Miracle-dz/UniFAR-FTUs huggingface dataset:
Does federalism enhance representative democracy? Perpetual reform and shifting power in a divided Belgium

ABSTRACT Theoretically federations are meant to divide and decentralise power with sub-national governments that are closer to the people, which can better accommodate diversity with policies best suited to each community. However, such differences may be inequitable and counter-productive rivalries among multiple governments can emerge. Thus the relationship between federalism and democracy is neither clearly beneficial nor non-existent. If federalism does enhance representative democracy, then the process of federalisation should strengthen it, with the comparatively younger Belgian federation providing a valuable case to interrogate this premise. In recent decades Belgium has been transformed from a unitary state into an intricate asymmetrical federation where power is primarily shared between the Dutch and French language groups. Several new sub-state polities have been created and empowered and minorities have been guaranteed political representation, but arguably they are not as locally oriented as they could be, and the executives are relatively powerful. The process of federalisation has been most concerned with holding the country together than advancing principles of good governance either at the federal level or through maximising decentralisation. Yet the Belgian federation has succeeded in improving representation compared to the previous unitary state and maintaining peace.
Sector Design Using Multiband Antenna With Metamaterial Reflector for Cellular UAV System

The use of Unmanned Aerial Vehicles (UAVs) is expected to provide a prompt communication service area under disasters and emergencies. Using the multiple frequency bands called carrier aggregation increases the system throughput and can support situations where urgent traffic could explode. We previously developed a multiband sector antenna with multiple reflectors employing the metamaterial for a UAV-mounted mobile base station. The metamaterial reflector, which has electromagnetic band gap (EBG) characteristics, can reflect the electromagnetic waves of the desired band, i.e. EBG band, and transmit those of the specific bands. It has been a challenge to adjust the beamwidth under the constraint of the metamaterial reflector. This paper proposes the beamwidth adjustment algorithm considering the EBG band and evaluates the system throughput performance utilizing the designed multiband sector antenna. The proposed algorithm designed the dual-band sector antenna with 90 degrees beamwidth in the horizontal plane of each frequency band (800 MHz and 2 GHz), and the measurements match the electromagnetic field simulations well. Moreover, the system-level simulation evaluates the system throughput performance utilizing the designed dual-band sector antenna.
Seashells and Oyster Shells: Biobased Fine Aggregates in Concrete Mixtures

The construction industry is the largest global consumer of materials, among which sand plays a fundamental role; now the second most used natural resource behind water, sand is the primary component in concrete. However, natural sand production is a slow process and sand is now consumed at a faster pace than it’s replenished. One way to reduce consumption of sand is to use alternative materials in the concrete industry. This paper reports the exploratory study on the suitability of aquaculture byproducts as fine aggregates in concrete mixtures. Seashell grit, seashell flour and oyster flour were used as sand replacements in concrete mixtures (10%, 30% and 50% substitution rates). All the mixtures were characterized in fresh and hardened states (workability, air content, compressive strength and water absorption). Based on compressive strength, measured at 7 and 28 days, seashell grit provided the most promising results: the compressive strength was found to be larger than for conventional concrete. Moreover, the compressive strength of the cubes was larger, when larger percentages of seashell grit were used, with the highest value obtained for 50% substitution. However, for oyster flour and seashell flour, only 10% sand substitution provided results comparable with the control mixture. For the three aggregates, workability of concrete decreases with fineness modulus decrease. For mixtures in which shell and oyster flour were used with 30% and 50% substitution percentages, it was necessary to increase the quantity of mixing water to allow a minimal workability. In conclusion, considering the promising results of the seashell grit, it is suggested to study further the characteristic of the material, also considering its environmental and physical properties, including acoustic and thermal performances. Higher substitution percentages should also be investigated. This research adds to the relevant literature in matter of biobased concrete, aiming at finding new biobased sustainable alternatives in the concrete industry.
Optimized Residual Convolutional Learning Neural Network for Intrapartum Maternal-Embryo Risk Assessment

An effective fetal electrocardiogram (FECG) and ultrasound sonography (USG) signals with continues watching is a testing tool utilized by obstetricians to assess the maternal and embryo stage classiﬁcation method are proposed utilizing a deep 2D convolutional neural network (CNN) which nowadays observe excellent presentation in the field of design identification. Because of the convolution and irregularity, a visible explanation of both Fetal Heart Rate or FHR and Uterine Contractions or UC signals utilizing usual instructions commonly obtained in remarkable individual inter-observer and intra-observer changeability. As a result, automated system depends on modern artificial intelligence (AI) innovation has nowadays been evolved to assist obstetricians in manufacturing targeted medical conclusions. The major object of this research was to make sure a novel, steady, strong, and effective model for maternal and embryo risk detection. Moreover, multiple CNNs is optimized through Genetic Algorithm (GA), overcomes the majority decision drawback in the traditional voting method. Improvement of the suggested classiﬁer incorporate different deep studying methods such as transfer learning, GA initialization, multiple convolutional layers, hybrid optimization SGD with Adam and dropout with softmax were used in the experiments. And then, we differentiated our CNN classiﬁer with four familiar CNN optimized models; such as SGD, Rmsprop, Adam and Adagrad. Depends on the experiment freely available database (CTU-UHB), we got good categorization presentation, after complete investigation, utilizing the proposed Optimized Residual Convolutional Learning Neural Network method with average cross-validation (10 fold) of the Acc, TPR, TNR, PPV, NPV, HM, Kappa, AUC, PRAUC, Log Loss, DR, Prevalence, DP and BA respectively. Once the proposed Optimized Residual Convolutional Learning Neural Network model with 10 layers is achieved 96.24 % accuracy in average with successfully trained with the 8 different risk factors, the corresponding automated system can be used as a potent device to detect maternal and embryo risk state objectively and accurately.
Markov Decision Process-based Potential Field Technique for UAV Planning

A BSTRACT . This study proposes a methodology for mission/path planning of an unmanned aerial vehicle (UAV) using an artificial potential field with the Markov Decision Process (MDP). The planning problem is formulated as an MDP. A low-resolution solution of the MDP is obtained and used to define an artificial potential field, which provides a continuous UAV mission plan. A numerical case study is conducted to demonstrate the validity of the proposed technique.
Exploring Spreadsheet Use and Practices in a Technologically Constrained Setting

This paper explores the impacts of spreadsheets on business operations in a water utility parastatal in Malawi, Sub-Saharan Africa. The organisation is a typical example of a semigovernment organisation operating in a technologically underdeveloped country. The study focused on spreadsheet scope of use and life cycle as well as organisation policy and governance. The results will help define future spreadsheet usage by influencing new approaches for managing potential risks associated with spreadsheets in the organization. Generally, findings indicate that the proliferation of spreadsheets in the organization has provided an enabling environment for business automation. The paper also highlights management, technological and human factor issues contributing to high risks associated with the pervasive spreadsheet use. The conclusions drawn from the research confirms that there is ample room for improvement in many areas such as implementation of comprehensive policies and regulations governing spreadsheet development processes and adoption.
Adaptive Fuzzy Funnel Control for Pure-Feedback Nonlinear System with Input Constraint

—For pure-feedback nonlinear system with input saturation constraint and system internal interference, the problem of prescribed performance control is considered. Firstly, a smooth nonlinear function is introduced to approach the saturation function of the input signal, which solves the design difﬁculty caused by nonlinearity of non-differential saturation. Then, the pure-feedback system is decoupled by using mean-value theorem, so that the system controller can be constructed by utilizing the backstepping technique directly. Next, a novel system controller is proposed by combining prescribed performance control and fuzzy logic control technology. In the framework of Lyapunov stability theory, the controller can ensure that all closed-loop signals of the system are uniformly and ultimately bounded, and the tracking error of the system converges to the allowable range under the funnel function, thus the transient and steady-state performance of the system was guaranteed. Finally, the effectiveness of the control scheme is veriﬁed by a simulation example.
Cosine: A Cloud-Cost Optimized Self-Designing Key-Value Storage Engine

Wepresent a self-designing key-value storage engine, Cosine, which can always take the shape of the close to “perfect” engine architecture given an input workload, a cloud budget, a target performance, and required cloud SLAs. By identifying and formalizing the first principles of storage engine layouts and core key-value algorithms, Cosine constructs a massive design space comprising of sextillion (1036) possible storage engine designs over a diverse space of hardware and cloud pricing policies for three cloud providers – AWS, GCP, and Azure. Cosine spans across diverse designs such as Log-Structured Merge-trees, B-trees, Log-Structured Hash-tables, in-memory accelerators for filters and indexes as well as trillions of hybrid designs that do not appear in the literature or industry but emerge as valid combinations of the above. Cosine includes a unified distribution-aware I/O model and a learned concurrency-aware CPU model that with high accuracy can calculate the performance and cloud cost of any possible design on any workload and virtual machines. Cosine can then search through that space in a matter of seconds to find the best design and materializes the actual code of the resulting storage engine design using a templated Rust implementation. We demonstrate that on average Cosine outperforms state-of-the-art storage engines such as write-optimized RocksDB, read-optimized WiredTiger, and very write-optimized FASTER by 53x, 25x, and 20x, respectively, for diverse workloads, data sizes, and cloud budgets across all YCSB core workloads and many variants. PVLDB Reference Format: Subarna Chatterjee, Meena Jagadeesan, Wilson Qin, and Stratos Idreos. Cosine: A Cloud-Cost Optimized Self-Designing Key-Value Storage Engine. PVLDB, 15(1): 112 126, 2022. doi:10.14778/3485450.3485461 1 REASONING ABOUT CLOUD COSTS Application Diversity. Key-value stores [71, 72, 87] serve as the storage backbone for a wide range of applications such as graph processing in social media [27, 59], event log processing [42], web applications [119], and online transaction processing [98]. Relational This work is licensed under the Creative Commons BY-NC-ND 4.0 International License. Visit https://creativecommons.org/licenses/by-nc-nd/4.0/ to view a copy of this license. For any use beyond those covered by this license, obtain permission by emailing info@vldb.org. Copyright is held by the owner/author(s). Publication rights licensed to the VLDB Endowment. Proceedings of the VLDB Endowment, Vol. 15, No. 1 ISSN 2150-8097. doi:10.14778/3485450.3485461 Cl ou dpr ov ide r a nd ha rd wa re sp ac e read w rit e LSM
The Videogame Affordances Corpus

Videogames are created for human players whose common-sense knowledge of real-world objects and interactions (and their familiarity with other games) primes them for successful play. Action games feature recurring formal elements including a directly controlled avatar, moving enemies, resource pickups, and portals to new map areas; mapping these onto culturally signiﬁcant symbols helps players learn to play quickly. We present a schema, annotation tool
Spot Price Characterization: What Are the Right Features to Model?

Amazon EC2 has been offering spot instances [2] since 2009 and a large segment of its “tenant”workloads has come to embrace these [6]. The appeal of spot instances lies in their low prices up to one-tenth of the prices of on-demand instances of equivalent (advertized) capacities. Unlike an on-demand instance, whose price changes very slowly (over months or years), a spot instance has a highly dynamic price that may change as frequently as once every few minutes. To procure a spot instance, a tenant needs to place a bid in the concerned marketplace. Following this, whenever the dynamic price for the requested type of spot instance (simply the spot price henceforth) falls below the bid, an instance is allocated to the tenant. However, when the bid falls below the spot price, a warning is issued to the tenant following which the instance is reclaimed/revoked by EC2. The tenant may choose to use this warning period (2 minutes as of this writing) to save all or some of that instance’s state. From a tenant’s point of view, a spot instance is a virtual machine (VM) that is cheaper than its on-demand counterpart but appears to have poorer availability. To benefit from the low prices of spot instances, a tenant must effectively deal with two sources of complexity: (i) it must predict relevant aspects of spot price, and (ii) it must combine these predictions with its application-specific trade-offs 1 to devise online instance procurement algorithms. Both these issues have received a lot of attention recently. In this paper, our focus is on (i). Our findings also have implications for (ii) and will inform our future work as we discuss in Section 4. A key challenge in using spot instances, as already identified by many recent papers, is the poor predictability of spot prices. Researchers have proposed prediction techniques for spot prices with varying degrees of modeling complexity. Among the simpler ones are techniques based on (dynamically updated) empirical probability distributions [3, 4, 7]. More sophisticated techniques based on auto-regressive time-series [1, 8, 10], Markovian models [9, 5], etc., have also been proposed. A key requirement for cost-effective procurement of a spot instance is the ability to predict its service contiguity, i.e., the contiguous duration for which it is likely to be available. Regardless of the modeling technique used, all existing approaches (to our knowledge) are based on creating a statistical model for the raw spot price itself (with underlying assumptions about its stationarity).
Drawcto: A Multi-Agent Co-Creative AI for Collaborative Non-Representational Art

Non-representational art–such as works by Wassily Kandin-sky, Joan Mitchell, Willem de Kooning, etc.–showcases diverse artistic expressions and challenges viewers with its interpretive open-endedness and lack of a clear mapping to our everyday reality. Human cognition and perception nonetheless aid us in making sense of, reasoning about, and discussing the perceptual features prevalent in such non-representational art. While there have been various Computational Creative systems capable of generating representational artwork, only a few existing Computational (Co)Creative systems for visual arts can produce non-representational art. How would a co-creative AI that incorporates elements of the human visual perception theory be able to collaborate with a human in co-creating a non-representational art? This paper explores this challenge in detail, describes potential machine learning and non-machine learning approaches for designing an AI agent and introduces a new web-based, multi-agent AI drawing application, called Drawcto , capable of co-creating non-representational artwork with human collaborators.
Comparative Analysis of Pitching Prediction Algorithms

Two algorithms are described in the paper; one of them is the Kalman filter, which is based on the use of a pitching mathematical model, and the second uses a neural network in which the model is considered unknown. The results of the algorithms sensitivity analysis to the parameters of the model and its influence on the potential accuracy of prediction are presented. A stationary narrow-band second-order Markov process is used as a model of the ship pitching, which was used to form the input signal of the algorithms. Also, the results of the algorithms simulation in predicting real data are presented.
Exploring the links between Physics and Astronomy education teaching and learning in the Philippines

Students consider physics to be one of the most prevalent and troublesome topics since they find physics to be a challenging course in high school and become more deceptive when they have it as an undergraduate course. The Rizal Technological University (RTU) in the Philippines offers undergraduate and graduate academic programs in astronomy. The university utilizes physics diagnostic tests to classify students’ suitability towards the course as RTU houses the Philippines’ most advanced optical telescopes and the Center for Astronomy Research and Development. The latter was established through the Department of Science and Technology to promote a research hub in the field of astronomy and space science, as well as providing support for research funding and scholarships. The study performed causal-comparative research design and correlational analytics toward students’ attitude and motivation, study habits, learning styles, professor factors, social factors, and academic grades in physics and astronomy. The study explored the links between physics and astronomy and revealed their faith in their study habits in both astronomy and physics. The findings of this study provide evidence of the link of physics and astronomy education in students’ academic programs and its value from the previous learnings of students and teaching of teachers as a critical role in predicting student performance. The increased enrolment in the academic program demonstrates the validity of the above-mentioned statements, as it enticed students to pursue astronomy as their scientific career. Other efforts are discussed in detail in this paper.
Pandemic as a Challenge for Human-AI Cooperation

Cooperation between human players and AI agents in games is a subject of great interest in the research community. In this paper we propose a new domain as a challenge for future AI research: the cooperative game Pandemic. This game represents a challenge because it requires cooperation between players in an environment with incomplete information. Ad-ditionally, we propose a ﬁrst approach for a cooperative agent for the game, that uses planning in conjunction with plan recognition. We also propose an experiment to test the mentioned approach. In this paper we argue why Pandemic makes a compelling domain for AI research, the status of our project, as well as which challenges remain to be addressed.
Variable area jet nozzle using shape memory alloy actuators in an antagonistic design

A significant reduction in noise and improved fuel consumption can be achieved by varying the area of a commercial jet engine's fan nozzle. A larger diameter at takeoff and approach can reduce jet velocity reducing noise. Adjusting the diameter in cruise, to account for varying Mach number, altitude, etc, can optimize fan loading and reduce fuel consumption. Boeing recently tested a scaled variable area jet nozzle capable of a 20% area change. Shape Memory Alloy actuators were used to position 12 interlocking panels at the nozzle exit. A closed loop control system was used to maintain a range of constant diameters with varying flow conditions and to vary the diameter under constant flow conditions. Acoustic data by side line microphones and flow field measurements at several cross-sections using PIV was collected at each condition. In this paper the variable area nozzle's design is described. The effect of the nozzle's diameter on its acoustic performance is presented for a range of Mach numbers and mass flow rates. Flow field data is shown including the effects of the joints between the interlocking panels.
Enhancement of Microhardness and Tribological Properties of Recycled AA 6063 Using Energy-Efficient and Environment-Friendly Friction Stir Processing

In this study, the surface microhardness, friction and wear performance of recycled aluminium alloy 6063 were improved via an energy-efficient surface engineering technique known as friction stir processing. Different tool rotational speeds of 1200 rpm, 1400 rpm, 1600 rpm, 1800 rpm and 2000 rpm with a fixed feed rate of 30 mm/min were used to process the recycled aluminium alloy 6063. The effects of rotational speed on the microstructure, surface microhardness and tribological performance of the samples were analyzed. The results show that the samples produced at a stirring speed of 1200 rpm achieved the greatest enhancement of 25 % in surface microhardness, 37 % in wear resistance and 33 % reduction in friction coefficient. This has significant implications for environmental sustainability as a relatively low rotational speed, hence a low energy input, is sufficient to enhance the surface properties of recycled aluminium alloy 6063. The benefits of superior tribological properties of recycled aluminium alloy afforded by such an energy-efficient surface engineering method include reduced exploitation of new resources, reduced carbon footprint, and enhanced product sustainability and durability.
Guiding Generative Graph Grammars of Dungeon Mission Graphs via Examples

Generative Graph grammars are an established technique for procedural content generation (PCG) of the mission space of dungeon levels, as they allow for explicit speciﬁcation of de-sign intention. However, they are difﬁcult to control beyond the initial speciﬁcation. Deﬁning effective grammars requires both design expertise and familiarity with how grammars work, and even then the resulting grammar is not guaranteed to generate missions that ﬁt a designer’s intention. In this paper, we propose a system that attempts to allow designers to affect an existing grammar’s generative space by allowing designers to control the probability distribution induced by the grammar. The system does this by learning the parameters of a probabilistic graph grammar from examples. Designers can control the generation of these examples via speciﬁcation of assessment criteria, and a threshold above or below which the output is generated. We conclude with a demonstration of the efﬁcacy of the system in shifting the distribution of the generative space.