Miracle-dz/UniFAR-FTUs huggingface dataset:
Recurrent Prediction With Spatio-Temporal Attention for Crowd Attribute Recognition

Crowd attribute recognition is a challenging task for crowd video understanding because a crowd video often contains multiple attributes from various types. Traditional deep learning-based methods directly treat this recognition problem as a multiple binary classification problem and represent the video by vectorizing and fusing the separately learned spatial and temporal features in the fully connected layers. Therefore, the correlations between these attributes may not be well captured. In this paper, a bidirectional recurrent prediction model with a semantic-aware attention mechanism is proposed to explore the spatio-temporal and semantic relations between the attributes for more accurate recognition. The ConvLSTM is introduced for feature representation to capture the spatio-temporal structure of the crowd videos and facilitate the visual attention. The bidirectional recurrent attention module is proposed for sequential attribute prediction by associating each subcategory attributes to corresponding semantic-related regions iteratively. The experiments and evaluations on the challenging WWW crowd video dataset not only show that our approach significantly outperforms the state-of-the-art methods but also verify that our approach can effectively capture the spatio-temporal and semantic relations of the crowd attributes.
Proceedings of the Workshop on Multi-source Multilingual Information Extraction and Summarization

Information extraction (IE) and text summarization (TS) are key technologies aiming at extracting relevant information from texts and presenting the information to the user in a condensed form. The ongoing information explosion makes IE and TS particularly critical for successful functioning within the information society. These technologies, however, face new challenges with the adoption of the Web 2.0 paradigm (e.g., blogs, wikis) due to their inherent multi-source nature. These technologies must no longer deal only with isolated texts or narratives, but with large-scale repositories or sources—possibly in several languages—containing a multiplicity of views, opinions, and commentaries on particular topics, entities and events. There is thus a need to adapt and/or develop new techniques to deal with these new phenomena. Recognising similar information across different sources and/or in different languages is of paramount importance in this multi-source, multi-lingual context. In information extraction, merging information from multiple sources can lead to increased accuracy, as compared to extraction from a single source. In text summarization, similar facts found across sources can inform sentence scoring algorithms. In question answering, the distribution of answers in similar contexts can inform answer-ranking components. Often, it is not the similarity of information that matters, but its complementary nature. In a multi-lingual context, information extraction and text summarization can provide solutions for crosslingual access: key pieces of information can be extracted from different texts in one or many languages, merged, and then conveyed in natural language in concise form. Applications need to be able to cope with the idiosyncratic nature of the new Web 2.0 media: mixed input, new jargon, ungrammatical and mixed-language input, emotional discourse, etc. In this context, synthesizing or inferring opinions from multiple sources is a new and exciting challenge for NLP. On another level, profiling of individuals who engage in the new social Web, and identifying whether a particular opinion is appropriate/relevant in a given context are important topics to be addressed. The objective of this second Multi-source Multilingual Information Extraction and Summarization (MMIES) workshop is to bring together researchers and practitioners in information-access technologies, to discuss recent approaches for dealing with multi-source and multi-lingual challenges.
A Grammar of Bantawa: Grammar, paradigm tables, glossary and texts of a Rai language of Eastern Nepal

This dissertation provides a comprehensive overview of the grammar of Bantawa, a Kiranti (Rai) language spoken in Eastern Nepal. Bantawa is an SOV language featuring rich verbal morphology. In Bantawa we find both ergative and accusative alignment patterns in verbal affix agreement, and an ergative / absolutive pattern of case marking. The grammar treats the syntax of all major syntactical constructions, including the highly productive verb compounding process, embedding of converbial and finite clauses, nominalisations and evidentiality. The semantics of nominalisations and the aspectual nuances of verbal compounds are discussed in detail. An annotated text corpus, comprehensive paradigm tables and a glossary complete the grammar.
SemEval-2014 Task 4: Aspect Based Sentiment Analysis

Sentiment analysis is increasingly viewed as a vital task both from an academic and a commercial standpoint. The majority of current approaches, however, attempt to detect the overall polarity of a sentence, paragraph, or text span, irrespective of the entities mentioned (e.g., laptops) and their aspects (e.g., battery, screen). SemEval2014 Task 4 aimed to foster research in the field of aspect-based sentiment analysis, where the goal is to identify the aspects of given target entities and the sentiment expressed for each aspect. The task provided datasets containing manually annotated reviews of restaurants and laptops, as well as a common evaluation procedure. It attracted 163 submissions from 32 teams.
Nutritional and Anti–Nutritional Evaluation of Garri Processed by Traditional and Instant Mechanical Methods

Aim: The aim of this study is to evaluate the nutritional and anti-nutritional composition of garri processed by traditional and instant mechanical methods. Place and Duration of Study: This study was carried out in Achara, Uturu and analyses were done at the Biochemistry and Central Laboratories of Gregory University, Uturu, Abia State between March and July, 2017. Methods: Cassava was harvested and processed in Achara area of Uturu, Abia State. For garri processed by instant mechanical method, cassava was grated and dewatered using hydraulic press and were roasted (fried) within 24 hours of harvest. For garri processed by traditional method, the grated garri was allowed to stay for 24 hours in the sack before dewatering using sticks. The dewatering process took 3 days before roasting. The nutritional and anti-nutritional composition of raw cassava mash, garri processed by traditional and instant mechanical methods were evaluated using standard methods. Results: The result of the analysis showed that garri processed by traditional method was higher in most of the nutritional factors but lower in all of the anti-nutritional factors investigated when compared with those of garri processed by instant mechanical method and raw cassava mash. Garri processed by traditional method was significantly higher in vitamin A but lower in vitamin C when compared with garri processed by instant mechanical method at p<0.05. Garri with palm oil has its cyanogenic glucoside significantly reduced when compared with garri without palm oil at p<0.05. Conclusion: Long period of fermentation (3 – 5 days) of cassava product is recommended as garri processed by traditional method was nutritionally better than that processed by instant mechanical method due to the longer period of fermentation.
Visually dependent nonverbal cues and video communication

Humans have a rich verbal and nonverbal language that allows us to communicate in powerful ways. Technology continues to play a significant role in enabling and enhancing the ways we communicate with each other when we are geographically separated. Machines capture our expressions and transmit them anywhere in the world for distant partners. But how expression is captured and how it is presented can have surprising effects on the way people communicate. In this dissertation, I present background, designs, and evaluations of a new video conferencing system called MultiView which aims to improve the group-to-group video conferencing experience. I will (1) show the dependence of spatial information for the effective communication of nonverbal information such—as eye contact—and formalize the spatial shortcomings of modern video conferencing system design, (2) introduce a design based on a new multiple-perspective display to address these shortcomings, and (3) show that the MultiView design can effectively capture and present nonverbal cues in a natural way and that this new ability dramatically improves trust formation between remotely meeting groups.
Towards more Secure and Efficient Password Databases

Password databases form one of the backbones of nowadays web applications. Every web application needs to store its users’ credentials (email and password) in an efficient way, and in popular applications (Google, Facebook, Twitter, etc.) these databases can grow to store millions of user credentials simultaneously. However, despite their critical nature and susceptibility to targeted attacks, the techniques used for securing password databases are still very rudimentary, opening the way to devastating attacks. Just in the year of 2016, and as far as publicly disclosed, there were more than 500 million passwords stolen in internet hacking attacks. To solve this problem we commit to study several schemes like property-preserving encryption schemes (e.g. deterministic encryption), encrypted data-structures that support operations (e.g. searchable encryption), threshold cryptography (e.g. secret sharing), fully/partially homomorphic encryption schemes, and commodity trusted hardware (e.g. Intel SGX). In this thesis we propose to make a summary of the most efficient and secure techniques for password database management systems that exist today and recreating them to accommodate a new and simple universal API. Additionally, in this thesis we will also build two new techniques for secure password database management, one based on cryptographic encryption and the other based on commodity trusted hardware. These solution will preserve security guarantees while allowing users to login under the “one second” usability mark. Furthermore, since password databases are particularly susceptible to snapshot adversaries (i.e. a hacker that gets a small time window of access to the database and makes a snapshot copy of all available information), the thesis will also study this adversary in detail and research how to efficiently counter it.
Generalization of Teacher-Student Network and CNN Pruning

—Convolutional neural network (CNN) pruning is an important way to adapt a large CNN model attained on general datasets to a more specialized task. It is time-consuming, for its needs for exploring many possible pruned variants and for the long time needed to train and test each pruned variant. Block-wise pruning is an idea to accelerate the process: If each block of layers in a CNN can be pruned and trained well, putting them together could compose many almost-ready variants of pruned CNN and hence save much time for training pruned CNN. Materializing the idea, however, faces a major barrier: how to effectively train an arbitrary block of CNN layers independently. This paper proposes to solve the problem through Generalized Teacher-Student Network (GTSN), a novel technique designed for ﬂexibly passing the knowledge of a large CNN to a pruned CNN at an individual layer level. It uncovers a series of insights on the effects of GTSN on CNN pruning, and integrates them into a block-wise CNN pruning framework, which speeds up CNN pruning by up to 202.8X for ResNet-50 and up to 30.2X for Inception-V3.
Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network

Taxi and sharing bike bring great convenience to urban transportation. A lot of efforts have been made to improve the efficiency of taxi service or bike sharing system by predicting the next-period pick-up or drop-off demand. Different from the existing research, this paper is motivated by the following two facts: 1) From a micro view, an observed spatial demand at any time slot could be decomposed as a combination of many hidden spatial demand bases; 2) From a macro view, the multiple transportation demands are strongly correlated with each other, both spatially and temporally. Definitely, the above two views have great potential to revolutionize the existing taxi or bike demand prediction methods. Along this line, this paper provides a novel Co-prediction method based on Spatio-Temporal neural Network, namely, CoST-Net. In particular, a deep convolutional neural network is constructed to decompose a spatial demand into a combination of hidden spatial demand bases. The combination weight vector is used as a representation of the decomposed spatial demand. Then, a heterogeneous Long Short-Term Memory (LSTM) is proposed to integrate the states of multiple transportation demands, and also model the dynamics of them mixedly. Last, the environmental features such as humidity and temperature are incorporated with the achieved overall hidden states to predict the multiple demands simultaneously. Experiments have been conducted on real-world taxi and sharing bike demand data, results demonstrate the superiority of the proposed method over both classical and the state-of-the-art transportation demand prediction methods.
Integrating a Photovoltaic Panel and a Battery Pack in One Module: From concept to prototype

Photovoltaic (PV) solar energy is variable and not completely predictable; therefore, different energy storage devices have been researched. Among the variety of options, electrochemical cells (commonly called batteries) are technically feasible because of their maturity and stability. However, PV-battery systems face multiple challenges such as high cost and complexity of installation. Cost is the main concern when trying to enable new solutions for the solar market, especially when competing with other renewable technologies, but most importantly, with fossil fuels to reduce the effects of climate change. As a consequence, a new concept that integrates all the components of a PVbattery system in a single device is introduced. By integrating a power electronics unit and a battery pack at the back of a PV panel, referred as PV-battery Integrated Module (PBIM), the cost of the total system can decrease and become a viable alternative for the solar market. Because the concept is relatively new and not all the challenges have been previously addressed, this dissertation strives to prove the feasibility of the concept and to fill the gaps that have been identified in the literature review. Firstly, an off-grid PV-battery system was selected, and a sizing methodology was proposed to investigate the limitations and boundaries of the integrated device. Having sized the system, the thesis explored the implementation of an energy management system in order to control smartly the direction and magnitude of the power delivery. Then, a thermal model was developed to characterize the thermal response of the PBIM and to recommend a thermal management system to decrease the temperature of operation of the battery pack and power electronics. Finally, by testing a PBIM prototype and developing an integrated model that reproduces the temperature and power flows expected, a battery testing methodology was developed for finding a suitable battery technology that can comply with the requirements set by the expected operating conditions of the device. Therefore, the research carried out in this dissertation proves that the integration of PV-battery system in one device is technically feasible.
Integrating innovation diffusion theory with technology acceptance model: supporting students’ attitude towards using a massive open online courses (MOOCs) systems

ABSTRACT This research intends to investigate factors affecting students’ behavioural intentions to use a massive open online courses (MOOCs) system. Integrating the technology acceptance model (TAM) with the innovation diffusion theory (IDT), the present research proposes an extended technology acceptance model. Testing of data collected from 1148 students using (MOOCs) system in Malaysia achieved the research objective. The findings obtained via a quantitative research method, based on analysing the results using a Structure Equation Modeling (SEM). The outcomes reveal that six views of novelty features swayed students’ a (MOOCs) scheme behavioural objective. The properties of the relative advantages, intricacy, trialability, observability, compatibility, and perceived enjoyment on the perceived ease of use are significant. Also, the effects of the relative advantages, complexity, trialability, observability, compatibility, and perceived enjoyment on the usefulness have a substantial impact. Therefore, the practical outcomes offer robust backing for the integrative method between TAM and IDT. The results recommend a prolonged model of TAM with IDT for the taking of MOOCs) system used to improve students learning performance, which can help decision makers in higher education, universities and colleges to planning, evaluating and executing the use of (MOOCs) systems.
A Deep Neural Network Approach for Model-based Gait Recognition

Since the declaration of war on terrorism, human identification has gained more popularity throughout various research communities. One of the sought-after techniques that is used in the identification process is Gait recognition. Gait recognition is a biometric recognition technique that uses body measurements and movements during walks. This technique is non-invasive and can be done without an awareness of a subject. In this work, we develop identification models for gait information extracted from sequences of walking by Microsoft Kinect, from Andersson and Araujo, using a Deep Neural Networks (DNN) combined with Majority Vote. The results show our proposed model yields accuracy of 95.0%, which outperforms Support Vector Machine, k-Nearest Neighbor, Multi-Layer Perceptron and solely Deep Neural Networks techniques.
Wetin dey with these comments? Modeling Sociolinguistic Factors Affecting Code-switching Behavior in Nigerian Online Discussions

Multilingual individuals code switch between languages as a part of a complex communication process. However, most computational studies have examined only one or a handful of contextual factors predictive of switching. Here, we examine Naija-English code switching in a rich contextual environment to understand the social and topical factors eliciting a switch. We introduce a new corpus of 330K articles and accompanying 389K comments labeled for code switching behavior. In modeling whether a comment will switch, we show that topic-driven variation, tribal affiliation, emotional valence, and audience design all play complementary roles in behavior.
Robust Selections of Various Hematopoietic Cell Fractions on the CliniMACS Plus Instrument

Cell separation technologies play a vital role in the graft engineering of hematopoietic cellular fractions, particularly with the rapid expansion of the field of cellular therapeutics. The CliniMACS Plus Instrument (Miltenyi Biotec) utilizes immunomagnetic techniques to isolate hematopoietic progenitor cells (HPCs), T cells, NK cells, and monocytes. These products are ultimately used for HPC transplantation and for the manufacture of adoptive immunotherapies. We evaluated the viable cell recovery and cell purity of selections and depletions performed on the CliniMACS Plus over a 10-year period at our facility, specifically assessing for the isolation of CD34+, CD4+, CD3+/CD56+, CD4+/CD8+, and CD25+ cells. Additionally, patient- and instrument-related factors affecting these parameters were examined. Viable cell recovery ranged from 32.3 ± 10.2% to 65.4 ± 15.4%, and was the highest for CD34+ selections. Cell purity ranged from 86.3 ± 7.2% to 99.0 ± 1.1%, and was the highest for CD4+ selections. Undesired cell fractions demonstrated a range of 1.2 ± 0.45 to 5.1 ± 0.4 log reductions. Red cell depletions averaged 2.12 ± 0.68 logs, while platelets were reduced by an average of 4.01 ± 1.57 logs. Donor characteristics did not impact viable cell recovery or cell purity for CD34+ or CD4+ cell enrichments; however, these were affected by manufacturing variables, including tubing size, bead quantity, and whether preselection platelet washes were performed. Our data demonstrate the efficient recovery of hematopoietic cellular fractions on the CliniMACS Plus that may be optimized by adjusting manufacturing variables.
The Translation and Interpreting Competence Questionnaire: an online tool for research on translators and interpreters

ABSTRACT Despite the growth of research on translation and interpreting, measures of competence in such activities typically stem from informal, non-validated instruments. This scenario casts doubts on the ensuing findings and hinders comparability across studies. To address the issue, we introduce the Translation and Interpreting Competence Questionnaire (TICQ), an online tool for collecting relevant quantitative and qualitative data. The instrument comprises three sections. Section A covers demographic data and aspects of language history and multilingual abilities. Sections B and C focus on translation and interpreting competence, respectively, via self-rating scales on modality-specific skills and questions about procedural, declarative, and otherwise professional factors. A stringent validation procedure based on data from 284 participants (including item reliability estimations, principal component analyses, and multigroup discriminant function analyses) revealed that the TICQ can robustly classify subjects in terms of their actual status (laymen, students, professionals) in the translation and interpreting communities –with an accuracy of roughly 80%. Importantly, the TICQ is available in three languages, it is fully customizable, and it can be administered online, locally or in pen-and-paper format. Briefly, this user-friendly tool provides comprehensive information for empirical investigations in the field, offering unprecedented opportunities to enhance sample selection, between-study comparisons, and meta-analytic research.