Reviewerly/exHarmony huggingface dataset:
Transparency Order of (n, m)-Functions—Its Further Characterization and Applications

The concept of transparency order is a useful measure for the robustness of (n, m)-functions (cryptographic S-boxes as mappings from \(GF(2)^n\) to \(GF(2)^m\)) to multi-bit Differential Power Analysis (DPA). The recently redefined notion of transparency order (\(\mathcal {RTO}\)), based on the cross-correlation coefficients, uses a very delicate assumption that the adversary has a priori knowledge about the so called pre-charged logic value (a constant register value set by a system) used in DPA-like attacks. Moreover, quite contradictorily, this constant value is used as a variable when maximizing \(\mathcal {RTO}\). To make the attack scenario more realistic, the notion of differential transparency order (\(\mathcal {DTO}\)) is defined for (n, m)-functions, which can efficiently eliminate the impact posed by this pre-charged logic value. By considering (4, 4) S-boxes which are commonly used in the design of lightweight block ciphers, we deduce in the simulated scenario that the information leakage using \(\mathcal {DTO}\) is usually larger compared to the standard indicator. Towards its practical applications, we illustrate that the correlation power analysis (CPA) based on the novel notion of \(\mathcal {DTO}\) performs better than that uses the classical notion of \(\mathcal {RTO}\). This conclusion is confirmed in two cases, i.e. CPA against MARVIN and CPA against PRESENT-128.
Logical Comparison of Cases

Comparison between cases is a core issue in case-based reasoning. In this paper, we discuss a logical comparison approach in terms of the case model formalism. By logically generalizing the formulas involved in case comparison, our approach identifies analogies, distinctions and relevances. An analogy is a property shared between cases. A distinction is a property of one case ruled out by the other case, and a relevance is a property of one case, and not the other, that is not ruled out by the other case. The comparison approach is applied to HYPO-style comparison (where distinctions and relevances are not separately characterized) and to the temporal dynamics of case-based reasoning using a model of real world cases.
A Color Image Encryption Based on 5D Hyper Chaos-Based System and DNA Dynamic Coding

An image encryption scheme based on a five-dimensional hyper chaos-based system was proposed and studied. In this scheme, the SHA-256 algorithm was used to generate the initial key of the original image, and the RGB color channels were separated to obtain three two-dimensional matrices. The five-dimensional hyperchaotic system was controlled by the image hash algorithm to generate a five-dimensional random sequence, in which the two related to the index replacement, and the other three concerned the dynamic DNA encoding of the digital image. The NPCR values of each layer of the encrypted image are 99.6120%, 99.6014% and 99.6204% respectively. The UACI values are 33.4845%, 33.4399% and 33.4915%, respectively. Accordingly the information-entropy is 7.9992, 7.9993 and 7.9993 respectively. It is found that the algorithm has a lower correlation between the neighboring pixels, the good security and the strong anti-attack ability.
Library Cataloging Work under the Background of Computer Network

Abstract With the rapid development of computer technology and network technology, the development of various social undertakings has gained new impetus. The cataloging work of the library has gradually changed from manual compilation to computer operation and networking. Online joint cataloguing links the catalogue resources of each relatively independent literature institution with computer network technology, forming a relatively unified and synchronous joint catalogue database. On the one hand, under the background of computer network, the efficiency of Library Cataloging can be improved, on the other hand, library cataloging has realized the unification of description format standard and standardized index. It can be said that actively carrying out the research of library cataloging work under the background of computer network will help to improve the level of cataloging work and innovate the ways and methods of cataloging work.
SMART Hospital Management Systems Based on Internet of Things: Challenges, Intelligent Solutions and Functional Requirements

Nowadays, Internet of Things (IoT) is invading almost all sectors of life since it is based on connecting living or non-living things together through computer technology. It is responsible for connecting physical objects together through the internet. Healthcare and hospitals are one of the most important sectors that require a lot of attention to transfer their old form of documentation into SMART management systems. It is essential to analyze health data in order to increase the quality of patient's care. Egypt being a development country is starting to substitute its old governmental systems into electronic SMART technology. IoT devices produce different types of data and transfer them to the cloud computing for storage and analysis. The benefits of using IoT in collecting, transferring, and analyzing patients' data for the hospitals are attracting a lot of researchers. Therefore, the arrangement of smarter and more money saving healthcare services are becoming highly required. Security and privacy, device communication, and data collection and management are some of the challenges that face the IoT technology especially when used with hospital's data. Accordingly, a proposed reference model for making SMART hospital management system is under construction in order to achieve the best performance. The model is taking into consideration both the functional and non-functional requirements of the different participants involved in the hospital management system.
Evaluation of Principal Component Analysis Algorithm for Locomotion Activities Detection in a Tiny Machine Learning Device

Data generated by human locomotion activities is a process that involves the analysis of hundreds or thousands of data in a reduced time, due to the very nature of the signals generated and the techniques for implementing classification models and event detection. It is advisable to try to reduce the number of characteristics or select the most important elements of the captured signals showing, in this paper, the use of principal component analysis (PCA) techniques. The use of machine learning techniques for reduced hardware devices in intelligent environments, allows generating a solution for the non-invasive supervision of activities, complementing the use of PCA with other classification algorithms suitable for the treatment of data with a high number of characteristics such as support vector machines (SVM). Therefore, the evaluation of PCA processes and SVM algorithms is shown, selecting the one that has the best performance during its implementation in IoT devices with low hardware resources. Finally, it is considered that the memory space consumed in the IoT device and the execution time of the processes are critical elements to make the comparison and contrast of the PCA models, allowing to select and develop a reliable and efficient model in small devices of IoT.
Ensemble Multi-Channel Neural Networks for Scientific Language Editing Evaluation

A huge and growing number of scientific papers are authored by non-native English speakers, driving increased demand for effective computer-based writing tools to help writers composing scientific articles. The Automated Evaluation of Scientific Writing (AESW) shared task promotes the use of natural language processing tools to improve the quality of scientific writing in English by predicting whether a given sentence needs language editing or not. In this study, we propose an Ensemble Multi-Channel Neural Networks (called EMC-NN) model for scientific language editing evaluation, comprised of three main parts: a multi-channel word embedding representation, a combination of Bidirectional Long Short-Term Memory and Convolutional Neural Networks, and a majority voting ensemble. Experimental results on 143,804 testing sentences show that our proposed EMC-NN achieved an F1-score of 0.6367, outperforming the winner of the AESW-2016 competition task and the recent BERT transformers. Based on a series of in-depth analyses comparing the number of channels, ensemble size and network architectures, the proposed EMC-NN model is a relatively simple, but effective approach that offers significant performance improvements for scientific writing evaluation task.
Stratified Data Integration.

We propose a novel approach to the problem of semantic heterogeneity where data are organized into a set of stratified and independent representation layers, namely: conceptual(where a set of unique alinguistic identifiers are connected inside a graph codifying their meaning), language(where sets of synonyms, possibly from multiple languages, annotate concepts), knowledge(in the form of a graph where nodes are entity types and links are properties), and data(in the form of a graph of entities populating the previous knowledge graph). This allows us to state the problem of semantic heterogeneity as a problem of Representation Diversity where the different types of heterogeneity, viz. Conceptual, Language, Knowledge, and Data, are uniformly dealt within each single layer, independently from the others. In this paper we describe the proposed stratified representation of data and the process by which data are first transformed into the target representation, then suitably integrated and then, finally, presented to the user in her preferred format. The proposed framework has been evaluated in various pilot case studies and in a number of industrial data integration problems.
Semantic Detection of Targeted Attacks Using DOC2VEC Embedding

The targeted attack is one of the social engineering attacks. The detection of this type of attack is considered a challenge as it depends on semantic extraction of the intent of the attacker. However, previous research has primarily relies on the Natural Language Processing or Word Embedding techniques that lack the context of the attacker's text message. Based on Sentence Embedding and machine learning approaches, this paper introduces a model for semantic detection of targeted attacks. This model has the advantage of encoding relevant information, which helps to improve the performance of the multi-class classification process. Messages will be categorized based on the type of security rule that the attacker has violated. The suggested model was tested using a dialogue dataset taken from phone calls, which was manually categorized into four categories. The text is pre-processed using natural language processing techniques, and the semantic features are extracted as Sentence Embedding vectors that are augmented with security policy sentences. Machine Learning algorithms are applied to classify text messages. The experimental results show that sentence embeddings with doc2vec achieved high prediction accuracy 96.8%. So, it outperformed the method applied to the same dialog dataset.
Research on the Security Algorithm of Reversible Information Hiding in Communication Encrypted Image

Abstract Considering the embedding capacity and the security of the algorithm, the paper proposes a reversible information hiding algorithm for encrypted images based on. The information concealer can find the transformed pixel group in the encrypted image according to the same key and use the embedding key to replace the corresponding pixel’s LSB to complete the reversible embedding of secret information. Simulation tests show that the proposed hiding method can realize the lossless recovery of the carrier image and the original image, which effectively reduces the transmission load and has high security.
The Talents Training Reform in Educational Management of Applied Institute Based On Internet and Information Technology

An important problem of education management is that education management needs professional advanced management tools and technologies. The key to the improvement and promotion of education management lies in the application of modern information technology to provide an efficient and professional platform for education management decision-making. In the era of big data, great changes have taken place in the understanding and application of information and data in universities. The education management practice of applied talents training based on big data platform, through data mining and big data orientation, realizes the mode transformation and mode innovation of education management theory, means, technology and culture. It can not only provide personalized learning services for students, but also innovate the traditional teaching concept, and then realize the "people-oriented" teaching principle, which improves the overall level and quality of universities to a certain extent.
Empirical Analysis of Financial Depth and Width Based on Convolutional Neural Network

There are great differences in financial and economic development in different regions. In different time series and different regions, the effects of financial depth and width on economic development are also different. This paper selects neural network to establish the economic benefit model of financial depth and breadth, which can deeply explore the relationship between financial data and economic data. In order to determine the optimal convolutional neural network parameters, the optimal convolutional neural network parameters are determined through comparative simulation analysis. The convolutional neural network model based on the optimal parameters is applied to the empirical analysis of the effect of financial and economic development in X region. In order to obtain the optimal convolutional neural network parameters, different convolution layers, convolution core size, and convolution core number are compared and simulated. The convolutional neural network model with optimal parameters is used to simulate the financial and economic data of X region. The simulation results show that the density of financial personnel has a certain impact on economic development, so it is necessary to improve the comprehensive quality of financial personnel and promote regional economic development. Therefore, this paper seeks an effective method to study the effect of financial breadth and depth on economic development which can provide a feasible idea for the in-depth research method of financial and economic development.
Research on the Implementation of Apprenticeship System in Primary School in Wuhan--A Study Based on Interview

Apprenticeship is a common school-based training method for new teachers.Although the apprenticeship system is of great significance for the professional development of new teachers, and it has gradually become the main training form for training new teachers in primary and secondary schools, there is little research on the actual operation effect of the apprenticeship system.Aiming at this problem, the researchers visited the front line of teaching and selected new and old teachers at different levels of primary schools in Wuhan to interview, trying to understand the true situation of the apprenticeship system in primary schools in Wuhan.
A Safety Evaluation Model of Intersections under Mixed Traffic Conditions Using Traffic Conflicts and Cloud Model

Safety evaluation of traffic conflict is a very important and challenging issue in evaluating intersection safety under incomplete traffic accident data conditions and is also one of the main safety surrogate measures of analyzing accident data recently. It helps to analyze and solve intersection problems comprehensively and deeply. From there, it helps to improve traffic safety as well as reduce the risk of traffic accidents at intersections. Various evaluation methods based on traffic conflict have been proposed to make conflict safety levels at intersections more consistent and objective. However, a major concern is that many existing measurements are still subjective and are not easy to obtain uniformly. This study aimed to develop a model for safety evaluation at intersections in a comprehensive way that may be expected to directly link to the severity of the accident from different evaluation indicators. First, the three factors, including time to collision (TTC), conflicting speed (CS), and deceleration rate (DR) to avoid a crash, are introduced into safety evaluation of conflicts as the indicators. And then, as regards the fuzziness and randomness of the evaluation indicators, the qualitative concept has to be converted into a quantitative one utilizing cloud model, which implements the natural transformation between the qualitative concept of the safety level of traffic conflict and the membership degree of the evaluation indicators corresponding to the different safety levels. Finally, an indicator weight model is built based on the information entropy and the AHP method to determine the safety level. We illustrate the practical implementation of the proposed method using actual data of a typical signalized intersection from Hanoi City of Vietnam. The results indicate that traffic conflict analyzed by the proposed method was appropriate with actual state of the intersection, and the proposed method is simple, effective, and feasible, so it has a certain application value.
Identifying Anomalies while Preserving Privacy

Identifying anomalies in data is vital in many domains, including medicine, finance, and national security. However, privacy concerns pose a significant roadblock to carrying out such an analysis. Since existing privacy definitions do not allow good accuracy when doing outlier analysis, the notion of sensitive privacy has been recently proposed. Sensitive privacy makes it possible to analyze data for anomalies with practically meaningful accuracy while providing a strong guarantee similar to differential privacy, which is the prevalent privacy standard today. In this work, we relate sensitive privacy to other important notions of data privacy so that one can port the technical developments and private mechanism constructions from these related concepts to sensitive privacy. Sensitive privacy critically depends on the underlying anomaly model. We develop a novel n-step lookahead mechanism to efficiently answer arbitrary outlier queries, which provably guarantees sensitive privacy if we restrict our attention to common a class of anomaly models. We also provide general constructions to give sensitively private mechanisms for identifying anomalies and show the conditions under which the constructions would be optimal.
Augmenting phishing squatting detection with GANs

Current solutions to tackle phishing employ blocklists that are built from user reports or automatic approaches. They, however, fall short in detecting zero-day phishing attacks. We propose the use of Generative Adversarial Networks (GANs) to automate the generation of new squatting candidates starting from a list of benign URLs. The candidates can be either manually verified or become part of a training set for existing machine learning models. Our results show that GANs can produce squatting candidates, some of which are previously unknown existing phishing domains.
Deep learning for tilted-wave interferometry

Abstract The tilted-wave interferometer is an interferometrical measurement system for the accurate optical form measurement of optical aspheres and freeform surfaces. Its evaluation procedure comprises a high-dimensional inverse problem to reconstruct the form of the surface under test from measured data. Recent work has used a deep learning hybrid approach to solve the inverse problem successfully in a simulation environment. A quantification of the model uncertainty was incorporated using ensemble techniques. In this paper, we expand the application of the deep learning approach from simulations to measured data and show that it produces results similar to those of a state-of-the-art method in a real-world environment.
SISTEM REWARD DAN PUNISHMENT SEBAGAI PEMICU DALAM MENINGKATKAN KINERJA KARYAWAN

A company certainly really wants employees to have good performance and have the motivation to work continuously so that the company is able to become a place to work that can make employees grow. There are various ways that can be done to improve employee performance. By using the right way will be able to improve employee performance better. The way that can be done to improve the performance of employees is to provide rewards when employees can achieve their goals and sanctions (punishments) when employees are negligent in their work. Giving punishment or reward is one of the ways used by companies to always motivate their employees to do their best for the company. The purpose of this reward and punishment system is to continue to encourage outstanding employees.
Data Invariants to Understand Unsupervised Out-of-Distribution Detection

Unsupervised out-of-distribution (U-OOD) detection has recently attracted much attention due to its importance in mission-critical systems and broader applicability over its supervised counterpart. Despite this increased attention, U-OOD methods suffer from important shortcomings. By performing a large-scale evaluation on different benchmarks and image modalities, we show in this work that most popular state-of-the-art methods are unable to consistently outperform a simple anomaly detector based on pre-trained features and the Mahalanobis distance (MahaAD). A key reason for the inconsistencies of these methods is the lack of a formal description of U-OOD. Motivated by a simple thought experiment, we propose a characterization of U-OOD based on the invariants of the training dataset. We show how this characterization is unknowingly embodied in the top-scoring MahaAD method, thereby explaining its quality. Furthermore, our approach can be used to interpret predictions of U-OOD detectors and provides insights into good practices for evaluating future U-OOD methods.
On the Reform of College English teaching in Chinese Private Universities under the Background of “Internet +” Era

In the era of "Internet+", traditional college English teaching in private universities and colleges in China is facing great challenges.Meanwhile, the rapid development of internet information technology has brought about rare opportunities and vitality into College English teaching, which accelerated the structural reform of College English teaching in China.This paper analyzes the problems and challenges faced by Chinese private universities under the background of the "Internet +" era.At the same time, aiming at these problems, this paper puts forward some countermeasures of reforming College English teaching in Chinese private universities, hoping to provide some advice for improving the quality of College English Teaching in Chinese private universities.
An Enhanced Meta-model to Generate Web Forms for Ontology Population

The process of manually inserting data in ontology instances is usually a cumbersome activity. Editing complex domain ontologies using Protégé and similar tools requires expert knowledge. Despite exhaustive research in this area, the existing ontology population tools are still not user-friendly enough to simplify this activity for end-users. To facilitate this process, we propose an approach to design an ontology to serve as a meta-model for the generation of user interface models. The user interface models are used to create web applications with dialog-based HTML forms, which are eventually used to populate instances of OWL ontologies. Our meta-model includes several patterns used to generate programming control structures used to populate ontology instances. On the one hand, the meta-model describes user interfaces, and on the other hand, it describes the structure of the output ontology instance. We also show a prototype of a tool that loads a simple meta-model file and creates a single-page application that populates an ontology instance.