Miracle-dz/UniFAR-FTUs huggingface dataset:
Audio-Visual Interpretable and Controllable Video Captioning

Video captioning [8] aims to automatically generate a natural language sentence to describe the dynamic, potentially complex multimodal scene inside of a video. Most of the previous works [8, 10] focus on exploring better visionand-language modelings and put less emphasis on the multimodal aspect of video captioning, where audio often reveals important in-scene and out-of-scene information and contributes to the language generation in its unique ways. For example, it adds the difficulty to describe the singing event by watching the audio-mute video in Fig. 1. Although this is not new to the multimedia community, many works over there aim to optimize the video captioning metrics with an uninterpretable fusion strategy. The basic questions as to what extent different modalities (auditory and visual) contribute to a particular sentence, and furthermore, to individual words in a sentence remain underexplored. It is our belief that unfolding these questions is valuable to making fundamental progress on video captioning. At first glance, it is seemingly impossible to answer the above questions. The first challenge is that there is no annotation denoting the individual contributions of the auditory or visual modality made to texts in any of the existing video captioning datasets—such a process is difficult to quantify without breakthroughs in Neurophysiology and Psychophysics. Instead, we study these questions from a computational perspective, where we mine signals from audio and video and compete their associations to text. The second challenge lies on the computational framework. Recurrent neural networks (RNNs) are widely used as decoders for video captioning. Despite the success in modeling sequential dependencies, RNN decoder-based architectures have inherent limitations to perform modalityinterpretable video captioning. When generating a word, besides using current provided/attended features and previous words, these models always exploit hidden states of the RNN decoder. The latter contains memorized information from different modalities, which makes the models impossible to disentangle the contributions from individual modalities for predicting the words. In this paper, we aim to disentangle the interplay of the two modalities and make the first attempt to interpretable audio-visual video captioning. Concretely, we propose a novel multimodal convolutional neural networkHumans: (1) people singing and dancing. (2) a group of people singing and dancing.
Students ’ self-beliefs and music instruction : A literature review

Background. Only recently research in psychology and education revealed positive correlations between students’ self-beliefs and their academic performance (e.g., Bandura, 1986; Pajares & Miller, 1997). Self-beliefs were found to be strongly related to students’ perceptions concerning their abilities and expectations for future success or failure, on the one hand, and their sense of control over their actions, on the other. Research findings also demonstrated that these beliefs may affect students’ academic achievement and their future choices with respect to elected school subjects (Zimmerman, 1995; Valentine, DuBois, & Cooper, 2004).
1 Arc Basalt Simulator Version 5 . 00 Tutorial

Arc Basalt Simulator Version 5.00 Tutorial 1 March 20, 2017 2 Jun-Ichi Kimura 3 Department of Solid Earth Geochemistry (DSEG) 4 Japan Agency for Marine-Earth Science and Technology (JAMSTEC) 5 Natsushima-cho 2-15, Yokosuka 237-0061, Japan 6 E-mail: jkimura@jamstec.go. jp 7 8 Updated from Arc Basalt Simulator Ver. 3.10 Tutorial, Nov. 28, 2011 version by J.-I. 9 Kimura (JAMSTEC) and R.J. Stern (U. Texas, Dallas) (Kimura, 2012) 10 1
Political Ignorance and Voting Participation of Rural Dwellers in Pakistan

This study explored political ignorance and voting participation of rural residents using a qualitative research design. Face-to-face interviews of 71 participants were conducted by visiting their houses using an unstructured interview guide. Data were analyzed and grouped into recurrent themes using content analysis. The results indicated that a large majority of rural inhabitants were politically ignorant and did not seem to be interested in political activities as they were unaware about the existing political system, the way government operates and affect their lives, current affairs, and rural development programs. These participants did not know the value of their votes as they used to cast their votes based on clan preferences, community leader’s endorsement, fear of enmity of village elite, and friendship with community leader. These results will be helpful for NGOs, civil society, mainstream mass-media, and community based organizations in shaping public opinion, overcoming socio-political inequalities, altering existing socio-political arrangements, and strengthening socio-political participation.
Assessing Moral Hazard Problem in Murabahah Financing

Murabahah is a dominant financing instrument in most Islamic banks all over the world. However, price volatility of the good being financed opens a chance for entrepreneurs to gain profit by pretending to be default (moral hazard). Assessment on condition triggering such moral hazard and probability of entrepreneurs to take risk of pretending to be default are being analyzed. Finally, Islamic bank can mitigate it through appropriate bank’s investigation and charging some cost as well as penalty.
Typing Training System on Touch Screen with Color Blinking Imaging

Acquiring reasonable touch-typing skills is essential for partially sighted students. The goal of our work was to strengthen touchscreen navigation and typing on virtual keyboard using residual visual resources. Visual color patterns displayed with the help of a single two-color light emitting diode coupled to glasses and special layout of tactile markers have been used to design the text entry self-training system. The tactile pointer 110×40 sq. mm provides 15 clearly detectable positions on the touchscreen and minimizes inadvertent selections. Our results show that multisensory approach reduces a period for tutorial. The adequate color stimulation could also promote deceleration of the degeneration of visual nerve.
Short-term Electricity Price Forecasting Using Generalized Additive Models

If one examines the spot price series of electrical power over the course of time, it is striking that the electricity price across the day takes a course that is determined by power consumption following a day and night rhythm. The daily course changes in its height and temporal extent in both, the course of the week, as well as with the course of the year. This study deals methodologically with this intra-day and seasonal behaviour. We contribute the usage of Generalized Additive Models (GAM) and apply these models with European data.
Establishing the Assessment Model for English Language Continuous Writing Component

Validation of assessment instruments has often been confined to ensuring that items in the test are a representative sample of the domain. However, structural validation of psychological instruments through multivariate statistical procedures can be used to enhance the construct validity of psychological scales by providing an empirical model of both the dimensionality of the construct itself and fidelity of the scale developed to measure the construct. The current research is an attempt to establish the assessment model of the English Language continuous writing component. The study uses exploratory factor analysis with orthogonal rotation techniques to map-out the dimensional structure the construct. Specifically, final examination composition and letter writing scores were submitted to a principal component analysis extraction method with Varimax rotation technique to arrive at a more parsimonious and theoretically meaningful language proficiency model. Items with significant loadings on the recovered components were subsequently used to name each component in the scale.
Java NIO

Java NIO explores the new I/O capabilities of version 1.4 in detail and shows you how to put these features to work to greatly improve the efficiency of the Java code you write. This compact volume examines the typical challenges that Java programmers face with I/O and shows you how to take advantage of the capabilities of the new I/O features. You'll learn how to put these tools to work using examples of common, real-world I/O problems and see how the new features have a direct impact on responsiveness, scalability, and reliability. Because the NIO APIs supplement the I/O features of version 1.3, rather than replace them, you'll also learn when to use new APIs and when the older 1.3 I/O APIs are better suited to your particular application.
STUDY ON CRASH OF COMPOSITE FUSELAGE WITH STRAIN RATE EFFECT

Composite material has been widely applied to design and manufacturing of aircraft. The energy absorption mechanism, damage and failure criteria of composites are very different from metals, and most study on crash of aircraft has focused on metal fuselage. Therefore, the study on crash of composite fuselage poses a challenge. In past studies, the simulation of crash of composite fuselage rarely considered the strain rate effect. In our work, we developed dynamic property test of composite and obtained its constitutive model and strength under high and medium strain rate. Based on the test data, we validated our simulation process by building block approach and analysed the crash of composite fuselage with strain rate considered.
Aid Volatility and Economic Growth of Pakistan: A Macroeconomic Policy Framework

This study explores the linkage among aid volatility, macroeconomic policies, and economic growth of Pakistan (1972-2015). We have empirically analyzed the impact of foreign aid volatility on economic growth conditional to the macroeconomic policy environment. For this purpose, we have constructed an index of macroeconomic policies through Principal Component Analysis. We find that volatile aid hinders the economic growth of Pakistan and impedes the expected positive outcomes of macroeconomic policies. By employing the Generalized Method of Moments (GMM) technique, this study shows that foreign aid and foreign aid volatility both negatively affect economic growth under the current macroeconomic policy framework of Pakistan. These results suggest that foreign aid inflows can only be supportive within finance development and social welfare projects.
Towards an Effective Syntax for Deterministic Regular Expressions

Deterministic regular expressions (DREGs) are a core part of XML Schema and used in other applications. But unlike regular expressions, DREGs do not have a simple syntax, instead they are defined in a semantic manner, which puts a burden on the user to develop XML Schema Definitions and to use DREGs. In this paper, we propose a syntax for DREGs, and prove that the class of DREGs is context-free. Next, one of the burdens for using DREGs is the automatic generation of DREGs which is indispensable in practice. Based on the context-free grammars for DREGs, we further design a generator for DREGs, which can generate sentences randomly. Experimental results demonstrate the efficiency and usefulness of the generator.
Real-Time Vehicle Speed Estimation Based on License Plate Tracking in Monocular Video Sequences

A method of estimating the vehicle speed from images obtained by a fixed over-the-road monocular camera is presented. The method is based on detecting and tracking vehicle license plates. The contrast between the license plate and its surroundings is enhanced using infrared light emitting diodes and infrared camera filters. A range of the license plate height values is assumed a priori. The camera vertical angle of view is measured prior to installation. The camera tilt is continuously measured by a micro-electromechanical sensor. The distance of the license plate from the camera is theoretically derived in terms of its pixel coordinates. Inaccuracies due to the frame rate drift, to the tilt and the angle of view measurement errors, to edge pixel detection and to a coarse assumption of the vehicle license plate height are analyzed and theoretically formulated. The resulting system is computationally efficient, inexpensive and easy to install and maintain along with the existing ALPR cameras. Copyright © 2016 IFSA Publishing, S. L.
Ethnomedicinal Antiepileptic Plants Used in Parts of Oyo and Osun States , Nigeria

Despite the relatively high prevalence of epilepsy in Nigeria, most of the patients do not have access to allopathic drugs. Others often default in continuing the therapy, or seek traditional methods of treatment. A survey of plants used in treating epilepsy was carried out in ten major markets of Oyo and Osun States, Nigeria. Sixty-two plants belonging to thirty-six families were collected. The families Fabaceae, Apocynaceae and Annonaceae had the highest number of antiepileptic species. Eight plants including Xylopia aethiopica, Alstonia boonei and Lannea welwitschii occurred frequently in the fourteen recipes recorded. Stem barks constituted 55% of the plant parts used in the formulations and the implication of this for conservation of the plants discussed. The phytomedicines were administered, mostly as decoctions and only two as charred residues.
Structural classifiers for contextual semantic labeling of aerial images

In this work we propose a novel approach to classify aerial images with structured predictors. They get high performances by encoding the contextual information that exists around a region of interest. More precisely, we learn a graph model that takes into account the structure existing between adjacent regions (or superpixels) belonging to various categories with a Structural Support Vector Machine. The whole image classification task is broken down in multiple local subtasks so that we can deal with large amounts of regions (more than 100k).
A survey of distributed denial-of-service attack, prevention, and mitigation techniques

Distributed denial-of-service is one kind of the most highlighted and most important attacks of today’s cyberworld. With simple but extremely powerful attack mechanisms, it introduces an immense threat to current Internet community. In this article, we present a comprehensive survey of distributed denial-of-service attack, prevention, and mitigation techniques. We provide a systematic analysis of this type of attacks including motivations and evolution, analysis of different attacks so far, protection techniques and mitigation techniques, and possible limitations and challenges of existing research. Finally, some important research directions are outlined which require more attentions in near future to ensure successful defense against distributed denial-of-service attacks.
Defragmenting the Internet of Things : Towards Integration of Bluetooth Low Energy

Integrating Bluetooth Low Energy (BLE) more tightly into the Internet of Things (IoT) by establishing it as one of the goto transports for IPand ICN-based networks has a high potential in the process of defragmenting the IoT. To achieve this goal, a deeper understanding, not only on network aspects, but also on system aspects is needed. This research proposal sketches a path toward this goal.
23 F eb 2 01 6 Learning Efficient Algorithms with Hierarchical Attentive Memory

In this paper, we propose and investigate a novel memory architecture for neural networks called Hierarchical Attentive Memory (HAM). It is based on a binary tree with leaves corresponding to memory cells. This allows HAM to perform memory access inΘ(logn) complexity, which is a significant improvement over the standard attention mechanism that requires Θ(n) operations, wheren is the size of the memory. We show that an LSTM network augmented with HAM can learn algorithms for problems like merging, sorting or binary searching from pure input-output examples. In particular, it learns to sortn numbers in timeΘ(n logn) and generalizes well to input sequences much longer than the ones seen during the training. We also show that HAM can be trained to act like classic data structures: a stack, a FIFO queue and a priority queue.
SYNTHESIZED STEREOMAPPING VIA DEEP NEURAL NETWORKS FOR NOISY SPEECH RECOGNITION

In our previous work, we extend the traditional stereo-based stochastic mapping by relaxing the constraint of stereo-data, which is not practical in real applications, via HMM-based speech synthesis to construct the “clean” channel data for noisy speech recognition. In this paper, we propose to use deep neural networks (DNNs) for stereo mapping compared with the joint Gaussian mixture model (GMM). The experimental results on Aurora3 databases show that our proposed DNN based synthesized stereo mapping can achieve consistently significant improvements of recognition performance over joint GMM based synthesized stereo mapping in the wellmatched (WM) condition among four different European languages.
GENDER ANALYSIS OF HIP ABDUCTOR AND ADDUCTOR FORCE RATIOS

The strength of hip abductors (ABD) and adductors (ADD) have been implicated in athletic injuries. This study assessed ABD:ADD in a variety of testing conditions and sought to assess gender differences therein. Fifteen men and fifteen women participated in this study. Subjects performed maximum voluntary isometric muscle actions for hip ABD and ADD against a portable force plate which was manually applied statically by research personal. Subjects were tested in four conditions included supine, supine with hip flexion, seated with knee extension, and standing. Results revealed no significant main effects or gender interaction for testing position for ABD:ADD, ABD force, or ADD force (p ≤ 0.05). Results show that easy to use, valid, and reliable methods of assessing ABD:ADD exist, irrespective of testing position.
Author ' s personal copy

Assessment of the genetic relatedness of walnut cultivars with phenotypic data and molecular markers allows progress in conservation and management of the genetic resources, breeding and protection of breeders’ intellectual property. In the present study we used 24 morphological traits, 25 random amplified polymorphic DNAs (RAPDs) and 7 simple sequence repeats (SSRs), to study genetic diversity and relationships in walnut cultivars at different levels. The 20 analyzed accessions represent some of the most important Romanian and international walnut cultivars and typify a genetic diversity characteristic of a germplasm collection. The distances based on morphological and SSR markers were significantly correlated. The two DNA marker systems were uncorrelated and RAPD markers failed to describe the pattern of molecular similarity. All marker systems detected polymorphisms that were adequate for the discrimination of all cultivars. Morphologicaland SSR-based genetic distances were related to a certain point and differed from RAPD-based genetic distances. Our data indicate that the type and the number of phenotypic traits evaluated can considerably alter the result of the analysis and combination of qualitative and quantitative data needs caution. Moreover, the data imply that the two molecular marker systems are useful for cultivar characterization, but SSR markers are more advisable to investigate genetic relationships. Also, they can be employed to complete and aid the traditional registration of varieties. We propose that, since the information provided by morphological and SSR marker systems in walnut is similar, they should serve for cultivar characterization and assessment in genebanks. © 2013 Elsevier B.V. All rights reserved.
A Billion Open Interfaces for Eve and Mallory: MitM, DoS, and Tracking Attacks on iOS and macOS Through Apple Wireless Direct Link

Apple Wireless Direct Link (AWDL) is a key protocol in Apple's ecosystem used by over one billion iOS and macOS devices for device-to-device communications. AWDL is a proprietary extension of the IEEE 802.11 (Wi-Fi) standard and integrates with Bluetooth Low Energy (BLE) for providing services such as Apple AirDrop. We conduct the first security and privacy analysis of AWDL and its integration with BLE. We uncover several security and privacy vulnerabilities ranging from design flaws to implementation bugs leading to a man-in-the-middle (MitM) attack enabling stealthy modification of files transmitted via AirDrop, denial-of-service (DoS) attacks preventing communication, privacy leaks that enable user identification and long-term tracking undermining MAC address randomization, and DoS attacks enabling targeted or simultaneous crashing of all neighboring devices. The flaws span across AirDrop's BLE discovery mechanism, AWDL synchronization, UI design, and Wi-Fi driver implementation. Our analysis is based on a combination of reverse engineering of protocols and code supported by analyzing patents. We provide proof-of-concept implementations and demonstrate that the attacks can be mounted using a low-cost ($20) micro:bit device and an off-the-shelf Wi-Fi card. We propose practical and effective countermeasures. While Apple was able to issue a fix for a DoS attack vulnerability after our responsible disclosure, the other security and privacy vulnerabilities require the redesign of some of their services.
Distraction-aware Shadow Detection Quanlong Zheng Xiaotian

Shadow detection is an important and challenging task for scene understanding. Despite promising results from recent deep learning based methods. Existing works still struggle with ambiguous cases where the visual appearances of shadow and non-shadow regions are similar (referred to as distraction in our context). In this paper, we propose a Distraction-aware Shadow Detection Network (DSDNet) by explicitly learning and integrating the semantics of visual distraction regions in an end-to-end framework. At the core of our framework is a novel standalone, differentiable Distraction-aware Shadow (DS) module, which allows us to learn distraction-aware, discriminative features for robust shadow detection, by explicitly predicting false positives and false negatives. We conduct extensive experiments on three public shadow detection datasets, SBU, UCF and ISTD, to evaluate our method. Experimental results demonstrate that our model can boost shadow detection performance, by effectively suppressing the detection of false positives and false negatives, achieving state-of-the-art results.
A Hybrid Unicast-Multicast Network Selection for Video Deliveries in Dense Heterogeneous Network Environments

Resource management in emerging dense heterogeneous network environments (DenseNets) is a challenging issue. The employment of multicast transmissions in this scenario has potential to address the problems. On one hand, the large number of smart user mobile devices and user expectations for high-quality rich media services has determined a growing demand for network resources; in DenseNets, mobile users have to make the choice in terms of the network to connect to, in order to balance energy saving and delivery performance. On the other hand, the proliferation of user accesses to the existing and future network infrastructure will bring along with it the operators need for optimizing the radio resource usage. This paper proposes a hybrid unicast-multicast utility-based network selection algorithm (HUMANS), which offers the additional option of selecting multicast transmissions in the network selection process during video delivery. By serving users with good channel conditions via unicast transmissions and users with poor channel quality conditions via multicast, HUMANS allows outperforming other solutions in terms of outage percentage and average quality of transmission, in both low- and high-density scenarios. Most importantly, at the same time it guarantees operators a more efficient resource utilization.