Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloud Computing based Multipurpose E-Service Application using Flutter

The internet is used by individuals worldwide to access information. Now, there is no need of a phone book for contact information, maps for directions, or to leave the house to go shopping anymore. These features can better be found online in seconds and have made so many previously essential things obsolete. Although online shopping has been around for consumers over the past several years, people still feel more comfortable with local trustworthy stores. Shopping in-store still has many benefits and is a prominent place in retail. Some things are particular to an in-character experience, which includes bodily inspecting a product and pricing. When a user searches for local goods in provincial markets, usually it turns into a troublesome task to pick up the specific place where the user could get the exact item. Also, there are not enough facilities available for the local storekeepers to display and advertise their store’s products. After considering all these issues, an e-service application was developed that will help people decide which trip is ideal for them before they leave home. This application also comprises a feature for the shopkeepers to enhance their sales. This application will save the time and energy of an individual for free of cost. And it would make the exploration of local goods and services effortless based on the user’s current location and preference. It would be effective for people by saving their time, energy, and money. It will simultaneously help the local traders to increase their sales. Our application would also help the people who wish to join this platform as a seller, by providing exposure to their shops. This would cause an increase in the rate of customer inflow.
Optimal Power Allocation Based Unlicensed Spectrum Sharing for Cellular Networks

Following 3G and 4G cellular networks, 5G is already deployed in some countries and 6G is under development looking for higher speed and lower latency. This evolution of wireless industry and mobile users requirements enhance the exploration of new spectral access strategies. Even though, millimeter waves offer promising bandwidth, it suffer from limited range due to high path loss. Therefore, new generations of cellular networks are interested in Sub-6GHz spectrum that has adequate propagation properties and has many bands released for unlicensed access. However, these frequencies are used by the majority of commercial wireless technologies such as WIFI, Bluetooth and 3G/4G cellular networks. Their spectral coexistence with new wireless services over unlicensed bands requires mutual interference control based on controlling the power levels of co-located operators. To address this issue, we present a coexistence solution based on power control enabling maximum cell transmission capacity. We formulate the interaction between two co-located operators as a competitive game. Each operator-user pair tries to maximize its sum transmission capacity over the target channels under maximum power constraint. We develop the analytical expressions of the optimal power allocation vectors, then we keep the base stations playing the game iterations until the convergence to a Nash equilibrium. We provide simulation results proving the performance of the proposed model.
Crystal structure of the homing endonuclease I-CvuI provides a new template for genome modification

Results: Structural and biochemical analysis shows that I-CvuI can be a new scaffold to engineer novel DNA specificities Conclusion: selectivity Significance: protein-DNA interactions ABSTRACT Homing endonucleases recognize and generate a DNA double-strand break, which has been used to promote gene targeting. These enzymes recognize long DNA stretches; they are highly sequence-specific enzymes and display a very low frequency of cleavage even in complete genomes. Although a large number of homing endonucleases have been identified, the landscape of possible target sequences is still very limited to cover the complexity of the whole eukaryotic genome. Therefore the finding and molecular analysis of homing endonucleases identified but not yet characterized may widen the landscape of possible target sequences. The previous characterization of protein-DNA interaction before the engineering of new homing endonucleases is essential for further enzyme modification. Here we report the crystal structure of I-CvuI in complex with its target DNA and with the target DNA of I-CreI, a homologue enzyme widely use in genome engineering. To characterize the enzyme cleavage mechanism we have solved the I-CvuI DNA structures in the presence of non-catalytic (Ca 2+ ) and catalytic ions (Mg 2+ ). We have also analyzed the metal dependence of DNA cleavage using Mg 2+ ions at different concentrations ranging from non-cleavable to cleavable concentrations obtained from in vitro cleavage experiments. The structure of I-CvuI homing endonuclease expands the current repertoire for engineering custom specificities, both by itself as a new scaffold alone or in hybrid constructs with other related homing endonucleases or other DNA binding protein templates.
Entity-Enhanced Graph Convolutional Network for Accurate and Explainable Recommendation

A recommendation engine that relies solely on interactions between users and items will be limited in its ability to provide accurate, diverse and explanation-rich recommendations. Side information should be taken into account to improve performance. Methods like Factorisation Machines (FM) cast recommendation as a supervised learning problem, where each interaction is viewed as an independent instance with side information encapsulated. Previous studies in top-K recommendation have incorporated knowledge graphs (KG) into the recommender system to provide rich information about the relationships between users, items and entities. Nevertheless, these studies do not explicitly capture the preference of users for the side information. Furthermore, some studies explain the recommendation, but there is no unified method of measuring explanation quality. In this work, we investigate the utility of Graph Convolutional Networks (GCN) and multi-task learning techniques to capture the tripartite relations between users, items and entities. Based on our study, we propose that in the hybrid structure of the KG, its rich relationships are an essential factor for successful recommendation from both an explanation and performance perspective. We propose a novel method named Light Knowledge Graph Convolutional Network (LKGCN) which explicitly models the high-order connectivities between user items and entities. Specifically, we use multi-task learning techniques and attention mechanisms in order to combine user preferences on items and entities. Additionally, we present a unified evaluation method PeX for explainable recommendation models. Extensive experiments on real-world datasets show that the LKGCN is conceptually superior to existing graph-based recommendation methods from two perspectives: recommendation accuracy and interpretation. We release the codes and datasets on github1.
Development of a Clinical Prediction Model for 1-Year Mortality in Patients With Advanced Cancer

Key Points Question Can a predictive model be developed and validated to calculate the 1-year risk of death among patients with advanced cancer by combining clinician responses to the surprise question (“Would I be surprised if this patient died in the next year? ”) with patients’ clinical characteristics and laboratory values? Findings In this prognostic study that included 867 patients with advanced cancer, the developed model combining the surprise question, clinical characteristics, and laboratory values had better discriminative ability in predicting 1-year risk of death than the surprise question, clinical characteristics, or laboratory values alone. A nomogram was developed to aid clinicians in identifying those at risk of dying within 1 year. Meaning These results suggest that the prediction model and nomogram developed for this study can be used by clinicians to identify patients who may benefit from palliative care and advance care planning.
Data-Driven Modeling and Control for Lane Keeping System of Automated Driving Vehicles: Koopman Operator Approach

This paper proposes the data-driven modeling and control method with the Koopman operator for the lane-keeping system. The vehicle can be modeled as a linear motion model but has underlying complicated nonlinear behavior. Thus, there exists a need to model the full vehicle dynamics effectively. To this end, we use the Koopman operator to express the full vehicle nonlinear dynamics as a linear structure. However, it is not practical to use the Koopman operator directly because it lies in infinite-dimensional space. Hence, we apply the extended dynamic mode decomposition to approximate the Koopman operator as a finite-dimensional linear operator. We conduct a comparative study between the linear model-based optimal control and the Koopman operator-based optimal control. As a result, it is observed that the proposed method reduces the system state by 20% compared to the linear model-based controller.
Alternators of a right alternative algebra

We show that in any right alternative algebra, the additive span of the alternators is nearly an ideal. We give an easy test to use to determine if a given set of additional identities will imply that the span of the alternators is an ideal. We apply our technique to the class of right alternative algebras satisfying the condition (a, a, b) = ? [a, [a, b]]. We show that any semiprime algebra over a field of characteristic #2, #3 which satisfies the right alternative law and the above identity with X:# 0 is a subdirect sum of (associative and commutative) integral domains. Introduction. In any nonassociative algebra, the associator (a, b, c) and the commutator [a, b] are defined by: (a, b, c) = (ab)c a(bc); [a, b] = ab ba. An alternator is an associator of the form (a, a, b), (a, b, a), or (b, a, a). We let M represent the subspace generated by all alternators. If R is a right alternative algebra, Thedy [5, Lemmas 11 and 12] has shown that L = M + RM is a left ideal and that L contains a two-sided ideal of R. Furthermore, M + MR is a two-sided ideal of R. There are known conditions which imply that M is itself a left ideal; one such condition is the identity [a, (b, b, a)] C M [5, p. 23]. We show exactly what identities are required to make M a left ideal, and we give an easy way to deduce if a given right alternative algebra satisfies them. Some work [3] has been done on the structure of right alternative algebras where M is assumed to be an ideal. Till now, the actual strength of this assumption was not known. We show that such an assumption is rather weak, and we give an easy process to see if M is an ideal. We finish by examining a variety of nonassociative algebras introduced by A. Sagle. These algebras arise from the Taylor series representation of an analytic multiplication on a manifold. They are right alternative algebras satisfying the additional identity (a, a, b) = X[a, [a, b]]. (1) When X = 0, equation (1) states that the algebra is alternative. If X # 0 and the algebra is semiprime, then it must in fact be associative and commutative. Presented to the Society, January 8, 1978; received by the editors January 18, 1977. AMS (MOS) subject classifications (1970). Primary 17A30.
A Study on Design Method of Synchronous Reluctance Motor Based on Nonlinear MEC Using Newton-Raphson Method

Recently, policies for carbon reduction are being implemented worldwide to solve environmental and energy consumption problems. Minimum Energy Performance Standard (MEPS) is a carbon reduction policy for reducing energy consumption by regulating the efficiency of industrial induction motors. The induction motors have a low manufacturing cost and a simple structure, and it can be operated without control and have great advantages in maintenance so it is applied in various industrial fields. However, compared with other types of motors, there is a limit to improving the efficiency due to the additional loss generated in the cage of rotor. Synchronous reluctance motors (SynRMs) are attracting the most attention as an alternative to an induction motor since they are composed of only a copper winding and a core, manufacturing cost and durability are excellent Moreover, unlike induction motors, there is no secondary loss; therefore, it is evaluated as a high-efficiency industrial motor corresponding to MEPS. However, because of their complex rotor shape composed of multiple barriers and segments, SynRMs are typically designed using finite-element analysis, requiring a significant amount of time and trial and error. In this paper, a basic design method for a synchronous reluctance motor based on a nonlinear magnetic equivalent circuit using the Newton-Rapson method is presented. Finally, the design method of the synchronous reluctance motor is validated through performance tests using a prototype.
Online SuBmodular + SuPermodular (BP) Maximization with Bandit Feedback

We investigate non-modular function maximization in an online setting with $m$ users. The optimizer maintains a set $S_q$ for each user $q \in \{1, \ldots, m\}$. At round $i$, a user with unknown utility $h_q$ arrives; the optimizer selects a new item to add to $S_q$, and receives a noisy marginal gain. The goal is to minimize regret compared to an $\alpha$-approximation to the optimal full-knowledge selection (i.e., $\alpha$-regret). Prior works study this problem under a submodularity assumption for all $h_q$. However, this is not ideally amenable to applications, e.g., movie recommendations, that involve complementarity between items, where e.g., watching the first movie in a series enhances the impression of watching the sequels. Hence, we consider objectives $h_q$, called \textit{BP functions}, that decompose into the sum of monotone submodular $f_q$ and supermodular $g_q$; here, $g_q$ naturally models complementarity. Under different feedback assumptions, we develop UCB-style algorithms that use Nystrom sampling for computational efficiency. For these, we provide sublinear $\alpha$-regret guarantees for $\alpha = 1/\kappa_{f} [1 - e^{-(1 - \kappa^g) \kappa_{f}} ]$, and $\alpha = \min\{1 - \kappa_f/e, 1 - \kappa^g\}$; here, $\kappa_f, \kappa^g$ are submodular and supermodular curvatures. Furthermore, we provide similar $\alpha$-regret guarantees for functions that are almost submodular where $\alpha$ is parameterized by the submodularity ratio of the objective functions. We numerically validate our algorithms for movie recommendation on the MovieLens dataset and selection of training subsets for classification tasks.
A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning

This work presents Unified Contrastive Arbitrary Style Transfer (UCAST), a novel style representation learning and transfer framework, that can fit in most existing arbitrary image style transfer models, such as CNN-based, ViT-based, and flow-based methods. As the key component in image style transfer tasks, a suitable style representation is essential to achieve satisfactory results. Existing approaches based on deep neural networks typically use second-order statistics to generate the output. However, these hand-crafted features computed from a single image cannot leverage style information sufficiently, which leads to artifacts such as local distortions and style inconsistency. To address these issues, we learn style representation directly from a large number of images based on contrastive learning by considering the relationships between specific styles and the holistic style distribution. Specifically, we present an adaptive contrastive learning scheme for style transfer by introducing an input-dependent temperature. Our framework consists of three key components: a parallel contrastive learning scheme for style representation and transfer, a domain enhancement (DE) module for effective learning of style distribution, and a generative network for style transfer. Qualitative and quantitative evaluations show the results of our approach are superior to those obtained via state-of-the-art methods. The code is available at https://github.com/zyxElsa/CAST_pytorch.
A Systematic Study on Object Recognition Using Millimeter-wave Radar

Millimeter-wave (MMW) radar is becoming an essential sensing technology in smart environments due to its light and weather-independent sensing capability. Such capabilities have been widely explored and integrated with intelligent vehicle systems, often deployed in industry-grade MMW radars. However, industry-grade MMW radars are often expensive and difficult to attain for deployable community-purpose smart environment applications. On the other hand, commercially available MMW radars pose hidden underpinning challenges that are yet to be well investigated for tasks such as recognizing objects, and activities, real-time person tracking, object localization, etc. Such tasks are frequently accompanied by image and video data, which are relatively easy for an individual to obtain, interpret, and annotate. However, image and video data are light and weather-dependent, vulnerable to the occlusion effect, and inherently raise privacy concerns for individuals. It is crucial to investigate the performance of an alternative sensing mechanism where commercially available MMW radars can be a viable alternative to eradicate the dependencies and preserve privacy issues. Before championing MMW radar, several questions need to be answered regarding MMW radar’s practical feasibility and performance under different operating environments. To answer the concerns, we have collected a dataset using commercially available MMW radar, Automotive mmWave Radar (AWR2944) from Texas Instruments, and reported the optimum experimental settings for object recognition performance using several deep learning algorithms in this study. Moreover, our robust data collection procedure allows us to systematically study and identify potential challenges in the object recognition task under a cross-ambience scenario. We have explored the potential approaches to overcome the underlying challenges and reported extensive experimental results.
The molybdenite deposits of the Rencontre East area, Newfoundland

Molybdenite occurs in the aplitic border phase of a Paleozoic batholith of granite and alaskite, which intrudes Cambrian quartzites and Ordovician (? ) volcanics. In the Ackley City orebody, molybdenite is disseminated in altered aplite; a honeycomb of alteration was formed by the mineralizing solutions that penetrated the aplite without marked structural control. Silicification preceded muscovitization in space, but the two processes may be in part contemporaneous.The mineralization is believed to be closely related genetically to the miarolitic aplite phase in which it occurs. A hypothesis is presented for the origin of the aplite, and its close relationship to the mineralization. The latter is believed to be due either to acid fluids--gases, or hydrothermal condensates, or to a combination of acid gases and alkaline liquids. Graton9s hypothesis that ore-forming fluids are alkaline liquids fails to explain the relationships. The main period of mineralization is high-temperature with intermediate-temperature late phases. The Ackley City orebody is classified as a "complex aplite. "The Wylie Hill deposit is of intermediate-temperature and the Crow Cliff deposit is a pegmatitic type.
Construction process monitoring of a large-span steel truss roof based on response-increment comparison strategy

Large-span spatial structures, with good appearance and plasticity are mostly used nowadays in urban landmarks. During the construction process, the loads are accumulated along with the construction conditions and the responses vary from the construction paths. Hence, a safety evaluation or monitoring of the construction process appears significant. During monitoring, the installation moment of strain gauges is not always the ideally non-loading status, the absolute or real strains in the structural components are difficult to acquire. Furthermore, the displacement monitoring for all key points reduces the construction efficiency. Herein, a response-increment real-time comparison strategy of stresses and unloading point displacements within numerical analysis is announced to avoid the above shortcomings. Based on a real large-span truss structure located in China, four construction schemes are proposed and investigated by simulation method. With the proposed construction scheme and monitoring strategy, a stress and displacement monitoring for the large-span truss structure was conducted. Through the comparison between the monitoring and simulation data, the proposed strategy is verified feasible for the construction monitoring, the efficiency is accelerated and the safety is guaranteed.
An Investigation of Latency-Accuracy Trade-off in Inter-frame Video Prediction using Quantized CNNs

Real-time video streaming has become the largest portion of internet traffic in recent years. Therefore, improving the efficiency of video coding remains an important research issue. Modern video codecs perform inter-frame prediction by motion estimation. However, inter-frame prediction is one of the most computationally expensive and time-consuming operations in video coding. Convolutional neural networks (CNN) have been used in recent research for inter-frame prediction tasks. The CNN architectures in previous work use floating point arithmetic whereas motion estimation in video codecs only use integer arithmetic. Thus, inter-frame prediction using CNNs instead of motion estimation may not always result in better time complexity. Floating point CNNs can be quantized into integer CNNs. Integer CNNs can reduce network latency but can also result in a loss of prediction accuracy. In this paper, we investigate the latency vs accuracy trade-off of quantized CNNs in inter-frame bi-prediction. We present experimental results which demonstrate that the integer CNN is at least 5% faster than the floating point CNN, while the prediction quality degradation of the integer CNN is no more than 0.6 dB in PSNR.