Miracle-dz/UniFAR-FTUs huggingface dataset:
Chat-to-Design: AI Assisted Personalized Fashion Design

In this demo, we present Chat-to-Design, a new multimodal interaction system for personalized fashion design. Compared to classic systems that recommend apparel based on keywords, Chat-to-Design enables users to design clothes in two steps: 1) coarse-grained selection via conversation and 2) fine-grained editing via an interactive interface. It encompasses three sub-systems to deliver an immersive user experience: A conversation system empowered by natural language understanding to accept users’ requests and manages dialogs; A multimodal fashion retrieval system empowered by a large-scale pretrained language-image network to retrieve re-quested apparel; A fashion design system empowered by emerging generative techniques to edit attributes of retrieved clothes.
RQC Revisited and More Cryptanalysis for Rank-Based Cryptography

In this paper, we revisit the Rank Quasi-Cyclic (RQC) (Melchor et al., IEEE IT, 2018) encryption scheme by proposing three possible variations for its design. Our first improvement relies on the introduction of Augmented Gabidulin codes, a new family of decodable codes exploiting the concept of support erasure for the rank metric. Following the work of Melchor et al. (PQCrypto, 2022), our second improvement uses multiple syndromes to increase the weight of the error to be decoded. As pioneered in Melchor et al. (NIST PQC, 2020), our third variation considers non-homogeneous error weights in order to decrease the parameters. These improvements can be combined together to design schemes offering various trade-offs in term of security and size. Our Multi-UR-AG (multiple syndromes, unstructured, augmented Gabidulin) scheme achieves a size of 11kB (public key + ciphertext) for 128 bits of security while featuring a conservative design as it relies on pure random instances without any ideal structure. Besides, our NH- Multi-RQC-AG (non-homogeneous error, multiple syndromes, ideal structure, augmented Gabidulin) achieves a size of 2.7 kB for 128 bits of security, namely a 50 % improvement with respect to classical RQC. Our second and third variations respectively rely on the security of the <inline-formula> <tex-math notation="LaTeX">$\textsf {RSL} $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\textsf {NHRSD} $ </tex-math></inline-formula> problems (or <inline-formula> <tex-math notation="LaTeX">$\textsf {NHRSL} $ </tex-math></inline-formula> when considered together). In this paper, we also provide new security analysis and attacks for these problems. While these results are important for our new schemes, they are of independent interest as well. Our security analysis for the <inline-formula> <tex-math notation="LaTeX">$\textsf {RSL} $ </tex-math></inline-formula> problem provides an improvement on the recent algebraic attacks for some instances. In addition, we show that the <inline-formula> <tex-math notation="LaTeX">$\textsf {RSL} $ </tex-math></inline-formula> problem can be solved in polynomial time when <inline-formula> <tex-math notation="LaTeX">$N \geq (k+1) r\frac {m}{m-r}$ </tex-math></inline-formula>, this improves the best known combinatorial attack (Gaborit et al., Crypto, 2017). We also propose the first combinatorial attack against the <inline-formula> <tex-math notation="LaTeX">$\textsf {NHRSD} $ </tex-math></inline-formula> problem along with a precise complexity analysis of the algebraic attack described Melchor et al. (NIST PQC, 2020). At last, we combine these analysis to provide an attack against the <inline-formula> <tex-math notation="LaTeX">$\textsf {NHRSL} $ </tex-math></inline-formula> problem.
Forecasting Busy-Hour Downlink Traffic in Cellular Networks

The dramatic growth in cellular traffic volume requires cellular network operators to develop strategies to carefully dimension and manage the available network resources. Forecasting traffic volumes is a fundamental building block for any proactive management strategy and is therefore of great interest in such a context. Differently from what found in the literature, where network traffic is generally predicted in the short-term, in this work we tackle the problem of forecasting busy hour traffic, i.e., the time series of observed daily maxima traffic volumes. We tackle specifically forecasting in the long term (one, two months ahead) and we compare different approaches for the task at hand, considering different forecasting algorithms as well as relying or not on a cluster-based approach which first groups network cells with similar busy hour traffic profiles and then fits per-cluster forecasting models to predict the traffic loads. Results on a real cellular network dataset show that busy hour traffic can be forecasted with errors below 10% for look-ahead periods up to 2 months in the future. Moreover, when clusters are available, we improve forecasting accuracy up to 8% and 5% for look-ahead of 1 and 2 months, respectively.
Microbiological Stability of Rice Tart Stored at Ambient Temperature after Baking

Very popular in Belgium, rice tart is a hot pastry sold in bakeries. It is then consumed at home, for dessert or snack. This study is conducted to investigate the microbiological stability of this foodstuff, from the end of baking to end user by consumers. In this purpose, 108 rice tart samples were collected from each of seven bakeries in five Belgium provinces. Physico-chimical analysis in addition to microbiological analysis were carried out in accordance with the European Regulation EC 2073/2005 and with references methods, to enumerate the total microorganisms count, Staphylococcus aureus, Bacillus cereus as well as Enterobacteria, susceptible likely to contaminate the tarts during the production or after baking. Even when the results meet the microbiological safety standards immediately after baking in all bakeries implicated, a significant (p 3,7 log cfu/g) was observed during the storage at nonrefrigerated temperature (28 to 30°C). A post-baking recontamination and other parameters like an insufficient baking time or a non uniform distribution of the oven heat could explain the observed bacteria growth. The present study shows that most rice tarts investigated are microbiologically safe. However, the possible increase of bacteria load in this foodstuff attributable to the favorable aw, pH and temperature conditions, highlight the importance of applying good hygienic practices and compliance with storage conditions after baking to ensure consumer safety.
Linguistic Approach to Time Series Forecasting

This paper proposes methods of predicting dynamic time series (including non-stationary ones) based on a linguistic approach, namely, the study of occurrences and repetition of so-called N-grams. This approach is used in computational linguistics to create statistical translators, detect plagiarism and duplicate documents. However, the scope of application can be extended beyond linguistics by taking into account the correlations of sequences of stable word combinations, as well as trends. The proposed methods do not require a preliminary study and determination of the characteristics of time series or complex tuning of the input parameters of the forecasting model. They allow, with a high level of automation, to carry out short-term and medium-term forecasts of time series, characterized by trends and cyclicality, in particular, series of publication dynamics in content monitoring systems. Also, the proposed methods can be used to predict the values of the parameters of a large complex system with the aim of monitoring its state, when the number of such parameters is significant, and therefore a high level of automation of the forecasting process is desirable. A significant advantage of the approach is the absence of requirements for time series stationarity and a small number of tuning parameters. Further research may focus on the study of various criteria for the similarity of time series fragments, the use of nonlinear similarity criteria, the search for ways to automatically determine the rational step of quantization of the time series.
Low probability states, data statistics, and entropy estimation

A fundamental problem in the analysis of complex systems is getting a reliable estimate of the entropy of their probability distributions over the state space. This is difficult because unsampled states can contribute substantially to the entropy, while they do not contribute to the maximum likelihood estimator of entropy, which replaces probabilities by the observed frequencies. Bayesian estimators overcome this obstacle by introducing a model of the low-probability tail of the probability distribution. Which statistical features of the observed data determine the model of the tail, and hence the output of such estimators, remains unclear. Here we show that well-known entropy estimators for probability distributions on discrete state spaces model the structure of the low-probability tail based largely on a few statistics of the data: the sample size, the maximum likelihood estimate, the number of coincidences among the samples, and the dispersion of the coincidences. We derive approximate analytical entropy estimators for undersampled distributions based on these statistics, and we use the results to propose an intuitive understanding of how the Bayesian entropy estimators work.
Geometrically-Shaped Multi-Dimensional Modulation Formats in Coherent Optical Transmission Systems

Shaping modulation formats in multi-dimensional (MD) space is an effective approach to harvest spectral efficiency gains in both the additive white Gaussian noise (AWGN) channel and the optical fiber channel. In the first part of this paper, existing MD geometrically-shaped modulations for fiber optical communications are reviewed. It is shown that large gains can be obtained by exploiting correlation in the dimensions or/and by increasing the cardinality of the modulation format. Practical limitations and challenges are also discussed together with efficient solutions. In the second part, we extend the recently proposed four-dimensional (4D) modulation format family based on the constraint of orthant-symmetry to high spectrum efficiencies up to 10 b/4D-sym by maximizing generalized mutual information for AWGN channel. Reach increases of up to 25% for a multi-span optical fiber transmission system are reported. Lastly, with the help of a recently introduced nonlinear interference (NLI) model, an optimization for designing nonlinear-tolerant 4D modulation formats is introduced for a single-span optical fiber system. Simulation results show that the proposed NLI model-based 4D modulation format could increase the effective SNRs by 0.25 dB with respect to the AWGN channel-optimal 4D modulation format.
Arigatō: Effects of Adaptive Guidance on Engagement and Performance in Augmented Reality Learning Environments

Experiential learning (ExL) is the process of learning through experience or more specifically “learning through reflection on doing”. In this paper, we propose a simulation of these experiences, in Augmented Reality (AR), addressing the problem of language learning. Such systems provide an excellent setting to support “adaptive guidance”, in a digital form, within a real environment. Adaptive guidance allows the instructions and learning content to be customised for the individual learner, thus creating a unique learning experience. We developed an adaptive guidance AR system for language learning, we call Arigatō (Augmented Reality Instructional Guidance & Tailored Omniverse), which offers immediate assistance, resources specific to the learner's needs, manipulation of these resources, and relevant feedback. Considering guidance, we employ this prototype to investigate the effect of the amount of guidance (fixed vs. adaptive-amount) and the type of guidance (fixed vs. adaptive-associations) on the engagement and consequently the learning outcomes of language learning in an AR environment. The results for the amount of guidance show that compared to the adaptive-amount, the fixed-amount of guidance group scored better in the immediate and delayed (after 7 days) recall tests. However, this group also invested a significantly higher mental effort to complete the task. The results for the type of guidance show that the adaptive-associations group outperforms the fixed-associations group in the immediate, delayed (after 7 days) recall tests, and learning efficiency. The adaptive-associations group also showed significantly lower mental effort and spent less time to complete the task.
Representation Heterogeneity

Semantic Heterogeneity is conventionally understood as the existence of variance in the representation of a target reality when modelled, by independent parties, in different databases, schemas and/ or data. We argue that the mere encoding of variance, while being necessary , is not sufficient enough to deal with the problem of representational heterogeneity, given that it is also necessary to encode the unifying basis on which such variance is manifested. To that end, this paper introduces a notion of Representation Heterogeneity in terms of the co-occurrent notions of Representation Unity and Representation Diversity . We have representation unity when two heterogeneous representations model the same target reality, representation diversity otherwise. In turn, this paper also highlights how these two notions get instantiated across the two layers of any representation, i.e., Language and Knowledge .
Offline Policy Optimization with Eligible Actions

Ofﬂine policy optimization could have a large im-pact on many real-world decision-making problems, as online learning may be infeasible in many applications. Importance sampling and its variants are a common used type of estimator in ofﬂine policy evaluation, and such estimators typically do not require assumptions on the properties and representational capabilities of value function or decision process model function classes. In this paper, we identify an important overﬁtting phe-nomenon in optimizing the importance weighted return, in which it may be possible for the learned policy to essentially avoid making aligned deci-sions for part of the initial state space. We propose an algorithm to avoid this overﬁtting through a new per-state-neighborhood normalization constraint, and provide a theoretical justiﬁcation of the proposed algorithm. We also show the limita-tions of previous attempts to this approach. We test our algorithm in a healthcare-inspired simulator, a logged dataset collected from real hospitals and continuous control tasks. These experiments show the proposed method yields less overﬁtting and bet-ter test performance compared to state-of-the-art batch reinforcement learning algorithms.
Simultaneous Dependence of Matter Clustering on Scale and Environment

In this work, we propose new statistical tools that are capable of characterizing the simultaneous dependence of dark matter and gas clustering on the scale and the density environment, and these are the environment-dependent wavelet power spectrum (env-WPS), the environment-dependent bias function (env-bias), and the environment-dependent wavelet cross-correlation function (env-WCC). These statistics are applied to the dark matter and baryonic gas density fields of the TNG100-1 simulation at redshifts of z=3.0-0.0, and to Illustris-1 and SIMBA at z = 0. The measurements of the env-WPSs suggest that the clustering strengths of both the dark matter and the gas increase with increasing density, while that of a Gaussian field shows no density dependence. By measuring the env-bias and env-WCC, we find that they vary significantly with the environment, scale, and redshift. A noteworthy feature is that at z = 0.0, the gas is less biased in denser environments of Δ ≳ 10 around 3 h Mpc−1, due to the gas reaccretion caused by the decreased AGN feedback strength at lower redshifts. We also find that the gas correlates more tightly with the dark matter in both the most dense and underdense environments than in other environments at all epochs. Even at z = 0, the env-WCC is greater than 0.9 in Δ ≳ 200 and Δ ≲ 0.1 at scales of k ≲ 10 h Mpc−1. In summary, our results support the local density environment having a non-negligible impact on the deviations between dark matter and gas distributions up to large scales.
Function-space Inference with Sparse Implicit Processes

Implicit Processes (IPs) represent a flexible framework that can be used to describe a wide variety of models, from Bayesian neural networks, neural samplers and data generators to many others. IPs also allow for approximate inference in function-space. This change of formulation solves intrinsic degenerate problems of parameter-space approximate inference concerning the high number of parameters and their strong dependencies in large models. For this, previous works in the literature have attempted to employ IPs both to set up the prior and to approximate the resulting posterior. However, this has proven to be a challenging task. Existing methods that can tune the prior IP result in a Gaussian predictive distribution, which fails to capture important data patterns. By contrast, methods producing flexible predictive distributions by using another IP to approximate the posterior process cannot tune the prior IP to the observed data. We propose here the first method that can accomplish both goals. For this, we rely on an inducing-point representation of the prior IP, as often done in the context of sparse Gaussian processes. The result is a scalable method for approximate inference with IPs that can tune the prior IP parameters to the data, and that provides accurate non-Gaussian predictive distributions.
Efficient Redundancy Techniques in Cloud and Desktop Grid Systems using MAP/G/c-type Queues

Abstract Cloud computing is continuing to prove its flexibility and versatility in helping industries and businesses as well as academia as a way of providing needed computing capacity. As an important alternative to cloud computing, desktop grids allow to utilize the idle computer resources of an enterprise/community by means of distributed computing system, providing a more secure and controllable environment with lower operational expenses. Further, both cloud computing and desktop grids are meant to optimize limited resources and at the same time to decrease the expected latency for users. The crucial parameter for optimization both in cloud computing and in desktop grids is the level of redundancy (replication) for service requests/workunits. In this paper we study the optimal replication policies by considering three variations of Fork-Join systems in the context of a multi-server queueing system with a versatile point process for the arrivals. For services we consider phase type distributions as well as shifted exponential and Weibull. We use both analytical and simulation approach in our analysis and report some interesting qualitative results.
Magnetic topology with axial flux concentration: a technique to improve permanent-magnet motor performance

This article presents a different topology for the magnetic circuit of permanent-magnet motors, using the concept of Axial Flux Concentration. By means of longitudinal extension of the rotor length beyond the stator core, a substantial increase in the flux per pole is enabled, improving the general performance of the machine. Even in low cost motors with ferrite magnets, this improvement is easily achieved utilizing the proposed scheme. With rare-earth magnets this technique permits a higher torque density, contributing to the compactness of the machine. This configuration is suitable for several magnetic topologies, but particularly for small diameter and low pole number permanent-magnet motors of the embedded-magnet type. The proposed topology is briefly described and the theoretical aspects presented. It is shown that, from the stator point of view, this scheme behaves like a conventional motor with fictitious equivalent magnets with augmented remanent flux density and recoil permeability. A 3D finite element modeling is performed to validate the theoretical analysis permitting some additional conclusions concerning magnetic saturation. The methodology is applied to a prototype brushless motor constructed for research purposes, composed of a stator and two rotors. Various characteristics are compared, concerning flux per pole, torque, power density and efficiency. The improvement in the performance of the axial flux concentration rotor is confirmed by experimental results, in accordance with the theoretical previsions.
http://ijape.iaescore.com Evaluation of brushless DC motor structure design for the electric impact using 3-D finite element

Info 2022 In this article, an analytical model is deployed to optimize and design an interior permanent magnet brushless DC motor (IPMBLDC motor) when compared to theoretical structure of IPMBLDC. In this motor, assume air-gap of magnet assembly, distribution of flux density coefficient, selection of functional point in the permanent magnet, Torque density, and permanent magnet dimension compares with air-gap were deployed in an ideal design model. An equivalent circuit of magnetic field was improved and compare with the total flux density distribution and the torque density efficiency is based on initial ideal design factors. In finite element of MAXWELL 3D method, an electromagnetic field is investigated and used to verify an advanced equivalent circuit of magnetic field and improve the IPMBLDC motor factors. Finally, simulation results of IPMBLDC motor are verify and compare with ideal factors of IPMBLDC density This is an open access article under the CC BY-SA
Bulletin of Electrical Engineering and Informatics

Received Mar 16, 2022 Revised May 17, 2022 Accepted Jun 1, 2022 Affective computing is the study of the deep extraction of emotional impacts that triggers humans for various reasons. Emotions directly reflect on human behaviour. The proposed analysis is inclined towards deep emotion extraction through a novel concept with less computation time. Designing a robust analysis model is focused on here. AMIGOS dataset on affect, and personality modelling is considered here. A Novel Gaussian ResiNet (GRN) algorithm is evaluated here. Any changes in the emotions of humans are the brainy response given to the actions faced. The features of the given physiological factors are considered for analysis, further with GMM-ResiNet (GRN) a low computational structure is used for classification. The Novel Gaussian ResiNet (GRN) is created from the given dataset for similar feature validations. The system predicts the correlated relative data from the training set and testing set and achieved the performance metrics using error rate (ER), Algorithm Computation Time (ACT), Full Computation Time (FCT), Accuracy (AUC) etc. Novel Gaussian ResiNet (GRN) is created and tested with processed data of the AMIGOS dataset. The model created is validated with state-of-art approaches and achieved an accuracy of 92.6%.