Reviewerly/exHarmony huggingface dataset:
A Design of Sparse Neural Network Accelerator for Memory Access Optimization

At present, convolutional neural networks have developed rapidly. How to improve computational efficiency and optimize memory access has become an important research direction in neural network accelerators. In this paper, we proposed a new sparse convolutional neural network accelerator. The new accelerator improves the efficiency of memory access. What's more, it can also effectively utilize the neuron sparsity in the sparse convolutional neural network, thus improving the computation speed of the accelerator. Compared with the existing sparse convolutional neural network accelerators, the experiments show that the memory access time of the accelerator is reduced by 60%, the average speedup of overall neural networks and the convolutional layer is 1.81x and 2.38x respectively.
Influence of Information Security Culture on the Information Security Governance Capabilities (Case Study: PT XYZ)

Objective – To analyze the relationship between a company’s information security approach/culture with its information security governance capabilities based on COBIT 5 framework and provide recommendations that can be used to improve the company's information security capabilities per COBIT 5 standard.&#x0D; Methodology – The research uses qualitative and quantitative methods by conducting interviews and distributing questionnaires to 3 members of the IT Department at PT XYZ.&#x0D; Findings – The research found that the measured COBIT 5 processes (APO13 and DSS05) failed to reach the expected target (level 4), with each DSS05 and APO13 can only reach level 1 and 2 respectively. In addition, several flaws were also found in the company’s information security culturethat may have contributed directly or indirectly to the current state of the company’s information security capabilities.&#x0D; Novelty – In this study, the researchers expand the previous study on information security culture conducted in 2010 by performing a security audit on a company's IT department to analyze the connection between corporate culture, especially information security culture and the capability level of information security governance. The company thus can make improvements or corrections to its information security approach/culture based on the recommendations provided with COBIT 5 framework.&#x0D; Keywords: Capability Level; COBIT; Governance; Information Security Culture.
An Entity Alignment Algorithm Based on GCN and Translation Model

Entity alignment (EA) is the core technology of building large-scale knowledge base and realizing knowledge fusion. In recent two years, many researches use the graph convolution neural network (GCN) for entity alignment and have achieved good results. However, the existing GCN-based EA algorithms do not effectively utilize the semantic information between entities. In this paper, an EA method combining GCN with translation model is proposed. It separately learns the embedded vectors of entities based on GCN and translation model, and then computes the distance of vectors to align entities. Experiments on real-world datasets show the effectiveness of the proposed approach.
Research on Music Education and Teaching in Colleges and Universities under the Dimension of Aesthetic Intersubjectivity

This article intends to take "intersubjectivity", which is derived from the philosophical concept of aesthetic intersubjectivity music education concept, as a breakthrough point, to make some discussions on how to build a harmonious system between teachers and students in music education teaching in colleges and universities. By drawing from aesthetic intersubjectivity about principle and related characteristics, from two aspects of theory and practice of college music education teaching to carry on the detailed and in-depth analysis and research, this paper discusses mainly the inter-subjective philosophy noun concept and intersubjectivity and music education in colleges and universities music education teaching, the necessity of research on the inter-subjective music education and the teaching strategy. To explore in the context of what kind of classroom teaching, the teaching strategy can make different classroom education teaching factors to form a balanced and effective system, to make music education teaching each link between each other to achieve a harmonious state of nature, in the hope of colleges and universities music education teaching classroom education of universities and colleges and the whole class can towards more vivid and orderly, healthy and harmonious development.
SCTIM: A Trusted Identity Model Based On Smart Contract

The anonymity of blockchain identities brings security risks to transactions. To solve this problem, this paper proposes a trusted identity model based on smart contracts and introduces the CA certificate of the PKI system to endorse the authenticity of the user's identity. The model structure is designed, the blockchain digital certificate format is given, the cross-domain authentication scheme based on smart contracts is described, and the security and efficiency of the model are analyzed. In terms of security, the model meets the needs of entity verification; in terms of efficiency, the use of a hash algorithm to construct a complete certificate chain, compared with existing solutions, significantly improves the efficiency of cross-domain authentication.
TidyTensor: Utilities for multidimensional arrays as named hierarchical structures

Deep learning applications commonly employ the use of tensors (multidimensional arrays) for data storage and manipulation.In addition to being space efficient tensors provide a common framework for, the engine of deep neural network training (Baydin et al., 2018).TidyTensor is an R package for inspecting and manipulating tensors.It provides an improved print() function for summarizing structure, named tensors, conversion to data frames, and high-level manipulation functions.Designed to complement keras package (or other packages that utilize native R arrays), functionality is layered on top of base R types.
Cognitive Consistency Models Applied to Data Clustering

Data clustering concerns the discovery of partitions in data such that items from the same groups are as similar to each other as possible, and items from different groups are as dissimilar to each other as possible. The literature presents vast diversity on data clustering approaches, including systems that model the behavior of social individuals from different species. This work proposes a clustering algorithm that is reasoned upon the social theory of cognitive dissonance and the psychological theory of balance. We investigate whether psychological balance aware decision-making capabilities would affect the data clustering task. Partial results revealed the superiority of the proposed approach according to 5 out of 9 clustering quality metrics, when compared to other clustering algorithms.
Optical Quantum Computing

Optical (also known as photonic or bosonic) quantum computing uses photons, so-called “flying qubits”, to encode \( \left| 0 \right\rangle \) or \( \left| 1 \right\rangle \). Several different methods exist for encoding \( \left| 0 \right\rangle \) and \( \left| 1 \right\rangle \), including the photon polarization or the spin angular momentum state (polarization encoding), different optical paths in an optical system (path encoding), the spatial distribution of light such as different optical modes in an optical waveguide (orbital angular momentum encoding), and time or frequency encoding. We have already examined the case of polarization encoding for quantum communication in Chap. 6. Let us consider the key optical elements used for optical quantum computing, using path encoding as an example.
On the Impact of Center Frequency Drifts on QKD Performance in WDM-based Nodes

Integrating Quantum Key Distribution (QKD) systems in existing optical infrastructure towards hybrid classical/quantum communication networks has arisen as a top priority for fiber infrastructure owners [1] . In this direction, important steps have been made using multiplexing techniques such as the recently reported record joint transmission of Continuous Variable-QKD (CV-QKD) with Wavelength Division Multiplexing (WDM) 18.3Tbps coherent data channels [2] . However, moving towards practical implementations where real-world deployments such as filtering nodes and lasers sources suffer from few GHz frequency drifts, a thorough study and a precise quantification of the impact of setups misalignments and the subsequent evaluation of the tolerance of QKD links are yet to be investigated. In this work, we attempt to focus on this setup misalignment by addressing the impact of filtering penalty in the WDM co-existence of QKD with classical bright signals.
Substitution inconsistencies in Transparent Intensional Logic

This paper presents several important results for Transparent Intensional Logic (TIL). The conversions that are standardly taken to be valid – namely restricted β-conversion by name and β-reduction by value – are shown to be invalid. The core principle on which their validity is based – the so-called Compensation Principle – is also shown to be invalid. Further, the paper demonstrates the flaws of the proof of the Compensation Principle.
Efficient data processing method for edge intelligence based on SVM

Recently, edge intelligence is emerging. It is widely used because it allows you to make business models more efficient through edge intelligence, but there is still a lack of models to use for edge intelligence. Artificial neural networks are being used a lot because they solve the problem of overfitting, which is a disadvantage of artificial neural networks and increase the speed by improving GPU performance. However as the independent variable to be learned increases, the learning time and characteristics of the artificial neural network slow down and the size of the neural network becomes very heavier. The shortage of these artificial neural networks make it difficult to make edge intelligence for classification or prediction in real life. Manufacturing lightweight models using heavy neural networks and high-performance GPUs also presents a lot of financial problems. However, SVM based on statistical mathematics exists between machine learning. Although it is relatively inferior to artificial intelligence, it is better light and has stable performing. To do this, we propose an efficient data processing method that improves the characteristics of the SVM (the overall performance, accuracy and learning speed of the SVM) and makes it easier for the SVM to learn the data. When four types of data processing were applied, the accuracy increased by more than 0.4% for the iris data set and 2% on average for the other data sets, and the training time for all data sets was also reduced. As a result, SVMs that can process data efficiently are much lighter than artificial neural networks and have very good training times versus accuracy. Therefore, in this paper, we propose an efficient data processing method based on SVM for edge intelligence.
An Overview of Technological Revolution in Deep Learning Architectures for Biomedical Named Entity Recognition

The research publications related to biomedical domain are increasing and a huge information is concealed in it. Most of these information are available in the unstructured format and it is not available in ready-to-use format for further experiments and analysis. Several techniques and features of Information Extraction (IE) and Natural Language Processing (NLP) in Text Mining (TM) are used in the field of biomedical domain. The main purpose of this paper is to review the various research works / practices which are being carried out, especially in the Biomedical Named Entity Recognition (BioNER) domain. Application of NLP in BioNER helps to extract knowledge on various entities like drug, disease, chemical, cell, gene, protein, etc. In this paper, a survey on BioNER which includes traditional rule-based methods, statistical Machine Learning (ML) and Deep Learning (DL) has been carried out to get key insights on research works in this area. This review proves that the DL models took over statistical ML and rule-based systems and proved to be the state-of-the-art in various perspectives.
Singularity Avoidance Strategies for Target Tracking Visual Servo Systems

A typical vision servo system for motion target tracking consists of a robotic manipulator and a camera mounted at its end. The image acquired by the camera is processed by a vision algorithm to obtain the position of the motion target in 3D space, whereby the servo controller controls the robotic manipulator to track the motion of the target and keep it in the camera’s field of view at all times. In this paper, we proposed an evaluation indicator to evaluate such tracking tasks, and proposed an adaptive damped least-square algorithm to avoid singularities during the motion of the robotic arm. An experimental platform consist of ZED binocular camera and UR5 robotic arm is built to conduct experiments on singularity avoidance and target tracking, verifying that the visual servo system with the singularity avoidance algorithm can achieve a good tracking result.
Mathematics and Variables ─ Independent Variables and Dependent Variables

This study discusses the usages of independent and dependent variables. Independent variables are used in two ways: to denote the input to a function and to define a function. Correspondingly, dependent variables are used to hold the output of a function and to describe the relationship between the inputs and outputs of a function. When translating independent variables and dependent variables of mathematics into predicate logic, mathematical variables are mapped onto both variables and function symbols of predicate logic.
A Behavioral Biometric system for Recognition and Authenticity via Internet in detecting attacks to provide Information Security Using multiple security interceptive biometric scanners (MSIBS)

The Internet and an individual's life span are analyzed as a technological oriented balancing lifestyle. Information is fetched from each person when they are surfing social media, games even for entertainment such as movies and songs also. The major drawback from usage of the internet is that it is highly insecure for information and threats are being increased day by day. When the necessity for computing the security system in an efficient manner is focused as main steps and also to avoid the data loss or even risking in human life from various situations. The sensitive features such as aadhar number, PAN number, driving license etc. are determined to take necessary actions in maintaining safety. Many organizations and human beings are identified and maintain individuality using their biometric authenticity only. Such that the fake or false information can be reduced. Authentication is also a token of security and avoids behavioral pattern forgery by unwanted people. Cyber security which includes the biometric scanner as important features in providing security by making cardinality in user matching as well as providing high security in their system. In the proposed system the information security which are implemented using Biometric scanners which can be used in various applications for pattern matching all individual users to maintain and have a activation access record separately. The main objective is to use the biometric scanners in fingerprint scanning system for customers, education, banking system etc to bring security safe. The authentication and privacy system which can be a given for a individual's when they are accessing internet makes them feel comfort and phishing attacks can also be monitored to update the vulnerable threat. Protection using the multiple security interceptive biometric scanners (MSIBS) with the Active Directory Federation Services (ADFS) which can make users alert by detecting the malicious users and hackers and keeps the internet information secure.
Research on Performance and Optimization of PV- Thermoelectric (PV-TE) Building Integrated System Based on Full Spectrum Utilization of Solar Energy

After China's reform and opening up, China's economy is booming, the real estate once was an important pillar industry of China's economy, but with the progress of China's economy and construction engineering industry, the problem of energy consumption is becoming increasingly serious. Solar energy is a new type of automatic and resource-saving renewable energy. The application of solar energy technology is of great significance to building energy saving, but the application conditions are still not mature. Therefore, in order to make efficient use of solar energy, the project focuses on combining photovoltaic power generation technology, photothermal conversion technology and thermoelectric generator to design an efficient and low-cost PV-thermoelectric (PV-TE) building integrated system that can cope with a variety of harsh environments.
Differential Leave-One-Out Cross-Validation for Feature Selection in Generalized Linear Dependence Models

Estimation of dependencies from empirical data in a growing class of models is inevitably concerned with choosing the value of a structural parameter responsible for the model’s complexity. The most popular cross-validation schemes, in particular, LOO, suffer from the necessity to multiply repeat the model estimation on different subsamples of the training set. In this paper, we propose the method of differential LOOCV for generalized linear models of arbitrary dependencies, which allows for estimation of the model only once with each tentative value of the structural parameter. The idea of the method is that, instead of complete deleting an object from the training set at a single step of the training process, we delete only an infinitesimally small part of each of them. The indicator of the model quality is computed as the average of partial derivatives of the errors at each of single objects by the weights of their occurrence in the training set. The computing of the model quality indicator does not increase the computational complexity of the estimation procedure.
Federated Automatic Speech Recognition in Federated Ecology

Automatic Speech Recognition (ASR) requires a lot of data for training, and more data is better than more advanced algorithms. However, the training data of speech recognition may expose personal information such as voice prints. We have heard some cases of fraud using synthetic voices. Existing federated learning methods have many limitations that restrict the further applications in large practical scenarios. In this paper, we propose a set of federated data methods to transfer training features, which eventually not only keep the convenience and effectiveness of ASR training by nature but also protect the speaker's personal information.
Visual Representation Learning for Automating Car Part Recognition in a Large-scale Car Sharing Platform

Among several components of post-accident procedures, the automation of car part recognition on images benefits efficient operations of car sharing platforms and insurance companies. The prior studies employed various computer vision algorithms to automate car part recognition tasks under the supervised learning paradigm; however, these studies bear several drawbacks to be applied in the real world. The supervised approaches required manual annotations on the dataset, and they assumed car part images always include the global shape of the vehicle while the real-world images do not follow the assumption. In pursuit of improving these limits, our study proposed a novel approach to automating car part recognition with the following contributions. First, we examined the self-supervised feature extractor better understands the visual representation of car part images rather than conventional methods. Second, we scrutinized resizing with interpolation and augmenting with the normalization most effectively highlights car part images' visual patterns. Third, we designed an automated car part recognition system with fewer human interventions than prior research. Lastly, we examined our automation approach trained with midsize car images can be transferred into other car types; thus, the practitioner can save resources in real-world practices. Although our automated car part recognition approach still bears a human intervention, we expect it would be a concrete baseline of further automation approaches to accomplish efficient post-accident procedures in the real world.
Animate Orientation Based on Visual Landmarks and Scene Recognition

A biologically-inspired approach to robot route following is presented. The ant of the genus Formica rufa (a red forest ant) is used as a model species. These ants actively use collective foraging, unlike many other ant species. The scout ant remembers the route to food and can transmit information about the food location to foraging ants. Foragers can independently reach this place using this data and return home. The basis of the proposed method is the memorization the way by visual landmarks and fuzzy control. The animate path description model consists of a sequence of scenes and includes compass to account for the direction. The behavior of the animate-scout is implemented using an algorithm that simulates the foraging behavior of ants. The animate-forager performs actions to reproduce the route, applying the developed set of rules. The forager behavior is based on the same principles as that of a scout. But the scout remembers the scenes, and the forager recognizes and compares the visible scene and the scene from the route description. The actions of animates are presented in the form of elementary behavioral procedures. Each behavioral procedure is implemented using a finite state machine. The experiments for solving the foraging problem were carried out using a modeling system based on the ROS framework. The simulation results confirm the effectiveness of the proposed method. The method does not require large computing power and advanced sensory capabilities from the robot. It can also be used in reconnaissance and patrol tasks.
Claims-based algorithms for common chronic conditions were efficiently constructed using machine learning methods

Identification of medical conditions using claims data is generally conducted with algorithms based on subject-matter knowledge. However, these claims-based algorithms (CBAs) are highly dependent on the knowledge level and not necessarily optimized for target conditions. We investigated whether machine learning methods can supplement researchers' knowledge of target conditions in building CBAs. Retrospective cohort study using a claims database combined with annual health check-up results of employees' health insurance programs for fiscal year 2016-17 in Japan (study population for hypertension, N = 631,289; diabetes, N = 152,368; dyslipidemia, N = 614,434). We constructed CBAs with logistic regression, k-nearest neighbor, support vector machine, penalized logistic regression, tree-based model, and neural network for identifying patients with three common chronic conditions: hypertension, diabetes, and dyslipidemia. We then compared their association measures using a completely hold-out test set (25% of the study population). Among the test cohorts of 157,822, 38,092, and 153,608 enrollees for hypertension, diabetes, and dyslipidemia, 25.4%, 8.4%, and 38.7% of them had a diagnosis of the corresponding condition. The areas under the receiver operating characteristic curve (AUCs) of the logistic regression with/without subject-matter knowledge about the target condition were .923/.921 for hypertension, .957/.938 for diabetes, and .739/.747 for dyslipidemia. The logistic lasso, logistic elastic-net, and tree-based methods yielded AUCs comparable to those of the logistic regression with subject-matter knowledge: .923-.931 for hypertension; .958-.966 for diabetes; .747-.773 for dyslipidemia. We found that machine learning methods can attain AUCs comparable to the conventional knowledge-based method in building CBAs.