Reviewerly/exHarmony huggingface dataset:
YOLO V3 and YOLO V4 for Masses Detection in Mammograms with ResNet and Inception for Masses Classification

Breast cancer is considered the most common type of cancer in women. It is the second leading cause of death all over the world among women. However, the process of mass detection and classification still pruned to errors. So, in the last two decades, the development of Computer-Aided Detection (CAD) systems became essential for early diagnosis. However, they should be improved for more accurate results, which can help obtain more confidence in the radiologist's decision. In this paper, we proposed a novel detection framework that detects the existent masses in the given mammograms then classifies them as benign or malignant by utilizing the transfer learning concept. The proposed model performed the detection and the classification process by combining the You Only Look Once (YOLO) detector using other classifiers to classify the predicted allocated masses. In this paper, YOLO-V3 and YOLO-V4 are compared by evaluating their performance in detecting masses. It has been observed by the experimental results that YOLO-V4 outperformed YOLO-V3 by obtaining more accurate detection results and a lower number of undetected mammograms, especially when its responsibility set to detect masses whatever their types instead of detecting benign and malignant masses. Consequently, the detected masses are classified by ResNet and Inception V3 achieving better results than YOLO's classification results.
Hybridized Metaheuristic Search Algorithm with Modified Initialization Scheme for Global Optimization

Metaheuristic search algorithms (MSAs) such as particle swarm optimization (PSO) and differential evolution (DE) have gained significant attentions from practitioners to solve different engineering applications. Nevertheless, these MSAs are only effective to solve certain problem categories due to No Free Lunch Theorem. A modified hybrid algorithm namely chaotic oppositional based hybridized DE with PSO (CO-HDEPSO) is proposed to address the aforementioned drawbacks in order to solve different global optimization problems more effectively. The chaotic oppositional based initialization scheme is first designed into CO-HDEPSO, aiming to produce initial population with better quality. A hybridization framework is introduced in CO-HDEPSO to combine the strengths of PSO and DE simultaneously for further optimization performance enhancement. Comprehensive analyses are performed to compare the performances of CO-HDEPSO with five MS algorithms using CEC 2014 benchmark functions. The proposed CO-HDEPSO is reported to demonstrate more competitive search accuracy than its peer algorithms in most benchmark functions and able to solve 26 out of 30 CEC 2014 test functions with best Fmean values.
Frame Deletion Detection Based on Optical Flow Orientation Variation

Frame deletion detection is an arrestive field for video forensics in recent years.This paper presents a frame deletion detection algorithm based on optical flow orientation variation.Optical flow field has been proved to be effective for the detection of the frame deletion operation.However, current opticalflow based methods have relatively poor detection performance for real-world videos.Our research aims to develop a new forensic method to detect frame deletion for real-world videos.The proposed algorithm firstly develops an analytical model for flow orientation variation between adjacent optical flow fields, and exhibits a novel forensic clue for frame deletion detection, under an ideal but reasonable assumption.Accordingly, an effective descriptor, namely pseudo flow orientation variation (PFOV), is created to approximate the flow orientation variation, as a discriminative description feature to capture the frame deletion trace.The Frobenius norm and a smooth function are also introduced to quantify each descriptor to form an one-dimensional time series, and further statistics technique is adopted to detect the anomalous points in the time series caused by frame deletion.In addition, to improve the descriptor performance, the Robust Principal Component Analysis (RPCA) is applied to extract moving objects from video sequences and compute the proposed descriptor on them.The tests are made on 324 real-world videos, and experiment results demonstrate that the true positive rate can reach 90.12%, meanwhile the false alarm rate is 7.71%, which indicates the superiority of the proposed method in the forensic algorithms for real-world video.
Improving abstractive summarization based on dynamic residual network with reinforce dependency

The Seq2Seq abstract summarization model based on long short-term memory (LSTM) is very effective for short text summarization. However, LSTM is limited by long-term dependencies, which can potentially result in salient information loss when long text is processed by the Seq2Seq model based on LSTM. To overcome the long-term dependence limitation, an encoder-decoder model based on the dynamic residual network is proposed in this work. The model can dynamically select an optimal state from the state history to establish a connection with the current state to improve the LSTM long sequence dependencies according to the current decoding environment. Because the dynamic residual connections will result in long-term connection-dependent words, a new method based on reinforcement learning is proposed to simulate the dependence between words, which is then implemented into the training process of the model. This model is verified using the CNN/Daily Mail and New York Times datasets, and the experimental results show that the proposed model achieves significant improvements in capturing long-term dependencies compared with the traditional LSTM-based Seq2Seq abstractive summarization model.
NAMF: A Nonlocal Adaptive Mean Filter for Removal of Salt-and-Pepper Noise

In this paper, a novel algorithm called a Nonlocal Adaptive Mean Filter (NAMF) for removing salt-and-pepper (SAP) noise from corrupted images is presented. We employ an efficient window detector with adaptive size to detect the noise. The noisy pixel is then replaced by the combination of its neighboring pixels, and finally, a SAP noise based nonlocal mean filter is used to reconstruct the intensity values of noisy pixels. Extensive experimental results demonstrate that NAMF can obtain better performance in terms of quality for restoring images at all levels of SAP noise.
Tackling Morpion Solitaire with AlphaZero-like Ranked Reward Reinforcement Learning

Morpion Solitaire is a popular single player game, performed with paper and pencil. Due to its large state space (on the order of the game of Go) traditional search algorithms, such as MCTS, have not been able to find good solutions. A later algorithm, Nested Rollout Policy Adaptation, was able to find a new record of 82 steps, albeit with large computational resources. After achieving this record, to the best of our knowledge, there has been no further progress reported, for about a decade. In this paper we take the recent impressive performance of deep self-learning reinforcement learning approaches from AlphaGo/AlphaZero as inspiration to design a searcher for Morpion Solitaire. A challenge of Morpion Solitaire is that the state space is sparse, there are few win/loss signals. Instead, we use an approach known as ranked reward to create a reinforcement learning self-play framework for Morpion Solitaire. This enables us to find medium-quality solutions with reasonable computational effort. Our record is a 67 steps solution, which is very close to the human best (68) without any other adaptation to the problem than using reward. We list many further avenues for potential improvement.
Threat Modeling—How to Visualize Attacks on IOTA?

Internet of Things (IoT) has been deployed in a vast number of smart applications with the aim to bring ease and comfort into our lives. However, with the expansion of IoT applications, the number of security and privacy breaches has also increased, which brings into question the resilience of existing security and trust mechanisms. Furthermore, the contemporaneous centralized technology is posing significant challenges viz scalability, transparency and efficiency to wide range of IoT applications such as smart logistics, where millions of IoT devices need to be connected simultaneously. Alternatively, IOTA is a distributed ledger technology that offers resilient security and trust mechanisms and a decentralized architecture to overcome IoT impediments. IOTA has already been implemented in many applications and has clearly demonstrated its significance in real-world applications. Like any other technology, IOTA unfortunately also encounters security vulnerabilities. The purpose of this study is to explore and highlight security vulnerabilities of IOTA and simultaneously demonstrate the value of threat modeling in evaluating security vulnerabilities of distributed ledger technology. IOTA vulnerabilities are scrutinized in terms of feasibility and impact and we have also presented prevention techniques where applicable. To identify IOTA vulnerabilities, we have examined existing literature and online blogs. Literature available on this topic is very limited so far. As far as we know IOTA has barely been addressed in the traditional journals, conferences and books. In total we have identified six vulnerabilities. We used Common Vulnerability Scoring System (CVSS v3.0) to further categorize these vulnerabilities on the basis of their feasibility and impact.
Application of artificial neural network to map the performance characteristics of boiler using different algorithms

The present work demonstrates the real time predictions of the performance characteristics of a 210 MW boiler using artificial neural network (ANN) approach. ANNs have been successfully applied to model the different characteristics such as power, outlet steam pressure, and efficiency of a coal-fired water tube boiler. The training and testing data to develop the ANN were obtained through a number of experiments from the boiler of a thermal power station. Back propagation algorithm was utilized to train the ANN. The inputs considered were coal flow rate, percentage of load, and steam flow rate. The application of the developed model exposed better results in terms of regression value (R2), accuracy, percentage error, and mean square error. The co-efficient of multiple determination value was 0.99874 for the developed model which is very close to 1. It is perceptible that the developed model could be a better tool in predicting the performance of the boiler. In addition, modeling was also done based on radial basis network and compared with the back propagation network. Comparison of results indicates that the back propagation algorithm network performs better than radial basis network.
Kalman Filtering With Adaptive Step Size Using a Covariance-Based Criterion

In Kalman filtering (KF), a tradeoff exists when selecting the filter step size. Generally, a smaller step size improves the estimation accuracy, yet with the cost of a high computational load. To mitigate this tradeoff influence on performance, a criterion that acts as a guideline for a reasonable choice of the step size is proposed. This criterion is based on the predictor-corrector error covariance matrices of the discrete KF. In addition, this criterion is elaborated to an adaptive algorithm, for the case of the time-varying measurement noise covariance. Two simulation examples and a field experiment using a quadcopter are presented and analyzed to show the benefits of the proposed approach.
The entanglements which make instruments musical: Rediscovering sociality

(2021). The entanglements which make instruments musical: Rediscovering sociality. Journal of New Music Research: Vol. 50, Special Issue: Socio-Cultural Role of Technology in Digital Musical Instruments. Guest Editors: Koray Tahiroğlu and Thor Magnusson, pp. 133-146.
A comparison of likelihood-free methods with and without summary statistics

Abstract Likelihood-free methods are useful for parameter estimation of complex models with intractable likelihood functions for which it is easy to simulate data. Such models are prevalent in many disciplines including genetics, biology, ecology and cosmology. Likelihood-free methods avoid explicit likelihood evaluation by finding parameter values of the model that generate data close to the observed data. The general consensus has been that it is most efficient to compare datasets on the basis of a low dimensional informative summary statistic, incurring information loss in favour of reduced dimensionality. More recently, researchers have explored various approaches for efficiently comparing empirical distributions of the data in the likelihood-free context in an effort to avoid data summarisation. This article provides a review of these full data distance based approaches, and conducts the first comprehensive comparison of such methods, both qualitatively and empirically. We also conduct a substantive empirical comparison with summary statistic based likelihood-free methods. The discussion and results offer guidance to practitioners considering a likelihood-free approach. Whilst we find the best approach to be problem dependent, we also find that the full data distance based approaches are promising and warrant further development. We discuss some opportunities for future research in this space. Computer code to implement the methods discussed in this paper can be found at https://github.com/cdrovandi/ABC-dist-compare .
Toward Seamless Transitions Between Shared Control and Supervised Autonomy in Robotic Assistance

Assistive robots aim to help humans with impairments execute motor tasks in everyday household environments. Controlling the end-effector of such robots directly, for instance with a joystick, is often cumbersome. Shared control methods, like Shared Control Templates (SCTs) [1], have therefore been proposed to provide support for robotic control. Moreover, depending on factors such as workload, system trust or engagement, users may like to freely adjust the level of autonomy, for instance by letting the robot complete a task by itself. In this letter, we present a concept for adjustable autonomy in the context of robotic assistance. We extend the SCT approach with an automatic control module that allows the user to switch between Shared Control and Supervised Autonomy at any time during task execution. As both support modes use the same action representation, transitions are seamless. We show the capabilities of this approach in a set of daily living tasks with our wheelchair-mounted robot EDAN and our humanoid robot Rollin' Justin. We highlight how automatic execution benefits from SCT features, like task-related constraints and whole-body control.
Learning lenient parsing &amp; typing via indirect supervision

Both professional coders and teachers frequently deal with imperfect (fragmentary, incomplete, ill-formed) code. Such fragments are common in STACKOVERFLOW; students also frequently produce ill-formed code, for which instructors, TAs (or students themselves) must find repairs. In either case, the developer experience could be greatly improved if such code could somehow be parsed & typed; this makes such code more amenable to use within IDEs and allows early detection and repair of potential errors. We introduce a lenient parser, which can parse & type fragments, even ones with simple errors. Training a machine learner to leniently parse and type imperfect code requires a large training set including many pairs of imperfect code and its repair; such training sets are limited by human effort and curation. In this paper, we present a novel, indirectly supervised, approach to train a lenient parser, without access to such human-curated training data. We leverage the huge corpus of mostly correct code available on Github, and the massive, efficient learning capacity of Transformer-based NN architectures. Using GitHub data, we first create a large dataset of fragments of code and corresponding tree fragments and type annotations; we then randomly corrupt the input fragments by seeding errors that mimic corruptions found in STACKOVERFLOW and student data. Using this data, we train high-capacity transformer models to overcome both fragmentation and corruption. With this novel approach, we can achieve reasonable performance on parsing & typing STACKOVERFLOW fragments; we also demonstrate that our approach performs well on shorter student error program and achieves best-in-class performance on longer programs that have more than 400 tokens. We also show that by blending Deepfix and our tool, we could achieve 77% accuracy, which outperforms all previously reported student error correction tools.
GKEEP: An Enhanced Graph‐Based Keyword Extractor With Error‐Feedback Propagation for Geoscience Reports

Abstract As the amount of published geoscience literature grows, reading and summarizing texts of large collections has become a challenging task. Publication keywords can be considered basic components of knowledge structure representations and have been used to reveal knowledge concerning research domains. In contrast to data used in other research domains, the works on textual geoscience data that entail keyword extraction are limited. In this paper, we propose an unsupervised algorithm, the graph‐based keyword extractor with error‐feedback propagation (GKEEP), that enhances graph‐based keyword extraction approaches by using an error‐feedback mechanism similar to the concept of backpropagation. The proposed approach comprises the following steps. A preprocessed document is used as the input of the proposed model and is represented as a weighted undirected graph, where the vertices represent words and the edges represent the cooccurrence relationship between the words constrained by a window size. Subsequently, its nodes are ranked by their importance scores calculated by a graph‐based ranking algorithm. Consequently, all the words have their own scores, and they are used to compute the scores of keyword candidates. Subsequently, the Word2Vec method is applied to recalculate the scores of keyword candidates and rank the keyword candidates to select the final keyword. It also utilizes error feedback to boost the rankings of the most salient terms that would otherwise be deemed less important. With empirical experiments on two real data sets (including our newly built data set), the proposed GKEEP model outperforms state‐of‐the‐art unsupervised models and the existing graph‐based ranking models. The proposed method can effectively reflect intrinsic keyword semantics and interrelationships.
Encrypted Negative Password Identification Exploitation RSA Rule

Security is the fundamental angle for setting solid passwords for all our online media or any kind of records that are protected The data which is given as an input are secured for privacy purpose, later it is hacked by the intruders. So for that reason, an outline called encoded negative password is proposed. After obtaining the secret phrase from the client, the psssword is hashed (Hashing the secret key by SHA). The hashing is scrambled to obtain the hashed secret key by utilizing two other calculations considered as RSA and AES. The two-way hashed and scrambled secret phrase which is produced is called a negative secret word in the database. The advantage of this technique is that when the data set is hacked additionally no issue with the genuine passwords of the clients due to the decoding of the negative secret phrase. This framework provides a space for true authentication stages.
Quantum probability-inspired graph neural network for document representation and classification

Recent studies have found that text can be represented in Hilbert space through a neural network driven by quantum probability, which provides a unified representation of texts with different granularities without losing the performance of downstream tasks. However, these quantum probability-inspired methods only focus on intra-document semantics and lack modeling global structural information. In this paper, we explore the potential of combining quantum probability with graph neural network, and propose a quantum probability-inspired graph neural network model to capture global structural information of interaction between documents for document representation and classification. We build a document interaction graph for a given corpus based on document word relation and frequency information, then learn a graph neural network driven by quantum probability on the defined graph. First, the proposed model represents each document node in the graph as a superposition state in a Hilbert space. Then the proposed model further computes density matrix representations for nodes to encode document interaction as mixed states. Finally, the model computes classification probability by performing quantum measurement on the mixed states. Experiments on four document classification benchmarks show that the proposed model outperforms a variety of classical neural network models and the previous quantum probability-inspired model with much smaller parameter size. Extended analyses also demonstrate the robustness of the proposed model with limited training data and its ability to learn semantically distinguishable document representation.
Binocular stereo vision based illuminance measurement used for intelligent lighting with LED

Abstract Intelligent lighting is responsible for providing an appropriate illuminance and balancing the environment and resources. An appropriate illuminance can improve visual effect of machine recognition, while it is always difficult to be measured and controlled. An illuminance measurement method by using depth measurement technology of binocular stereo vision is proposed in this study. Illuminance analysis models of single LED and combined LEDs are established, and theoretical calculation equations for analyzing the relationship between power, illuminance, and depth are proposed. The illuminance distribution of single LED and combined LEDs are measured. The difference between single LED and combined LEDs are obtained in terms of illuminance, power, energy efficiency, effective illuminance area, etc. The results show that the illuminance is proportional to power of LED consumed and inversely proportional to the square of depth of image. The higher the energy conversion efficiency under the high illuminance for both single LED and combined LEDs. The depth of image is measured by binocular stereo vision technology, which has a good measurement performance. The binocular stereo vision based illuminance measurement method proposed in this study is efficient, reliable and robust, which is in good agreement with the experimental results, and the relative error is less than 3.8 %. The illuminance measurement is realized without increasing the equipment and image acquisition workload, which lays the foundation for dynamic monitoring and controlling of illuminance. The research results can also be applied to related fields as well, such as the intelligent lighting of street lamp, high beam in automatic driving and supplement lamps in video monitoring.