Miracle-dz/UniFAR-FTUs huggingface dataset:
Action segmentation and understanding in RGB videos with convolutional neural networks

In this work, we propose three techniques for accelerating a modern action recognition pipeline. For reaching this point, we carried out an extensive study about the current state of action recognition. First, the traditional action recognition methods based on handcrafted features where reviewed, along with the ones focused on using machine learning as well as those which use deep learning techniques. Valuable insights were extracted from them, as well as the difficulties the task of action recognition carries out. Subsequently, we explored numerous video datasets available for properly train deep models in the task of understanding human actions. Then, several video action recognition works from the past three years where thoroughly studied. Coming back to the three proposed techniques, we first selected two of the reviewed deep learning works. Specifically, (1) Temporal Segment Networks (TSN), a Convolutional Neural Network (CNN) framework that makes use of a small number of video frames for obtaining robust predictions which have allowed to win the first place in the 2016 ActivityNet challenge; (2) MotionNet, a network that is capable of inferring optical flow from Red Green and Blue (RGB) frames by means of simple and transposed convolutions. As the third proposal we selected NVIDIA Video Loader (NVVL), a new Graphics Processing Unit (GPU) video decoding software developed by NVIDIA. Useful for efficiently reducing the data transfer and storage bottleneck of video deep learning applications. Finally, we combine the first and third technologies. For this, we trained the RGB stream of the TSN network in videos loaded with NVVL on a subset of daily actions from the University of Central Florida 101 (UCF101) dataset. Thus proving their validity for their integration into a deep learning action recognition system that is both faster and effective.
Analyzing event related potentials using adaptive filter

ERPs (Event Related Potentials) are EEG signals which are directly measured from cortical electrical response to external stimuli such as feelings, sensual or cognitive events. The evaluation of the amplitude and latency of the ERP wave has important significance in evaluating neurological reflex. However, the ERP wave amplitude is small compared with the EEG wave, and considerably affected by the noise such as eyes, muscles, heart motion etc. In this paper, datasets are collected from ERPLAB and journals provided available datasets with the stimulus of sound and light. Using adaptive noise cancellation (ANC) combined with LMS algorithm the waves P300 of ERP were detected and separated. The algorithm was evaluated by the ratio SNR and average value. Results were compared with other published tools such as P300 calculation algorithm of ERPLAB softwar.
A Review of Business Intelligence and Analytics in Small and Medium-Sized Enterprises

Business intelligence and analytics (BI&A) has become a cornerstone of many organizations in making informed decisions. Despite the importance of small and medium-sized enterprises (SMEs) to all world economies, research in BI&A focuses mostly on large enterprises. To aid in closing this gap, this study provides a comprehensive review of the literature sources in this domain. Through a systematic literature review, this study collects, categorizes, synthesizes, and analyzes 78 articles related to BI&A in SMEs. The research topics that are addressed include BI&A components and solutions, Mobile BI&A, Cloud BI&A, and BI&A application, adoption, implementation, and benefits. Further, the research gaps and suggestions for future research are presented to facilitate the progression of BI&A in SME research.
AN INVESTIGATION ON THE INTERNATIONAL TOURISTS’ EXPENDITURES IN THAILAND: A MODELLING APPROACH

As a result of the increase in both the international touristsâ€™ expenditures and tourist arrivals to Thailand, there is a growing interest in determining the trend of international touristsâ€™ expenditures based on time-series modelling. In our article secondary data were used to produce forecasts of the international touristsâ€™ expenditures in Thailand between 2009 and 2010. The forecasting method is based on the ARFIMAX (0, 0.197, 0, 0.033) model. Furthermore, this method predicted that international touristsâ€™ expenditures in Thailand between 2009 and 2010 will have to contract and slow down. This paper seeks to determine whether the international touristsâ€™ expenditures are affected by other circumstances. The results of this study revealed that the international tourist arrivals to Thailand will also have to slow down. However, from the results, there is solid evidence to support such a claim.
Recommended system of personalized corporate e-learning based on ontologies

Corporate e-learning is becoming general, the use of electronic learning systems is constantly expanding. Corporate training is characterized by the availability of a corporate knowledge base, which is constantly expanding. In order to achieve stable development of company, transparency and access to knowledge and information is necessary. This ensures a thorough understanding of the executed work by all employees of the company. Therefore, a key objective in this context is a generalization, accumulation and transmission of corporate knowledge to any employee of the company under the corporate training. In addition, it is crucial to find the suitable content in the learning process in order to save time and quickly get involved in the workflow. Thus, new systems are needed that can effectively recommend the necessary educational material for users on the basis of their preferences.
An Empirical Study on Optimizing Flipped Classroom of Advanced English Course: from Learner’s Perspective

The exploration of flipping classroom is in full swing, and the reform has come to the stage of optimization, that is, to reflect on the effectiveness of the classroom. The paper conducted an empirical study on whether the flipping classroom of advanced English course has achieved marked improvement in respect of the learners’ comprehensive competence compared with that in traditional classrooms. Results indicated that flipped classroom contributed to the active attitude of the learners and the enhancement of their language competence, communication skills, and other competences, especially in the second round of reform. Based on the feedback from the learners in the first round of teaching and learning, some optimization measures in view of input materials, evaluation means, and ways of activities have been taken to give full play to learners’ initiative and enthusiasm, and thus to improve the overall competence of the learners.
Profit Maximization for Admitting Requests with Network Function Services in Distributed Clouds

Traditional networks employ expensive dedicated hardware devices as middleboxes to implement Service Function Chains of user requests by steering data traffic along middleboxes in the service function chains before reaching their destinations. Network Function Virtualization (NFV) is a promising virtualization technique that implements network functions as pieces of software in servers or data centers. The integration of NFV and Software Defined Networking (SDN) further simplifies service function chain provisioning, making its implementation simpler and cheaper. In this paper, we consider dynamic admissions of delay-aware requests with service function chain requirements in a distributed cloud with the objective to maximize the profit collected by the service provider, assuming that the distributed cloud is an SDN that consists of data centers located at different geographical locations and electricity prices at different data centers are different. We first formulate this novel optimization problem as a dynamic profit maximization problem. We then show that the offline version of the problem is NP-hard and formulate an integer linear programming solution to it. We third propose an online heuristic for the problem. We also devise an online algorithm with a provable competitive ratio for a special case of the problem where the end-to-end delay requirement of each request is negligible. We finally evaluate the performance of the proposed algorithms through experimental simulations. The simulation results demonstrate that the proposed algorithms are promising.
Strategies for e-Business: Creating Value Through Electronic and Mobile Commerce: Concepts and Cases

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A Study on Organizational Boundary Formation and Key Mechanisms

This study focuses on the organizational boundary formation from a business founder identity perspective.We wonder how business founders with different identities make initial decisions at the founding period of new ventures,and thus demarcate organizational boundaries along respective logics that are based on efficiency,power,competence and identity.The study looks at contingent roles of task and institutional environment in influencing the founder decision making as well as the organizational boundary formation.The study contributes to the understanding towards an extension of theories of firms in entrepreneurship context,the role of entrepreneurs in crafting organizational characteristics at the firm level as well as offering new insights to organizational imprinting studies.Moreover,the study sheds lights on entrepreneurs' self-management,as well as suggests governments and supporting agencies to incubate diversified new ventures rather than only efficiency-based firms in a new era.
Usage of Social Media in City Marketing: A Research on 30 Metropolitan Municipalities in Turkey

Rapid changes in information and communication technologies cause serious developments in marketing field similar to every sector. In parallel with these developments, the social media channels which appear as Web 2.0 applications have a number of advantages in comparison with traditional media channels. Social media gained a place in marketing activities because of it s advantages. Social media is added on instruments in line with these developments that countries benefit in marketing activities for attracting more tourists, investment and becoming prominent against the other cities. Cities intensively benefit from social media because of social media ’ s properties like reaching the large masses, low marketing cost, interaction, receiving feedbacks rapidly, etc. Within this context, the purpose of this research is to determine the use statuses of social media by metropolitan municipalities as part of city marketing. In accordance with this purpose, official social media accounts of 30 metropolitan municipalities a re investigated between the dates of 01/09/2016 and 30/09/2016, then their shares in social media channels a re investigated in the context of city marketing. It is observed that two metropolitan municipalities do not have official Facebook accounts, four metropolitan municipalities do not have an official Twitter account as well. Again, it is found that 19 metropolitan municipalities do not have an official Instagram account and 17 metropolitan municipalities do not have official YouTube account. In consequence, it is observed that, our metropolitan municipalities do not benefit from social media effectively for city marketing activities.
The role of robustness analysis for sustainable performance assessment frameworks

Sustainability criteria are often contradictory and the choice of a practice compared to another for performance improvement, should take into account the conflicts of criteria to find a compromise. The choice of a practice and the quantification of its impacts are determined by qualitative scales containing many ambiguities, and may lead to misinterpretation of the results. Inspired by research on performance measurement in sustainable Supply Chain Management, we introduce robustness issues in sustainable performance assessment frameworks and propose a process to help decision-maker to anticipate changes and to react to complexity and uncertainty.
Holographic model on the mechanism of memory encoding, storage, and retrieval in the brain

Since it was discovered that hippocampus plays a vital role in human memory, enormous investigations on brain memory have been carried out in the last a few decades. However, so far, there has been no report demonstrating how and in what form that the memory is encoded, stored and retrieved. In this paper, we propose a holographic model with theoretical analysis to describe the mechanism of memory encoding, storage and retrieval in brain. The model suggests that memory encoding in brain is achieved through the process of hologram generation in synapses via the interference of the signal wave carrying the outside information and the energy wave emitted by neurons of hippocampus. The holograms are formed by modulated synapses with the structures of the interference patterns. In memory retrieval process, hippocampus emits the same energy wave as that used for encoding with consciousness to induce the reconstruction of the signal wave from the hologram. Experiment was carried out to simulate hologram encoding and retrieval. The discussion presented in the paper shows that the proposed model can reasonably explain some phenomena related to brain memory.
Lagoons for livestock manure.

Lagooning for disposal of wastes is a field of sanitary engineering that has received much study. Papers have dealt with oxidation ponds for raw and primary wastewater, and for all kinds of industrial wastes. It is natu ral that the agricultural segment of our society also would look to lagooning as a method of disposing of its wastes, of which livestock manure is the primary material. The livestock farmer today is faced with a Herculean chore of manure management. Farming has changed from the family farm of a generation ago to large-scale specialized "factory farms. Hundreds of dairy cows, thousands of beef animals, or tens of thousands of chickens or turkeys are kept in close confinement on very lim ited acreage. Tremendous quantities of manure are produced each day, and disposal is hampered by its sheer vol ume and by lack of land on which it can be spread. The manure is not even readily disposed of to nearby crop farmers because hauling and spreading manure frequently costs more than the use of chemical fertilizers. Many live stock farmers would gladly accept a system that destroys the manure rather than be faced with the logistics of col lection, processing, storing, marketing, hauling, and spreading it. Thus, la goons have been considered seriously and attempted as a method of reduc ing the manure management chore.
Route choice based on passenger classification for the urban mass transit network

This paper discusses passenger travel route choice behavior for the urban mass transit network.According to the familiarity of the passengers on the network,the passengers are divided into two types,familiar type and unfamiliar type,and the different route choice behaviour and characteristics between the two types of passengers are analysed.The different travel impedances are built up,and ratio correction method of multi-route distribution model is improved.Calculation and analysis of the local network of Beijing rail transit prove that the results are more realistic after the passenger classification.
multiple-valued logic circuit architechure and supplementary symmetrical logic circuit structure(SUS-LOC)

The present invention relates to the design and generation of a circuit structure and the resulting circuit diagram of a multi-value logic, the circuit configuration is arbitrary r- value of the logical function of the n digits (r is an integer greater than this, n is an integer greater than 0) permits. This structure is called symmetric Oh replacement logic circuit structure (SUS-LOC, SUpplementary Symmetrical LOgic Circuit structure). In a circuit mixing the SUS-LOC, branch circuit it is recognized as unique transfer circuit and the output response. For some circuits, because the operating characteristics of the switching elements, additional circuit elements or state are to be combined in order to prevent back-biasing (back biasing). SUS-LOC is fully activated. Only active elements performing a logical integrated, and resistors and / or as other passive load (load), their cone poneonteu not directly related to the logic integrated circuit are excluded from the protection task. r- value designed by using the definition of SUS-LOC, a multi-value or multi-value generation of the logic circuit can be achieved with known techniques, materials and devices.
Enterprise Environmental Strategy Formation Mechanism: An Analysis Framework from the Micro-dynamic Perspective

The enterprises' environmental value orientation and behaviors determine the environmental quality of one country.According to the impact levels of different environmental driving forces inside and outside the enterprises,the paper proposes an analysis framework and the corresponding research contents about the environmental strategy formation from the perspectives of green consumes,environmental regulation,market structure,internal drive and institution influence.In order to reflect the unity between environmental strategy and other strategy in one enterprise,this paper argues that the green feature and traditional feature of the enterprise product should be the fundamental analysis cell.In this way,the behavior mechanism of the enterprise confronted multiple environmental pressure will be uncovered.
Above-Average Debt Ratio and the Relationship with Return on Equity: The Case of the Vietnamese Listed Seafood Enterprises

Many studies have presented the relationship between the leverage of firms and return on equity (ROE) in order to analyze how significant leverage has influence on the ROE of enterprises. Exploring the data set from financial statements and annual reports of the Vietnamese listed seafood enterprises from 2009 to 2013, the paper aims to examine the determinants of these firms having a greater debt ratio than the average of the industry. In addition, the relationship between ROE and a firm’s leverage is analyzed by applying a fixed-effects regression. It can be found that firms having an above-average debt ratio have a lower ROE than their counterparts by 50.94%.
A Kinetic Neural Network Approach for Absolute Quantification and Change Detection in Positron Emission Tomography.

When dealing with absolute quantification of the tracer kinetics in Positron Emission Tomography (PET), one of the most reliable family of methods are the physiologically-consistent compartmental ODE models [3]. In this work we introduce the kinetic neural Network (kNN), a novel technique for absolute quantification inspired by compartmental models and Neural Networks commonly used in Deep Learning. The ODE model's explicit solution is computed symbolically with the Python package sympy [2], by using the Pade approximation of the exponential matrix [1]. The model is evaluated voxel-by-voxel with PET data and fitted using the Gradient Descent technique. We applied this method on a set of PET images of the same subjects before and after an intervention. At last, we compared the derived parameter images by using Hostelling's t-squared statistic to evaluate the regions of the brain affected by the treatment. References [1] M. Arioli, B. Codenotti, and C. Fassino, The Pade method for computing the matrix exponential , Linear Algebra and its Applications, 240 (1996), pp. 111–130. [2] A. Meurer, C. P. Smith, M. Paprocki, O. Certik, et al., SymPy: symbolic computing in Python , PeerJ Computer Science, 3 (2017), p. e103. [3] G. L. Zeng, A. Hernandez, D. J. Kadrmas, and G. T. Gullberg, Kinetic parameter estimation using a closed-form expression via integration by parts. , Physics in medicine and biology, 57 (2012), pp. 5809–21.
Who owns Internet of Thing devices?

Although Internet of Things (IoT) has been recently receiving attention from the research community, undoubtedly, there still exists several privacy concerns about those devices. In particular, IoT devices in the cyberspace are reachable and visible through IP addresses. This article uniquely exploits to qualify the distribution of owner information of IoT devices based on the observation; consumers may write relevant details into the application-layer service on the IoT devices, such as company or usernames. We propose to automatically extract owner annotation by utilizing a set of techniques (network scanning, machine learning, and natural language processing). We use the probing and classifier to determine whether the response data come from an IoT device. The natural language-processing technique is used to extract owner information from IoT devices. We have conducted real-world experiments to evaluate our integrated approach empirically. The results show that the precision is 97% and the coverage is 96%. Furthermore, our approach is running on a more larger unlabeled dataset consisting of 93 million response packets from the whole IPv4 space. Our analysis has drawn upon nearly 4.3 million IoT devices exposed to the public, and it is a typical trail effect of the owner information distribution.
VEHICLE ROUTE SELECTION WITH AN ADAPTIVE NEURO FUZZY INFERENCE SYSTEM IN UNCERTAINTY CONDITIONS

A useful routing system should have the capability of supporting the driver effectively in deciding on an optimum route to his preference. This paper describes the problem of choice of road route under conditions of uncertainty which drivers are faced with as they carry out their task of transportation. The choice of road route depends on the needs stated in the transport requirements, the location of the users and the conditions under which the transport task is performed. The route guidance system developed in this paper is an Adaptive Neuro Fuzzy Inference Guidance System (ANFIGS) that provides instructions to drivers based upon "optimum" route solutions. A dynamic route guidance (DRG) system routes drivers using the current traffic conditions. ANFIGS can provide actual routing advice to the driver in light of the real-time traffic conditions. In the DRG system for the choice of road route, the experiential knowledge of drivers and dispatchers is accumulated in a neuro-fuzzy network which has the capability of generalizing a solution. The adaptive neuro-fuzzy network is trained to select an optimal road route on the basis of standard and additional criteria. As a result of the research, it is shown that the suggested adaptable fuzzy system, which has the ability to learn, has the capability of imitating the decision making process of the drivers and dispatchers and of showing a level of competence which is comparable with the level of their competence.
Une nouvelle approche à l'extraction de lexiques bilingues à partir de corpus comparables

We present in this article a new method for automatic extraction of bilingual lexicons from comparable corpora. We first anaylze the assumptions underlying the research works led in this domain, and then detail the associated algorithms. Lastly, we evaluate our approach on two different corpora, and show how the combination of our method with standard ones significantly improves the quality of the extracted lexicons.