Miracle-dz/UniFAR-FTUs huggingface dataset:
Web image annotation by fusing visual features and textual information

In this paper, we propose a novel web image annotation method, namely FMD (Fused annotation by Mixed model graph and Decision tree), which combines visual features and textual information to conceptualize the web images. The FMD approach consists of three main processes: 1) construct the visual-based model, namely ModelMMG, 2) construct the textual-based model, namely ModelDT, and 3) fuse ModelMMG and ModelDT as ModelFMD for annotating the images. The purpose of visual-based annotation model is to objectify the image not only by the global content of the image but also by its local content of composing objects. The textual-based annotation model is to handle the problems of user-specified dependency of keywords and the complex computation due to high dimensionalities in text features. The experimental results reveal that the proposed FMD method is very effective for web image annotation in terms of accuracy through the integration of two different types of features.
Sense size‐dependent dust loading and emission from space using reflected solar and infrared spectral measurements: An observation system simulation experiment

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) satellite mission observes hyperspectral Earth reflected solar (RS) and emitted infrared radiance (IR). Such measurements span an additional dimension on spectrally dependent scattering and absorption of dust, the critical signals for particle size. Through a suite of observation system simulation experiments (OSSEs), this study assesses the capability of CLARREO's measurements for recovering size‐dependent dust emissions in GEOS‐Chem chemistry transport model (CTM). To this end, another CTM (Flow‐following finite‐volume Icosahedral Model‐Chem, or FIM‐Chem) is used for the nature run to simulate CLARREO spectral radiances. The spectral signals are then used for analyzing the sensitivities and error characteristics of dust optical depth (DOD) under three observations scenarios (IR only, RS only, and combined IR and RS) using an optimal estimation technique. Next, these synthetic data are assimilated into GEOS‐Chem adjoint model to constrain dust emissions of four particle sizes with radii from 0.1 μm to 6.0 μm. The OSSEs results indicate (1) the IR spectra are most sensitive to dust of the third size bin (1.8–3.0 μm) and least sensitive to the smallest bin (0.1–1.0 μm); (2) the RS spectra are most sensitive to dust of the smallest size bin and the sensitivity decreases as dust size increases; (3) combining IR and RS spectra can fully characterize DOD across all sizes, providing the best constraints for size‐resolved dust emissions; and (4) CLARREO data fail to constrain the spatial distribution of dust sources due to its narrow swath and joint observations from CLARREO‐calibrated sensors with wide swath are desirable.
Enhance hardware security using FIFO in pipelines

Cryptographic functions are foundation of many information security applications. Embedded cryptographic hardware is vulnerable to side channel attacks such as power analysis- and fault- attacks. In this paper, we propose a design architecture which enhances the hardware security against such attacks. The key idea is to insert FIFOs in between two successive pipeline stages and randomly vary the duration when the data stay at FIFOs in every pipeline loop. This can make the time instant of executing certain operation unpredictable. The more timing variation appears, the less probability the attack succeeds. The proposed approach provides more possible configurations than previous approaches as well as it can be realized in both ASIC and FPGA implementations. We implemented the required controller and data path. Furthermore, the AES (Advanced Encryption Standard) algorithm was realized with the proposed pipeline of four stages.
Music on YouTube: Engagement with User-appropriated Videos

YouTube is the leading Internet video service and one of the most popular websites in 2014. Music videos hold top positions in different YouTube charts, but the music video types or engagement patterns with them have not been systematically studied. In this paper we present three studies that focus on YouTube music. We first show that music videos are the most popular content genre in YouTube. We then present a typology of traditional and user-generated music videos discovered in YouTube. It includes twelve subtypes of music videos under three main types: traditional, user-appropriated, and derivative. Last, we present findings on user engagement statistics that go beyond view, comment, and vote counts. These metrics show that while music videos gather more views, engagement differences with other content genres are miniscule. However, there are notable differences in engagement between different music video types. This is prominent between different artists on one hand, and between traditional and usergenerated videos on the other hand. We synthesize these findings by discussing the importance of user-generated videos in YouTube’s music ecosystem.
Crowdsourcing with Fewer Workers

The crowdsourcing mechanism, as an effective and economic alternative in data science study, has attracted substantial research interests in recent years. However, despite of numerous successful crowdsourcing applications, many crowdsourcers still suffer from the high cost issue. This is mainly due to the fact that most of the crowd workers are usually not professional experts, and thus, in order to ensure the quality of crowdsourcing, extra investment is introduced by hiring many crowd workers to work on every task multiple times to inhabit the noisy submissions. In this article, we propose an approach that can mitigate the crowdsourcer's investment by using less human efforts while at the same time still has provable guarantees for the refined results. Experimental results on two real-world datasets are inspiring and consistent with the theoretical analysis.
Near Real-time Performance of Population-based Nature-Inspired Algorithms on Cheaper and Older Smartphones

For solving optimization problems, stochastic population-based nature-inspired algorithms use inspirations from nature. Despite their applicability in real-world environments, their bottleneck is high time complexity. Usually, they are searching for optimal solutions on computing devices of full computational power. However, in some situations, we deal with devices of limited computational power. Some examples of such devices are smartphones, which have been becoming very powerful for running various applications. However, there is still a lack of researches that would study the performance of nature-inspired algorithms on these devices. In this paper, we analyze the performance of one member of the nature-inspired algorithms, the so-called Bat algorithm, on the Android smartphone. Although smartphones nowadays offer a computational power comparable with the personal computer, we focus on the cheaper and older smartphones that are most widespread today.
Conformational Changes of G Protein‐Coupled Receptors During Their Activation by Agonist Binding

Abstract The superfamily of G protein‐coupled receptors (GPCRs) is the largest and most diverse group of transmembrane proteins involved in signal transduction. Many of the over 1000 human GPCRs represent important pharmaceutical targets. However, despite high interest in this receptor family, no high‐resolution structure of a human GPCR has been resolved yet. This is mainly due to difficulties in obtaining large quantities of pure and active protein. Until now, only a high‐resolution x‐ray structure of an inactive state of bovine rhodopsin is available. Since no structure of an active state has been solved, information of the GPCR activation process can be gained only by biophysical techniques. In this review, we first describe what is known about the ground state of GPCRs to then address questions about the nature of the conformational changes taking place during receptor activation and the mechanism controlling the transition from the resting to the active state. Finally, we will also address the question to what extent information about the three‐dimensional GPCR structure can be included into pharmaceutical drug design programs.
Unbinding rationality: a single‐union agreement for contractors at a UK oil refinery

Aims to determine why, in this “critical case”, a single‐union agreement was implemented in a top‐down manner, affected only contractors and was unique. Reports on in‐depth interviews of a representative sample of 20 managers, contractors, trade union officials and industry body representatives to triangulate archival data supplied by the refinery. Environmental changes were similar for all UK refineries. The implementation of a single‐union agreement is the result of a number of these but, crucially, new appointments in the case study refinery served to break with tradition. Suggests, however, that management does not always perceive itself to be a powerful agency; rather, it seems to need to proceed carefully and rationally in order to establish its own legitimacy to its peer group and within its own executive group to maintain its credibility as professional. States that this was an uncertain and risky venture, especially for a bureaucratic major oil company sensitive to public opinion.
Disagreement between symptom-reflux association analysis parameters in pediatric gastroesophageal reflux disease investigation.

AIM To assess the agreement within 3 commonly used symptom-reflux association analysis (SAA) parameters investigating gastroesophageal reflux disease (GERD) in infants. METHODS Twenty three infants with suspected GERD were included in this study. Symptom index (SI), Symptom sensitivity index (SSI) and symptom association probability (SAP) related to cough and irritability were calculated after 24 h combined pH/multiple intraluminal impedance (MII) monitoring. Through defined cut-off values, SI, SSI and SAP values are differentiated in normal and abnormal, whereas abnormal values point towards gastroesophageal reflux (GER) as the origin of symptoms. We analyzed the correlation and the concordance of the diagnostic classification of these 3 SAA parameters. RESULTS Evaluating the GER-irritability association, SI, SSI and SAP showed non-identical classification of normal and abnormal cases in 39.2% of the infants. When irritability was taken as a symptom, there was only a poor inter-parameter association between SI and SSI, and between SI and SAP (Kendall's tau b = 0.37, P < 0.05; Kendall's tau b = 0.36, P < 0.05, respectively). Evaluating the GER-cough association, SI, SSI and SAP showed non-identical classification of normal and abnormal cases in 52.2% of the patients. When cough was taken as a symptom, only SI and SSI showed a poor inter-parameter association (Kendall's tau b = 0.33, P < 0.05). CONCLUSION In infants investigated for suspected GERD with pH/MII-monitoring, SI, SSI and SAP showed a poor inter-parameter association and important disagreements in diagnostic classification. These limitations must be taken into consideration when interpreting the results of SAA in infants.
The Clubhouse model and employment following brain injury

The Clubhouse model has a long history of effective support among people who experience disability due to psychiatric impairment. However, it had not been used with other groups until its recent adaptation among people who experience disability following brain injury. The model is known for its consumer direction, community basis, and long term supports. Preliminary results indicate that the' model may also be very effective in supporting community and vocational tenure among this new group of individuals.
TrimZero: A Torch Recurrent Module for Efficient Natural Language Processing

Deep learning framework supported by CUDA parallel computing platform boosts advances of studies on machine learning. The advantage of parallel processing largely comes from an efficiency of matrix-matrix multiplication using many CUDA-enabled graphics processing units (GPU). Therefore, for recurrent neural networks (RNNs), the usage of a zero-filled matrix representing variable lengths of sentences for a learning batch is forced for that reason, however, it is still true that these zeros are wasting computational resources. We propose an efficient algorithm which is trimming off zeros in the batch for RNNs providing the same result. The benchmark results validate our method with approximately 25% faster learning. Empirically, a natural language task confirms our results.
Surgery for secondary spontaneous pneumothorax: risk factors for recurrence and morbidity.

OBJECTIVES Secondary spontaneous pneumothorax (SSP) is more common in elderly patients; it has high rates of recurrence and mortality, even if surgery is performed. There has been little study on the surgical treatment of SSP. Therefore, we analysed the outcomes of surgical treatment of SSP patients, and investigated the risk factors of recurrence and morbidity. METHODS We studied 97 consecutive surgical treatments on 94 patients with SSP who had emphysematous changes of lung retrospectively. Emphysematous changes on preoperative computed tomography image were evaluated by the Goddard score, which is a visual scoring system. First, video-assisted thoracoscopic surgery was performed, followed by bullectomy for the responsible lesions. RESULTS The rate of morbidity was 20.6% and that of mortality was 4.1%. Recurrence rate was 9.3%. By multivariate analysis, a Goddard score≥7 (odds ratio: 8.93, P=0.033) and treatment of bulla without the use of staplers (odds ratio: 11.57, P=0.019) were significant risk factors for morbidity, while pulmonary fibrosis tended to increase the risk of recurrence (hazard ratio: 4.21, P=0.051), and a Goddard score≥7 (hazard ratio: 7.79, P=0.023) was a significant risk factor for recurrence. CONCLUSIONS Surgical treatment in patients with SSP had favourable results. Treatment in which the base of the bulla cannot be definitely shut off with staplers is associated with increased morbidity. Significant emphysematous change on preoperative computed tomography image and pulmonary fibrosis are predictors of recurrence. Patients with these findings should be investigated in terms of the indications of surgery and additional treatment, not only bullectomy.
Uncoverning Groups via Heterogeneous Interaction Analysis

With the pervasive availability of Web 2.0 and social networking sites, people can interact with each other easily through various social media. For instance, popular sites like Del.icio. us, Flickr, and YouTube allow users to comment shared content (bookmark, photos, videos), and users can tag their own favorite content. Users can also connect to each other, and subscribe to or become a fan or a follower of others. These diverse individual activities result in a multi-dimensional network among actors, forming cross-dimension group structures with group members sharing certain similarities. It is challenging to effectively integrate the network information of multiple dimensions in order to discover cross-dimension group structures. In this work, we propose a two-phase strategy to identify the hidden structures shared across dimensions in multi-dimensional networks. We extract structural features from each dimension of the network via modularity analysis, and then integrate them all to find out a robust community structure among actors. Experiments on synthetic and real-world data validate the superiority of our strategy, enabling the analysis of collective behavior underneath diverse individual activities in a large scale.
Parental Smoking in Pregnancy and the Risks of Adult-Onset Hypertension

Fetal exposure to parental smoking may lead to developmental adaptations and promote various diseases in later life. This study evaluated the associations of parental smoking during pregnancy with the risk of hypertension in the daughter in adulthood, and assessed whether these associations are explained by birth weight or body weight throughout life. We used data on 33 086 participants of the Nurses’ Health Study II and the Nurses’ Mothers’ Cohort. Cox proportional hazards models were used to examine the associations of maternal and paternal smoking during pregnancy with the nurse daughter, with self-reported physician-diagnosed hypertension from 1989 until 2007. Overall, 8575 (25.9%) mothers and 18 874 (57.0%) fathers smoked during pregnancy. During follow-up, 7825 incident cases of adult-onset hypertension were reported. Both maternal and paternal smoking of ≥15 cigarettes/d during pregnancy were associated with increased risks of hypertension (rate ratio, 1.19; 95% CI, 1.09–1.29; and rate ratio, 1.18; 95% CI, 1.12–1.25, respectively) in the age-adjusted models. Further adjustment for birth weight did not affect the effect estimates appreciably, whereas additional adjustment for body shape and weight until age 18, or current body mass index, attenuated the associations with both maternal and paternal smoking (rate ratio, 1.07; 95% CI, 0.98–1.16; and rate ratio, 1.06; 95% CI, 1.01–1.12, respectively). The associations of parental smoking during pregnancy with the risk of hypertension in the offspring were largely explained by body weight throughout life, suggesting that these associations may not reflect direct intrauterine mechanisms.
A Survey of Moving Target Defenses for Network Security

Network defenses based on traditional tools, techniques, and procedures (TTP) fail to account for the attacker’s inherent advantage present due to the static nature of network services and configurations. To take away this asymmetric advantage, Moving Target Defense (MTD) continuously shifts the configuration of the underlying system, in turn reducing the success rate of cyberattacks. In this survey, we analyze the recent advancements made in the development of MTDs and highlight (1) how these defenses can be defined using common terminology, (2) can be made more effective with the use of artificial intelligence techniques for decision making, (3) be implemented in practice and (4) evaluated. We first define an MTD using a simple and yet general notation that captures the key aspects of such defenses. We then categorize these defenses into different sub-classes depending on what they move, when they move and how they move. In trying to answer the latter question, we showcase the use of domain knowledge and game-theoretic modeling can help the defender come up with effective and efficient movement strategies. Second, to understand the practicality of these defense methods, we discuss how various MTDs have been implemented and find that networking technologies such as Software Defined Networking and Network Function Virtualization act as key enablers for implementing these dynamic defenses. We then briefly highlight MTD test-beds and case-studies to aid readers who want to examine or deploy existing MTD techniques. Third, our survey categorizes proposed MTDs based on the qualitative and quantitative metrics they utilize to evaluate their effectiveness in terms of security and performance. We use well-defined metrics such as risk analysis and performance costs for qualitative evaluation and metrics based on Confidentiality, Integrity, Availability (CIA), attack representation, QoS impact, and targeted threat models for quantitative evaluation. Finally, we show that our categorization of MTDs is effective in identifying novel research areas and highlight directions for future research.
Automatic Semantic Network Generation from Unstructured Documents – The Options

There is plenty of information that exists in freeform or web-based text and whose use from a computing perspective is limited because of its unstructured nature. This data first needs to be structured as a knowledge base to facilitate tasks such query, search, re-use, sharing, keeping information current, question answering (Q&A), prediction, disambiguation and summarization. Structuring through topic models, database base systems and taxonomies have been tried but these tend to be domain specific and limited in scalability to the level of diverse unstructured data available. Knowledge representation to create knowledge bases can be done through linking data as used in applications such as LinkedIn, Facebook, BabelNet, Google Knowledge Graph, among others. This essentially forms a semantic structure also called a semantic network (SN). The automatic generation of these structures is a problem largely unresolved. This paper concentrates on development of a model that uses machine learning to automatically generate semantic networks from web data. SNs are an important component in the development of applications that support semantic search, Q&A, information retrieval among others. Automatic generation, also known as semantic network induction, reduces the complete reliance on manual and time-consuming semantic network authoring.
Investigating University Students’ Beliefs about Language Learning

The aim of the study is to investigate beliefs students usually held about language learning, based on the Beliefs About Language Learning Inventory (BALLI) questionnaire (Horwitz, 1988). For this purpose, 423 University learners of English in Iran were selected. Running descriptive statistics and the scree plot test, five factors were extracted: Formal Learning and Motivation for English Learning, Learner’s Confidence in Learning English, Strategy and Attitude in Learning English, Aptitude and Strategy in Learning English, and the Importance of English and Formal Learning. The findings of the current study suggest that teachers should be aware of learners’ beliefs of language learning as well as their own in order to assist less successful language learners to become successful.
Age Estimation Guided Convolutional Neural Network for Age-Invariant Face Recognition

While very promising results have been shown on face recognition related problems, age-invariant face recognition still remains a challenge. Facial appearance of a person changes over time, which results in significant intraclass variations. In order to address this problem, we propose a novel deep face recognition network called age estimation guided convolutional neural network (AE-CNN) to separate the variations caused by aging from the personspecific features which are stable. The carefully designed CNN model can learn age-invariant features for face recognition. To the best of our knowledge, this is the first attempt to use age estimation task for obtaining age-invariant features. Extensive results on two well-known public domain face aging datasets: MORPH Album 2 and CACD show the effectiveness of the proposed approach.
Enhanced message addressing capabilities for computer networks

Three message addressing modes are described: 1) Logical adderssing, in which a permanantly,assigned address denotes one or more physical addresses. This permits multiple connections from the subscriber to the network, as well as other functions. 2) Broadcast adderssing, in which a messase is addressed to all subscribers. 3) Group addressing and multidestination addressing, in which a message carries the name of a list of addresses, or the list itself. These methods facilitate many new ways of using computer networks. The paper focuses on two basic issues for each method: efficiency and reliability, and recommends implementation approaches in each case. Significant performance improvements are possible if these addressing methods are implemented with efficient delivery mechanisms. A distinction is made between virtual circuit and datagram systems; virtual circuits are superior for logical addressing, while datagrams are preferable for broadcast, group, and multidestination addressing.