Miracle-dz/UniFAR-FTUs huggingface dataset:
Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization

Most previous seq2seq summarization systems purely depend on the source text to generate summaries, which tends to work unstably. Inspired by the traditional template-based summarization approaches, this paper proposes to use existing summaries as soft templates to guide the seq2seq model. To this end, we use a popular IR platform to Retrieve proper summaries as candidate templates. Then, we extend the seq2seq framework to jointly conduct template Reranking and template-aware summary generation (Rewriting). Experiments show that, in terms of informativeness, our model significantly outperforms the state-of-the-art methods, and even soft templates themselves demonstrate high competitiveness. In addition, the import of high-quality external summaries improves the stability and readability of generated summaries.
A concept to simplify authoring of adaptive hypermedia eLearning structures

ABSTRACT Adaptive Learning Systems have been developed to improve learning success by increasing learner satisfaction, learning speed, and educational effectiveness. However, authoring adaptive eLearning structures is still a complicated task for lecturers, because they are unaccustomed to the way of thinking and to the workflows that are needed for the creation process. This paper describes a research project developing a concept that helps authors with the structure creation process, focussed on sequencing of content. Therefore, the issue was approached from the authors' perspective. First, storytelling theory has helped understand content structures and provided insights into how authors work. Second, by investigating the linear structure of learning content, information for sequencing could be extracted, providing the basis for an approach that is based on relations of different authors' content, and defining interdependencies that delinearise the content structure.
Economical Revenue Maximization in Cache Enhanced Mobile Edge Computing

Mobile edge computing (MEC) has emerged as a potential paradigm to enhance the processing capabilities of mobile user equipments (MUEs), while edge caching has become a promising means of alleviating traffic in the backhual. In this paper, we formulate a stochastic optimization problem to maximize the average economical profit of MEC server by jointly optimizing offloading decision and caching decision making, and the allocation of radio, computing, and caching resources in a cellular network, with network stability taken into account. To tackle this problem, we develop an online algorithm referred to as {\bf d}ynamic {\bf j}oint computation {\bf o}ffloading, {\bf r}esource allocation, and content {\bf c}aching algorithm (DJORC) based on Lyapunov optimization theory. Specifically, the proposed DJORC only needs the current states of the system, and without requiring any prior-knowledge. By further using 0-1 integer programming and linear programming, the closed-form solution of the formulated problem is obtained. Simulation results are presented to verify the performance of DJORC under different parameter settings, as well as the performance gains obtained by DJORC over other existing schemes.
The real Bigfoot: a pes from Wyoming, USA is the largest sauropod pes ever reported and the northern-most occurrence of brachiosaurids in the Upper Jurassic Morrison Formation

A set of associated left pedal elements of a sauropod dinosaur from the Upper Jurassic Morrison Formation in Weston County, Wyoming, is described here. Several camarasaurids, a nearly complete small brachiosaur, and a small diplodocid have been found at this locality, but none match the exceptionally large size of the pedal elements. Next to the associated pedal elements, an isolated astragalus, phalanx and ungual were found, which match the large metatarsals in size. The elements cannot be ascribed to diplodocids due to the lack of a ventral process of metatarsal I. Moreover, the morphology of metatarsal V has a broad proximal end, with a long and narrow distal shaft, which differs from Camarasaurus. The size of the material and a medially beveled distal articular surface of metatarsal IV imply an identification as a brachiosaurid. This is the largest pes ever reported from a sauropod dinosaur and represents the first confirmed pedal brachiosaur elements from the Late Jurassic of North America. Furthermore, this brachiosaur material (the pes and the small nearly complete specimen) is the northernmost occurrence of brachiosaurids in the Morrison Formation.
Understanding Online Game Cheating: Unpacking the Ethical Dimension

ABSTRACT Using an online survey approach, this study examines the ethical dimensions and factors operating behind the game cheating behavior. Although the data indicated that young gamers were overall critical of cheating behavior in online games, cheating was found to be a common practice in online gaming community. It is found that gamers tended to dismiss the cheating action as morally insignificant due to peer influence and the play nature of online gaming. With regard to the ethical antecedents of cheating decisions, our results revealed that the personal moral philosophy of idealism had both direct and indirect influence on game cheating intention and behavior (partial mediation), meanwhile, the personal moral philosophy of relativism had only significant indirect influence on game cheating intention (full mediation) and cheating behavior (distal mediation).
#MeToo Alexa: How Conversational Systems Respond to Sexual Harassment

Conversational AI systems, such as Amazon’s Alexa, are rapidly developing from purely transactional systems to social chatbots, which can respond to a wide variety of user requests. In this article, we establish how current state-of-the-art conversational systems react to inappropriate requests, such as bullying and sexual harassment on the part of the user, by collecting and analysing the novel #MeTooAlexa corpus. Our results show that commercial systems mainly avoid answering, while rule-based chatbots show a variety of behaviours and often deflect. Data-driven systems, on the other hand, are often non-coherent, but also run the risk of being interpreted as flirtatious and sometimes react with counter-aggression. This includes our own system, trained on “clean” data, which suggests that inappropriate system behaviour is not caused by data bias.
Back-Translation-Style Data Augmentation for end-to-end ASR

In this paper we propose a novel data augmentation method for attention-based end-to-end automatic speech recognition (E2E-ASR), utilizing a large amount of text which is not paired with speech signals. Inspired by the back-translation technique proposed in the field of machine translation, we build a neural text-to-encoder model which predicts a sequence of hidden states extracted by a pre-trained E2E-ASR encoder from a sequence of characters. By using hidden states as a target instead of acoustic features, it is possible to achieve faster attention learning and reduce computational cost, thanks to sub-sampling in E2E-ASR encoder, also the use of the hidden states can avoid to model speaker dependencies unlike acoustic features. After training, the text-to-encoder model generates the hidden states from a large amount of unpaired text, then E2E-ASR decoder is retrained using the generated hidden states as additional training data. Experimental evaluation using LibriSpeech dataset demonstrates that our proposed method achieves improvement of ASR performance and reduces the number of unknown words without the need for paired data.
Coxiella burnetii Blocks Intracellular Interleukin-17 Signaling in Macrophages

Coxiella burnetii is an obligate intracellular bacterium and the etiological agent of Q fever. Successful host cell infection requires the Coxiella type IVB secretion system (T4BSS), which translocates bacterial effector proteins across the vacuole membrane into the host cytoplasm, where they manipulate a variety of cell processes. ABSTRACT Coxiella burnetii is an obligate intracellular bacterium and the etiological agent of Q fever. Successful host cell infection requires the Coxiella type IVB secretion system (T4BSS), which translocates bacterial effector proteins across the vacuole membrane into the host cytoplasm, where they manipulate a variety of cell processes. To identify host cell targets of Coxiella T4BSS effector proteins, we determined the transcriptome of murine alveolar macrophages infected with a Coxiella T4BSS effector mutant. We identified a set of inflammatory genes that are significantly upregulated in T4BSS mutant-infected cells compared to mock-infected cells or cells infected with wild-type (WT) bacteria, suggesting that Coxiella T4BSS effector proteins downregulate the expression of these genes. In addition, the interleukin-17 (IL-17) signaling pathway was identified as one of the top pathways affected by the bacteria. While previous studies demonstrated that IL-17 plays a protective role against several pathogens, the role of IL-17 during Coxiella infection is unknown. We found that IL-17 kills intracellular Coxiella in a dose-dependent manner, with the T4BSS mutant exhibiting significantly more sensitivity to IL-17 than WT bacteria. In addition, quantitative PCR confirmed the increased expression of IL-17 downstream signaling genes in T4BSS mutant-infected cells compared to WT- or mock-infected cells, including the proinflammatory cytokine genes Il1a, Il1b, and Tnfa, the chemokine genes Cxcl2 and Ccl5, and the antimicrobial protein gene Lcn2. We further confirmed that the Coxiella T4BSS downregulates macrophage CXCL2/macrophage inflammatory protein 2 and CCL5/RANTES protein levels following IL-17 stimulation. Together, these data suggest that Coxiella downregulates IL-17 signaling in a T4BSS-dependent manner in order to escape the macrophage immune response.
Unsupervised Learning of Distributional Relation Vectors

Word embedding models such as GloVe rely on co-occurrence statistics to learn vector representations of word meaning. While we may similarly expect that co-occurrence statistics can be used to capture rich information about the relationships between different words, existing approaches for modeling such relationships are based on manipulating pre-trained word vectors. In this paper, we introduce a novel method which directly learns relation vectors from co-occurrence statistics. To this end, we first introduce a variant of GloVe, in which there is an explicit connection between word vectors and PMI weighted co-occurrence vectors. We then show how relation vectors can be naturally embedded into the resulting vector space.
Early metal use and crematory practices in the American Southeast

Significance Chemical sourcing of a Late Archaic (ca. 4100–3980 cal B.P.) copper artifact reveals extensive trade networks linking the coastal southeastern United States with the Great Lakes. Found alongside the cremated remains of at least seven individuals and in the direct center of a plaza defined by a circular shell midden, the copper artifact demonstrates the existence of long-distance networks that transmitted both objects and mortuary practices. In contrast with models that assume coastal hunter-gatherer-fishers typically lived in small, simple societies, we propose that trading for and utilizing copper is evidence of emergent hierarchical social organization during the Archaic and the likelihood that power was gained and displayed during large-scale gatherings and ceremonial events. Long-distance exchange of copper objects during the Archaic Period (ca. 8000–3000 cal B.P.) is a bellwether of emergent social complexity in the Eastern Woodlands. Originating from the Great Lakes, the Canadian Maritimes, and the Appalachian Mountains, Archaic-age copper is found in significant amounts as far south as Tennessee and in isolated pockets at major trade centers in Louisiana but is absent from most of the southeastern United States. Here we report the discovery of a copper band found with the cremated remains of at least seven individuals buried in the direct center of a Late Archaic shell ring located in coastal Georgia. Late Archaic shell rings are massive circular middens thought to be constructed, in part, during large-scale ritual gatherings and feasting events. The exotic copper and cremated remains are unique in coastal South Carolina and Georgia where Archaic-age cremations are conspicuously absent and no other Archaic copper objects have been reported. Elemental data produced through laser ablation inductively coupled plasma mass spectrometry shows the copper originated from the Great Lakes, effectively extending Archaic copper exchange almost 1,000 km beyond its traditional boundaries. Similarities in mortuary practices and the presence of copper originating from the Great Lakes reveal the presence of long-distance exchange relations spanning vast portions of the eastern United States and suggest an unexpected level of societal complexity at shell ring localities. These findings are consistent with the hypothesis that elite actors solidified their positions through ritual gatherings and the long-distance exchange of exotic objects during the Archaic.
The Effects of Visual and Control Latency on Piloting a Quadcopter Using a Head-Mounted Display

Recent research has proposed teleoperation of robotic and aerial vehicles using head motion tracked by a head-mounted display (HMD). First-person views of the vehicles are usually captured by onboard cameras and presented to users through the display panels of HMDs. This provides users with a direct, immersive and intuitive interface for viewing and control. However, a typically overlooked factor in such designs is the latency introduced by the vehicle dynamics. As head motion is coupled with visual updates in such applications, visual and control latency always exists between the issue of control commands by head movements and the visual feedback received at the completion of the attitude adjustment. This causes a discrepancy between the intended motion, the vestibular cue and the visual cue and may potentially result in simulator sickness. No research has been conducted on how various levels of visual and control latency introduced by dynamics in robots or aerial vehicles affect users' performance and the degree of simulator sickness elicited. Thus, it is uncertain how much performance is degraded by latency and whether such designs are comfortable from the perspective of users. To address these issues, we studied a prototyped scenario of a head motion controlled quadcopter using an HMD. We present a virtual reality (VR) paradigm to systematically assess the effects of visual and control latency in simulated drone control scenarios.
A Provably Secure Anonymous Authenticated Key Exchange Protocol Based on ECC for Wireless Sensor Networks

In wireless sensor networks, users sometimes need to retrieve real-time data directly from the sensor nodes. Many authentication protocols are proposed to address the security and privacy aspects of this scenario. However, these protocols still have security loopholes and fail to provide strong user anonymity. In order to overcome these shortcomings, we propose an anonymous authenticated key exchange protocol based on Elliptic Curves Cryptography (ECC). The novel protocol provides strong user anonymity such that even the gateway node and the sensor nodes do not know the real identity of the user. The security of the proposed protocol is conducted in a well-defined security model under the CDH assumption. Compared with other related protocols, our protocol is efficient in terms of communication and enjoys stronger security. The only disadvantage is that our protocol consumes more computation resources due to the usage of asymmetric cryptography mechanisms to realize strong anonymity. Consequently, our protocol is suitable for applications which require strong anonymity and high security in wireless sensor networks.
Relationships between Motor Proficiency and Academic Performance in Mathematics and Reading in School-Aged Children and Adolescents: A Systematic Review

Positive associations exist between physical activity, cognition, and academic performance in children and adolescents. Further research is required to examine which factors underpin the relationships between physical activity and academic performance. This systematic review aimed to identify, critically appraise, and synthesize findings of studies examining relationships between motor proficiency and academic performance in mathematics and reading in typically developing school-aged children and adolescents. A systematic search of electronic databases was performed to identify relevant studies. Fifty-five eligible articles were critically appraised and key data was extracted and synthesized. Findings support associations between several components of motor proficiency and academic performance in mathematics and reading. There was evidence that fine motor proficiency was significantly and positively associated with academic performance in mathematics and reading, particularly during the early years of school. Significant positive associations were also evident between academic performance and components of gross motor proficiency, specifically speed and agility, upper-limb coordination, and total gross motor scores. Preliminary evidence from a small number of experimental studies suggests motor skill interventions in primary school settings may have a positive impact on academic performance in mathematics and/or reading. Future research should include more robust study designs to explore more extensively the impact of motor skill interventions on academic performance.
Coordination and Common Knowledge on Communication Networks

Protest is a collective action problem and can be modeled as a coordination game in which two or more people each take an action with the potential to achieve shared mutual benefits, only if their actions coincide. In the context of protest participation, successful coordination requires that people know each others' willingness to participate, and that this information is common knowledge. Social networks can facilitate the creation of common knowledge through the flow of messages. Although there is a rich experimental literature that documents behavior in coordination games with and without communication, little is known about how people coordinate behaviors within a social network and how different types of communication structures affect behavior. In this paper, we develop a theoretically based on-line experiment with Amazon Mechanical Turk participants to characterize the emergence of common knowledge and coordination through interactions within a network. Our experiment is designed to identify the effects of both social network topology and communication and to falsify the game-theoretic predictions. Our data reveal that choices are affected by the network structure and they move towards the theoretical predictions with communication. We use our behavioral findings to simulate dynamics in more complex networks through agent-based modeling. Thus, we combine human behaviors identified in experiments with realistic social network structures to reveal patterns not previously observed.
Participatory learning culture and community formation in connectivist MOOCs

ABSTRACT The purpose of this research is to better understand community formation in MOOCs through employing combined lenses of connectivism, rhizomatic learning, actor-network theory, community of practice, and community of inquiry. In a sequential explanatory mixed methodology design, social network analysis and nethnography were used to analyze and interpret data from a five-week, open and freely available MOOC, #humanMOOC, conducted in late 2015 and early 2016. The findings revealed that both intrinsic and extrinsic drives have unique roles in community formation process and support. These include respect and transparency in mutual communication, being socially visible and building a digital identity, seeing the relationships and patterns among the ideas, and being emotionally present and creating a welcoming, safe place are some effective internal drives, while opportunities to connect to personally meaningful sources or nodes, being able to wander among open ecologies, using nonhuman elements to facilitate learning, creating a safe base ground for initial activities, and creating community goods and well-designed learning spaces that meet diverse needs of the learners are some external drives.
Generalised Spatial Carrierless Amplitude and Phase Modulation in Visible Light Communication

Generalised spatial carrierless amplitude and phase modulation (GS-CAP) is developed in this paper to improve the spectral efficiency of the conventional CAP in indoor visible light communication. The proposed GS-CAP preserves the attractive low complexity transceiver of CAP by exploiting the spatial domain for performance improvement. Bit error rate (BER) analysis is derived for the performance evaluation of the proposed GS-CAP and is shown to be in excellent agreement with the simulation results. The impact of different system parameters, such as the number of active light emitting diodes (LEDs) and the number of receiving photodiodes (PDs), on the performance of GS-CAP are investigated. In addition, a precoding technique is developed for improving the BER performance of GS-CAP when the channel gains are similar. Finally, it is found that for different GS-CAP system configurations with the same spectral efficiency, the configuration with the smallest number of active LEDs results in optimum power efficiency and BER performance.
Target-Sensitive Memory Networks for Aspect Sentiment Classification

Aspect sentiment classification (ASC) is a fundamental task in sentiment analysis. Given an aspect/target and a sentence, the task classifies the sentiment polarity expressed on the target in the sentence. Memory networks (MNs) have been used for this task recently and have achieved state-of-the-art results. In MNs, attention mechanism plays a crucial role in detecting the sentiment context for the given target. However, we found an important problem with the current MNs in performing the ASC task. Simply improving the attention mechanism will not solve it. The problem is referred to as target-sensitive sentiment, which means that the sentiment polarity of the (detected) context is dependent on the given target and it cannot be inferred from the context alone. To tackle this problem, we propose the target-sensitive memory networks (TMNs). Several alternative techniques are designed for the implementation of TMNs and their effectiveness is experimentally evaluated.
Accurate Timeout Detection Despite Arbitrary Processing Delays

Timeout is widely used for failure detection. This paper proposes SafeTimer, a mechanism to enhance existing timeout detection protocols to tolerate long delays in the OS and the application: at the heartbeat receiver, Safe-Timer checks whether there are any pending heartbeats before reporting a failure; at the heartbeat sender, Safe-Timer blocks the sender if it cannot send out heartbeats in time. We have proved that SafeTimer can prevent false failure report despite arbitrary delays in the OS and the application. This property allows existing protocols to relax their timing assumptions and use a shorter timeout interval for faster failure detection. Our evaluation shows that the overhead of SafeTimer is small and applying SafeTimer to existing systems is easy.
Improving Topic Quality by Promoting Named Entities in Topic Modeling

News related content has been extensively studied in both topic modeling research and named entity recognition. However, expressive power of named entities and their potential for improving the quality of discovered topics has not received much attention. In this paper we use named entities as domain-specific terms for news-centric content and present a new weighting model for Latent Dirichlet Allocation. Our experimental results indicate that involving more named entities in topic descriptors positively influences the overall quality of topics, improving their interpretability, specificity and diversity.