Reviewerly/exHarmony huggingface dataset:
Improving Speed of Dilithium’s Signing Procedure

Dilithium is a round 2 candidate for digital signature schemes in NIST initiative for post-quantum cryptographic schemes. Since Dilithium is built upon the “Fiat Shamir with Aborts” framework, its signing procedure performs rejection sampling of its signatures to ensure they do not leak information about the secret key. Thus, the signing procedure is iterative in nature with a number of rejected iterations, which serve as unnecessary overheads hampering its overall performance. As a first contribution, we propose an optimization that reduces the computations in the rejected iterations through early-evaluation of the conditional checks. This allows to perform an early detection of the rejection condition and reject a given iteration as early as possible. We also incorporate a number of standard optimizations such as unrolling and inlining to further improve the speed of the signing procedure. We incorporate and evaluate our optimizations over the software implementation of Dilithium on both the Intel Core i5-4460 and ARM Cortex-M4 CPUs. As a second contribution, we identify opportunities to present a more refined evaluation of Dilithium’s signing procedure in several scenarios where pre-computations can be carried out. We also evaluate the performance of our optimizations and the memory requirements for the pre-computed intermediates in the considered scenarios. We could yield speed-ups in the range of 6% upto 35%, considering all the aforementioned scenarios, thus presenting the fastest software implementation of Dilithium till date.
Rough Terrain Autonomous Vehicle Control Using Google Cloud Vision API

Computer vision (CV) represents a technology using which information contained in a visual scenery is obtained by capturing and digital processing of the images. Computer vision is becoming more popular and widely used in automated vehicles, robotics to emulate human vision capability and industrial automation. Google cloud vision API is a remote real time image processing platform to process, classify and acquire required metadata from image and videos. In this work, design and implementation of a rough terrain autonomous vehicle using Rocker Bogie mechanism and Google cloud vision API for obstacle avoidance is presented. Rocker Bogie mechanism is employed for maneuvering the robot on rough terrains.
A Mobile Educational Lab Kit for Fragile Contexts

In this paper, we discuss an innovative approach to learning engineering and building a pathway to entrepreneurial innovations through a versatile educational lab kit, the EngStarter. The EngStarter emerged as a result of three years of experience in offering university-level engineering to refugee learners. This engineering kit is an open-source hardware educational kit designed to provide students with the necessary tools, instruments, and pedagogies to build real-world solutions to their problems while learning fundamental concepts of electronic circuits, programming, and the internet of things. EngStarter is designed to amplify educational practitioners' ability to facilitate curricula that are socially driven by demonstrating different ways that technology can help students to solve local problems and thereby foster self-reliance and sustainability.
Planning a Shortest Path Based on Detour and Path Shortening

This article aims to study path planning for autonomous unmanned vehicles, which includes the shortest path and the obstacle avoidance function. The path planning method in this article is based on a known environment for the robot in a point-to-point situation, thus it is different from the obstacle avoidance and path optimization method made by the probabilistic roadmap planners or the heuristic search method. The first task is to obtain a rough path, by avoiding obvious obstacles on the path using the concept of detour, and then apply the Constrained Delaunay triangulation to find the shortest path by applying the Dijkstra algorithm if the starting and/or destination points are in the concave obstacle. Finally, the Path Shortening method is used to obtain a suboptimal solution.
Zero-Order Moving Horizon Estimation

Moving Horizon Estimation (MHE) is an optimization-based approach to nonlinear state estimation. The computational burden associated with the online solution of the corresponding nonlinear optimization problems poses a major challenge when applying MHE in practice. Motivated by these considerations, we introduce zero-order MHE, an inexact, but computationally less expensive variant of exact MHE. Zero-order MHE is based on the Gauss-Newton algorithm and avoids online evaluation of derivatives and factorizations. As for exact MHE, the estimation error produced by zero-order MHE would become zero in the absence of noise and model-plant mismatch, and grows linearly with the noise level. In addition, we present a structure-exploiting approach for recursive factorization of the Gauss-Newton Hessian approximation which allows for efficient arrival cost updates. Zero-order MHE is compared to exact and linear MHE both theoretically, in terms of estimation error bounds, and numerically, by applying the methods to a state estimation example.
Fractional Reservation Based Mempool Processing in Blockchains

A massive traffic events that impact all the nodes in the distributed system may cause a Denial of Service (DoS). Managing DoS attacks is even harder in peer-to-peer projects because of multiple equal in rights nodes (miners or maintainers) that communicate across the globe to secure the network. In most blockchains, users can send transactions. Moreover, as a systems throughput is limited, the ability to send transactions should be limited in some honest and transparent way. Otherwise, the pool of unconfirmed (pending) transactions mempool could be overloaded, and it may cause DoS. In Bitcoin, users pay a fee for each transaction to address this issue. Steem.io introduced an alternative approach based on the fractional reservation of the blockchain block space. This approach is an adaptation of similar ones from the network routing and banking systems. The block space fractional reservation for blockchains in terms of a score function is introduced in this paper. Authors made a private blockchain project demo on Exonum framework. The score function influences only on mempool processing, and other blockchains can also make use of it.
Approximation in L<sup>p</sup>(µ) with deep ReLU neural networks

We discuss the expressive power of neural networks which use the non-smooth ReLU activation function ρ(x) = max{0, x} by analyzing the approximation theoretic properties of such networks. The existing results mainly fall into two categories: approximation using ReLU networks with a fixed depth, or using ReLU networks whose depth increases with the approximation accuracy. After reviewing these findings, we show that the results concerning networks with fixed depth - which up to now only consider approximation in L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> (λ) for the Lebesgue measure λ - can be generalized to approximation in L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> (μ), for any finite Borel measure μ. In particular, the generalized results apply in the usual setting of statistical learning theory, where one is interested in approximation in L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (P), with the probability measure P describing the distribution of the data.
A Study on Low-resource Language Identification

Modern language identification (LID) systems require a large amount of data to train language-discriminative models, either statistical (e.g., i-vector) or neural (e.g., x-vector). Unfortunately, most of languages in the world have very limited accumulation of data resources, which result in limited performance on most languages. In this study, two approaches are investigated to deal with the LID task on low-resource languages. The first approach is data augmentation, which enlarges the data set by incorporating various distortions into the original data; and the second approach is multi-lingual bottleneck feature extraction, which extracts multiple sets of bottleneck features (BNF) based on speech recognition systems of multiple languages. Experiments conducted on both the i-vector and x-vector models demonstrated that the two approach are effective, and can obtain promising results on both in-domain data and out-of-domain data.
Machine Learning Is the Future for Lung Cancer Prognosis and Prediction

Lung cancer is one of the major reasons for the death if it is not diagnosed in the early stages of cancer. It is the one among the most dreadful disease which affects in the lungs function. It can be identified only after the disease spread into the deeper parts of the lungs and then only it will make a life threading problem. Lung cancer prognosis which was done based on the various parameters such as age, sex, condition of smoking, duration of smoking and count of smoking per day. The proceedings were done using the algorithm for the time to first cigarette after awakening which is represented as TTFC. The expert doctor says that the back-propagation network is a great deal in the recognition of the lung cancer without any involvement by them. This research is based on the classification of lung cancer and its stages using the establishment of the BPN and predicts the recurrence. Similarly, with this BPN, an algorithm that is inspired from its habitat known as ant lion optimization algorithm is also used in the optimization of weights and parameters of the BPN. The use of the ALO algorithm provides an improved convergence mechanism by improving the proposed technique's accuracy. The use of this proposed method with the BPN optimizes the network and the ALO optimizer provides an accurate prediction of the lung cancer by the earlier stage and even predicts the changes for reoccurrence after diagnosis. The prognosis analysis was made by the various comparative study between the characteristic features of HIV and the unaffected person using the algorithm such as the Wilcoxon rank-sum test. This algorithm will continuously classify the viral load and CD4 count which is based on factors such as age, sex, and smoking activities. It will be useful for early diagnosis and future prediction. Lung cancer rates can be analyzed based on the incident rates of affected and unaffected persons to HIV infections.
Design of an autonomous electric single-seat vehicle based on environment recognition algorithms

Today's autonomous vehicles are equipped with a variety of sensors, control techniques and actuators depending on the objectives and requirements of the addressing issue. In research, some even employ commercial cars to house their devices. The high cost of all these components represents a limitation in the development of an autonomous vehicle system. Therefore, researchers have no option but to rely on experimentation under simulated environments or to use small vehicles that in many cases do not accurately represent a real parametric dynamic system. This article describes the design of an autonomous electric vehicle prototype, which includes control systems, microcontrollers, encoders, motor controllers, LIDAR, and cameras, with the aim of having a reference in the literature that is low-cost and whose program code is employable in a real-life context. The present vehicle, whose structure is based on a conventional tubular go-kart, has been provided with the capability of moving autonomously by using computer vision and artificial intelligence algorithms. This document records the first steps of the design taking into account parameters of a real-life environment and the flexibility of A.I. algorithms.
Side Channel Attack Resistant AES Design Based on Finite Field Construction Variation

The Advanced Encryption Standard (AES) is the current standard for symmetric key cipher and is algorithmically secure. Side channel attacks that target power consumption can reveal the secret key in AES implementations. Masking data with random variables is one of the main methods used to thwart power analysis attacks. Data can be masked with multiple random variables to prevent higher-order attacks at the cost of a large increase in area. A novel masking scheme for AES resistant to second-order attacks is proposed. Instead of using an additional mask, variation in finite field construction is exploited to increase resistance to second-order attacks. As a result, the area requirement is reduced. For an example AES encryptor, the proposed design is 12% smaller compared to the previous best design, with a very small drop in achievable security level.
Accuracy Comparison of Classification Techniques for Mouse Dynamics-Based Biometric CaRP

Combining more than one authentication schemes enhances the robustness of a system against cyber-attacks. In this paper, we introduce a novel mouse dynamics-based biometric CaRP (CAPTCHA as gRaphical Password). It combines mouse dynamics-based biometric authentication scheme with knowledge-based authentication scheme. This study primarily focuses on the comparison of classification accuracy of binary decision tree, SVM, and ANN for proposed mouse dynamics-based authentication scheme of CaRP.
Forensic detection of Median filtering in Images using Local Tetra Patterns and J-Divergence

This paper presents a novel application of local tetra patterns to the median filtering detection problem. The premise of the proposed method is based on the ability of the local tetra patterns in identifying the streaking fingerprints left over by the application of a median filter on an image. These streaking fingerprints serve as a clue in determining the authenticity of an image towards the application of a median filter. The streaking pixels are identified by establishing the relationship of every pixel with respect to its neighboring pixels. The relationship is in the form of horizontal and vertical derivative directions and magnitudes followed by the tetra pattern and magnitude assignment. The feature vector generated utilizing the local tetra patterns is reduced by using the J-divergence in-order to keep the computational complexity low. Experimental testing for the proposed method along with comparative analysis carried out with existing state-of-the-art methods shows good performance at reduced computational complexity for the proposed method.
Decision Support System For Determining The Single Tuition Group (UKT) In State University Of Medan Using Fuzzy C-Means

The Single Tuition System (UKT) is a portion of the Single Tuition Fee that is paid to each student based on his economic ability. UKT which applied at State Universities in Indonesian is a policy aimed at helping and reducing the cost of student education. Decree of Menristekdikti Number 91/M/KPT/2018 concerning Single Tuition Fee (BKT) and Single Tuition (UKT) at State Universities in the Ministry of Research, Technology, and Higher Education in 2018 divides UKT into seven groups. The purpose of this study is to develop a decision support system to determine the UKT group using the Fuzzy C-Means Clustering method. The decision support system is developed based on the web using the Fuzzy C-Means Clustering method. The data used as a sample is the data of undergraduate students of the Faculty of Mathematics and Natural Sciences, The State University of Medan in 2018/2019. The parameters used for grouping UKT are: the amount of parents' income, the number of siblings based on the Family Card, the condition of the Father (Life or Death), the condition of the Mother (Life or Death), the Building Land Tax (PBB), the Motor Vehicle Tax (PKB) total last one year, and the last one month electricity costs. The grouping results showed that 76% were in groups 1 to 4, and 24% were in groups 5 to 7. However, the results of this clustering are not final due to inaccuracies in filling data for several parameters, so that data validation is required by checking from other sources of information.
Roger that! Learning How Laypersons Teach New Functions to Intelligent Systems

Intelligent systems are rather smart today but still limited to built-in functionality. To break through this barrier, future systems must allow users to easily adapt the system by themselves. For humans the most natural way to communicate is talking. But what if users want to extend the systems' functionality with nothing but natural language? Then intelligent systems must understand how laypersons teach new skills. To grasp the semantics of such teaching sequences, we have defined a hierarchical classification task. On the first level, we consider the existence of a teaching intent in an utterance; on the second, we classify the distinct semantic parts of teaching sequences: declaration of a new function, specification of intermediate steps, and superfluous information. We evaluate twelve machine learning techniques with multiple configurations tailored to this task ranging from classical approaches such as naïve-bayes to modern techniques such as bidirectional LSTMs and task-oriented adaptations. On the first level convolutional neural networks achieve the best accuracy (96.6%). For the second task, bidirectional LSTMs are the most accurate (98.8%). With the additional adaptations we are able to improve both classifications distinctly (up to 1.8%).
Combined Item Sets Generation using Modified Apriori Algorithm

Association rule mining is used to find association relationships among data sets. Apriori algorithm is one of the classical algorithms of association rule mining. It generates the association rules from transaction data, such as, if item 'a' is bought then what are the chances to buy item 'b'. It uses support and confidence values to generate the association rule.
A Group Mutual Exclusion protocol for the Use Case of IoT-Blockchain Integration In Work-Safe Scenario

The Blockchain empowers IoT ecosystems more secure and transparent. AS a result, integrating these two technologies can have a significant impact across multiple industries. In this paper, a sample use case related to safe-work has been discussed. A group mutual exclusion (GME) protocol has been proposed for the consensus operation of different authorities/stakeholder to update the distributed ledger of the hybrid system. The protocol utilises a single token to enable various processes of different stakeholders to enter into a common session to reach the consensus among themselves. In other words, we have introduced a novel consensus protocol-proof of presence. The protocol offers a crucial characteristic-the concurrency, throughput, and waiting time can be tailored by adjusting the period for which a session is declared. The protocol also ensures no starvation in the system. Furthermore, this algorithm works out for the Extended Group Mutual Exclusion problem as well.
I Know Who You are: A Learning Framework to Profile Smartphone Users

The volume of traffic belonging to mobile applications over the Internet has already crossed the traffic generated due to traditional desktop-based Internet browsing. To protect security and privacy of the mobile user, many smartphone applications use encryption to encapsulates communications over the Internet. However it is not possible to decode the actual message contents, even then the statistical information present in the traffic is useful to identify application and the associated activity. This paper proposes a machine learning based framework to analyze the encrypted mobile traffic with the objective of finding the mobile application usage patterns of smartphone users. This information can be used by various authorities to profile mobile users with respect to their age, gender profession, etc. which would further help them to look for any suspicious or anomalous behaviour. Proposed framework has been tested on the network traffic data pertaining to popular smartphone applications such as GMail, Facebook, and Youtube and have achieved 97.46% accuracy for application identification. Further, the proposed framework also achieved a fairly high accuracy, 77.37% when used for the classification of exact activity performed by the smartphone user in different applications.
But anyone can mix their labor: a reply to Cheneval

Francis Cheneval has recently argued that people have property rights over personal data about themselves. Until now, the discussion on data ownership has primarily been a discussion among legal theorists and economists. Cheneval contribution to the discussion is a very welcome input from academic philosophy. Cheneval attempts to reach his conclusion through two distinct strategies. One strategy is to reach the conclusion through a Lockean inspired libertarian rights-based theory of property. The second strategy is to reach his conclusion through a Rawlsian account of distributive justice. According to Cheneval, his conclusion can be reached both ways. In this reply, I will focus exclusively on Cheneval argument that people have Lockean inspired libertarian property rights over personal data. I will offer an objection, which– if correct –demonstrates demonstrates how Cheneval Lockean argument runs into a dilemma.
Towards Blockchain for Decentralized Self-Organizing Wireless Networks

Distributed consensus mechanisms have been widely researched and made popular with a number of blockchain-based token applications, such as Bitcoin, and Ethereum. Although these general- purpose platforms have matured for scale and security, they are designed for human incentive and continue to require currency reward and contract functions that are not requisite in machine communications. Redes Chain is a new blockchain, built to support fully decentralized self-organization in wireless networks - without a cryptocurrency or contract dependency.
Research on the Ability Evaluation of "Double-Qualified and Double-Ability" Teacher in M University Based on Fuzzy-AHP

As a local undergraduate university, M University is just in the process of the application-oriented transformation. The "double-qualified and double-ability" teachers are the key factor for the applied talent training. But how to evaluate a teacher's abilities in this field? Here, firstly, we construct the index system of ability evaluation of the "double-qualified and double-ability" teacher which includes the elements of the basic teaching ability, the professional and technical ability and the innovative and entrepreneurial ability. Then, the indicator weights are carried out by the AHP method and the comprehensive evaluation made by the Fuzzy. Finally, empirical analysis being made to investigate a teacher's abilities of "double-qualified" and "double-ability".
Research on Interface Improvement of English Vocabulary Learning APP

This study is mainly to explore the improvement of the auxiliary learning English vocabulary APP, to stimulate the motivation of autonomous learning and the interest in language learning, and to promote the lifelong learning of mobile devices. The research method first analyzes the usability of the existing English vocabulary learning APP, and then improves the new version of the vocabulary learning APP for the image and the use process, and then compares the items of the usability problem with the questionnaire. From the comparison of interface layout, image and pleasure, we can clearly understand the needs of people's visual experience, and then show the connection between learning experience and learning effect, and achieve better learning results.