Reviewerly/exHarmony huggingface dataset:
Patapasco: A Python Framework for Cross-Language Information Retrieval Experiments

While there are high-quality software frameworks for information retrieval experimentation, they do not explicitly support cross-language information retrieval (CLIR). To fill this gap, we have created Patapsco, a Python CLIR framework. This framework specifically addresses the complexity that comes with running experiments in multiple languages. Patapsco is designed to be extensible to many language pairs, to be scalable to large document collections, and to support reproducible experiments driven by a configuration file. We include Patapsco results on standard CLIR collections using multiple settings.
Neural reality of argument structure constructions

In lexicalist linguistic theories, argument structure is assumed to be predictable from the meaning of verbs.As a result, the verb is the primary determinant of the meaning of a clause.In contrast, construction grammarians propose that argument structure is encoded in constructions (or form-meaning pairs) that are distinct from verbs.Decades of psycholinguistic research have produced substantial empirical evidence in favor of the construction view.Here we adapt several psycholinguistic studies to probe for the existence of argument structure constructions (ASCs) in Transformerbased language models (LMs).First, using a sentence sorting experiment, we find that sentences sharing the same construction are closer in embedding space than sentences sharing the same verb.Furthermore, LMs increasingly prefer grouping by construction with more input data, mirroring the behaviour of non-native language learners.Second, in a "Jabberwocky" priming-based experiment, we find that LMs associate ASCs with meaning, even in semantically nonsensical sentences.Our work offers the first evidence for ASCs in LMs and highlights the potential to devise novel probing methods grounded in psycholinguistic research. Transitive DitransitiveCaused-motion Resultative Throw Anita threw the hammer.Chris threw Linda the pencil.Pat threw the keys onto the roof.Lyn threw the box apart. GetMichelle got the book.Beth got Liz an invitation.Laura got the ball into the net.Dana got the mattress inflated
AdaSpeech 4: Adaptive Text to Speech in Zero-Shot Scenarios

Adaptive text to speech (TTS) can synthesize new voices in zero-shot scenarios efficiently, by using a well-trained source TTS model without adapting it on the speech data of new speakers. Considering seen and unseen speakers have diverse characteristics, zero-shot adaptive TTS requires strong generalization ability on speaker characteristics, which brings modeling challenges. In this paper, we develop AdaSpeech 4, a zero-shot adaptive TTS system for high-quality speech synthesis. We model the speaker characteristics systematically to improve the generalization on new speakers. Generally, the modeling of speaker characteristics can be categorized into three steps: extracting speaker representation, taking this speaker representation as condition, and synthesizing speech/mel-spectrogram given this speaker representation. Accordingly, we improve the modeling in three steps: 1) To extract speaker representation with better generalization, we factorize the speaker characteristics into basis vectors and extract speaker representation by weighted combining of these basis vectors through attention. 2) We leverage conditional layer normalization to integrate the extracted speaker representation to TTS model. 3) We propose a novel supervision loss based on the distribution of basis vectors to maintain the corresponding speaker characteristics in generated mel-spectrograms. Without any fine-tuning, AdaSpeech 4 achieves better voice quality and similarity than baselines in multiple datasets.
ECQ$$^{\text {x}}$$: Explainability-Driven Quantization for Low-Bit and Sparse DNNs

Abstract The remarkable success of deep neural networks (DNNs) in various applications is accompanied by a significant increase in network parameters and arithmetic operations. Such increases in memory and computational demands make deep learning prohibitive for resource-constrained hardware platforms such as mobile devices. Recent efforts aim to reduce these overheads, while preserving model performance as much as possible, and include parameter reduction techniques, parameter quantization, and lossless compression techniques. In this chapter, we develop and describe a novel quantization paradigm for DNNs: Our method leverages concepts of explainable AI (XAI) and concepts of information theory: Instead of assigning weight values based on their distances to the quantization clusters, the assignment function additionally considers weight relevances obtained from Layer-wise Relevance Propagation (LRP) and the information content of the clusters (entropy optimization). The ultimate goal is to preserve the most relevant weights in quantization clusters of highest information content. Experimental results show that this novel Entropy-Constrained and XAI-adjusted Quantization (ECQ $$^{\text {x}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mi>x</mml:mi> </mml:msup> </mml:math> ) method generates ultra low-precision (2–5 bit) and simultaneously sparse neural networks while maintaining or even improving model performance. Due to reduced parameter precision and high number of zero-elements, the rendered networks are highly compressible in terms of file size, up to 103 $$\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> compared to the full-precision unquantized DNN model. Our approach was evaluated on different types of models and datasets (including Google Speech Commands, CIFAR-10 and Pascal VOC) and compared with previous work.
Dungeons &amp; Replicants II: Automated Game Balancing Across Multiple Difficulty Dimensions via Deep Player Behavior Modeling

Video game testing has become a major investment of time, labor, and expense in the game industry. Particularly the balancing of in-game units, characters, and classes can cause long-lasting issues that persist years after a game's launch. While approaches incorporating artificial intelligence have already shown successes in reducing manual effort and enhancing game development processes, most of these draw on heuristic, generalized, or optimal behavior routines, while actual low-level decisions from individual players and their resulting playing styles are rarely considered. In this article, we apply <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deep player behavior modeling</i> to turn atomic actions of 213 players from six months of single-player instances within the MMORPG Aion into generative models that capture and reproduce particular playing strategies. In a subsequent simulation, the resulting generative agents (“replicants”) were tested against common NPC opponent types of MMORPGs that iteratively increased in difficulty, respective to the primary factor that constitutes this enemy type (Melee, Ranged, Rogue, Buffer, Debuffer, Healer, Tank, or Group). As a result, imbalances between classes as well as strengths and weaknesses regarding particular combat challenges could be identified and regulated automatically.
Developing a Credit Card Fraud Detection Model using Machine Learning Approaches

The growing application and usage of e-commerce applications have given an exponential rise to the number of online transactions.Though there are several methods for completing online transactions, however, credit cards are most commonly used.The increased number of transactions has given the opportunity to the fraudsters to mislead the customers and make them execute fraudulent transactions.Therefore, there is a need for such a method that can automatically classify detect fraudulent transactions.This research study aims to develop a credit-card fraud detection model that can effectively classify an online transaction as fraudulent or genuine.Three supervised machine learning approaches have been applied to develop a credit-card fraud classifier.These techniques include logistic regression, artificial intelligence and support vector machine.The classification accuracy achieved by all the classifiers is almost similar.This research has used the confusion matrix and area under the curve to demonstrate the score of the different performance measures and evaluate the overall performance of the classifiers.Several performance measures such as accuracy, precision, recall, F1-measure, Matthews correlation coefficient, receiver operating characteristic curve have been computed and analysed to evaluate the performance of the credit-card fraud detection classifiers.The analysis demonstrates that the support vector machine-based classifier outperforms the other classifiers.
PSYCHOLOGICAL SUPPORT USING VIRTUAL REALITY IN A STUDY WITH THREE-DAY DRY IMMERSION

Usability and validity of a based on virtual reality (VR) psychological support program were tested in a 3-day dry immersion study with participation of female subjects. Test results advocate that VR can be beneficial for managing the psychophysiological reactions in the period of acute adaptation to dry immersion.
Multisensory 360° Videos Under Varying Resolution Levels Enhance Presence

Omnidirectional videos have become a leading multimedia format for Virtual Reality applications. While live 360 ° videos offer a unique immersive experience, streaming of omnidirectional content at high resolutions is not always feasible in bandwidth-limited networks. While in the case of flat videos, scaling to lower resolutions works well, 360 ° video quality is seriously degraded because of the viewing distances involved in head-mounted displays. Hence, in this article, we investigate first how quality degradation impacts the sense of presence in immersive Virtual Reality applications. Then, we are pushing the boundaries of 360 ° technology through the enhancement with multisensory stimuli. 48 participants experimented both 360 ° scenarios (with and without multisensory content), while they were divided randomly between four conditions characterised by different encoding qualities (HD, FullHD, 2.5K, 4K). The results showed that presence is not mediated by streaming at a higher bitrate. The trend we identified revealed however that presence is positively and significantly impacted by the enhancement with multisensory content. This shows that multisensory technology is crucial in creating more immersive experiences.
One to Rule Them All? A First Look at DNS over QUIC

The DNS is one of the most crucial parts of the Internet. Since the original DNS specifications defined UDP and TCP as the underlying transport protocols, DNS queries are inherently unencrypted, making them vulnerable to eavesdropping and on-path manipulations. Consequently, concerns about DNS privacy have gained attention in recent years, which resulted in the introduction of the encrypted protocols DNS over TLS (DoT) and DNS over HTTPS (DoH). Although these protocols address the key issues of adding privacy to the DNS, they are inherently restrained by their underlying transport protocols, which are at strife with, e.g., IP fragmentation or multi-RTT handshakes—challenges which are addressed by QUIC. As such, the recent addition of DNS over QUIC (DoQ) promises to improve upon the established DNS protocols. However, no studies focusing on DoQ, its adoption, or its response times exist to this date—a gap we close with our study. Our active measurements show a slowly but steadily increasing adoption of DoQ and reveal a high week-over-week fluctuation, which reflects the ongoing development process: As DoQ is still in standardization, implementations and services undergo rapid changes. Analyzing the response times of DoQ, we find that roughly 40% of measurements show considerably higher handshake times than expected, which traces back to the enforcement of the traffic amplification limit despite successful validation of the client’s address. However, DoQ already outperforms DoT as well as DoH, which makes it the best choice for encrypted DNS to date.
Virtual microscopy as a teaching–learning tool for histology in a competency-based medical curriculum

Histology forms an important component of first-year medical education. Unfortunately, it is limited to the practical laboratory due to the need for a microscope and good quality slides. Virtual microscopy is a recent advancement, which uses computers as an alternative to microscopes. The aim of the study was to compare virtual microscopy (VM)-based practical classes with traditional microscopy (TM)-based practical classes for two cohorts of first-year medical students, by comparing learning achieved using two different test scores as well as a qualitative assessment of student and faculty perspectives regarding the feasibility and usefulness of VM.Each cohort of students was divided into two equal batches and each batch underwent eight histology modules of which, four utilised traditional microscopes and four utilised virtual microscopes. Quantitative analysis was performed using a theory test (which assessed preparation, theory knowledge and understanding) as well as a spotter test (which assessed identification skills, reasoning, and recall). Qualitative analysis was performed using a structured questionnaire and focus group discussions.Modules using VM were better when compared with those using TM, showing statistically significant and better grades. Qualitative analysis performed, yielded important information as to how this technology can serve as a good adjunct to traditional histology classes in the competency-based curriculum by increasing student interest, enabling self-study, and reducing students dependence on the tutor.VM forms a good adjunct as well as a standalone modality of learning to TM, as it improves accessibility to slides and promotes self-learning.
Neural rendering in a room

We, as human beings, can understand and picture a familiar scene from arbitrary viewpoints given a single image, whereas this is still a grand challenge for computers. We hereby present a novel solution to mimic such human perception capability based on a new paradigm of amodal 3D scene understanding with neural rendering for a closed scene. Specifically, we first learn the prior knowledge of the objects in a closed scene via an offline stage, which facilitates an online stage to understand the room with unseen furniture arrangement. During the online stage, given a panoramic image of the scene in different layouts, we utilize a holistic neural-rendering-based optimization framework to efficiently estimate the correct 3D scene layout and deliver realistic free-viewpoint rendering. In order to handle the domain gap between the offline and online stage, our method exploits compositional neural rendering techniques for data augmentation in the offline training. The experiments on both synthetic and real datasets demonstrate that our two-stage design achieves robust 3D scene understanding and outperforms competing methods by a large margin, and we also show that our realistic free-viewpoint rendering enables various applications, including scene touring and editing. Code and data are available on the project webpage: https://zju3dv.github.io/nr_in_a_room/.
Exploring Chinese cyber discourse: integrating political and legal perspectives

Abstract Cyberspace, with the rapidly growing network of users and communication technologies, provides venues for myriad social and political interactions. The very technology that enables the development of cyberspace itself also makes detailed and cumulative observation possible. This study aims to investigate cyber discourse in the context of China from the perspective of political and legal integration. Theoretical insight is transformed from viewing cyberspace as a research object to regarding it as a research ontology. To fill in this uncharted domain, this study aims to examine (a) the hidden connotations of the political discourse in cyberspace, (b) the new features of legal regulation in cyberspace, and (c) the relation between political discourse and legal practice. Centering on both political and legal discourse on cyberspace, the findings of this study indicate that there is a dialectical relationship between political discourse and legal practice in cyberspace. With the findings, this study contributes to the discursive construction by extending the discourse studies into cyberspace and integrating the discourse studies with politics and law.
Scrumban: An Agile Integration of Scrum and Kanban in Software Engineering

Software Engineering (SE) technologies are emerging day by day and seeking ABPR (Agile Business Process Reengineering) for Software Engineering Management (SEM) frameworks in software development organizations. BPR can enable organizational capabilities to initiate and implement critical change in execution. Under the roof of agile and on the base of empiricism, the Scrum has been proven itself as an ABPR approach for software engineering management in the software development organizations, across the world, by improving productivity, self-organization and collaboration for standard software development processes. Scrum has been leading software project development practices using its own characteristics: Artifacts, Pillars, Values, Events and Roles. But still software project development organizations are facing some issues with their software project development and management processes, like no documentation policy which results into inaccurate estimation, internal states of each work item flowing through the Scrum Board, Sprint tracker and prediction towards goal achievement which can be considered as challenges for Scrum due its limitations as well as Scrum does not allow, any alteration in its rules. Such issues have raised a question against implementation of Scrum and have opened the door for the next level of research to answer the question, how to overcome the limitations of Scrum. Kanban can provide the solution to some of these issues but it can’t provide complete SEM solutions to software Development Organizations. The aim of this research study is empirical analysis about how the formation of hybrid framework Scrumban as an integration of Scrum with Kanban, can resolve challenges of Scrum; using literature reviews, case study reviews, and research surveys; and this research has proposed a conceptual customized Scrumban framework, by keeping Kanban in the center of customization under the bound of Scrum rules. This research also concludes limitations of Scrumban, like project documentation, planning, large scaled project, distributed environment, team capabilities etc.; as each method has its own limitations.
Hand Gesture to Speech and Text Conversion Device

Sign language and facial expressions are the major means of communication for the speech flawed people. General people can understand the facial expression to an extent but cannot understand the sign language. Dumb people are unable to express their thoughts to normal humans. To reduce this gap of communication, this paper presents an electronic system which will help the mute people to exchange their ideas with the normal person in emergency situations. The system consists of a glove that can be worn by the subject which will convert the hand ges-tures to speech and text. The message displayed will also help deaf people to understand their thoughts. This prototype involves raspberry pi 3 as the micro-controller along with the flex sensors, accelerometer sensor. The resistance of the flex sensor changes due to the bending moment of the fingers of the subject. The accelerometer measures the angular displacement of the wrist along the y-axis. The microcontroller takes the input from the two sensors and matches it with the pre-programmed values and plays the respective message. The system makes use of python and its libraries for microcontroller programming.
Yewno Discover

Yewno Discover helps students and researchers find precisely the right information they are searching for, regardless of discipline and especially if it is interdisciplinary information. This new kind of visual knowledge discovery tool uses machine learning and computational linguistics to literally read over 200 million full-text articles and can guide users to the precise paragraph of information within this collection that is most valuable to their search. &#x0D; This practical workshop will enable you to learn more how you can use Yewno Discover to engage your patrons to get more of the library resources. Be sure to bring your laptop!
Fuzzy neuro-genetic approach for feature selection and image classification in augmented reality systems

In this paper, a new approach for implementing an Augmented Reality system by applying fuzzy genetic neural networks is proposed. It consists of two components namely feature selection and classification modules. For feature detection, extraction and selection, the proposed model uses a fuzzy logic based incremental feature selection algorithm which has been proposed in this work in order to recognize the important features from 3D images. Moreover, this paper explains the implementation and results of the proposed algorithms for an Augmented Reality system using image recognition, feature extraction, feature selection and classification by considering the global and local features of the images. For this purpose, we propose a three layer fuzzy neural network that has been implemented based on weight adjustments using fuzzy rules in the convolutional neural networks with genetic algorithm for effective optimization of rules. The classification algorithm is also based on fuzzy neuro-genetic approach which consists of two phases namely Training phase and testing phase. During the training phase, rules are formed based on objects and these rules are applied during the testing phase for recognizing the objects which can be used in robotics for effective object recognition. From the experiments conducted in this work, it is proved that the proposed model is more accurate in 3D &lt;br /&gt; object recognition.
An Attention-Based Friend Recommendation Model in Social Network

In social networks, user attention affects the user’s decision-making, resulting in a performance alteration of the recommendation systems. Existing systems make recommendations mainly according to users’ preferences ... | Find, read and cite all the research you need on Tech Science Press
Multi-view video codec using compressive sensing for wireless video sensor networks

In monitoring applications, different views are needed to be captured by multi-view video sensor nodes for understanding the scene clearly. These multi-view sequences have large volume of redundant data which affects the storage, transmission, bandwidth and lifetime of wireless video sensor nodes. A low complex coding technique is required for addressing these issues and for processing multi-view sensor data. Hence, in this paper, a framework on CS-based multi-view video codec using frame approximation technique (CMVC-FAT) is proposed. Quantisation with entropy coding based on frame skipping is adopted for achieving efficient video compression. For better prediction of skipped frame at receiver, a frame approximation technique (FAT) algorithm is proposed. Simulation results reveal that CMVC-FAT framework outperforms the existing method with achievement of 86.5% reduction in time and bits. Also, it shows 83.75% reduction in transmission energy compared with raw frame.