Reviewerly/exHarmony huggingface dataset:
S-TREK: Sequential Translation and Rotation Equivariant Keypoints for local feature extraction

In this work we introduce S-TREK, a novel local feature extractor that combines a deep keypoint detector, which is both translation and rotation equivariant by design, with a lightweight deep descriptor extractor. We train the S-TREK keypoint detector within a framework inspired by reinforcement learning, where we leverage a sequential procedure to maximize a reward directly related to keypoint repeatability. Our descriptor network is trained following a "detect, then describe" approach, where the descriptor loss is evaluated only at those locations where keypoints have been selected by the already trained detector. Extensive experiments on multiple benchmarks confirm the effectiveness of our proposed method, with S-TREK often outperforming other state-of-the-art methods in terms of repeatability and quality of the recovered poses, especially when dealing with in-plane rotations.
Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous driving

Existing datasets for autonomous driving (AD) often lack diversity and long-range capabilities, focusing instead on 360° perception and temporal reasoning. To address this gap, we introduce Zenseact Open Dataset (ZOD), a large- scale and diverse multimodal dataset collected over two years in various European countries, covering an area 9×that of existing datasets. ZOD boasts the highest range and resolution sensors among comparable datasets, coupled with detailed keyframe annotations for 2D and 3D objects (up to 245m), road instance/semantic segmentation, traffic sign recognition, and road classification. We believe that this unique combination will facilitate breakthroughs in long-range perception and multi-task learning. The dataset is composed of Frames, Sequences, and Drives, designed to encompass both data diversity and support for spatio-temporal learning, sensor fusion, localization, and mapping. Frames consist of 100k curated camera images with two seconds of other supporting sensor data, while the 1473 Sequences and 29 Drives include the entire sensor suite for 20 seconds and a few minutes, respectively. ZOD is the only large-scale AD dataset released under a permissive license, allowing for both research and commercial use. More information, and an extensive devkit, can be found at zod.zenseact.com.
MB-TaylorFormer: Multi-branch Efficient Transformer Expanded by Taylor Formula for Image Dehazing

In recent years, Transformer networks are beginning to replace pure convolutional neural networks (CNNs) in the field of computer vision due to their global receptive field and adaptability to input. However, the quadratic computational complexity of softmax-attention limits the wide application in image dehazing task, especially for high-resolution images. To address this issue, we propose a new Transformer variant, which applies the Taylor expansion to approximate the softmax-attention and achieves linear computational complexity. A multi-scale attention refinement module is proposed as a complement to correct the error of the Taylor expansion. Furthermore, we introduce a multi-branch architecture with multi-scale patch embedding to the proposed Transformer, which embeds features by overlapping deformable convolution of different scales. The design of multi-scale patch embedding is based on three key ideas: 1) various sizes of the receptive field; 2) multi-level semantic information; 3) flexible shapes of the receptive field. Our model, named Multi-branch Transformer expanded by Taylor formula (MB-TaylorFormer), can em-bed coarse to fine features more flexibly at the patch embedding stage and capture long-distance pixel interactions with limited computational cost. Experimental results on several dehazing benchmarks show that MB-TaylorFormer achieves state-of-the-art (SOTA) performance with a light computational burden. The source code and pre-trained models are available at https://github.com/FVL2020/ICCV-2023-MB-TaylorFormer.
Householder Projector for Unsupervised Latent Semantics Discovery

Generative Adversarial Networks (GANs), especially the recent style-based generators (StyleGANs), have versatile semantics in the structured latent space. Latent semantics discovery methods emerge to move around the latent code such that only one factor varies during the traversal. Recently, an unsupervised method proposed a promising direction to directly use the eigenvectors of the projection matrix that maps latent codes to features as the interpretable directions. However, one overlooked fact is that the projection matrix is non-orthogonal and the number of eigenvectors is too large. The non-orthogonality would entangle semantic attributes in the top few eigenvectors, and the large dimensionality might result in meaningless variations among the directions even if the matrix is orthogonal. To avoid these issues, we propose Householder Projector, a flexible and general low-rank orthogonal matrix representation based on Householder transformations, to parameterize the projection matrix. The orthogonality guarantees that the eigenvectors correspond to disentangled interpretable semantics, while the low-rank property encourages that each identified direction has meaningful variations. We integrate our projector into pre-trained StyleGAN2/StyleGAN3 and evaluate the models on several benchmarks. Within only 1% of the original training steps for fine-tuning, our projector helps StyleGANs to discover more disentangled and precise semantic attributes without sacrificing image fidelity. Code is publicly available via https://github.com/KingJamesSong/HouseholderGAN.
Decoding Fear: Exploring User Experiences in Virtual Reality Horror Games

This preliminary study investigated user experiences in VR horror games, highlighting fear-triggering and gender-based differences in perception. By utilizing a scientifically validated and specially designed questionnaire, we successfully collected questionnaire data from 23 subjects for an early empirical study of fear induction in a virtual reality gaming environment. The early findings suggest that visual restrictions and ambient sound-enhanced realism may be more effective in intensifying the fear experience. Participants exhibited a tendency to avoid playing alone or during nighttime, underscoring the significant psychological impact of VR horror games. The study also revealed a distinct gender difference in fear perception, with female participants exhibiting a higher sensitivity to fear stimuli. However, the preference for different types of horror games was not solely dominated by males; it varied depending on factors such as the game’s pace, its objectives, and the nature of the fear stimulant.
From Centralized to Decentralized Remote Electronic Voting

Elections generally involve the simple tasks of counting votes and publishing the final tally to voters. Depending on the election's scope, these processes require sophisticated methods embedded in the electorate's various technological and societal factors (e.g., the voting culture). An election's integrity is the pinnacle of the trust placed in the voting process and its final results. Previous research on cryptographic voting schemes continuously refined voting protocols to achieve private and verifiable elections. These cryptographic schemes enable novel ways to allow remote voting systems to (i) provide a remote voting alternative to on-site voting with a ballot box and (ii) offer a technical path to building an end-to-end verifiable Electronic Voting system by applying cryptographic primitives. This chapter explicitly addresses Remote Electronic Voting (REV), which enables vote casting in an uncontrolled environment and vote transmission over communication channels (e.g., over the Internet). Further, this chapter clarifies how the Distributed Ledger (DL) technology can enhance the decentralization of the electoral process, ensuring transparency and the ability to verify results while guaranteeing the confidentiality of voters. Thus, necessary cryptographic fundamentals and examples of voting schemes, their properties, and trade-offs will be investigated. The different approaches toward REV systems are detailed, followed by an overview of related work. Further, a centralized REV voting system architecture, all stakeholders involved, and critical trust assumptions are outlined. This leads to the proposal of a fully decentralized architecture, which is being evaluated in qualified discussions with respect to long-term privacy, verifiability, and voter authentication. Finally, open research aspects are complemented by overall considerations on decentralized REV.
Introducing artificial intelligence (AI)-based digital writing assistants for teachers in writing scientific articles

Artificial intelligence (AI) technologies have played essential roles in the development of teachers. In recent years, AI-based digital writing assistants have received increasing attention among teachers. Thus, this study aims to know the use of AI-based digital writing assistants to help teachers write scientific articles. It used a sequential explanatory mixed methods study to gain the research data. Descriptive analysis was completed for the quantitative data, and thematic analysis was used for the qualitative data. The instrument used was surveys, consisting of pre and post-survey. Interestingly, the pre-survey found that teachers are not very familiar with digital writing assistants. Some of the participants had no prior knowledge of AI-assisted writing tools, while others only had limited experience. This means that more people need to be aware of the potential that AI offers in facilitating scientific writing. Furthermore, it is essential to teach teachers how to use this technology, as it can help them save time and effort while they write their articles. The post-survey conducted at the end of the project found that teachers found the process of writing a scientific article to be easier and more enjoyable with the help of AI. The results suggest that AI-based digital writing assistants may be able to provide an alternative method for teachers to compose their scientific articles. This tool could help to reduce errors and enable more efficient writing with greater accuracy. Additionally, the feedback suggests that AI-based tools could aid in improving students’ engagement and interest in writing.
Using Action Strategy in Improving Speaking Skill in English Language Teaching

This research aims to improve the speaking skill through the use of of Action Learning Strategy of the Eight Grade Students in English Language Teaching. This research was conducted in SMPN 3 Soromandi Kabupaten Bima, NTB. This research is designed quantitatively by using time series design. The sample was taken through cluster random sampling. There were 40 students were taken as a sample. In collecting the data, the instrument used was speaking test to know the improvement of student ability in speaking skill. The test were pre-test and post-test. The result of data analysis shows that an improvement of students’ scores in pretest 1 to 4. The mean score of pretests 1 is 59.13, in pretest 2 was 62.0, in pretest 3 was 56.5, and pretest 4 was 58.38. On the contrary, the result of posttest shows the improvement of the students score in posttest 1 is 70.38, posttest 2 is 72.75, posttest 3 is 76.75, and posttest 4 is 74.90. Based on the result of pretest and posttest shows that the use of Action Learning Strategy can improve the speaking skill in English Language Teaching. In conclusion, the use of Action Learning Strategy is effective in improving the speaking skill in English Language Teaching.
Comparison of Apriori Algorithm and FP-Growth in Managing Store Transaction Data

The role and position of data in today's digital era are very important, data can be likened to a resource that can be explored further to produce new information or knowledge. Seeing the importance of data position, several solutions can be offered in getting more value from data, one of which is the use of Data Mining techniques with association techniques, several types of association techniques are a priori algorithms and FP-Growth algorithms. Based on the research results, the a priori algorithm produces a combination of goods with a confidence value of 98.4 and a support value of 98.4, and the algorithm produces a combination of goods with a support value of 95.2 and a confidence value of 95.2. The comparison of these two algorithms in making associations results in a faster execution time of the FP-Growth algorithm than Apriori, and the Apriori algorithm produces more varied itemset combinations.
Some operations of fuzzy sets in UP-algebras with respect to a triangular norm

This paper aim is to apply the notions of the intersection and the union of any fuzzy set to UP-algebras. We investigate properties of the intersection and the union of T-fuzzy UP-subalgebras, T-fuzzy near UP-filters, T-fuzzy UP-filters, T-fuzzy UPideals, T-fuzzy strongly UP-ideals, anti-T-fuzzy UP-subalgebras, and anti-T-fuzzy near UP-filters of UP-algebras.
Exploring the Impact of Digital Leadership on Sustainable Performance with Mediating Role of Artificial Intelligence

Purpose: The aim of this study is to investigate the impact of digital leadership on sustainable performance, specifically focusing on the mediating role of artificial intelligence within the business environment.&#x0D; Design/Methodology/Approach: &#x0D; This study surveyed 230 employees from several industrial estate businesses using a case study technique. Questionnaires were used to collect the data, which was subsequently analyzed using structural equation modeling (SEM). The findings indicated that there is an indirect effect on long-term performance due to the multidirectional links between mediators and digital leadership. This study found, specifically, that AI and digital leadership greatly enhance inventive talents, which in turn enhances sustainable performance.&#x0D; Findings: This study's findings highlight the critical role that digital leadership plays in fusing sustainability and artificial intelligence, two concepts that when coupled have the potential to improve an organization's performance and productivity.&#x0D; Implications/Originality/Value: This study presents new insights into the role that technology skills and innovative digital behaviors play in achieving long-term performance.
A Descriptive and Historical Study of Bibliography with Applications to Medical Science Using WASPAS Method

Several topics in medical science aim to explain how it human body functions. Beginning with fundamental biology, it is typically separated into specialized fields like anatomy, pharmacology, and diseases with a little physiology, microbiology, structural biology, and genetics thrown in. Several topics in medical science aim to explain how it human body functions. Beginning with fundamental biology, it is typically separated into specialist fields like anatomy, pharmacology, and diseases with a little physiology, microbiology, structural biology, and genetic thrown in. You can switch from a biological science to a physician career, yes. You can apply to graduate entrance program in medicine or pharmacy. It makes it possible for graduates in biological sciences and the healthcare industry to earn an MD degree swiftly. With additional study and a certificate in medical science, you can still find meaningful employment in the larger fields of medical, pharmaceutical, and health sciences. You will also have a strong basis for becoming a doctor or dentist. Alternative: Male students, Female students, and Female interns. Evaluation Option: Students' views of learning, educational self, perceptions of the environment, and social self of students’ perception of teachers from the result it is seen that Students’ perception of atmosphere and is got the first rank whereas is the Students’ perception of learning got is having the lowest rank. The value of the dataset for medical science in WASPAS method shows that it results in Students’ perception of atmosphere and top ranking
Selecting information systems development models based on organizational culture: an integrated approach of DEMATEL and ANP

Purpose This study aims to investigate the effects of organizational culture factors on the selection of software process development models and develops a conceptual model for selecting and adopting process development models with an organizational culture approach, using 12 criteria and their sub-criteria defined in Fey and Denison’s model (12 criteria). Design/methodology/approach The research hypotheses were investigated using statistical analysis, and then the criteria and sub-criteria were selected based on Fey and Denison’s model and the experts’ viewpoints. Afterward, the organizational culture of the selected company was measured using the data from 2016 and 2017, based on Fey and Denison’s questionnaire. Due to the correlation between the criteria, using the decision-making trial and evaluation technique, the correlation between sub-criteria were determined, and by analytical network process method and using Super-Decision software, the process development model was preferred to the 12 common models in information systems development. Findings Results indicated a significant and positive effect of organizational culture factors (except the core values factor) on the selection of development models. Also, by changing the value of organizational culture, the selected process development model changed either. Sensitivity analysis performed on the sub-criteria implied that by changing and improving some sub-criteria, the organization will be ready and willing to use the agile or risk-based models such as spiral and win-win models. Concerning units where the mentioned indicators were at moderate and low limits, models such as waterfall, V-shaped and incremental worked more appropriately. Originality/value While many studies were performed in comparing development models and investigating their strengths and weaknesses, and the impact of organizational culture on the success of information technology projects, literature indicated that the impact of organizational sub-culture prevailing in the selection of development process models has not been investigated. In this study, new factors and indicators were addressed affecting the selection of development models with a focus on organizational culture. Correlation among the factors and indicators was also investigated and, finally, a conceptual model was proposed for proper adoption of the models and methodologies of system development.
Adversarial Policy Switching with Application to RTS Games

Complex games such as RTS games are naturally formalized as Markov games. Given a Markov game, it is often possible to hand-code or learn a set of policies that capture the diversity of possible strategies. It is also often possible to hand-code or learn an abstract simulator of the game that can estimate the outcome of playing two strategies against one another from any state. We consider how to use such policy sets and simulators to make decisions in large Markov games. Prior work has considered the problem using an approach we call minimax policy switching. At each decision epoch, all policy pairs are simulated against each other from the current state, and the minimax policy is chosen and used to select actions until the next decision epoch. While intuitively appealing, we show that this switching policy can have arbitrarily poor worst case performance. In response, we describe a modified algorithm, monotone policy switching, whose worst case performance, under certain conditions, is provably no worse than the minimax fixed policy in the set. We evaluate these switching policies in both a simulated RTS game and the real game Wargus. The results show the effectiveness of policy switching when the simulator is accurate, and also highlight challenges in the face of inaccurate simulations.
Cheap Deployable Networked Sensors for Environmental Use

We profile the utility and limits in deployment of inexpensive disposable sensor networks by amateurs to achieve environmental monitoring goals. Four current technology movements — PC-on-a-chip processors such as Arduino, prebuilt sensors, 3-D printing, and Wi-Fi and mesh networking — enable rapid sensor platform creation and make it easy for non-specialists to create general sensor-bearing platforms. Deployment of a cluster of multiple sensor-bearing platforms is, however, communications-limited in terms of both range and number of devices supported, and generally requires a base station with internet access. We examine how inexpensive technology supports scenarios for short-term environmental modelling by average citizens.
Estimating Solar Radiation Intensity from Other Meteorological Data

Estimates of solar radiation (Ri) by 15-day periods were calculated from other available meteorological data by multiple regression analysis of Eo = f(Ri, u, Ta) ed), and then solving for Ri from the most significant equation of the model given above. Only solar radiation wind speed (u) and air temperature (Ta) were found to correlate significantly with Eo. The solution in terms of Ri (equation /4/) then was used to obtain estimates of solar radiation for 1- to 5-day periods. The mean ratio of estimated Ri to measured Ri approached unity (0.95), while the coefficient of variation was 8.9 percent, as compared to 5.1 percent for the original 15-day period data. It was found when these were compared to measured values that neglect of air temperature reduced precision of the estimates. Equation /4/ (the indirect solution) next was evaluated on the basis of foreign data, representing extreme meteorological conditions as those in Bet Dagan, Israel and Cristobal, Panamá. This equation also was compared at the same time to a direct solution of Ri by multiple regression analysis (equation /6/), a solution which directly minimizes the deviations about Ri. Statistical data are presented which compare the precision of the estimates by either equation (/4/ and /6/).
Computers as a Language Learning Tool

The subject of CALL has only recently been introduced to language teachers inWest Germany. While in other European countries many institutions are involved inprojects dealing with the use of computers in language learning. The Audio-VisualMedia Centre (AVMZ), Bergische Universitat-GH Wuppertal, 5600 Wuppertal 1, isone of the few places in this country investigating various approaches to CALL.This article describes the general concept of the project and the kind of programsbeing developed and used. Furthermore, it is intended to solicit comments fromscholars and institutions in the USA and thus established contacts and initiate anexchange of ideas.
An Effective Project-Based Embedded System Design Teaching Method

To ensure the course uses the latest design technology from the industry, we formed a close partnership between academia and industry from the very beginning of this course development.The work described in this paper is the result of the close cooperation between Harvey Mudd College and Digilent Inc.This partnership provides valuable technical assistance to enable a faster bring up of the new embedded system development platform.For example, during the early phase of Zedboard development board bring up, we received pre-leases of documents and detailed instruction for booting the Linux system from Digilent.We also participate in the Zedboard development board user group where news, documents, projects, support, and partner information are available and shared by all participants.We also received technical assistant from Xilinx technical support to resolve many tool related problems