Miracle-dz/UniFAR-FTUs huggingface dataset:
Advancing Methodology for Social Science Research Using Alternate Reality Games: Proof-of-Concept Through Measuring Individual Differences and Adaptability and their impact on Team Performance

While work in fields of CSCW (Computer Supported Collaborative Work), Psychology and Social Sciences have progressed our understanding of team processes and their effect performance and effectiveness, current methods rely on observations or self-report, with little work directed towards studying team processes with quantifiable measures based on behavioral data. In this report we discuss work tackling this open problem with a focus on understanding individual differences and its effect on team adaptation, and further explore the effect of these factors on team performance as both an outcome and a process. We specifically discuss our contribution in terms of methods that augment survey data and behavioral data that allow us to gain more insight on team performance as well as develop a method to evaluate adaptation and performance across and within a group. To make this problem more tractable we chose to focus on specific types of environments, Alternate Reality Games (ARGs), and for several reasons. First, these types of games involve setups that are similar to a real-world setup, e.g., communication through slack or email. Second, they are more controllable than real environments allowing us to embed stimuli if needed. Lastly, they allow us to collect data needed to understand decisions and communications made through the entire duration of the experience, which makes team processes more transparent than otherwise possible. In this report we discuss the work we did so far and demonstrate the efficacy of the approach.
Patterns of Arctiid Diversity

Arctiids have attracted a great deal of attention for their striking behavior and ecology, yet our knowledge of their patterns of diversity, particularly in tropical regions, is still surprisingly small. is chapter summarizes current knowledge of arctiid diversity along large-scale gradients of latitude, medium-scale gradients of elevation and disturbance, and small-scale gradients between the forest understory and the canopy.
Chemical and sensory attributes of ghee residue burfi supplemented with corn flour

An attempt was made to utilize ghee residue in the formulation of burfi type confection. The formulation was initially standardized for the levels of ghee residue, khoa and sugar levels. On the basis of sensory parameters, 40% ghee residue, 60% khoa and 30g sugar was optimized. The formulation served as control for the subsequent treatment. Further, different levels of corn flour viz. 3%, 6% and 9% were incorporated (by replacing the respective proportions of khoa) in the ghee residue burfi. The developed products were assessed for physico-chemical, colour and sensory parameters. A significant (P<0.05) effect on the pH and titratable acidity was observed at 9% level of CF incorporation, however, at 6% level the value was comparable with the control and 3%. A significant (P<0.05) decline in fat and lactose values were observed at 6% CF level while the sensory scores of burfi for flavour and overall acceptability containing 6% corn flour were higher than the control. Thus, ghee residue burfi, prepared using 6% corn flour is appreciable for its physico-chemical properties and acceptability.
Automatic error classification with multiple error labels

Although automatic classiﬁcation of machine translation errors still cannot provide the same detailed granularity as manual error classiﬁcation, it is an important task which enables estimation of translation errors and better understanding of the analyzed MT system, in a short time and on a large scale. State-of-the-art methods use hard decisions to assign single error labels to each word. This work presents ﬁrst re-sults of a new error classiﬁcation method, which assigns multiple error labels to each word. We assign fractional counts for each label, which can be interpreted as a con-ﬁdence for the label. Our method generates sensible multi-error suggestions, and improves the correlation between manual and automatic error distributions.
Structured Exploration via Hierarchical Variational Policy Networks

Reinforcement learning in environments with large state-action spaces is challenging, as exploration can be highly inefficient. Even if the dynamics are simple, the optimal policy can be combinatorially hard to discover. In this work, we propose a hierarchical approach to structured exploration to improve the sample efficiency of on-policy exploration in large state-action spaces. The key idea is to model a stochastic policy as a hierarchical latent variable model, which can learn low-dimensional structure in the state-action space, and to define exploration by sampling from the low-dimensional latent space. This approach enables lower sample complexity, while preserving policy expressivity. In order to make learning tractable, we derive a joint learning and exploration strategy by combining hierarchical variational inference with actor-critic learning. The benefits of our learning approach are that 1) it is principled, 2) simple to implement, 3) easily scalable to settings with many actions and 4) easily composable with existing deep learning approaches. We demonstrate the effectiveness of our approach on learning a deep centralized multi-agent policy, as multi-agent environments naturally have an exponentially large state-action space. In this setting, the latent hierarchy implements a form of multi-agent coordination during exploration and execution (MACE). We demonstrate empirically that MACE can more efficiently learn optimal policies in challenging multi-agent games with a large number (~20) of agents, compared to conventional baselines. Moreover, we show that our hierarchical structure leads to meaningful agent coordination.
University of Dundee Automated Unbounded Verification of Stateful Cryptographic Protocols with Exclusive

Exclusive-or (XOR) operations are common in cryptographic protocols, in particular in RFID protocols and electronic payment protocols. Although there are numerous applications, due to the inherent complexity of faithful models of XOR, there is only limited tool support for the verification of cryptographic protocols using XOR. The TAMARIN prover is a state-of-the-art verification tool for cryptographic protocols in the symbolic model. In this paper, we improve the underlying theory and the tool to deal with an equational theory modeling XOR operations. The XOR theory can be freely combined with all equational theories previously supported, including user-defined equational theories. This makes TAMARIN the first tool to support simultaneously this large set of equational theories, protocols with global mutable state, an unbounded number of sessions, and complex security properties including observational equivalence. We demonstrate the effectiveness of our approach by analyzing several protocols that rely on XOR, in particular multiple RFID-protocols, where we can identify attacks as well as provide proofs.
Analogies Explained: Towards Understanding Word Embeddings

Word embeddings generated by neural network methods such as word2vec (W2V) are well known to exhibit seemingly linear behaviour, e.g. the embeddings of analogy “woman is to queen as man is to king” approximately describe a parallelogram. This property is particularly intriguing since the embeddings are not trained to achieve it. Several explanations have been proposed, but each introduces assumptions that do not hold in practice. We derive a probabilistically grounded definition of paraphrasing that we re-interpret as word transformation, a mathematical description of “wx is to wy”. From these concepts we prove existence of linear relationships between W2V-type embeddings that underlie the analogical phenomenon, identifying explicit error terms.
Apoptosis and Clearance of Apoptotic Cells

The human body generates 10–100 billion cells every day, and the same number of cells die to maintain homeostasis in our body. Cells infected by bacteria or viruses also die. The cell death that occurs under physiological conditions mainly proceeds by apoptosis, which is a noninflammatory, or silent, process, while pathogen infection induces necroptosis or pyroptosis, which activates the immune system and causes inflammation. Dead cells generated by apoptosis are quickly engulfed by macrophages for degradation. Caspases are a large family of cysteine proteases that act in cascades. A cascade that leads to caspase 3 activation mediates apoptosis and is responsible for killing cells, recruiting macrophages, and presenting an “eat me” signal(s). When apoptotic cells are not efficiently engulfed by macrophages, they undergo secondary necrosis and release intracellular materials that represent a damage-associated molecular pattern, which may lead to a systemic lupus-like autoimmune disease. 489 Click here to view this article's online features: • Download figures as PPT slides • Navigate linked references • Download citations • Explore related articles • Search keywords ANNUAL REVIEWS Further A nn u. R ev . I m m un ol . 2 01 8. 36 :4 89 -5 17 . D ow nl oa de d fr om w w w . a nn ua lr ev ie w s. or g A cc es s pr ov id ed b y U ni ve rs id ad d e G ua da la ja ra o n 04 /0 5/ 19 . F or p er so na l u se o nl y. IY36CH19_Nagata ARI 23 March 2018 8:38
ELICITATION BY SELLERS AND THIRD-PARTY PLATFORMS IN B 2 B MARKETS

Customers rely substantially on online reviews to make informed decisions. Despite the rich body of literature on online reviews, little is known about the role of online reviews in business-to-business (B2B) markets. In particular, it is unclear how providing past customers with incentives in exchange for their reviews (that is, review elicitation) affects the resulting reviews in such markets. Using a comprehensive data set from a large B2B online review platform, we investigate different types of elicitation that include email invitations, donations to charity, and gift cards. In contrast to previous research, we also observe review elicitation from both the third-party platform and individual sellers. In line with our hypotheses derived from relationship marketing and theory on intrinsic versus extrinsic motivation, we find that seller-initiated elicitation is associated with an increase in review ratings, whereas elicitation initiated by the third-party platform is associated with a decrease in ratings. In addition, the correlation between any type of elicitation and review text length is negative except for gift cards given by the seller. Our results come with substantial managerial implications for B2B review platforms, sellers, and customers.
A Sociotechnical Negotiation Mechanism to Support Component Markets in Software Ecosystems

Organizations have opened up their software platforms and reusable assets to others, including partners and third-party developers around the world, creating software ecosystems (SECOs). This perspective can contribute to minimize nontechnical barriers of software reuse in industry because it explores potential benefits from the relations among companies and stakeholders. An inhibitor is the complexity in defining value for reusable assets in a scenario where producers try to meet customers’ expectations, and vice-versa. In this paper, we present a value-based mechanism to support component negotiation and socialization processes in a reuse repository in the SECO context as an extension of the Brechó-EcoSys environment. Social resources were integrated into the mechanism in order to aid component negotiation. An evaluation of the negotiation mechanism was initially performed based on an analysis of its elements and functions against critical factors in the negotiation within a SECO, identified in a previous systematic literature review. In addition, an analysis of the social resources supporting the negotiation mechanism was performed against popular sociotechnical elements for SECOs, identified in a previous survey with experts in the field. Finally, the negotiation process and the potential support provided by sociotechnical resources were investigated through an observational study where participants were engaged in some tasks playing as consumer and producers using the sociotechnical negotiation mechanism at Brechó-EcoSys environment. We concluded that sociotechnical resources (e.g., forum and tag cloud) support component producers and consumers with useful information from the SECO community.
An Enhanced Data Transformation Framework for the Sonification of Simulated Rigid Bodies

While simulated rigid bodies hold a wealth of information that can be understood through sound (Table 1), current interpretive methods will often overlook important features of their data. This proves to be detrimental when placing the same data in the context of an auditory display where the user might wish to analyse or express specific dimensions under a range of circumstances. The following investigation describes a framework for a model-induced parameter mapping technique which allows for an explicit level of control over the flow of information, supported by a number of key conditions in both the auditory and visual channels. Given that the formative decisions behind this design tailors the data to meet the purposes of sonification, the user is presented with a viable alternative that overcomes a number of limitations inherent to employing a more conventional physical modelling approach. Property Type Example Position Vector (11.2, 4.3, 1.0) Orientation Quaternion (1.0, 0.0, 0.0, 0.0) Angular Velocity Vector (0.0, -0.8, 0.0) Linear Velocity Vector (0.2, -9.4, 0.4) Angular Momentum Vector (0.8, 34.0, 4.8) Linear Momentum Vector (9.0, 1.5, 12.5)
Title Understanding hydrogen-bonding structures of molecularcrystals via electron and NMR nanocrystallography

Right © The Author(s) 2019. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
When nonmanuals meet semantics and syntax : a practial guide for the segmentation of sign language discourse

This paper aims to contribute to the segmentation o f sign language (SL) discourses by providing an ope rational synthesis of the criteria that signers use to segment a SL discourse. Such pr o edure was required when it came to analyse the ro le of buoys as discourse markers (DMs), which is part of a PhD on DMs in French Belgi an SL (LSFB). All buoy markers found in the data had to be differentiated in terms of scope: some markers (like most list buoy markers) seemed t o be long range markers, whereas others (like most fragment buoy markers) seemed to have a local scope only. Our pra ctical guide results from a hierarchized and operat ion lized synthesis of the criteria, which explain the segmentation judgments of deaf (native and non-native) and hearing (non-na tive) signers of LSFB who were asked to segment a small-scale (1h) corpus. These c riteria are a combination of non-manual, semantic a nd syntactic cues. Our contribution aims to be shared, tested on other SLs and hopefully improved to provide SL researchers w ho conduct discourse studies with some efficient and easy-to-use guidelines, and void them extensive (and time-consuming) annotati on of the manual and non-manual cues that are related to the marking of boundaries in SLs.
The Impact of Alemtuzumab Scheduling on Graft Versus Host Disease Following Unrelated Donor Fludarabine and Melphalan Allografts

Alemtuzumab conditioning is highly effective at reducing the incidence of acute and chronic graft versus host disease (GVHD) in reduced intensity fludarabine and melphalan transplantation with ciclosporin monotherapy. Less frequent and lower dose scheduling may be used with sibling donors but an optimal regimen for matched unrelated donors has not been defined. In this retrospective observational
Database Query Execution on Heterogeneous Architecture

Graphics processors (GPUs) have emerged as a powerful co-processor for general-purpose computation. GPUs have been very widely used for various applications including database query execution. GPUs have an order of magnitude higher computation power as well as the memory bandwidth but they have a bottleneck of limited GPU device memory and transferring data between CPU main memory and GPU device memory. The database optimizer generates a query plan i.e. a tree structure which contains operators as nodes and edges represent the dependency between the nodes. The problem is how to divide the nodes of this tree-structured query plan, given by MonetDB[1], into the heterogeneous environment (CPU and GPU). We have proposed greedy approach and dynamic programming approach for the chain trees i.e., trees in which nodes have exactly one child. We have also verified that HEFT (Heterogeneous Earliest Finish Time) algorithm[14] works very well for non-chain trees i.e., trees in which nodes have many child nodes. We have also implemented operators for hybrid environment (CPU-GPU) i.e., running a single operator in the hybrid environment by using data parallelism. The thesis also includes the resource configurable implementation of filter and hash join operators.
Genetic characterization of T-DNA insertions in the genome of the sumo1/2 knock-down line

Sumoylation is a post-translation modification that affects a plethora of processes in the model plant Arabidopsis thaliana. It consists in the conjugation of SUMO proteins onto lysines of protein targets. The Arabidopsis genome contains eight SUMO genes. SUMO1 and SUMO2 code for the main substrates used for sumoylation. Their essential role is highlighted by the embryo-lethality of the sumo1 sumo2 double knockout mutant. Consequently, a line where SUMO1 is knocked out and SUMO2 is knocked down (sumo1/2) via the insertion of an artificial microRNA construct (amiR-SUMO2) was created that constitutes a valuable tool to investigate the role of SUMO in planta for genetic approaches. However, the number of amiR-SUMO2 construct insertions and their localization in the genome remained undetermined. Thus, the genotyping of the sumo1/2 mutant partially relies on the segregation of the kanamycin resistance trait linked with the amiR-SUMO2 construct, which makes the genetic works tedious. Here, we sequenced the genome of sumo1/2 with Ion Torrent Technology and used a rapid method to identify both the number of insertions and their localization. We identified two insertions, whose presences were eventually confirmed by PCR, making genotyping possible using PCRs. These results facilitate genetic studies with the sumo1/2 line.
An Analysis of Corporate Insider Trading and Earnings Announcements in India Murugappa

Corporate insiders have at least two major avenues to influence the perceptions of investors in stock markets. Firstly, they file mandated reports that convey key information, including accounting earnings. Secondly, corporate insiders can trade in their own stocks and thus reveal private information. We document the extent and some cross-sectional properties of insider trading in India, using a large sample of trades and announcements from 2008-2014. We show that insider trades are profitable; they can predict subsequent stock returns for up to one year. We show that a portion of these profits arise from trades both before an earnings announcement, from foreknowledge of earnings news, and after an earnings announcement, from exploiting the under-reaction to the news. We then document that the market reacts to both insider trades and earnings news, and that, overall, these two signals are perceived neither as information substitutes nor as complements. However, for the case where both signals indicate bad news, we find evidence that they are substitutes.
Realization Of Parallel Non-Alignment Based Approach To Find Longest Common Subsequence Using Hadoop MapReduce

The Longest Common Subsequence (LCS) identiﬁcation of biological sequences has signiﬁcant applications in bioinformatics. Due to the emerging growth in bioinformatics applications, new biological sequences with longer length have been used for processing, making it a great challenge for sequential LCS algorithms. Few parallel LCS algorithms have been proposed buttheir efﬁciency and effectivenessare not satisfactory with increasing complexity and size of the biological data. A parallel non-alignment based map reduce approach which help to solve the problem using distributed platform, is realized using Hadoop MapReduce.
USING GRAPH CONVOLUTIONAL NETWORK

Multi-document summarization transforms a set of related documents into a concise summary. Existing Indonesian news article summarization does not take relationships between sentences into account and depends heavily on Indonesian language tools and resources. This study employed Graph Convolutional Network (GCN) which allows for word embedding sequence and sentence relationship graph as input for Indonesian news article summarization. The system in this study comprised four main components: preprocess, graph construction, sentence scoring, and sentence selection components. Sentence scoring component is a neural network that uses Recurrent Neural Network and GCN to produce scores for all sentences. This study used three different representation types for the sentence relationship graph. The sentence selection component then generates a summary with two different techniques: by greedily choosing sentences with the highest scores and by using the Maximum Marginal Relevance (MMR) technique. The evaluation showed that the GCN summarizer with Personalized Discourse Graph, a graph representation system, achieved the best results with an average Received: 17 Julai 2018 Accepted: 8 January 2019 Published: 10 June 2019 Journal of ICT, 18, No. 3 (July) 2019, pp: 345–365 346 ROUGE-2 recall score of 0.370 for a 100-word summary and 0.378 for a 200-word summary. Sentence selection using the greedy technique gave better results for generating a 100-word summary, while the MMR performed better for generating a 200-word summary.