Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors Predicting Exclusive Breastfeeding among The First Time Chinese Mothers

Abstract: This study examined the factors that predict exclusive breastfeeding at 4 months and 6 months among the first-time Chinese mothers. Based on the theory of planned behavior, a descriptive and correlational research design was utilized. A systematic random sampling method was applied and a total of 272 first-time Chinese mothers who gave birth at a hospital in Shanghai were recruited. Three instruments, the Breastfeeding Attrition Prediction Tool, the Breastfeeding Knowledge Scale, and a Demographic Questionnaire were used to measure breastfeeding knowledge, attitude, subjective norm, perceived control, and breastfeeding practice. The data were analyzed using descriptive statistics, Point-biserial, Pearson’s product moment correlation, and Logistic regression analysis. Results revealed that the breastfeeding initiation rate was 93.75%, but exclusive breastfeeding rates at 4 months and 6 months had reduced to 34.19% and 3.31%, respectively.There were significant positive relationships between exclusive breastfeeding at 4 months and breastfeeding knowledge, attitude, subjective norm, and perceived breastfeeding control.A negative relationship was found between exclusive breastfeeding at 4 months and working status. A logistic regression analysis indicated that all of five breastfeeding factors could significantly predict exclusive breastfeeding at 4 months. The implication of this study to nursing practice lies in that nurses should provide enough breastfeeding knowledge,encourage new mothers’ positive breastfeeding attitude, change their significant others,and strengthen their breastfeeding control.
Asset Pricing Implications of Strategic Trading and Activism November 2 , 2018

This paper studies the asset pricing implications of investor activism. We consider a setting where a blockholder can both trade the firm’s shares and influence the firm value by exerting costly effort. The blockholder’s ability to add value varies over time. To understand the effects of blockholder transparency, we consider two regimes: one in which the market observes the blockholder’s ability and another where it does not. i) When the market observes the blockholder’s ability, the blockholder’s trading is characterized by Coasian dynamics: despite his large size, the blockholder is unable to exploit his market power and trades at a competitive price. Furthermore, when his ability to add value increases, the blockholder sells shares which in turn diminishes his incentives to boost the firm’s productivity. ii) By contrast, when the blockholder privately observes his ability, the dynamics of trading and activism change drastically: as the blockholder’s ability improves, he buys shares. He does so gradually to mitigate the price impact of his trading. As the blockholder’s stock-holdings grow, a virtuous circle unravels that boosts the firm’s productivity and reduces the firm’s cost of capital. In the long-run, the presence of information asymmetry about blockholder ability leads to a more concentrated ownership, more intense activism, and higher firm productivity. However, it also leads to more volatile cash flows, and a higher risk-premium. The implications for the firm’s stock price are ambiguous.
Stochastic DCM of the DMN using resting-state fMRI: test-retest reliability

Model 3 parameter estimates are summarised in Fig.4. Mean (SD) intraclass correlation coefficient (ICC) between sessions 2 and 3 is 0.321 (0.253), which is comparable to functional connectivity results reported by Shehzad et al. (2009); however, ICCs between session 1 and 2 or 1 and 3 are poor. Overall . SPM8: fMRI data were realigned; anatomical scans underwent unified (“new”) segmentation, yielding a bias-corrected skull-stripped image; fMRI data were co-registered to this image; spatial normalisation used warps from unified segmentation; 6mm smoothing was applied.
An assessment of metaphor retrieval methods

There has been growing interest in using corpora in metaphor research in recent years, and as a result a number of tools and techniques have been proposed and used for metaphor identification. However, very little is known about their ability to retrieve all and only metaphors from corpora. The aim of this paper is report on a quantitative assessment of methods for metaphor retrieval; but it must be stressed that this assessment is not a final evaluation, since performance of any one of these methods may be altered by different test corpora. Out of the many different techniques and instruments reported in the metaphor, corpus linguistics and Natural Language Processing literature, three procedures and three computer tools were selected for assessment.
Algorithms Based on Artificial Neural Networks for Intrusion Detection in Heavy Traffic Computer Networks

The current network is complicated owing to the high throughput and the multi-uniformity of actions. An intrusion detection system is a critical component for reliable information management. Network Intrusion Detection Systems, which are part of the layered defense scheme, must be capable of meeting these organizational objectives in order to be effective. This work presents the research carried out on different neural network structures using a number of algorithms for intrusion detection. Latest algorithms have been presented which have faster convergence and improved performance in Intrusion Detection System (IDS). The objective of this paper is to implement a new combination of artificial neural network algorithm which would be efficient in detecting intrusion in a networked environment. The performances of different methods have been compared.
Answer Pointer Alternatives for Unanswerable Questions in SQuAD 2 . 0

Machine reading comprehension tasks where questions are answered by a segment of text in a passage are commonly approached by training a model to point to the start and end indices of the answer span within the passage. With the introduction of unanswerable questions in SQuAD2.0, the answer pointer method may be adapted to point to a zero-length span at the start of the input to indicate no answer. At the time of writing, fine-tuning BERT attached to an answer pointer "head" (final layer) achieves respectable results on SQuAD2.0, with pretrained weights for BERT readily available and the head easily interchangeable, making BERT an excellent candidate for running experiments. Using pretrained BERT, we investigate various alternative techniques in an attempt to beat the traditional answer pointer method: pre-classifying answerability, joint modeling with unanswerable questions filtered out during training, and "heat map" span prediction via token classification. While none of these methods significantly outperformed the answer pointer approach, error analysis reveals limitations and insights that may better motivate extensions and future work.
Low-Dimensional Knowledge Graph Embeddings via Hyperbolic Rotations

Knowledge graphs (KGs) capture rich relationships between a large number of entities. Embeddings of these structures must preserve these relationships with high fidelity. Recently, hyperbolic embedding methods have achieved state-ofthe-art quality in graph representation learning tasks; when embedding certain graphs, they can produce parsimonious embeddings that have higher fidelity while using much fewer dimensions than their Euclidean counterparts. Mirroring work in Euclidean space, we are the first to leverage trainable hyperbolic rotations, a key notion in providing sufficiently rich representations for the complex logical patterns found in KGs. Coupled with trainable curvature, this approach yields improved embeddings in fewer dimensions. We evaluate our method, ROTATIONH, on the WN18RR link prediction task; in the low-dimensional setting, we improve on previous Euclidean-based efforts by 2.2% in mean reciprocal rank (MRR), and in the high-dimensional setting, we achieve a new state-of-the-art MRR of 49.2%, improving on existing methods by 1.1%.
Road Traffic RFID Pedestrians Detecting System for Vehicles

Safe road traffic, clean environment, environmentally friendly vehicles and eco-buildings are components of the world’s vision of immediate future supported by scientists and statesmen. Aspiration for healthy environment and sustainable development of technologies give life for idea of smart cities which consist of all these visions towards modern world. The aim of research was connection pedestrians and alarm system of the vehicle to prevent road incidents with the help of RFID technology. There were proposed vehicle RFID system and tags for pedestrians. System was designed on the basis of scientists’ research in this area. The main factors influencing the construction of proposed system were working range of the system and coverage width of the reading devices. Further were compared existent subsidiary systems for drivers versus designed system. The study represents the expected effectiveness of the proposed RFID system as well as the ability to implement it even in old vehicle control systems.
Purchases of hypnotics, sedatives and minor tranquillizers anong 2,566 individuals in the country of Jämtland, Sweden

Data from a continuous recording of drug prescriptions to 16,600 individuals in the county of Jäatland, Sweden, revealed that 2,566 patients (15.5%) obtained prescriptions for hyponotics, sedatives and minor tranquillizers in 1970. Occasional use (one pruchase only, Group A) was seen in 7.4% of the population, intermediate use (two to six purchases, Group B) in 6.9% whereas 1.2% were regular users (seven purchases or more, Group C). For each group as a whole there was 5 years later a highly significant intriandividual reduction in the purchases of these durgs as well as of other psychotropic durgs. In all groups 10–23% had increased their purchases, most of them insignificantly. Fifteen of 30 patients with a marked increase in consumption developed a regualr purchase pattern, but signs of oversuse or abuse were sen in only foru persons. During the studied period the benzodiazepines increased their share of the total from 45 to 60%. Anithistamines also increased in all groups while the proportion of barbiturates and combined preprations decreased markedly.
OD2WD: From Open Data to Wikidata through Patterns

We present OD2WD, a semi-automatic pattern-based framework for populating the Wikidata knowledge graph with (originally tabular) data from Open Data portals. The motivation is twofold. One, our framework can further enrich Wikidata as a central RDF-oriented knowledge graph with a large amount of data coming from public or government sources. Two, our framework can help the discovery of data from such Open Data portals and its integration with Linked Data without forcing the Open Data portals to provide Linked Data infrastructure by themselves, as the latter may not always be feasible due to various technical, budgetary, or policy reasons. Throughout the transformation process, we identify several reengineering and alignment patterns. We implement the framework as an online service and API accessible at http://od2wd.id, currently tailored for three Indonesian Open Data portals: Satu Data Indonesia, Jakarta Open Data, and Bandung Open Data.
Deep Audio-visual System for Closed-set Word-level Speech Recognition

Audio-visual understanding is usually challenged by the complementary gap between audio and visual informative bridging. Motivated by the recent audio-visual studies, a closed-set word-level speech recognition scheme is proposed for the Mandarin Audio-Visual Speech Recognition (MAVSR) Challenge in this study. To achieve respective audio and visual encoder initialization more effectively, a 3-dimensional convolutional neural network (CNN) and an attention-based bi-directional long short-term memory (Bi-LSTM) network are trained. With two fully connected layers in addition to the concatenated encoder outputs for the audio-visual joint training, the proposed scheme won the first place with a relative word accuracy improvement of 7.9% over the solitary audio system. Experiments on LRW-1000 dataset have substantially demonstrated that the proposed joint training scheme by audio-visual incorporation is capable of enhancing the recognition performance of relatively short duration samples, unveiling the multi-modal complementarity.
Evidence for Communicative Compensation in Debt Advice with Reduced Multimodality

Research has found that professional advice with empathy displays and signs of listening lead to more successful outcomes. These skills are typically displayed through visual nonverbal signals, whereas reduced multimodal contexts have to use other strategies to compensate for the lack of visual nonverbal information. Debt advice is often a highly emotional scenario but to date there has been no research comparing fully multimodal with reduced multimodal debt advice. The aim of the current study was to compare explicit emotional content (as expressed verbally) and implicit emotional content (as expressed through paralinguistic cues) in face to face (FTF) and telephone debt advice recordings. Twenty-two debt advice recordings were coded as emotional or functional and processed through emotion recognition software. The analysis found that FTF recordings included more explicit emotion than telephone recordings did. However, linear mixed effects modelling found substantially higher levels of arousal and slightly lower levels of valence in telephone advice. Interaction analyses found that emotional speech in FTF advice was characterised by lower levels of arousal than during functional speech, whereas emotional speech in telephone advice had higher levels of arousal than in functional speech. We can conclude that there are differences in emotional content when comparing full and reduced multimodal debt advice. Furthermore, as telephone advice cannot avail of visual nonverbal signals, it seems to compensate by using nonverbal cues present in the voice.
Towards More Impactful Recommender Systems Research

A number of ways exist in which a recommender system can have an impact on users and business. However, only a small number of them can be reasonably addressed with today’s predominant and narrow research approach based on offline experimentation and accuracy measures. It sometimes even stands to question if small increases in prediction accuracy will actually lead to a better system in any of the ways in which a recommender system can impact users and create value. We therefore argue for a more impact-oriented approach to research in the field of recommender systems. With such a refocused lens, we hope that the corresponding research results are also more impactful and relevant in reality. To foster such research, we present in this work a first taxonomy describing the various facets to consider when developing impact-oriented research, ranging from the expected value of a recommender for different stakeholders to the potential risks that come with such applications.
Perfect Forward Security of SPAKE2

SPAKE2 is a balanced password-authenticated key exchange (PAKE) protocol, proposed by Abdalla and Pointcheval at CTRSA 2005. Due to its simplicity and efficiency, SPAKE2 is one of the balanced PAKE candidates currently under consideration for standardization by the CFRG, together with SPEKE, CPace, and J-PAKE. In this paper, we show that SPAKE2 achieves perfect forward security in the random-oracle model under the Gap Diffie-Hellman assumption. Unlike prior results, which either did not consider forward security or only proved a weak form of it, our results guarantee the security of the derived keys even for sessions that were created with the active involvement of the attacker, as long as the parties involved in the protocol are not corrupted when these sessions take place. Finally, our proofs also demonstrate that SPAKE2 is flexible with respect to the generation of its global parameters M and N. This includes the cases where M is a uniform group element and M=N or the case where M and N are chosen as the output of a random oracle.
Computed Tomography in the Evaluation of Trauma

As known, adventure and experience about lesson, entertainment, and knowledge can be gained by only reading a book. Even it is not directly done, you can know more about this life, about the world. We offer you this proper and easy way to gain those all. We offer many book collections from fictions to science at all. One of them is this computed tomography in the evaluation of trauma that can be your partner.
CorrFeat: Correlation-based Feature Extraction Algorithm using Skin Conductance and Pupil Diameter for Emotion Recognition

To recognize emotions using less obtrusive wearable sensors, we present a novel emotion recognition method that uses only pupil diameter (PD) and skin conductance (SC). Psychological studies show that these two signals are related to the attention level of humans exposed to visual stimuli. Based on this, we propose a feature extraction algorithm that extract correlation-based features for participants watching the same video clip. To boost performance given limited data, we implement a learning system without a deep architecture to classify arousal and valence. Our method outperforms not only state-of-art approaches, but also widely-used traditional and deep learning methods.
Revisiting Leakage Abuse Attacks

Encrypted search algorithms (ESA) are cryptographic algorithms that support search over encrypted data. ESAs can be designed with various primitives including searchable/structured symmetric encryption (SSE/STE) and oblivious RAM (ORAM). Leakage abuse attacks attempt to recover client queries using knowledge of the client’s data. An important parameter for any leakage-abuse attack is its known-data rate; that is, the fraction of client data that must be known to the adversary. In this work, we revisit leakage abuse attacks in several ways. We first highlight some practical limitations and assumptions underlying the well-known IKK (Islam et al. NDSS ’12) and Count (Cash et al., CCS ’15) attacks. We then design four new leakage-abuse attacks that rely on much weaker assumptions. Three of these attacks are volumetric in the sense that they only exploit leakage related to document sizes. In particular, this means that they work not only on SSE/STE-based ESAs but also against ORAM-based solutions. We also introduce two volumetric injection attacks which use adversarial file additions to recover queries even from ORAM-based solutions. As far as we know, these are the first attacks of their kind. We evaluated all our attacks empirically and considered many experimental settings including different data collections, query selectivities, known-data rates, query space size and composition. From our experiments, we observed that the only setting that resulted in reasonable recovery rates under practical assumptions was the case of high-selectivity queries with a leakage profile that includes the response identity pattern (i.e., the identifiers of the matching documents) and the volume pattern (i.e., the size of the matching documents). All other attack scenarios either failed or relied on unrealistic assumptions (e.g., very high known-data rates). For this specific setting, we propose several suggestions and countermeasures including the use of schemes like PBS (Kamara et al, CRYPTO ’18), VLH/AVLH (Kamara and Moataz, Eurocrypt ’19), or the use of padding techniques like the ones recently proposed by Bost and Fouque (Bost and Fouque, IACR ePrint 2017/1060).
Comparing Pedestrian Navigation Methods in Virtual Reality and Real Life

Mobile navigation apps are among the most used mobile applications and are often used as a baseline to evaluate new mobile navigation technologies in field studies. As field studies often introduce external factors that are hard to control for, we investigate how pedestrian navigation methods can be evaluated in virtual reality (VR). We present a study comparing navigation methods in real life (RL) and VR to evaluate if VR environments are a viable alternative to RL environments when it comes to testing these. In a series of studies, participants navigated a real and a virtual environment using a paper map and a navigation app on a smartphone. We measured the differences in navigation performance, task load and spatial knowledge acquisition between RL and VR. From these we formulate guidelines for the improvement of pedestrian navigation systems in VR like improved legibility for small screen devices. We furthermore discuss appropriate low-cost and low-space VR-locomotion techniques and discuss more controllable locomotion techniques.
”Paint that object yellow”: Multimodal Interaction to Enhance Creativity During Design Tasks in VR

Virtual Reality (VR) has always been considered a promising medium to support designers with alternative work environments. Still, graphical user interfaces are prone to induce attention shifts between the user interface and the manipulated target objects which hampers the creative process. This work proposes a speech-and-gesture-based interaction paradigm for creative tasks in VR. We developed a multimodal toolbox (MTB) for VR-based design applications and compared it to a typical unimodal menu-based toolbox (UTB). The comparison uses a design-oriented use-case and measures flow, usability, and presence as relevant characteristics for a VR-based design process. The multimodal approach (1) led to a lower perceived task duration and a higher reported feeling of flow. It (2) provided a higher intuitive use and a lower mental workload while not being slower than an UTB. Finally, it (3) generated a higher feeling of presence. Overall, our results confirm significant advantages of the proposed multimodal interaction paradigm and the developed MTB for important characteristics of design processes in VR.
Polynomials Whose Secret Shares Multiplication Preserves Degree for 2-CNF Circuits Over a Dynamic Set of Secrets

One of the most interesting research topics in cryptography is nding e cient homomorphic encryption schemes, preferably information-theoretically secure, which are not based on unproven computational hardness assumptions. The most signi cant breakthrough in this eld was made by Gentry [12] in 2009, and since then, there were various developments. We suggest here an information-theoretically secure secret sharing scheme that e ciently supports one homomorphic multiplication of secrets in addition to homomorphic additions of, practically, any number of such multiplied secrets. In particular, our scheme enables sharing a dynamic set of secrets amongst N participants, using polynomials of degree N − 1. Quadratic functions and 2-CNF circuits over the set of secrets can then be homomorphically evaluated, while no information is revealed to any single participant, both before and after the computation. Our scheme is statistically secure against coalitions of less than N − 1 participants. One possible application of our scheme is a secure homomorphic evaluation of multi-variate quadratic functions and 2-CNF circuits.