Reviewerly/exHarmony huggingface dataset:
Why and When: Understanding System Initiative during Conversational Collaborative Search

In the last decade, conversational search has attracted considerable attention. However, most research has focused on systems that can support a \emph{single} searcher. In this paper, we explore how systems can support \emph{multiple} searchers collaborating over an instant messaging platform (i.e., Slack). We present a ``Wizard of Oz'' study in which 27 participant pairs collaborated on three information-seeking tasks over Slack. Participants were unable to search on their own and had to gather information by interacting with a \emph{searchbot} directly from the Slack channel. The role of the searchbot was played by a reference librarian. Conversational search systems must be capable of engaging in \emph{mixed-initiative} interaction by taking and relinquishing control of the conversation to fulfill different objectives. Discourse analysis research suggests that conversational agents can take \emph{two} levels of initiative: dialog- and task-level initiative. Agents take dialog-level initiative to establish mutual belief between agents and task-level initiative to influence the goals of the other agents. During the study, participants were exposed to three experimental conditions in which the searchbot could take different levels of initiative: (1) no initiative, (2) only dialog-level initiative, and (3) both dialog- and task-level initiative. In this paper, we focus on understanding the Wizard's actions. Specifically, we focus on understanding the Wizard's motivations for taking initiative and their rationale for the timing of each intervention. To gain insights about the Wizard's actions, we conducted a stimulated recall interview with the Wizard. We present findings from a qualitative analysis of this interview data and discuss implications for designing conversational search systems to support collaborative search.
Calculating the many-body density of states on a digital quantum computer

Quantum statistical mechanics allows us to extract thermodynamic information from a microscopic description of a many-body system. A key step is the calculation of the density of states, from which the partition function and all finite-temperature equilibrium thermodynamic quantities can be calculated. In this work, we devise and implement a quantum algorithm to perform an estimation of the density of states on a digital quantum computer which is inspired by the kernel polynomial method. Classically, the kernel polynomial method allows to sample spectral functions via a Chebyshev polynomial expansion. Our algorithm computes moments of the expansion on quantum hardware using a combination of random state preparation for stochastic trace evaluation and a controlled unitary operator. We use our algorithm to estimate the density of states of a non-integrable Hamiltonian on the Quantinuum H1-1 trapped ion chip for a controlled register of 18 qubits. This not only represents a state-of-the-art calculation of thermal properties of a many-body system on quantum hardware, but also exploits the controlled unitary evolution of a many-qubit register on an unprecedented scale.
TransPoser: Transformer as an Optimizer for Joint Object Shape and Pose Estimation

We propose a novel method for joint estimation of shape and pose of rigid objects from their sequentially observed RGB-D images. In sharp contrast to past approaches that rely on complex non-linear optimization, we propose to formulate it as a neural optimization that learns to efficiently estimate the shape and pose. We introduce Deep Directional Distance Function (DeepDDF), a neural network that directly outputs the depth image of an object given the camera viewpoint and viewing direction, for efficient error computation in 2D image space. We formulate the joint estimation itself as a Transformer which we refer to as TransPoser. We fully leverage the tokenization and multi-head attention to sequentially process the growing set of observations and to efficiently update the shape and pose with a learned momentum, respectively. Experimental results on synthetic and real data show that DeepDDF achieves high accuracy as a category-level object shape representation and TransPoser achieves state-of-the-art accuracy efficiently for joint shape and pose estimation.
ReBotNet: Fast Real-time Video Enhancement

Most video restoration networks are slow, have high computational load, and can't be used for real-time video enhancement. In this work, we design an efficient and fast framework to perform real-time video enhancement for practical use-cases like live video calls and video streams. Our proposed method, called Recurrent Bottleneck Mixer Network (ReBotNet), employs a dual-branch framework. The first branch learns spatio-temporal features by tokenizing the input frames along the spatial and temporal dimensions using a ConvNext-based encoder and processing these abstract tokens using a bottleneck mixer. To further improve temporal consistency, the second branch employs a mixer directly on tokens extracted from individual frames. A common decoder then merges the features form the two branches to predict the enhanced frame. In addition, we propose a recurrent training approach where the last frame's prediction is leveraged to efficiently enhance the current frame while improving temporal consistency. To evaluate our method, we curate two new datasets that emulate real-world video call and streaming scenarios, and show extensive results on multiple datasets where ReBotNet outperforms existing approaches with lower computations, reduced memory requirements, and faster inference time.
TriPlaneNet: An Encoder for EG3D Inversion

Recent progress in NeRF-based GANs has introduced a number of approaches for high-resolution and high-fidelity generative modeling of human heads with a possibility for novel view rendering. At the same time, one must solve an inverse problem to be able to re-render or modify an existing image or video. Despite the success of universal optimization-based methods for 2D GAN inversion, those applied to 3D GANs may fail to extrapolate the result onto the novel view, whereas optimization-based 3D GAN inversion methods are time-consuming and can require at least several minutes per image. Fast encoder-based techniques, such as those developed for StyleGAN, may also be less appealing due to the lack of identity preservation. Our work introduces a fast technique that bridges the gap between the two approaches by directly utilizing the tri-plane representation presented for the EG3D generative model. In particular, we build upon a feed-forward convolutional encoder for the latent code and extend it with a fully-convolutional predictor of tri-plane numerical offsets. The renderings are similar in quality to the ones produced by optimization-based techniques and outperform the ones by encoder-based methods. As we empirically prove, this is a consequence of directly operating in the tri-plane space, not in the GAN parameter space, while making use of an encoder-based trainable approach. Finally, we demonstrate significantly more correct embedding of a face image in 3D than for all the baselines, further strengthened by a probably symmetric prior enabled during training.
Persistent Nature: A Generative Model of Unbounded 3D Worlds

Despite increasingly realistic image quality, recent 3D image generative models often operate on 3D volumes of fixed extent with limited camera motions. We investigate the task of unconditionally synthesizing unbounded nature scenes, enabling arbitrarily large camera motion while maintaining a persistent 3D world model. Our scene representation consists of an extendable, planar scene layout grid, which can be rendered from arbitrary camera poses via a 3D decoder and volume rendering, and a panoramic skydome. Based on this representation, we learn a generative world model solely from single-view internet photos. Our method enables simulating long flights through 3D landscapes, while maintaining global scene consistency--for instance, returning to the starting point yields the same view of the scene. Our approach enables scene extrapolation beyond the fixed bounds of current 3D generative models, while also supporting a persistent, camera-independent world representation that stands in contrast to auto-regressive 3D prediction models. Our project page: https://chail.github.io/persistent-nature/.
Matryoshka Policy Gradient for Entropy-Regularized RL: Convergence and Global Optimality

A novel Policy Gradient (PG) algorithm, called Matryoshka Policy Gradient (MPG), is introduced and studied, in the context of max-entropy reinforcement learning, where an agent aims at maximising entropy bonuses additional to its cumulative rewards. MPG differs from standard PG in that it trains a sequence of policies to learn finite horizon tasks simultaneously, instead of a single policy for the single standard objective. For softmax policies, we prove convergence of MPG and global optimality of the limit by showing that the only critical point of the MPG objective is the optimal policy; these results hold true even in the case of continuous compact state space. MPG is intuitive, theoretically sound and we furthermore show that the optimal policy of the standard max-entropy objective can be approximated arbitrarily well by the optimal policy of the MPG framework. Finally, we justify that MPG is well suited when the policies are parametrized with neural networks and we provide an simple criterion to verify the global optimality of the policy at convergence. As a proof of concept, we evaluate numerically MPG on standard test benchmarks.
Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models

State-of-the-art machine learning models often learn spurious correlations embedded in the training data. This poses risks when deploying these models for high-stake decision-making, such as in medical applications like skin cancer detection. To tackle this problem, we propose Reveal to Revise (R2R), a framework entailing the entire eXplainable Artificial Intelligence (XAI) life cycle, enabling practitioners to iteratively identify, mitigate, and (re-)evaluate spurious model behavior with a minimal amount of human interaction. In the first step (1), R2R reveals model weaknesses by finding outliers in attributions or through inspection of latent concepts learned by the model. Secondly (2), the responsible artifacts are detected and spatially localized in the input data, which is then leveraged to (3) revise the model behavior. Concretely, we apply the methods of RRR, CDEP and ClArC for model correction, and (4) (re-)evaluate the model's performance and remaining sensitivity towards the artifact. Using two medical benchmark datasets for Melanoma detection and bone age estimation, we apply our R2R framework to VGG, ResNet and EfficientNet architectures and thereby reveal and correct real dataset-intrinsic artifacts, as well as synthetic variants in a controlled setting. Completing the XAI life cycle, we demonstrate multiple R2R iterations to mitigate different biases. Code is available on https://github.com/maxdreyer/Reveal2Revise.
Avaliação de indivíduos diabéticos quanto ao autocuidado e ao estágio de mudança de comportamento segundo o Modelo Transteórico na Atenção Secundária do Distrito Federal

CASE REPORTSa middle-aged man with acute intestinal obstruction due to an ileo-ileal intussusception of inverted Meckel's diverticulum with a lipoma that was managed by laparoscopy.Worthy of note is the very scarce number of case reports on this exceeding uncommon association.
Zilele Academice Ieșene – Sărbătoarea științei la Academia Română Filiala Iași

For 37 years, in the fall, in Iași, there have been large-scale scientific events organized by the Iași Branch of the Romanian Academy during the Iași Academic Days. On these occasions numerous scientists, from the country and abroad, present their own results and debates take place. Through these actions to promote scientific results, but also to create the institutional framework for dialogue, the institution wants to present its own achievements to the general public and to transfer beyond the borders of science laboratories results that could be useful to the economic, social or cultural environment.
Word prediction using CNN for ancient manuscripts

In this paper, we propose a CNN model for character recognition in Tamil palm-leaf manuscripts. Palm leaves served as the primary source for retaining information in numerous domains such as science, literature, medicine, history and so on in ancient times. Tamil is one of the ancient languages, and there are several manuscripts that provide important information. These manuscripts were destroyed of some bacteria. As a result, by predicting the missing letter and providing suggestions for identifying the word with a high prediction rate, this Convolutional neural network can play a significant role in training the data set and recovering valuable information from the manuscripts.
Analysis of Influential Features with Spectral Features for Modeling Dialectal Variation in Malayalam Speech Using Deep Neural Networks

Over the past few decades, research has focused heavily on automatic speech recognition (ASR). Although ASR for a few languages is close to reality; ASR for the low resource languages like Malayalam is still in its infancy. Here, in this work, the authors discuss the experiment conducted on accented data of Malayalam speech using two approaches. One approach uses the spectral features for modeling using deep convolutional neural network and the other uses the influential features of speech signals for modeling using LSTM-RNN approach. The proposed methodology comprises three distinct stages; dataset preparation, feature extraction, classification, and hence the construction of deep learning models that recognize the accent-based spoken sentences in the Malayalam language. Mel-frequency cepstral coefficient (MFCC) algorithm, short-term Fourier transform (STFT), and mel spectrogram methodologies are used for feature engineering and hence the features that represent the speech signals are used for constructing the accented ASR system for the Malayalam language using long short-term memory (LSTM) a recurrent neural network (RNN). The spectrogram dataset has been constructed for the speech dataset and used for constructing the ASR model with deep convolutional neural network (DCNN). The result of the experiment shows that LSTM-based RNN outperforms DCNN for the proposed dataset that has been constructed for the experiment in the natural recording environment.
Advanced Tree-Seed Algorithm for Large Sized JSP Problems

Globalizing economies force manufacturing companies to develop themselves and take new measures.Correct planning of the production process is vital in order to increase the competitiveness of companies.For this reason, the Job shop Scheduling Problem (JSP) has a great role in the planning of production in companies.In JSPs, the determined jobs in the manufacturing companies must be run in the correct order on existing and suitable machines.Planning to complete the work in the company in the shortest possible time on the smachines and in the order, is a combinatorial difficult optimization problem.Both exact methods and meta-heuristic approaches are used in solving JSPs, which is an NP-hard optimization problem.Depending on the number of jobs and machines in the companies, the size of this optimization problem changes.In JSPs where the number of jobs and machines is high, precise methods may be insufficient to produce solutions in operational time.Therefore, meta-heuristic algorithms are frequently used in solving JSPs.In this work, the exploration and exploitation capabilities in the Tree Seed Algorithm (TSA) are enhanced with the swap, symmetry, and shift mutation operators.The proposed new TSA (Advanced TSA-ATSA) algorithm is compared on large-sized JSPs with meta-heuristic algorithms which are well known in the literature.The proposed ATSA has shown promising performance in experimental studies.
Çoklu Ezme Yüzey Bitirme İşleminin AISI 8620 Sementasyon Çeliği Yüzey Özelliklerine Etkilerinin Karşılaştırılmalı İncelenmesi

Developments in machine manufacturing technology necessitate the quality performance of the finished surfaces to be in parallel.The burnishing method is carried out to surfaces in different geometries, and its effect on the normal machined surface quality has been demonstrated by various scientific studies.However, by subjecting the surface to more than one burnishing process, it is limited to obtain sufficient data on the surface properties of the material.In this study, AISI 8620 cementation steel, which is one of the materials commonly used in machine manufacturing, was preferred especially since a toughness in the core, a hard surface resistant to abrasion on the peripheral surface, and a ductile structure in impact work at required.The material was prepared 20 mm diameter with the turning method under the same machining parameters.On 4 of the 5 leveled surfaces prepared; In the examinations and comparisons made after the burnishing process applied from 1 burnishing number to 4 burnishing numbers, the best results were obtained in terms of surface roughness in 2 burnishing operations and surface hardness in 3 burnishing operations.The worst values were obtained in 1 burnishing operation for surface roughness and 4 burnishing operations for surface hardness.
A névtudomány, a nevezéktan és a terminológia viszonya, érintkezési pontjai

Onomastics, Nomenclature and Terminology. Relations and Differences Onomastics, nomenclature and terminology are partially interrelated disciplines as they explore similar, sometimes the same linguistic items, while adopting different perspectives and methodologies. Their differing purposes separate these three fields of science. Proper names, scientific names and terms display several common and unique characteristics. Shared features include the relevance of identification; the primacy of denotation, motivation and information at the expense of etymological sense, connotation and affective shade in their semantics; and the importance of finding their target-language equivalents in translation. Scientific names and terms classify, systematize, store and transfer knowledge elements. They have a fixed relationship to conceptual categories and explicitly defined meanings. Designed for international professional use, professionals aim to minimise their discrepancies across languages in harmonization processes. Scientific names identify entities of the tangible world. They are formed consciously with the help of predetermined linguistic elements and rules. Terms usually identify abstract concepts, and they are mostly formed spontaneously based on general elements and rules. Proper names identify psychosocially salient real or fictive entities; they are formed spontaneously or consciously from restricted sets of elements of natural languages, and are partly based on genre-specific rules. They are strongly culture-dependent linguistic units, but as universals, they are present in all natural languages. Scientific names are nouns; proper names are nominal expressions (words, phrases, clauses, sentences); terms (if linguistic units) can be nouns, verbs, adjectives, adverbs, or nominal, verbal, adjectival and adverbial expressions as well.
Bug Tracker: Comprehensive Bug Tracking and Modification System

It is important that information provided in bug reports is relevant and complete in order to help resolve bugs quickly. However, often such information trickles to developers after several iterations of communication between Developers and reporters. Poorly designed bug tracking systems are partly to blame for this exchange of information being stretched over time. Our paper addresses the concerns of bug tracking systems by proposing four broad directions for enhancements. As a proof-of-concept, we also demonstrate a prototype interactive bug tracking system that gathers relevant information from the user and identifies files that need to be fixed to resolve the bug.
TEMPORAL AND SPATIAL ANALYSIS OF EARTHQUAKE IN STOLAC ON APRIL 22ND, 2022

The territory of Bosnia and Herzegovina represents a seismically relatively active zone, given that it is under the influence of the convergent movement of the African plate towards the Eurasian plate. Evidence of the seis- micity of Bosnia and Herzegovina is the earthquakes recorded in the earthquake catalogs. The strongest earth- quake recorded on the territory of Bosnia and Herzegovina in the 21st century is the earthquake that occurred in Stolac on April 22, 2022. The area of Stolac is characterized by numerous normal, oblique and overturned folds as well as reverse faults. The reverse faulting is the cause of the earthquake that occurred, which was proven by the analysis of the focal mechanism of the earthquake. Based on the GIS software of the National In- stitute of Geophysics and Volcanology of Italy for earthquake dislocations, the maximum possible earthquake magnitude was determined, which for the Stolac area is 7 degrees on the Richter scale. In view of this, it is nec- essary to design, build and reconstruct buildings so that they are resistant to earthquakes. Keywords: earthquake, seismicity, beachball diagrams, hazard
Using Cooperative Script to Enhance Students' Comprehension of Narrative Text

This study aims to determine the efficacy of implementing cooperative scripts to improve students’ reading comprehension of narrative texts in the first grade at SMAN 8 South Tangerang during the 2020/21 academic year. This study includes 39 students from X Science 1 and 39 from X Science 2 as samples. The English test results of the students measure the success of this study. Based on the stated purpose, the researcher designed a quasi-experimental study. After taking the English Test, the student acquired reading comprehension skills. The examination was based on the tenth-grade English textbook. Through pre-test and post-test, the researcher obtained the results. The researcher in the data analysis procedure utilized normality, homogeneity, and a T-test. The examination of the data revealed a substantial difference between the reading comprehension achievement of students in experimental classes, which were taught using the Cooperative Script technique, and students in control classes, who were not. It might be seen by comparing the pre-test and post-test results of students. The experimental class had a mean pre-test score of 59.69 and a post-test score of 73.94. In contrast, the mean score on the pretest for the control group was 58.87, while the mean score on the posttest was 70.41.