Miracle-dz/UniFAR-FTUs huggingface dataset:
Nesting Refuge Structures Are Ineffective at Protecting Bell's Turtle (Myuchelys bellii) Nests from Red Fox (Vulpes vulpes) Depredation

Abstract. Invasive red foxes (Vulpes vulpes) are a serious conservation issue for Australia's freshwater turtle species, including the endangered Bell's turtle (Myuchelys bellii). As many as 96% of Australian freshwater turtle nests may be depredated in a season by foxes. Current methods of turtle nest protection rely on early detection of nesting activity, followed by nest-specific structures to prevent predation. An alternative method to provide protection against fox raiding was tested: nesting refuge structures based on a design successfully used in the United States to protect diamondback terrapin (Malaclemys terrapin) nests. Six wood and chicken wire structures were placed at different sites beside large riverine pools on the Macdonald and Gwydir rivers, northeastern New South Wales, Australia, in the summers of 2019–2020 and 2020–2021. Sites were chosen for known previous nesting activity or presence of mature females, and each structure was placed in typical Bell's turtle nesting habitat at known nesting sites. Prior to placement, the soil was tilled with a rotary hoe to make the interior of the structure more enticing as nesting habitat, because Bell's turtles had been previously seen to nest in disturbed soils. Although females did approach the structures and in one case entered, no females were recorded nesting inside. Further, severe flooding in both years damaged and/or displaced 4 of the 6 structures. Rigid nest protection structures were therefore not shown to be an effective nest protection method for this species, despite their success in other regions for other species. Negative results such as these are important for conservation studies because they guide conservation efforts away from expending limited resources on ineffective methods and strategies.
Leveraging Co-Creation for Computing-Infused Lessons

Computing-infused lessons serve as a way to provide all K-12 stu- dents with computer science learning opportunities. Positioning high school interns as curricular co-creators with K-12 teachers can help to create more engaging lessons. I examine the quality and impact of these co-created computing infused lessons.
RDeF: Prosuming Interactive Stories to Train RDF and SPARQL

Traditional database courses mainly focus on managing relational databases, but understanding NoSQL databases is becoming more relevant to computer science students. This also applies to Linked Data with the standards RDF and SPARQL where training material is scarce. Scarcity can be countered by involving students in the creation process. This paper introduces RDeF --- an open-source prosumer framework where students can consume and produce interactive stories to train their RDF and SPARQL skills. Stories are composed of scenes that are created with RDF triples from templates. Within stories, players must identify hidden RDF triples to build the knowledge base and query it by executing SPARQL requests to answer questions.
Teacher Education and Computational Thinking: Measuring Pre-service Teacher Conceptions and Attitudes

In recent years, there has been a growing awareness of the need for computer science education opportunities and particularly for engaging students in computational thinking ideas and practices to help them understand how computing influences our world. At its core, computational thinking (CT) is seen as understanding how computational practices (such as, abstraction) and tools (such as modeling software) can be used to explore phenomena, solve problems, and influence our lives and society. The push for CT integration has called for preparing future educators to learn CT practices and tools through stand-alone courses and within the context of disciplinary pedagogy/methods courses. However, there are few instruments that measure pre-service teachers' attitudes towards computational thinking and its role in formal schooling. In this study, we developed and validated an instrument to measure pre-service teachers' attitudes towards CT, its role in students' lives, and their own self-efficacy to incorporate CT into their teaching. We report results from a principal components factor analysis on survey responses from 260 pre-service teachers to identify patterns and reduce the number of dimensions of comparison in the analysis. We discuss how pre-service teacher conceptions have implications for preparing future teachers to integrate computational thinking into their instruction.
Simulation-Based Learning via Cisco Packet Tracer to Enhance the Teaching of Computer Networks

Teaching and learning computer networks is a crucial part of a computing undergraduates education. However, it is an abstract topic with theory that can be challenging for students to comprehend. Simulation-based learning can be used as a teaching tool to enhance networking-based lecture topics and help students more easily understand and visualise how it really works in a safe, user-friendly environment. One such simulation tool, Cisco Packet Tracer, can be used to good effect, and this paper reports on the practical implementation of the tool as identified in existing literature. Additionally, this paper discusses how Packet Tracer was utilised as part of a UK based first year undergraduate module 'Computers and Security', and details the content of nine practical sessions, whilst also identifying the main benefits and challenges of their implementation from a practitioner perspective. It was found that while Packet Tracer can be an additional benefit to augment the teaching of networking concepts, there are limitations and challenges which educators must be aware of if implementing the tool. The paper therefore concludes with providing recommendations which should help educators and curriculum designers in creating and delivering more effective and interactive networking sessions.
SCINTILLATIONS IN LIQUID HELIUM.

Scintillations in liquid helium due to the passage of a charged particle were first observed by Simmons and Perkins in 1961.1 Subsequent investigations have revealed that the intensity of the scintillations as a function of temperature is different from that for scintillators near room temperature and is almost certainly connected with the superfluid nature of liquid helium below the X point. This temperature effect is discussed in terms of current theories of liquid helium and in the light of the present state of knowledge relating to the scintillation phenomenon. Both a and 3 particles have been used to excite the liquid helium and it is significant that the effect of temperature on the scintillations depends on the particle producing the scintillation. The most thorough investigation of a-induced cintillations has been carried out by the University of Virginia group2 who have observed scintillations down to O.3°K. Their results are shown in Fig. 1. The scintillation intensity drops steadily from 4.2°K to the X point (2.18°K) where it drops sharply. Below the X point the intensity decreases by aboUt 15% to what appears to be a constant value. The decrease from 4.2°K to 2.18°K can be explained by a change in the density of the liquid which causes a decrease in the a-path length and hence an increase in the fraction of scintillation light absorbed on the source holder. The sharp drop at the X point is probably due to the cessation of internal boiling in the liquid. The superfluid nature of helium below the A point gives rise to a very large thermal conductivity which prevents enough heat being localised to form a bubble. The fit to the data below the A point produced by the authors of reference 2 is discussed later. They noted in this experiment that if all the emitted photons were collected then no decrease was observed below the A point. These observations clearly require an explanation in terms of a mechanism which delays photon emission to a degree that depends on the temperature of the liquid. A similar investigation of the effect of temperature on the scintillation intensity induced by . particles gave the results shown3 in Fig. 2. It can be seen here that the intensity remains constant with temperature apart from a discontinuity at the A point. As the path length of electrons is about two orders of magnitude greater than that of
Peripheral mechanisms of chronic pain

Abstract Acutely, pain serves to protect us from potentially harmful stimuli, however damage to the somatosensory system can cause maladaptive changes in neurons leading to chronic pain. Although acute pain is fairly well controlled, chronic pain remains difficult to treat. Chronic pain is primarily a neuropathic condition, but studies examining the mechanisms underlying chronic pain are now looking beyond afferent nerve lesions and exploring new receptor targets, immune cells, and the role of the autonomic nervous system in contributing chronic pain conditions. The studies outlined in this review reveal how chronic pain is not only confined to alterations in the nervous system and presents findings on new treatment targets and for this debilitating disease.
One step preparation of green reduced copper oxide nanorods using Citrus sinensis L. peel extracts and evaluation of their photocatalytic degradation of Rose Bengal dye and antibacterial activity

ABSTRACT Modern development in nanoscience and nanotechnology has contributed to the varied uses of metal and metal oxide nanoparticles in numerous arena of sciences, research organisations and industries. Among all metal oxides, copper oxide nanoparticles (CuO NPs) has reached more responsiveness as a result of its unique properties and applications. Herein, we report a one-step hydrothermal preparation of a photocatalytically stable copper oxide nanorods (g-CuO NRs) in the presence of Citrus sinensis L. peel extracts. The as prepared g-CuO NRs was used for photocatalytic activity in degradation of Rose Bengal (RB) under visible light irradiation. Upon 30 min of visible light irradiation, the g-CuO NRs photocatalyst exhibits an excellent photocatalytic activity (94.5%) for degradation RB. In addition, the g-CuO NRs were further tested for their antibacterial activity against gram-negative Escherichia coli , Klebsiella pneumoniae and gram-positive Staphylococcus aureus , Streptococcus pneumoniae bacteria and the test results showed improved antibacterial activity with the increase concentration of g-CuO NRs. The outcome of this work highlights the g-CuO NRs to be an interesting material for treatment of organic dyes and biomedical applications. Graphical abstract
Simulation of deep eutectic solvents: Progress to promises

Deep eutectic solvents (DESs) are binary or ternary mixtures of compounds that possess significant melting point depressions relative to the pure isolated components. The discovery of DESs has been a major breakthrough with multiple fields benefitting from their low cost and tunable physiochemical properties. However, tailoring DESs for specific applications through their practically unlimited synthetic combinations can be as much a hindrance as a benefit given the expense and time‐required to perform large‐scale experimental measurements. This emphasizes the need for fast computational tools capable of making accurate predictions of DES physiochemical properties exclusively from molecular structure. Yet, these systems are not trivial to model or simulate at the atomic level given their exceedingly nonideal behaviors, asymmetry of components, and the complexity of their molecular electrostatic interactions. Despite the challenge, computational reports featuring quantum mechanical (QM) methods have provided significant understanding into the relationship between the melting point depression and the unique and complex hydrogen bond network present in DESs. Classical molecular dynamics (MD) methods have examined bulk‐phase solvent organization in conjunction with thermodynamic and transport properties. Machine learning (ML) algorithms have shown great potential as structure–property prediction tools. Overall, this review highlights computational accomplishments that have meaningfully advanced our understanding of DESs and strives to give the reader a sense of the overall strengths and drawbacks of the methodologies employed while hinting at promises of advances to come.
Statistical Inversion of Gaia Photometry for Asteroid Spins and Shapes

The Gaia satellite will provide us with a large amount of photometric asteroid data, sparse in time. We have used two methods to analyse simulated Gaia data to obtain the spin and shape solution for an asteroid-like object. A convex inversion method was used to obtain the spin state and convex shape for the target, and a spherical harmonics technique to obtain a set of non-convex shapes that reproduce the observed data. Our results show that the period of asteroids may be determined accurately using Gaia data alone, while the pole solution and the related convex shape are constrained to specific regions. Nonconvex features cannot uniquely be extracted from the data, but a set of non-convex solutions characterizes the limits where the correct shape lies.
Gene symbol recognition with GeneOCR

Motivation Figures in biomedical publications often contain lists of gene symbols. Human intelligence and manual curation is required to recognize, extract, and validate the symbols in order to reuse them. The task is laborious, error prone, and amenable to automation. Results We introduce GeneOCR, a web-based software to recognize, extract, and check gene symbols from figures. It features both an interactive user interface and programmatic access. Simulation-based benchmark reveals that GeneOCR recognizes or suggests correct gene symbols in more than 80% cases. The performance qualifies GeneOCR as an enhancement to manual curation. Availability The source code of GeneOCR is available with GNU v3 license at https://github.com/bedapub/GeneOCR-UI (frontend) and https://github.com/bedapub/GeneOCR-API (backend). A Docker/Singularity image is available via BioContainers. Contact jitao_david. zhang@roche.com
New Collaborative Caching Scheme for D2D Content Sharing in 5G

Device-to-Device (D2D) content sharing is an excellent solution to offload the traffic from the core 5G networks and backhaul links. Caching the most popular files at the network edge and in users’ devices to support user proximity services in 5G helps to offload traffic in the core network and increases the cache hit probability. D2D communication in 5G can be utilized to share the cached files between a pair of devices with a minimal involvement from the base station. This paper presents a collaborative content caching scheme with the primary objective to maximize the overall system offloading gain and the cache hit probability in downloading the popular file contents. The proposed system model exploits the social-networking concept within the WiFi Direct radio range, assuming the cell structure present in a densely populated area such as a university campus or an auditorium. The performance of the proposed cache scheme is evaluated through extensive simulations taking into consideration the device mobility and disconnection, and the results are presented.
This paper is included in the Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation.

Network telemetry is essential for service availability and performance in large-scale production environments. While there is recent advent in novel measurement primitives and algorithms for network telemetry, a challenge that is not well studied is Change . Facebook runs fast-evolving networks to adapt to varying application requirements. Changes occur not only in the data collection and processing stages but also when interpreted and consumed by applications. In this paper, we present PCAT, a production change-aware telemetry system that handles changes in fast-evolving networks. We propose to use a change cube abstraction to systematically track changes, and an intent-based layering design to conﬁne and track changes. By sharing our experiences with PCAT, we bring a new aspect to the monitoring research area: improving the adaptivity and evolvability of network telemetry.
This paper is included in the Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation.

Datacenter operators today strive to support microsecond-latency applications while also using their limited CPU resources as efﬁciently as possible. To achieve this, several recent systems allow multiple applications to run on the same server, granting each a dedicated set of cores and reallocating cores across applications over time as load varies. Unfortunately, many of these systems do a poor job of navigating the tradeoff between latency and efﬁciency, sacriﬁcing one or both, especially when handling tasks as short as 1 µ s. While the implementations of these systems (threading libraries, network stacks, etc.) have been heavily optimized, the policy choices that they make have received less scrutiny. Most systems implement a single choice of policy for allocating cores across applications and for load-balancing tasks across cores within an application. In this paper, we use simulations to compare these different policy options and explore which yield the best combination of latency and efﬁciency. We conclude that work stealing performs best among load-balancing policies, multiple policies can perform well for core allocations, and, surprisingly, static core allocations often outperform reallocation with small tasks. We implement the best-performing policy choices by building on Caladan, an existing core-allocating system, and demonstrate that they can yield efﬁciency improvements of up to 13-22% without degrading (median or tail) latency.
This paper is included in the Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation.

Abstract The rackless data center (RDC) is a novel network architecture that logically removes the rack boundary of traditional data centers and the inefﬁciencies that come with it. As modern applications generate more and more inter-rack trafﬁc, the traditional architecture suffers from contention at the core, imbalanced bandwidth utilization across racks, and longer network paths. RDC addresses these limitations by enabling servers to logically move across the rack boundary at run-time. Our design achieves this by inserting circuit switches at the network edge between the ToR switches and the servers, and by reconﬁguring the circuits to regroup servers across racks based on the trafﬁc patterns. We have performed extensive evaluations of RDC both in a hardware testbed and packet-level simulations and show that RDC can speed up a 4:1 oversubscribed network by 1 . 78 × ∼ 3 . 9 × for realistic applications and more than 10 × in large-scale simulation; furthermore, RDC is up to 2 . 4 × better in performance per watt than a conventional non-blocking network.
Backdraft: a Lossless Virtual Switch that Prevents the Slow Receiver Problem

Virtual switches, used for end-host networking, drop packets when the receiving application is not fast enough to consume them. This is called the slow receiver problem, and it is important because packet loss hurts tail communication latency and wastes CPU cycles, resulting in application-level performance degradation. Further, solving this problem is challenging because application throughput is highly variable over short timescales as it depends on workload, memory contention, and OS thread scheduling. This paper presents Backdraft, a new lossless virtual switch that addresses the slow receiver problem by combining three new components: (1) Dynamic Per-Flow Queuing (DPFQ) to prevent HOL blocking and provide on-demand memory usage; (2) Doorbell queues to reduce CPU overheads; (3) A new overlay network to avoid congestion spreading. We implemented Backdraft on top of BESS and conducted experiments with real applications on a 100 Gbps cluster with both DCTCP and Homa, a state-of-the-art congestion control scheme. We show that an application with Backdraft can achieve up to 20x lower tail latency at the 99 th percentile.
Bots in�uence opinion dynamics without direct human-bot interaction: the mediating role of recommender systems

Bots’ ability to influence public discourse is difficult to estimate. Recent studies found that hyperpartisan bots are unlikely to influence public opinion because bots often interact with already highly polarized users. However, previous studies focused on direct human-bot interactions (e.g., retweets, at-mentions, and likes). The present study suggests that political bots, zealots, and trolls may affect people’s views indirectly via the mediating role of a platform’s content recommendation system, thus influencing opinions even in the absence of direct human-bot interaction. Using an agent-based opinion dynamics simulation, we isolated the effect of a single bot – representing 1% of nodes in a network – on the opinion formed by rational Bayesian agents after removing direct human-bot connections. We compare this experimental condition with an identical baseline condition where such a bot is absent. We used the same random seed in both simulations so that conditions remained identical except for the presence of the bot. Results show that, even in the absence of direct interactions, the mere presence of the bot is sufficient to shift the average population opinion. Virtually all nodes – not only nodes directly interacting with the bot – shifted towards more extreme opinions. Overall, these findings offer a proof of concept that bots and hyperpartisan accounts can influence population opinions not only by directly interacting with humans but also by secondary effects, such as shifting platforms’ recommendation engines’ internal representations. The mediating role of recommender systems creates indirect causal pathways of algorithmic opinion manipulation.
Who is the Lone Terrorist? A Study of Vehicle-Borne Attackers in Israel and the West Bank

ABSTRACT Lone actor terrorism has become a significant challenge for Western democracies. Previous studies have failed to point out a comprehensive profile of lone terrorists, and suggested that examining more specific sub-groups of lone actors, sharing contextual factors or ideology, may produce such a profile. The current study examines the sub-group of vehicle-borne lone terrorists, who committed their attacks in Israel and the West Bank between January 2000 and March 2016. Based on confidential and open-source data, we find that general sociodemographic characteristics did not produce a unique profile of attackers. However, a deeper examination of behavioral factors preceding the attack yields common traits. Specifically, we find that previous experience—both in different forms of unlawful behavior and in training related to the attack method—was significantly related to a successful attack. Similarities in regards to the triggers for the attack and personal motivations also emerge, suggesting that while operating independently, lone actors are very much influenced by ongoing events. We conclude that focusing on a sub-group of lone attackers following a spatio-methodological-oriented approach contributes to the construction of a profile for lone terrorists, and discuss these findings in the context of mitigation.