Reviewerly/exHarmony huggingface dataset:
Robustness and Deployability of Deep Object Detectors in Autonomous Driving

Many successful deep learning networks for object detection have been proposed in recent years, but their direct comparison is difficult due to different resolutions, training and validation data and frameworks used to train these networks. Moreover, suitability of these object detectors in context of autonomous driving is not compared or studied extensively. Autonomous driving has a wide operational domain containing diverse open world scenarios like different weather, daylight and road conditions etc. Most of the object detection datasets are from specific operational domains, e.g. KITTI dataset is mostly urban scenarios. In this work, we aim to first club many open source datasets (simulated and real) available for object detection in autonomous driving scenario. Clubbing the datasets makes them diverse by including different weather, lighting, time of the day and road conditions. Secondly, by clubbing most of the publicly available object detection datasets, we show the generalization ability of object detection networks in diverse scenarios. We also provide comparative study of accuracy vs resolution and the results of DNN based object detection on real images by training only on simulated data. Additionally, range of detection is really important for autonomous driving basic functionality like highway driving, and market trend is to move towards higher resolution images, yet most of the well known DNN based object detectors operate at very small image resolution which restricts range of detection. We demonstrate that the use of ROI instead of entire image can help in increasing detection range. Finally, in the context of autonomous driving these DNNs needs to run on low powered Edge devices. We measure the deployability of these DNNs by measuring their run time performance on Nvidia Drive PX2.
An Adaptive Latent Factor Model via Particle Swarm Optimization for High-Dimensional and Sparse Matrices

Latent factor (LF) models are greatly efficient in extracting valuable knowledge from High-Dimensional and Sparse (HiDS) matrices which are commonly seen in many industrial applications. Stochastic gradient descent (SGD) is an efficient scheme to build an LF model, yet its convergence rate depends vastly on the learning rate which should be tuned with care. Therefore, automatic selection of an optimal learning rate for an SGD-based LF model is a significant issue. To address it, this study incorporates the principle of particle swarm optimization (PSO) into an SGD-based LF model for searching an optimal learning rate automatically. With it, we further propose an adaptive Latent Factor (ALF) model. Empirical studies on two HiDS matrices from industrial applications indicate that an ALF model obviously outperforms an LF model in terms of convergence rate, and maintains competitive prediction accuracy for missing data.
Simple and Efficient KDM-CCA Secure Public Key Encryption

We propose two efficient public key encryption (PKE) schemes satisfying key dependent message security against chosen ciphertext attacks (KDM-CCA security). The first one is KDM-CCA secure with respect to affine functions. The other one is KDM-CCA secure with respect to polynomial functions. Both of our schemes are based on the KDM-CPA secure PKE schemes proposed by Malkin, Teranishi, and Yung (EUROCRYPT 2011). Although our schemes satisfy KDM-CCA security, their efficiency overheads compared to Malkin et al.'s schemes are very small. Thus, efficiency of our schemes is drastically improved compared to the existing KDM-CCA secure schemes. We achieve our results by extending the construction technique by Kitagawa and Tanaka (ASIACRYPT 2018). Our schemes are obtained via semi-generic constructions using an IND-CCA secure PKE scheme as a building block. We prove the KDM-CCA security of our schemes based on the decisional composite residuosity (DCR) assumption and the IND-CCA security of the building block PKE scheme. Moreover, our security proofs are tight if the IND-CCA security of the building block PKE scheme is tightly reduced to its underlying computational assumption. By instantiating our schemes using existing tightly IND-CCA secure PKE schemes, we obtain the first tightly KDM-CCA secure PKE schemes whose ciphertext consists only of a constant number of group elements.
A Novel Encryption of Text Messages Using Two Fold Approach

Background: The amount of digital data created and shared via internet has been increasing every day. The number of security threats has also increased due to the vulnerabilities present in the network hardware and software. Cryptography is the practice and study of techniques to secure communication in the presence of third parties. Though there are several cryptosystems to secure the information, there is a necessity to introduce new methods in order to protect information from the attackers. Objective: To propose a new encryption method using Binary Tree Traversal (BTT) and XOR operation to protect the text messages. Methods: The proposed method uses both transposition and substitution techniques for converting plaintext into ciphertext. The notion binary tree traversal is adapted as transposition and bitwise XOR operation is used for substitution. Results: The repeating letters appeared in the plaintext are replaced with different cipher letters and placed on different location in the cipher text. Hence, it is infeasible to identify the plaintext message easily. The time taken by the proposed method for encryption is very less. Conclusion: A simple encryption method using binary tree traversals and XOR operation is developed. Encrypting data using binary tree traversals is a different way while compared with other traditional encryption methods. The proposed method is fast, secure and can be used to encrypt short messages in real time applications.
On the Complexity of Collision Resistant Hash Functions: New and Old Black-Box Separations

The complexity of collision-resistant hash functions has been long studied in the theory of cryptography. While we often think about them as a Minicrypt primitive, black-box separations demonstrate that constructions from one-way functions are unlikely. Indeed, theoretical constructions of collision-resistant hash functions are based on rather structured assumptions. We make two contributions to this study:
Optimal Number of Clusters Finding Using the Fireworks Algorithm

The main goal of this paper is to find the optimal number of clusters for data set ungrouped using an optimization algorithm; in this case, we are using Fireworks Algorithm (FWA). The optimal number of clusters will be finding based on centroids and to determine the maximum K centroids of the interval where to search the Fireworks Algorithm, we are introducing two statistics rules: Sturges Law and the square root of N, also we are introducing two metrics to evaluate the clusters, which are Intra-clusters and Inter-clusters.
A Shape Comparison Reinforcement Method Based on Feature Extractors and F1-Score

Evaluating object segmentation is a topic of great interest for shape comparison techniques. In this work, ad-hoc metrics for a detailed segmentation analysis and a novel keypoint based method for comparing pairs of shapes are presented. As references, two different segmentation approaches were used: a handmade segmentation and an automatic one based on a Convolutional Neural Network (CNN). The proposed comparison approach consists of a combination between a keypoint extractor and an invariant scale shape identifier. The overall validation process is established according to different steps, which allow to measure the similarity between shapes. First, Reinforced Matched (RM) and Reinforced Ratio (RR) strategies are implemented. Moreover, five different state-of-the-art keypoint extractors are compared, i.e., SIFT, SURF, ORB, A-KAZE, and BRISK. Experimental tests were performed on a popular collection of images, i.e., the Berkeley Segmentation Dataset and Benchmark 300 (BSDS300), which contains shapes segmented both manually and automatically. The experimental results have shown the effectiveness of the proposed method.
eXITs: An Ensemble Approach for Imputing Missing EHR Data

Missing data points are prevalent in electronic health records (EHRs) and are an impedance to utilizing machine learning for predictive and classification tasks in healthcare. For this challenge, we developed eXITs - a stacked ensemble learner that employs 6 base models to perform imputation on time series data from 13 different laboratory tests across 8, 267 patients in the MIMIC-III database provided in the ICHI 2019 Data Analytics Challenge on Missing Data Imputation (DACMI). The results show that our ensemble model (avg. nRMSE = 0.200) outperforms the reference model, 3D-MICE (avg. nRMSE = 0.222) by 9.69%.
$X-BAND$ : Expiration Band for Anonymizing Varied Data Streams

The Internet of Things (IoT) has formed a whole new layer of the world built on the Internet, reaching every connected device, actuator, and sensor. Many organizations utilize IoT data streams for research and development purposes. To make value out of these data streams, the data handling party must ensure the privacy of the individuals. The most common approach to provide privacy preservation is anonymization. IoT data provide varied data streams due to the nature of the individual's preference and versatile devices pool. The conventional single-tuple expiration-driven sliding window method is not adequate to provide efficient anonymization. Furthermore, the minimization of missingness has to be considered for the varied data stream anonymization. Therefore, we propose the X-BAND algorithm that utilizes the new expiration-band mechanism for handling varied data streams to achieve efficient anonymization, and we introduce weighted distance function for X-BAND to reduce missingness of published data. Our experiment on real data sets shows that X-BAND is effective and efficient compared to the famous conventional anonymization algorithm FADS. X-BAND demonstrated 5%-11% and 1%-3% less information loss on real data sets Adult and PM2.5, respectively, while performing similar on clustering, comparable to reusing suppression and runtime. Also, the new weighted distance function is effective for reducing missingness for anonymization.
A Comparative Study on New Classification Algorithm using NASA MDP Datasets for Software Defect Detection

Software Defect (SD) prediction motivates as basic position in upgrading software program and also it helps for software testing in reducing time as well as value of it. Defect prediction in software program is distinct as an enormously vital capacity when arranging a software task and considerably more endeavour is needed to comprehend this complicated problem utilizing a software measurements and defect dataset. Measurements are the association among the numerical and categorical attribute value and it applied at the software consequently its miles utilized for predicting defect. In this paper, we discussed about a study on new classification algorithm as know as Mixed Mode Database Miner (MMDBM) utilizing NASA MDP datasets. Finally the proposed algorithm MMDBM classifier progress to less processing time and high level of accuracy rate.
Is Information-Theoretic Topology-Hiding Computation Possible?

Topology-hiding computation (THC) is a form of multi-party computation over an incomplete communication graph that maintains the privacy of the underlying graph topology. Existing THC protocols consider an adversary that may corrupt an arbitrary number of parties, and rely on cryptographic assumptions such as DDH. In this paper we address the question of whether information-theoretic THC can be achieved by taking advantage of an honest majority. In contrast to the standard MPC setting, this problem has remained open in the topology-hiding realm, even for simple “privacy-free” functions like broadcast, and even when considering only semi-honest corruptions. We uncover a rich landscape of both positive and negative answers to the above question, showing that what types of graphs are used and how they are selected is an important factor in determining the feasibility of hiding topology information-theoretically. In particular, our results include the following. Given the strong impossibilities, we put forth a weaker definition of distributional-THC, where the graph is selected from some distribution (as opposed to worst-case).
Deep Learning Based Lesion Detection For Mammograms

Mammogram plays an essential role in reducing breast cancer deaths by detecting cancer early. In this study, Convolutional Neural Networks (CNN) based Faster R-CNN model was applied to detect mass and calcification in breast cancer effectively. Mammography dataset contains public dataset including DDSM, INbreast, and BCD, as well as private dataset, which was obtained from Shenzhen People’s Hospital, China. Final detection result for public dataset was 0.804 in Average Precision (AP), and 0.975 in recall for mass detection, and 0.686 AP and 0.925 recall in calcification detection. Result for private dataset was higher with 0.902 AP and 0.978 recall for mass detection, and 0.605 AP and 0.834 recall for calcification detection.
Effects of time-energy correlation strength in molecular entangled photon spectroscopy

In this paper, we explore the time-energy domain quantum-classical transition comparing a classical pump-probe experiment on a diatomic molecule to its quantum enhanced counterpart, where the pump and probe pulses are substituted by the signal and idler beams of a spontaneous parametric down conversion (SPDC) source. Absorption of biphotons generated with SPDC exploits quantum time-energy entanglement to enhance the overall yield and selectivity of the process, when compared with a classical pump-probe setup, while maintaining femtosecond time resolution. We systematically study the effects of correlation strength on process efficiency and selectivity, comparing the results to classical pump-probe spectra. An excitation scheme to improve the yield based on spectral narrowing of biphotons is shown. The results indicate that the quantum improvements in yield are caused by a more efficient use of the total power available for the process.
Fibonacci multi-modal optimization algorithm in noisy environment

Noises are very common in practical optimization problems. It will cause interference on optimization algorithms and thus makes the algorithms difficult to find a true global extreme point and multiple local extreme points. For the problem, this paper proposes a Fibonacci multi-modal optimization (FMO) algorithm. Firstly, the proposed algorithm alternates between global search and local optimization in order not to fall into local optimum points and to retain multiple optimum points. And then, a Fibonacci regional scaling criterion is proposed in the FMO algorithm to alleviate the effects of noise, and the position of optimum point is determined according to its probability distribution under noise interference. In experiments, we evaluate the performance of the proposed FMO algorithm through 35 benchmark functions. The experimental results show that compared with Particle Swarm Optimization (PSO) algorithm, three improved versions of PSO, and Genetic algorithm (GA), the proposed FMO algorithm can gain more accurate location of optimum point and more global and local extreme points under noisy environment. Finally, an example of practical optimization in radio spectrum monitoring is used to show the performance of the FMO algorithm.
Expectations on the Product Owner Role in Systems Engineering - A Scrum Team's Point of View

Product Owners (PO) in the Scrum framework have an important role in the development process. While the POs primal description is straightforward - the actual implementation of this role in industries is not. Many companies face the difficulty of assigning the right level of tasks and responsibilities to this role. To provide solutions to this problem, the Product Owner role became a subject of research. However, most of the studies focus on the actual state of the role to propose solutions on how to tailor it. What is missing is the Scrum Teams perspective. Therefore, we were interested in the Teams expectations on the Product Owner role. Which tasks do they expect to be performed by this role and how would they characterize a perfect Product Owner? In an attempt to answer this question, we conducted an online survey in the oil and gas industry and gathered feedback from all members of the Scrum Team. Our findings indicate that the Scrum Team expects the Product Owner to write user stories and prioritize the backlog solely. Other tasks are expected to be done either in direct collaboration with the management or the scrum master or are not expected to be done by the Product Owner role at all. Also, the respondents would characterize a perfect Product Owner primarily as trustworthy.
Towards Complexity Level Classification of Driving Scenarios Using Environmental Information

A driver’s driving performance and behavior are highly related to their cognitive workload, which corresponds as well to the complexity of their driving scenarios. Their performance might be impacted when driving through downtown on a rainy night, compared with driving on a remote countryside road on a sunny afternoon. Therefore, advanced intelligent vehicles should have the ability to assess the complexity level of the current driving scenario and therefore understand a driver’s cognitive workload to prevent potential hazard situations. In this study, environmental information extracted from the OpenStreetMap (OSM) domain, surrounding vehicle information derived from the video, and prior environmental knowledge such as weather, time and driving location are employed to explore the capability of classifying the complexity level of driving scenarios. A 0.855 F1-score was achieved using the random forest algorithm and combining all features extracted from the environmental information. Conclusions with potential benefits of the development of intelligent vehicles and future study directions are also discussed.
Application of the Gravitational Search Algorithm for Constructing Fuzzy Classifiers of Imbalanced Data

The presence of imbalance in data significantly complicates the classification task, including fuzzy systems. Due to a large number of instances of bigger classes, instances of smaller classes are not recognized correctly. Therefore, additional tools for improving the quality of classification are required. The most common methods for handling imbalanced data have several disadvantages. For example, methods for generating additional instances of minority classes can worsen classification if there is a strong overlap of instances from different classes. Methods that directly modify the fuzzy classification algorithm lead to a decline in the interpretability of the model. In this paper, we study the efficiency of the gravitational search algorithm in the tasks of selecting the features and tuning the term parameters for fuzzy classifiers of imbalanced data. We consider only data with two classes and apply the algorithm based on extreme values of classes to construct models with a minimum number of rules. In addition, we propose a new quality metric based on the sum of the overall accuracy and the geometric mean with the presence of a priority coefficient between them.
Dynamic Object Tracking and 3D Surface Estimation using Gaussian Processes and Extended Kalman Filter

Dynamic object tracking is a prerequisite for the successful application of driving assistance and autonomous driving functions. With the advent of high resolution Light Detection and Ranging (LiDAR) sensors, more than one measurement emerges from the object's surface per measurement cycle which leads to an extended object tracking approach. The main research questions of this work are how to model the object's surface and the measurement model and how to estimate this surface recursively along with the kinematic state, e.g., position and velocities.We model the object extent in spherical coordinates as the radius over the azimuth and elevation angle in object centered coordinates. To this end we apply a recursive formulation of a Gaussian Process (GP) which estimates the surface radius and provides a very flexible and parameter-free model of the object surface. An augmented state space system is formulated with the state comprised of the kinematic state and the radius estimates for a set of basis points. Due to the non-linear measurement equation, an Extended Kalman Filter (EKF) is applied to recursively estimate mean and covariance of the state online over time. The proposed approach is evaluated in simulation given a point cloud model of an actual vehicle.Evaluation results show, that the GP formulation is capable of accurately representing any complex star-shaped surface in a parameter-free fashion. A recursive approximation of the GP enables recursive online tracking performance.
Tackling Imbalanced Class on Cross-Project Defect Prediction Using Ensemble SMOTE

Abstract The dataset with imbalanced class can reduce the performance of the classifiers. In this study proposed a cross-project software defect prediction model that applies the SMOTE (Synthetic Minority Oversampling Technique) to balance classes in datasets and ensembles technique to reduce misclassification. The ensemble technique using AdaBoost and Bagging algorithms. The results of the study show that the model that integrates SMOTE and Bagging provides better performance. The proposed model can find more software defects and more precise.
Bit Plane Slicing-Based Progressive Visual Secret Sharing for Grayscale and Color Images

This paper presents a new technique for generating a set of shared image from a single secret image under the bit plane slicing-based approach. The proposed method inherits the usability and effectiveness of the former Progressive Visual Secret Sharing (PVSS) task. The proposed scheme also overcomes the slight limitation of former scheme on the lossy recovered secret image. As documented in Experimental Section, the proposed method achieves lossless recovered secret image, at the same time, it offers better performance compared to the former scheme. In addition, this finding is also supported with the theoretical analysis.
Spoken Language to Sign Language Translation System Based on HamNoSys

Sign languages use visual-manual modality to convey information, thereby enabling communication between hearing-impaired people. However, there is no fully developed technology that enables the communication between hearing-impaired people and those with no hearing disabilities. Current approaches to sign language translation are based on videos and pictures that are difficult to edit after being recorded. Here, we propose a new framework based on speech recognition, natural language processing, and 3D virtual human technology. Our current method (1) provides a new translation method based on a visual human, and the gestures can be easily edited using the HamNoSys keyboard; (2) can be used in the translation of various languages, including Chinese sign language(CSL), American sign language (ASL), and British sign language(BSL); (3) achieves grammar conversion between sign and spoken languages. Through the preliminary test of a simple conversation, the unilateral translation from spoken to sign languages is achieved. Further improvements will be obtained by the incorporation of more vocabulary and grammar conversion rules.