Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis

Extracting features from crowd flow analysis has become an important research challenge due to its social cost and the impact of inadequate planning of high-quality services and security monitoring on the lives of citizens. This paper descriptively reviews and compares existing crowd analysis approaches based on different data sources. This survey provides the fundamentals of crowd analysis and considers three main approaches: crowd video analysis, crowd spatio-temporal analysis, and crowd social media analysis. The key research contributions in each approach are presented, and the most significant techniques and algorithms used to improve the precision of results that could be integrated into solutions to enhance the quality of services in a smart city are analyzed.
INTERNAL DESTINATION MARKETING : A CASE STUDY

Because marketing of destinations requires a collaborative approach based on a destination’s complex and nested nature, Destination Marketing Organisations (DMOs) aim to facilitate this notion. A DMO that is a non-party organisation and represents the tourism industry acts as a glue in terms of creating a favourable destination experience that attracts both tourists and investors. That glue among the stakeholders requires internal destination marketing (IDM) as a strategic tool to generate established support and a powerful synergy at the same time. This study will outline the dimensions of IDM by conducting qualitative analysis through a case study.
Comparison of Linear Poly Ethylene Imine (LPEI) and Poly L-Lysine (PLL) in Fabrication of CHOK1 Cell-Loaded Multilayer Alginate Microcapsules

Purpose: Poly l-lysine (PLL) has been introduced as a strengthening covering layer for alginate microcapsules which are the most convenient way for cell encapsulation. Some disadvantages of PLL such as high price and low biocompatibility have prompted scientists to find better alternatives. Linear poly ethylene imine (LPEI), thanks to its highly similar structure to PLL, could be considered as a proper cost-effective alternative. In this study LPEI and PLL were compared as covering layers of cell-loaded alginate-LPEI-alginate (cALA) and alginate-PLL-alginate (cAPA) microcapsules. Methods: In addition to the physico-mechanical properties, the encapsulation efficiency, cell survival post encapsulation, cell viability, and cellular metabolic activity within the microcapsules were evaluated using trypan blue, live/dead cell staining, and MTT test, respectively. Results: Physico-mechanical evaluation of the microcapsules revealed that the cell microencapsulation process did not affect their shape, size, and mechanical stability. Although the encapsulation efficiency for cALA and cAPA was not different (P >0.05), cell survival post encapsulation was higher in cALA than in cAPA (P<0.05) which could be the reason for the higher cell viability and also cellular metabolic activity within these microcapsules in comparison to cAPA. Conclusion: Here, based on these results, ALA could be introduced as a preferable alternative to APA for cell encapsulation.
Aquatic Macrophytes are Seasonally Important Dietary Resources for Moose

Moose (Alces alces) are generalist herbivores, but are important aquatic-terrestrial ecotone specialists. Aquatic macrophytes are a high-quality food source for moose during summer, but the importance of aquatic food sources to the moose diet is difficult to study. We used stable isotope analysis of carbon and nitrogen from moose hooves and forage (terrestrial plants, aquatic macrophytes, and arboreal lichen) to assess the diet of moose at Isle Royale National Park, Michigan, USA, using Bayesian mixing models. We also evaluated the isotopic variability along chronologies of serially sampled hooves. Overall, our mixing models indicate that 13%–27% of the summer moose diet was aquatic in origin. Among moose that died during winter, body condition was impaired and hoof 15N was higher where aquatic habitats were sparse. Although isotope chronologies preserved in hooves could significantly enhance our understanding of ungulate foraging ecology, interpretation of such chronologies is presently limited by our lack of knowledge pertaining to hoof growth rate and seasonal growth variability related to age and health. Distinct isotopic values among terrestrial plants, aquatic macrophytes, and arboreal lichens indicate that continued methodological advances in stable isotope ecology will lead to more precise estimates of the contribution of aquatic feeding to moose population dynamics and other ungulates.
ADDIS ABABA UNIVERSITY SCHOOL OF GRADUATE STUDIES TRADE AMONG COMESA COUNTRIES : A GRAVITY APPROACH BY EDEN KASSAYE

This study aimed at examining whether Common Market for Eastern and Southern Africa (COMESA) is effective in promoting trade among its member states. The study uses gravity model to estimate the trade intensity among its members for the year 1981 and 1999. The gravity model is applied to trade intensity indices and the estimation of the gravity equation is carried out using Tobit regression. The empirical results indicate that the standard gravity model variables have the expected effect on the trade linkage in COMESA. The main focus of the study is, on the estimation of COMESA, which is found to be insignificant in promoting trade among members. A comparison of the two periods result reveals a very similar pattern before and after the formation of COMESA. Thus, the result confirms the ineffectiveness of COMESA in enlarging trade among its members. Accordingly, the study suggests the requirement of joint effort of the member countries in coordinating, harmonizing and implementing policies, improving their human and institutional capabilities, increasing production capacity and expanding markets by involving active participation of the private sector so as to raise intra-COMESA trade.
ADVANCED MOLECULAR AND CELL BASED THERAPIES FOR ATHEROSCLEROSIS

Cardiovascular disease (CVD) is the most frequent cause of premature death in modern industrialized countries, accounting for 4.35 million deaths each year in Europe, and 35% of deaths in the United Kingdom. Atherosclerosis is pathological process characterized by formation of subintimal plaques that can reduce or obstruct blood flow through the vessel. It is a result of normal blood vessels ageing, with initial lesion developing before age 30 (so-called fatty streaks). Risk factors that significantly accelerate this process are serum lipid impairment, hypertension, cigarette smoking, diabetes mellitus, obesity, physical inactivity and hereditary factors i.e. positive family history of cardiovascular disorders. Compelling experimental and clinical evidence has demonstrated that both systemic and local inflammation might play a prominent role in the pathogenesis of atherosclerosis and its clinical complications. Since inflammatory processes accompany all stages of atherosclerosis, measurement of plasma/serum concentrations of circulating inflammatory biomarkers might aid in identifying individuals at high risk for cardiovascular disease (CVD). Currently employed treatment strategies include risk factor modification, conventional pharmacotherapy, and percutaneous or surgical revascularization. Yet despite these interventions disease progression often results in recurrent symptoms, with many patients exhausting all therapeutic options. KEY WORDS: Atherosclerosis, Risk Factors, Pathophysiology, Inflammation. Biomarkers for Atherosclerosis, Polyphenols, Molecular and Cell Based Therapies
Multi-Classification Model for Spoken Language Understanding

The spoken language understanding (SLU) is an important part of spoken dialogue system (SDS). In the paper, we focus on how to extract a set of act-slot-value tuples from users’ utterances in the 1st Chinese Audio-Textual Spoken Language Understanding Challenge (CATSLU). This paper adopts the pretrained BERT model to encode users’ utterances and builds multiple classifiers to get the required tuples. In our framework, finding acts and values of slots are recognized as classification tasks respectively. Such multi-task training is expected to help the encoder to get better understanding of the utterance. Since the system is built on the transcriptions given by automatic speech recognition (ASR), some tricks are applied to correct the errors of the tuples. We also found that using the minimum edit distance (MED) between results and candidates to rebuild the tuples was beneficial in our experiments.
Encrypted Databases: New Volume Attacks against Range Queries

We present a range of novel attacks which exploit information about the volume of answers to range queries in encrypted database. Our attacks rely on a strategy which is simple yet robust and effective. We illustrate the robustness of our strategy in a number of ways. We show how i) to adapt the attack for several variations of a basic usage scenario ii) to defeat countermeasures intended to thwart the premise of our basic attack and iii) to perform partial reconstruction of secret data when unique reconstruction is information theoretically impossible. Furthermore, over the state of the art, our attacks require one order of magnitude fewer queries. We show how to improve the attacks even further, under the assumption that some partial information is known to the adversary. We validate experimentally all of our attacks through extensive experiments on real-world medical data and justify theoretically the effectiveness of our strategy for the basic attack scenario. Our new attacks further underscore the difficulty of striking an appropriate functionality-security trade-off for encrypted databases.
Communicative Signals and Social Contextual Factors in Multimodal Affect Recognition

One research branch in Affective Computing focuses on using multimodal ‘emotional’ expressions (e.g. facial expressions or non-verbal vocalisations) to automatically detect emotions and affect experienced by persons. The field is increasingly interested in using contextual factors to better infer emotional expressions rather than solely relying on the emotional expressions by themselves. We are interested in expressions that occur in a social context. In our research we plan to investigate how we can; a) utilise communicative signals that are displayed during interactions to recognise social contextual factors that influence emotion expression and in turn b) predict/recognise what these emotion expressions are most likely communicating considering the context. To achieve this, we formulate three main research questions: I) How do communicative signals such as emotion expressions co-ordinate behaviours and knowledge between interlocutors in interactive settings? , II) Can we use behavioural cues during interactions to detect social contextual factors relevant for interpreting affect? and III) Can we use social contextual factors and communicative signals to predict what emotion experience is linked to an emotion expression?
Continuous Emotion Recognition in Videos by Fusing Facial Expression, Head Pose and Eye Gaze

Continuous emotion recognition is of great significance in affective computing and human-computer interaction. Most of existing methods for video based continuous emotion recognition utilize facial expression. However, besides facial expression, other clues including head pose and eye gaze are also closely related to human emotion, but have not been well explored in continuous emotion recognition task. On the one hand, head pose and eye gaze could result in different degrees of credibility of facial expression features. On the other hand, head pose and eye gaze carry emotional clues themselves, which are complementary to facial expression. Accordingly, in this paper we propose two ways to incorporate these two clues into continuous emotion recognition. They are respectively an attention mechanism based on head pose and eye gaze clues to guide the utilization of facial features in continuous emotion recognition, and an auxiliary line which helps extract more useful emotion information from head pose and eye gaze. Experiments are conducted on the Recola dataset, a database for continuous emotion recognition, and the results show that our framework outperforms other state-of-the-art methods due to the full use of head pose and eye gaze clues in addition to facial expression for continuous emotion recognition.
Engagement Intensity Prediction withFacial Behavior Features

This paper describes an approach for the engagement prediction task, a sub-challenge of the 7th Emotion Recognition in the Wild Challenge (EmotiW 2019). Our method involves three fundamental steps: feature extraction, regression and model ensemble. In the first step, an input video is divided into multiple overlapped segments (instances) and the features extracted for each instance. The combinations of Long short-term memory (LSTM) and Fully connected layers deployed to capture the temporal information and regress the engagement intensity for the features in previous step. In the last step, we performed fusions to achieve better performance. Finally, our approach achieved a mean square error of 0.0597, which is 4.63% lower than the best results last year.
Discrete Sliding Mode Control Design for Piezoelectric Actuator

Piezoelectric bimorph actuators have been employed in several applications. In this paper, the piezoelectric actuator is discretized and its hysteresis function is studied, then a digital sliding mode controller is designed. Furthermore, a perturbation estimation technique is applied and an observer is no longer needed. In addition, simulations are performed also using the traditional PID controller in order to validate the proposed controller scheme performance.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.
The Bitcoin Backbone Protocol Against Quantum Adversaries

Bitcoin and its underlying blockchain protocol have received recently signiﬁcant attention in the context of building distributed systems as well as from the perspective of the foundations of the consensus problem. At the same time, the rapid development of quantum technologies brings the possibility of quantum computing devices from a theoretical concept to an emerging technology. Motivated by this, in this work we revisit the formal security of the core of the Bitcoin protocol, called the Bitcoin backbone, in the presence of an adversary that has access to a scalable quantum computer. We prove that the protocol’s essential properties stand in the post-quantum setting assuming a general quantum adversary with suitably bounded number of queries in the Quantum Random Oracle (QRO) model. In order to achieve this, we investigate and bound the quantum complexity of a Chain-of-Proofs-of-Work search problem which is at the core of the blockchain protocol. Our results imply that security can be shown by bounding the quantum queries so that each quantum query is worth O ( p − 1 / 2 ) classical ones and that the wait time for safe settlement is expanded by a multiplicative factor of O ( p − 1 / 6 ), where p is the probability of success of a single classical query to the protocol’s underlying hash function.
Task-independent Multimodal Prediction of Group Performance Based on Product Dimensions

This paper proposes an approach to develop models for predicting the performance for multiple group meeting tasks, where the model has no clear correct answer. This paper adopts ”product dimensions” [Hackman et al. 1967] (PD) which is proposed as a set of dimensions for describing the general properties of written passages that are generated by a group, as a metric measuring group output. This study enhanced the group discussion corpus called the MATRICS corpus including multiple discussion sessions by annotating the performance metric of PD. We extract group-level linguistic features including vocabulary level features using a word embedding technique, topic segmentation techniques, and functional features with dialog act and parts of speech on the word level. We also extracted nonverbal features from the speech turn, prosody, and head movement. With a corpus including multiple discussion data and an annotation of the group performance, we conduct two types of experiments thorough regression modeling to predict the PD. The first experiment is to evaluate the task-dependent prediction accuracy, in the situation that the samples obtained from the same discussion task are included in both the training and testing. The second experiments is to evaluate the task-independent prediction accuracy, in the situation that the type of discussion task is different between the training samples and testing samples. In this situation, regression models are developed to infer the performance in an unknown discussion task. The experimental results show that a support vector regression model archived a 0.76 correlation in the discussion-task-dependent setting and 0.55 in the task-independent setting.
WiBend: Wi-Fi for Sensing Passive Deformable Surfaces

We present WiBend, a system that recognizes bending gestures as the input modalities for interacting on non-instrumented and deformable surfaces using WiFi signals. WiBend takes advantage of off-the-shelf 802.11 (Wi-Fi) devices and Channel State Information (CSI) measurements of packet transmissions when the user is placed and interacting between a Wi-Fi transmitter and a receiver. We have performed extensive user experiments in an instrumented laboratory to obtain data for training the HMM models and for evaluating the precision of WiBend. During the experiments, participants performed 12 distinct bending gestures with three surface sizes, two bending speeds and two different directions. The performance evaluation results show that WiBend can distinguish between 12 bending gestures with a precision of 84% on average.
PPA_A_224529 1701..1709

1Key Lab of Health Technology Assessment, National Health Commission, School of Public Health, Fudan University, Shanghai, People’s Republic of China; 2Department of Health Technology Assessment Research, Shanghai Health Development Research Center, Shanghai Medical Information Center, Shanghai, People’s Republic of China; 3The Second Clinical Medical School of Nanjing Medical University, Nanjing, People’s Republic of China; 4Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China; 5West China School of Pharmacy, Sichuan University, Chengdu, People’s Republic of China; 6Fudan University Shanghai Cancer Center, Shanghai, People’s Republic of China; 7Sun Yat-Sen University Cancer Center, Guangzhou, People’s Republic of China Objective: The study aims to quantify patients’ risk-benefit preferences for chemotherapy
A smart glove with integrated triboelectric nanogenerator for self-powered gesture recognition and language expression

ABSTRACT Flexible electronics with great functional characteristics have proved to be a stepping stone in the field of wearable devices. Amongst all, gesture-sensing techniques have been widely studied for human-machine interfaces. In this paper, we propose a self-powered gesture-sensing system attached to the back of the hands, which has the capability of distinguishing hand gestures by measuring the triboelectric nanogenerator output signal. By attaching the sensor on the back of the hand, we can sense the displacement of tendons to detect the gestures. In addition, humidity resistance and durability of the device were tested and validated. Furthermore, we have established a set of rules to define the relationship between gestures and corresponding English letters. Therefore, the proposed sensor can further serve as an electronic sign language translator by converting gestures into words. Finally, we can integrate this system into gloves to enhance the applicability and utility. Overall, we have developed a real-time self-powered back-of-hand sensing system which can recognize various hand gestures. GRAPHICAL ABSTRACT
Non-thermal inactivation of Listeria spp. in a typical dry-fermented sausage: “Bergamasco” salami

Aim of the present study was the evaluation of the growth potential of Listeria spp. inoculated in the typical North Italian dry fermented sausage “Bergamasco” salami during its production. As it was necessary to carry out the challenge test in the production line of the industry, according to the guidelines of the European Reference Laboratory for Listeria monocytogenes, a non-pathogenic “surrogate” microorganism was used: for the inoculum, two strains of Listeria innocua (1 ATCC, 1 strain isolated from a similar substrate) were used. The inoculation of the samples occurred during grinding and mixing of the sausage mass, before the filling. To avoid cross-contamination, the control samples were produced before the contaminated ones. After the dripping, salamis were subjected to the normal production process (drying and maturation in five steps at specific temperatures and humidity rates). The inoculated products were subjected to the enumeration of Listeria spp. at T0 (day of inoculation) and at T4 (post-drying), and every 10 days during curing (T10, T20, T30, T40, T50, T60, T70, T80 and T90), as this salami is generally sold as whole piece with varying levels of curing (from T20 to T90). Since the product may be cut in half and vacuumpacked, at each of the times starting from T20, half salami was vacuum-packed and stored for 30 days at 12°C, at the end of the which Listeria spp. enumeration was performed again. At all times and for each type of samples of each of the three batches, the enumeration of the natural microflora (Total Viable Count, lactic acid bacteria, Pseudomonas spp., Enterobacteriaceae) and the determination of water activity and pH were performed on control samples. The product was characterized by a high concentration of microflora (8-8.5 Log UFC/g), consisting mainly of lactic acid bacteria, added to the mixture at the beginning of the production process. The pH showed a decrease over time, expected for this type of products, due to the development of lactic acid bacteria (final pH: 5.42-5.55). The water activity reached values able to inhibit the development of Listeria spp. (final aw: 0.826-0.863). Listeria counts in the tested batches of “Bergamasco” salami showed the absence of significant growth in the product with a reduction of loads if compared to T0, between -0.59 and -1.04 Log CFU/g. Even in the samples subjected to vacuum packaging and storage at 12°C, the absence of significant increase of lactic acid bacteria in the product was highlighted with further decrease of bacterial loads (-0.70/-0.79 Log CFU/g if compared to T20). Considering the worst case scenario (thus the batch with the highest growth potential), in the products stored in the curing room at 14-16°C, at humidity of 80% and in the samples stored at 12°C and vacuum packaged, the threshold indicated by the EURL Lm guidelines (+0.5 Log CFU/g) for the growth of Listeria spp. was not reached, allowing to classify “Bergamasco” salami in the category 1.3 of the EC Reg. 2073/2005 as “Ready-to-eat food unable to support the growth of Listeria monocytogenes”.