Miracle-dz/UniFAR-FTUs huggingface dataset:
Photosynthetic biofilms in pure culture harness solar energy in a mediatorless bio-photovoltaic cell (BPV) system†

Microbial fuel cells are an emerging technology for converting organic substrates into electrical power. Recent research has shown that biofilms of some bacterial species are capable of self-mediated extracellular electron transfer. The prospect of exploiting this trait in photoautotrophic microbes that do not require an organic substrate has important implications for the future development of renewable solar energy technologies. Here we report on light-driven electrical power generated with biofilms grown from photosynthetic fresh water or marine species without the addition of an artificial electron-shuttling mediator. Green alga (Chlorella vulgaris, Dunaliella tertiolecta) or cyanobacteria (Synechocystis sp. PCC 6803, Synechococcus sp. WH 5701) strains were grown directly on a transparent, conductive anode (indium tin oxide-coated polyethylene terephthalate) and power generation under light and dark conditions was evaluated using a single-chamber bio-photovoltaic cell (BPV) system. Increased power outputs were observed for all strains upon illumination, with the largest light effect observed for Synechococcus (maximum 10.3 mW m−2 total power output recorded under 10 W m−2 white light). Further experiments conducted with Synechococcus and C. vulgaris showed that photosynthetic oxygen evolution rates were consistent with BPV power outputs under different light regimes (red, green and blue light), indicating a direct link between power output and photosynthetic activity. Biofilm power generation in these BPV devices was self-sustained for several weeks and was highly sensitive to ambient light levels. When connected in series, four BPVs (each 0.011 m2) generated enough power to run a commercial digital clock.
Frequency analysis of pressure fluctuation in fluidized bed plenum.

Pressure fluctuations in the plenum chamber of a solid-gas fluidized bed were measured and analyzed in order to develop a method for the diagnosis of fluidization conditions.Power spectral density functions obtained by FFT analysis of the pressure fluctuation data had three principal frequencies. These frequencies were compared with the bubble eruption frequency observed at the bed surface and the bubble generation frequency, which was obtained by an optic fiber probe installed above the distributor opening, and two of the three frequencies were found to agree well respectively with the obtained frequencies of bubble generation and of eruption. The third principal frequency was judged to be a natural frequency of the fluidized bed, since the frequency depended on the particle content in the fluidized bed and on the plenum volume. The frequency corresponding to bubble generation was observed to be influenced significantly by the natural frequency of the fluidized bed when the natural frequency had a low value.
Veterinary endectocides for malaria control and elimination: prospects and challenges

Residual transmission is the persistence of malaria transmission after scale-up of appropriate vector control tools and is one of the key challenges for malaria elimination today. Although long associated with outdoor biting, other mosquito behaviours such as partly feeding upon animals contribute greatly to sustaining transmission. Peri-domestic livestock can be used as decoy to protect humans from blood-seeking vectors but this approach often leads to an increased malaria risk in a phenomenon known as zoopotentiation. Treating the said livestock with drugs capable of killing intestinal parasites as well as mosquitoes that feed upon them has the potential to tackle malaria through a previously unexplored mechanism. The advantages and challenges associated with this approach are briefly discussed here. Numerous references are purposely provided. This article is part of the theme issue ‘Novel control strategies for mosquito-borne diseases’.
Novel control strategies for mosquito-borne diseases

Mosquito-borne diseases are an increasing global health challenge, threatening over 40% of the world's population. Despite major advances in malaria control since 2000, recent progress has stalled. Additionally, the risk of Aedes-borne arboviruses is rapidly growing, with the unprecedented spread of dengue and chikungunya viruses, outbreaks of yellow fever and the 2015 epidemic of Zika virus in Latin America. To counteract this growing problem, diverse and innovative mosquito control technologies are currently under development. Conceptually, these span an impressive spectrum of approaches, from invasive transgene cassettes with the potential to crash mosquito populations or reduce the vectorial capacity of a population, to low-cost alterations in housing design that restrict mosquito entry. This themed issue will present articles providing insight into the breadth of mosquito control research, while demonstrating the requirement for an interdisciplinary approach. The issue will highlight mosquito control technologies at varying stages of development and includes both opinion pieces and research articles with laboratory and field-based data on control strategy development. This article is part of the theme issue ‘Novel control strategies for mosquito-borne diseases'.
Dynamics of gas–surface interactions: 3D generalized Langevin model applied to fcc and bcc surfaces

A three dimensional generalized Langevin formalism is presented and applied to Ar and Xe interactions with Pt (111). Approximations for the random force and friction terms are proposed which permit realistic description of the motion and response of the surface atoms, including proper correlations among neighboring atoms. A ’’ghost atom’’ technique is developed which provides convenient numerical solution of the generalized Langevin equations in such a way that the fluctuation–dissipation theorem relating the random force and friction is satisfied rigorously. A simple prescription for determining parameters in the random force and friction is outlined. The prescription is applied to a four‐atom active zone to obtain explicit relationships for all parameters, depending only on the bulk and surface Debye frequencies, for fcc (100), (110), and (111), and bcc (100) and (110) surfaces. Calculations of energy accommodation, sticking probabilities, and thermal desorption rates are reported for Ar and Xe on Pt(11.
Objective Prediction of Social Skills Level for Automated Social Skills Training Using Audio and Text Information

Although Social Skills Training is a well-known effective method to obtain appropriate social skills during daily communication, getting such training is difficult due to a shortage of therapists. Therefore, automatic training systems are required to ameliorate this situation. To fairly evaluate social skills, we need an objective evaluation method. In this paper, we utilized the second edition of the Social Responsiveness Scale (SRS-2) as an objective evaluation metric and developed an automatic evaluation system using linear regression with multi-modal features. We newly adopted features including 28 audio features and BERT-based sequential similarity (seq-similarity), which indicates how well the meaning of users remains consistent within their utterances. We achieved a 0.35 Pearson correlation coefficient for the SRS-2's overall score prediction and 0.60 for the social communication score prediction, which is a treatment sub-scale score of SRS-2. This experiment shows that our system can objectively predict the levels of social skills. Please note that we only evaluated the system on healthy subjects since this study is still at the feasibility phase. Therefore, further evaluation of real patients is needed in future work.
ACL-SQL: Generating SQL Queries from Natural Language

Relational databases are prevalent in handling large and complex databases; however, writing the SQL queries can be a tedious task for databases. Recent studies for automating the task of translating natural language text to SQL query are not robust to queries having multi-table dependencies. Also, considering the lack of such research in scientific domain, we introduce ACL-SQL, a dataset with complex queries depending on up to five tables and benchmark the dataset on a simple approach. Evaluation shows that our approach is reasonably precise and can be adopted for practical applications. The dataset and codes are available at https://github.com/rohitshantarampatil/sql-nlp.
MEMOS: A Multi-modal Emotion Stream Database for Temporal Spontaneous Emotional State Detection

Mental health applications are increasingly interested in using audio-visual and physiological measurements to detect the emotional state of a person, where significant researches aim to detect episodic emotional state. The availability of wearable devices and advanced signals is attracting researchers to explore the detection of a continuous sequence of emotion categories, referred to as emotion stream, for understanding mental health. Currently, there are no established databases for experimenting with emotion streams. In this paper, we make two contributions. First, we collect a Multi-modal EMOtion Stream (MEMOS) database in the scenario of social games. Audio-video recordings of the players are made via mobile phones and aligned Electrocardiogram (ECG) signals are collected by wearable sensors. Totally 40 multi-modal sessions have been recorded, each lasting between 25 to 70 minutes. Emotional states with time boundaries are self-reported and annotated by the participants while watching the video recordings. Secondly, we propose a two-step emotional state detection framework to automatically determine the emotion categories with their time boundaries along the video recordings. Experiments on the MEMOS database provide the baseline result for temporal emotional state detection research, with average mean-average-precision (mAP) score as 8.109% on detecting the five emotions (happiness, sadness, anger, surprise, other negative emotions) in videos. It is higher than 5.47% where the emotions are detected by averaging the frame-level confidence scores (obtained by Face++ emotion recognition API) in the segments from a sliding window. We expect that this paper will introduce a novel research problem and provide a database for related research.
Enforcing Multilabel Consistency for Automatic Spatio-Temporal Assessment of Shoulder Pain Intensity

The standard clinical assessment of pain is limited primarily to self-reported pain or clinician impression. While the self-reported measurement of pain is useful, in some circumstances it cannot be obtained. Automatic facial expression analysis has emerged asa potential solution for an objective, reliable, and valid measurement of pain. In this study, we propose a video based approach for the automatic measurement of self-reported pain and the observer pain intensity, respectively. To this end, we explore the added value of three self-reported pain scales, i.e., the Visual Analog Scale(VAS), the Sensory Scale (SEN), and the Affective Motivational Scale(AFF), as well as the Observer Pain Intensity (OPI) rating for a reliable assessment of pain intensity from facial expression. Using a spatio-temporal Convolutional Neural Network - Recurrent Neural Network (CNN-RNN) architecture, we propose to jointly minimize the mean absolute error of pain scores estimation for each of thesescales while maximizing the consistency between them. The reliability of the proposed method is evaluated on the benchmark database for pain measurement from videos, namely, the UNBC-McMaster Pain Archive. Our results show that enforcing the consistency be-tween different self-reported pain intensity scores collected using different pain scales enhances the quality of predictions and improve the state of the art in automatic self-reported pain estimation.The obtained results suggest that automatic assessment of self-reported pain intensity from videos is feasible, and could be used as a complementary instrument to unburden caregivers, specially for vulnerable populations that need constant monitoring.
SIDEKICKS AND SOCIETY: A study of the generational evolution of values in UK's Society through minor characters in popular English literature

Using an etic lens, this study deals with identifying and deliberating upon culture in the United Kingdom as represented in the literary works of Arthur Conan Doyle, Roald Dahl and JK Rowling. We posit that literature is an art form that invariably represents the zeitgeist, the beliefs and the biases of the society the author inhabited (either directly through social commentary or indirectly through allegory and subtext). This cultural paradigm changes over time as societies evolve. Building upon our prior work, this study looks at evaluating the culture of the UK, using Hofstede's Cultural Dimensions of Power Distance and Masculinity, and looking specifically at the characters in the works of these authors. Arguably, minor characters, that are generally used for world-building, are the ones that better represent the society contemporary to the author, bespoke for the audience. We conducted four online workshops with Indian participants well-versed with the chosen texts. Through these workshops we analyzed minor characters for the Power Distance and Masculinity they reflect. Furthermore, even more meta discussions about societies and characters unfolded, giving us a rich tapestry of the cultural evolution of the United Kingdom's society. We found that the sets of characters in the fictional universes of the stories had grown to become more diverse and the authors had grown more bold with their criticisms of society over time. This paper also lays the groundwork for future culture studies that look into literature of a society and builds a platform for a deeper and more insightful understanding of persona building and design fiction.
See me Speaking? Differentiating on Whether Words are Spoken On Screen or Off to Optimize Machine Dubbing

Dubbing is the art of finding a translation from a source into a target language that can be lip-synchronously revoiced, i. e. , that makes the target language speech appear as if it was spoken by the very actors all along. Lip synchrony is essential for the full-fledged reception of foreign audiovisual media, such as movies and series, as violated constraints of synchrony between video (lips) and audio (speech) lead to cognitive dissonance and reduce the perceptual quality. Of course, synchrony constraints only apply to the translation when the speaker's lips are visible on screen. Therefore, deciding whether to apply synchrony constraints requires an automatic method for detecting whether an actor's lips are visible on screen for a given stretch of speech or not. In this paper, we attempt, for the first time, to classify on- from off-screen speech based on a corpus of real-world television material that has been annotated word-by-word for the visibility of talking lips on screen. We present classification experiments in which we classify
On-Device Document Classification using multimodal features

From small screenshots to large videos, documents take up a bulk of space in a modern smartphone. Documents in a phone can accumulate from various sources, and with the high storage capacity of mobiles, hundreds of documents are accumulated in a short period. However, searching or managing documents remains an onerous task, since most search methods depend on meta-information or only text in a document. In this paper, we showcase that a single modality is insufficient for classification and present a novel pipeline to classify documents on-device, thus preventing any private user data transfer to server. For this task, we integrate an open-source library for Optical Character Recognition (OCR) and our novel model architecture in the pipeline. We optimise the model for size, a necessary metric for on-device inference. We benchmark our classification model with a standard multimodal dataset FOOD-101 and showcase competitive results with the previous State of the Art with 30% model compression.
Reverse Dictionary Using an Improved CBoW Model

A reverse dictionary is a tool that solves the Tip-of-the-Tongue problem by predicting the target word given a meaning or description. This paper proposes a modified Continuous Bag-Of-Words(CBOW) model inspired by the Recurrent Neural Network(RNN), to implement a reverse dictionary. The model possesses the merits of both the CBOW model and RNN while stripping away the complexity associated with RNN. We evaluate the model by measuring accuracy based on the top-2 predictions.
Scalable Database Normalization Powered by the Crowd

In the age of information, there is a growing need to process data, both structured and unstructured, with ease and accuracy. Such an intensive task might be achieved through machine learning based applications, but not always. Moreover, the major problems with existing methodologies are scalability and complexity. Crowdsourcing is an alternate solution that deploy mechanisms for breaking the major task at hand into smaller segments (microtasks) requiring quick (often binary) decisions to be exercised by the amateur human participants. The use of such collective and collaborative intelligence can simplify few, if not most, of the difficult database management tasks. In this paper, we address one such problem, precisely determining functional dependencies (FDs) in a database. Our motivation is to pursue scalable normalization of a database through a game-like crowdsourcing approach. The FD realization is an interesting outcome of our research.
Detecting Data Accuracy Issues in Textual Geographical Data by a Clustering-based Approach

Data are published to encourage data exploitation. However, data quality issues threaten data consumption and require data consumers investing time and effort in data cleansing. By focusing on textual geographical data, we aim to detect inaccurate values, such as typos, truncated values, and propose corrections by a clustering-based approach. Our method is mainly based on a dictionary of correct values, the Agglomerative clustering to group data in clusters, and Levenshtein and Fuzzy string searching for computing word similarity. We test our approach on real open datasets published by the Campania region, heterogeneous in the topic, size, and type of errors by showing the positive results of using Levenshtein and Fuzzy Matching and exploiting clustering methods in detecting and correcting quality issues in textual geographical data. The achieved results are useful for data producers and consumers, both for the academy and the industry, in any application domain.
Assessment of Situation Awareness during Robotic Surgery using Multimodal Data

The use of robotic surgical systems disrupts existing team dynamics inside operating rooms and constitutes a major challenge for the development of crucial non-technical skills such as situation awareness (SA). Techniques for assessing SA mostly rely on subjective assessments and questionnaires; few leverage multimodal measures combining physiological, behavioural, and subjective indicators. We propose a conceptual model relating SA with mental workload, stress and communication, supported by measurable behaviours and physiological signals. To validate this model, we collect subjective, behavioural, and physiological data from surgical teams performing radical prostatectomy using robotic surgical systems. Statistical analyses will be performed to establish relationships between SA, subjective assessment of stress and mental workload, communication processes, and the surgeons' physiological signals.
Inferring customer occupancy status in for-hire vehicles using PU Learning

Data from Global Positioning Systems (GPS) and fare-meters in For-Hire vehicles (FHVs) have been used for various applications – both in research as well as organizational decision-making. The utility of such exercises largely depend on the accuracy of the data. This study looks at an environment where the data is partially mislabeled. Specifically, we take a common real-world setting where vehicle operators choose to render transportation services to customers without the use of a fare-meter, often by negotiating a fixed rate with the customer. This practice, which to different degrees, has been observed and documented across urban areas in the world, leads to various undesirable effects. In this study, we seek to identify cases of such behavior in the dataset. Typically, a supervised learning classifier could be built to predict the occupancy status from GPS traces, which can then be used, to look for anomalies between the predicted and stated behaviors. However, in our case the training dataset also contains instances of incorrect tagging. We address this problem by casting it as one of learning from Positive and Unlabeled instances (PU Learning) . This is owing to the fact that we observe the phenomenon of one-sided label noise, where trips tagged ‘vacant’ by the taximeter could be truly vacant or occupied, whereas trips tagged ‘occupied’ are expected to be occupied in reality as well. To support this novel formulation, we apply three state-of-the-art PU Learning algorithms on a real-world trajectory data set from an organization plying 170 active vehicles over a period of two months. We compare these to the baselines of standard supervised learning. Validation is carried out by the organization through alternate channels of investigation which is not indicated in the data set. The results show that the PU Learners provide a significant improvement in classification across a range of metrics when compared to the baseline approaches. This translates to a significant increase in identifying or reclassifying the mislabeled rides.
What do Teachers Seek?: A study into understanding the needs of teachers who educate children with visual impairments

It is of importance that the children with visual impairments have equal educational and occupational opportunities as enjoyed by their sighted peers in the field of Science and Math. This paper follows a small qualitative research done with five teachers who teach children with visual impairments. These teachers were from three different schools for the blind, present across the state of Karnataka, India. Through this research, we understand a teacher's process when preparing for a classroom, thus identify their tasks, needs, and challenges. We collected the data via telephonic interviews, and it was analyzed using thematic analysis. The paper eventually discusses the implications of these findings in the design of instructional content intended to aid the teacher in achieving an efficient transference of concepts in Math and Science to children with visual impairments.
Determinants of Morning Stiffness in Rheumatoid Arthritis: Comment on the Article by Orange et al

profiles (see Supplementary Table 2, http://onlin elibr ary.wiley.com/ doi/10.1002/art.41472/ abstract) in COVID-19 patients, including detailed information regarding LAC test results and immunoassays, and serial studies of aPL to demonstrate persistence. In conclusion, there is an opportunity to discover potentially common mechanisms that will inform our understanding of both COVID-19 and APS. However, this will require correct use and measurement of aPL.