Reviewerly/exHarmony huggingface dataset:
Asynchronous training of quantum reinforcement learning

The development of quantum machine learning (QML) has received a lot of interest recently thanks to developments in both quantum computing (QC) and machine learning (ML). One of the ML paradigms that can be utilized to address challenging sequential decision-making issues is reinforcement learning (RL). It has been demonstrated that classical RL can successfully complete many difficult tasks. A leading method of building quantum RL agents relies on the variational quantum circuits (VQC). However, training QRL algorithms with VQCs requires significant amount of computational resources. This issue hurdles the exploration of various QRL applications. In this paper, we approach this challenge through asynchronous training QRL agents. Specifically, we choose the asynchronous training of advantage actor-critic variational quantum policies. We demonstrate the results via numerical simulations that within the tasks considered, the asynchronous training of QRL agents can reach performance comparable to or superior than classical agents with similar model sizes and architectures.
Adaptive Dynamic Global Illumination

We present an adaptive extension of probe based global illumination solution that enhances the response to dynamic changes in the scene while while also enabling an order of magnitude increase in probe count. Our adaptive sampling strategy carefully places samples in regions where we detect time varying changes in radiosity either due to a change in lighting, geometry or both. Even with large number of probes, our technique robustly updates the irradiance and visibility cache to reflect the most up to date changes without stalling the overall algorithm. Our bandwidth aware approach is largely an improvement over the original \textit{Dynamic Diffuse Global Illumination} while also remaining orthogonal to the recent advancements in the technique.
SemPPL: Predicting pseudo-labels for better contrastive representations

Learning from large amounts of unsupervised data and a small amount of supervision is an important open problem in computer vision. We propose a new semi-supervised learning method, Semantic Positives via Pseudo-Labels (SemPPL), that combines labelled and unlabelled data to learn informative representations. Our method extends self-supervised contrastive learning -- where representations are shaped by distinguishing whether two samples represent the same underlying datum (positives) or not (negatives) -- with a novel approach to selecting positives. To enrich the set of positives, we leverage the few existing ground-truth labels to predict the missing ones through a $k$-nearest neighbours classifier by using the learned embeddings of the labelled data. We thus extend the set of positives with datapoints having the same pseudo-label and call these semantic positives. We jointly learn the representation and predict bootstrapped pseudo-labels. This creates a reinforcing cycle. Strong initial representations enable better pseudo-label predictions which then improve the selection of semantic positives and lead to even better representations. SemPPL outperforms competing semi-supervised methods setting new state-of-the-art performance of $68.5\%$ and $76\%$ top-$1$ accuracy when using a ResNet-$50$ and training on $1\%$ and $10\%$ of labels on ImageNet, respectively. Furthermore, when using selective kernels, SemPPL significantly outperforms previous state-of-the-art achieving $72.3\%$ and $78.3\%$ top-$1$ accuracy on ImageNet with $1\%$ and $10\%$ labels, respectively, which improves absolute $+7.8\%$ and $+6.2\%$ over previous work. SemPPL also exhibits state-of-the-art performance over larger ResNet models as well as strong robustness, out-of-distribution and transfer performance. We release the checkpoints and the evaluation code at https://github.com/deepmind/semppl .
Analyzing Resource Utilization in an HPC System: A Case Study of NERSC Perlmutter

Resource demands of HPC applications vary significantly. However, it is common for HPC systems to primarily assign resources on a per-node basis to prevent interference from co-located workloads. This gap between the coarse-grained resource allocation and the varying resource demands can lead to HPC resources being not fully utilized. In this study, we analyze the resource usage and application behavior of NERSC's Perlmutter, a state-of-the-art open-science HPC system with both CPU-only and GPU-accelerated nodes. Our one-month usage analysis reveals that CPUs are commonly not fully utilized, especially for GPU-enabled jobs. Also, around 64% of both CPU and GPU-enabled jobs used 50% or less of the available host memory capacity. Additionally, about 50% of GPU-enabled jobs used up to 25% of the GPU memory, and the memory capacity was not fully utilized in some ways for all jobs. While our study comes early in Perlmutter's lifetime thus policies and application workload may change, it provides valuable insights on performance characterization, application behavior, and motivates systems with more fine-grain resource allocation.
Learning to Summarize Videos by Contrasting Clips

Video summarization aims at choosing parts of a video that narrate a story as close as possible to the original one. Most of the existing video summarization approaches focus on hand-crafted labels. As the number of videos grows exponentially, there emerges an increasing need for methods that can learn meaningful summarizations without labeled annotations. In this paper, we aim to maximally exploit unsupervised video summarization while concentrating the supervision to a few, personalized labels as an add-on. To do so, we formulate the key requirements for the informative video summarization. Then, we propose contrastive learning as the answer to both questions. To further boost Contrastive video Summarization (CSUM), we propose to contrast top-k features instead of a mean video feature as employed by the existing method, which we implement with a differentiable top-k feature selector. Our experiments on several benchmarks demonstrate, that our approach allows for meaningful and diverse summaries when no labeled data is provided.
Open-vocabulary Object Segmentation with Diffusion Models

The goal of this paper is to extract the visual-language correspondence from a pre-trained text-to-image diffusion model, in the form of segmentation map, i.e., simultaneously generating images and segmentation masks for the corresponding visual entities described in the text prompt. We make the following contributions: (i) we pair the existing Stable Diffusion model with a novel grounding module, that can be trained to align the visual and textual embedding space of the diffusion model with only a small number of object categories; (ii) we establish an automatic pipeline for constructing a dataset, that consists of {image, segmentation mask, text prompt} triplets, to train the proposed grounding module; (iii) we evaluate the performance of open-vocabulary grounding on images generated from the text-to-image diffusion model and show that the module can well segment the objects of categories beyond seen ones at training time; (iv) we adopt the augmented diffusion model to build a synthetic semantic segmentation dataset, and show that, training a standard segmentation model on such dataset demonstrates competitive performance on the zero-shot segmentation(ZS3) benchmark, which opens up new opportunities for adopting the powerful diffusion model for discriminative tasks.
Model Kumon as a Learning Sequence: Improving Students’ Imla’ Writing Skills

This study aims to improve the writing skills of first graders at Madrasah Diniyah Ula Baramij At-Ta'lim ‎Wa At-‎Ta'lim (MD.BAW) which consists of five classes with a total sample of 104 students by applying the Kumon model to reduce errors in writing pronouncements ', for example, letter writing errors, punctuation rules (tanwin), use of mad letters, connecting letters, and shifting letter sounds which have an impact on changing the meaning of a sentence. This ‎type of ‎research uses Classroom Action Research through ‎four cycles with ‎four stages proposed by Kurt Lewin: planning, action, ‎observation, and reflection. While the data ‎collection techniques ‎were carried out using observation, ‎interviews, pre-test, Kumon ‎worksheets, observation ‎instruments, field notes, and ‎documentation. Qualitative data analysis uses the model put forward by Miles and Huberman namely data reduction, data presentation, and drawing conclusions. As for the quantitative data using the Anas Sudijono percentage instrument by calculating the percentage frequency and the amount. The results of this study indicate that improving writing skills using the Kumon model was successfully carried out through four cycles by always providing motivation, observing, guiding, and providing intensive training in each cycle so that students can write more independently, thoroughly, and in an orderly manner according to the rules of Imla'.
Design of Water Source Alternative Based on SWAT Model Simulation

Water shortage problem was often happened in many places in Indonesia. Water availability is very important not only for domestic needs, but also for other activities particularly for agricultural activities. To solve water shortage problem, this study aimed to design a water source alternative. The rain is a free water source and collecting rain in the small dam or reservoir is one of the effective rainwater harvesting technique that can increase water availability. This research was conducted at Sekaran Village and the analysis had done using SWAT (Soil and Water Assessment Tools) model. The planned rainfall and discharge were analysed by 5 years return period to calculate the water potential for reservoir charging. The results of this study indicated that the location of the reservoir was planned in a forest area with a slope of 8-25%, on coordinates 111º38'11.39"E and 7º6'14.22"S and has a storage volume of 13737 m3. The reservoir has 4356 m2 surface area with a depth of 3 m. Based on the SWAT model simulation, the highest average discharge potential occured in February with a discharge of 0.312 m3/s and the lowest average discharge occured in October with a discharge of 0.044 m3/s. The total volume of potential annual water that can fill the reservoir is 61.166 m3/s per year based on a planned rainfall of 115.38 mm and a planned flood discharge of 2.54 m3/s with a return period of 5 years. The reservoir construction required a cost of IDR 1,200,747,000. Keywords: Cost, Location, Rainwater Harvesting, Reservoir, SWAT
Do Payment Technology Innovations Affect Currency Demand in Tunisia?

This study investigates the impact of non-cash payment technologies on demand for currency in Tunisia using the autoregressive distributed lag (ARDL) cointegration approach from 2010M12 to 2018M7. In this context, our findings indicate a negative influence of EFT-POS terminals on currency demand. We have also shown that the demand for currency is positively influenced by the increase in the number of ATMs and the volume of transactions made by bank cards in circulation, reflecting the frequent use of cards to receive cash in Tunisia. Whereas, the impact of increase in check payments on demand for currency is negative and not significant. The empirical evidence shows that there is preference for cash in payment habits by Tunisian consumers. So, it should be noted that despite the development of payment systems, cash still attractive and an easier substitute for non-cash payments in Tunisia.
Arbitrary Style Transfer with Semantic Content Enhancement

Arbitrary style transfer is an import topic which changes the style of a source image according to a reference one. It is useful for artistic creation and intelligent imaging applications. The main challenge of the style transfer is that it is difficult to balance the semantic feature transformation and original semantic content. In this paper, we introduce a semantic content enhancement module to mitigate the affect of color distribution and semantic feature transformation for the style transfer while keeping the original semantic structure as much as possible. Meanwhile, we also introduce a channel attention module to enhance the style features by fusing with the style attention network. With the enhancement of both features, our network achieves excellent result that balances original semantic structure and transfer stylized visualization. In addition, we also migrate the algorithm to 3D space and it also performs stably for 3D scene-based style transfer. Experiments show that our method can handle various style transfer tasks.
Robust Anomaly Detection and Localization via Simulated Anomalies

Anomaly detection refers to identifying abnormal images and localizing anomalous regions. Reconstruction-based anomaly detection is a commonly used method; however, traditional reconstruction-based methods perform poorly as deep models generalize successfully enough that even anomalous regions can be well-restored. In this paper, we propose a new method to address the single pseudo-anomaly type and high false positive detection of the existing methods. Specifically, we design a novel pseudo-anomaly simulation module that can generate several types of anomalies on normal images. Furthermore, we propose an effective reconstruction network to improve the robustness of the model against distractors. Finally, we employ a segmentation network to localize anomalous regions. This simple but effective method can detect various anomalies in the real world, even those that are subtle and rare. Extensive experiments on the MVTec anomaly detection dataset demonstrate the effectiveness and superiority of the proposed method, yielding an AUROC score of 98.2% in image-level anomaly detection and 97.8% in pixel-level anomaly localization.
Exploring Text Selection in Augmented Reality Systems

Text selection is a common and essential activity during text interaction in all interactive systems. As Augmented Reality (AR) head-mounted displays (HMDs) become more widespread, they will need to provide effective interaction techniques for text selection that ensure users can complete a range of text manipulation tasks (e.g., to highlight, copy, and paste text, send instant messages, and browse the web). As a relatively new platform, text selection in AR is largely unexplored and the suitability of interaction techniques supported by current AR HMDs for text selection tasks is unclear. This research aims to fill this gap and reports on an experiment with 12 participants, which compares the performance and usability (user experience and workload) of four possible techniques (Hand+Pinch, Hand+Dwell, Head+Pinch, and Head+Dwell). Our results suggest that Head+Dwell should be the default selection technique, as it is relatively fast, has the lowest error rate and workload, and has the highest-rated user experience and social acceptance.
Graphic Design and Evaluation of an Augmented Reality for Advergame

This letter attempts to introduce an Augmented Reality Advergame (ARA), which is designed to eliminate the pain-points present of current advergames. i.e., cumbersome interface design, excessive entertainment elements lead to user distraction, and low participation among middle-aged and elderly users. For this purpose, our ARA improves color scheme, optimizes graphic proportions, and reduces typographical complexity of interface design. Furthermore, we introduce an augmented reality way in interaction. Eye-tracking studies demonstrate that ARA is better at directing user attention to key information than the current advergames. Perceptive studies confirm that ARA user engagement is 25% higher than the control advergame.
Touch Trick the mind: Haptic Display Augmented the Mind-Map of internal locus of control

Previous work in VR has demonstrated that the haptic remapped technique uses visual domination of proprioceptive position sense to map virtual hand positions to match the internal locus of control. However, when the offset between virtual and physical hands gradually increases, the internal locus of control has difficulty in self-attributing the virtual hand movement, and the user becomes aware of the remapping process. Hence, we hypothesize that haptics can influence spatial perception by affecting the internal locus of control in a way that confuses the remapping process. In this paper, we present an experiment to evaluate performance in different cognitive environments to verify that haptics enhances people's perception of space by confusing the original threshold of vision to create illusions. And a haptic redirection is proposed to verify the effect of haptics on internal position control. We designed two experiments to estimate the perception of haptics for internal position sense. Our results quantitatively show that haptics can indeed reduce people's perceptual thresholds for spatial distance as well as depth in a way that alters internal perception by increasing illusion, and that a haptic-visual coherent environment can make the remapping of mismatches between virtual and physical locations less obvious.
Multi-Lingual Mobile Application to Improve the Pharmaceutical Care of Patients in Ethiopia

Delivering essential information for patients concerning medication they’re taking is vital and improves healthcare services. Due to heavy burdens in every pharmacy, patients in Ethiopia do not get proper advice about the medicinal drugs they’re taking from the pharmacist. Moreover, it is far tough to recognize and interpret the various written instruction on remedy labels or package deal leaflet while not heaving the domain knowledge. As Ethiopia is an importer of many medicinal drugs from abroad, very often the package leaflets are not written in the local language. Therefore, due to language barrier, most of the patients cannot comprehend the information on medication labels and on package leaflet. In this regards most patients purchase the medication without a proper understanding of how to use the medication. This necessitates a technological based solution which serves patients in their language when they want an advice. This study demonstrates the design and implementation of a mobile-based solution to minimize such challenges and improve patient access to the medicinal drugs information. The requirements of the proposed system gathered using interview from pharmacist, doctors, technical expert, and client. Finally, a mobile application prototype is developed and evaluated for its effectiveness.
Language contacts and deonyms in contemporary journalistic Italian language

This paper focuses its attention on the presence of deonyms – in this case, new words derived from the surnames of foreign heads of state – in presentday journalistic Italian language. The research was conducted by analyzing the historical archive of two of the most relevant newspapers in Italy: la Repubblica and La Stampa. The purpose is to emphasize the main different semantic nuances of the words consisting of politicians’ surnames and Italian suffixes and to demonstrate the language contacts between Italian and other languages. As it can easily be guessed, most of the words investigated in this study were derived from names of American presidents as a result of the influence of American politics on the Italian system.
Place names between popular and administrative culture

The paper focuses on differences and similarities between popular (oral) place names and administrative (written) ones, starting from the observation of repertoires collected in Azzano d’Asti (Piedmont, north-western Italy). The analysis involves both quantitative and qualitative aspects. On the one hand, the paper investigates the creation of the repertoires, considering their different functions; on the other hand, the observation is focused on differences and similarities between oral and written place names used to designate the same place (taking into account phonetic, morphological, syntactic and semantic elements).
Between identity and otherness. Stereotypical forms of ethnonyms

We aim to investigate ethnic names from an interdisciplinary perspective embracing not only an onomastic viewpoint, but also an ethnological, anthropological and sociological one. Generally speaking, ethnonyms as group names are derived from toponyms, but their referents are the people who inhabit a specific geographical area and who have a particular cultural identity. Still, the identity of a group just like that of a person is validated only by referring it to a different one. Therefore, in addition to the official name (‘the endonym’), the others, who speak a different language and have a different culture and mindset, as a result of their way of perceiving the world, will employ an exonym, a word of their own creation which usually has nothing to do with the geographical area, but rather with the habits and customs of the inhabitants. Sometimes these onymic formulas may even become offensive and function as stereotypes which generalise and preserve not pertinent character traits, but collateral ones, in relation to the referent (the Scot = scrooge; the Brit = snob; the German = organised and precise; the Japanese = punctual; the Italian = associated with the Mafia; the Norwegian = cold and introverted). This phenomenon is found especially in multicultural environments where the convergence point of two cultures becomes a source of alterity also affecting the level of onomastics. We define by linguistic means the peculiarities of some peoples, i.e. the Italians, Americans, Romanians and Germans.
Onomastic reflections of Italian emigration to Argentina: interlinguistic contacts and identity reestablishment

This paper aims to retrace the most significant phases in the formation of modern Argentine onomastics, as a result of the construction of national identity, a process which assimilates and recomposes the variety of the original ethnic components. A diachronic reading of the data, starting from the baptismal registers of a rural community north of Córdoba, will allow for an evaluation of the onomastic choices, indicating the contours of the progressive reworking of the identity profile of Italian migrants, modified by means of interlinguistic and intercultural contacts.
Integration and service composition based on model

In the paper we would like to introduce a frame- work for designing and developing Semantic Web Service application that integrates a several enterprises and composes results from a diﬀerent services by applying techniques, methodologies and notations provided by Software Engineering and Business process modeling. We propose to use existing business modeling techniques (BPMN, BPLWS, OWL-S, WSDL, WebML) for modeling the cross enterprise processes, for enterprise integrations and web service compositions. The main purpose is to designing and developing semantically rich Web applications, with semiautomatic defining semantic descriptions from the business process model that increase the eﬃciency of design and decrease the extra work necessary for processing semantically annotated information which passing through the enterprises.