Miracle-dz/UniFAR-FTUs huggingface dataset:
Groups in Violent Conflicts When Suffering Begets Suffering : The Psychology of Competitive Victimhood Between Adversarial

Inter-group competitive victimhood (CV) describes the efforts of members of groups involved in violent conflicts to establish that their group has suffered more than their adversarial group. Such efforts contribute to conflicts’ escalation and impede their peaceful resolution. CV stems from groups’ general tendency to compete with each other, along with the deep sense of victimization resulting from conflicts. The authors point to biases that contribute to groups’ engagement in CV, describe five dimensions of victimhood over which groups may compete, and contend that such competition serves various functions that contribute to the maintenance of conflicts. Drawing on the Needs-Based Model, they suggest that CV may reflect groups’ motivations to restore power or moral acceptance. They then review evidence of the negative consequences of CV for inter-group forgiveness and suggest potential strategies to reduce CV. Finally, the authors discuss potential moderators and directions for future research.
Outcomes from Osteochondral Autograft Transplant or Mosaicplasty in 26 Patients with Type V Osteochondral Lesions of the Talus

Background Mosaicplasty osteochondral transplantation (MOT) has been proved a feasible choice for the treatment of talus osteochondral injury. It can reduce ankle pain and allow performance of daily living activities and elementary sports. However, there are very little data on the restoration of normal life with large lesions. Our investigation focused on assessing outcomes in patients with large lesions. Material/Methods The MOT operation was used, and the donor site was located at the lateral condyle of the femur. Clinical evaluation included the Berndt and Harty outcome question, Visual Analog Scale (VAS) pain score (during rest, walking and running), American Orthopedic Foot and ankle Association (AOFAS) score, International Knee Documentation Committee (IKDC) subjective knee evaluation form, and the Short Form 36 (SF-36). Twenty-four months after surgery, the graft binding was assessed by MRI using the magnetic resonance observation of cartilage repair tissue (MOCART) score. Results We assessed 26 patients, including 14 males and 12 females. The average follow-up time was 33.9±5.2 months. The average lesion size was 173.1±23.5 mm2. The average VAS score during rest improved from 4.62±0.85 preoperatively to 1.08±0.27 postoperatively (P<0.05), the score during walking improved from 5.19±0.63 preoperatively to 1.15±0.37 postoperatively (P<0.05), and the score during running improved from 6.08±0.74 preoperatively to 1.39±0.57 postoperatively (P<0.05). The average postoperative AOFAS ankle score improved to 91.5±2.6 (range, 88 to 96), compared with preoperative 75.0±2.8 (range, 70 to 79) (P<0.05). Two patients developed pain in the donor site of the knee joint, and both of them had 2 or more osteochondral plugs harvested. The postoperative SF-36 score increased to 92.2±2.4 (P<0.05). In the last follow-up, 18 patients answered the Berndt and Harty outcome question, all with “good” as the result. Conclusions MOT is a feasible choice for patients with larger lesions. It can improve the VAS/AOFAS/IKDC/SF-36 scores, and has high patient satisfaction. Graft incorporation helps improve patient quality of life. Our results indicate that a larger osteochondral plug area increases the risk of developing knee donor site pain.
The necessity of organic production regulation : are public certification schemes better than private ones ? 1

In recent years many private actors emerged in envi ro mental governance. This appearance reveals itself in different forms one of which bein g certification and labelling schemes. However, some investigations exposed cases when the private certification and labelling schemes were not sufficiently transparent, reliable or effective. The focus of this research is on the organic certif ica ion and labelling schemes. Certification of organic products has one unique element when compar ed to other schemes. In most developed countries private standards of the organic producti on have became marginalized by the successive development of public standards. This ha ppened in the US, Japan, the EU and some other countries. The private schemes continue to ex ist alongside with the public ones. In this case the product can be certified against several standa rds which is costly and the effect of that is not well known. The case study of this research is the EU legal fra mework for the certification and labelling of organic production. EU regulations of organic produ ction are an example of an attempt to impose legal control of voluntary actions. The main objective of this paper is to answer the question if governmental interruption is necessary and if legally controlled schemes can provide better legitimateness, credibleness and overall eff ectiveness. These three aspects are usually identified as the main drawbacks of the private sch emes.
A reliability-operational analysis of a track-side CCTV cabinet taking into account interference

Manuscript submitted 2020-10-25, revised 2021-02-06, initially accepted for publication 2021-02-18, published in April 2021 Abstract. The paper presents issues associated with the impact of electromagnetic interference on track-side cabinets of a closed-circuit television system (CCTV) functioning in a railway transport environment. The measurements of an electromagnetic field emitted by a track-side CCTV cabinet were presented. Designs of this kind are operated in railway facilities; therefore, they should not disturb the functioning of other equipment, the rail traffic control systems, in particular (the so-called inner compatibility). An analysis of the obtained results facilitated developing a research model, and, further, improving its reliability and conducting an operational analysis, considering electromagnetic interference. This enabled us to determine a relationship allowing us to specify the probability of a track-side CCTV cabinet staying in a state of full ability. The presented discussions regarding a track-side CCTV cabinet, considering electromagnetic interference, allow us for the numerical assessment of different types of solutions (technical and organizational), which can be implemented in order to mitigate the impact of electromagnetic interference on the system functioning.
On the effects of increased vertical mixing 1 on the Arctic Ocean and sea ice 2 3

23 Against the backdrop of Arctic sea ice decline, vertical mixing in the interior 24 Arctic Ocean will most likely change, but it is still unclear how the Arctic Ocean and 25 sea ice will respond. In this paper, a sea ice-ocean model with a simple 26 parameterization for interior background mixing is used to investigate the Arctic 27 Ocean and sea ice response to a scenario of increased vertical mixing. It is found that 28 more vertical mixing reduces sea ice thickness all year round and decreases 29 summertime sea ice concentration. More vertical mixing leads to a cooling of the cold 30 halocline layer and Atlantic Water layer below. The increased vertical mixing speeds 31 up vertical heat and salinity exchange, brings the underlying warm and saline water 32 into the surface layer, and contributes to the sea ice decline. Vertical salinity gradient 33 of the cold halocline layer reduces together with a much fresher Atlantic Water layer, 34
Predictive Many-core Allocation Method for Edge Off-Road Services

: In recent years, edge computing has been attracting attention for the purpose of offloading advanced processing by IoT and robot services. It needs to efficiently provide services to multiple robotics services while dynamically switching abundant computational resources such as many-core resources. In Many-core research, many studies have been conducted to increase the degree of parallelism of tasks to improve responsiveness and computational efficiency. However, there is still not enough discussion on how to effectively allocate resources in a way that is suitable for integrated services and to achieve both efficiency and responsiveness. In this study, we construct an efficient resource allocation prediction formula as a study of an efficient Many-core allocation method for edge offload. In addition, as a specific service, we decided to evaluate the offload of AI processing of communication robots. In the construction of the prediction formula, it was confirmed that sufficient processing performance and responsiveness can be obtained with the minimum core by automatically calculating the optimum number of cores from the actual application operation.
Metabolome-wide association study on ABCA7 demonstrates a role for ceramide metabolism in impaired cognitive performance and Alzheimer's disease

Genome-wide association studies (GWAS) have identified genetic loci associated with risk of Alzheimer's disease (AD), but underlying mechanisms are largely unknown. Using untargeted mass spectrometry, we conducted a metabolome-wide association study (MWAS) that identified the association of lactosylceramides (LacCer)s with AD-related single nucleotide polymorphisms (SNPs) in ABCA7 (P = 5.0x 10-5 to 1.3 x 10-44). Independent support for the association came through the discovery of differences in concentrations of sphingomyelins, ceramides, and hexose-ceramides in brain tissue from ABCA7-null mice compared to wild type (P =0.049 -1.44 x10-5). We showed that plasma LacCer concentrations are associated with cognitive performance in humans. We found evidence for a potentially causal association of LacCer with AD risk using Mendelian randomisation analysis. Our work suggests that AD risks arising from functional variations in ABCA7 expression are mediated at least in part through ceramides, the metabolism or downstream signalling of which offers new therapeutic opportunities.
Using Machine Learning to Classify Schizophrenia Based on Retinal Images

Objectives: Thinning of retinal layers has been documented in patients with chronic schizophrenia using standard metrics of optical coherence tomography (OCT) devices. We demonstrate the effectiveness of machine learning (ML) techniques to differentiate between schizophrenia patients and healthy controls using OCT images. Methods: Features extracted from a convolutional neural network (CNN) designed to segment retinal layers from OCT images represented abstracted data from the OCT images of 14 first episode (FEP) and 18 chronic schizophrenia patients, and their respective 20 and 18 age-matched controls. The abstracted data and OCT machine metrics were used separately to train support vector classification (SVC) models to differentiate between control and schizophrenia samples and test them. Results: SVCs operating on OCT machine metrics did not classify unseen samples of FEP schizophrenia patients and controls with performance better than chance, while those looking at chronic schizophrenia did, paralleling results obtained using parametric statistics. In contrast, SVCs operating on OCT image data extracted from the CNN classified unseen samples from both populations with performance greater than chance. Conclusion: These results suggest that ML techniques can detect patterns in patients with FEP schizophrenia with greater performance using features extracted from OCT images than metrics provided by OCT machines.
VoiSeR: A New Benchmark for Voice-Based Search Refinement

Voice assistants, e.g., Alexa or Google Assistant, have dramatically improved in recent years. Supporting voice-based search, exploration, and refinement are fundamental tasks for voice assistants, and remain an open challenge. For example, when using voice to search an online shopping site, a user often needs to refine their search by some aspect or facet. This common user intent is usually available through a “filter-by” interface on online shopping websites, but is challenging to support naturally via voice, as the intent of refinements must be interpreted in the context of the original search, the initial results, and the available product catalogue facets. To our knowledge, no benchmark dataset exists for training or validating such contextual search understanding models. To bridge this gap, we introduce the first large-scale dataset of voice-based search refinements, VoiSeR, consisting of about 10,000 search refinement utterances, collected using a novel crowdsourcing task. These utterances are intended to refine a previous search, with respect to a search facet or attribute (e.g., brand, color, review rating, etc.), and are manually annotated with the specific intent. This paper reports qualitative and empirical insights into the most common and challenging types of refinements that a voice-based conversational search system must support. As we show, VoiSeR can support research in conversational query understanding, contextual user intent prediction, and other conversational search topics to facilitate the development of conversational search systems.
Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase

We introduce a data augmentation technique based on byte pair encoding and a BERT-like self-attention model to boost performance on spoken language understanding tasks. We compare and evaluate this method with a range of augmentation techniques encompassing generative models such as VAEs and performance-boosting techniques such as synonym replacement and back-translation. We show our method performs strongly on domain and intent classification tasks for a voice assistant and in a user-study focused on utterance naturalness and semantic similarity.
Adaptive Mixed Component LDA for Low Resource Topic Modeling

Probabilistic topic models in low data resource scenarios are faced with less reliable estimates due to sparsity of discrete word co-occurrence counts, and do not have the luxury of retraining word or topic embeddings using neural methods. In this challenging resource constrained setting, we explore mixture models which interpolate between the discrete and continuous topic-word distributions that utilise pre-trained embeddings to improve topic coherence. We introduce an automatic trade-off between the discrete and continuous representations via an adaptive mixture coefficient, which places greater weight on the discrete representation when the corpus statistics are more reliable. The adaptive mixture coefficient takes into account global corpus statistics, and the uncertainty in each topic’s continuous distributions. Our approach outperforms the fully discrete, fully continuous, and static mixture model on topic coherence in low resource settings. We additionally demonstrate the generalisability of our method by extending it to handle multilingual document collections.
Context-aware Neural Machine Translation with Mini-batch Embedding

It is crucial to provide an inter-sentence context in Neural Machine Translation (NMT) models for higher-quality translation. With the aim of using a simple approach to incorporate inter-sentence information, we propose mini-batch embedding (MBE) as a way to represent the features of sentences in a mini-batch. We construct a mini-batch by choosing sentences from the same document, and thus the MBE is expected to have contextual information across sentences. Here, we incorporate MBE in an NMT model, and our experiments show that the proposed method consistently outperforms the translation capabilities of strong baselines and improves writing style or terminology to fit the document’s context.
Through the Looking Glass: Learning to Attribute Synthetic Text Generated by Language Models

Given the potential misuse of recent advances in synthetic text generation by language models (LMs), it is important to have the capacity to attribute authorship of synthetic text. While stylometric organic (i.e., human written) authorship attribution has been quite successful, it is unclear whether similar approaches can be used to attribute a synthetic text to its source LM. We address this question with the key insight that synthetic texts carry subtle distinguishing marks inherited from their source LM and that these marks can be leveraged by machine learning (ML) algorithms for attribution. We propose and test several ML-based attribution methods. Our best attributor built using a fine-tuned version of XLNet (XLNet-FT) consistently achieves excellent accuracy scores (91% to near perfect 98%) in terms of attributing the parent pre-trained LM behind a synthetic text. Our experiments show promising results across a range of experiments where the synthetic text may be generated using pre-trained LMs, fine-tuned LMs, or by varying text generation parameters.
STAR: Cross-modal [STA]tement [R]epresentation for selecting relevant mathematical premises

Mathematical statements written in natural language are usually composed of two different modalities: mathematical elements and natural language. These two modalities have several distinct linguistic and semantic properties. State-of-the-art representation techniques have demonstrated an inability in capturing such an entangled style of discourse. In this work, we propose STAR, a model that uses cross-modal attention to learn how to represent mathematical text for the task of Natural Language Premise Selection. This task uses conjectures written in both natural and mathematical language to recommend premises that most likely will be relevant to prove a particular statement. We found that STAR not only outperforms baselines that do not distinguish between natural language and mathematical elements, but it also achieves better performance than state-of-the-art models.
Handling Out-Of-Vocabulary Problem in Hangeul Word Embeddings

Word embedding is considered an essential factor in improving the performance of various Natural Language Processing (NLP) models. However, it is hardly applicable in real-world datasets as word embedding is generally studied with a well-refined corpus. Notably, in Hangeul (Korean writing system), which has a unique writing system, various kinds of Out-Of-Vocabulary (OOV) appear from typos. In this paper, we propose a robust Hangeul word embedding model against typos, while maintaining high performance. The proposed model utilizes a Convolutional Neural Network (CNN) architecture with a channel attention mechanism that learns to infer the original word embeddings. The model train with a dataset that consists of a mix of typos and correct words. To demonstrate the effectiveness of the proposed model, we conduct three kinds of intrinsic and extrinsic tasks. While the existing embedding models fail to maintain stable performance as the noise level increases, the proposed model shows stable performance.
Segmenting Subtitles for Correcting ASR Segmentation Errors

Typical ASR systems segment the input audio into utterances using purely acoustic information, which may not resemble the sentence-like units that are expected by conventional machine translation (MT) systems for Spoken Language Translation. In this work, we propose a model for correcting the acoustic segmentation of ASR models for low-resource languages to improve performance on downstream tasks. We propose the use of subtitles as a proxy dataset for correcting ASR acoustic segmentation, creating synthetic acoustic utterances by modeling common error modes. We train a neural tagging model for correcting ASR acoustic segmentation and show that it improves downstream performance on MT and audio-document cross-language information retrieval (CLIR).
BERTective: Language Models and Contextual Information for Deception Detection

Spotting a lie is challenging but has an enormous potential impact on security as well as private and public safety. Several NLP methods have been proposed to classify texts as truthful or deceptive. In most cases, however, the target texts’ preceding context is not considered. This is a severe limitation, as any communication takes place in context, not in a vacuum, and context can help to detect deception. We study a corpus of Italian dialogues containing deceptive statements and implement deep neural models that incorporate various linguistic contexts. We establish a new state-of-the-art identifying deception and find that not all context is equally useful to the task. Only the texts closest to the target, if from the same speaker (rather than questions by an interlocutor), boost performance. We also find that the semantic information in language models such as BERT contributes to the performance. However, BERT alone does not capture the implicit knowledge of deception cues: its contribution is conditional on the concurrent use of attention to learn cues from BERT’s representations.
Acquiring a Formality-Informed Lexical Resource for Style Analysis

To track different levels of formality in written discourse, we introduce a novel type of lexicon for the German language, with entries ordered by their degree of (in)formality. We start with a set of words extracted from traditional lexicographic resources, extend it by sentence-based similarity computations, and let crowdworkers assess the enlarged set of lexical items on a continuous informal-formal scale as a gold standard for evaluation. We submit this lexicon to an intrinsic evaluation related to the best regression models and their effect on predicting formality scores and complement our investigation by an extrinsic evaluation of formality on a German-language email corpus.
Exploiting Emojis for Abusive Language Detection

We propose to use abusive emojis, such as the “middle finger” or “face vomiting”, as a proxy for learning a lexicon of abusive words. Since it represents extralinguistic information, a single emoji can co-occur with different forms of explicitly abusive utterances. We show that our approach generates a lexicon that offers the same performance in cross-domain classification of abusive microposts as the most advanced lexicon induction method. Such an approach, in contrast, is dependent on manually annotated seed words and expensive lexical resources for bootstrapping (e.g. WordNet). We demonstrate that the same emojis can also be effectively used in languages other than English. Finally, we also show that emojis can be exploited for classifying mentions of ambiguous words, such as “fuck” and “bitch”, into generally abusive and just profane usages.
End-to-End Argument Mining as Biaffine Dependency Parsing

Non-neural approaches to argument mining (AM) are often pipelined and require heavy feature-engineering. In this paper, we propose a neural end-to-end approach to AM which is based on dependency parsing, in contrast to the current state-of-the-art which relies on relation extraction. Our biaffine AM dependency parser significantly outperforms the state-of-the-art, performing at F1 = 73.5% for component identification and F1 = 46.4% for relation identification. One of the advantages of treating AM as biaffine dependency parsing is the simple neural architecture that results. The idea of treating AM as dependency parsing is not new, but has previously been abandoned as it was lagging far behind the state-of-the-art. In a thorough analysis, we investigate the factors that contribute to the success of our model: the biaffine model itself, our representation for the dependency structure of arguments, different encoders in the biaffine model, and syntactic information additionally fed to the model. Our work demonstrates that dependency parsing for AM, an overlooked idea from the past, deserves more attention in the future.