Miracle-dz/UniFAR-FTUs huggingface dataset:
Dawn: Co-programming Distributed Applications with Network Control

Traditionally, distributed applications are programmed without knowing network's behaviors, while network is also separately controlled. However, simple network abstraction in current applications is no longer able to handle more complicated and programmable networks. Dawn raises the novel idea of co-programming the applications with network control in the context of end hosts. By annotating each application flow with direct network control, programmers can express general distributed applications correctly and efficiently under arbitrary networks. We demonstrate Dawn's expressivity with multiple application case studies.
Visualising Large Document Collections by Jointly Modeling Text and Network Structure

Many large text collections exhibit graph structures, either inherent to the content itself or encoded in the metadata of the individual documents. Example graphs extracted from document collections are co-author networks, citation networks, or named-entity-cooccurrence networks. Furthermore, social networks can be extracted from email corpora, tweets, or social media. When it comes to visualising these large corpora, either the textual content or the network graph are used. In this paper, we propose to incorporate both, text and graph, to not only visualise the semantic information encoded in the documents' content but also the relationships expressed by the inherent network structure. To this end, we introduce a novel algorithm based on multi-objective optimisation to jointly position embedded documents and graph nodes in a two-dimensional landscape. We illustrate the effectiveness of our approach with real-world datasets and show that we can capture the semantics of large document collections better than other visualisations based on either the content or the network information.
Improving the Cross-Subject Performance of the ERP-Based Brain–Computer Interface Using Rapid Serial Visual Presentation and Correlation Analysis Rank

The brain–computer interface (BCI) is a system that is designed to provide communication channels to anyone through a computer. Initially, it was suggested to help the disabled, but actually had been proposed a wider range of applications. However, the cross-subject recognition in BCI systems is difficult to break apart from the individual specific characteristics, unsteady characteristics, and environmental specific characteristics, which also makes it difficult to develop highly reliable and highly stable BCI systems. Rapid serial visual presentation (RSVP) is one of the most recent spellers with a clean, unified background and a single stimulus, which may evoke event-related potential (ERP) patterns with less individual difference. In order to build a BCI system that allows new users to use it directly without calibration or with less calibration time, RSVP was employed as evoked paradigm, then correlation analysis rank (CAR) algorithm was proposed to improve the cross-individual classification and simultaneously use as less training data as possible. Fifty-eight subjects took part in the experiments. The flash stimulation time is 200 ms, and the off time is 100 ms. The P300 component was locked to the target representation by time. The results showed that RSVP could evoke more similar ERP patterns among subjects compared with matrix paradigm. Then, the included angle cosine was calculated and counted for averaged ERP waveform between each two subjects. The average matching number of all subjects was 6 for the matrix paradigm, while for the RSVP paradigm, the average matching number range was 20 when the threshold value was set to 0.5, more than three times as much larger, quantificationally indicating that ERP waveforms evoked by the RSVP paradigm produced smaller individual differences, and it is more favorable for cross-subject classification. Information transfer rates (ITR) were also calculated for RSVP and matrix paradigms, and the RSVP paradigm got the average ITR of 43.18 bits/min, which was 13% higher than the matrix paradigm. Then, the receiver operating characteristic (ROC) curve value was computed and compared using the proposed CAR algorithm and traditional random selection. The results showed that the proposed CAR got significantly better performance than the traditional random selection and got the best AUC value of 0.8, while the traditional random selection only achieved 0.65. These encouraging results suggest that with proper evoked paradigm and classification methods, it is feasible to get stable performance across subjects for ERP-based BCI. Thus, our findings provide a new approach to improve BCI performances.
CoronaVR: A Computational Resource and Analysis of Epitopes and Therapeutics for Severe Acute Respiratory Syndrome Coronavirus-2

In December 2019, the Chinese city of Wuhan was the center of origin of a pneumonia-like disease outbreak with an unknown causative pathogen. The CDC, China, managed to track the source of infection to a novel coronavirus (2019-nCoV; SARS-CoV-2) that shares approximately 79.6% of its genome with SARS-CoV. The World Health Organization (WHO) initially declared COVID-19 as a Public Health Emergency of International Concern (PHEIC) and later characterized it as a global pandemic on March 11, 2020. Due to the novel nature of this virus, there is an urgent need for vaccines and therapeutics to control the spread of SARS-CoV-2 and its associated disease, COVID-19. Global efforts are underway to circumvent its further spread and treat COVID-19 patients through experimental vaccine formulations and therapeutic interventions, respectively. In the absence of any effective therapeutics, we have devised h bioinformatics-based approaches to accelerate global efforts in the fight against SARS-CoV-2 and to assist researchers in the initial phase of vaccine and therapeutics development. In this study, we have performed comprehensive meta-analyses and developed an integrative resource, “CoronaVR” (http://bioinfo.imtech.res.in/manojk/coronavr/). Predominantly, we identified potential epitope-based vaccine candidates, siRNA-based therapeutic regimens, and diagnostic primers. The resource is categorized into the main sections “Genomes,” “Epitopes,” “Therapeutics,” and Primers. ” The genome section harbors different components, viz, genomes, a genome browser, phylogenetic analysis, codon usage, glycosylation sites, and structural analysis. Under the umbrella of epitopes, sub-divisions, namely cross-protective epitopes, B-cell (linear/discontinuous), T-cell (CD4+/CD8+), CTL, and MHC binders, are presented. The therapeutics section has different sub-sections like siRNA, miRNAs, and sgRNAs. Further, experimentally confirmed and designed diagnostic primers are earmarked in the primers section. Our study provided a set of shortlisted B-cell and T-cell (CD4+ and CD8+) epitopes that can be experimentally tested for their incorporation in vaccine formulations. The list of selected primers can be used in testing kits to identify SARS-CoV-2, while the recommended siRNAs, sgRNAs, and miRNAs can be used in therapeutic regimens. We foresee that this resource will help in advancing the research against coronaviruses.
The Effect of Strengths-Based Performance Appraisal on Perceived Supervisor Support and the Motivation to Improve Performance

Strengths-based performance appraisal focuses on identifying, appreciating, and developing employee’s qualities in line with the company goals. Based on self-determination theory (SDT), we hypothesized that strengths-based performance appraisals will bring about a stronger motivation to improve (MTI) performance, by making subordinates feel supported by their supervisor and thereby fulfill their need for relatedness. Moreover, we hypothesized that strengths-based performance appraisal will reduce the threat to the relationship between supervisor and subordinate when the performance rating is relatively low. To investigate our hypotheses, we distributed a questionnaire to employees working for a large Dutch consultancy firm right after their yearly appraisal (N = 422) and linked the questionnaire data to their official performance ratings. Conditional process analysis indicated that strengths-based performance appraisal had a positive effect on perceived supervisor support (PSS), and in turn on MTI performance. Furthermore, the effect of strengths-based performance appraisal was particularly strong, when the performance rating was relatively low. Our findings may inspire future research into strengths-based performance appraisal as a relational approach to employee development.
Reinforcement Learning based Strategy Selection in StarCraft: Brood War

Artificial intelligence (AI) applied on real-time strategy (RTS) games, such as StarCraft has become a hot topic in recent years. Currently, many researches mainly focused on the aspects of applying reinforcement learning (RL) on the aspects, such as the building order, map analysis, macro- and micro-management, etc. This paper innovatively proposes a mechanism applying reinforcement learning on pre-designed game strategies, which can be considered as human experience and combinations of both the macro- and micro-management. In our work, game strategies are formed by many sub-strategy modules that consist a series of orderly actions corresponding to certain components and game stages. Further, by applying RL on the strategy modules, the proposed mechanism can effectively improve selection of the game strategies, and balance the AI decision and human experience. Our bot was tested against some bots selected from AIIDE 2017-2018. The results show that with the RL framework, our bot can improve the overall winning rate from 42.79% to 59.36%. Particularly, the improvements on opponent 32.06%, 25.18%, and 30.32%, against the Zerg bot, Arrakhammer, the Protoss bot, Xelnaga, the Terran bot, UAlbertaBot.
NetworkAPI: An In-band Signalling Application-aware Traffic Engineering using SRv6 and IP anycast

Application-aware Traffic Engineering (TE) plays a crucial role in ensuring quality of services (QoS) for recently emerging applications such as AR, VR, cloud gaming, and connected vehicles. While a deterministic application-aware TE is required for these mission-critical applications, a negotiation procedure between applications and network operators needs to undergo major simplification to fulfill the scalability of the application based on emerging microservices and container-based architecture. In this paper, we propose a NetworkAPI framework which allows an application to indicate a desired TE behavior inside IP packets by leveraging Segment Routing over IPv6 (SRv6). In the NetworkAPI framework, the TE behavior provided by the network operator is expressed as an SRv6 Segment Identifier (SID) in the form of a 128-bit IPv6 address. Because the IPv6 address of an SRv6 SID is distributed using IP anycast, the application can utilize the unchanged SRv6 SID regardless of the application's location, as if the application controls an API on the transport network. We implement a prototype of the NetworkAPI framework on a Linux kernel. On the prototype implementation, a basic packet forwarding performance is evaluated to demonstrate the feasibility of our framework.
Activité protéolytique de souches de lactobacilles thermophiles isolées de levains et de Comté. II. Applications en sites industriels

Resume Cette etude a permis de verifier en site industriel, l'aptitude proteolytique de souches de lactobacilles thermophiles isolees de levains et de Comte et selectionnees a partir de tests d'activites sur cultures (dosages des groupements -NH2 par l'acide 2,4,6 trinitrobenzene sulfonique et de l'activite leucine aminopeptidasique). L'importance et la nature de la proteolyse observees sur fromages n'ont pas de relation etroite avec l'activite proteolytique des souches mesuree en laboratoire. En association avec des streptocoques thermophiles, les especes' L delbrueckii subsp lactis produisent davantage d'acides amines et de petits peptides que les associations avec L helveticus. La comparaison de profils peptidiques obtenus par CLHP pour les 2 especes montre que la surface cumulee des pics en fin de chromatogramme, plus importante chez les fromages fabriques avec les souches de L helveticus L79 et L80, est correlee avec la note d'amertume.
Chinese Calligraphy Character Image Recognition and Its Applications in Web and Wechat Applet Platform

Calligraphy is an important part of traditional Chinese culture. Authentic calligraphy works are usually preserved on paper, bamboo slips and stone tablets, which are easy to be destroyed and not easily appreciated by most calligraphy lovers anytime and anywhere. Therefore, in order to facilitate the preservation and appreciation of calligraphy works, digital technology and computer technology are used for digital storage, management and service. The time complexity of image recognition algorithm based on traditional method is relatively high, especially the algorithm based on feature matching has low tolerance to Chinese character deformation. Therefore, this paper tries to use deep learning to study the image recognition of Chinese characters. However, some existing data sets of calligraphic images have a small number of corresponding images, which cannot support deep learning network training. In view of this problem, a data augmentation method based on character glyph and stroke characteristics is proposed. This method starts from two aspects of Chinese character glyph and stroke. Chinese character glyph is changed by affine changes of the restricted conditions. The stroke information of Chinese characters is extracted by super pixel and the stroke information is changed to expand the diversity of Chinese characters in the data set. In the process of character recognition, mixup data augmentation algorithm is combined with Inception v4 network which has excellent performance in Imagenet classification tasks? for network recognition training to improve network generalization ability. Experiments show that the algorithm proposed in this paper based on deep learning has a higher recognition effect than previous algorithms, especially in cursive and running recognition. The digital calligraphy knowledge service system based on web terminal and WeChat small program terminal is designed and implemented, which can provide the digital calligraphy knowledge service of calligraphy work segmentation and calligraphy character recognition.
The common house spider, Parasteatoda tepidariorum, maintains silk gene expression on sub-optimal diet

Orb-web weaving spiders and their relatives spin multiple task-specific fiber types. The unique material properties of each silk type result from differences in amino acid sequence and structure of their component proteins, primarily spidroins (spider fibrous proteins). Amino acid content and gene expression measurements of spider silks suggest some spiders change expression patterns of individual protein components in response to environmental cues. We quantified mRNA abundance of three spidroin encoding genes involved in prey capture in the common house spider, Parasteatoda tepidariorum (Theridiidae), fed different diets. After 10 days of acclimation to the lab on a diet of mealworms, spiders were split into three groups: (1) individuals were immediately dissected, (2) spiders were fed high-energy crickets, or (3) spiders were fed low-energy flies, for 1 month. All spiders gained mass during the acclimation period and cricket-fed spiders continued to gain mass, while fly fed spiders either maintained or lost mass. Using quantitative PCR, we found no significant differences in the absolute or relative abundance of dragline gene transcripts, major ampullate spidroin 1 (MaSp1) and major ampullate spidroin 2 (MaSp2), among groups. In contrast, prey-wrapping minor ampullate spidroin (MiSp) gene transcripts were significantly less abundant in fly-fed than lab-acclimated spiders. However, when measured relative to actin, cricket-fed spiders showed the lowest expression of MiSp. Our results suggest that house spiders are able to maintain silk production, even in the face of a low-quality diet.
CHCI: A Crowdsourcing Human-computer Interaction Framework for Cultural Heritage Knowledge

This paper aims to extract knowledge including entities and relationships, from multi-source heterogeneous cultural heritage (CH) resources. The proposed crowdsourcing human-computer interaction framework utilizes museum-user-algorithm cooperation to achieve high-quality and scalable CH knowledge extraction. This paper also proposes crowdsourcing optimization mechanisms to improve participation and quality of crowdsourcing project. Finally, this paper discusses how extracted knowledge can support CH digital resource construction and knowledge-driven intelligent applications in Museum.
Where attention goes, energy flows: enhancing individual sustainability in software engineering

Software engineers are plagued by the same troubles as many others in highly skilled jobs and digitized environments: Ever-expanding to-do lists, time to market pressure from management, deadline-driven development, continuous interruption during working tasks, and the juggle of balancing that with other areas of life (physical, mental and emotional health, family, household, finance, friends, hobbies and community service). These demands of life in combination with a seemingly ever-increasing pace wear or burn out many people in the long run. Specifically, as software engineers, this also leads to decreased creativity and less efficiency in problem-solving. Generally offered solutions are reducing screen time and spending more time outdoors, both of which are hard to do within the work of a software engineer. On a meta level, if the developers of the systems that run most of our world do not develop individual sustainability with a balanced pace of life, that imbalance propagates into the systems we develop (similar to Conway's Law). We argue that mindfulness practices like yoga poses (asanas), breathing practices, and meditation exercises can help individually, and even more effectively in combination. In this exploratory paper, we discuss related work that explores the application of these mitigations in other application domains and propose a research agenda to explore their use within software engineering education and practice. Engaging with mindfulness practices in the context of software engineering promises to enhance creativity and cognitive problem-solving skills, leading to more efficiency and effectiveness during software development and increased individual sustainability. This, in turn, leads to better team spirit as well as increased economic profit, both in terms of maintaining human capital and customer contract deliverables.
Linking minimal and detailed models of CA1 microcircuits reveals how theta rhythms emerge and how their frequencies are controlled

The wide variety of cell types and their inherent biophysical complexities pose a challenge to our understanding of oscillatory activities produced by cellular-based computational models. This challenge stems from the high-dimensional and multi-parametric nature of these systems. To overcome this issue, we implement systematic comparisons of minimal and detailed models of CA1 microcircuits that generate intra-hippocampal theta rhythms (3-12 Hz). We leverage insights from minimal models to guide detailed model explorations and obtain a cellular perspective of theta generation. Our findings distinguish the pyramidal cells as the theta rhythm initiators and reveal that their activity is regularized by the inhibitory cell populations, supporting an ‘inhibition-based tuning’ mechanism. We find a strong correlation between the pyramidal cell input current and the resulting LFP theta frequency, establishing that the intrinsic pyramidal cell properties underpin network frequency characteristics. This work provides a cellular-based foundation from which in vivo theta activities can be explored.
Three-dimensional random walk models of individual animal movement and their application to trap counts modelling

Background Random walks (RWs) have proved to be a powerful modelling tool in ecology, particularly in the study of animal movement. An application of RW concerns trapping which is the predominant sampling method to date in insect ecology, invasive species, and agricultural pest management. A lot of research effort has been directed towards modelling ground-dwelling insects by simulating their movement in 2D, and computing pitfall trap counts, but comparatively very little for flying insects with 3D elevated traps. Methods We introduce the mathematics behind 3D RWs and present key metrics such as the mean squared displacement (MSD) and path sinuosity, which are already well known in 2D. We develop the mathematical theory behind the 3D correlated random walk (CRW) which involves short-term directional persistence and the 3D Biased random walk (BRW) which introduces a long-term directional bias in the movement so that there is an overall preferred movement direction. In this study, we consider three types of shape of 3D traps, which are commonly used in ecological field studies; a spheroidal trap, a cylindrical trap and a rectangular cuboidal trap. By simulating movement in 3D space, we investigated the effect of 3D trap shapes and sizes and of movement diffusion on trapping efficiency. Results We found that there is a non-linear dependence of trap counts on the trap surface area or volume, but the effect of volume appeared to be a simple consequence of changes in area. Nevertheless, there is a slight but clear hierarchy of trap shapes in terms of capture efficiency, with the spheroidal trap retaining more counts than a cylinder, followed by the cuboidal type for a given area. We also showed that there is no effect of short-term persistence when diffusion is kept constant, but trap counts significantly decrease with increasing diffusion. Conclusion Our results provide a better understanding of the interplay between the movement pattern, trap geometry and impacts on trapping efficiency, which leads to improved trap count interpretations, and more broadly, has implications for spatial ecology and population dynamics.
Flipping Perceptions, Engagements and Realities: A Case Study

Flipped learning is considered as an active and constructivist teaching and learning approach to enhance the learning experience of both students and teachers. This research, conducted within the framework proposed by Jonathan Bergmann, explores the students’ as well as teachers’ perception of flipped learning. The study uses both quantitative and qualitative methods to collect data from the respondents. Separate questionnaires were used to collect the information from teachers and students, and a structured interview was conducted with teachers. Total of 56 students and 14 teachers from an undergraduate business program participated in this study, and all the respondents had experience in flipped learning. Results show both the students and teachers hold a positive perception towards flipped approach, and they consider this approach as an innovative way of teaching and learning. Study also identified the factors affecting the effectiveness of flipped classrooms and provides necessary recommendations for its successful implementation.