Reviewerly/exHarmony huggingface dataset:
The role of blockchain for intellectual capital enhancement and business model innovation

Blockchain is considered as one of the most disruptive technologies, with an estimated growth from USD 1.2 billion in 2018 to USD 23.3 billion by 2023 worldwide. Blockchain has the potential to disrupt all business activities as much as the internet, social media, and mobile technologies did in the past. It enhances the creation of new business models, and forces organizations to work differently. The chapter aims to analyze the potentiality and the effects of blockchain on current and future business models from an intellectual capital perspective, by applying a Structured Literature Review (SRL) on the most recent scientific as well as professional production on the topic. The findings highlight how the design principles of blockchain technology can enhance the intellectual capital of an organization, as well as lead to a business model innovation.
Innovar enseñando: la educación del futuro. Las TICs como factor motivador en la enseñanza

This research is based on the analysis of the different technological tools that the teacher has at their disposal.According to statistical data on internet consumption, social networks and the applications most used by users, the objective of the research is focused on making available to the teacher the necessary information about the technological resources that teachers have at their disposal in a single click, as well as what I can incorporate into their teaching process using the applications that we usually use on their devices (mobile phones, computers and tablets) and, with this, encourage and motivate both teachers and students in the use of specific tools to improve the quality of teaching.On the one hand, information on technological resources available to teachers is presumed vital, given the rapid and continuous technological innovation; and on the other, the lack of material time available to teachers to devote to these purposes, without forgetting elements such as technophobia as a barrier that must be broken peacefully to reduce the digital divide in teaching and to visualize technological processes as an ally of the teacher.
DeepFusion: Fusing User-Generated Content and Item Raw Content towards Personalized Product Recommendation

Personalized recommender systems, as effective approaches for alleviating information overload, have received substantial attention in the last decade. Learning effective latent factors plays the most important role in recommendation methods. Several recent works extracted latent factors from user-generated content such as ratings and reviews and suffered from the sparsity problem and the unbalanced distribution problem. To tackle these problems, we enrich the latent representations by incorporating user-generated content and item raw content. Deep neural networks have emerged as very appealing in learning effective representations in many applications. In this paper, we propose a novel deep neural architecture named DeepFusion to jointly learn user and item representations from numerical ratings, textual reviews, and item metadata. In this framework, we utilize multiple types of deep neural networks that are best suited for each type of heterogeneous inputs and introduce an extra layer to obtain the joint representations for users and items. Experiments conducted on the Amazon product data demonstrate that our approach outperforms multiple state-of-the-art baselines. We provide further insight into the design selections and hyperparameters of our recommendation method. In addition, we further explore the relative importance of various item metadata information on improving the rating prediction performance towards personalized product recommendation, which is extremely valuable for feature extraction in practice.
Relationship between shear ratio and reconstruction accuracy in lateral shearing interferometry

The shear ratio is an essential parameter of the lateral shearing interferometry system that affects dynamic range, signal-to-noise ratio, and accuracy. Some qualitative conclusions about the shear ratio’s influence can be found in existing reports, but no systematical analysis or quantified universal conclusion has been given. Using the sheared phase to reconstruct wavefront is the unique feature of the shearing interferometry, but it also affects the measurement accuracy significantly. The wavefront reconstruction methods can be divided into zonal and modal methods. We use the FFT reconstruction method and the least-square reconstruction method as the typical methods of modal and zonal methods to analyze the source of the reconstruction error and its relationship with the shear ratio. We conclude that the FFT reconstruction method and the least-square reconstruction method are equivalent to each other. The lost information during the shearing corresponds to some incalculable spectral values in the frequency domain and the pistons of different subgrids in the spatial domain. More spectral information will be lost if the shear ratio increased, which leads to more significant reconstruction errors. When the shear ratios are 1.6% and 6.3%, the corresponding relative reconstruction errors are about 1% and 1%, respectively. The error will be lower if the test wavefront can be represented by a limited amount of basis functions.
Joint spectral intensity of entangled photon pairs from a warm atomic ensemble via stimulated emission beat interferometry

Photonic quantum states generated from atomic systems play prominent roles in long-distance quantum networks and scalable quantum communication, because entangled photon pairs from atomic ensembles possess a universal identity and narrow spectral bandwidth for quantum repeaters. In this study, we propose and demonstrate a novel, to the best of our knowledge, method for the joint spectral intensity measurement of narrowband continuous wave (CW)-mode photon pairs from a warm atomic ensemble using stimulated emission and beat interferometry for the first time. Our approach offers the advantage of sub-megahertz resolution, absolute optical frequency measurements with megahertz-level accuracy, fast collection time, and high signal-to-noise ratio; thus, our method can find important applications in the characterization of narrowband photon pairs generated from sources including atoms and artificially structured material.
[Multiple linear regression models with natural logarithmic transformations of variables].

In general, the application conditions of linear regression models could be met after the natural logarithmic transformation of data. From the practical perspective, this paper introduced the linear regression models with natural logarithmic transformation of independent variable, dependent variable, and both independent and dependent variables in detail. The paper illustrated why the equation and coefficients could not be directly explained after the natural logarithmic transformation of data. The percentage changes of X and/or Y were applied to elaborate the principle and method for the explanation of the equation and coefficients. Three examples were used to fit simple linear models with natural logarithmic transformation of independent, dependent, and both independent and dependent variables and the results of theses models were explained in detail.一般情况下，数据自然对数转换可使变量满足线性回归的应用条件。本文从实际应用出发，介绍了在进行线性回归分析时，自变量自然对数转换、因变量自然对数转换和两者都进行自然对数转换后，不能直接用X和/或Y的改变量解释回归方程和系数的原因，并阐述了用X和/或Y的百分比改变来解释回归方程和系数的原理。最后，用三个实例演示自变量、因变量、两者都进行自然对数转换，对拟合线性回归的结果进行了详细解释。.
Detection of Hate and Offensive Speech in Text

With online social platforms becoming more and more accessible to the common masses, the volume of public utterances on a range of issues, events, and persons etc. has increased profoundly. Though most of the content is a manifestation of personal feelings of the individuals, yet a lot of this content often comprises of hate and offensive speech. Exchange of hate and offensive speech has now become a global phenomenon with increased intolerance among societies. However companies running these social media platforms need to discern and remove such unwanted content. This article focuses on automatic detection of hate and offensive speech from Twitter data by employing both conventional machine learning algorithms as well as deep learning architectures. We conducted extensive experiments on a benchmark 25K Twitter dataset with traditional machine learning algorithms as well as using deep learning architectures. The results obtained by us using deep learning architectures are better than state-of-the-art methods used for hate and offensive speech detection.
Use of blockchain technology as supply chain management system involving air transport

This article explores Blockchain Technology, and how it can be used in the context of information exchange between stakeholders in international supply chains and networks involving air transport. The article reviews the global experience with this technology of iconic companies (Maersk, Lufthansa, Walmart, Unilever, Nestle, Samsung, British Airways) in logistics and supply chain management. It highlights the features of international chains using air transport, which predominantly consist of two categories of goods: goods with high added value (electronic devices and their components, fashion industry products, jewelry) and goods with strict delivery time requirements (pharmaceuticals, live animals, perishable products, e-commerce products and mail). The study shows that perishable goods have great potential for increasing airline cargo turnover. However, at present, there is no single universal tool for managing their supply chains since the tools used (ERP, SRM, TMS, WMS) do not provide complete transparency of information exchange between all stakeholders. The most problematic stage in the delivery to control steps in the delivery of perishable goods by air occur on the ground, while goods are being handled at the airport of departure and the airport of destination. Difficulties arise while checking the correctness of paperwork, labeling and information authenticity about the product origin. The article suggests using block chain blockchain technology in combination with IoT and smart-contract technologies for SCM of perishable products using air transport. The use of this technology allows to reduce the time of document verification procedures. Customs clearance and phytosanitary control could be vastly improved. Overall tracking and monitoring of a product from the moment of its production to the destination simplifies the identification of any counterfeits, improves the quality of food safety control. A greater level of control becomes possible in logistics operations, to determine liability in possible delivery conditions’ violations cases. Overall optimization of the logistic processes, automatization of the execution of verified financial transactions can be achieved. The use of these technologies in combination with innovative technologies for food quality control will significantly reduce logistics costs in the supply chains of perishable products.
State Transition Modeling of the Smoking Behavior Using LSTM Recurrent Neural Networks

The use of sensors has pervaded everyday life in several applications including human activity monitoring, healthcare, and social networks. In this study, we focus on the use of smartwatch sensors to recognize smoking activity. More specifically, we have reformulated the previous work in detection of smoking to include in-context recognition of smoking. Our presented reformulation of the smoking gesture as a state-transition model that consists of the mini-gestures hand-to-lip, hand-on-lip, and hand-off-lip, has demonstrated improvement in detection rates nearing 100% using conventional neural networks. In addition, we have begun the utilization of Long-Short-Term Memory (LSTM) neural networks to allow for in-context detection of gestures with accuracy nearing 97%.
Monomial Generalized Almost Perfect Nonlinear Functions

Generalized almost perfect nonlinear (GAPN) functions were defined to satisfy some generalizations of basic properties of almost perfect nonlinear (APN) functions for even characteristic. In particular, on finite fields of even characteristic, GAPN functions coincide with APN functions. In this paper, we study monomial GAPN functions for odd characteristic. We give monomial GAPN functions whose algebraic degree are maximum or minimum on a finite field of odd characteristic. Moreover, we define a generalization of exceptional APN functions and give typical examples.
Deep learning with graph-structured representations

In this thesis, we propose novel approaches to machine learning with structured data. Our proposed methods are largely based on the theme of structuring the representations and computations of neural network-based models in the form of a graph, which allows for improved generalization when learning from data with both explicit and implicit modular structure.
Automatic traditional Chinese painting classification: A benchmarking analysis

Summary In the recent years, there is a growing trend toward digitization of cultural heritage for better accessibility and preservation. For instance, the development of image processing techniques in traditional Chinese painting (TCP) has begun to attract researchers' attention in the computer vision field. TCP is one of the representative of Chinese traditional arts. Evidenced by the successes of development in image processing techniques in various applications, this article aim to apply the deep learning approach on TCP for several purposes, which include automatic establishment of unified image library, facilitating update‐to‐date data in the database, reduction of cost required for image classification and retrieval. First, a unified database is established, that consists of more than a thousand of images from six major TCP themes. Then, several deep learning algorithms that are based on mathematical models are applied to examine the classification performance. In addition, the salient regions that denote significant features are identified, by adopting the instance segmentation technique. As a result, the modified pretrained neural network is capable to achieve 99.66% recognition accuracy. Qualitative results are also presented to demonstrate the effectiveness of the proposed method. We also note that this is the first work that performs multiclass classification on six categories in this domain. Furthermore, a 10‐class classification result of 96% is obtained when performing on one of the painting types, namely, ghost‐and‐god.
V0LTpwn: Attacking x86 Processor Integrity from Software

Fault-injection attacks have been proven in the past to be a reliable way of bypassing hardware-based security measures, such as cryptographic hashes, privilege and access permission enforcement, and trusted execution environments. However, traditional fault-injection attacks require physical presence, and hence, were often considered out of scope in many real-world adversary settings. In this paper we show this assumption may no longer be justified. We present V0LTpwn, a novel hardware-oriented but software-controlled attack that affects the integrity of computation in virtually any execution mode on modern x86 processors. To the best of our knowledge, this represents the first attack on x86 integrity from software. The key idea behind our attack is to undervolt a physical core to force non-recoverable hardware faults. Under a V0LTpwn attack, CPU instructions will continue to execute with erroneous results and without crashes, allowing for exploitation. In contrast to recently presented side-channel attacks that leverage vulnerable speculative execution, V0LTpwn is not limited to information disclosure, but allows adversaries to affect execution, and hence, effectively breaks the integrity goals of modern x86 platforms. In our detailed evaluation we successfully launch software-based attacks against Intel SGX enclaves from a privileged process to demonstrate that a V0LTpwn attack can successfully change the results of computations within enclave execution across multiple CPU revisions.
A Simple Approach to Case-Based Reasoning in Knowledge Bases

We present a surprisingly simple yet accurate approach to reasoning in knowledge graphs (KGs) that requires \emph{no training}, and is reminiscent of case-based reasoning in classical artificial intelligence (AI). Consider the task of finding a target entity given a source entity and a binary relation. Our approach finds multiple \textit{graph path patterns} that connect similar source entities through the given relation, and looks for pattern matches starting from the query source. Using our method, we obtain new state-of-the-art accuracy, outperforming all previous models, on NELL-995 and FB-122. We also demonstrate that our model is robust in low data settings, outperforming recently proposed meta-learning approaches.
The continuous categorical: a novel simplex-valued exponential family

Simplex-valued data appear throughout statistics and machine learning, for example in the context of transfer learning and compression of deep networks. Existing models for this class of data rely on the Dirichlet distribution or other related loss functions; here we show these standard choices suffer systematically from a number of limitations, including bias and numerical issues that frustrate the use of flexible network models upstream of these distributions. We resolve these limitations by introducing a novel exponential family of distributions for modeling simplex-valued data - the continuous categorical, which arises as a nontrivial multivariate generalization of the recently discovered continuous Bernoulli. Unlike the Dirichlet and other typical choices, the continuous categorical results in a well-behaved probabilistic loss function that produces unbiased estimators, while preserving the mathematical simplicity of the Dirichlet. As well as exploring its theoretical properties, we introduce sampling methods for this distribution that are amenable to the reparameterization trick, and evaluate their performance. Lastly, we demonstrate that the continuous categorical outperforms standard choices empirically, across a simulation study, an applied example on multi-party elections, and a neural network compression task.
Context Aware Local Differential Privacy

Local differential privacy (LDP) is a strong notion of privacy for individual users that often comes at the expense of a significant drop in utility. The classical definition of LDP assumes that all elements in the data domain are equally sensitive. However, in many applications, some symbols are more sensitive than others. This work proposes a context-aware framework of local differential privacy that allows a privacy designer to incorporate the application's context into the privacy definition. For binary data domains, we provide a universally optimal privatization scheme and highlight its connections to Warner's randomized response (RR) and Mangat's improved response. Motivated by geolocation and web search applications, for $k$-ary data domains, we consider two special cases of context-aware LDP: block-structured LDP and high-low LDP. We study discrete distribution estimation and provide communication-efficient, sample-optimal schemes and information-theoretic lower bounds for both models. We show that using contextual information can require fewer samples than classical LDP to achieve the same accuracy.
Automatic Shortcut Removal for Self-Supervised Representation Learning

In self-supervised visual representation learning, a feature extractor is trained on a for which labels can be generated cheaply, without human annotation. A central challenge in this approach is that the feature extractor quickly learns to exploit low-level visual features such as color aberrations or watermarks and then fails to learn useful semantic representations. Much work has gone into identifying such features and hand-designing schemes to reduce their effect. Here, we propose a general framework for mitigating the effect shortcut features. Our key assumption is that those features which are the first to be exploited for solving the pretext task may also be the most vulnerable to an adversary trained to make the task harder. We show that this assumption holds across common pretext tasks and datasets by training a network to make small image changes that maximally reduce performance in the pretext task. Representations learned with the modified images outperform those learned without in all tested cases. Additionally, the modifications made by the lens reveal how the choice of pretext task and dataset affects the features learned by self-supervision.
Analysis of University Students Employment Recommendation System Based on Apriori Algorithm

“Employment Recommendation System for College Students Based on Apriori Algorithm”, which has taken the big data of the web recruitment platform in China as its technology and service foundation, has adopted Apriori recommendation algorithm to restructure relevant quality information on employment intelligently by data analyzing information on employment of all large recruitment platforms. The system has achieved the accurate employment recommendation for college students, which has improved not only the job satisfaction and employment rate of college students, but the work efficiency of employment service for college students.
Building powerful and equivariant graph neural networks with message-passing

Message-passing has proved to be an effective way to design graph neural networks, as it is able to leverage both permutation equivariance and an inductive bias towards learning local structures in order to achieve good generalization. However, current message-passing architectures have a limited representation power and fail to learn basic topological properties of graphs. We address this problem and propose a powerful and equivariant message-passing framework based on two ideas: first, we propagate a one-hot encoding of the nodes, in addition to the features, in order to learn a local context matrix around each node. This matrix contains rich local information about both features and topology and can eventually be pooled to build node representations. Second, we propose methods for the parametrization of the message and update functions that ensure permutation equivariance. Having a representation that is independent of the specific choice of the one-hot encoding permits inductive reasoning and leads to better generalization properties. Experimentally, our model can predict various graph topological properties on synthetic data more accurately than previous methods and achieves state-of-the-art results on molecular graph regression on the ZINC dataset.