Reviewerly/exHarmony huggingface dataset:
Improved Leakage-Resistant Authenticated Encryption based on Hardware AES Coprocessors

We revisit Unterstein et al.’s leakage-resilient authenticated encryption scheme from CHES 2020. Its main goal is to enable secure software updates by leveraging unprotected (e.g., AES, SHA256) coprocessors available on low-end microcontrollers. We show that the design of this scheme ignores an important attack vector that can significantly reduce its security claims, and that the evaluation of its leakage-resilient PRF is quite sensitive to minor variations of its measurements, which can easily lead to security overstatements. We then describe and analyze a new mode of operation for which we propose more conservative security parameters and show that it competes with the CHES 2020 one in terms of performances. As an additional bonus, our solution relies only on AES-128 coprocessors, an
The Exploration of Academic English Vocabulary Teaching Model Based on POA

At present, domestic vocabulary teaching, including academic English vocabulary teaching, mostly adopts traditional teaching methods.The current situation is that students with limited academic vocabulary have low academic competence.It is English teachers who have the duty and responsibility to help students expand their academic English vocabulary.Guided by the Production-oriented approach (POA), this article manages to construct the teaching design of academic English vocabulary teaching and explores effective ways to enhance students' academic English proficiency.This article finds that under the guidance of POA theory, academic English vocabulary teaching can more effectively stimulate students' internal motivation, promote the completion of production tasks, enhance students' sense of acquisition, and more accord with China's foreign language teaching practice.
Binary defocusing technique based on complementary decoding with unconstrained dual projectors

Abstract Binary defocusing technique can effectively break the limitation of hardware speed, which has been widely used in the real-time three-dimensional (3D) reconstruction. In addition, fusion technique can reduce captured images count for a 3D scene, which helps to improve real-time performance. Unfortunately, it is difficult for binary defocusing technique and fusion technique working simultaneously. To this end, our research established a novel system framework consisting of dual projectors and a camera, where the position and posture of the dual projectors are not strictly required. And, the dual projectors can adjust defocusing level independently. Based on this, this paper proposed a complementary decoding method with unconstrained dual projectors. The core idea is that low-resolution information is employed for high-resolution phase unwrapping. For this purpose, we developed the low-resolution depth extraction strategy based on periodic space-time coding patterns and the method from the low-resolution order to high-resolution order of fringe. Finally, experimental results demonstrated the performance of our proposed method, and the proposed method only requires three images for a 3D scene, as well as has strong robustness, expansibility, and implementation.
Benchmarking Analysis of the Accuracy of Classification Methods Related to Entropy

In the machine learning literature we can find numerous methods to solve classification problems. We propose two new performance measures to analyze such methods. These measures are defined by using the concept of proportional reduction of classification error with respect to three benchmark classifiers, the random and two intuitive classifiers which are based on how a non-expert person could realize classification simply by applying a frequentist approach. We show that these three simple methods are closely related to different aspects of the entropy of the dataset. Therefore, these measures account somewhat for entropy in the dataset when evaluating the performance of classifiers. This allows us to measure the improvement in the classification results compared to simple methods, and at the same time how entropy affects classification capacity. To illustrate how these new performance measures can be used to analyze classifiers taking into account the entropy of the dataset, we carry out an intensive experiment in which we use the well-known J48 algorithm, and a UCI repository dataset on which we have previously selected a subset of the most relevant attributes. Then we carry out an extensive experiment in which we consider four heuristic classifiers, and 11 datasets.
State of the Art on Microservices Autoscaling: An Overview

The adoption of microservices architecture has taken on great pro-portions due to its benefits and popularization of containers driven tools, such as Kubernetes and Docker. Besides, the development of microservice-based applications is a complex task, specially because they can be composed of multiple heterogeneous parts. In particular, one of the main challenges is how to conduct the microservices autoscaling (i.e., adding or removing resources on demand), while still avoiding resource waste, such as CPU and memory. This paper presents the state of the art of approaches to solve the problem of micro services autoscaling, the main characteristics to be considered as well as the important future directions that need to be still investigated.
Cross-lingual transfer of sentiment classifiers

Word embeddings represent words in a numeric space so that semantic relations between words are represented as distances and directions in the vector space. Cross-lingual word embeddings transform vector spaces of different languages so that similar words are aligned. This is done by mapping one language’s vector space to the vector space of another language or by construction of a joint vector space for multiple languages. Cross-lingual embeddings can be used to transfer machine learning models between languages, thereby compensating for insufficient data in less-resourced languages. We use cross-lingual word embeddings to transfer machine learning prediction models for Twitter sentiment between 13 languages. We focus on two transfer mechanisms that recently show superior transfer performance. The first mechanism uses the trained models whose input is the joint numerical space for many languages as implemented in the LASER library. The second mechanism uses large pretrained multilingual BERT language models. Our experiments show that the transfer of models between similar languages is sensible, even with no target language data. The performance of cross-lingual models obtained with the multilingual BERT and LASER library is comparable, and the differences are language-dependent. The transfer with CroSloEngual BERT, pretrained on only three languages, is superior on these and some closely related languages.
FINANCIAL STABILITY AND ECONOMIC GROWTH NEXUS: EVIDENCE FROM SUB-SAHARAN AFRICA USING PANEL DATA

This paper examines the effect of financial stability on the economic growth of countries in Sub-Saharan Africa using the World Development Indicators Data. To select the appropriate model best fits for the data, the Hausman test was used to select Random Effect Estimator over Fixed Effect Estimator to assess the relationship between the variables. Panel data was collected on Sub-Saharan Africa countries from 2010 to 2019 to predict the effect of financial stability on economic growth. The paper revealed that financial stability accounted for 71.8% of the variation of a country's economic growth for the period other things being equal. It recommends that financial regulators within Sub-Saharan African should provide prudential policies aim at attaining higher economic growth should target both monetary and fiscal policies as well market discipline.Keywords: Sub-Saharan African, economic growth, financial stability, panel data, Fixed effects estimatorJEL Classifications: E50, E58, F43, O47DOI: https://doi.org/10.32479/ijefi.11407
Multimedia Concealed Data Detection Using Quantitative Steganalysis

In current days, there is a constant evolution in modern technology. The most predominant usage of technology by society is the internet. There are many ways and means on the internet through which data is transmitted. Having such rapid and fast growth of communicating media also increases the exposure to security threats, causing unintellectual information ingress. Steganography is the main aspect of communicating in an aspect that hides the extent of communication. Steganalysis is another essential concern in data concealing, which is the art of identifying the existence of steganography. A framework has been designed to identify the concealed data in the multimedia file in the proposed system. This work's main strength is analyzing concealed data images without embedding and extracting the image's payloads. A quantitative steganalysis approach was considered to accomplish the proposed objective. By using this approach, the results were achieved with 98% accuracy.
Selecting miners within blockchain-based systems using evolutionary algorithms for energy optimisation

In this paper, we represent the problem of selecting miners within a blockchain-based system as a subset selection problem. We formulate the problem of minimising blockchain energy consumption as an optimisation problem with two conflicting objectives: energy consumption and trust. The proposed model is compared across different algorithms to demonstrate its performance.
Reinforcement-Learning-Based Route Generation for Heavy-Traffic Autonomous Mobile Robot Systems

Autonomous mobile robots (AMRs) are increasingly used in modern intralogistics systems as complexity and performance requirements become more stringent. One way to increase performance is to improve the operation and cooperation of multiple robots in their shared environment. The paper addresses these problems with a method for off-line route planning and on-line route execution. In the proposed approach, pre-computation of routes for frequent pick-up and drop-off locations limits the movements of AMRs to avoid conflict situations between them. The paper proposes a reinforcement learning approach where an agent builds the routes on a given layout while being rewarded according to different criteria based on the desired characteristics of the system. The results show that the proposed approach performs better in terms of throughput and reliability than the commonly used shortest-path-based approach for a large number of AMRs operating in the system. The use of the proposed approach is recommended when the need for high throughput requires the operation of a relatively large number of AMRs in relation to the size of the space in which the robots operate.
A Compact Laser Shearography System for On-Site Robotic Inspection of Wind Turbine Blades

Shearography is an optical technique in the field of non-destructive evaluation (NDE) of various materials. Its main advantages are that it is non-contact type and can cover a large area in a single inspection. As a result, although it has been widely acknowledged as an effective technique particularly for NDE of composite materials to detect subsurface defects such as delamination, disbond, cracks and impact damages, the use of shearography for on-site inspection of wind turbine blades (WTBs) has not been reported. This is due to wind causing structural vibration in the WTB. The solution in this paper is to make the shearography sit on the WTB during inspection when the WTB is parked, so that the relative motion between the shearography and the WTB is minimized within the tolerance of the shearography system. The ultimate goal of the solution is to enable a robot assisted shearography system to inspect the WTB on-site. This paper presents the research work on a new shearography design for integration with a robotic climber for on-site WTB inspection. The approach is tested and evaluated in experimental settings, and comparative assessment of the approach with other robotic NDE techniques is carried out. The results demonstrate the potential benefits and suitability of the approach for on-site robotic inspection of WTBs.
Smart Stacking of Deep Learning Models for Granular Joint Intent-Slot Extraction for Multi-Intent SLU

These days' multi-intent utterances have become very important for the spoken language understanding (SLU). The multi-intent systems and algorithms add more complexity (Compare to the single-intent-based system) to the SLU. As, it requires an accurate system, which can identify intents and slots at fine-grain (i.e., word/token) level and also able to handle the relation between intents and slots locally at utterance level. In this case, intents may belong to multiple domains and multiple different classes. Similarly, slots may also belong to multiple different classes, and slots of the same class may be related to multiple different intent classes. Unfortunately, very few works have been done till now to address these issues at the fine-grain level. To solve this problem, we propose a smart stacking-ensemble strategy. The first stage of this system uses a combination of three different types of powerful multitasking NLP models, developed on top of pre-trained BERT, XLNet, and Elmo. Finally, a stacking ensemble layer learns to predict the best possible results. We have evaluated our model on four publicly available datasets. The evaluation results on the state-of-the-art public datasets show that our devised system outperforms the existing multi-intent-based systems at token-level and sentence-level.
Bee Colony Optimization and Its Applications

The objective of this chapter is to promote the bee colony optimization (BCO) algorithm as a simple and effective metaheuristic method. BCO is a stochastic, random-search population-based technique, motivated by the foraging behavior of honey bees that belongs to the class of swarm intelligence methods. First, we give a description of the biological background behind BCO and then we provide a detailed explanation of the BCO algorithm. A significant part of this chapter is devoted to various recent applications of BCO. Until now, BCO has been successfully applied to numerous hard, combinatorial optimization problems, mostly in the field of traffic, transportation and logistics, location, and scheduling. Some applications in the continuous optimization field, as well as in stochastic optimization (where BCO is combined with fuzzy logic), appeared recently. This chapter sublimates most of scientific papers published since 2015. A comprehensive literature review covering the previous period was already published in 2015.
Generalized Completeness for SOS Resolution and its Application to a New Notion of Relevance

Abstract We prove the SOS strategy for first-order resolution to be refutationally complete on a clause set N and set-of-support S if and only if there exists a clause in S that occurs in a resolution refutation from $$N\cup S$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>N</mml:mi><mml:mo>∪</mml:mo><mml:mi>S</mml:mi></mml:mrow></mml:math> . This strictly generalizes and sharpens the original completeness result requiring N to be satisfiable. The generalized SOS completeness result supports automated reasoning on a new notion of relevance aiming at capturing the support of a clause in the refutation of a clause set. A clause C is relevant for refuting a clause set N if C occurs in every refutation of N . The clause C is semi-relevant , if it occurs in some refutation, i.e., if there exists an SOS refutation with set-of-support $$S = \{C\}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>S</mml:mi><mml:mo>=</mml:mo><mml:mo>{</mml:mo><mml:mi>C</mml:mi><mml:mo>}</mml:mo></mml:mrow></mml:math> from $$N\setminus \{C\}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>N</mml:mi><mml:mo>\</mml:mo><mml:mo>{</mml:mo><mml:mi>C</mml:mi><mml:mo>}</mml:mo></mml:mrow></mml:math> . A clause that does not occur in any refutation from N is irrelevant , i.e., it is not semi-relevant. Our new notion of relevance separates clauses in a proof that are ultimately needed from clauses that may be replaced by different clauses. In this way it provides insights towards proof explanation in refutations beyond existing notions such as that of an unsatisfiable core.
Application of hydration heat inhibitor in crack control of mass concrete of tunnel side wall

The structural deformation caused by temperature change is the main reason for cracking of mass concrete. In order to avoid or reduce the crack of the side wall of cast-in-place mass concrete in tunnel, the effects of different dosage of hydration heat inhibitor on the internal temperature rise curve of concrete, strength and the properties of the mixture are analyzed through experimental research, and the optimal dosage of 1% of cementing material is finally determined. The engineering application results show that after adding hydration heat inhibitor to the tunnel side wall concrete, the maximum temperature rise in the tunnel side wall is obviously reduced, and the arrival time of the temperature peak is delayed. No cracks appear in the tunnel side wall, and the crack control effect is good.
A Functional Clipping Approach for Policy Optimization Algorithms

Proximal policy optimization (PPO) has yielded state-of-the-art results in policy search, a subfield of reinforcement learning, with one of its key points being the use of a surrogate objective function to restrict the step size at each policy update. Although such restriction is helpful, the algorithm still suffers from performance instability and optimization inefficiency from the sudden flattening of the curve. To address this issue we present a novel functional clipping policy optimization algorithm, named Proximal Policy Optimization Smoothed Algorithm (PPOS), and its critical improvement is the use of a functional clipping method instead of a flat clipping method. We compare our approach with PPO and PPORB, which adopts a rollback clipping method, and prove that our approach can conduct more accurate updates than other PPO methods. We show that it outperforms the latest PPO variants on both performance and stability in challenging continuous control tasks. Moreover, we provide an instructive guideline for tuning the main hyperparameter in our algorithm.
Fast Codebook Generation Using Pattern Based Masking Algorithm for Image Compression

Vector Quantization (VQ) is a classical block coding technique used for image compression which achieves high compression using simple encoding and decoding process. Codebook generation is an important factor in VQ design, which directly influences computational cost and the quality of the reconstructed image. Linde-Buzo-Gray (LBG) is considered as a state of art technique, which uses k-mean clustering algorithm for codebook design. Various optimization techniques are applied for searching the optimal codebook, such as Bat Algorithm (BA), Particle swarm optimization (PSO), and Firefly Algorithm (FA). These algorithm suffers mainly with high time consumption due to unavailability of the optimal solution in search space. This research proposes a novel approach, where peak values of the histogram are applied to predefined pattern masks to predict the image patterns for codebook design. From the experimental results, it is indicated that when compared with other algorithms, the proposed pattern based masking (PBM) algorithm requires fewer iterations and converges at a faster speed, particularly at the bitrates ≥0.375 without compromising on peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM).
Supportive Smart Home Systems: Utilization Assessment for Internet Service Provider Networks

The use of residence-based well-being assessment through ambient and, in some cases, wearable sensors has led to many research projects for residential monitoring systems and supportive smart homes that are connected over the Internet to cloud processing. Many of these systems have been pilot tested and some early examples are entering commercial release. This report focuses on a key aspect for scaled use that has not been extensively considered which is the telecommunications provider network between the residence and the cloud. This work reports a 10-fold difference in network traffic generated between systems performing the same functionality and predominance of very small packets which must be routed. The project compares two smart home systems providing well-being monitoring and two smart bed sensors that assess vital signs and sleep. The results show how the design of sensing systems can vary greatly and that widespread deployment, such as many residences in a multi-tenant building, will force consideration of these effects within the telecommunication provider services.
Decoding Methods for Neural Narrative Generation

Narrative generation is an open-ended NLP task in which a model generates a story given a prompt.The task is similar to neural response generation for chatbots; however, innovations in response generation are often not applied to narrative generation, despite the similarity between these tasks.We aim to bridge this gap by applying and evaluating advances in decoding methods for neural response generation to neural narrative generation.In particular, we employ GPT-2 and perform ablations across nucleus sampling thresholds and diverse decoding hyperparameters-specifically, maximum mutual information-analyzing results over multiple criteria with automatic and human evaluation.We find that (1) nucleus sampling is generally best with thresholds between 0.7 and 0.9; (2) a maximum mutual information objective can improve the quality of generated stories; and (3) established automatic metrics do not correlate well with human judgments of narrative quality on any qualitative metric.
What transfers in morphological inflection? Experiments with analogical models

This paper investigates how abstract processes like suffixation can be learned from morphological inflection task data using an analogical memory-based framework.In this framework, the inflection target form is specified by providing an example inflection of another word in the language.This model is capable of near-baseline performance on the Sig-Morphon 2020 inflection challenge.Such a model can make predictions for unseen languages, allowing one-shot inflection for natural languages and the investigation of morphological transfer with synthetic probes.Accuracy for one-shot transfer can be unexpectedly high for some target languages (88% in Shona) and language families (53% across Romance).Probe experiments show that the model learns partially generalizable representations of prefixation, suffixation and reduplication, aiding its ability to transfer.The paper argues that the degree of generality of these process representations also helps to explain transfer results from previous research.
Cross-site Scripting Attack Detection Using Machine Learning with Hybrid Features

This study aims to measure the classification accuracy of XSS attacks by using a combination of two methods of determining feature characteristics, namely using linguistic computation and feature selection. XSS attacks have a certain pattern in their character arrangement, this can be studied by learners using n-gram modeling, but in certain cases XSS characteristics can contain a certain meta and synthetic this can be learned using feature selection modeling. From the results of this research, hybrid feature modeling gives good accuracy with an accuracy value of 99.87%, it is better than previous studies which the average is still below 99%, this study also tries to analyze the false positive rate considering that the false positive rate in attack detection is very influential for the convenience of the information security team, with the modeling proposed, the false positive rate is very small, namely 0.039%