Reviewerly/exHarmony huggingface dataset:
Coherent Personalized Paragraph Generation for a Successful Landing Page

Social media has become an important place for online marketing like never before. Businesses use various techniques to identify and reach potential customers across multiple platforms and deliver a message to grab their attention. A notable post could attract potential customers to the product landing page. However, the acquisition is only the beginning. The landing page should respond to the visitor's need for persuasion to increase conversion rates. Showing every visitor the same page is far from that goal. Even if the product meets everyone's needs, their priorities may differ. In this study, we propose a pipeline that includes gathering and identifying potential customers from Twitter, determining their priorities by understanding the context of their message, and creating a coherent paragraph that addresses the issue to display on the landing page.
Conv2NeXt: Reconsidering Conv NeXt Network Design for Image Recognition

Currently, transformer in computer vision, whether in image recognition, image segmentation or target detection, are widely popular due to their excellent performance. Traditional convolutional neural networks (CNNs) are dwarfed by Transformers, but the disadvantage of Transformers is that they require huge resources for cultivation. In 2022, Liu et al. rethought CNN s and proposed a series of models that refer to the structure of Transformers (e.g., Swin Transformers). In contrast to Transformers, Liu's CNN models, dubbed ConvNeXt, use the standard convolutional neural network. The accuracy of ConvNeXt on the ImageNet dataset is surprisingly close to that of Swin Transformers, proving that CNN s still have superior performance. However, ConvNeXt does not perform as well on low-resolution datasets, such as CIFAR-10, CIFAR-100, and Tiny ImageN et, and there is still much room for improvement. To enhance the generalisation of ConvNeXt, we propose Conv2N eXt models, which consist of two patch embedding layers and a symmetric convolutional block of repeated applications. In addition, Conv2N eXt incorporates some attention modules to refine feature maps, making them more robust. In experiments, Conv2NeXt surpasses most well-known and widely used state-of-the-art performances (SOT As) with an accuracy of 98.23 % in CIFAR-10, 83.82% in CIFAR-100, and 71.94% in Tiny ImageNet. Most importantly, these performances were trained by only one customer-based GPU, achieving almost SOTAs with a much lower number of parameters (Params). The open source code and checkpoint of this work are accessible at https://github.com/southerly7/Conv2NeXt.
Towards a Framework for Improving Transparency in the Software Engineering Process

Transparency is a relatively new and emerging non-functional quality requirement yet to be fully theoretically and empirically explored in the software engineering process (SEP). This paper presents a framework for formalising and improving the transparency of software products. The study argues that improving the transparency of software products improves their quality and maintainability, achieves effective communication and meets stakeholders’ transparency requirements concerns. Empowered by a standard data collection procedure, it utilises well-known concepts in developing a framework that captures the complex and multifaceted nature of a transparency improvement process. Current assertions by various transparency researchers and empirical evidence, coupled with the lack of measurement of transparency in SEP, gave rise to our proposition that the application of a transparency improvement programme in the early phase of SEP would lead to more transparency in software artefacts and a better quality of the final software system. In developing the framework, the study fills a significant gap in the transparency literature and potentially offers stakeholders, including requirements engineers, developers and organisations, a model-based framework approach to aid transparency evaluation and improvement. The study concluded by suggesting that the framework contributes significantly to providing a formal basis for theory building in transparency implementation and quality improvement by enabling a shared understanding among stakeholders in SEP.
On the Effectiveness of BGP Hijackers That Evade Public Route Collectors

Routing hijack attacks have plagued the Internet for decades. After many failed mitigation attempts, recent Internet-wide BGP monitoring infrastructures relying on distributed route collection systems, called route collectors, give us hope that future monitor systems can quickly detect and ultimately mitigate hijacks. In this paper, we investigate the effectiveness of public route collectors with respect to future attackers deliberately engineering longer hijacks to avoid being recorded by route collectors. Our extensive simulations (and attacks we device) show that monitor-based systems may be unable to observe many carefully crafted hijacks diverting traffic from thousands of ASes. Hijackers could predict whether their attacks would propagate to some BGP feeders (i.e., monitors) of public route collectors. Then, manipulate BGP route propagation so that the attack never reaches those monitors. This observation remains true when considering plausible future Internet topologies, with more IXP links and up to 4 times more monitors peering with route collectors. We then evaluate the feasibility of performing hijacks not observed by route collectors in the real-world.We experiment with two classifiers to predict the monitors that are dangerous to report the attack to route collectors, one based on monitor proximities (i.e., shortest path lengths) and another based on Gao-Rexford routing policies.We show that a proximity-based classifier could be sufficient for the hijacker to identify all dangerous monitors for hijacks announced to peer-to-peer neighbors. For hijacks announced to transit networks, a Gao-Rexford classifier reduces wrong inferences by ≥ 91% without introducing new misclassifications for existing dangerous monitors.
Paraphrase Generation Model Integrating Transformer Architecture, Part-of-Speech Features, and Pointer Generator Network

In recent years, hardware advancements have enabled natural language processing tasks that were previously difficult to achieve due to their intense computing requirements. This study focuses on paraphrase generation, which entails rewriting a sentence using different words and sentence structures while preserving its original meaning. This increases sentence diversity, thereby improving the performance of downstream tasks, such as question–answering systems and machine translation. This study proposes a novel paraphrase generation model that combines the Transformer architecture with part-of-speech features, and this model is trained using a Chinese corpus. New features are incorporated to improve the performance of the Transformer architecture, and the pointer generation network is used when the training data contain low-frequency words. This allows the model to focus on input words with important information according to their attention distributions.
An Efficient Coverage Area Re-Assignment Strategy for Multi-Robot Long-Term Surveillance

This study deals with a new strategy of the re-assignment for multi-robot seamless coverage tasks using the concept of propagation in a multi-robot surveillance system (MRSS). In the context of MRSSs, multi-robot coverage tasks play a critical role. These tasks require generating paths for two or more robots to cover an entire area, with the objective of minimizing the time needed to complete the task. However, over time, robots may need to be excluded from coverage missions due to issues such as battery charging or malfunctions. It is important to handle these situations efficiently in order to maintain the completeness and balance of the coverage mission. Typically, it can be resolved by either recomputing the coverage algorithm for the remaining robots or redistributing the coverage task of the excluded robot to its neighbors. However, in the proposed method, the amount of coverage area of the excluded robot is equally and efficiently assigned to the remaining robots. First off, a relational graph between robots and a tree based on the excluded robot are sequentially constructed to necessarily know how the robots are geometrically arranged in the given area centered on the excluded robot. The excluded robot becomes the root of the tree, and the depth of the tree indicates the proximity of the coverage areas. Subsequently, the amount of the original coverage area of the excluded robot can be differently assigned to its nearest neighbor robots according to the size of the subtree. Then, the coverage area of the robots corresponding to the second level of the tree are added from the partial coverage area of their parent robot to keep their coverage area balanced, respectively. The similar process is continuously performed, such as propagation, until the re-assignment of the coverage area over the leaf nodes is complete. Finally, balanced coverage area is re-assigned to the remaining robots, which is time-efficiently computed. Simulations were performed on two occupancy grid maps that were acquired from a simultaneous localization and mapping method. The proposed method was evaluated against conventional methods on three factors such as the balanced re-assignment of the coverage area (Balancing), the variation of the individual coverage area before and after the re-assignment process (Seamless Coverage), and the total computational efficiency over time (Time-efficiency). The coverage area was uniformly re-allocated after the proposed method was applied. In addition, the proposed method had a short calculation time and enables seamless coverage even after re-allocation. In the future, probabilistic maps related to the importance rate, accident rate, and crowds in the coverage area will also be taken into consideration.
A 334 μW 0.158 mm<sup>2</sup> ASIC for Post-Quantum Key-Encapsulation Mechanism Saber With Low-Latency Striding Toom–Cook Multiplication

Lattice-based cryptography is a novel approach to public key cryptography (PKC), of which the mathematical investigation (so far) resists attacks from quantum computers. By choosing a module learning with errors (MLWE) algorithm as the next standard, the National Institute of Standards and Technology (NIST) follows this approach. The multiplication of polynomials is the central bottleneck in the computation of lattice-based cryptography. Because PKC is mostly used to establish common secret keys, the focus is on compact area, power, and energy budget and, to a lesser extent, on throughput or latency. While most other work focuses on optimizing number theoretic transform (NTT)-based multiplications, in this article, we highly optimize a Toom–Cook-based multiplier. We demonstrate that a memory-efficient striding Toom–Cook with lazy interpolation results in a highly compact, low-power implementation, which, on top, enables a very regular memory access scheme. To demonstrate the efficiency, we integrate this multiplier into a Saber post-quantum accelerator, one of the four NIST finalists. Algorithmic innovation to reduce active memory, timely clock gating, and shift-add multiplier has helped to achieve 38% less power than state-of-the-art post-quantum cryptography (PQC) core, 4 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> less memory, 36.8% reduction in multiplier energy, and 118 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> reduction in active power with respect to state-of-the-art Saber accelerator (not silicon verified). This accelerator consumes 0.158- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {mm}^{2}$ </tex-math></inline-formula> active area, which is the lowest reported to date despite the process disadvantages of the state-of-the-art designs.
Impact of digital transformation on sustainable performance

This study came to examine the impact of Digital transformation on sustainable performance. The research population consisted of employees of Industrial companies listed in Amman stock exchange that is 23 companies. Because of the difficulty of collecting data using a complete census, a random sample of 530 employees was determined. 397 responses were used for the purposes of the statistical analysis of this research. In order to find the impact of digital transformation on sustainable performance, the structural equation modeling (SEM) technique was used. Research results show that digital transformation dimensions had a positive impact on sustainable performance. Big data had the greatest impact. Accordingly, the researchers recommend Industrial companies invest more in manufacturing modern technologies, represented by Digital transformation technologies that give companies the ability to provide high-quality performance and prepare an infrastructure to operate these technologies.
Towards a Real-Time, Low-Resource, End-to-End Object Detection Pipeline for Robot Soccer

This work presents a study for building a Deep Vision pipeline suitable for the Robocup Standard Platform League, a humanoid robot soccer tournament. Specifically, we focus on end-to-end trainable object detection for effective perception using Aldebaran NAO v6 robots. The implementation of such a detector poses two major challenges, those of speed, and resource-effectiveness with respect to memory and computational power. We benchmark architectures using the YOLO and SSD detection paradigms, and identify variants that are able to achieve good detection performance for ball detection, while being able to perform rapid inference. To add to the training data for these networks, we also create a dataset from logs collected by the UT Austin Villa team during previous competitions, and set up an annotation pipeline for training. We utilize the above results and training pipeline to realize a practical, multi-class object detector that enables the robot's vision system to run 35 Hz while maintaining good detection performance.
Reward systems for cohort data sharing: An interview study with funding agencies

Data infrastructures are being constructed to facilitate cohort data sharing. These infrastructures are anticipated to increase the rate of data sharing. However, the lack of data sharing has also been framed as being the consequence of the lack of reputational or financial incentives for sharing. Some initiatives try to confer value onto data sharing by making researchers' individual contributions to research visible (i.e., contributorship) or by quantifying the degree to which research data has been shared (e.g., data indicators). So far, the role of downstream evaluation and funding distribution systems for reputational incentives remains underexplored. This interview study documents the perspectives of members of funding agencies on, amongst other elements, incentives for data sharing. Funding agencies are adopting narrative CVs to encourage evaluation of diverse research outputs and display diversity in researchers' profiles. This was argued to diminish the focus on quantitative indicators of scientific productivity. Indicators related to open science dimensions may be reintroduced if they are fully developed. Shifts towards contributorship models for research outputs are seen as complementary to narrative review.
Can Negation Be Depicted? Comparing Human and Machine Understanding of Visual Representations

There is a widely held view that visual representations (images) do not depict negation, for example, as expressed by the sentence, "the train is not coming." The present study focuses on the real-world visual representations of photographs and comic (manga) illustrations and empirically challenges the question of whether humans and machines, that is, modern deep neural networks, can recognize visual representations as expressing negation. By collecting data on the captions humans gave to images and analyzing the occurrences of negation phrases, we show some evidence that humans recognize certain images as expressing negation. Furthermore, based on this finding, we examined whether or not humans and machines can classify novel images as expressing negation. The humans were able to correctly classify images to some extent, as expected from the analysis of the image captions. On the other hand, the machine learning model of image processing was only able to perform this classification at about the chance level, not at the same level of performance as the human. Based on these results, we discuss what makes humans capable of recognizing negation in visual representations, highlighting the role of the background commonsense knowledge that humans can exploit. Comparing human and machine learning performances suggests new ways to understand human cognitive abilities and to build artificial intelligence systems with more human-like abilities to understand logical concepts.
How people reason with counterfactual and causal explanations for Artificial Intelligence decisions in familiar and unfamiliar domains

Few empirical studies have examined how people understand counterfactual explanations for other people's decisions, for example, "if you had asked for a lower amount, your loan application would have been approved". Yet many current Artificial Intelligence (AI) decision support systems rely on counterfactual explanations to improve human understanding and trust. We compared counterfactual explanations to causal ones, i.e., "because you asked for a high amount, your loan application was not approved", for an AI's decisions in a familiar domain (alcohol and driving) and an unfamiliar one (chemical safety) in four experiments (n = 731). Participants were shown inputs to an AI system, its decisions, and an explanation for each decision; they attempted to predict the AI's decisions, or to make their own decisions. Participants judged counterfactual explanations more helpful than causal ones, but counterfactuals did not improve the accuracy of their predictions of the AI's decisions more than causals (Experiment 1). However, counterfactuals improved the accuracy of participants' own decisions more than causals (Experiment 2). When the AI's decisions were correct (Experiments 1 and 2), participants considered explanations more helpful and made more accurate judgements in the familiar domain than in the unfamiliar one; but when the AI's decisions were incorrect, they considered explanations less helpful and made fewer accurate judgements in the familiar domain than the unfamiliar one, whether they predicted the AI's decisions (Experiment 3a) or made their own decisions (Experiment 3b). The results corroborate the proposal that counterfactuals provide richer information than causals, because their mental representation includes more possibilities.
Presence and simulator sickness predict the usability of a virtual reality attention task

Attention is the ability to actively process specific information within one's environment over longer periods of time while disregarding other details. Attention is an important process that contributes to overall cognitive performance from performing every day basic tasks to complex work activities. The use of virtual reality (VR) allows study of the attention processes in realistic environments using ecological tasks. To date, research has focused on the efficacy of VR attention tasks in detecting attention impairment, while the impact of the combination of variables such as mental workload, presence and simulator sickness on both self-reported usability and objective attention task performance in immersive VR has not been examined. The current study tested 87 participants on an attention task in a virtual aquarium using a cross-sectional design. The VR task followed the continuous performance test paradigm where participants had to respond to correct targets and ignore non-targets over 18 min. Performance was measured using three outcomes: omission (failing to respond to correct targets), commission errors (incorrect responses to targets) and reaction time to correct targets. Measures of self-reported usability, mental workload, presence and simulator sickness were collected. The results showed that only presence and simulator sickness had a significant impact on usability. For performance outcomes, simulator sickness was significantly and weakly associated with omission errors, but not with reaction time and commission errors. Mental workload and presence did not significantly predict performance. Our results suggest that usability is more likely to be negatively impacted by simulator sickness and lack of presence than performance and that usability and attention performance are linked. They highlight the importance of considering factors such as presence and simulator sickness in attention tasks as these variables can impact usability.The online version contains supplementary material available at 10.1007/s10055-023-00782-3.
From Double Pushout Grammars to Hypergraph Lambek Grammars With and Without Exponential Modality

We study how to relate well-known hypergraph grammars based on the double pushout (DPO) approach and grammars over the hypergraph Lambek calculus HL (called HL-grammars). It turns out that DPO rules can be naturally encoded by types of HL using methods similar to those used by Kanazawa for multiplicative-exponential linear logic. In order to generalize his reasonings we extend the hypergraph Lambek calculus by adding the exponential modality, which results in a new calculus HMEL0; then we prove that any DPO grammar can be converted into an equivalent HMEL0-grammar. We also define the conjunctive Kleene star, which behaves similarly to this exponential modality, and establish a similar result. If we add neither the exponential modality nor the conjunctive Kleene star to HL, then we can still use the same encoding and show that any DPO grammar with a linear restriction on the length of derivations can be converted into an equivalent HL-grammar.
Malicious website identification using design attribute learning

Malicious websites pose a challenging cybersecurity threat. Traditional tools for detecting malicious websites rely heavily on industry-specific domain knowledge, are maintained by large-scale research operations, and result in a never-ending attacker–defender dynamic. Malicious websites need to balance two opposing requirements to successfully function: escaping malware detection tools while attracting visitors. This fundamental conflict can be leveraged to create a robust and sustainable detection approach based on the extraction, analysis, and learning of design attributes for malicious website identification. In this paper, we propose a next-generation algorithm for extended design attribute learning that learns and analyzes web page structures, content, appearances, and reputation to detect malicious websites. Results from a large-scale experiment that was conducted on more than 35,000 websites suggest that the proposed algorithm effectively detects more than 83% of all malicious websites while maintaining a low false-positive rate of 2%. In addition, the proposed method can incorporate user feedback and flag new suspicious websites and thus can be effective against zero-day attacks.