Miracle-dz/UniFAR-FTUs huggingface dataset:
NBER WORKING PAPER SERIES TECHNOLOGY , INFORMATION AND THE DECENTRALIZATION OF THE FIRM Daron Acemoglu

This paper develops a framework to analyze the relationship between the diffusion of new technologies and the decentralization decisions of firms. Centralized control relies on the information of the principal, which we equate with publicly available information. However, the manager can use her informational advantage to make choices that are not in the best interest of the principal. As the available public information about the specific technology increases, the trade-off shifts in favor of centralization. We show that firms closer to the technological frontier, firms in more heterogeneous environments and younger firms are more likely to choose decentralization. Using three datasets of French and British firms in the 1990s we report robust correlations consistent with these predictions. Daron Acemoglu MIT Department of Economics E52-380B 50 Memorial Drive Cambridge MA 02142-1347 and NBER daron@mit.edu Philippe Aghion Department of Economics Harvard University Cambridge, MA 02138 and NBER paghion@fas.harvard. edu Claire Lelarge National Institute of Statistics and Economic Studies Direction des Études et Synthèses Économiques 15 Boulevard Gabriel Peri 92244 Malakoff Cedex, FRANCE John Van Reenen Department of Economics London School of Economics Houghton Street London WC2A 2AE UNITED KINGDOM Fabrizio Zilibotti Institute for International Economic Studies Stockholm University SE 10691Stockholm SWEDEN
Automatic offline trajectory generation for surface coverage problem from a 3D drawing

Surface coverage with a robot manipulator is widely used in different industrial processes. For this reason having an automated way to program these robots is really important.In this work we implement an iterative algorithm to automate the process of trajectory generation for surface coverage, given the 3D model of the target object, with a particular focus on spray painting process.
Acoustic Scene Classification Using Deep Learning-based Ensemble Averaging

In our submission to the DCASE 2019 Task 1a, we have explored the use of four different deep learning based neural networks architectures: Vgg12, ResNet50, AclNet, and AclSincNet. In order to improve performance, these four network architectures were pre-trained with Audioset data, and then ﬁne-tuned over the development set for the task. The outputs produced by these networks, due to the diversity of feature front-end and of architecture differences, proved to be complementary when fused together. The ensemble of these models’ outputs improved from best single model accuracy of 77.9% to 83.0% on the validation set, trained with the challenge default’s development split. For the challenge’s evaluation set, our best ensemble resulted in 81.3% of classiﬁcation accuracy.
Predicting Voting Behavior Using Digital Trace Data

A major concern arising from ubiquitous tracking of individuals’ online activity is that algorithms may be trained to predict personal sensitive information, even for users who do not wish to reveal such information. Although previous research has shown that digital trace data can accurately predict sociodemographic characteristics, little is known about the potentials of such data to predict sensitive outcomes. Against this background, we investigate in this article whether we can accurately predict voting behavior, which is considered personal sensitive information in Germany and subject to strict privacy regulations. Using records of web browsing and mobile device usage of about 2,000 online users eligible to vote in the 2017 German federal election combined with survey data from the same individuals, we find that online activities do not predict (self-reported) voting well in this population. These findings add to the debate about users’ limited control over (inaccurate) personal information flows.
Sound Event Detection and Direction of Arrival Estimation using Residual Net and Recurrent Neural Networks

This paper presents deep learning approach for sound events detection and localization, which is also a part of detection and classification of acoustic scenes and events (DCASE) challenge 2019 Task 3. Deep residual nets originally used for image classification are adapted and combined with recurrent neural networks (RNN) to estimate the onset-offset of sound events, sound events class, and their direction in a reverberant environment. Additionally, data augmentation and post processing techniques are applied to generalize and improve the system performance on unseen data. Using our best model on validation dataset, sound events detection achieves F1-score of 0.89 and error rate of 0.18, whereas sound source localization task achieves angular error of 8 ° and 90% frame recall.
Modulation-Based Simultaneous Wireless Information and Power Transfer

Due to the rapid growth of IoT and other connected devices, developing battery-less and/or self-sustainable devices to enable green communication is crucial. This letter presents a practical simultaneous wireless information and power transfer (SWIPT) scheme based on an <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>-ary quadrature-amplitude modulation (QAM) modulation based technique (<inline-formula> <tex-math notation="LaTeX">${MS} $ </tex-math></inline-formula>). The proposed scheme employs hybrid constellation shaping system (HCS) to improve the spectral efficiency. We compare <inline-formula> <tex-math notation="LaTeX">${MS} $ </tex-math></inline-formula> with the traditional power splitting scheme based SWIPT (<inline-formula> <tex-math notation="LaTeX">${PS} $ </tex-math></inline-formula>) technique. We demonstrate that our proposed scheme outperforms <inline-formula> <tex-math notation="LaTeX">${PS} $ </tex-math></inline-formula> in terms of the error rate performance. <inline-formula> <tex-math notation="LaTeX">${MS} $ </tex-math></inline-formula> scheme is mainly focused on improving the reliability of low powered IoT sensors and it offers better achievable information rate at operating SNR region as compared to that of <inline-formula> <tex-math notation="LaTeX">${PS} $ </tex-math></inline-formula>. Those improvements come at the expense of a slight reduction in the maximum achievable spectral efficiency.
Optimal sizing and operating strategy of a stand‐alone generation–load–storage system: An island case study

Component sizing optimization and technoeconomic feasibility studies for stand‐alone power systems have been actively discussed in recent years. However, most models and studies overlook the importance of individual component performance, input data resolution and their integration with the system. This article compares the rapidly evolving battery storage technology applications using the context of a stand‐alone system, through a case study on Bruny Island, Tasmania, Australia, where the current electricity infrastructure is approaching its end of life. We constructed a mixed‐integer linear programming model to achieve optimal balance between modeling accuracy and computational complexity. The model was constructed to obtain the optimal component sizing on the basis of minimum lifetime net present cost. It was shown in our work that any transition of islands to become fully stand alone should be carefully considered, where a limited grid connection could still provide significant value to the infrastructure, greatly reduce its stress over peak hours. This concept was illustrated further with another set of model scenarios aiming to explore the performance of battery storage systems with different power output allowances, limited by the time‐series data resolution, were developed. It was shown that the selection of incomplete input data cycle, reliance on standardized average values and utilization of low resolution time‐series data could heavily skew the results of a model, as the use of incomplete dataset and oversimplified average values subtly conceal some of the critical sizing and operation issues in a technoeconomic feasibility study, impacting the accuracy of the model to reflect the real problem.
Joint Beamforming Design and Resource Allocation for Terrestrial-Satellite Cooperation System

In this paper, we investigate a multicast beamforming terrestrial-satellite cooperation system to optimize the communication capacity and quality of service. Different from traditional link-based terrestrial network, we design the terrestrial and satellite beamforming vectors cooperatively based on the required contents of users in order to realize more reasonable resource allocation. Meanwhile, the backhaul links between content provision center and satellite and base stations are limited, and the users always need high quality of service, considering these, our object is maximizing the sum of user minimum ratio under the constraints of resource allocation, backhaul link and quality of service in reality. We first formulate the optimization problem and propose a joint optimization iterative algorithm to design the beamforming vectors of satellite and base stations cooperatively. Then, to obtain the global optimum solution, we propose a Bound-based algorithm and solve the optimization problem by shrinking the upper bound and lower bound of the optimization feasible region. To decrease the complexity, we then design a heuristic scheme to solve the problem. The simulation results show that, our proposed cooperative optimization algorithms have better performance than non-cooperative methods, and the heuristic scheme has little poor performance but has significant advantage in low complexity.
Plagiarism Detection Application Uses Winnowing Algorithm with Synonym Recognition for Indonesian Text Documents

Plagiarism is the act of recognizing someone else's work as a result of personal work without the original owner's permission work. Plagiarism in the form of documents has happened a lot in this digital era. In response to this, through this scientific work a system will be developed that can be used for detect plagiarism between text documents, namely Rejecting Algorithms with Synonym Recognition. Winnowing Algorithm is a document fingerprint method that is used to detect similarities between text documents using hashing techniques. This algorithm was chosen because Reject is one of the best algorithms to get the value of similarity between document text both in terms of accuracy and the performance. From the results of testing in table 8, the conclusion is that the greater the number of grams and window=2 are used, the process time varies and the percentage of similarity varies, tends to decrease. And also by adding the text processing process and the synonym recognition varies greatly increasing the processing time and decreasing the percentage of similarity.
Transition from Paper to Electronic Nursing Documentation in Residential Aged Care: an Actor Network Theory Analysis

Objectives: To examine the role of technology when introduced into the specific setting of residential aged care and then analyse the associated changes to this complex socio-technical network of human and technology actors on the introduction of this technology using the rich lens of Actor Network Theory. Methods: An exploratory qualitative single case study was conducted. The specific focus being the implementation of a nursing information system in an aged care context, i.e. the transition from paper-based nursing documentation to electronic nursing documentation. A series of 19 semi structured interviews with facility managers, nursing coordinators, and the nursing and care staff were conducted. The collected data were analysed using standard qualitative techniques such as thematic analysis and a priori themes were developed from the application of Actor Network Theory. Results: A priori themes coupled with emergent themes served to highlight the impact of a disruptive technology solution into a complex context. Conclusion: An Actor Network Theory analysis enables a rich theoretical lens to be used to examine the introduction of a disruptive technology into a complex context. On such examination critical success factors were identified as well as key barriers. Moreover, people issues were found to be central to the success of such a solution.
Efficient Event Scheduling of Network Update

Changes in network state are a common source of instability in networks. An update event typically involves multiple flows that compete for network resources at the cost of rescheduling and migrating some existing flows. Previous network updating schemes tackle such flows independently, rather than as the entity of an update event. They only optimize the flow-level metrics for the flows involved in an update event. In this paper, we present an event-level abstraction of network update that groups flows of an update event and schedules them together to minimize the event completion time (ECT). We then study the scheduling problem of multiple update events for achieving high scheduling efficiency and preserving fairness. The designed least migration traffic first (LMTF) method schedules all update events in the FIFO order, but it avoids head-of-line blocking by randomly fine-tuning the queue order of some events. It can considerably reduce the update cost, the average, and tail ECTs of update events. In addition, we design a general parallel-LMTF (P-LMTF) method to guarantee fairness and further improve scheduling efficiency among update events. This improves the LMTF method by opportunistically updating multiple events simultaneously. The comprehensive evaluation results indicate that the average ECT of our approach is up to $10 \times $ faster than the flow-level scheduling method for network update events, and its tail ECT is up to $6 \times $ faster. Our P-LMTF method incurs a 75% reduction in the average ECT compared with FIFO when the network utilization exceeds 70%, and it achieves a 42% reduction in tail ECT.
Failure-Based Controller Placement in Software Defined Networks

Software Defined Networking (SDN) takes out the control plane of the network elements and distributes it to separate entities named as controllers. The main aim of the Controller Placement Problem (CPP) is to compute the optimum locations for controller deployment and assignment of switches to respective controllers. Every controller failure results in disconnection of switches from the controller leading to reassignment of switches to one or more of the active controllers. In this paper, we propose a controller placement strategy for multiple link failures, considering the network properties, to minimize the worst case latencies. We propose the solution using decision making under uncertainty (and non-competitive situations). Our objective is to find the optimum positions in the network for controller placement with reduced Maximum Worst Case Latency (MWCL) and maximum utilization of the controller. In addition, we have proposed the constraint-based solution for reassignment of switches to active controllers in the network. We have evaluated the proposed methodologies on various network topologies from Internet Topology Zoo. It is found that the decision making under uncertainty provides a better solution on CPP as compared to the existing mathematical methods for any SDN network topology.
Steady Flow Through Modeled Glottal Constriction

The airflow in the modeled glottal constriction was simulated by the solutions of the Navier-Stokes equations for laminar flow, and the corresponding Reynolds equations for turbulent flow in generalized, nonorthogonal coordinates using a numerical method. A two-dimensional model of laryngeal flow is considered and aerodynamic properties are calculated for both laminar and turbulent steady flows. Three configurations of vocal folds, corresponding to convergent, parallel or rectangular, and divergent glottis are used to study the effects of glottal shape on the airflow. Results are reported for several types of calculations. Results are obtained for the velocity and pressure distributions, and friction coefficient and turbulent kinetic energy in the constriction. Flow separation was found to exist even at low Reynolds numbers, and the separation region grew in size with increasing Reynolds number. The glottal shape has a marked influence on the flow pattern and the pressure drop.
Design and Research of SDN Unified Controller in Large Data Center

Firstly, this paper introduces the current situation and requirements of software defined network (SDN) controller in large data center, and then analyzes the mainstream SDN controller system, and then summarizes the development trends of the data center and SDN controller system. Next, this paper mainly introduces the design architecture and implementation technology of the SDN unified controller. Finally, it summarizes the advantages of the SDN unified controller system and discusses the challenges of future work on the project.
Android-based Image and Video Steganography System

Steganography is a form of security measure wherein confidential information is hidden within a cover media. This study aims to contribute to the security of digital information, particularly of audio data using image and video steganography. A total of six technique combinations were derived from three-pixel location strategies and spatial domain embedding in order to successfully mask and impose audio data into cover images and videos. The final prototype comes in the form of an integrated system that operates on three environments. The first platform is an android application, which serves as the input/output gateway, followed by a Matlab program which serves as the main processor, and a server that allows two-way exchange of information at both ends. The goal of the prototype is to successfully hide the audio data into the cover media with minimal to negligible visual and audible changes between the original files and output files. To evaluate the system’s performance, both qualitative and quantitative bases are used. Quantitative assessment includes the MSE and PSNR of the steganography output, the BER and accuracy of the recovered audio data. Meanwhile, qualitative assessment is conducted in the form of a survey. Results from quantitative and qualitative assessment strongly verify that the proposed integrated system is verified and hereby recognized as consistent, reliable and accurate.
ARTIFICIAL INTELLIGENCE BASED PERSONAL ASSISTANT

—Many people like business men, executives and management persons need personal assistant to help them with their day to day tasks. Personal assistant perform many tasks including replying to emails, managing appointments, scheduling different events etc. Now a days concept of virtual intelligent personal assistant’s concept is quite popular. Virtual intelligent personal assistant performs tasks like playing music, dialling phone numbers, searching information on Internet etc. Concept of smart equipments like switching on and off lights and fans etc is also popular. The need of a reliable low cost system has remained an ever desired requirement. This paper combines both concept of personal assistant and control of home appliances. This paper is based on interactive personal assistant capable of processing voice commands given by the user to execute the desired operations like search anything from the internet, fetching stock market information, weather report, basic calculations, opening mailbox, etc. With the help of IOT, proposed system will be able to control home appliances (Smart home concept), thereby making our lives easier and will prove to be a boon for the disabled.
Adversarial robustness of linear models: regularization and dimensionality

Many machine learning models are sensitive to adversarial input, meaning that very small but carefully designed noise added to correctly classiﬁed examples may lead to misclassiﬁcation. The reasons for this are still poorly understood, even in the simple case of linear models. Here, we study linear models and oﬀer a number of novel insights. We focus on the eﬀect of regularization and dimensionality. We show that in very high dimensions adversarial robustness is inherently very low due to some mathematical properties of high-dimensional spaces that have received little attention so far. We also demonstrate that—although regularization may help—adversarial robustness is harder to achieve than high accuracy during the learning process. This is typically overlooked when researchers set optimization meta-parameters.
A Generative Dialogue System for Reminiscence Therapy

With people living longer than ever, the number of cases with neurodegenerative diseases such as Alzheimer’s or cognitive impairment increases steadily. In Spain it affects more than 1.2 million patients and it is estimated that in 2050 more than 100 million people will be affected. While there are not effective treatments for this terminal disease, therapies such as reminiscence, that stimulate memories of the patient’s past are recommended, as they encourage the communication and produce mental and emotional benefits on the patient. Currently, reminiscence therapy takes place in hospitals or residences, where the therapists are located. Since people that receive this therapy are old and may have mobility difficulties, we present an AI solution to guide older adults through reminiscence sessions by using their laptop or smartphone. Our solution consists in a generative dialogue system composed of two deep learning architectures to recognize image and text content. An Encoder-Decoder with Attention is trained to generate questions from photos provided by the user, which is composed of a pretrained Convolution Neural Network to encode the picture, and a Long Short-Term Memory to decode the image features and generate the question. The second architecture is a sequence-to-sequence model that provides feedback to engage the user in the conversation. Thanks to the experiments, we realise that we obtain the best performance by training the dialogue model with Persona-Dataset and fine-tuning it with Cornell Movie-Dialogues dataset. Finally, we integrate Telegram as the interface for the user to interact with Elisabot, our trained conversational agent.
Delay-Limited Rate-Compatible Protograph LDPC Codes

—This paper produces a family of rate-compatible protograph-based low-density parity-check (PLDPC) codes that have superior performance under strict constraints of short block length and low decoding iterations. The design of such practical codes is a challenging task since the constraints imposed by structured designs (PLDPC), rate-compatibility, small block length, as well as a small number of decoding iterations, are hard to meet simultaneously. As the block length and the number of decoding iterations decrease, the typical LDPC design based only on the coding threshold is no longer effective due to imperfection in the modeling of LDPC decoders in the short block length regime. We propose a code design method that takes the code simulations and the number of the decoding iterations as inputs to optimize the new codes. Analytical and simulation results conﬁrm that the new codes produced by the proposed approach outperform the state-of-the-art codes in a wide range of code rates. None of the newly optimized codes has the error-ﬂoor behavior even below the frame error rate of 10 − 5 or bit error rate of 10 − 6 .