Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Optimisation based Energy Storage Operation for System Congestion Management

 Abstract — Power system operation faces an increasing level of uncertainties from renewable generation and demand, which may cause large-scale congestion under ineffective operation. This paper applies energy storage (ES) to reduce system peak and congestion by robust optimisation, considering the uncertainties from ES State of Charge (SoC), flexible load, and renewable energy. First, a deterministic operation model for ES, as a benchmark, is designed to reduce the variance of branch power flow based on the least-square concept. Then, a robust model is built to optimise ES operation with the uncertainties in the severest case from load, renewable and ES SoC that are converted into branch flow budgeted uncertainty sets by the cumulant and Gram-Charlier expansion method. The ES SoC uncertainty is modelled as an interval uncertainty set in the robust model, solved by duality theory. These models are demonstrated on a grid supply point (GSP) to illustrate the effectiveness of congestion management technique. Results illustrate that the proposed ES operation significantly improves system performance in reducing system congestion. This robust optimisation based ES operation can further increase system flexibility to facilitate more renewable energy and flexible demand without triggering large-scale network investment. dispatch model under uncertain PV output. For the uncertainty sets in robust optimisation, box and budgeted uncertainty sets are widely used. Papers [20, 21] consider budgeted uncertainty sets in modelling. The correlation between load and wind
Deadline-Aware Fast One-to-Many Bulk Transfers over Inter-Datacenter Networks

An increasing number of cloud services are operated globally, where the service data are frequently replicated across geographically distributed datacenters to improve service quality and reliability. Such replication generates many one-to-many bulk data transfers over inter-datacenter networks from one datacenter to many receiver datacenters. To provide end-users with guaranteed services, these data transfers are usually required to be completed within designated deadlines. Despite the exponential growth in data demand, there has been little work on guaranteeing deadlines for one-to-many transfers, which is the subject of this paper. This paper proposes a centralized admission control coupled with a scheduling algorithm, named deAdline-Guaranteed transfEr (AGE), to guarantee the deadline of admitted data transfers and utilize the network capacity efficiently. The key idea is to flexibly select the source datacenter for receiver datacenters and allow the remaining receivers to obtain a replica from either the original source or the other receivers that have already received a copy. By jointly allocating the source for receivers and the bandwidth and routing paths for every data transfer, AGE maximizes the number of deadline-satisfied transfers. Our simulations show that compared to the state-of-the-art, AGE guarantees the deadline for up to 70 percent more transfers, achieves at least 2× higher network throughput, and reduces the completion time up to 80 percent.
Protocol to query a White Space Database

Regulatory entities in many countries are making spectrum previously used by television stations available for secondary use as a result of the switch from analog to digital. The spectrum in such cases is still owned by the primary user to whom it is licensed. However parts of the spectrum may be unused at a given location or time and hence can be made available for secondary use. In order to use such spectrum a device has to query a database in order to obtain a list of available channels or spectrum at a given location and time. This document specifies a protocol that can be used to query a white space database by a device.
Differential Privacy-Based Location Protection in Spatial Crowdsourcing

Spatial crowdsourcing (SC) is a location-based outsourcing service whereby SC-server allocates tasks to workers with mobile devices according to the locations outsourced by requesters and workers. Since location information contains individual privacy, the locations should be protected before being submitted to untrusted SC-server. However, the encryption schemes limit data availability, and existing differential privacy (DP) methods do not protect the tasks’ location privacy. In this paper, we propose a differential privacy-based location protection (DPLP) scheme, which protects the location privacy of both workers and tasks, and achieves task allocation with high data utility. Specifically, DPLP splits the exact locations of both workers and tasks into noisy multi-level grids by using adaptive three-level grid decomposition (ATGD) algorithm and DP-based adaptive complete pyramid grid (DPACPG) algorithm, respectively, thereby considering the grid granularity and location privacy. Furthermore, DPLP adopts an optimal greedy algorithm to calculate a geocast region around the task grid, which achieves the trade-off between acceptance rate and system overhead. Detailed privacy analysis demonstrates that our DPLP scheme satisfies <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math><alternatives><mml:math><mml:mi>ε</mml:mi></mml:math><inline-graphic xlink:href="lin-ieq1-2920643.gif"/></alternatives></inline-formula>-differential privacy. The extensive analysis and experiments over two real-world datasets confirm high efficiency and data utility of our scheme.
Recent Progress in Real‐Time Adaptable Digital Neuromorphic Hardware

It has been three decades since neuromorphic engineering was first brought to public attention, which aimed to reverse‐engineer the brain using analog, very large‐scale, integrated circuits. Vigorous research in the past three decades has enriched neuromorphic systems for realizing this ambitious goal. Reverse engineering the brain essentially implies the inference and learning capabilities of a standalone neuromorphic system—particularly, the latter is referred to as embedded learning. The reconfigurability of a neuromorphic system is also pursued to make the system field‐programmable. Bearing these desired attributes in mind, recent progress in digital neuromorphic hardware is overviewed, with an emphasis on real‐time inference and adaptation. Real‐time adaptation, that is, learning in realtime, highlights the feat of spiking neural networks with inherent rich dynamics, which allows the networks to learn from environments embodying an enormous amount of data. The realization of real‐time adaptation imposes severe constraints on digital neuromorphic hardware design. Herein, the constraints and recent attempts to cope with the challenges arising from the constraints are addressed.
Calibration of a six-axis parallel manipulator based on BP neural network

Purpose This paper aims to provide a simple and flexible calibration method of parallel manipulators for improving the position accuracy only using partial pose information. Design/methodology/approach The overall idea of this method is to use BP neural network to fit the relationship between calibration parameters and measurement parameters and then adjust calibration parameters according to measurements. Findings The calibration method significantly improves the position accuracy of the six-axis parallel manipulator. Simulation shows that the accuracy can be improved by increasing the number of positions consisted of samples to train BP neural network, and when the position number is increased, the descent velocity of fitting error is decreased. Originality/value The method is general for various parallel mechanisms and simple for measurement process. It can be applied to the calibration of various mechanisms without analyzing the mathematical relationship between measurements and calibration parameters. The measurement parameters can be flexibly selected to simplify measurement process, which saves calibration cost and time.
Adversarial Training for Unknown Word Problems in Neural Machine Translation

Nearly all of the work in neural machine translation (NMT) is limited to a quite restricted vocabulary, crudely treating all other words the same as an < unk> symbol. For the translation of language with abundant morphology, unknown (UNK) words also come from the misunderstanding of the translation model to the morphological changes. In this study, we explore two ways to alleviate the UNK problem in NMT: a new generative adversarial network (added value constraints and semantic enhancement) and a preprocessing technique that mixes morphological noise. The training process is like a win-win game in which the players are three adversarial sub models (generator, filter, and discriminator). In this game, the filter is to emphasize the discriminator’s attention to the negative generations that contain noise and improve the training efficiency. Finally, the discriminator cannot easily discriminate the negative samples generated by the generator with filter and human translations. The experimental results show that the proposed method significantly improves over several strong baseline models across various language pairs and the newly emerged Mongolian-Chinese task is state-of-the-art.
Design of 2 dof arm robot control system using ultrasonic sensor

This paper explains about design of 2 DOF hand-shaped manipulator robot prototype with articulated manipulator joints configuration. The manipulator robot joint driven by a servo motor in a closed loop that was controlled using an Arduino uno microcontroller. Movements of joints and end-effector robots were well-designed and allowed real-time observation interfaced by Arduino uno-based computer software. The manipulator robot that is developed can move objects to the right and left. The robotic gripper can grab objects that are less than 5 cm in size and have the ability to move objects with a maximum weight of 1 kg.
Towards secure multi-party computation on the Internet : few rounds and many parties

Multi-Party Computation (MPC) protocols allow a set of mutually distrustful parties to securely compute on their joint private inputs, maintaining the privacy of those. Since its early conception in the eighties, a plethora of research as well as some real world deployments have taken place, demonstrating the relevance of such an interesting mathematical problem. Nevertheless, most of the research and all deployments have focused on scenarios where both the number of parties and network latency are very low. This thesis lays out the first steps towards practical deployments of large-scale MPC, where many parties may be involved and they might only be connected through a high-latency network such as the Internet. Both theoretical and empirical evidence shows that non-constant-round protocols cannot perform well on slow networks for many functions. In order to overcome this issue, all the works in this thesis include concretely efficient constant-round protocols based on multi-party garbled circuits. Chapter 3 and Chapter 4 deal with the very strong setting where an active adversary corrupts all but one parties. The former achieves such goal by using homomorphic encryption for low-depth circuits. The latter, based on subsequent work, improves the theoretical understanding of the problem and utilises oblivious transfer to improve efficiency. In Chapter 5 we provide both constant and non-constant round protocols that can handle large numbers of parties more efficiently. We leverage our performance improvements by relaxing the setting where all but one parties are corrupted to one where a small minority of honest participants can be assumed. Concretely, such change allows to distribute secret key material so that each party only holds a ‘short’ part of the key. Security is then based on the concatenation of all honest parties’ keys rather than on each party’s individual key, improving both the communication and computation complexities.
Las neuronas espejo en el neuromarketing: una estrategia peligrosa cuando no se aplican normas éticas

Los avances en las investigaciones neurocientíficas, en pocos años, no más de dos décadas, están teniendo un impacto importante en un amplio espectro de la investigación científica humana. Cada descubrimiento en el complejo tejido neuronal humano-animal, que refleja alguna interacción endógena o exógena a él, genera nuevas formas de aproximaciones científicas. Este ensayo aborda las neuronas espejos como uno de los descubrimientos más importante en el campo de la neurociencia, con significativas aplicaciones en el estudio científico del marketing, práctica que ha adoptado el nombre de neuromarketing. Por otra parte, presenta el valor que le confiere a la investigación neurocientífica y que apoya el desarrollo humano, siempre que se tomen en consideración normas éticas que regulen el uso de aplicaciones que degraden lo humano. Además, analiza como las ONG, las religiones y las empresas incorporan técnicas que tienen explicaciones fundamentadas en la forma cómo reaccionan las neuronas espejos, y vaticina los peligros que pueden desencadenar las malas prácticas llevada a cabo en un mercado competitivo. Culmina con una invitación a la reflexión ética sobre las aplicaciones de la neurociencia para generar consumo y crear necesidades absurdas en los consumidores, solo por vender.
Design of broadband high-efficiency power amplifier based on elliptic low-pass and band-pass matching network

A novel approach is proposed for the design of broadband and high-e ﬃ ciency power ampli ﬁ er (PA), with elliptic low-pass and band-pass ﬁ ltering matching networks in this letter. Based on the equivalent circuit model of the GaN HEMT, the optimal topology of output matching network (MN) is designed as elliptic low-pass matching network with two attenuation poles, and the input MN is designed as band-pass match- ing network in the distributed form. A series of design procedure of the proposed topology is presented in detail. In this design, a 10-W GaN HEMT device (CGH40010F) was used. Results of measurement show that a broadband high-e ﬃ ciency PA is realized from 1.45 to 2.55GHz (fractional bandwidth = 55%) with measured drain e ﬃ ciency of 70% – 84% and output power of 40.4 – 42.4dBm.
Adapting Text Categorization for Manifest based Android Malware Detection

There are mainly three different approaches to detect malwares: i) static, ii) dynamic, and iii) hybrid. Static approach uses static source of the program without executing it. Dynamic approach, on the other hand, executes the program in a controlled environment and obtains information from operating system during runtime. Hybrid approach, as its name implies, is the combination of these two approaches. Although static approach may seem to have some disadvantages, it is highly preferred because of its lower cost. In this paper, we assume that obfuscated malware is processed by dynamic analysis and perform static malware detection based on text categorization methods. To reach our goal, we apply text mining techniques like feature extraction by using bag-of-words, n-grams, etc. from \texttt{manifest content} of programs to investigate the effectiveness of the malware detection. Our experimental results revealed that our approach is capable of detecting malicious applications with an accuracy between 94.0% and 99.3%.
Promotion of learner autonomy within the framework of a flipped EFL instructional model: perception and perspectives

Abstract This study investigated the effects of the flipped classroom model on EFL learner autonomy in a content-based instructional context. The participants involved were students enrolled in two linguistics classes, divided into an experimental group (n = 64) which received a flipped classroom (FC) instruction and a control group (n = 60) taught in a non-flipped classroom. In the FC, various functions were embedded in a Moodle course management system (CMS), including video lectures, online quizzes, resources and e-journals. The comparison between the pre- and post-questionnaire on perceived learner autonomy showed no difference between the groups prior to instruction; however, significant statistical differences were found between the groups following instruction, especially in the constructs of learner autonomous strategy, learner behavior and learner confidence. The examination of e-journals revealed that learner autonomy had been improved in terms of strategy use, behavior, interaction with the materials, the use of social resources and self-management of learning. The CMS log analysis showed significant correlations between online learning activities and perceived learner autonomy. Furthermore, the data collected from the pre- and post-questionnaires, e-journal and interviews were analysed and interpreted to explain the fostered learner autonomy from psychological, technical, sociocultural and political–critical perspectives.
A Faceted Prior for Scalable Wideband Imaging: Application to Radio Astronomy

Wideband radio-interferometric (RI) imaging consists in estimating images of the sky across a whole frequency band from incomplete Fourier data. Powerful prior information is needed to regularize the inverse imaging problem. At the extreme resolution and dynamic range of interest to modern telescopes, image cubes will far exceed Terabyte sizes, with data volumes orders of magnitude larger, making image estimation a very challenging task. The computational cost and memory requirements of corresponding iterative image recovery algorithms are extreme and call for high parallelism. A data-splitting strategy was recently introduced to parallelize computations over data blocks within an advanced primal-dual convex optimization algorithm. Building on the same algorithm, we propose an image faceting approach that consists in splitting the image cube into 3D overlapping facets with their own prior, reducing the computational bottleneck from full image to facet size. Simulation results suggest our prior provides similar if not superior reconstruction quality to the corresponding state-of-the-art non-faceted approach, with facet parallelization offering acceleration and therefore increased potential of scalability to large data and image sizes.
Segment Routing Part I

Over the past ten years, operators have heavily invested in network infrastructure because of exploding growth in IP traffic. Moreover, the lack of close interaction between applications and network infrastructure has made the situation even worse:Traffic engineering solutions fell short of expectations, and as a result, network infrastructures are largely underutilized. Implementation and operational complexity have led to a surge in operating expenditures, along with greater network instability. Segment Routing (SR) addresses these challenges by focusing on operational simplicity, better scale, and better application utilization of the installed infrastructure. SR achieves this by moving state from the network to the packet header. This simplifies and scales the network while putting the application in control. For the first time in the IP industry, applications program inter-domain SLA-enabled policies at scale through the Data Center, the metro/aggregation and the backbone. Segment Routing has been designed from day one for SDN and seeks the right balance between distributed intelligence, centralized optimization, and application-based policy creation. Many operators across different market segments Over-The-Top, Hyper-Scale Cloud, Service Providers, Enterprise have publicly expressed their investment into Segment Routing as the de-facto SDN architecture. This book aims at sharing and explaining the technology to a broad audience. Formal concepts are introduced together with the use-cases that lead to their definition. Doing so, the reader follows the insights and experience of the inventor of the technology and understands why it was designed this way. To further enhance the practical understanding of the technology, the authors have collected opinions from many operators who worked with them during this project. These insights help the reader to better understand the objective and benefits of the technology and its numerous deployment use-cases. This book is also available in Kindle ebook format
Vibrotactile Quality Assessment: Hybrid Metric Design Based on SNR and SSIM

The emerging mulsemedia (MULtiple SEnsorial MEDIA) introduces new sensorial data (haptic, olfaction, gustation, etc.), significantly augmenting the conventional audio-visual communication. This can be used in many areas, such as immersive entertainment and innovative education. Previous research has been dedicated to evaluating the impact of other sensorial data on conventional multimedia; however, standalone quality evaluation of new sensorial data, especially vibrotactile data (a type of haptic data), has not been covered. To the best of our knowledge, this paper is the first to empirically demonstrate that the common statistical metrics in audio and visual domains, i.e. signal-to-noise ratio (SNR) and Structural SIMilarity (SSIM), are highly correlated with human vibrotactile perception as well. To be specific, we propose a testing protocol for vibrotactile quality evaluation and conduct subjective experiments. The results suggest that SNR and SSIM are applicable to vibrotactile quality assessment. We also consider a practical scenario where the quality of vibrotactile data varies with time. Based on the validation of SNR and SSIM in the first part, we present an objective metric as a hybrid composition of SNR and SSIM. Instead of assessing the quality of data using an overall score, the hybrid metric evaluates the quality in a time-varying manner. Subjective experiments are conducted and the results demonstrate that the correlation coefficient can be significantly increased using the hybrid metric.
AN IMPROVEMENT OF LONGITUDINAL FLYING QUALITY OF HSCT AEROELASTIC AIRCRAFT MODEL USING GPC TECHNIQUE

Applying automatic control methods to flexible aircrafts such as High Speed Civil Transport (HSCT) is a way to overcome the difficulties of the coupling between the elastic modes and the rigid body modes. The difficulties involved by sensing the flexure motion and the rigid body motion, which is a source of additional dipoles near the imaginary axis of the s-plan, make the choice of a suitable automatic control method a challenge. To this purpose, this paper intends to investigate the applicability of control strategies, which would be able to compensate the difficulties in stabilizing the aircraft. The proposed method is the so-called Generalized Predictive Control (GPC) technique. After a brief overview of HSCT modeling we recall the basic principles of the GPC. Then, we are interested in the application of this method with four different choices of the tuning parameters. In our study, the objective is not only to stabilize the pitch rate step response, but also to have this response accepted from point of view flying quality by controlling the elevator deflection.
Developing Interactive Learning Environments that can be used by all the classes having access to computers. The case of Aplusix for algebra

Our research team has developed and experimented software for the learning of algebra, named Aplusix, with the idea of being usable and useful for all the classes having access to computers, and of helping teachers to teach the curriculum. In this paper, we list 19 principles that we consider relevant to this goal and we briefly describe the Aplusix system. This system is distributed in France since early 2005 and will be distributed in many countries from 2006. It has proven to be efficient (students learn) and to facilitate the teacher's work.