Reviewerly/exHarmony huggingface dataset:
Trajectory planning in 3D dynamic environment with non-cooperative agents via fast marching and Bézier curve

In this paper, we propose a framework for trajectory planning in a 3D dynamic environment where other non-cooperative agents may obstruct the active quadrotor. A trajectory predictor is designed for the non-cooperative agent via least squares. In addition, based on Euclidean signed distance field (ESDF), a velocity map configuration is developed for both dynamic and static obstacles, and then utilized in fast marching method to find a feasible discrete path. The smooth and dynamic feasible trajectory generation problem is formulated as a quadratic programming (QP) problem, where a safe flight corridor generator is developed for safe constraints in a dynamic environment. Piecewise Bézier curves are used to represent the generated trajectory. With the help of flight corridor, along with the convex hull property of Bézier curve, the quadratic programming problem turns into a convex optimization problem. Simulation results show that the proposed approach is validated for collision avoidance in a dynamic environment.
Handwritten Manipuri Meetei-Mayek Classification Using Convolutional Neural Network

A new technique for classifying all 56 different characters of the Manipuri Meetei-Mayek (MMM) is proposed herein. The characters are grouped under five categories, which are Eeyek Eepee (original alphabets), Lom Eeyek (additional letters), Cheising Eeyek (digits), Lonsum Eeyek (letters with short endings), and Cheitap Eeyek (vowel signs. Two related works proposed by previous researchers are studied for understanding the benefits claimed by the proposed deep learning approach in handwritten Manipuri Meetei-Mayek. (1) Histogram of Oriented (HOG) with SVM classifier is implemented for thoroughly understanding how HOG features can influence accuracy. (2) The handwritten samples are trained using simple Convolutional Neural Network (CNN) and compared with the proposed CNN-based architecture. Significant progress has been made in the field of Optical Character Recognition (OCR) for well-known Indian languages as well as globally popular languages. Our work is novel in the sense that there is no record of work available to date that is able to classify all 56 classes of the MMM. It will also serve as a pre-cursor for developing end-to-end OCR software for translating old manuscripts, newspaper archives, books, and so on.
Albanian Text Classification: Bag of Words Model and Word Analogies

Abstract Background: Text classification is a very important task in information retrieval. Its objective is to classify new text documents in a set of predefined classes, using different supervised algorithms. Objectives: We focus on the text classification for Albanian news articles using two approaches. Methods/Approach: In the first approach, the words in a collection are considered as independent components, allocating to each of them a conforming vector in the vector’s space. Here we utilized nine classifiers from the scikit-learn package, training the classifiers with part of news articles (80%) and testing the accuracy with the remaining part of these articles. In the second approach, the text classification treats words based on their semantic and syntactic word similarities, supposing a word is formed by n-grams of characters. In this case, we have used the fastText, a hierarchical classifier, that considers local word order, as well as sub-word information. We have measured the accuracy for each classifier separately. We have also analyzed the training and testing time. Results: Our results show that the bag of words model does better than fastText when testing the classification process for not a large dataset of text. FastText shows better performance when classifying multi-label text. Conclusions: News articles can serve to create a benchmark for testing classification algorithms of Albanian texts. The best results are achieved with a bag of words model, with an accuracy of 94%.
The Influence of Generational Diversity Management and Leader–Member Exchange on Innovative Work Behaviors Mediated by Employee Engagement

This study investigates the influence of generational diversity management and the quality of leader–member exchanges on innovative work behaviors, mediated by employee engagement. A group of 1839 employees at Indonesia’s largest telecommunications company, which represents three generations – Baby Boomers, Gen X, and Gen Y – participated in the study. The results suggesting that employee engagement strengthens the influence of quality in leader–member exchanges, and diversity management leads to positive innovative work behaviors. Generational diversity, leader–member exchange, and innovative work behaviors are lowest among Gen Y, but are highest among Gen X. Employee engagement peaks in Baby Boomers.
Multi-View Saliency-Guided Clustering for Image Cosegmentation

Image cosegmentation aims at extracting the common objects from multiple images simultaneously. Existing methods mainly solve cosegmentation via the pre-defined graph, which lacks flexibility and robustness to handle various visual patterns. Besides, similar backgrounds also confuse the identifying of the common foreground. To address these issues, we propose a novel Multi-view Saliency-Guided Clustering algorithm (MvSGC) for the image cosegmentation task. In our model, the unsupervised saliency prior is used as partition-level side information to guide the foreground clustering process. To achieve robustness to noises and missing observations, similarities on instance-level and partition-level are both considered. Specifically, a unified clustering model with cosine similarity is proposed to capture the intrinsic structure of data and keep partition result consistent with the side information. Moreover, we leverage multi-view weight learning to integrate multiple feature representations to further improve the robustness of our approach. A K-means-like optimization algorithm is developed to proceed the constrained clustering in a highly efficient way with theoretical support. Experimental results on three benchmark datasets (i.e., the iCoseg, MSRC and Internet image dataset) and one RGB-D image dataset demonstrate the superiority of applying our clustering method for image cosegmentation.
Proof-of-Prestige: A Useful Work Reward System for Unverifiable Tasks

As cryptographic tokens and altcoins are increasingly being built to serve as utility tokens, the notion of useful work consensus protocols, as opposed to number-crunching PoW consensus, is becoming ever more important. In such contexts, users get rewards from the network after they have carried out some specific task useful for the network. While in some cases the proof of some utility or service can be proved, the majority of tasks are impossible to verify. In order to deal with such cases, we design “Proof-of-Prestige” (PoP)-a reward system that can run on top of Proof-of-Stake blockchains. PoP introduces “prestige” which is a volatile resource and, in contrast to coins, regenerates over time. Prestige can be gained by performing useful work, spent when benefiting from services and directly translates to users minting power. PoP is resistant against Sybil and Collude attacks and can be used to reward workers for completing unverifiable tasks, while keeping the system free for the end-users. We use two exemplar use-cases to showcase the usefulness of PoP and we build a simulator to assess the cryptoeconomic behaviour of the system in terms of prestige transfer between nodes.
Survey of Hyperledger Blockchain Frameworks

There are many blockchain frameworks have been introduced to user, developer, and customer and they have to make a decision which one is most suitable for them in order for developing their applications. This paper describes an empirical research to benchmark the properties of frameworks in Hyperledger family. The purpose is to show the advantage & disadvantage of each framework. We apply the deeply study inside the framework and do the experiment to measure the properties as well as give literature and numerical review of each framework. Our results show the status of tested frameworks and do not demonstrate how to employ the framework as well as apply to the detail application. The main contribution of this paper is a statistical analysis of the effectiveness of Hyperledger frameworks as a tool for developer to develop their applications.
Repair-Based Degrees of Database Inconsistency

We propose and investigate a concrete numerical measure of the inconsistency of a database with respect to a set of integrity constraints. It is based on a database repair semantics associated to cardinality-repairs. More specifically, it is shown that the computation of this measure can be intractable in data complexity, but answer-set programs are exhibited that can be used to compute it. Furthermore, its is established that there are polynomial-time deterministic and randomized approximations. The behavior of this measure under small updates is analyzed, obtaining fixed-parameter tractability results. We explore abstract extensions of this measure that appeal to generic classes of database repairs. Inconsistency measures and repairs at the attribute level are investigated as a particular, but relevant and natural case.
Effects of measurements on correlations of software code metrics

Software metrics play a significant role in many areas in the life-cycle of software including forecasting defects and foretelling stories regarding maintenance, cost, etc. through predictive analysis. Many studies have found code metrics correlated to each other at such a high level that such correlated code metrics are considered redundant, which implies it is enough to keep track of a single metric from a list of highly correlated metrics. Software is developed incrementally over a period. Traditionally, code metrics are measured cumulatively as cumulative sum or running sum. When a code metric is measured based on the values from individual revisions or commits without consolidating values from past revisions, indicating the natural development of software, this study identifies such a type of measure as organic. Density and average are two other ways of measuring metrics. This empirical study focuses on whether measurement types influence correlations of code metrics. To investigate the objective, this empirical study has collected 24 code metrics classified into four categories, according to the measurement types of the metrics, from 11,874 software revisions (i.e., commits) of 21 open source projects from eight well-known organizations. Kendall's τ-B is used for computing correlations. To determine whether there is a significant difference between cumulative and organic metrics, Mann-Whitney U test, Wilcoxon signed rank test, and paired-samples sign test are performed. The cumulative metrics are found to be highly correlated to each other with an average coefficient of 0.79. For corresponding organic metrics, it is 0.49. When individual correlation coefficients between these two measure types are compared, correlations between organic metrics are found to be significantly lower (with p < 0.01) than cumulative metrics. Our results indicate that the cumulative nature of metrics makes them highly correlated, implying cumulative measurement is a major source of collinearity between cumulative metrics. Another interesting observation is that correlations between metrics from different categories are weak. Results of this study reveal that measurement types may have a significant impact on the correlations of code metrics and that transforming metrics into a different type can give us metrics with low collinearity. These findings provide us a simple understanding how feature transformation to a different measurement type can produce new non-collinear input features for predictive models.
Region-Based Context Enhanced Network for Robust Multiple Face Alignment

The recent studies for face alignment have involved developing an isolated algorithm on well-cropped face images. It is difficult to obtain the expected input by using an off-the-shelf face detector in practical applications. In this paper, we attempt to bridge between face detection and face alignment by establishing a novel joint multi-task model, which allows us to simultaneously detect multiple faces and their landmarks on a given scene image. In contrast to the pipeline-based framework by cascading separate models, we aim to propose an end-to-end convolutional network by sharing and transform feature representations between the task-specific modules. To learn a robust landmark estimator for unconstrained face alignment, three types of context enhanced blocks are designed to encode feature maps with multi-level context, multi-scale context, and global context. In the post-processing step, we develop a shape reconstruction algorithm based on point distribution model to refine the landmark outliers. Extensive experiments demonstrate that our results are robust for the landmark location task and insensitive to the location of estimated face regions. Furthermore, our method significantly outperforms recent state-of-the-art methods on several challenging datasets including 300 W, AFLW, and COFW.
New types of graphs induced by topological spaces

In this article, a technique to construct a new type of topological structures by graphs called topological graphs is introduced. We use the concept of a homeomorphism between topological graphs as a topological property to prove the isomorphic between graphs. We construct a computer program that builds graphs and its topological graphs. This model helps in studying many topological properties such as continuity, connectedness, compactness and separation axioms on graphs. Finally, we present some examples of different types of graphs and their topological graphs and study some of their topological and algebraic properties.
A Two-stage Method For Hand-Raising Gesture Recognition in Classroom

With the development of visual technology, automatic classroom behavior analysis has attracted more and more attention, hand-raising gesture recognition is the most essential one of them. There exist a lot of challenging cases in the uncontrolled environment, such as unknown number of students and their position, occluded body parts and low-resolution hands area. In order to solve the above challenges, we propose a two-stage method for identifying hand-raising gesture in real classroom scenarios. The first stage performs a multi-stage pose estimation to get the candidate hand-based area of each student, only assemble keypoints on the raising arm and use multi-scale information fusion to get finer results. The second stage identify the gesture style through binary classi-fication network. The experiment has proved the effectiveness of our proposed method and achieved an accuracy of 94.76% with our self-built hand-raising dataset.
Hierarchical neural query suggestion with an attention mechanism

Abstract Query suggestions help users of a search engine to refine their queries. Previous work on query suggestion has mainly focused on incorporating directly observable features such as query co-occurrence and semantic similarity. The structure of such features is often set manually, as a result of which hidden dependencies between queries and users may be ignored. We propose an Attention-based Hierarchical Neural Query Suggestion (AHNQS) model that uses an attention mechanism to automatically capture user preferences. AHNQS combines a session-level neural network and a user-level neural network into a hierarchical structure to model the short- and long-term search history of a user. We quantify the improvements of AHNQS over state-of-the-art recurrent neural network-based query suggestion baselines on the AOL query log dataset, with improvements of up to 9.66% and 12.51% in terms of Recall@10 and MRR@10, respectively; improvements are especially obvious for short sessions and inactive users with few search sessions.
Authenticated Call Stack

Shadow stacks are the go-to solution for perfect backward-edge control-flow integrity (CFI). Software shadow stacks trade off security for performance. Hardware-assisted shadow stacks are efficient and secure, but expensive to deploy. We present authenticated call stack (ACS), a novel mechanism for precise verification of return addresses using aggregated message authentication codes. We show how ACS can be realized using ARMv8.3-A pointer authentication, a new low-overhead mechanism for protecting pointer integrity. Our solution achieves security comparable to hardware-assisted shadow stacks, while incurring negligible performance overhead (< 0.5%) but requiring no additional hardware support.
A dual privacy-preservation scheme for cloud-based eHealth systems

With the development of wireless sensor network and internet, ubiquitous eHealth care systems are booming. However, a great deal of data computing and storage processing restrict the scalability of ubiquitous eHealth. As a solution, cloud-based eHealth is coming up. However, using cloud-computing model often causes some privacy concerns. Massive medical data in eHealth system contain numerous sensitive information and electronic health records of users. Meanwhile, if data encryption is adopted to protect privacy, cloud center cannot process data. To solve this problem, a scheme based on fully homomorphism conception for privacy protection and data processing of eHealth is proposed in this paper. Our scheme not only meets the aforementioned dual requirements but also limit the arbitrary actions of patients and doctors.
A Survey on Privacy Preserving Data Mining Approaches and Techniques

In recent years, the importance of the Internet in our personal as well as our professional lives cannot be overstated as can be observed from the immense increase of its users. It therefore comes as no surprise that a lot of businesses are being carried out over the internet. It brings along privacy threats to the data and information of an organization. Data mining is the processing of analyze larger data in order to discover patterns and analyze hidden data concurring to distinctive sights for categorize into convenient information which is collected and assembled in common areas and other information necessities to eventually cut costs and increase revenue. In fact, the data mining has emerged as a significant technology for gaining knowledge from vast quantities of data. However, there was been growing concern that use of this technology is violating individual privacy. This tool aims to find useful patterns from large amount of data using by mining algorithms and approaches. The analysis of privacy preserving data mining (PPDM) algorithms should consider the effects of these algorithms in mining the results as well as in preserving privacy. Therefore, the success of privacy preserving data mining algorithms is measured in term of its performances, data utility, level of uncertainty, data anonymization, data randomization and so on based on data mining techniques and approaches are presented in this paper to analyze.
Securing Cryptographic Circuits by Exploiting Implementation Diversity and Partial Reconfiguration on FPGAs

Adaptive and reconfigurable systems such as Field Programmable Gate Arrays (FPGAs) play an integral part of many complex embedded platforms. This implies the capability to perform runtime changes to hardware circuits on demand. In this work, we make use of this feature to propose a no-vel countermeasure against physical attacks of cryptographic implementations. In particular, we leverage exploration of the implementation space on FPGAs to create various circuits with different hardware layouts from a single design of the Advanced Encryption Standard (AES), that are dynamically exchanged during device operation. We provide evidence from practical experiments based on a modern Xilinx ZYNQ UltraScale+ FPGA that our approach increases the resistance against physical attacks by at least factor two. Furthermore, the genericness of our approach allows an easy adaption to other algorithms and combination with other countermeasures.
Incremental Proofs of Sequential Work

A proof of sequential work allows a prover to convince a verifier that a certain amount of sequential steps have been computed. In this work we introduce the notion of incremental proofs of sequential work where a prover can carry on the computation done by the previous prover incrementally, without affecting the resources of the individual provers or the size of the proofs. To date, the most efficient instance of proofs of sequential work [Cohen and Pietrzak, Eurocrypt 2018] for N steps require the prover to have $$\sqrt{N}$$ memory and to run for $$N + \sqrt{N}$$ steps. Using incremental proofs of sequential work we can bring down the prover's storage complexity to $$\log N$$ and its running time to N. We propose two different constructions of incremental proofs of sequential work: Our first scheme requires a single processor and introduces a poly-logarithmic factor in the proof size when compared with the proposals of Cohen and Pietrzak. Our second scheme assumes $$\log N$$ parallel processors but brings down the overhead of the proof size to a factor of 9. Both schemes are simple to implement and only rely on hash functions (modelled as random oracles).
Touch-Stroke Dynamics Authentication Using Temporal Regression Forest

Touch-stroke dynamics is a relatively recent behavioral biometrics. It authenticates an individual by observing his behavior when swiping a “stroke” on a smartphone or tablet. Several studies have attempted to determine the optimum authentication accuracy of classifiers, but none of them has used time series or temporal machine learning techniques. We postulate that when a user performs a series of touch strokes in a continuous manner, it can be perceived as a temporal behavior characteristic of the person. In this letter, we propose the use of a temporal regression forest to unearth this hidden but vital temporal information. By incorporating this temporal information in the authentication process, the proposed model is able to achieve average equal error rates of ~4.0% and ~2.5% on the Serwadda dataset and Frank dataset, respectively.
Improving Head Pose Estimation with a Combined Loss and Bounding Box Margin Adjustment

We address a problem of estimating pose of a person's head from its RGB image. The employment of CNNs for the problem has contributed to significant improvement in accuracy in recent works. However, we show that the following two methods, despite their simplicity, can attain further improvement: (i) proper adjustment of the margin of bounding box of a detected face, and (ii) choice of loss functions. We show that the integration of these two methods achieve the new state-of-the-art on standard benchmark datasets for in-the-wild head pose estimation. The Tensorflow implementation of our work is available at https://github.com/MingzhenShao/HeadPose.
Distributed Ledger Technologies for M2M Communications

Distributed Ledger Technologies (DLT) are seen as data bases of the future, which reduce the cost of trust and revolutionize transactions between individuals, companies and governments. They are considered as one of the main drivers of future applications, especially in field of Machine to Machine (M2) communications which are one of the basic technologies for Internet of Things (IoT). Therefore, Distributed Ledger Technologies with their inherent property to provide security, privacy and decentralized operation are engine for todays and future reliable, autonomous and trusted IoT platforms. For this reason, IoT using DLT can be considered as “Internet of Trusted Things”. This paper considers tree basic DLT technologies according to their architecture: blockchain, tangle and hashgraph. Their characteristics are compared considering number of criteria, with the aim to find a proper architecture for a specific M2M application.