And hence when the hacker has the password database, he might get confused with the real and fake passwords. Here we make the hacker to fall into our trap by confusing him. Once he tries to enter a false password the administrator will get a notification and the hacker gets identified.
GeoStyle: Discovering Fashion Trends and Events

Understanding fashion styles and trends is of great potential interest to retailers and consumers alike. The photos people upload to social media are a historical and public data source of how people dress across the world and at different times. While we now have tools to automatically recognize the clothing and style attributes of what people are wearing in these photographs, we lack the ability to analyze spatial and temporal trends in these attributes or make predictions about the future. In this paper we address this need by providing an automatic framework that analyzes large corpora of street imagery to (a) discover and forecast long-term trends of various fashion attributes as well as automatically discovered styles, and (b) identify spatio-temporally localized events that affect what people wear. We show that our framework makes long term trend forecasts that are > 20% more accurate than prior art, and identifies hundreds of socially meaningful events that impact fashion across the globe.
Combining Faster R-CNN and Model-Driven Clustering for Elongated Object Detection

While analyzing the performance of state-of-the-art R-CNN based generic object detectors, we find that the detection performance for objects with low object-region-percentages (ORPs) of the bounding boxes are much lower than the overall average. Elongated objects are examples. To address the problem of low ORPs for elongated object detection, we propose a hybrid approach which employs a Faster R-CNN to achieve robust detections of object parts, and a novel model-driven clustering algorithm to group the related partial detections and suppress false detections. First, we train a Faster R-CNN with partial region proposals of suitable and stable ORPs. Next, we introduce a deep CNN (DCNN) for orientation classification on the partial detections. Then, on the outputs of the Faster R-CNN and DCNN, the algorithm of adaptive model-driven clustering first initializes a model of an elongated object with a data-driven process on local partial detections, and refines the model iteratively by model-driven clustering and data-driven model updating. By exploiting Faster R-CNN to produce robust partial detections and model-driven clustering to form a global representation, our method is able to generate a tight oriented bounding box for elongated object detection. We evaluate the effectiveness of our approach on two typical elongated objects in the COCO dataset, and other typical elongated objects, including rigid objects (pens, screwdrivers and wrenches) and non-rigid objects (cracks). Experimental results show that, compared with the state-of-the-art approaches, our method achieves a large margin of improvements for both detection and localization of elongated objects in images.
Blind Schnorr Signatures and Signed ElGamal Encryption in the Algebraic Group Model

The Schnorr blind signing protocol allows blind issuing of Schnorr signatures, one of the most widely used signatures. Despite its practical relevance, its security analysis is unsatisfactory. The only known security proof is informal and in the combination of the generic group model (GGM) and the random oracle model (ROM) assuming that the “ROS problem” is hard. The situation is similar for (Schnorr-)signed ElGamal encryption, a simple CCA2-secure variant of ElGamal. We analyze the security of these schemes in the algebraic group model (AGM), an idealized model closer to the standard model than the GGM. We first prove tight security of Schnorr signatures from the discrete logarithm assumption (DL) in the AGM+ROM. We then give a rigorous proof for blind Schnorr signatures in the AGM+ROM assuming hardness of the one-more discrete logarithm problem and ROS. As ROS can be solved in sub-exponential time using Wagner’s algorithm, we propose a simple modification of the signing protocol, which leaves the signatures unchanged. It is therefore compatible with systems that already use Schnorr signatures, such as blockchain protocols. We show that the security of our modified scheme relies on the hardness of a problem related to ROS that appears much harder. Finally, we give tight reductions, again in the AGM+ROM, of the CCA2 security of signed ElGamal encryption to DDH and signed hashed ElGamal key encapsulation to DL.
The Importance of Modeling Social Factors of Language: Theory and Practice

Natural language processing (NLP) applications are now more powerful and ubiquitous than ever before.With rapidly developing (neural) models and ever-more available data, current NLP models have access to more information than any human speaker during their life.Still, it would be hard to argue that NLP models have reached human-level capacity.In this position paper, we argue that the reason for the current limitations is a focus on information content while ignoring language's social factors.We show that current NLP systems systematically break down when faced with interpreting the social factors of language.This limits applications to a subset of information-related tasks and prevents NLP from reaching human-level performance.At the same time, systems that incorporate even a minimum of social factors already show remarkable improvements.We formalize a taxonomy of seven social factors based on linguistic theory and exemplify current failures and emerging successes for each of them.We suggest that the NLP community address social factors to get closer to the goal of humanlike language understanding.
Latest Tools for Data Mining and Machine Learning

Nowadays, Data Mining is used everywhere for extracting information from the data and in turn, acquires knowledge for decision making. Data Mining analyzes patterns which are used to extract information and knowledge for making decisions. Many open source and licensed tools like Weka, RapidMiner, KNIME, and Orange are available for Data Mining and predictive analysis. This paper discusses about different tools available for Data Mining and Machine Learning, followed by the description, pros and cons of these tools. The article provides details of all the algorithms like classification, regression, characterization, discretization, clustering, visualization and feature selection for Data Mining and Machine Learning tools. It will help people for efficient decision making and suggests which tool is suitable according to their requirement.
Artificial intelligence to identify genetic alterations in conventional histopathology

Precision oncology relies on the identification of targetable molecular alterations in tumor tissues. In many tumor types, a limited set of molecular tests is currently part of standard diagnostic workflows. However, universal testing for all targetable alterations, especially rare ones, is limited by the cost and availability of molecular assays. From 2017 to 2021, multiple studies have shown that artificial intelligence (AI) methods can predict the probability of specific genetic alterations directly from conventional hematoxylin and eosin (H&E) tissue slides. Although these methods are currently less accurate than gold standard testing (e.g. immunohistochemistry, polymerase chain reaction or next-generation sequencing), they could be used as pre-screening tools to reduce the workload of genetic analyses. In this systematic literature review, we summarize the state of the art in predicting molecular alterations from H&E using AI. We found that AI methods perform reasonably well across multiple tumor types, although few algorithms have been broadly validated. In addition, we found that genetic alterations in FGFR, IDH, PIK3CA, BRAF, TP53, and DNA repair pathways are predictable from H&E in multiple tumor types, while many other genetic alterations have rarely been investigated or were only poorly predictable. Finally, we discuss the next steps for the implementation of AI-based surrogate tests in diagnostic workflows. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Low-Rank 2-D Neighborhood Preserving Projection for Enhanced Robust Image Representation

2-D neighborhood preserving projection (2DNPP) uses 2-D images as feature input instead of 1-D vectors used by neighborhood preserving projection (NPP). 2DNPP requires less computation time than NPP. However, both NPP and 2DNPP use the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> norm as a metric, which is sensitive to noise in data. In this paper, we proposed a novel NPP method called low-rank 2DNPP (LR-2DNPP). This method divided the input data into a component part that encoded low-rank features, and an error part that ensured the noise was sparse. Then, a nearest neighbor graph was learned from the clean data using the same procedure as 2DNPP. To ensure that the features learned by LR-2DNPP were optimal for classification, we combined the structurally incoherent learning and low-rank learning with NPP to form a unified model called discriminative LR-2DNPP (DLR2DNPP). By encoding the structural incoherence of the learned clean data, DLR-2DNPP could enhance the discriminative ability for feature extraction. Theoretical analyses on the convergence and computational complexity of LR-2DNPP and DLR-2DNPP were presented in details. We used seven public image databases to verify the performance of the proposed methods. The experimental results showed the effectiveness of our methods for robust image representation.
WristCam: A Wearable Sensor for Hand Trajectory Gesture Recognition and Intelligent Human–Robot Interaction

As a promising component for body sensor networks, the wearable sensors for hand gesture recognition have increasingly received great attention in recent years. By interpreting human intentions through hand gestures, the natural human-robot interaction can be realized in the smart home where the youth and the elderly can perform hand gestures to control the household robot or the robotic wheelchair. Here, a wearable wrist-worn camera sensor was shown to recognize hand trajectory gestures. The moving velocity of the user's hand was deduced from the matched speeded up robust features keypoints of the moving background of the video sequence. Furthermore, the segmentation of continuous gestures was achieved by detecting the predefined gesture starting signal from the hand region of the image, which was segmented by the lazy snapping algorithm. In this paper, 10 types of gestures and 1350 gesture samples collected from 15 subjects at three different scenes were classified by the dynamic time warping algorithm and the results achieved an average recognition accuracy up to 97.6%. Moreover, the practicability of the proposed system was further demonstrated by controlling a cooperative robot to draw letters on paper.