Reviewerly/exHarmony huggingface dataset:
The New A(I)rt Movement and Its Copyright Protection: Immoral or E-Moral?

Abstract Artificial intelligence (‘AI’) has left few well-established laws and norms unchallenged, and copyright is not an exception. Nowadays art can be created by algorithms and continental Europe’s copyright law ‒ which inter alia includes Greece ‒ no longer seems fit for purpose, persisting as it does in searching for a human behind each creative outcome. This paper aims to analyse the issues algorithmic generated art brings in terms of copyright protection, and to explain why the current legislation and proposed solutions are inadequate to resolve them. Inspired by a theory already applicable in Germany in certain civil law cases, this paper will then propose a new twofold sui generis right that includes a new, partial e-moral right and a sui generis economic right. The paper will then explain how this proposed sui generis right manages to avoid potential pitfalls from granting legal personhood to AI, while letting humans reap the financial benefits.
Application analysis of cryptography in blockchain

The purpose of blockchain technology is to solve the trust problem between people or institutions and make the communication data and network communication of the Internet. In the past, cryptography lost money. Passwords are used to protect data, and the cost is relatively high. But with blockchain, cryptography becomes valuable. The formation of blockchain has made new contributions to cryptography and done something we could not do in the past. With blockchain, cryptography is "valuable". In fact, there are many cryptographic primitives used in blockchain, such as hash, digital signature, and etc. Moreover, digital signature not only uses standard digital signature, but also uses ring signature, connectable ring signature, one-time signature, borromer ring signature, multi signature, homomorphic encryption, homomorphic commitment, accumulator, zero knowledge proof, etc. As well as the recently popular password signature toss. As mentioned above, the popularity of blockchain technology will completely break the centralized pattern, indicating the advent of a new era in the future - Web3.0. This paper focuses on the application of encryption technology in blockchain and expounds in detail the applications of such as hash function and ring signature in blockchain. This study analyzes the application of cryptography in blockchain and discusses to the development of encryption technology in the future.
Virtual reality environment-based collaborative exploration of fashion design

Driven by rapid advances in digital technology, virtual reality environments (VREs) have emerged as a new collaborative design tool. In this study, we present an experimental investigation of cognitive characteristics of VRE-based collaborative conceptual exploration in fashion design. Eight graduate students participated in two empirical experiments in which they collaboratively developed design concepts using a VRE and an online teleconferencing environment (OTE). The collected data were analysed according to a coding scheme to investigate the cognitive patterns involved in the process of collaborative exploration. The results indicate that collaborative exploration via VRE elicited more external representation-related cognitive activity linked to conceptual evaluation and enhanced interaction with a given design tool. They also show that OTE-based collaborative exploration was associated with a conceptual cognitive process model with a sequential flow of steps, which did not produce specific joint environment-related changes. In contrast, the VRE-based approach induced a flexible mental flow of various cognitive behaviours. This research contributes to the theoretical and empirical understanding of VRE needed to develop a methodological framework for VRE-based fashion design collaboration and related education.
Butterfly network: a convolutional neural network with a new architecture for multi-scale semantic segmentation of pedestrians

The detection of multi-scale pedestrians is one of the challenging tasks in pedestrian detection applications. Moreover, the task of small-scale pedestrian detection, i.e., accurate localization of pedestrians as low-scale target objects, can help solve the issue of occluded pedestrian detection as well. In this paper, we present a fully convolutional neural network with a new architecture and an innovative, fully detailed supervision for semantic segmentation of pedestrians. The proposed network has been named butterfly network (BF-Net) because of its architecture analogous to a butterfly. The proposed BF-Net preserves the ability of simplicity so that it can process static images with a real-time image processing rate. The sub-path blocks embedded in the architecture of the proposed BF-Net provides a higher accuracy for detecting multi-scale objective targets including the small ones. The other advantage of the proposed architecture is replacing common batch normalization with conditional one. In conclusion, the experimental results of the proposed method demonstrate that the proposed network outperform the other state-of-the-art networks such as U-Net + + , U-Net3 + , Mask-RCNN, and Deeplabv3 + for the semantic segmentation of the pedestrians.
Advanced Process Monitoring through Fault Detection and Classification for Robust Statistical Process Control of Tantalum Nitride Reactive Sputtering

This paper discusses Fault Detection and Classification (FDC) deployed in high-volume manufacturing for the Tantalum Nitride (TaN) reactive sputtering process. TaN thin film resistors (TFR) with an average sheet resistance (R <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</inf> ) of 50 ohms/sq with high uniformity and negative temperature coefficient of resistance (TCR) were achieved. Optimizing interdiction capabilities for real-time prevention and detection of parameter excursions strengthened statistical process control (SPC) and improved yield.
BRAS: Bidirectional Reflectance Adjustment Strategy for 3-D Reconstruction of Mirror-Like Surface

A mirror-like surface (MLS) reflects highlight, aggravating the image saturation in structured light 3-D reconstruction systems and precluding defect detection based on reconstructed 3-D profiles. Previous studies have focused on a strategy of limiting the luminous flux entering the camera. However, the high intensity of the reflected highlight forces the limitation to be strengthened, which heavily reduces the modulation in the images for the 3-D reconstruction. Therefore, we propose a new strategy to adjust the source of the reflected highlight, i.e., the bidirectional reflectance (BR) of the MLS, which fundamentally suppresses the highlight and removes the limitation on the luminous flux. To execute the proposed strategy, an unfixed view structured light system (UVSLS) is established. The UVSLS converts the reflection viewer from the real camera to the virtual camera, realizing the flexible adjustment of the MLS BR. Finally, an MLS 3-D reconstruction framework is constructed to obtain the 3-D profile of the MLS. Experiments demonstrate that the proposed framework reduces the saturated pixels by 73.72% compared with the conventional method. Compared with the previous methods, the saturated pixels are reduced by an average of 47.62%.
Beyond Playing to Win: Creating a Team of Agents With Distinct Behaviors for Automated Gameplay

In this article, we present an approach to generate a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">team</i> of general video game playing agents with differentiated behaviors that can ultimately assist in the game development process. We consider the agent behavior as the corresponding outcomes of playing the game: rate of wins, score, exploration, enemies killed, items collected, etc. We create and identify agents that are expected to achieve particular goals but do not necessarily simulate human behavior during gameplay. We present a solution that, by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">heuristic diversification</i> , provides a controller with different heuristics and a corresponding set of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">weights</i> , driving its actions. Given the simplicity of this <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">behavior-encoding</i> and its easiness to evolve, we use Multidimensional Archive of Phenotypic Elites to generate different solutions that elicit particular behaviors and assemble a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">team</i> . The resulting agents are allocated in a feature space, used to identify the expectations of each of them. We generate a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">team</i> for four games of the General Video Game Artificial Intelligence framework and find six different <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">behavior-type</i> agents in each. We include an experiment to check the portability of these agents when playing alternative levels and an exploratory work aiming to use them to detect design flaws in game levels.
Pushing Visualization Research Frontiers: Essential Topics Not Addressed by Machine Learning

Unsurprisingly, we have observed tremendous interests and efforts in the application of machine learning (ML) to many data visualization problems, which are having success and leading to new capabilities. However, there is a space in visualization research that is either completely or partly agnostic to ML that should not be lost in this current VIS+ML movement. The research that this space can offer is imperative to the growth of our field and it is important that we remind ourselves to invest in this research as well as show what it could bear. This Viewpoints article provides my personal take on a few research challenges and opportunities that lie ahead that may not be directly addressable by ML.
TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution

Image Super Resolution is a potential approach that can improve the image quality of low-resolution optical sensors, leading to improved performance in various industrial applications. It is important to emphasize that most state-of-the-art super resolution algorithms often use a single channel of input data for training and inference. However, this practice ignores the fact that the cost of acquiring high-resolution images in various spectral domains can differ a lot from one another. In this paper, we attempt to exploit complementary information from a low-cost channel (visible image) to increase the image quality of an expensive channel (infrared image). We propose a dual stream Transformer-based super resolution approach that uses the visible image as a guide to super-resolve another spectral band image. To this end, we introduce Transformer in Transformer network for Guidance super resolution, named TnTViT-G, an efficient and effective method that extracts the features of input images via different streams and fuses them together at various stages. In addition, unlike other guidance super resolution approaches, TnTViT-G is not limited to a fixed upsample size and it can generate super-resolved images of any size. Extensive experiments on various datasets show that the proposed model outperforms other state-of-the-art super resolution approaches. TnTViT-G surpasses state-of-the-art methods by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.19 \sim 2.3dB $ </tex-math></inline-formula> , while it is memory efficient.
A Deep Learning Approach for Identifying and Discriminating Spoken Arabic Among Other Languages

Spoken Language Identification (SLID) is an important step in speech-to-speech translation systems and multi-lingual automatic speech recognition. In recent research, deep learning mechanisms have been the prevailing approaches for spoken language identification. This paper aims to study, detect, and analyze spoken languages similar to Arabic in pronouncing certain words and then proposes a deep learning-based architecture, specifically the Bidirectional Long Short Term Memory (BLSTM), for spoken Arabic language identification and discrimination between these similar languages, namely, German, Spanish, French, and Russian, all of which are taken from Mozilla speech corpus languages. Additionally, our work involves a linguistic study of these considered languages. A total of ten thousand speakers are chosen for all five languages, and the BLSTM architecture is designed and implemented using acoustic signal features and applied to five experiments in this paper. The results show a precision of 98.97%, 98.73%, 98.47%, and 99.75% for identifying the spoken Arabic language separately along with German, Spanish, French, and Russian, respectively. Additionally, we achieved an average accuracy of 95.15% for discriminating between all these considered five languages in terms of the pronunciation of words. Our findings confirm that a BLSTM architecture is able to distinguish between observable similar pronunciations of words in considered languages.
Color measurement system based on convergent binocular stereo vision

Aiming at the problems of single camera color measuring system, a method of 3D object color measurement based on convergent binocular stereo vision is proposed. By taking a pair of 2D images, an 3D image is reconstructed in a 3D point cloud model, in which color of each point is restored by fusing colors of corresponding point of 2D images. Based on color charts with 240 and 24 colors, a distinctive 11-term polynomial is trained to convert colors from image RGB to CIELAB. An experiment was conducted to test the proposed method. The results show that the color prediction accuracy for the proposed model was good enough.
Gated Region-Refine pose transformer for human pose estimation

Implementing the transformer for global fusion is a novel and efficient method for pose estimation. Although the computational complexity of modeling dense attention can be significantly reduced by pruning possible human tokens, the accuracy of pose estimation still suffers from the problem of high overlap of candidate regions and severe background noise. Moreover, the undifferentiated fusion of features from different views also leads to a sizeable effective information loss. To address these challenges, we propose a Gated Region-Refine Pose Transformer (GRRPT) for human pose estimation. The proposed GRRPT can obtain the general area of the human body from the coarse-grained tokens and then embed it into the fine-grained ones to extract more details of the joints. Experimental results on COCO demonstrate that performing the Multi-Resolution Attention mechanism learns more refined candidate regions and improves accuracy. Furthermore, we design a Fusion Gate module consisting of two gates to pixel-wise select valid information from the auxiliary views, which significantly alleviates information redundancy. Finally, we evaluate the effectiveness of our method on Human3.6M and our dataset FDU-Motion and achieve state-of-the-art performance.
Reconciliation of statistical and spatial sparsity for robust visual classification

Recent image classification algorithms, by learning deep features from large-scale datasets, have achieved significantly better results comparing to the classic feature-based approaches. However, there are still various challenges of image classifications in practice, such as classifying noisy image or image-set queries, and training deep image classification models over the limited-scale dataset. Instead of applying generic deep features, the model-based approaches can be more effective and data-efficient for robust image and image-set classification tasks, as various image priors are exploited for modeling the inter- and intra-set data variations while preventing over-fitting. In this work, we propose a novel Joint Statistical and Spatial Sparse representation scheme, dubbed J3S, to model the image or image-set data for classification. J3S utilized joint sparse representation to reconcile both the local image structures and global Gaussian distribution mapped into Riemannian manifold. The learned J3S models are used for robust image and image-set classification tasks. Experiments show that the proposed J3S-based image classification scheme outperforms the popular or state-of-the-art competing methods over FMD, UIUC, ETH-80 and YTC databases.
Should robotics engineering education include software engineering education?

Multiple universities across the United States now offer bachelor's degrees in robotics, which aim to prepare students to work in the robotics industry. To judge how well these programs are providing software engineering training, we evaluate whether these programs teach the software engineering practices that are required for robotics software engineering. We compile an updated list of robotics bachelor's degree programs and measure whether the curriculum of each program claims to teach a specific practice. We find that some of these practices are not mentioned in the curricula, and that some are only taught implicitly in long-term project courses. These project courses vary in scope, guidance, and structure. This implies that robotics bachelor's degrees may not be preparing students to engage with the practices in the workforce.
A Software Vulnerability Dataset of Large Open Source C/C++ Projects

Automated tools, namely Static Analysis Tools (SATs) and Penetration Testing Tools, are frequently used by developers to detect vulnerabilities. However, research and practice show that the effectiveness of those tools in large-scale projects is low, being prone to both false positives and false negatives. Thus, there is an urgent need for more effective techniques, which ultimately require representative field data for driving their design and testing. In this paper, we present a dataset of vulnerabilities from five large open-source C/C++ projects: Mozilla, Linux Kernel, Xen, httpd, and Glibc. For collecting the data, we designed an automated process grounded on vulnerabilities collected from the Common Vulnerability and Exposures (CVE) Details website. For each vulnerability, we retrieve the corresponding source code units from the project repository (including both vulnerable and fixed versions). We then compute a large set of Software Metrics (SMs) for those code units and run two SATs to collect security alerts (i.e., potential vulnerabilities and/or weaknesses). The dataset currently includes 5214 vulnerabilities. To demonstrate its usefulness, we explore the use of the dataset to train machine learning models to detect vulnerable C/C++ functions. Results clearly show that the dataset can be used in practice and is a key contribution for researchers working in software security.
Voting System using Facial and FingerPrint Authentication

The proposed Online Voting machine is a digital vote casting biometric authentication device, which is of the human fingerprint to in addition beautify the extent of belief and confidentiality of the electorate inside the system and make this process Globally accessible as possible which would be done through the internet. The vital problems faced throughout the team of workers elections carried out inside the University of Ibadan network that’s one of the case studies. In this System, voter registration and authentication process are performed as the desktop Based application or module. In this System, voter registration and authentication method are accomplished because of the computing device is Based software or module. The voter desires to have his or her fingerprints captured and the sample this is extracted is saved within the database. This is executed to save you more than one registration or identity. so, during the authentication section of time, the voter undergoes an identical checking of their fingerprints to the values saved withinside the database. The unique number is given to the voter at the time of registration and that number is matched during the authentication of a voter then the fingerprint is matched. This system has achieved a high success rate in the identification. This keeps a blank for the multiple registrations because of the unique identification number given to the voter at the registration phase. If a voter is registering a second time it shows the voter already registered as his fingerprint, I registered with a unique identification number. Voters can retain the web module of the task to forge their votes via the net and any internet-related device. The Token number is the secret key that is sent to the email by each voter through or her email. This voting system should be done in the stipulated amount of time which is sent to the voters by email by the administration. This System offers a digital vote casting machine that makes to resolve the highlighted demanding situations of the gadget undertaking workforce elections in the University of Ibadan which gives citizens a trusted machine. This system can be used in staff elections and student elections conducted in the university.
Characterizing Deep Learning Neural Network Failures Between Algorithmic Inaccuracy and Transient Hardware Faults

Deep Neural Networks (DNNs) have been widely deployed in safety-critical applications such as autonomous vehicles, healthcare, and space applications. Though DNN models have long suffered intrinsic algorithmic inaccuracies, the increasing number of hardware transient faults in computer systems has been raising safety and reliability concerns in safety-critical applications. This paper investigates the impact of DNN misclassifications that caused by hardware transient faults and intrinsic algorithmic inaccuracy in safety-critical applications. We first extend a state-of-the-art fault injector for TensorFlow application, TensorFI, to support fault injections on modern DNN models in a scalable way, then characterize the outcome classes of the models, analyzing them based on safety related metrics. Finally, we conduct a large-scale fault injection experiment to measure the failures according to the metrics and study their impact on safety. We observe that failures caused by hardware transient faults could have much more significant impact (up to 4 times higher probability) on safety-critical applications than that of the DNN algorithmic inaccuracies, advocating the potential needs to protect DNNs from hardware faults in safety-critical applications.