Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Patent Grants within Patent Families : Why are some Patents within the same Patent Family Granted in One Country but not in Another?

This paper analyzes differences in the grant/no-grant status of patents within patent families by country. Many companies apply for the same patent across multiple countries with the aim of acquiring rights to goods and services globally. However, it is often the case that patents within the same patent family are granted in one country but not in another. We analyze this discrepancy from two aspects: information on the patent, such as the number of claims and citations, and words used in the patent specifications, such as the usage of “seasonal” words also used in articles published around the time the patent was filed. In this study, we focus on pharmaceutical patents filed in the United States, India, and Brazil. We then build a machine learning model to predict the grant/no-grant status of patents within the same patent family, including the case where a patent is granted in one country but not in another. Experimental results show that our model predicts the grant/no-grant status of patents within a patent family with high accuracy.
A Critical Story About Sample Size, Outliers, and Normality Criteria in Structural Equation Modelling

This paper bring a brief discussion about sample size, outlier, and normality tests which have been not considered in some previous studies related to Structural Equation Modelling technique. The intended audience is statisticians, mathematicians, or methodologists who either know about SEM or simple basic statistics especially in regression and linear/nonlinear modelling, and Ph.D. students in statistics, mathematics, management, psychology, and even computer science.
Development of Technology Strategies under Volatility and Uncertainty : A concept for the Continuous-Iterative Analysis of the Firm and its Environment

The dynamics and uncertainty of the environment in which companies operate has been increasing steadily over the past years. Subsumed under the acronym VUCA, more and more industries are confronted with transformative change in ever shorter time. The more dynamic and uncertain the environment becomes, the more important it becomes for companies to ensure a meaningful long-term development orientation of their capability base. Maintaining and expanding sustainable competitive advantage through building the technological capability basis is the mandate of a technology strategy. However, established methodologies for this are very time-consuming and only hardly situation-adaptive due to their discrete-sequential approach in combination with extensive analyses. Hence, a continuous iterative procedure for high turbulence environments is proposed. For such, the re-conception of the strategic analysis phase must be emphasized in order to achieve shorter development and reaction times while ensuring high decision quality. Therefore, the present paper aims to conceptualize such an analysis model. For this, the preliminary work for the overall methodology is briefly presented as the overarching framework of the analysis model. Subsequently, two analysis modes are differentiated, and the model structure is derived.
A Lightweight Convolutional Neural Network Model for Concrete Damage Classification using Acoustic Emissions

In this study, a convolutional neural network (CNN) model was developed for non-destructive damage classification of concrete materials based on acoustic emission techniques. The raw acoustic emission signal is used as the network model input, while the damage type is used as the output. In the study, 15,000 acoustic emission signals were used as the dataset, of which 12,000 signals were used for training, 1,500 signals for validation, and 1,500 signals for testing. Adaptive moment estimation (Adam) was used as the learning algorithm. Batch normalization and dropout layers were used to solve the overfitting problem generated in earlier versions of the model. The proposed model achieves an accuracy of 99.70% with 20,243 parameters, which provides a significant improvement over previous models. As a result, the classification of damages and decisions based upon them in non-destructive structural health monitoring applications can be improved.
Studying the Properties of Cellular Materials with GPU Acceleration

There has always been a great interest in cellular behaviour. From the molecular level, studying the chemistry of the reactions that occur in cell, and the physical interactions between those molecules, to the scale of the cell itself and its behaviour in response to various phenomena. Suffice it to say, that cellular behaviour is highly complex and, therefore, it is difficult to predict how cells will behave or to even describe their behaviour in detail. Traditionally cell biology has been done solely in the laboratory. That has always yielded interesting results and science. There are some aspects of phenomena that, due to cost, time, or other factors, need to be studied computationally. Especially if these stimuli occur on very short or long time scales. Therefore, a number of models have been proposed in order to study cell behaviour. Unfortunately, these methods can only be used in certain situations and circumstances. These methods can, and do, produce interesting and valid results. Yet there is not really any model available that can be used to model more than one or two kinds of cell behaviour. For example, methods that can show cell sorting do not necessarily show packing. Furthermore, many of the models in the literature represent cells as collections of points, or polygons, so cellular interactions at interfaces cannot be studied efficiently. The goal of the work presented here was to develop a three dimensional model of cells using Molecular Dynamics. Cells are represented as spherical meshes of mass points. And these mass points are placed in a force field that emulates cellular interactions such as adhesion, repulsion, and friction. The results of this work indicates that the model developed can reproduce qualitatively valid cellular behaviour. And the model can be extended to include other effects. It must also be recognized that Molecular Dynamics (MD) is very expensive computationally. Especially in the case of this model as many mass points are needed in the cellular mesh to ensure adequate spatial resolution. Higher performance is always needed either to study larger systems or to iterate on smaller systems more quickly. The most obvious way to alleviate this problem is too use high performance hardware. It will be shown that this performance is most accessible, after some effort, with Graphics Processing Unit (GPU) acceleration. The model developed in this work will be implemented with GPU acceleration. The code generated in this way is quite fast.
150-GHz Vector Modulator Phase Shifter in 65-nm CMOS

A D-band vector modulator-type phase shifter (VMPS) is presented using a 65-nm CMOS process. The proposed VMPS is based on a single Gilbert cell configuration to reduce insertion loss and minimize the effects of the parasitic components. Quadrature signals are generated from the Lange coupler with low insertion loss. A D-band output signal is generated from the vector summation from the quadrature signals controlled with the gate bias of the Gilbert cell. The output transformer is designed to simultaneously achieve low loss combining in the quadrature signals and impedance matching. In the EM-circuit co-simulation results, the proposed VMPS, implemented on the size 0.4 × 0.47 mm2 including all pads, shows that a gain of −12.5(±3) dB and an input P1dB of 7 dBm with a power consumption of 15.7 mW at the operating frequency of 150 GHz.
MAPPARAT: A Resource Constrained FPGA-Based Accelerator for Sparse-Dense Matrix Multiplication

Matrix Multiplication has gained importance due to its wide usage in Deep Neural Networks. The presence of sparsity in matrices needs special considerations to avoid redundancy in computations and memory accesses. Sparsity becomes relevant in the choice of compression format for storage and memory access. In addition to compression format, the choice of the algorithm also influences the performance of the matrix multiplier. The interplay of algorithm and compression formats results in significant variations in several performance parameters such as execution time, memory, and total energy consumed. This paper presents MAPPARAT, a custom FPGA-based hardware accelerator for $sparse \times dense$ matrix multiplication. Our analysis show that the choice of the compression format is heavily dependent on sparsity of the input matrices. We present two variants of MAPPARAT based on the algorithm used for $sparse \times dense$ matrix multiplication, viz., row-wise and column-wise product. A difference in speedup of $2.5\times$ and a difference in energy consumption by about $2.6\times$ is seen between the two variants. We show that an intelligent choice of algorithm and compression format based on the variations in sparsity, matrix dimensions, and device specifications is necessary for performance acceleration. For identical sparse matrices, a speedup of up to $3.6\times$ is observed, when the dense format is chosen for one of the matrices for sparsity in the range of 30% to 90%. MAPPARAT on a resource-constrained device shows performance efficiency of up to 7 GOPs-per-second-per-watt.
SEVA: Structural Analysis based Security Evaluation of Sequential Locking

The protection of hardware intellectual properties (IPs) against reverse engineering and piracy has emerged as a critical need over the past decade. These threats are expected to remain dominant due to the following reasons: (1) prevalence of IP-based System on Chip (SoC) design flow; (2) highly globalized supply chain of hardware IP blocks; and (3) the presence of multiple untrusted parties in the SoC life cycle. Consequently, design solutions to safeguard hardware IPs against these threats have become a topic of active research. Hardware obfuscation or locking techniques have shown tremendous promise to provide robust low-cost protection against these threats. However, while researchers have explored innovative locking-based protections, a vast body of research on effective attacks on these techniques exposing their vulnerabilities has been presented. The latter forms an important and valuable contribution to the field of hardware security since it helps to understand potential vulnerabilities of locking solutions; perform security evaluation, and mitigate known vulnerabilities to entail more robust protection. In this paper, we present SEVA, a novel paradigm of structural analysis attack on sequential locking. SEVA aims at identifying and removing the inserted logic and the sequential elements in a design that is used to transform and lock its state space. We demonstrate that the proposed attack is highly scalable and precise, and widely applicable to many state-of-the-art sequential locking techniques. Based on the available information to an attacker, SEVA offers two attack modes, supervised and unsupervised machine learning (ML) analysis. We have performed the security evaluation to multiple sequential techniques and successfully identified the added state elements (SEs) with an average accuracy of 97%.
Deep Reinforcement Learning based Haptic Enhancement for Tele-Diagnosis

Tele-Diagnosis is beneficial for medical care in areas with inadequate resources, which helps control the spread of Covid-19 in the current pandemic. Most teleoperated diagnostics are dependent on humans, possibly leading to cognitive issue caused by distanced communication. In this paper, we propose a local haptic enhancement framework to facilitate the remote palpation. The deep deterministic policy gradient (DDPG) algorithm is exploited to compensate for signal transmission due to latency, allowing human to operate without the sense of delay. With the help of weighted recursive least squares (WRLS) method, the interactive force can be estimated on the patient’s side despite the lack of force sensors. Fuzzy inference is used to diagnose and classify the extent of disease based on the estimated force and motion state on the remote side, thereby leveraging the remote sensory information to conduct autonomous reasoning. Finally, the final diagnosis is derived by performing minimum risk Bayesian decision based on local and remote inference results. Comparative simulation results have validated the superior performances of the proposed scheme.
Structuring the Core Competencies of a Major Car Manufacturer in a Technology Field: Applying Semantic Anchor Points to General Motors’ Gearing Technology

In order to stay competitive, companies develop a set of core competencies, many of which are based on technological know-how. This technological know-how, along with the efforts of R&D-projects, is displayed in those companies’ patent portfolios. Large companies often have a large patent portfolio, comprising various technologies. In order to understand a company’s position within a technology, it seems helpful to structure its core competencies. For a similar purpose, several authors developed a method based on semantic anchor points. While this method works well for smaller companies with a limited number of patents in one field, the question arises, whether and how this method can be applied to a major manufacturer with a comparatively large number of patents. In this paper, General Motors and their patents in the technology field of gearings are used to test the method. We have to divide the data set of General Motors due to their special patenting strategy. Using the cluster-specific semantic anchor points, specific technological know-how of General Motors is identified, and the competencies are analyzed in the course of time. For instance, three clusters of competencies could be identified. Two clusters describe the core competence of General Motors - the application of planetary gear sets. One cluster comprises a basic set of diverse competencies, which we call the follower-cluster.
A Bear on a Wall: Disruptive Agents of Space- and Place-Based Ethical Boundaries

Viral videos of human-animal interactions are prevalent among social-media sites. The comments sections of these videos are sites of debate over the ethics of place. In this study, we analyze comments on three viral videos to investigate three questions: Who has, or who creates, authority in construction of a space- or place-based ethic? In what ways are arguments about environmental ethics regarding space and place formed, and how do they emerge in social media spaces? What can we learn from an investigation of specific viral videos, including their associated comments, as they relate to place and space-based ethics? This study suggests interesting possibilities for the way ethics-related information moves, and for the ways we consider place and space in relation to ourselves and our world.
Approximate Adders for Deep Neural Network Accelerators

In this paper we propose a high-accuracy approximate carry lookahead adder that can be used with high reliability to realize DNN hardware accelerators. The Generate only Approximate adder design uses two successive group generate signals to approximate the carry. The design has an accuracy of 99.6 % while reducing the delay and PDP by 21.0 %, compared to an exact design. We have used three popular DNN architectures (LeNet, VGG16, and ResNet20) to test the suitability of the approximate design. With our design, the accuracy is degraded by a maximum of only 2.4 % for the ResNet20 architecture, whereas for the low accuracy configurations of published approximate designs, the error is as high as 90 %. Compared to high accuracy configurations, our design reduces the delay and PDP by as much as 20 % and 46 % respectively.
Monocular Visual Traffic Surveillance: A Review

To facilitate the monitoring and management of modern transportation systems, monocular visual traffic surveillance systems have been widely adopted for speed measurement, accident detection, and accident prediction. Thanks to the recent innovations in computer vision and deep learning research, the performance of visual traffic surveillance systems has been significantly improved. However, despite this success, there is a lack of survey papers that systematically review these new methods. Therefore, we conduct a systematic review of relevant studies to fill this gap and provide guidance to future studies. This paper is structured along the visual information processing pipeline that includes object detection, object tracking, and camera calibration. Moreover, we also include important applications of visual traffic surveillance systems, such as speed measurement, behavior learning, accident detection and prediction. Finally, future research directions of visual traffic surveillance systems are outlined.
Facial Recognition Technology Codes of Ethics: Content Analysis and Review

This paper reports insights from content and rhetorical analyses of codes of ethics (COEs) for designing facial recognition technology (FRT). The paper considers 16 new codes of ethics created for engineers to follow as they design FRT considering its social implications. It contributes to the literature by providing insights into what domains and dimensions emerge from codes of ethics specifically designed to encourage accountability in FRT design. We searched several research databases and conducted Google searches to identify FRT codes of ethics published between 2010 and 2020. The search yielded 16 lists of principles. Analysis revealed that FRT codes of ethics retain many domains typical of engineering codes of ethics, including professionalism and abiding by industry standards. However, the nature of FRT also required additional domains, including rights, consent, and accountability. Themes not only invoked characteristics necessary for engineers to follow but also expected FRT characteristics, such as transparency about errors and bias. FRT codes of ethics show what the industry expects of the actor-network created by FRT deployments and the engineers who design them. FRT codes of ethics are closed-system symbolic and persuasive processes that establish collective perceptions derived from prescriptions from leadership or professional organizations.
Using Extended Character Feature in Bi-LSTM for DGA Domain Name Detection

The detection of DGA domain name has become a central topic in network security in recent years. With DGA (Domain Generation Algorithm), a large number of pseudo-random domain names could be generated and used in DDoS (Distributed Denial of Service) attacks, reflection amplification attacks and other network attacks. Traditional deep learning algorithms based on character feature have the advantages of automatic feature extraction and shorter training time in DGA domain name detection. But for wordlist-based DGA domain name, the accuracy is lower. In order to improve the detection accuracy, semantic features of domain name are extracted to extend character features for the first time. Experiment results show that using extended character feature in Bi-LSTM (Bi-Directional Long Short-Term Memory) could improve the detection performance. In multi-classification, micro average of F1 score reaches up to 98.72%.
Scalable Hybrid Cache Coherence Using Emerging Links for Chiplet Architectures

Multi-die or chiplet architectures provide the most prominent solution to overcome the manufacturing constraints of conventional monolithic-chip architectures and enable scalability to large core counts. However, communication across the chiplets, especially inter-chiplet coherence poses major challenge for high-performance many core applications. To enable scalable coherence in chiplet architectures, we propose novel hybrid snoopy-over-directory coherence enabled through emerging broadcast-capable links with local directory for intra-chiplet coherence and global snoopy for coherence between chiplets. Evaluations show that it improves runtime by $2.8\times$, while requiring 90% and 60% lower storage and energy respectively as compared to hierarchical directory protocol.
FFEDN: Feature Fusion Encoder Decoder Network for Crack Detection

Crack detection plays a crucial role in structural health monitoring tasks to ensure the reliability of the transportation infrastructures. However, the automatic detection of cracks remains a challenging task due to the complicated background. Especially, tiny crack detection should be attached importance because of its weak feature and background interference. Therefore, an end-to-end network Feature Fusion Encoder Decoder Network (FFEDN) with two novel modules is proposed to improve the crack detection accuracy. For one thing, the representation capability for tiny cracks is enhanced by introducing the attention mechanism, which redistributes and fuses different features of both the encoder and the decoder. For another, because high-level feature contains less interference, a shape semantic prior module is developed to learn the shape prior map that provides the rough shape and location information of cracks. This map is fed into the lower-level feature and helps it focus on crack areas, thereby suppressing background interference. To demonstrate the effectiveness of the proposed network, several experiments are implemented on three publicly available crack datasets. Compared with state-of-the-art crack detection methods, the novel network shows better performance on all the six evaluation metrics.
Technical, Organizational, and Process Impacts of Business Intelligence Implementation Failure

Business intelligence (BI) software applications enable rapid decision-making in an environment of vastly expanding amounts of data and constantly evolving market dynamics. However, the literature suggests that about 70% of BI implementation efforts fail to deliver productive outcomes for decision-makers. For a comprehensive assessment of BI implementation failures, this research considers three perspectives: technical, organizational, and process (TOP) and the significant criteria that comprise them. These represent failures as well the absence of success factors. A literature review was performed to determine the criteria that are the elements of the TOP perspectives. A taxonomy of perspectives, criteria, and factors were developed, representing the TOP impacts of BI implementation failures. The review included detailed definitions of each element. This is a critical stage in building a decision model for the assessment of successful types of BI implementations.
The Impact of Data Utilization Capability on Japanese Companies : Findings from the Questionnaire Surveys

This study investigates the impact of data utilization on the business results of Japanese companies by using the dataset of the questionnaire survey conducted in 2020 by Research Institute of Economy, Trade and Industry. To capture the landscape of companies' data abilities across industries, we used data utilization capability (DUC) scores and conducted industry-wise comparison on the relationship between DUC and business results. The results of the analysis show that the DUC's effect in the service industries is statistically positive, whereas that in the manufacturing industries is not. The evidence suggests that the benefit of data utilization is much evident in the service industries than that in the manufacturing industries. Additional analysis supports this difference and poses a risk that the gap between both industries will not be closed. This fact suggests that we should consider such industry-wise difference when planning corporate strategies and industrial policies.