Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of sound in inducing storytelling in immersive environments

Sound design has been a fundamental component of audiovisual storytelling. However, with technological developments things are rapidly changing. More sensory information is available and, at the same time, the user is gaining agency upon the narrative, being offered the possibility of navigating or making other decisions. These new characteristics of immersive environments bring new challenges to storytelling in interactive narratives and require new strategies and techniques for audiovisual narrative progression. Can technology offer an immersive environment where the user has the sensation of agency, of choice, where her actions are not mediated by evident controls but subliminally induced in a way that it is ensured that a narrative is being followed? Can sound be a subliminal element that induces attentional focus on the most relevant elements for the narrative, inducing storytelling and biasing search in an audiovisual immersive environment? Herein, we present a literature review that has been guided by this prospect. With these questions in view, we present our exploration process in finding possible answers and potential solution paths. We point out that consistency, in terms coherency across sensory modalities and emotional matching may be a critical aspect.
Composable Instructions and Prospection Guided Visuomotor Control for Robotic Manipulation

Deep neural network-based end-to-end visuomotor control for robotic manipulation is becoming a hot issue of robotics field recently. One-hot vector is often used for multi-task situation in this framework. However, it is inflexible using one-hot vector to describe multiple tasks and transmit intentions of humans. This paper proposes a framework by combining composable instructions with visuomotor control for multi-task problems. The framework mainly consists of two modules: variational autoencoder (VAE) networks andlong short-term memory (LSTM) networks. Perceptioninformation of theenvironment is encoded by VAE into a small latent space. The embedded perception information and composable instructions are combined by the LSTM module to guide robotic motion based on different intentions. Prospection is also used to learn the purposes of instructions, which means not only predicting the next action but also predicting a sequence of future actions at the same time. To evaluate this framework, a series of experiments are conducted in pick-and-place application scenarios. For new tasks, the framework could obtain a success rate of 91.2%, which means it has a good generalization ability.
Research on Novel Bearing Fault Diagnosis Method Based on Improved Krill Herd Algorithm and Kernel Extreme Learning Machine

In this paper, a novel bearing intelligent fault diagnosis method based on a novel krill herd algorithm (NKH) and kernel extreme learning machine (KELM) is proposed. Firstly, multiscale dispersion entropy (MDE) is used to extract fault features of bearings to obtain a set of fault feature vectors composed of dispersion entropy. Then, it is imported into the kernel extreme learning machine for fault diagnosis. But considering the kernel function parameters σ and the error penalty factor C will affect the classification accuracy of the kernel extreme learning machine, this paper uses the novel krill herd algorithm (NKH) for their optimization. The opposite populations are added to the NKH in the initialization of population to improve its speed and prevent local optimum, and during the period of looking for the optimal solution, the impulse operator is introduced to ensure it has enough impulse to rush out of the local optimal once into the local optimum. Finally, in order to verify the effectiveness of the proposed method, it was applied to the bearing fault experiment of Case Western Reserve University and XJTU-SY bearing data set. The results show that the proposed method not only has good fault diagnosis performance and generalization but also has fast convergence speed and does not easily fall into the local optimum. Therefore, this paper provides a method for fault diagnosis under different loads. Meanwhile, the new method (NKH-KELM) is compared and analyzed with other mainstream intelligent bearing fault diagnosis methods to verify the effectiveness and accuracy of the proposed method.
Application of Supply-Demand-Based Optimization for Parameter Extraction of Solar Photovoltaic Models

Modeling solar photovoltaic (PV) systems accurately is based on optimal values of unknown model parameters of PV cells and modules. In recent years, the use of metaheuristics for parameter extraction of PV models gains more and more attentions thanks to their efficacy in solving highly nonlinear multimodal optimization problems. This work addresses a novel application of supply-demand-based optimization (SDO) to extract accurate and reliable parameters for PV models. SDO is a very young and efficient metaheuristic inspired by the supply and demand mechanism in economics. Its exploration and exploitation are balanced well by incorporating different dynamic modes of the cobweb model organically. To validate the feasibility and effectiveness of SDO, four PV models with diverse characteristics including RTC France silicon solar cell, PVM 752 GaAs thin film cell, STM6-40/36 monocrystalline module, and STP6-120/36 polycrystalline module are employed. The experimental results comparing with ten state-of-the-art algorithms demonstrate that SDO performs better or highly competitively in terms of accuracy, robustness, and convergence. In addition, the sensitivity of SDO to variation of population size is empirically investigated. The results indicate that SDO with a relatively small population size can extract accurate and reliable parameters for PV models.
Design patterns for angular hotdraw

The number of web frameworks available for use is growing. Web developers need to learn how to use them effectively and efficiently. Working through the design patterns presented in this paper for the Angular Hotdraw application assists web developers in this task. Web developers should be able to make a start with a the Angular web framework and have a foundation to learn from.
Extraction of test cases procedures from textual use cases to reduce test effort: Test Factory Experience Report

The optimization of processes in the software development life cycle has become mandatory, since companies need to reduce the costs of their projects and shorten the time to market without affecting the quality of the product. With regards to software products, activities related to the testing process play a major role in their quality, since they assure that the product complies with the elicited requirements. However, companies usually do not perform testing activities on software projects due to financial and time constraints. These costs may be even higher in testing processes that rely on manual execution of activities, which is error-prone and can take too much time. To mitigate this situation, test automation allows the reduction of the effort required. This paper presents an experience report in a Test Factory about the use of a tool that partially automates the specification of test procedures based on use cases. The tool aims to read the textual use cases from the requirement management system, extract the test case procedures and required inputs to manual editing, and then send the test cases to the test management system. This solution is then applied to software from the telecommunications industry. This paper also presents how the tool was used and the lessons learned during the whole process.
Egret: simplifying traffic management for physical and virtual network functions

Traffic migration is a common procedure performed by operators during planned maintenance and unexpected incidents to prevent/reduce service disruptions. However, current practices of traffic migration often couple operators' intentions (e.g. device upgrades) with network setups (e.g. load-balancers), resulting in poor re-usability and substantial operational complexities. Our study of 205 Methods of Procedure (MOPs) from a major U.S. carrier suggests that generalizing traffic migration with a unified model is feasible. Such generalization along with SDN's automation capability is key to scalable and flexible management of traffic, especially for virtualized network functions with unprecedented scale, heterogeneity, and fast iteration. In this paper, we propose Egret, a generic traffic migration system that simplifies traffic management for physical and virtual network functions. Egret (1) hides intricate implementation details from operators with generic intention-based interfaces, and (2) modularizes common traffic migration procedures to enable plug-and-play by developers and vendors. Leveraging a novel mask-based abstraction of traffic migration jobs, Egret can further simplify reverse traffic migration and enable job interleaving.
Using auxiliary artifacts during code inspection activity: findings from an exploratory study

Code inspection is an important activity to identify defects in the source code and improve software quality. However, even when using techniques such as checklists, inspectors consider implicit decision-making knowledge. In this paper, we perform an exploratory study with groups of inspectors (Group1 and Group2) with two objectives: 1) to present findings on how (and if) auxiliary artifacts interfere in decision making during the code inspection activity, and 2) to show whether there is any influence on the number of defects identified by inspectors when using or not auxiliary artifacts. Both groups used the computational code inspection support of the CRISTA tool, but only Group1 used auxiliary artifacts (requirements, UML diagrams, software metrics). We identified 10 findings. All of them are related to the inspectors' decision making and the influence of using artifacts on defects identification. The findings suggested that when inspectors use auxiliary artifacts, their effectiveness in identifying defects is improved. Besides, their decision making is more homogeneous than that of inspectors who do not use auxiliary artifacts. However, more investigations are necessary to make the results more generalizable. As future work, different strategies for code inspection techniques can be defined based on the findings.
XuanYuan: An AI-Native Database

In big data era, database systems face three challenges. Firstly, the traditional empirical optimization techniques (e.g., cost estimation, join order selection, knob tuning) cannot meet the high-performance requirement for large-scale data, various applications and diversiﬁed users. We need to design learning-based techniques to make database more intelligent. Secondly, many database applications require to use AI algorithms, e.g., image search in database. We can embed AI algorithms into database, utilize database techniques to accelerate AI algorithms, and provide AI capability inside databases. Thirdly, traditional databases focus on using general hardware (e.g., CPU), but cannot fully utilize new hardware (e.g., ARM, GPU, AI chips). Moreover, besides relational model, we can utilize tensor model to accelerate AI operations. Thus, we need to design new techniques to make full use of new hardware. To address these challenges, we design an AI-native database. On one hand, we integrate AI techniques into databases to provide self-conﬁguring, self-optimizing, self-monitoring, self-diagnosis, self-healing, self-assembling, and self-security capabilities. On the other hand, we enable databases to provide AI capabilities using declarative languages in order to lower the barrier of using AI. In this paper, we introduce ﬁve levels of AI-native databases and provide several open challenges of designing an AI-native database. We also take autonomous database knob tuning, deep reinforcement learning based optimizer, machine-learning based cardinality estimation, and autonomous index/view advisor as examples to showcase the superiority of AI-native databases.
Effect of different feeding management on the respiratory methane emission and feces-derived methane yield of goat

Objective: This study aimed to evaluate the respiratory methane emission and ultimate methane yield (B0) of goat feces that fed roughage consisted of Pennisetum purpureum and Gliricidia) and fed roughage and concentrate with different protein source in the ration (fish meal and soybean meal). Materials and Methods: Fifteen Kacang bucks were allocated to the control group (T0): goats were fed roughage only, T1: goats were fed roughage and concentrate with fish meal as protein sources, and T2: goats were fed roughage and concentrate and the protein source in the ration was soybean meal. Results: The protein content of feces from T0 was significantly lower (p < 0.05) than that from the other treatments. The same phenomenon was also found in the respiratory methane emission in terms of l/head/d, l/kg digestible dry matter, and l/kg body weight. However, there was no significant effect (p > 0.05) of different ration composition on the ultimate methane yield (B0) of goat feces. This study found that B0 of goat feces from treatment T0, T1, and T2 was 17.40%, 25.78%, and 61.29%, respectively, higher than that from the international default value for developing countries. Conclusion: Feeding grass and legume can reduce methane respiration emission in goat. B0 of feces in the present study was higher than that in the international default value; therefore, the potential emission of goat manure in tropical developing countries could be higher than that in the present estimation.
Long Non-Coding RNA NEAT1 Promoted Hepatocellular Carcinoma Cell Proliferation and Reduced Apoptosis Through the Regulation of Let-7b-IGF-1R Axis

Background and aim Long non-coding RNA nuclear-enriched abundant transcript 1 (NEAT1) is abnormally expressed in various human malignancies, including hepatocellular carcinoma (HCC). Let-7b is a miRNA with the effect of a tumor suppressor gene, and its expression level in various tumor tissues is lower than that in normal tissues. Studies have found that IGF-1R can be abnormally activated in the process of hepatocyte deterioration, and the expression level of IGF-1R in HCC is significantly up-regulated. The aim of this study was to investigate the functional mechanism of NEAT1/let-7b-IGF-1R axis in HCC. Methods The expressions of NEAT1 and microRNA (miR)-let-7b in HCC tissues and cell lines were quantified by quantitative real-time PCR (qRT-PCR). The effect of NEAT1 on tumor growth was observed in a mice model of transplanted hepatoma. The effects of down-regulation or up-regulation of NEAT1 expression in HCC cell lines were analysed from the perspectives of cell viability and apoptosis. The binding sites of NEAT1 and miR-let-7b were predicted by biological software. The expression of the miR-let-7b target molecules IGF-1R was detected by Western blotting. Results The results showed that the expressions of NEAT1 were significantly increased, while the expressions of miR-let-7b were decreased in the HCC tissues and cell lines. Additionally, it was found that the expressions of NEAT1 and miR-let-7b showed a negative correlation in HCC tissues. The mouse model experiments confirmed that the interference with NEAT1 expression inhibited the tumor growth. Meanwhile, the cell viability of HepG2/Huh7 cell lines was significantly decreased via the downregulation of NEAT1, whereas the corresponding rates of apoptosis were significantly increased. It was further proven that there was a certain negative regulatory mechanism between NEAT1 and miR-1et-7b, which was related to the expression of IGF-1R. Conclusion The over-expression of NEAT1 could promote the proliferation of HCC cells by inhibiting the expression of the miR-let-7b regulated by IGF-1R.
A study comparing shape, colour and texture as visual labels in audio sample browsers

Searching through vast libraries of sound samples can be a daunting and time-consuming task. Modern audio sample browsers use mappings between acoustic properties and visual attributes to visually differentiate displayed items. There are few studies focused on the effect of these mappings on the time it takes to search for a specific sample. We designed a study to compare shape, colour and texture as visual labels in a known-item search task. We describe the motivation and implementation of the study, then present a preliminary analysis of results. We find that shape and colour outperform texture. Based on these results we propose modifications to the study and avenues for further analysis.
Robust Protocol-Based ℋ∞ Consensus Control of Time-Varying Uncertain Multiagent Systems Subject to Missing Measurements

In this paper, we consider the design problem of a robust consensus controller for discrete time-varying uncertain multiagent systems (DTVUMASs) with stochastic communication protocol (SCP) and missing measurements. The SCP is described by a set of random variables with a known probability to arrange signal transmission of addressed multiagent systems. Moreover, we depict the missing measurement phenomenon by a sequence of Bernoulli-distributed random variables having known probabilities. The controller parameters are designed to ensure that the closed-loop DTVUMASs satisfy the performance with the satisfactory consensus criterion. Together with the completing squares approach and the stochastic analysis methodology, some sufficient conditions are proposed by solving coupled backward recursive Riccati difference equations (BRRDEs) to guarantee the consensus performance. Finally, we present a numerical simulation example to illustrate the effectiveness of the designed controller design scheme.
HyperTester: high-performance network testing driven by programmable switches

Modern network research and operations are inseparable from network testers to evaluate performance limits of proofs-of-concept, troubleshoot failures, etc. Existing network testers suffer from either constrained flexibility or a low performance-cost ratio. In this paper, we propose a new network tester, HyperTester. The core of HyperTester is to leverage new-generation programmable switches for generating and capturing test traffic with high performance, low cost, and remarkable flexibility. We design a series of efficient mechanisms, including template-based packet generation, false-positive-free counter-based queries, and stateless connections to realize various network testing tasks upon switches with limited programmability and resources. Meanwhile, to facilitate developing testing tasks upon HyperTester, we provide a high-level network testing API. We have implemented HyperTester on the Tofino switch and built dozens of network testing tasks. The evaluations on the hardware testbed show that HyperTester supports line-rate packet generation (400Gbps in the testbed) with highly-accurate rate control, while HyperTester can save $40150 per Tps and 9225W per Tbps when compared with the software network testers.
II-Learn - A Novel Metric for Measuring the Intelligence Increase and Evolution of Artificial Learning Systems

Department of Electrical Engineering and Information Technology, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, Gheorghe Marinescu 38, Targu Mureș, 540139, Romania Department of Computer Science and Biomedical Informatics, University of Thessaly, Papasiopoulou 2-4, Lamia, 35131, Greece Department of Mathematics and Informatics, Vasile Alecsandri University of Bacau, Calea Marasesti 157, Bacau, 600115, Romania
A Web Prototype to Teach Music and Computational Thinking Through Building Blocks

This paper presents the recent evolution of a Web prototype originally conceived to teach music and computational thinking to preschool and primary school learners through a gamification approach. The software tool, called Legato, is based on the metaphor of building blocks, whose characteristics (e.g., position in space, shape, and color) can be associated with basic music parameters (e.g., pitch, rhythm, and timbre). Legato is a Web app written using standard languages, such as HTML5, CSS and JavaScript; besides, it adopts the Web MIDI API to produce sounds. The prototype is made publicly available for evaluation and use in an educational context.
PURR: a primitive for reconfigurable fast reroute: hope for the best and program for the worst

Highly dependable communication networks usually rely on some kind of Fast Re-Route (FRR) mechanism which allows to quickly re-route traffic upon failures, entirely in the data plane. This paper studies the design of FRR mechanisms for emerging reconfigurable switches. Our main contribution is an FRR primitive for programmable data planes, PURR, which provides low failover latency and high switch throughput, by avoiding packet recirculation. PURR tolerates multiple concurrent failures and comes with minimal memory requirements, ensuring compact forwarding tables, by unveiling an intriguing connection to classic "string theory" (i.e., stringology), and in particular, the shortest common supersequence problem. PURR is well-suited for high-speed match-action forwarding architectures (e.g., PISA) and supports the implementation of arbitrary network-wide FRR mechanisms. Our simulations and prototype implementation (on an FPGA and Tofino) show that PURR improves TCAM memory occupancy by a factor of 1.5x--10.8x compared to a naïve encoding when implementing state-of-the-art FRR mechanisms. PURR also improves the latency and throughput of datacenter traffic up to a factor of 2.8x--5.5x and 1.2x--2x, respectively, compared to approaches based on recirculating packets.
Functional BDNF rs7124442 Variant Regulated by miR-922 is Associated with Better Short-Term Recovery of Ischemic Stroke

Background Brain-derived neurotrophic factor (BDNF) is the most abundant neurotrophin, which contributes to the neuronal survival and synaptic plasticity. This study investigated the associations of BDNF polymorphisms at the 3ʹ-untranslated region with risk and outcome of ischemic stroke in a Chinese Han population. Methods 500 patients and 520 controls were enrolled for BDNF rs7124442 genotyping. The binding of miR-922 to BDNF rs7124442 was examined by luciferase assay; BDNF expression was assessed using qRT-PCR. Results Alcohol consumption, cigarette smoking, diabetes, hypertension (all P < 0.001) and higher serum triglycerides concentration (P = 0.009) were associated with an increased risk of developing ischemic stroke. After adjusted for age and sex, logistic regression analysis showed that IS patients harbored with rs7124442 TC genotype had a milder initial stroke (Dominant model: OR = 0.45, 95% CI = 0.25–0.81, P = 0.015), and also showed a better short-term recovery (Dominant model: OR = 0.39, 95% CI =0.24–0.68, P = 0.003). Furthermore, we found that co-transfection of hsa-miR-922 mimics with BDNF 3ʹ-UTR containing the mutated allele C changed luciferase activity when compared to co-transfection with BDNF 3ʹ-UTR containing the wild-type allele. Besides, patients carrying BDNF rs712444 TC or CC genotype had an increased level of BDNF compared with patients with the TT genotype. Conclusion Our study demonstrates that the SNP rs7124442 in BDNF 3ʹ-UTR, through affecting the regulatory role of miR-922 in BDNF expression, might act as a protective factor for the outcome of patients with ischemic stroke.