Miracle-dz/UniFAR-FTUs huggingface dataset:
Intangible assets and the trend in the accruals-cash flow association

Recent research documents a weakening of the negative association between accruals and cash flows and raises the concern that accounting usefulness may be declining. We provide evidence that the documented weakening of the association does not result from a loss of accounting usefulness per se, but from deficiencies due to a specific accounting practice and a specific design choice made by researchers, which can be corrected. At the core of the problem is the departure from the matching principle resulting from the current accounting treatment of intangible investments. More specifically, the weakening of the negative association is driven by the increasing level of intangible investments, which are expensed rather than capitalized, and by researchers’ choice to scale accruals and cash flows by total assets, which are understated for intangible-intensive firms. Treating intangible expenditures as capitalized investments and using alternative scaling options practically eliminate the weakening trend in the correlation between accruals and cash flows.
Power Collection from Multiple Hydrokinetic Generators Utilizing Advanced Control

A common DC bus power collection scheme is considered for a two-unit array of cross-flow hydrokinetic turbines utilizing Advanced control schemes. These controllers increase mechanical rotor efficiency while introducing large, transient spikes of bidirectional current. A numerical simulation in the Simscape Power Systems environment driven by results from laboratory-scale turbine experiments is constructed to evaluate system efficiency from mechanical power input to active power delivered to the grid. The combined effects of advanced control and power collection result in a 13% system efficiency increase over a baseline case, while the collection scheme alone results in a 5% increase. Generator losses are determined to most significantly impact system performance. Results of this study are intended to inform the completion of an electromechanical emulation machine to further evaluate generators and power electronics specified for field deployment. Allowances for heat dissipation and the interconnection of units are anticipated to be major engineering challenges that must be overcome for reliable, cost-effective implementation once deployed at sea.
Automatic feature engineering from very high dimensional event logs using deep neural networks

As communication networks have grown, event logs have increased in both size and complexity at a very fast rate. Thus, mining event logs has become very challenging due to their high variety and volume. The traditional solution to model raw event logs is to transform the raw logs into features with fewer dimensions through manual feature engineering. However, feature engineering is very time-consuming, and its quality is highly dependent on data scientists' domain knowledge. Furthermore, repeatedly preprocessing event logs significantly delays the scoring process, which must scan all items in the logs. In this paper, we present our recent study on mining high-dimensional event logs using deep neural networks. We propose a Midway Neural Network (MNN) to avoid both manual feature engineering and the re-preprocessing of event logs. MNN embeds an input feature vector from a particular time window into a dense representation and memorizes these midway representations for incremental training and prediction. The experimental results demonstrated that the proposed method minimized human intervention, decreased the time for training and scoring, and decreased the memory and storage costs while maintaining a similar modeling performance compared to traditional solutions. We hope that our insights and knowledge can inspire colleagues who are working on similar problems.
Fractal IoT: A Scalable IoT Framework for Energy Management in Connected Buildings

Reported status of traditional energy and renewable energy resources indicate that their shortage is increasing at a rate twice that of the energy demand. This has motivated researchers to explore techniques for efficient, systematic management of energy. In this work we propose a fractal IoT architecture for smart community to addressed the challenges such as variation of energy availability, accessibility and demand. The architecture is deployed and tested in several scenarios such as Smart Building Smart Grid and Microgrid. A solar Microgrid setup is installed in a tribal village in Kerala, India consisting of 42 homes. This paper explains how the different features are integrated to develop smart buildings, and self-sustainable autonomous neighborhood's using these multiple case studies. The experimental result shows that the system is able to achieve the IoT features such as adaptability, interoperability and scalability by providing consistent performance in the hostel buildings with 800 occupants as well as homes totaling of 126 occupants in the tribal village.
Setting up the mediboard HIS for use in hospital routine

After conducting a comparative study of nine different open source hospital information systems (OSHIS): we used the DeLone & McLean model to evaluate the quality of information systems, and we used the SQALE method for the technical evaluation of source code, and we used the statistics published by Open Source communities referential to compare OSHIS related activities. We were able to choose MediBoard, which has a minimal technical debt of 42.16%. The activity statistics proved the potential of MediBoard with 2381 commits and 12 contributions in 2016. But this SIH would not yet have a standard usability. In fact, the current installation of MediBoard only allows 3 modules out of 63. To resolve this problem, we have used ISO 12207: the software life cycle standard, and we used ISO 14764: the software maintenance standard to have a complete and standard HIS. As a result, and after the implementation of our solution, we have been successful in making the usability of MediBoard standard.
A survey on hand biometry

Using hand to perform person recognition is a very old technique used since prehistoric era. This paper proposes an overview of different methods used in hand biometric systems. Modalities offered by the hand that can be used instead of the classical fingerprint are presented, and advantages and drawbacks of each modality are highlighted. Different concepts and techniques of multibiometric hand systems, which combine different biometric traits to increase recognition rate, are also exposed. Finally, an accuracy comparison of different state-of-art methods is provided, and examples of commercialized multibiometric systems are introduced.
Learning job representation using directed graph embedding

In recent years, embedding technologies have gained popularity in many areas of machine learning, such as NLP, computer vision, information retrieval, etc. In this paper, we propose a latent representation of job positions consisting of job title and company pairs, which can capture not only similarity relations but also ordering relations among job positions. We first construct a directed graph of job positions from the user's job transition history in the resume data, then we train the job position embedding using an asymmetric relation preserving graph embedding algorithm. Experimental results on a career move prediction task using real-world data set demonstrated that the proposed embedding solution can outperform state-of-the-art embedding methods.
Landmark-based Automated Guided Vehicle Localization Algorithm for Warehouse Application

Automated guided vehicle (AGV) is a solution for warehouse goods transportation, but robot localization is crucial for this application and existing methods are expensive. Therefore, in this paper, a low-cost landmark based AGV algorithm localization algorithm with single camera is proposed for warehouse application. The proposed algorithm includes the computer vision algorithm to recognize the landmark and estimate the distance between the landmark and AGV with single camera. Previous localization algorithm based on triangulation is using three landmarks for localization, the proposed localization algorithm uses only two landmarks which is based on concept of intersection of two circles. The landmarks in the scene were detected with Canny edge detection method and transformed back to straight square from skewed image with perspective transform to provide consistent landmark recognition result. The landmark then was recognized with Tesseract open source character recognition library and custom trained database. The performance of the proposed algorithm was evaluated using images captured by a single camera setup on a trolley and maneuvered through the library and laboratory at Universiti Sains Malaysia with landmarks. The recognition accuracy for landmark is 93.26% overall. The average error of the localization algorithm was 237.29mm and standard deviation 184.27mm. As a conclusion, landmark based AGV localization algorithm for warehouse application was successfully developed.
Non-invasive Imaging the Relative Changes in Cerebral Blood Volume during Total Aortic-arch Replacement Using Electrical Impedance Tomography: a First Study

Total aortic-arch replacement (TAR) is one of the major cardiovascular surgeries, which is by far the most effective emergency treatment of Acute Aortic Dissection type-A (AADA). In TAR, cardiopulmonary bypass is established and the aortic arch is replaced and reconstructed. Though hypothermic circulatory arrest (HCA) and unilateral antegrade cerebral perfusion (uACP) are often used for brain protection, parts of the brain may still encounter insufficient blood supply. With prolonged HCA time, this insufficiency causes brain injuries. Currently, there lacks an effective, real-time cerebral imaging technique that can be used in cardiac surgeries. Electrical impedance tomography (EIT) is a low-cost, non-invasive imaging method, which is sensitive to the bio-electrical impedance of tissues. In this study, we are the first using electrical impedance tomography (EIT) intraoperatively to image and monitor the relative changes in cerebral blood volume (rc-CBV). A subject with AADA is monitored using EIT in TAR surgeries and EIT images during the HCA + uACP period are reconstructed and analyzed. A blood factorization model was proposed to estimate the relative changes in blood proportion using EIT images. We found that (1) EIT images are sensitive to the changes in the rate of cerebral perfusion. (2) During uACP, the right hemisphere is perfused directly via cannulation into the right subclavian artery and the circulation to the left cerebral is mainly compensated through the circle of Willis. EIT and rc-CBV images show relatively high impedance and low blood ratios in the left hemisphere, which indicates that the patient may have insufficient collateral circulation. (3) EIT has moderate spatial resolutions, which provides additional information regarding the pathological changes in different regions deep in the brain. We conclude that cerebral EIT is a promising imaging method and it may provide early warning signs regarding insufficient cerebral blood supply in TAR surgeries.
Automatic Detection of Major Depressive Disorder via a Bag-of-Behaviour-Words Approach

In recent years, machine learning has been increasingly applied to the area of mental health diagnosis, treatment, support, research, and clinical administration. In particular, using less-invasive wearables combined with the artificial intelligence to monitor, or diagnose the mental diseases has tremendous needs in real practice. To this end, we propose a novel approach for automatic detection of major depressive disorder. Firstly, spontaneous activity physical data are recorded by a watch-type device equipped with an activity monitor. Subsequently, a bag-of-behaviour-words approach is applied to extract higher representations from the raw sensor data in an unsupervised scenario. Finally, a support vector machine is selected as the classifier to make the predictions on screening the major depressive disorder. There are 69 healthy control subjects, and 14 major depressive disorder patients involved in this study. The experimental results demonstrate the effectiveness of the proposed method in a rigorous subject-independent test, which achieves an unweighted average recall at 59.3 % (an accuracy of 66.0 %). This unweighted average recall significantly (p < .05, onetailed z-test) outperforms human hand-crafted features with an unweighted average recall at 53.6 % (an accuracy of 61.7 %).
Blockchain technology impact on the maritime supply chain

Blockchain technology is an emergent technology that has been implemented in different areas: finance, maritime industry, trade, education, land registration and supply chain management. The field we are discussing in this paper is the maritime industry. This paper will discuss the impact of the supply chain management, challenges and how Blockchain technology can solve them.
Whole Genome Sequence Analysis of Influenza a Virus from Four Outbreaks between 1968-2013

Influenza A is a rapidly mutating virus that can be easily transmitted in both humans and animals. Influenza A is divided into subtypes based on two glycoproteins in the virus: the hemagluttinin (HA) and the neuraminidase (NA). The virus has been the cause of many epidemics and pandemics throughout history and thus, the utilization of vaccines against the virus is paramount. The study used the program MUSCLE to determine the evolution and mutation of influenza A pathogens. Through this, it was found that the eight segments that make up the influenza A pathogens produced different levels of mutations with some segments being highly conserved and others undergoing major changes. The highly conserved regions in segments 2 and 3 are good targets in developing new vaccines. Genes encoding ofr surface proteins were highly mutated across the strains and prompts vaccine developers to continually update their viral epitope targets. Further studies are encouraged to identify all genes in the highly conserved regions in the hopes of finding a gene product on the surface of the virus that can be locked on to as an epitope target of vaccine development.
A Circle of Friends: Persuasive Tools to Improve Heart Health

Cardiovascular disease (CVD) is the leading causes of death in the United States and worldwide. While CVD risk factors are well-known and many can be changed with diet and exercise, more research is needed to understand how to design effective interventions that help patients reduce CVD risk. In this paper, we present the results of a content analysis of the Health Freedom Circle of Friends (COF) Walking Program, a community-based health program run by a public health non-profit that has been shown to reduce CVD risks. We examine the design to better understand the persuasive tools used as well as parts of the design that might benefit from a technological intervention.
On the Bottleneck Structure of Congestion-Controlled Networks

In this paper, we introduce theTheory of Bottleneck Ordering, a mathematical framework that reveals the bottleneck structure of data networks. This theoretical framework provides insights into the inherent topological properties of a network in at least three areas: (1) It identifies the regions of influence of each bottleneck; (2) it reveals the order in which bottlenecks (and flows traversing them) converge to their steady state transmission rates in distributed congestion control algorithms; and (3) it provides key insights into the design of optimized traffic engineering policies. We demonstrate the efficacy of the proposed theory in TCP congestion-controlled networks for two broad classes of algorithms: Congestion-based algorithms (TCP BBR) and loss-based additive-increase/multiplicative-decrease algorithms (TCP Cubic and Reno). Among other results, our network experiments show that: (1) Qualitatively, both classes of congestion control algorithms behave as predicted by the bottleneck structure of the network; (2) flows compete for bandwidth only with other flows operating at the same bottleneck level; (3) BBR flows achieve higher performance and fairness than Cubic and Reno flows due to their ability to operate at the right bottleneck level; (4) the bottleneck structure of a network is continuously changing and its levels can be folded due to variations in the flows' round trip times; and (5) against conventional wisdom, low-hitter flows can have a large impact to the overall performance of a network.
A systematic literature review on intelligent user interfaces: preliminary results

The user interfaces promote the interaction with the software system to achieve the users' goals. In this way different types of interaction are provided, such as direct manipulation, web UI or tangible interaction. These interfaces have evolved, including artificial intelligence and adaptation mechanisms to answer the evolution of the technological areas. From this evolution emerged the intelligent user interfaces that aim to be more effective, efficient, and natural. Considering the importance of IUI nowadays, we have performed a systematic literature review to investigate the design trends of IUIs in the context of contemporary software systems, such as software systems based on internet of things (IoT) or dedicated to smart cities. Preliminary results show which models and technologies are most used to develop IUIs, and which application domain is mostly represented.
Achieving Efficient Routing in Reconfigurable DCNs

With the fast growth of cloud services and network scales, the heavy and highly dynamic traffic demands pose great challenges to the efficient traffic engineering in today's data center networks (DCNs) [21]. The DCN flows can be broadly classified into two main categories: delay-sensitive small flows (e.g., queries or realtime small messages) and throughput-sensitive large flows (e.g., the backup traffic). In general, more than 80% flows in data centers are small flows, while the majority of the traffic volume is contributed by the top 10% large flows [3, 7]. To handle the mixed traffic, today's data centers [1, 14] generally follow the tree-based topologies (e.g., fat-tree) and take the load-agnostic routing strategies based on random path selection (e.g., ECMP1) [14, 19]. Although it is applicable for routing small flows which are highly random, these strategies are likely to route several large flows through the same output link and lead to long-lived congestions [2, 8]. With the limited switch buffer occupied by large flows for a long time, small flows are reported to experience one order of magnitude larger delay, which compromises the performance of DCNs and makes the users suffer [3].
BK.Synapse: A scalable distributed training framework for deep learning

Training neural networks efficiently is a thoroughly-researched topic that plays an important role in their adoption. Major advancements have been made, including the use of multiple nodes to further decrease training time. However, training at scale usually means adding on multiple layers of complex deployment logic and parallelization concerns, distracting researchers from the core of their algorithms. This paper presents a framework called BK.Synapse that can facilitate distributed training while maintaining clarity, simplicity, and user-friendliness. The design is modular, allowing flexible and easy deployment on a variety of hardware specifications. The framework is benchmarked in a case study: training a neural network for an object detection problem. Our results show a good amount of improvements over conventional training, with very few modifications to the existing codebase. The resulting model also performs relatively well upon further testing.
Interaction Models for Detecting Nodal Activities in Temporal Social Media Networks

Detecting nodal activities in dynamic social networks has strategic importance in many applications, such as online marketing campaigns and homeland security surveillance. How peer-to-peer exchanges in social media can facilitate nodal activity detection is not well explored. Existing models assume network nodes to be static in time and do not adequately consider features from social theories. This research developed and validated two theory-based models, Random Interaction Model (RIM) and Preferential Interaction Model (PIM), to characterize temporal nodal activities in social media networks of human agents. The models capture the network characteristics of randomness and preferential interaction due to community size, human bias, declining connection cost, and rising reachability. The models were compared against three benchmark models (abbreviated as EAM, TAM, and DBMM) using a social media community consisting of 790,462 users who posted over 3,286,473 tweets and formed more than 3,055,797 links during 2013–2015. The experimental results show that both RIM and PIM outperformed EAM and TAM significantly in accuracy across different dates and time windows. Both PIM and RIM scored significantly smaller errors than DBMM did. Structural properties of social networks were found to provide a simple and yet accurate approach to predicting model performances. These results indicate the models’ strong capability of accounting for user interactions in real-world social media networks and temporal activity detection. The research should provide new approaches for temporal network activity detection, develop relevant new measures, and report new findings from large social media datasets.
Comparison of an Optimized Fractional Order Fuzzy and Fuzzy Controllers Based MPPT using PSO for Photovoltaic Applications

This paper presents comparison of an improved MPPT controller using particle swarm optimization (PSO) tuned fractional order fuzzy logic controller (FO-FLC) and conventional fuzzy logic controller for photovoltaic applications. The optimization was carried out in two steps: the first step compute optimal values of scaling factors and fractional operator while the second step optimize Controller output membership functions. The optimization design objective is to minimize Root Mean of Squared Error (RMSE) between a measured power and desired maximum power. The optimal fractional operator shortens the tracking time and eliminate oscillations around MPP. The evaluation of the performance of the proposed controller and the simulations were carried out and the results shows that the FO-FLC provides better performance in terms of tracking speed and accuracy at different atmospheric conditions.
Designing an Open-hardware Remotely Controllable Phototurbidostat for Studying Algal Growth

Keeping an algal culture at a constant turbidity requires expensive and complex devices. We designed a low-cost, user friendly but also highly configurable phototurbidostat using 3D-printing, open-source software and electronics. The device is able to monitor in real time a culture in photobioreactor, and dynamically adjust the conditions to maintain the turbidity at a desired value. It can accommodate to a large set of volumes or laboratory equipments with little effort thanks to its modular and scalable design. Each module (such as light, pumps or sensors) are autonomous and controllable via Wi-Fi. Furthermore, our phototurbidostat is fully open-source and can be remotely controlled by a smartphone or a computer via a web-based graphical user interface. Finally, the device can be reproduced easily for a cost ranging between 1/20th and 1/50th of the price of a classical commercial device.