Reviewerly/exHarmony huggingface dataset:
Optimization Model of Waste Transportation from Garbage Dump (GD) to Final Landfill (FL)

In the process of waste management, collection and transportation of waste is the most important and expensive aspect due to massive labor intensity and vehicle use. A bad process of transporting waste causes a buildup of garbage in the garbage dump (GD). For this reason, a decision model is demanded to optimize such a transportation process. The purpose of this study is to produce a decision support model in selecting multiple GDs that are the top priority in the process of transporting waste to final landfill (FL) and prototyping apps. The method for the process of weighting waste management parameters is fuzzy logic. The process of optimizing the presentation of data and determining the best alternative is performed using the hill-climbing method. Based on the results of the study, the parameters of the amount of waste have the highest level of importance with a value of 0.177. GD, which has the highest total rating (f(x)) based on the selected hill-climbing optimization process.
Deconstructing Language Borders through the Hybrid. A Topical Approach to Margaret Atwood’s <i>Dark Lady</i>

Abstract Starting from the premise that cultures assume myriads of foreign elements, alterities, and differences, this paper analyses a phenomenon that becomes a conscious and an intentional one, namely language hybridity. Our purpose is to give thoughtful attention to certain instances of hybridity perceived at the syntactic, semantic, and lexical levels. Since language users make their choice in any situational context, we witness a great degree of linguistic blending: e. g. the borrowing of words and phrases becomes tied to new ways of making meaning. Additionally, we face a dynamic increase of mixed language registers, styles, and voices that form a complex linguistic repertoire in a literary work. For exemplification, we will analyse Margaret Atwood’s experimentations across genre and linguistic boundaries encountered in her short story Dark Lady , integral part of the short fiction collection Stone Mattress. Nine Wicked Tales (2014). This narrative is characterized by a mixture of heterogeneous elements: hybrid phrases created as a result of borrowing words, elevated language (sprinkled with widely known Latin sayings), and alteration of idioms by one-word substitution. Hybridity becomes a way through which Margaret Atwood deconstructs language borders. In Dark Lady , the Canadian writer shows that hybridity stimulates innovation since the individual is allowed to move freely between spaces of meaning.
A Small-Footprint End-to-End KWS System in Low Resources

In this paper, we propose an efficient end-to-end architecture, based on Connectionist Temporal Classification (CTC), for low-resource small-footprint keyword spotting (KWS) system. For a low-resource KWS system, it is difficult for the network to thoroughly learn the features of keywords. The intuition behind our new model is that a priori information of the keyword is available. In contrast to the conventional KWS system, we modify the label set by adding the preset keyword(s) to the original label set to enhance the learning performance and optimize the final detection task of the system. Besides, CTC is applied to address the sequential alignment problem. We employ GRU as the encoding layer in our system because of the dataset small. Experiments using the WSJ0 dataset show that the proposed KWS system is significantly more accurate than the baseline system. Compared to the character-level-only KWS system, the proposed system can obviously improve the performance. Furthermore, the improved system works well in terms of low resource conditions, especially for long words.
Design and Analysis of an Effective Securing Consensus Scheme for Decentralized Blockchain System

Blockchain, as a decentralized network system, has been attracting increasing attention in recent years. In a blockchain system, there must be a consensus mechanism to ensure the distributed consensus among all parties. Such consensus mechanism may also be applied to guarantee fairness, correctness, and sustainability of such decentralized systems. In this paper, we propose a novel consensus mechanism, named Proof-of-Credibility (PoC), which is an improved version of Proof-of-Work (PoW). Compared with existing consensus mechanisms, PoC provides strong resistance to resource centralization and other malicious attacks. First, we present the Serial Mining Puzzle (SMP) to resist collusive mining. SMP guarantees that participants only get negligible advantage by parallel solving. Second, PoC considers the influence of participant credibility, which is reflected by the mining behaviour of a participant. Thus, credible participants get higher probability of winning the mining competition than incredible ones. Finally, the performance of PoC is analyzed in terms of common prefix, chain quality and power cost. Our analysis indicates that PoC is security and incentive compatible with suitable security parameter settings.
Entropy-Based Model Selection Using Monte Carlo Method

In this investigation, we propose a new kind of simplification specialized for Multiple Regression Analysis (MRA) using Random sampling. We propose a novel approach to simplify MRA models for dimension reduction while preserving amount of information. After applying Principle Component Analysis (PCA) to explanatory variables of interests to simplify relationship among them, we reduce the variables (dimensions) quickly to avoid loss of entropy in an efficient manner. We show an experimental results to see the effectiveness of this approach. Our main idea comes from random sampling with the tight relationship between entropy and multiple correlation coefficients (MCC).
Research and Implementation of Multi-chain Digital Wallet Based on Hash TimeLock

In terms of interoperability between different blockchains issue and achieving value exchange between different blockchains, a convertible multi-chain digital wallet solution is proposed. The wallet realizes the exchange of different digital currency assets by hash timelock technology. In traditional blockchain projects, each blockchain is a closed island. Users need to exchange different digital currencies through centralized Exchanges, this is contrary to the idea of decentralization and non-tamperability of blockchains, safety and reliability depend entirely on Exchanges. By introducing hash timelock technology, multi-chain digital wallet can realize the exchange of digital assets in different blockchains without third-party intermediary, ensure its de-centralization characteristics, safety and reliability.
SAR Target Recognition Based on Cholesky Decomposition Weighted Kernel Extreme Learning Machine

An SAR target recognition algorithm based on extreme learning machine is proposed. The traditional extreme learning machine cannot overcome the problem of sample noise and imbalance. To solve the problem, this paper introduces the weighted extreme learning machine algorithm, and uses KFCM algorithm combined with the proportion of samples to obtain the sample weight matrix. At the same time, in view of the problem that ordinary extreme learning machine uses matrix inverse to train the process, a calculation method based on choleksy decomposition is proposed. The experimental results show that the algorithm in this paper is faster and has higher recognition rate than ordinary algorithms such as KELM, SVM and BP neural network.
Ensemble-Based Software Engineering for Modern Computing Platforms

Software systems have evolved from being 'stand-alone systems' to 'systems of systems' to meet the challenging needs of societies. Contemporary software systems such as socio-technical systems are composed of distributed and heterogeneous agents, the em- bedded environment, and software components. Addressing the disruptions caused by run-time changes in the environment while designing software systems remains a challenging task in practice. Ensembles enable systems with collective adaptability to be built as emergent aggregations of autonomous and self-adaptive agents. The phenomenal aspect of this type of systems allows agents (i.e, services, people and things) a ected by an issue to adapt collabo- ratively with minimal impact on their own preferences through a collective resolution process. In this paper we report the outcomes of the 2nd ACM SIGSOFT International Workshop on Ensemble- Based Software Engineering for Modern Computing Platforms (EnSEmble 2019), which was held as part of the 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2019) in Tallinn, Estonia, on August 26th, 2019.
Intelligent Interactive Edge Detection Method of Image

Edge detection is a fundamental problem in image processing and computer vision, it plays an important role in the field of computer vision. Edge detection highlights significant changes in the image by identifying points in the digital image where the brightness changes significantly, and filters the secondary information while retaining the key features of the image, thus reducing the amount of data.
A Pipelining Strategy for Accelerating Convolutional Networks on ARM Processors

Convolutional neural networks (CNN) is playing an important role in many fields. Many applications are able to run the inference process of CNN with pre-trained models on mobile devices in these days. Improving performance of embedded processors such as ARM-based CPUs makes it possible to meet the requirement of real-time processing. In this paper, a pipelining strategy is proposed to accelerate convolution networks on ARM processors. We implement a $$3\times 3$$ convolution with Neon instructions which are single instruction and multiple data (SIMD) instructions supported by ARM processors. In order to reduce stalls in the pipeline, issue orders of instructions are rearranged according to the out-of-order execution and dual-issue mechanism on ARM processors. A tiling method is exploited to increase data reuse. The input feature map is divided into multiple $$6\times 6$$ tiles, and the computations within the tile is highly optimized using our proposed pipelining strategy. The speedup of proposed method is 2.88 compared with gcc compiled codes on RK3288. The effect of our optimizing method is measured by a performance profiling tool, cycles and cache misses are decreased significantly. The multi-thread version implemented with openMP achieve speedup of 6.8 compared with single-thread gcc complied version.
An Efficient Path Planning Methodology Based on the Starting Region Selection

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Automated parking is an efficient way to solve parking difficulties and path planning is of great concern for parking maneuvers [&lt;span class="xref"&gt;1&lt;/span&gt;]. Meanwhile, the starting region of path planning greatly affects the parking process and efficiency. The present research of the starting region are mostly determined based on a single algorithm, which limits the flexibility and efficiency of planning feasible paths. This paper, taking parallel parking and vertical parking for example, proposes a method to calculate the starting region and select the most suitable path planning algorithm for parking, which can improve the parking efficiency and reduce the complexity. The collision situations of each path planning algorithm are analyzed under collision-free conditions based on parallel and vertical parking. The starting region for each algorithm can then be calculated under collision-free conditions. After that, applicable starting regions for parking can be obtained, and each of those regions corresponds to a parking path planning algorithm. However, there always exists overlapped starting regions, which can be applied to multiple parking path planning algorithms. In order to select the most suitable algorithm to plan the parking path, the priority order of algorithms is decided based on the preference criterion function. The collision-free parking path can be generated following the priority order. Based on the modified B-spline curves, a continuous-curvature path is presented. The simulation results based on MATLAB/Simulink and PreScan show that the methodology can smoothly judge the feasibility of automated parking in vehicle’s current position and plan the most suitable parking path. The proposed methodology can calculate the starting region of automated parking rapidly and plan more efficient parking path compared with other methods.&lt;/div&gt;&lt;/div&gt;
A Robust Random Number Generator Based on Chaotic Ring Oscillators

A robust chaos-based random number generator (RNG) system is newly proposed. Output bitstreams generated from this new RNG system is tested on NIST-800-22 test suite. Proposed RNG circuit fulfill all fifteen randomness tests of NIST-800-22 without undergoing any post-processing implementations except for the X OR operation. While classical ring oscillator based RNG systems needs at least 25 rings, each of them containing 13 inverters, and 25 D flip-flops to pass all the tests, newly designed RNG system pass all the tests successfully with 10 chaotic new subcells containing 20 ring oscillators with 13 inverters and each subcell includes two resistors, a capacitor and two diodes. These are enough for the new system to generate sufficient randomness without any D flip-flops. Experimental results are demonstrated and comparisons are made among the new design, classical ring oscillator based RNGs and another chaotic ring oscillator based RNG. Noise analysis of the new RNG is also performed to show that the system has a nondeterministic entropy source which secures the system against possible synchronization attacks.
Case Study for Digital Cultural Content Mining

In this chapter we consider an application case of Deep Learning in the task of architectural recognition. The main objective is to identify both: architectural styles and specific architectural structures. We are interested in attention mechanisms in Deep CNNs and explain how real visual attention maps built upon human gaze fixations can help in the training of deep neural networks.
Patents – Direct Infringement, Individual Infringement, and “Digital” Infringement

Chapter 5 turns to patent infringement. It introduces a fundamental tension between patent holders and good faith users of the technology. 3D printing will expose unsuspecting individuals and 3D print shops to patent infringement liability when they print patented objects. To spare unintentionally infringing individuals and 3D print shops the ruinous costs of litigation, I explore options for exemptions and safe harbors. At the same time, if 3D printing enables massive, individualized manufacturing, and if the law exempts each individual act of infringement, patent rights would be eviscerated. Squaring this circle will not be easy, but the chapter explores ways to alleviate the tension, particularly by strengthening indirect infringement claims and limiting exemptions to cases where the accused infringer had no knowledge of the patent. In addition, Chapter 5 demonstrates that 3D printable files will not infringe traditional patent claims directed to tangible objects. Moreover, the most commonly traded 3D printable file format will not infringe a computer-readable medium (Beauregard) claim. Attempting to alleviate some of the protection gaps for patent holders while balancing the needs of users, the chapter considers a novel theory of “digital patent infringement.”
The Influence of the Test Operator on the Expressive Power of PDL-like Logics

Abstract Berman and Paterson proved that test-free propositional dynamic logic (PDL) is weaker than PDL. One would raise questions: does a similar result also hold for extensions of PDL? For example, is test-free converse-PDL (CPDL) weaker than CPDL? In what circumstances the test operator can be eliminated without reducing the expressive power of a PDL-based logical formalism? These problems have not yet been studied. As the description logics $\mathcal{ALC}_{trans}$ and $\mathcal{ALC}_{reg}$ are, respectively, variants of test-free PDL and PDL, there is a concept of $\mathcal{ALC}_{reg}$ that is not equivalent to any concept of $\mathcal{ALC}_{trans}$. Generalizing this, we prove that there is a concept of $\mathcal{ALC}_{reg}$ that is not equivalent to any concept of the logic that extends $\mathcal{ALC}_{trans}$ with inverse roles, nominals, qualified number restrictions, the universal role and local reflexivity of roles. We also provide some results for the case with RBoxes and TBoxes. One of them states that tests can be eliminated from TBoxes of the deterministic Horn fragment of $\mathcal{ALC}_{reg}$.
Semisupervised learning based on nuclear norm regularization

The semisupervised learning method can use a small amount of tagged data and a large amount of unlabeled information at the same time to achieve a better learning effect than using only one kind of data information. This has aroused wide interest in theory and practice. Recently, integrating new knowledge sources such as pairwise constraints into various classification and learning tasks with insufficient training data has been actively studied in machine learning. In this book, we introduce a semisupervised learning method with enhanced spectral kernel. The framework comprises these main parts: nuclear norm regularized model, modified fixed point, label propagation, and valid kernel.
Mind mapping in artificial intelligence for data democracy

One of the main hurdles in the quest for data democracy is the information overload problem that end users have to suffer when trying to analyze all information available to be informed and make sensible decisions. In this chapter, I talk about the definition, causes, consequences, and possible solutions to information overload. One of the solutions lies in the use of artificial intelligence (AI). But this is a double-edged word because, in some cases, it helps to reduce information overload and, in other cases, it helps to increase the problem. I introduce the use of the mind mapping (MM) technique to help reduce information overload in all cases and specifically in cases where AI has a negative effect on this problem. For readers not privy to the MM technique, I provide some introductory references that can be useful to them. I do also provide a detailed example of the advantages of MM in the visualization of the results of the semantic analysis of a complex text using IBM Watson NLU and MM automation software developed by myself to create MindManager mind maps. This is one of those cases where AI produces a large amount of information that has to be organized in the best possible way. MM is, in my opinion, the most adequate way to organize that kind of information. The MM technique is also a very important tool to achieve data democracy through the empowerment of end users when gathering and analyzing complex information. Mind maps are single compressed files containing information, notes, graphs, and links that simplify the organization and treatment of complex information. Finally, an HTML5 version of the mind map used is made available to all readers so that they can appreciate the look and feel of the MM experience by simply using their Internet browser.
Effects of Color Systems' Transformation on Optical Flow Estimation of Noisy and Degraded Images

Varying illumination and image blur are some of major challenges faced by contemporary methods of optical flow estimation. Despite significant advancement, these aspects have not received much of attention by modern-day methods. Latest work in this field is heavily affected and produce adverse results when dealing with images containing variable illumination and blur. In this paper, we investigate the effects of color space transformations on optical flow estimation from degraded and noisy images. In our experiments, clean and noisy images have been used. These images contain different kinds of blur and atmospheric effects such as fog, mist, shadows and dark regions. By estimating optical flow with three types of sequences in parallel (super clean, clean and noisy), and using four popular color systems, the effects of color space transformation have been observed on the estimated flow fields. The four color systems include RGB (red, blue, green), HSV (hue, saturation, value), HSL (hue, saturation, lightness) and XYZ (as standardized by the International Commission on Illumination in 1931). It is found that output of an optical flow algorithm not only depends on the color system adopted, but some color spaces tend to favor some special type of image sequences. For instance, XYZ color system is more favorable for the images abiding by the brightness constancy assumption while HSV color space is more suitable for blurry and noisy images. While keeping rest of the parameters unchanged but only transforming the color-space, we estimated the optical flow. Obviously the results of an algorithm applied to clean images for optical flow, would not be consistent with a flow estimated from same images containing noise. The objective is to compare this adversative effect for different color spaces. The flow estimation errors in four color systems have been reported and compared, and the best color-space is pointed out in each case. The paper also discusses the possible factors behind these variable outcomes with an insight into the basic frameworks of traditional methods for optical flow.
Problem Identification of Usability of Web Frameworks

Actually, web frameworks are widely used by developers, since they allow streamlining the process of developing web applications, by reusing functionalities already developed and tested. The learning and use of these tools is often difficult because of several factors, including documentation. The documentation has provided to users is a key aspect for the usability of the framework, since its objective is to facilitate and help to them in the use of the framework and to optimize the development process, reducing time, effort and costs. Due to the scarcity of usability proposals (methods, evaluation models, guidelines or guides) specific for web framework, this study proposes to identify, characterize and analyze the usability problems of resources and documentation supports that currently they are available to developers. From these results, a device is provided that allows to identify problems of usability of web frameworks, in a simple, economic and fast way.
IFME: Influence Function Based Model Explanation for Black Box Decision Systems

Due to the high precision and the huge prediction needs, machine learning models based decision systems has been widely adopted in all works of life. They were usually constructed as a black box based on sophisticated, opaque learning models. The lack of human understandable explanation to the inner logic of these models and the reason behind the predictions from such systems causes a serious trust issue. Interpretable Machine Learning methods can be used to relieve this problem by providing an explanation for the models or predictions. In this work, we focus on the model explanation problem, which study how to explain the black box prediction model globally through human understandable explanation. We propose the Influence Function based Model Explanation (IFME) method to provide interpretable model explanation based on key training points selected through influence function. First, our method introduces a novel local prediction interpreter, which also utilizes the key training points for local prediction. Then it finds the key training points to the learning models via influence function globally. Finally, we provide the influence function based model agnostic explanation to the model used. We also show the efficiency of our method through both theoretical analysis and simulated experiments.