Miracle-dz/UniFAR-FTUs huggingface dataset:
A positive feedback loop between two C-type lectins originated from gene duplication and relish promotes the expression of antimicrobial peptides in Procambarus clarkii

Gene duplication (GD) leads to the expansion of gene families that contributes organisms adapting to stress or environment and dealing with the infection of various pathogens. C-type lectins (CTLs) in crustaceans undergo gene expansion and participate in various immune responses. However, the functions of different CTL produced by GD are not fully characterized. In the present study, two CTL genes (designated as PcLec-EPS and PcLec-QPS, respectively) were identified from Procambarus clarkii. PcLec-EPS and PcLec-QPS originate from GD and the main difference between them is exon 3. PcLec-EPS and PcLec-QPS respectively contains EPS and QPS motif in their carbohydrate recognition domain. The mRNA levels of PcLec-EPS and PcLec-QPS in hemocytes, gills, intestine and lymph underwent time-dependent enhancement after D-Mannose and D-Galactose challenge. Recombinant PcLec-EPS and PcLec-QPS could bind to carbohydrates and microbes, and agglutinate bacteria. The results of experiments on recombinant protein injection and RNA interference indicate that PcLec-EPS and PcLec-QPS can respectively strong recognize and bind D-Mannose and D-Galactose, activate the Relish transcriptional factor, and further upregulate the expression of different antimicrobial peptides (AMPs). In addition, these two CTLs and Relish could positively regulate the expression of each other, suggesting that there is a positive feedback loop between two CTLs and Relish that regulates the expression of AMPs. It may contribute to the expansion of the immune response for host quickly and efficiently eliminating pathogenic microorganisms. This study provides new knowledge for clear understanding the significance and function of different CTL generated by GD in immune defenses in crustacean.
Quantifying affordances through information theory

Affordances are directly perceived environmental possibilities for action. Born within ecological psychology, they have been proposed to be one of the main building blocks to explain cognition from and embodied and situated perspective. Despite the interest, a formal definition of affordances in information theory terms that would allow to exploit their full potential in models of cognitive systems is still missing. We explore the challenge of quantifying affordances by using information-theoretical measures. Specifically, we propose that empowerment (i.e., information quantifying how much influence and control an agent has over the environment it can perceive) can be used to formally capture information about the possibilities for action (the range of possible behaviors of the agent in a given environment), which in some cases can constitute affordances. We test this idea in a minimal model reproducing some aspects of a classical example of body-scaled affordances: an agent passing through an aperture. We use empowerment measures to characterize the affordance of passing through the aperture. We find out that empowerment measures yield a similar transition to the one found in experimental data in humans in the specialized literature on ecological psychology. The exercise points to some limitations for formalizing affordances and allows us to pose questions regarding how affordances can be differentiated from more generic possibilities for action.
Design and Analysis of an Ultra-Dense, Low-Leakage, and Fast FeFET-Based Random Access Memory Array

High static power associated with static random access memory (SRAM) represents a bottleneck in increasing the amount of on-chip memory. Novel, emerging nonvolatile memories such as spin-transfer torque magnetic random access memory (STT-RAM), resistive random access memory (RRAM), and ferroelectric field effect transistor-based random access memory (FeFET-RAM) are alternatives for replacing hardware kernels such as SRAM-based last level caches (LLC) due to their fast access times and lower leakage. In this paper, we study an ultra-dense FeFET-RAM based on 1-FeFET memory cells, and address potential disturbance issues at the array level. Disturbances are studied experimentally and via simulation. Experimental measurements are well correlated with modeling results suggesting that we have a good understanding of how disturbance issues will manifest themselves. That said, previous WRITE schemes for 1-FeFET arrays may: 1) exacerbate disturbances and 2) significantly degrade figures of merit (FoM) such as WRITE power. To address these issues, we propose the use of columnwise body connections to simultaneously overcome disturbances and reduce leakage currents during WRITES. We present detailed studies on how 1-FeFET memory cells and arrays (with columnwise body bias) fare when compared to traditional SRAM approaches and other emerging technologies. Notably, we benchmark the 1-FeFET memory against 1T * 1FeFET and 2T * 1FeFET designs proposed in early works, as well as SRAM, STT-RAM, and RRAM. Our evaluation of a <inline-formula> <tex-math notation="LaTeX">$64\times 64$ </tex-math></inline-formula> FeFET-RAM array shows that the area, READ delay, and static power are reduced by <inline-formula> <tex-math notation="LaTeX">$\sim 5.3\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\sim 1.5\times $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$\sim 74\times $ </tex-math></inline-formula>, respectively, when compared to an SRAM equivalent. Also, the 1-FeFET memory cell design shows <inline-formula> <tex-math notation="LaTeX">$\sim 50\times $ </tex-math></inline-formula> improvements in terms of WRITE energy with respect to STT-RAM and RRAM counterparts.
A Trust-based Π-ATMS for the Future Internet

In this paper we propose a simple model of trust for the agents in a distributed environment. In the absence of sufficient information about the source and the data produced by it, we argue that the uncertainty in the data is proportional to the trustworthiness of the source. We model an agent consisting of a Π-ATMS. The agent assigns uncertainty values to the hypotheses and justifications about the world and inputs it to the Π-ATMS to obtain a list of conclusions with varying levels of uncertainty that help it to make a decision. We show how this approach can solve the problem applied to a crowd sourced garbage collection use case.
qSCMS: post-quantum security credential management system for vehicular communications.

With the increasing demand for intelligent transportation systems (ITS), security and privacy requirements are paramount. This demand led to many proposals aimed at creating a Vehicular Public Key Infrastructure (VPKI) able to address such prerequisites. Among them, the Security Credential Management System (SCMS) is particularly promising, providing data authentication in a privacy-preserving manner and supporting the revocation of misbehaving vehicles. Namely, one of the main benefits of SCMS is its so-called butterfly key expansion process, which issues arbitrarily large batches of pseudonym certificates through a single request. Despite SCMS’s appealing design, in this document, we show that its certificate issuing process can be improved. Namely, this protocol originally requires the vehicle to provide two separate public/private key pairs to registration authorities; we now propose an improved approach that unifies them into a single key pair. We also show that such performance gains come with no negative impact in terms of security, flexibility or scalability when compared to the original SCMS. Besides the improvement on the initial Elliptic Curve based protocol, we present a post-quantum version of the protocol using Ring Learning-with-errors (R-LWE) assumption. This new protocol has the same shape and features of the original one, but using R-LWE-based signature and encryption as underlying schemes and Lattices operation for the key issuing instead of Elliptic Curves.
QuaIIe: A Data Quality Assessment Tool for Integrated Information Systems

Data is central to decision-making in enterprises and organizations (e.g., smart factories and predictive maintenance), as well as in private life (e.g., booking platforms). Especially in artificial intelligence applications, like self-driving cars, trust in data-driven decisions depends directly on the quality of the underlying data. Therefore, it is essential to know the quality of the data in order to assess the trustworthiness and to reduce the uncertainty of the derived decisions. In this paper, we present QuaIIe (Quality Assessment for Integrated Information Environments, pronounced [’kvAl@]), a Java-based tool for the domain-independent ad-hoc measurement of an information system’s quality. QuaIIe is based on a holistic approach to measure both schema and data quality and covers the dimensions accuracy, correctness, completeness, pertinence, minimality, and normalization. The quality measurements are presented as machineand human-readable reports, which can be generated periodically in order to observe how data quality evolves. In contrast to most existing data quality tools, QuaIIe does not necessarily require domain knowledge and thus offers an initial ad-hoc estimation of an information system’s quality.
DI5GUISE: A highly Dynamic Framework for Real-Time Simulated 5G Evaluation

As the next generation of cellular networks (5G) nears deployment, autonomous smart nodes, such as Internet of Things (IoT) and vehicular devices, are being developed to take advantage of increased throughput and improved resiliency offered by the new link layer protocols of 5G. Evaluating the impact of real-time 5G air-interface scheduling algorithms on the stringent real-time delivery needs of these devices and their new applications is no small feat. In this short paper, we expand upon the mmwave (5G) module for NS-3 and present DI5GUISE, a configurable and highly dynamic real-time simulated testbed framework, through which the impact of these smart nodes can be investigated. We utilise real-time video delivery as an example of applications with high throughput requirements. The experimental results illustrate that even on low cost commodity hardware, such as Raspberry Pis, over 80Mbps per client of real-time streaming of 4K content can be achieved.
MLC STT-MRAM-Aware Memory Subsystem for Smart Image Applications

Next-generation memories with high storage capacity, high performance, and low power consumption are being researched due to the ever-growing demand for artificial intelligence and high-definition applications. Among such future memories, a multi-level cell (MLC) spin-transfer magnetic torque random access memory (STT-MRAM) attracts considerable attention as an alternative to static or dynamic random access memories. An MCL STT-MRAM has the advantages of capacity and non-volatility, but the disadvantages of performance, power consumption, and endurance resulting from complicated resistance state transition and detection processes. In particular, such issues are exacerbated in the latest smart image applications employing block-based processing algorithms. In this paper, we propose a memory subsystem that mitigates the MLC STT-MRAM disadvantages in smart image applications. Our main idea is threefold: MLC-aware image buffer composing, block-aware pixel-to-memory mapping, and prediction-aware image-to-buffer allocating techniques that all make multi-step resistance state transition and detection processes less required. Experimental results show that the proposed memory subsystem achieves 24.5% shorter application execution time, and 96.4% lower memory power consumption than the conventional memory subsystems for industrial smart image applications. In addition, our memory subsystem increases the lifetime of MLC STT-MRAMs via 93.8% fewer multi-step resistance state transition processes.
Popularity-Based Online Scaling for RAID Systems Under General Settings

The ever-increasing demand of storage capacity and system performance leads to the scaling requirement in redundant arrays of independent disks (RAID)-structured storage systems. Existing approaches mainly focus on minimizing data migration in offline scenario, but not consider the user accesses issued by applications. However, in online scenario, the scaling I/Os and user I/Os usually interfere with each other, and result in significant performance degradation. Thus, it is of big significance to develop an efficient online scaling scheme to mitigate the impact of I/O interference. In this paper, we propose popularity-based online scaling (POS), which exploits workload locality by scaling storage zones with high popularity in a higher priority. POS can efficiently alleviate the interference between scaling I/Os and user I/Os, and it is also general enough to scale various RAID systems like RAID-0, RAID-5, and RAID-6. It can also be readily deployed atop various conventional RAID scaling approaches to improve their performance. To demonstrate the effectiveness of POS, we implement various conventional RAID scaling approaches, and also implement POS on top of these approaches for comparison. Extensive simulations with real-world workloads show that POS can efficiently reduce the response time of user requests and scaling I/Os, and also improves the sequentiality of data accesses.
Integrating Security Considerations Into a Safety Case Construction

— Wide-spread reliance on networking in modern safety-critical control systems makes security increasingly interwoven with safety. Hence, we need novel methodologies integrating security consideration into the process of system development and safety case construction. Safety case is a structured argument justifying system safety. In this paper, we propose an approach that relies on the systems-theoretic analysis to construct security-aware safety cases. We define a number of generic patterns facilitating definition of security-aware safety cases. Our approach allows the developers to analyse the mutual interdependencies between safety and security in the design of networked control systems. It provides the engineers with a systematic top-down method for deriving constraints that should be imposed on the system and software behavior to guarantee safety in the presence of accidental and malicious faults.
Learning-Aided Optimization for Energy-Harvesting Devices With Outdated State Information

This paper considers utility optimal power control for energy-harvesting wireless devices with a finite capacity battery. The distribution information of the underlying wireless environment and harvestable energy is unknown, and only outdated system state information is known at the device controller. This scenario shares similarity with Lyapunov opportunistic optimization and online learning but is different from both. By a novel combination of Zinkevich’s online gradient learning technique and the drift-plus-penalty technique from Lyapunov opportunistic optimization, this paper proposes a learning-aided algorithm that achieves utility within <inline-formula> <tex-math notation="LaTeX">$O(\epsilon)$ </tex-math></inline-formula> of the optimal, for any desired <inline-formula> <tex-math notation="LaTeX">$\epsilon >0$ </tex-math></inline-formula>, by using a battery with an <inline-formula> <tex-math notation="LaTeX">$O(1/\epsilon)$ </tex-math></inline-formula> capacity. The proposed algorithm has low complexity and makes power investment decisions based on system history, without requiring knowledge of the system state or its probability distribution.
QAnalySeries – a cross-platform time series tuning and analysis tool

We introduce a computer program for time series tuning and analysis. The well known in paleo-climatic community program AnalySeries after Paillard et al. is restricted to the Mac OS (32-bit) and, according to Apple plans to move entirely onto the 64-bit system, it will not be supported in the future upgrades of Macintosh OS. QAnalySeries is an attempt to re-implement the major functionality of AnalySeries thus providing the community with a useful tool. QAnalySeries is written using Qt SDK as a free software and can be run on Macintosh, Windows and Linux systems. Paillard, D. , L. Labeyrie and P. Yiou (1996), Macintosh program performs time-series analysis, Eos Trans. AGU, 77: 379.
ST-DeLTA: A Novel Spatial-Temporal Value Network Aided Deep Learning Based Intelligent Network Traffic Control System

Deep learning has emerged as a popular Artificial Intelligence (AI) technique to make conventional cyber physical systems become intelligent and sustainable. Recently, deep learning has been widely used in the network domain. With the aid of powerful deep neural networks, the communication network can carry out packets forwarding actions intelligently to avoid possible failure and congestion. However, with the high computing cost and process limitation in only the static network scenario, the existing deep learning based network traffic control algorithms cannot satisfy the sustainable requirement of next generation large scale dynamic network. To conquer the existing problems, a novel spatial-temporal value network aided deep learning based intelligent traffic control algorithm referred as ST-DeLTA is proposed in this paper. In ST-DeLTA, the value matrix and spatial temporal training model (ST model) are employed to intelligently extract the spatial as well as temporal features of traffic patterns and make adaptive packets forwarding decision in large scale and dynamic networks. The mathematical analysis gives the computing cost reduction of our proposal, and the computer simulation demonstrates that our proposal has significantly better training and network performance compared with traditional algorithms in terms of training accuracy, transmission throughput, and average packets loss rate.
Java Swing

From the Publisher: Swing is a fully featured user interface development kit for Java applications. Building on the foundations of the Abstract Window Toolkit (AWT), Swing enables cross-platform applications to use any of several pluggable look-and-feels. Swing developers can take advantage of its rich, flexible features and modular components, building elegant user interfaces with very little code. This second edition of Java Swing thoroughly covers all the features available in Java 2 SDK 1.3 and 1.4. More than simply a reference, this new edition takes a practical approach. It is a book by developers for developers, with hundreds of useful examples, from beginni.g level to advanced. All these features mean that there's a lot to learn. Even setting aside its platform flexibility, Swing compares favorably with any widely available user interface toolkit -- it has great depth. Swing makes it easy to do simple things but is powerful enough to create complex, intricate interfaces.
The English Language Scientific Literature Classification Based on Abstract Using Rocchio Algorithm

The need for the documents in the form of journals or scientific articles is currently increasing which means the need of documents available also increasing, in another side it makes difficult to find and present journals. Therefore, a method is needed to classify journals automatically according to the categories of the journal. One method that can help organize documents according to their categories is classification. In this study, Rocchio algorithm is used as a method to classify journals. The data used comes from Google Scholar. Journal consists of 4 (four) categories: Computer Vision and Pattern Recognition, Artificial Intelligent, Data Mining and Analysis, and Computer System. System testing is carried out by taking 160 journals for training data and 48 journals for testing data. The results of this study produced a web-based journal classification system and showed the method used was able to classify journals with accuracy result is 93%.
STUDENT’S PARTICIPATION IN THE DESIGN PROCESS: A STUDY ON USER EXPERIENCE OF AN EDUCATIONAL GAME-LIKE APPLICATION

Gamification is increasingly used in the education context due to its engaging and motivating features that could enhance the learning/teaching process. While there is rich and growing work on game-like applications across fields, there is little multidisciplinary dialogue between pupils, researchers and game designers about how to design them for more effective mobile educational games. To be able to make the learning experiences more engaging, different methodologies have been used to provide designers with input from their target. Although it is broadly accepted that end users should be consulted when designing information and communication technologies (ICT), when the participants are children, the extent and type of consulting is more controversial. Although some considerations are necessary regarding the conclusions drawn from children’s enthusiastic play behaviours and reactions, they are the tomorrow’s power-users of everything, from the Internet to communications technologies. This paper explores the concept of participatory design in the process of designing a mobile gamified learning application about recycling and its effects on the ocean. This approach aims to understand how the inclusion of pupils from the beginning of the design process could influence the directions of the prototype. The consideration of pupils’ game-based learning experiences intends to ensure that the prototype design will adapt to the interests, knowledge and styles of the learners who will use it and, therefore, promote a more engaging and effective learning experience. Based on Druin’s framework of children’s roles (as a user, as a tester, and as an informant), pupils (end users) participated since the early stages in the design process, providing different types of inputs. The sample of this study consists of 36 children in Portugal and the UK (PT=20, UK=16) within the age group 12-14 years (digital natives). Pupils were able to bring and discuss ideas related to game elements (e.g. badges and leader boards), game mechanics (e.g. challenging, progression), graphic image (e.g. illustrations, iconography) and interaction (e.g. animal behaviour). The results suggest that when children are brought into the design process, they can produce new ideas and highlight system interactions in the way they interpret the context.
Beauty Is in the Eye of the Beholder

Aesthetics is a subjective concept that is likely to be perceived differently among people of different ages, genders, and cultural backgrounds. While techniques that directly compute this concept in images has seen increasing attention by the multimedia and machine-learning community, there are very few attempts at encoding the influences from the photographer’s viewpoint. This work demonstrates how the aesthetic quality of photos can be better learned by accounting for the demographic background of a photographer. A new AVA-PD (Photographer Demographic) dataset is created to supplement the AVA dataset by providing photographers’ age, gender and location attributes. Two deep convolutional neural network (CNN) architectures are proposed to utilize demographic information for aesthetic prediction of photos; both are shown to yield better prediction capabilities compared to most existing approaches. By leveraging on AVA-PD meta-data, we also present some additional machine-learnable tasks such as identifying the photographer and predicting photography styles from a person’s gallery of photos.
Building information models are dirty

Building Information Modelling (BIM) processes imply an intensive use of information. Nevertheless, several studies revealed critical issues in the data quality of information models. While some studies presented interesting works in the evaluation of model quality with reference to IFC. The analysis of the data quality issues in native models remains a research gap as well as the understanding of where these issues are generated. This research proposes an analysis of four information models to evaluate and classify data quality issues according to three dimensions, i.e. accuracy, coherence and completeness. Results highlighted user behaviours and/or technological limitations in real-world applications.