Miracle-dz/UniFAR-FTUs huggingface dataset:
POST-PROCESSING TO IMPROVE HEVC

In this paper, we propose a frame-based dynamic metadata post-processing scheme in HEVC. Video sequence is classified into different categories contains complexity of video content and quality indicator for each frame, an up-to-one byte flag embedded in the bitstream is transferred as side information. Meanwhile dynamic metadata contains classification information indicates the offline training of separate network models. Specifically, we adopt a 20-layers CNN (Convolutional Neural networks) model to extract more meaningful information from the reconstructed error and improve the filtering performance. Experimental results shows that our proposed post-processing scheme leads on average 1.6% BDrate reduction compared with HEVC baseline on the six sequences given in 2017 ICIP Grand Challenge.
Industrial and Systems Engineering Multi-Agent Image Classification via Reinforcement Learning

We investigate a classification problem using multiple mobile agents that are capable of collecting (partial) pose-dependent observations of an unknown environment. The objective is to classify an image (e.g, map of a large area) over a finite time horizon. We propose a network architecture on how agents should form a local belief, take local actions, extract relevant features and specification from their raw partial observations. Agents are allowed to exchange information with their neighboring agents and run a decentralized consensus protocol to update their own beliefs. It is shown how reinforcement learning techniques can be utilized to achieve decentralized implementation of the classification problem. Our experimental results on MNIST handwritten digit dataset demonstrates the effectiveness of our proposed framework.
Mission of the company, prosocial attitudes and job preferences: A discrete choice experiment

We conduct a discrete choice experiment to investigate how the mission of high-tech companies affects job attractiveness and induces self-selection of science and engineering graduates with respect to their prosocial attitudes. We characterize mission by whether or not the company combines its profit motive with a mission on innovation or corporate social responsibility (CSR). Furthermore, we vary job design (e.g. autonomy) and contractible job attributes (e.g. job security). We find that companies with a mission on innovation or CSR are considered more attractive. Women and individuals who are more altruistic and less competitive feel particularly attracted to such companies.
ENHANCING FLEXIBLE MARKETING POSTPONEMENT STRATEGY AND CUSTOMER RELATED PERFORMANCE: THE ROLE OF NEW TECHNOLOGY AND INNOVATION

This paper examines the importance of incorporating new technology and innovation in supply chain management (SCM) processes in Malaysian companies. The study measures senior production or SCM managers’ perceptions regarding new technology and innovation in supply chain management processes and level of performances in their companies. The paper specifically investigates relationship between new technological and innovation in SCM, Marketing postponement flexibility and customer related performance (CRP) and these associations were analyzed through statistical methods such as Pearson’s correlation, cluster analysis and Structural Equation Modelling (SEM). The findings suggest that new technology and innovation in SCM processes, and its adoptions have significant correlations with Marketing postponement flexibility and customer related performance. In addition, the SEM result demonstrates that new technology and innovation in SCM proxies namely ‘technology and innovation in product and process design’, ‘technology and innovation in new product development’ and ‘technology and innovation in production line process’ appear to be of primary importance and exhibit significant impact on Marketing postponement flexibility and customer-related performance. Findings of the study provide striking demonstrations of the power and importance of new technology and innovation in enhancing performances of companies. The result indicates that companies should emphasize greater attention to the product and process design as well as new product development aspects of the
Shocks and Frictions in US Business Cycles with Heterogeneous Agents ∗ Preliminary and Incomplete

Does market incompleteness change our view on the sources of business cycle fluctuations? Using a Bayesian likelihood approach, we estimate a Heterogeneous Agent New Keynesian (HANK) model in which aggregate demand and investment depend on aggregate changes in idiosyncratic income risk and portfolio liquidity, which are partly exogenous and partly respond to other aggregate shocks. We use the estimated model to compare the decomposition of U.S. GDP, consumption, and investment growth to various New Keynesian models without incomplete markets. We find that demand shocks explain a larger share of the business cycle, even the long run. Shocks to income risk and portfolio liquidity partly replace unobservable shocks to the risk premium. JEL codes: E32, E52
A bidirectional LSTM network for pitch estimation of speech sounds

​This article presents the construction of a bidirectional long short-term memory neural network for pitch estimation in human speech. Pitch estimation is a challenging task and state-of-the-art methods have not achieved perfection yet. Dealing with octave errors and the voicing decision is part of the task. Candidates for the pitch are generated by the debiased autocorrelation function (debiased ACF) (Boersma 1993) and the network is trained in choosing the correct candidate on a database consisting of recorded speech and the corresponding laryngograph signal (Atake ​et al 2000), which is treated as a ground truth. After training, the network is evaluated against the algorithm currently implemented in Praat. It makes more errors in choosing the correct candidate for the pitch, but fewer errors in its voicing decision than Praat does. It is also discovered that training in batches rather than online leads to slightly better results in this task. The results are promising, especially on the voicing decision, but more research is needed to be able to make the network better than the debiased ACF in Praat. The code for this work can be found at ​https://github.com/JonaBenja/pitch_lstm​.
Performance-Oriented Neural Architecture Search

Hardware-Software Co-Design is a highly successful strategy for improving performance of domain-specific computing systems. We argue for the application of the same methodology to deep learning; specifically, we propose to extend neural architecture search with information about the hardware to ensure that the model designs produced are highly efficient in addition to the typical criteria around accuracy. Using the task of keyword spotting in audio on edge computing devices, we demonstrate that our approach results in neural architecture that is not only highly accurate, but also efficiently mapped to the computing platform which will perform the inference. Using our modified neural architecture search, we demonstrate 0.88% increase in TOP-I accuracy with $ 1.85\times$ reduction in latency for keyword spotting in audio on an embedded SoC, and $ 1.59\times$ on a high-end GPU.
Efficacy of Doctor WoRM’s Module for Year Four Low Achievers

Mathematics is a core subject teaching in primary school education in Malaysia. Every pupil has to be mastered in four basic arithmetic skills. In this case, single-digit multiplication is important as it is the basis for other mathematics operations. However, there are still some pupils who face challenges in the skills even by Year Four. In this study, the researcher had carried out a quasi-experimental study to determine the efficacy of DoCtor WoRM’s Module. A pre-test was carried out to select the pupils who have not mastered the multiplication skills. Sixty pupils from four National-Type Chinese Primary Schools were selected as the respondents in this study. They were divided equally into experimental and control groups. The experimental group went through a teaching and learning using DoCtor WoRM’s Module, while the control group learnt the same content using traditional teaching. Three months of intervention was carried out. Then, a post-test was carried out to both groups of respondents. Data was analysed using inferential analysis. Dependent t-test had showed that there was a significant increase in the achievement in multiplication skills from pre-test (M = 59.47, SD = 27.242) to post-test (M = 91.53, SD = 12.566). The independent t-test showed that the achievement of multiplication skills in post-test of experimental group (M = 91.53, SD = 12.566) is higher than the post-test of control group (M = 79.19, SD = 22.588). As a conclusion, DoCtor WoRM’s Module is effective in improving multiplication skills among Year Four low achievers in SJKC. This study implicated that module is an effective tool for low achievers to improve their mathematics learning.
Original Possion Blending Deep Harmonization Pix 2 Pix GCC-GAN ( Ours ) Foreground Mask

Compositing a realistic image is a challenging task and usually requires considerable human supervision using professional image editing software. In this work we propose a generative adversarial network (GAN) architecture for automatic image compositing. The proposed model consists of four sub-networks: a transformation network that improves the geometric and color consistency of the composite image, a refinement network that polishes the boundary of the composite image, and a pair of discriminator network and a segmentation network for adversarial learning. Experimental results on both synthesized images and real images show that our model, Geometrically and Color Consistent GANs (GCC-GANs), can automatically generate realistic composite images compared to several state-of-the-art methods, and does not require any manual effort.
Extending WASP : providing context to a personal web archive From re-finding to finding Gijs Hendriksen July 1 , 2019

Using WASP, a system for archiving, revisiting and searching through previously visited websites, users can have a better overview of their browsing behavior and find pages they remember visiting more easily. In this thesis, we introduce an extension to WASP that not only allows for searching through your browsing history, but also through pages related to your interests. It does so by crawling possibly relevant pages in the background, and indexing them if they are deemed relevant enough.
Artificial Intelligence Techniques for Security Vulnerability Prevention

Computer security has been a concern for decades and artificial intelligence techniques have been applied to the area for nearly as long. Most of the techniques are being applied to the detection of attacks to running systems, but recent improvements in machine learning (for example, in natural language processing) have enabled the opportunity to process software and specifications to detect vulnerabilities in a system before it is deployed. This paper presents a survey of artificial intelligence techniques (including machine learning) to detect or repair security vulnerabilities before product introduction. In the surveyed papers, techniques are presented for using NLP to analyze requirements documents for security standard completeness, performing neural fuzz testing of software, generating exploits to detect risk, and more. We categorize current techniques into 3 groups: vulnerability detection, vulnerability repair, and specification analysis. Generally, while AI techniques have become quite useful in this area, we show that AI techniques still tend to be limited in scope, providing a collection of tools which can augment but not replace careful system development to reduce vulnerability risks.
Brain Controlled Robots : A Survey

A few decades back, the notion of controlling a robot with the mind would have seemed ludicrous and almost fictional, though with extensive research in the field of bio-mechatronics the idea today has become a reality. Brain controlled exoskeletons, prosthetics and wheelchairs have progressed from machines in sci-fi to nearly commercialized products. Brain controlled robots are based on Brain Computer Interface (BCI). This paper is delves into the technical aspects involved in BCIs and also provides an overview of some state-of-the-art BCIs. KeywordsElectroencephalogram (EEG), Magnetoencephalography(MEG), Brain Computer Interface (BCI), Motor Imagery (MI), brain controlled robots, Human Machine Interaction (HMI)
Pn TOMOGRAPHY, GEOPHYSICAL MODELS, CROSS CORRELATION, AND LOCATION IN EURASIA

This paper covers several facets of the location and geophysical modeling efforts at Los Alamos National Laboratory (LANL). In location we continue to develop kriged correction surfaces for Eurasian stations and show results from relocations of GT25 or better events using small (3 to 10 station) regional networks in Asia. With empirical corrections, locations for 70% of tested events were improved using first P only, while 82% improved using first P and first S arrivals. We are exploring model-based corrections using velocity models derived from Pn tomography of China and Siberia. Relocations in Asia using travel times calculated from Pn velocity perturbations show a 10-30% improvement over IASPEI91 travel times alone; similar tests will be performed in northeastern Asia. Pn tomography in northeastern Asia indicates higher velocities under the Siberian platform, with generally lower velocities under regions of more active tectonics. In Central Asia, we are using correlation repicking, satellite imagery and pattern recognition software to identify potential mines and tie correlated seismic events to mining locations. The pattern recognition software has identified several mining locations that we are linking to clusters of correlated seismic events, which are identified from the Kazakh National Data Center (KNDC) bulletins. For geophysical models, we have recently developed a new joint inversion method using gravity and surface wave group velocity data. This method has been applied to the Tarim and Junggar basins and shows high upper mantle velocities beneath the Tarim, with differences in lower-crust and upper-mantle shear velocities between the eastern and western Tarim.
The physiological response of two tolerant Persea americana rootstocks to Phytophthora cinnamomi and flooding

Avocado trees are extremely sensitive to both soil-flooding and the root rot caused by Phytophthora cinnamomi. It is well known that flooding exacerbates the effects of Phytophthora root rot (PRR), causing significant damage that leads to devastating losses worldwide. Early symptoms of plant stress often include decreases in stomatal conductance (gs) and net CO2 assimilation (A). Greenhouse studies were conducted to investigate the synergism between the effect of PRR and flooding on avocado. Plants were separated into four treatments; control (uninfected, non-flooded), flooded, infected, and flooded and infected. Net CO2 assimilation, transpiration, stomatal conductance, and CO2 partial pressure (Ci) were measured for two P.cinnamomi-tolerant rootstocks, ‘R0.09’ and ‘Duke 7’ over a period of four weeks.
Comparative diversity analysis of morphological traits in maize inbred lines using RAPD , SSR markers

Fourteen maize inbred lines were used for molecular diversity analysis using 11 RAPD and 25 SSR primers. The inbred lines were divided into two main clusters based on marker data suggesting the presence of diversity at DNA level. Further diversity based on morphological traits across two seasons kharif and rabi 2013 was studied. In 2013 kharif the inbred lines were grouped into seven clusters out of which five were solitary and in 2013rabi the inbred lines were grouped into five clusters with two being solitary. The consistency of cluster pattern based morphological diversity ofthe genotypes across seasons was compared with grouping of inbred lines obtained using molecular markers which indicating genetic distance at DNA level. Comparison of clustering pattern obtained by using molecular markers and morphological characters showed less similarity.
44o CONGRESO ESPAÑOL DE ACÚSTICA ENCUENTRO IBÉRICO DE ACÚSTICA EAA EUROPEAN SYMPOSIUM ON ENVIRONMENTAL ACOUSTICS AND NOISE MAPPING

Most Europeans now live in urban or suburban areas. The cities must thus offer their citizens good environmental conditions. The acoustic environment is one the primary indicators of the perceived quality of life in cities. Fulfilling the requirements of the European Environmental Noise Directive of 2002 (END) aiming at reducing and managing noise in large agglomerations must be taken as only part of an overall strategy that all city councils must embrace. Urban areas with differentiated soundscapes, such as parks, are well frequented by the urban populations for escaping the surrounding more aggressive areas and for psychological restoration. A study of urban parks in cities in Portugal and Brazil have shown that the citizens perceive a much higher sound quality in urban parks due not just to lower sound pressure levels but also to a higher correlation with their activities and expectations. Urban parks can represent quiet areas in the END sense within easy reach and with wealthy sound compositions. The research program and some findings are described and discussed.
Using Dafny for programs and proof

We propose a unified logical framework for specifying and proving both termination and non-termination of various programs. Our framework is based on a resource logic which captures both upper and lower bounds on resources used by the programs. By an abstraction, we evolve this resource logic for execution length into a temporal logic with three predicates to reason about termination, non-termination or otherwise. We introduce a new logical entailment system for temporal constraints and show how Hoare logic can be seamlessly used to prove termination and non-termination in our unified framework. We then report on the usability and practicality of our approach by verifying both termination and non-termination properties for about 300 programs, collected from a variety of sources. This adds a modest 5-10% verification overhead to an existing partial-correctness verification system.
VECTOR BASED MODEL COMBINING TEXTUAL AND VISUAL WORDS FOR MULTIMEDIA INFORMATION RETRIEVAL

This paper presents a multimedia retrieval method which combines textual information with image features to descri be the content of multimedia documents. Text and image are processed separately using a same bag-of-words approach and a tf.idf weighting scheme. We obtain two vectors of textual and visual terms that are linearly combined. We stud y the sensitivity of several factors which could impact on the performance of our model. We evaluate our method on the ImageCLEF’08 collection of about 150’000 Wikipedia documents. Experiments show that our model is competitive compared to other participants and yields top performance (2nd rank). Moreover, results prove that a multimedia model outperforms a text only one, which encourages to use multipl e modalities rather than a single one.
The Centrencephalic Space of Functional Integration: a Model for Complex Intelligent Systems 

If we have recently begun to understand how DNA gives life to embryos and then to individuals, only very little is understood of the intricate interactions between the biological bases of life, the environment and the human brain. The exponential acceleration of technological change could change many, perhaps all, the rules that have guided our civilization so far. It is very likely that these intelligent artificial entities will take much less time to understand the codes that constitute them, gaining forms of (self) awareness, decision-making skills, introspective capacities, mind reading and even free will. If all this is achieved, in the coming decades humanity will be destined to a profound cultural, epistemological and even physiological transformation. In this paper, we aim to show how the success or failure of a balanced man-machine co-evolution will also depend on some answers to fundamental scientific questions that have remained unexplored, such as consciousness and decision-making, creativity, but above all to the adaptive factor that more radically sustained and pushed the evolution beyond the constraints of our genetic code: improvisation. This entanglement of neuronal matrices could be at the origin of an intermodal communication — consists of a stream of semantic phenomena, mental images and more, tuned thanks to “pattern recognition” in centrencephalic space of functional integration — thus explaining “remote spectrum actions” at the base of primary adaptive unconscious and experiences life.
Critical Success Factors in ERP System Adoption: Comparative Analysis of the Private and the Public Sector

This research provides useful insight into differences in the perception of signifi cance and the degree of fulfi llment of Critical Success Factors (CSFs) of Enterprise Resource Planning (ERP) adoption between private and public sector organizations. The survey was conducted on a sample of 77 Serbian organizations, with approximately equal representation of those from the private and the public sector. A comprehensive list of CSFs, compiled as a result of an extensive literature review, was included in the questionnaire. The collected data and statistical procedures applied revealed the CSFs that differentiate the organizations from the two observed sectors. Involvement of end users in ERP adoption and implementation activities has proven to be the CSF in relation to which the assessments and attitudes of organizations from the two sectors differ the most. Results of this research indicate that differences between private and public sector organizations are more distinct in terms of their level of fulfi llment of ERP adoption CSFs than they are in terms of their assessment of CSFs’ signifi cance. This points to a conclusion that differences in the way of functioning and governance of organizations from these two sectors infl uence their ability to tackle the problems much more than their ability to recognize them. The practical contribution of this research is in providing some useful fi ndings that can greatly assist relevant stakeholders to achieve a higher success rate in an ERP adoption. In addition to responding to the initial research questions, this paper has also revealed new issues, suggesting the need for further research.
General Identifiability with Arbitrary Surrogate Experiments

We study the problem of causal identification from an arbitrary collection of observational and experimental distributions, and substantive knowledge about the phenomenon under in-vestigation, which usually comes in the form of a causal graph. We call this problem g-identifiability , or gID for short. The gID setting encompasses two well-known problems in causal inference, namely, identifiability [Pearl, 1995] and z-identifiability [Bareinboim and Pearl, 2012] — the former assumes that an observational distribution is necessarily available, and no experiments can be performed, conditions that are both relaxed in the gID setting; the latter assumes that all combinations of experiments are available, i.e., the power set of the experimental set Z , which gID does not require a priori. In this paper, we introduce a general strategy to prove non-gID based on hedgelets and thickets , which leads to a necessary and sufficient graphical condition for the corresponding decision problem. We further develop a procedure for systematically computing the target effect, and prove that it is sound and complete for gID instances. In other words, failure of the algorithm in returning an expression implies that the target effect is not computable from the available distributions. Finally, as a corollary of these results, we show that do-calculus is complete for the task of g-identifiability.