Reviewerly/exHarmony huggingface dataset:
Logistics Management Using Blockchain

As logistics companies continue to expand due to the revolution of Logistics 4.0, the complexity of the multiple connected organizations makes it impossible for a clear view of logistics operation. Since customer information is shared between companies, unauthorized access to personal information is inevitable, and it poses several threats to customers. To address this challenge, blockchain with some fascinating properties like enhanced security and transparency will be deployed. Blockchain is a technology that can be used to improve efficiency, visibility, and security in logistics management. This chapter will explore the current applications of blockchain in logistics management based on an analysis of the findings of several scholars. The change from traditional logistics to digital logistics, digital logistics issues, as well as blockchain principles, this study also provides useful insights into how blockchain can disrupt conventional operations in logistics management. It also lays the groundwork for future study into blockchain's applicability in digital fleet management.
Fuzzy Partitioning of Continuous Attributes Through Crisp Discretization

Designing good MFs is an essential problem when developing a fuzzy decision tree classifier because it requires the partition all continuous attributes. In this chapter, a two-step algorithm is described to generate MFs from data. It uses the accuracy of a FDT as a quality measure of the generated MFs and employs the crisp discretization as an initial partitioning. In short, the process is as follows. First, the discretization algorithm divides the attribute's domain into crisp parts, and then for each part we define a membership function.
Text Representation Model for Multiple Language Forms in Spoken Chinese Expression

Mixture of multiple language forms in spoken Chinese is a common but unfavorable issue.. It increases the difficulty of intent understanding and leads to inconvenience for information communication. Existing studies on intent recognition mainly focus on single language form or parallel multilingual language while paying little attention to spoken texts including multiple language forms. In considering that it is hard to capture the semantics of an expression with multiple language forms, it is important to study the problem. To solve this issue, a text representation model for the spoken Chinese expression mixed with English and Chinese Pinyin is proposed. And the feature matrix is built to mine the composition information of English and Pinyin. Besides, the model can efficiently distinguish English from Chinese Pinyin even though both fragments are composed of English letters. Meanwhile, it can effectively process the problem of hidden text information since the problem has been transformed into the Chinese translation task of English and Pinyin. In addition, to verify the performance of the model, the texts processed by this model are used as the input of the classifier. extensive experiments on a large online logistics manual customer service corpus show that this text representation model is correct and effective. It can not only eliminate the obstacles of the mixing of multiple language forms but also bring better results for intent understanding.
Automated Construction of Continuous Delivery Pipelines from Architecture Models

Continuous Delivery (CD) aims at reducing the cycle time from changes to software release while also increasing the software quality. To automate CD, delivery process models, defining all delivery activities need to be designed. Quality properties of delivery process models, such as maintainability, still oppose challenges. Previous research indicates that the quality of such models can be improved by aligning them with the software architecture. While software architecture knowledge is only incorporated implicitly, deep technical and process knowledge is required. On this basis, this paper introduces a new kind of delivery process models that focus mainly on software architecture knowledge. Hereby, we discard the current activity-centric view and shift to an artifact-centric view. Moreover, we outsource the required process- and technical knowledge to a transformation activities knowledge base. In order to make an artifact-based delivery process model executable, we provide a model-to-model transformation which constructs a CD pipeline from an artifact-based model with the help of the transformation activities knowledge base. We evaluated our approach by conducting a small industrial qualitative user study. It showed that low-experienced developers benefit from the reduced knowledge requirements of the artifact-based modeling approach.
Toothless Rhetoric or Strategic Polemic? A Textual and Contextual Analysis of Japan’s Hate Speech Law

In May, 2016 the Diet passed a law on the "Promotion of efforts to eliminate unfair discriminatory speech and behaviour against people originating from outside Japan", widely referred to as ヘイトスピーチ (Heito Supiichi Hō /Hate Speech Law). For some residents of Japan it had been a long time coming. Without any laws specifically prohibiting racially discriminatory speech or writing, aggrieved parties had hitherto been forced to resort to indirect lines of protection. In 1999, for example, a Brazilian national ejected from a jewelry shop displaying a poster saying "No foreigners allowed" obtained a favourable ruling citing Japan's ratification of the International Convention on the Elimination of All Forms of Racial Discrimination; and in 2013 an injunction for defamation and obstruction of business was granted on behalf of a school for children of North Korean descent repeatedly subjected to provocative demonstrations. But others questioned the need to reinforce limits on freedom of expression even in the face of aggressive taunts, with some claiming that incidents of racial discrimination in Japan lacked the historical, entrenched and violent dimensions that had prompted hate speech laws in Europe and elsewhere. When the text of the proposed law became public there was also debate about its utility as such an abstract measure seemed inapplicable to many potential victims and lacked punitive sanctions. Against this criticism it could be argued that the law went about as far as the government could expect to go if it were to get it passed; that it appears to be curtailing a particularly aggressive form of hate speech; and that it has ushered in a number of more specific initiatives, especially at local level. This study will begin with the 2016 text itself, drawing on the semiotic framework of Systemic Functional Grammar to explore how it prioritises general principles over specific regulations. This textual analysis will be followed by a contextual account of why the Law was constructed as it was, how it has influenced awareness of hate speech, and where it fits in with an existing genre of non-coercive legislation in Japan.The online version contains supplementary material available at 10.1007/s11196-022-09883-9.
Hand Gesture Recognition using Deep learning Method

In human-device interaction, hand signals are extremely significant. Despite the growing relevance of human hand signals, hand gesture recognition technology is still unreliable due to complicated hand gesture feedback or environmental influences. We suggest Recurrent Neural Networks (RNN) as a technology to solve this issue in this article. RNN has the benefit of being able to learn time series data, which significantly improves the efficiency of human-device interaction. We used a Sinuous antenna with a bandwidth of 6.0-8.5 GHz and a gain of 8 dB to collect data from the five hand signals and conditioned them using RNN. The proposed RNN's classification result was accurate to the tune of 80%
A Tabnet based Card Fraud detetion Algorithm with Feature Engineering

With the development of technology, there are some inventions has been raised, such as credit cards for paying in electronic transaction. People can trade in a short time with credit cards. However, it will be a double-edged sword because it provided a chance for criminals, and in turn, increased fraud rate. Therefore, there is an urgent need for researchers to research and proposed some methods. In our article, we utilize TabNet as our model. The dataset is provided by IEEE-CIS and it contains many transaction records and whether the transaction is fraud. In the feature engineering process, we compress the datasets and fill the missing value with -999. In the training process, the cross validation has been used, we split the dataset into 5 folds, and train them one by one. And we do some improvement to compress the memory. Finally, to evaluate how well TabNet performed, we compare it with other classical models: Naïve Bayes and XGBOOST. The result shows that our model has the best performance using the metrics ----accuracy and AUC-ROC score among these models. It is obvious that TabNet owns the highest AUC-ROC score and accuracy, and they are 0.884 and 0.965 respectively. In the future, we can improve the model and data based on this information to improve the classification effect and apply the model to other fraud detection problems.
EZCrop: Energy-Zoned Channels for Robust Output Pruning

Recent results have revealed an interesting observation in a trained convolutional neural network (CNN), namely, the rank of a feature map channel matrix remains surprisingly constant despite the input images. This has led to an effective rank-based channel pruning algorithm [23], yet the constant rank phenomenon remains mysterious and unexplained. This work aims at demystifying and interpreting such rank behavior from a frequency-domain perspective, which as a bonus suggests an extremely efficient Fast Fourier Transform (FFT)-based metric for measuring channel importance without explicitly computing its rank. We achieve remarkable CNN channel pruning based on this analytically sound and computationally efficient metric, and adopt it for repetitive pruning to demonstrate robustness via our scheme named Energy-Zoned Channels for Robust Output Pruning (EZCrop), which shows consistently better results than other state-of-the-art channel pruning methods. The codes and Appendix are publicly available at: https://github.com/ruilin0212/EZCrop.
AngHNE

Real-world networks often show heterogeneity. A frequently encountered type is the bipartite heterogeneous structure, in which two types of nodes and three types of edges exist. Recently, much attention has been devoted to representation learning in these networks. One of the essential differences between heterogeneous and homogeneous learning is that the former structure requires methods to possess awareness to node and edge types. Most existing methods, including metapath-based, proximity-based and graph neural network-based, adopt inner product or vector norms to evaluate the similarities in embedding space. However, these measures either violates the triangle inequality, or show severe sensitivity to scaling transformation. The limitations often hinder the applicability to real-world problems. In view of this, in this paper, we propose a novel angle-based method for bipartite heterogeneous network representation. Specifically, we first construct training sets by generating quintuples, which contain both positive and negative samples from two different parts of networks. Then we analyze the quintuple-based problem from a geometry perspective, and transform the comparisons between preferred and non-preferred samples to the comparisons of angles. In addition, we utilize convolution modules to extract node features. A hinge loss, as the final objective, is proposed to relax the angular constraint for learning. Extensive experiments for two typical tasks show the efficacy of the proposed method, comparing with eight competitive methods.
Collaborative Curating for Discovery and Expansion of Visual Clusters

In many visually-oriented applications, users can select and group images that they find interesting into coherent clusters. For instance, we encounter these in the form of hashtags on Instagram, galleries on Flickr, or boards on Pinterest. The selection and coherence of such user-curated visual clusters arise from a user's preference for a certain type of content as well as her own perception of which images are similar and thus belong to a cluster. We seek to model such curation behaviors towards supporting users in their future activities such as expanding existing clusters or discovering new clusters altogether. This paper proposes a framework, namely COLLABORATIVE CURATING that jointly models the interrelated modalities of preference expression and similarity perception. Extensive experiments on real-world datasets from a visual curating platform show that the proposed framework significantly outperforms baselines focusing on either clustering behaviors or preferences alone.
Evaluating 360o Video's Learning Effectiveness and Experiences

Technology constantly provides tools with an educational potential, such as 360o videos. Despite the growing body of research regarding their impact on learning, primary school students are not a common target group. To fill this gap, the study compared the learning outcomes from the use of 360o videos presented using head-mounted displays (HMDs) with that of 360o videos presented using PC monitors and with printed material, having as a target group 46 students, aged 11 to 12. Virtual tours of archaeological sites was the theme of the learning content. A within-subjects research design was applied. The learning outcomes and students' enjoyment were better in the HMDs condition. However, there were no significant differences in motivation and ease of use. Immersion and subjective usefulness were greater only compared to printed material. None of the above factors had an impact on the learning outcomes. Overall, while it can be supported that 360o videos provide positive learning experiences, further studies will help to better understand their exact impact on learning.
FARM-A-CATION: A support for farmers

Technology has evolved every sector in this world more useful. Agriculture is the backbone of our country. But nowadays farmers economic is affected drastically in the modern world compared to the older days. The major affecting factor is their employment, so we decide to solve the problem using our system. It is basically the concept of hiring the farmer giving them work and paying per hourly/day charges. We also incorporated the concept of sell/share/hire of land in which farmers can sell their land if they cannot maintain it, sharing concept is the method in which the farmers will get help from outsider money and they will share half of the profit with the co-share person then the hiring part is a farmer can hire a farmland and do agricultural works and then they pay rent for the hired land. We also aim to make the people to experience the farming and can know how farmer is struggling in making the cultivation. This is the ultimate purpose of the system Farm-a-cation: a support for farmer.
Detecting and Rectifying Adversarial Images Dealt by Deep Learning Models

Adversarial attacks on images are minute noises intentionally added to an image that can fool a deep learning model to make a wrong classification. There are few methods developed recently to identify adversarial images, we are using the PCA (principal Component Analysis) whitening method. This research work focuses on reconstructing the adversarial image by removing maximum possible adversarial content while retaining maximum possible original data which brings out the new possibility of rectification after detection of adversarial images. This involves reconstructing the image from the PCA whitened image by reversing the process of whitening. The clean and adversarial images are tested on this process and the deep learning model could classify with an accuracy of 83 percentage. In the future, finer noise filtration processes can be researched on, to retain the original quality of the image.
Detection and Classification of Breast Cancer Using CNN

Breast cancer has been a major type of cancer and has been responsible for countless deaths of women in the last few years. Scientists have been trying their best to come with the latest and greatest innovation in the treatment of this deadly disease. The challenge is it takes a lot of time to observe and analyze the cancer’s images manually. These challenges have been overcome to a very large extent with the advancements in the research area of machine learning and deep learning. In our proposed model, ConvNet is designed to extract features from histological image of breast cancer and classifications done based upon obtained relevant feature by same network contrary to the current model where the classification is performed by using convolutional neural networks (CNN) algorithm. In our model, we have obtained 98.5% accuracy, 97.01% sensitivity and f-score 95.7%. We performed comparison with some of the existing technologies and proved that the proposed CNN has given better performance.
A Bayesian Network Learning Method with Easy Reasoning

In connection with the current Bayesian network to the process of application, the reasoning efficiency is a little low, which can not be applied to the actual production and life. In order to improve the reasoning efficiency of the Bayesian, in this paper, an easy reasoning Bayesian network learning method based on reasoning complexity of Bayesian network is proposed. By adding the influence factors of reasoning efficiency into the original scoring function of Bayesian network, the influence factors of single reasoning and multiple reasoning are added into the learning process of Bayesian network. Based on the traditional hill-climbing method to learn Bayesian network, the Bayesian network association container is constructed. Under certain constraints, the easy reasoning Bayesian network is found. Experimental results show that the Bayesian network learning results of the proposed method can effectively reduce the reasoning complexity of Bayesian network, so as to improve the reasoning efficiency of Bayesian network in practical application.
Multimode Tree-Coding of Speech with Pre-/Post-Weighting

As speech-coding standards have improved over the years, so complexity has increased, and less emphasis been placed on low encoding/decoding delay. We present a low-complexity, low-delay speech codec based on tree-coding with sample-by-sample adaptive long- and short-code generators that incorporates pre- and post-filtering for perceptual weighting and multimode speech classification with comfort noise generation (CNG). The pre-/post-weighting filters adapt based on the code generator parameters available at both the encoder and decoder rather than the usual method that uses the input speech. The coding of the multiple speech modes and comfort noise generation is accomplished using the code generator adaptation algorithms, again, rather than using the input speech. Codec complexity comparisons are presented and operational rate distortion curves for several standardized speech codecs and the new codec are given. Finally, codec performance is shown in relation to theoretical rate distortion bounds.
CELL: Counter Estimation for Per-flow Traffic in Streams and Sliding Windows

Measurement capabilities are fundamental for a variety of network applications. Typically, recent data items are more relevant than old ones, a notion we can capture through a sliding window abstraction. These capabilities require a large number of counters in order to monitor the traffic of all network flows. However, SRAM memories are too small to contain these counters. Previous works suggested replacing counters with small estimators, trading accuracy for reduced space. But these estimators only focus on the counters&#x2019; size, whereas often flow ids consume more space than their respective counters. In this work, we present the CELL algorithm that combines estimators with efficient flow representation for superior memory reduction.We also extend CELL to the sliding window model, which prioritizes the recent data, by presenting two variants named RAND-CELL and SHIFT-CELL. We formally analyze the error and memory consumption of our algorithms and compare their performance against competing approaches using real-world Internet traces. These measurements exhibit the benefits of our work and show that CELL consumes at least 30% less space than the best-known alternative. The code is available in open source.
GDPR-Compliant Data Processing: Practical Considerations

We provide actionable guidance to organizations needing to comply with the European General Data Protection Regulation (GDPR). We use a data processing pipeline – Data collection, Data protection, and Data operations – to structure the discussion around regulation requirements (with references to specific articles and recitals), socio-technical challenges, and applicable security best practices and techniques. Ensuring compliance is critical since fines for infringements can mount up to 4% of the total worldwide annual turnover of the organization in the preceding financial year.
Hyper Parameter Optimization of Convolutional Neural Networks for Breast Cancer Classification

The Convolutional Neural Networks (CNN) have recently become the most effective way of classifying images. Numerous researchers have demonstrated the value of neural network design to improve efficiency by optimizing the hyperparameters of the neural network. Some researchers have shown the importance of the neuron’s activation by using various activation functions and tuning numerous parameters in deep neural networks. This study presents efficiently classified breast cancer images as Benign or Malignant by using deep learning schemes. The proposed scheme uses the pre-processing techniques for images such as normalization and standardization, and performs the classification by using convolutional neural network. Also hyper-parameter tuning of convolutional neural network is performed by using two efficient optimization techniques as grid search and randomized search technique. The experiment is conducted by using the INbreast dataset. The results demonstrated reveals that the hyper-parameter tuning/optimization techniques along with normalization and standardization techniques is the most effective technique for the mammogram image classification with CNN to classify breast cancer significantly.
A Survey on Neural Machine Translation Applied to Sign Language Generation

Sign languages are the primary medium of communication for the deaf and hearing-impaired community, and the number of human beings with hearing impairment is increasing at a rapid rate. There is a significant communication barrier between the hearing-impaired and normal-hearing communities. One solution to this issue is to offload the effort of text understanding for the deaf and hearing-impaired community in the form of Sign Language Generation (SLG). In this paper, first of all, we systematically review the mainstream Neural Machine Translation (NMT)-based SLG approaches. Specifically, according to their model architectures, we divide them into two categories: the Recurrent Neural Networks (RNNs)-based and Transformer-based models, and discuss their advantages and limitations. Next, we introduce the existing publicly available datasets and the data preprossessing methods in the field of SLG. Then, we summarize the performance evaluation methods for NMT-based SLG models. Finally, we offer two possible future research directions for better development in the field of SLG.