Miracle-dz/UniFAR-FTUs huggingface dataset:
A Blockchain-based Application to Protect Minor Artworks

A new emerging trend concerns the implementation of services and distributed applications through the blockchain technology. A blockchain is an append-only database, which guarantees security, transparency and immutability of records. Blockchains can be used in the field of Cultural Heritage to protect minor artworks, i.e. artistic relevant works not as famous as masterpieces. Minor artworks are subjected to counterfeiting, thefts and natural disasters because they are not well protected as famous artworks. This paper describes a blockchain-based application, called MApp (Minor Artworks application), which lets authenticated users (private people or organizations), store the information about their artworks in a secure way. The use of blockchain produces three main advantages. Firstly, artworks cannot be deleted from the register thus preventing thieves to remove records associated stolen objects. Secondly, artworks can be added and updated only by authorized users, thus preventing counterfeiting in objects descriptions. Finally, records can be used to keep artworks memory in case of destruction caused by a natural disaster.
The Vehicle Data Value Chain as a Lightweight Model to Describe Digital Vehicle Services

Digitalization has become an important driver of innovation in the automotive industry. For instance, the Quantified Self-movement has recently started spreading to the automotive domain, resulting in the provision of novel digital vehicle services for various stakeholders such as individual drivers and insurance companies. In this direction, a growing number of ICT start-ups from outside Europe have entered the market. Their digital vehicle services are grounded on the availability of vehicle Big Data. Hence, to better understand and capture this ongoing digital transformation, we introduce the Vehicle Data Value Chain (VDVC) as a lightweight model to describe and examine digital vehicle services. Furthermore, we classify current digital vehicle services offered by four start-ups and five car manufacturers by applying the VDVC, thereby identifying commonalities and differences within three crucial steps: data generation, acquisition, and usage. Additionally, we apply the VDVC to describe a digital mobility service provided by a European industry consortium. This exemplary application serves to evaluate the VDVC and show its general applicability in a practical context. We end our paper with a brief conclusion and an outlook on various current activities of standardization organizations, the European Commission and car manufacturers related to the future of vehicle services.
Learning Method of Recurrent Spiking Neural Networks to Realize Various Firing Patterns using Particle Swarm Optimization

Recently it has been reported that artificial spiking neural networks (SNNs) are computationally more powerful than the conventional neural networks. In biological neural networks of living organisms, various firing patterns of nerve cells have been observed, typical examples of which are burst firings and periodic firings. In this paper we propose a learning method which can realize various firing patterns for recurrent SNNs (RSSNs). We have already proposed learning methods of RSNNs in which the learning problem is formulated such that the number of spikes emitted by a neuron and their firing instants coincide with given desired ones. In this paper, in addition to that, we consider several desired properties of a target RSNN and proposes cost functions for realizing them. Since the proposed cost functions are not differentiable with respect to the learning parameters, we propose a learning method based on the particle swarm optimization.
Automatic Extraction of Legal Citations using Natural Language Processing

The accessibility of legal documents to the different actors of the judicial system needs to be ensured for the implementation of a strong international rule of law. The gap of such accessibility is being addressed by the Jus Mundi multilingual search-engine for International Law. The data updated on this platform is qualified by skilled lawyers. However, the interconnection of references within such documents, is a key feature for lawyers since, a major part of the legal research is analysing such citations to support their arguments. The process of interconnecting such references can prove to be expensive as well as time-consuming, if completed manually. Hence, the purpose of this research is to automatically extract such legal citations within international law, using Natural Language Processing (NLP), enabling the interconnectivity of documents on Jus Mundi. This study also discusses and addresses research gaps within this subject, especially in the domain specific to International Law. The method followed to achieve the automation is building an adaptable model through Regular-Expression based annotation language named JAPE (Java Annotation Patterns Engine). This set of automatically extracted links are then to be integrated with the search engine, having direct implication in the enablement of smoother navigation, making the law more accessible. This research also contributes to the state of the art bringing closer the eventual use of NLP in applications used to interact with International Law documents.
Reinventing the Cube: An Alternative Agent Design for Shape-shifting Technology

In 2002 William Butera suggested the concept of a paintable computer. It is a blend of the concept of the internet of things (IoT) and multiagent computing. He describes agents that function on a basic level, ideally suspended in a liquid, so that they can be dispersed evenly but also be used to enhance the surface onto which they are painted (Butera, 2002). Expanding on this concept to create a liquid computer that could change its physical shape, i.e. it would be able to create unique 3D structures depending on user requirements. Such an interface would be in a quasi-liquid state when inactive and could become solid when in use, comparable to a Non-Newtonian fluid. This paper details an agent design that is orientated towards this kind of shape-shifting interface technology.
Exploring the Intra-organizational Journey of a Vague Management Concept through a Translation Theory Lens

In the quest for change, management at times introduces vague management concepts in organizations. The use of such a concept instigates translation journeys, which may end in affecting an organizations competitive advantage. While performance outcomes of change are pertinent, it is the journey towards change that is emphasized here based on translation theory. This study explores the intra-organizational journey of the vague concept “knowledge platform” within a business school. The empirical data originates from 102 written documents dating from 2009 to 2018 produced by faculty and departmental levels. The data coding reveals six distinct subseries which together depicts how the knowledge platform passes through phases of de-contextualization and contextualization as it travels iteratively between the source and recipient contexts.
Supporting Taxonomy Development and Evolution by Means of Crowdsourcing

Information overload continues to be a challenge. By dividing the material into many different small subsets, classification based on a taxonomy makes data exploration and retrieval faster and more accurate. Instead of having to know the exact keywords that describe the knowledge resource, users can browse and search for them by selecting the categories that the resource is most likely to belong. Nevertheless, developing taxonomies is not an easy task. It requires the authors to have a certain amount of knowledge in the domain. Furthermore, the workload will increase as any new taxonomy needs to be frequently updated to remain relevant and useful. To combat these problems, this paper proposes another approach to crowdsource taxonomy development and evolution. We describe in this paper the concept of this approach along with different types of evaluations targeting on the one hand to demonstrate the feasibility of the approach and the usability of the initial prototype as well as on the other hand the quality and effectiveness of the chosen method.
What are the pathologies of nonlesional epilepsy: One pathologist’s perspective?

Abstract Surgery is a well established option in treating patients with medically intractable epilepsy. Better outcomes are achieved in surgical candidates who have magnetic resonance imaging (MRI) lesions present prior to surgery. In a minority of cases, no discernible MRI lesion is evident prior to surgery (nonlesional). The most commonly documented histopathologic findings in this clinical setting are focal cortical dysplasias, nonspecific gliosis, hippocampal sclerosis and no identifiable light microscopic alteration. There are a number of potential explanations for the absence of light microscopic findings in a subset of MRI-negative or nonlesional epilepsy patients related to the surgical resection being off-target, pathologic tissue sampling, interobserver and intraobserver variability in the diagnosis of certain pathologies, evolving pathology and lesions that are beyond light microscopic detection. This paper reviews the definition of nonlesional epilepsy, the pathologies encountered in this setting, possible explanations for the absence of apparent light microscopic abnormalities in some cases, and future directions.
APNEA: Intelligent Ad-Bidding Using Sentiment Analysis

Online advertising is one of the most lucrative forms of advertising, making it an important channel of advertising media. Contextual Advertising is a type of online display advertising that takes cues from the content of the triggering page and displays advertisements that are relevant to the current context. However, on several occasions, the context may have a negative connotation, and displaying advertisements that are relevant to it might prove to be detrimental to the advertiser. We refer to such a scenario as an unfortunate placement. In this work, we propose APNEA (Ad Positive NEgative Analysis), a light-weight system that uses a sentiment-oriented approach to rank the advertisers such that positively correlated brands are ranked higher than brands that are neutral or negatively correlated. Experiments show that APNEA helps avoid unfortunate placements while maintaining ad-relevance. It outperforms several baselines in terms of accuracy on human-annotated test data while having a lower run-time, which is crucial for real-time bidding systems. CCS CONCEPTS • Information systems → Computational advertising; Content match advertising; Display advertising; Sentiment analysis.
Association and Temporality between News and Tweets

With the advent of social media, the boundaries of mainstream journalism and social networks are becoming blurred. User-generated content is increasing, and hence, journalists dedicate considerable time searching platforms such as Facebook and Twitter to announce, spread, and monitor news and crowd check information. Many studies have looked at social networks as news sources, but the relationship and interconnections between this type of platform and news media have not been thoroughly investigated. In this work, we have studied a series of news articles and examined a set of related comments on a social network during a period of six months. Specifically, a sample of articles from generalist Portuguese news sources published on the first semester of 2016 was clustered, and the resulting clusters were then associated with tweets of Portuguese users with the recourse to a similarity measure. Focusing on a subset of clusters, we have performed a temporal analysis by examining the evolution of the two types of documents (articles and tweets) and the timing of when they appeared. It appears that for some stories, namely Brexit and the European Football Cup, the publishing of news articles intensifies on key dates (event-oriented), while the discussion on social media is more balanced throughout the months leading up to those events.
Predicting Outcomes of Active Sessions Using Multi-action Motifs

Web sites and online services increasingly engage with users through live chats to provide support, advice, and offers. Such approaches require reliable methods to predict the user’s intent and make an informed decision when and how to intervene during an active session. Prior work on predicting purchase intent involved click-stream data mining and feature construction in an ad-hoc manner with a moderate success (AUC 0.70 range). We demonstrate the use of the consumer Purchase Decision Model (PDM) and a principled way of constructing features predictive of the purchase intent. We show that the Logistic Regression (LR) classifiers, trained with multi-action motifs, perform on par with the state-of-the-art LSTM sequence model achieving comparable AUC (0.95 vs 0.96) and performing better for the sparse purchase sessions, with higher recall (0.85 vs 0.61) and higher F1 score (0.73 vs 0.66). While LSTM performs better than LR in terms of weighted averages of F1, precision, and recall, it requires 7 times longer to train and offers no insights about the predictive model in terms of the user actions and the purchase decision stages. The LR predictors are robust and effective in simulating real-time interventions, achieving F1 of 0.84 and AUC of 0.85 after observing only 50% of an active session. For non-purchase sessions that leaves room for live intervention, on average within 8 actions before the session ends. CCS CONCEPTS • Information system → Misc.
Modified Differential Evolution in the Load Balancing Problem for the iFDAQ of the COMPASS Experiment at CERN

In general, state-of-the-art data acquisition systems in high energy physics experiments must satisfy high requirements in terms of reliability, efficiency and data rate capability. The paper introduces the Load Balancing (LB) problem of the intelligent, FPGA-based Data Acquisition System (iFDAQ) of the COMPASS experiment at CERN and proposes a solution based on genetic algorithms. Since the LB problem is N P-complete, it challenges analytical and heuristic methods in finding optimal solutions in reasonable time. Differential Evolution (DE) is a type of evolutionary algorithms, which has been used in many optimization problems due to its simplicity and efficiency. Therefore, the Modified Differential Evolution (MDE) is inspired by DE and is presented in more detail. The MDE algorithm has newly-designed crossover and mutation operator and its selection mechanism is inspired by Simulated Annealing (SA). Moreover, the proposal uses an adaptive scaling factor and recombination rate affecting the exploration and exploitation of the MDE algorithm. Thus, the MDE represents a new efficient stochastic search technique for the LB problem. The proposed MDE algorithm is examined on two LB test cases and compared with other LB solution methods.
Endometriosis Malignant Transformation Review: Rhabdomyosarcoma Arising From an Endometrioma

Background: Endometriosis is a widely known benign disease, but 0.5%–1% of cases are associated with malignancy. It has been linked with ovarian neoplasms, particularly endometrioid and clear cell adenocarcinoma histology. Rhabdomyosarcomas are rarely associated with endometriosis. Case: A 35-year-old patient underwent surgical management of endometriomas to optimize infertility treatment. She later developed abdominal pain with rapid recurrence of ovarian masses. This prompted additional surgery with biopsies diagnosing ovarian rhabdomyosarcoma. Retroactive review of pathologic specimens from her prior surgery demonstrated the neoplasm originated from her prior endometrioma. Focal areas suggested possible underlying ovarian adenosarcoma with stromal overgrowth. Discussion: The incidence of rhabdomyosarcoma arising from endometriosis is exceedingly rare. The accuracy of diagnosing endometriosis and ruling out neoplasm requires coordinated efforts of a multidisciplinary team, involving radiologists, pathologists, oncologists, and gynecologic surgeons.
Lymph Node Status after Neoadjuvant Chemoradiation Therapy for Esophageal Cancer according to Radiation Field Coverage

Background To explore the effect of radiation on metastatic lymph nodes (LNs) after neoadjuvant chemoradiation therapy (nCRT), we examined the metastatic features of LNs according to their inclusion in the radiation field. Methods The patient group included 88 men and 2 women, with a mean age of 61.1±8.1 years, who underwent esophagectomy and lymphadenectomy after nCRT. Dissected LNs were compared in terms of clinical suspicion of metastasis, nodal station, and inclusion in the radiation field. Results LN positivity did not differ between LNs that were inside (in-field [IF]) and outside (out-field [OF]) of the radiation field (IF: 40 of 465 [9%], OF: 40 of 420 [10%]; p=0.313). In clinical N+ nodal stations, IF stations had a lower incidence of metastasis than OF stations (IF/cN+: 16 of 142 [11%], OF/cN+: 9/30 [30%]; p=0.010). However, in clinical N- nodal stations, pathological positivity was not affected by whether the nodal stations were included in the radiation field (IF/cN-: 24 of 323 [7%], OF/cN-: 31 of 390 [8%]; p=0.447). Conclusion Radiation therapy for nCRT could downstage clinically suspected nodal metastasis. However, such therapy was ineffective when used to treat nodes that were not suspicious for metastasis. Because significant numbers of residual metastases were identified irrespective of coverage by the radiation field, lymphadenectomy should be performed to ensure complete removal of residual nodal metastases after nCRT.
Novel Association of a Familial TGFBR1 Mutation in Loeys-Dietz Syndrome with Concomitant Hematologic Malignancy

Concomitant Loeys-Dietz syndrome (LDS) and hematologic malignancies are exceptionally rare. This is the first report of a patient operated on for aortic root dilation who had been previously diagnosed with LDS and B-cell-lymphoma. After completion of chemotherapy and complete remission, an elective valve-sparing aortic root replacement (using the David-V method) was performed. Due to the positive family history, pre-operative genetic counseling was conducted, and revealed LDS with a TGFBR1 (transforming growth factor beta receptor type I) mutation in 6 probands of the family, albeit in 1 of them posthumously. This missense mutation has been previously described in relation to aortic dissection, but a causative relationship to malignancy has so far neither been proposed nor proven.
Enzyme Degradation Reagents Effectively Remove Mycotoxins Deoxynivalenol and Zearalenone from Pig and Poultry Artificial Digestive Juices

Mycotoxin removers include enzymes and adsorbents that may be used in animal feeds to eliminate the toxic effects of mycotoxins. This study aimed to determine the removability of two different types of mycotoxin removers, adsorbents and enzyme degradation reagents (EDRs), in the simulated gastrointestinal conditions of pigs and poultry. Seven commercial mycotoxin removers, including five EDRs and two adsorbents, were tested in vitro. In this study, the supplemented dosages of mycotoxin removers used in pig and poultry feeds were the commercial recommendation ranging from 0.05% to 0.2%. For pigs, the in vitro gastric and small intestinal simulations were performed by immersing the mycotoxin-tainted feed in artificial gastric juice (AGJ) at pH 2.5 for 5 h or in artificial intestinal juice (AIJ) at pH 6.5 for 2 h to mimick in vivo conditions. For poultry, mycotoxin-tainted feeds were immersed in AGJ for 2 h at pH 4.5 and 0.5 h at pH of 2.5, respectively, to simulate crop/glandular stomach and gizzard conditions; the small intestinal simulation was in AIJ for 2 h at pH 6.5. For the pig, EDRs and adsorbents had deoxynivalenol (DON) removability (1 mg/kg) of 56% to 100% and 15% to 19%, respectively. Under the concentration of 0.5 mg/kg, the zearalenone (ZEN) removability by EDRs and adsorbents was 65% to 100% and 0% to 36%, respectively. For the simulation in poultry, the removability of DON by EDRs and adsorbents (5 mg/kg) was 56% to 79% and 1% to 36%, respectively; for the concentration of 0.5 mg/kg, the removability of ZEN by EDRs and adsorbents was 38% to 69% and 7% to 9%, respectively. These results suggest that EDRs are more effective in reducing DON and ZEN contamination compared to the adsorbent methods in the simulated gastrointestinal tracts of pig and poultry. The recoveries of DON and ZEN of pig in vitro gastrointestinal simulations were higher than 86.4% and 84.7%, respectively, with 88.8% and 85.9%, respectively, in poultry. These results demonstrated the stability and accuracy of our mycotoxin extraction process and in vitro simulation efficiency.
Effect of Native and Acetylated Dietary Resistant Starches on Intestinal Fermentative Capacity of Normal and Stunted Children in Southern India

The health benefits of dietary amylase resistant starch (RS) arise from intestinal microbial fermentation and generation of short chain fatty acids (SCFA). We compared the intestinal fermentative capability of stunted and nonstunted (‘healthy’) children in southern India using two types of RS: high amylose maize starch (HAMS) and acetylated HAMS (HAMSA). Twenty children (10 stunted and 10 healthy) aged 2 to 5 years were fed biscuits containing HAMS (10 g/day) for two weeks followed by a 2-week washout and then HAMSA biscuits (10 g/day) for 2 weeks. Fecal samples were collected at 3-4 day intervals and pH and SCFA analyzed. At entry, stunted children had lower SCFA concentrations compared to healthy children. Both types of RS led to a significant decrease in fecal pH and increase in fecal acetate and propionate in both healthy and stunted children. However, while HAMS increased fecal butyrate in both groups of children, HAMSA increased butyrate in healthy but not stunted children. Furthermore, healthy children showed a significantly greater increase than stunted children in both acetate and butyrate when fed either RS. No adverse effects were reported with either RS. Stunted children have impaired capacity to ferment certain types of RS which has implications for choice of RS in formulations aimed at improving microbial function in stunted children.
Determinants of Self-rated Cognitive Health among Older Korean Americans.

BACKGROUND AND OBJECTIVES In response to the dearth of information on cognitive health in older ethnic minorities, in the present study, we examined factors associated with self-rated cognitive health (SRCH) in older Korean Americans. Drawing from the World Health Organization's framework of social determinants of health, we examined how a broad spectrum of factors might influence the way in which older Korean Americans perceive and evaluate their own cognitive health. RESEARCH DESIGN AND METHODS Using data from the Study of Older Korean Americans (SOKA; N = 2,061, mean age = 73.2), a series of hierarchical linear regression models of SRCH was tested with sequential entry of predictors: (1) Mini-Mental State Examination (MMSE) score of cognitive status, (2) demographic variables, (3) physical and mental health status, (4) health behaviors, and (5) socio-cultural and community factors. RESULTS In addition to cognitive screening outcomes, each set of variables made a significant contribution to the predictive model of SRCH. Positive ratings of cognitive health were observed among older individuals with greater education, good physical and mental health, involvement in regular exercise, and socio-cultural resources (social networks, acculturation, and family solidarity). DISCUSSION AND IMPLICATIONS The SRCH of older adults goes beyond cognitive screening outcomes, suggesting a need to incorporate the various sources of social determinants when seeking to promote the cognitive health of older populations.