Miracle-dz/UniFAR-FTUs huggingface dataset:
F-DR Net:Face detection and recognition in One Net

The face multi-task analysis is high-profile in recent years. Face detection and recognition are more challenging in one net. We present a new parallel network architecture for two face tasks in one net, achieving end-to-end face detection and recognition. Firstly, we train a better face detection network. Then, the selection of the shared layers has a signification impact on the result in speed and accuracy for recognition, so we determine the optimal shared layers by experiments. Finally, shared layers contains discriminative information for face recognition, and we put the recognition network under the shared layer of the detection network. We achieve parallel end-to-end face detection and recognition in one net, comprehensively evaluated this method on several face detection and recognition benchmark datasets, including the Labeled Faces in the Wild (LFW) and Face Detection Datasets and Benchmark (FDDB). We get better detection and recognition accuracy on LFW and FDDB, and achieve faster speed compared to other methods. Our results demonstrate the effectiveness of the proposed approach.
A Machine Learning Approach for the Classification of Indoor Environments Using RF Signatures

Efficient deployment of Internet of Things (IoT) sensors primarily depends on allowing the adjustment of sensor power consumption according to the radio frequency (RF) propagation channel which is dictated by the type of the surrounding indoor environment. This paper develops a machine learning approach for indoor environment classification by exploiting support vector machine (SVM) based on RF signatures computed from real-time measurements. Results obtained demonstrate that the combination of received signal strength (RSS) and channel transfer function (CTF) yields a classification accuracy of 83.0% for identifying the type of the indoor environment.
What is Programming? Putting all Together - A Set of Skills Required

Developing programming skills is not an easy task for teaching and learning. Recently, research has been made in order to study the difficulties that students have for learning programming. Other research proposals have suggested innovative methodologies and tools to support learning programming. However, difficulties still remain. In this paper we propose an over-all view of programming, putting all together the elements which are involved in the programming tasks. Whit this proposal we try to make conscience in teachers and students about considering all these elements and to choose the better ways for instruction. We started this initiative by introducing a set of skills, which are going to be validated by students enrolled in programming courses.
Great Idea, Now What? Three Essays Examining The Relationship Between Organization Design And Commercialization Of Knowledge

Innovation is a key driver of a firm’s overall performance. Within an organization, innovation involves multiple actors transforming a firm’s knowledge into a final market offering. How an organization is designed can shape this transformation by influencing actors’ behaviors and interactions. However, despite prior studies, our understanding of the relationship between organization design and innovation is somewhat limited. In this dissertation, I offer a framework in which I conceptualize innovation as a process consisting of upstream tasks around invention, and downstream tasks around product development and commercialization. This enables me to combine both knowledgeand incentivesbased views of the firm to develop a more complete theoretical understanding of the relationship between organization design and innovation. The design attribute I focus upon is the degree of organizational centralization. On the one hand, more centralized designs are associated with enhanced intra-organizational knowledge flows, which can enhance innovation. On the other hand, more decentralized designs are associated with higher observability of effort and facilitate the more effective use of incentives, which can increase innovation efforts. I empirically examine this trade-off in the context of the pharmaceutical industry. I use a unique dataset of firms’ patents, clinical trials, sales and organization structures supplemented by 61 interviews with senior managers from 28 of my sample firms. I find that greater decentralization while yielding higher numbers of inventions is associated with less original inventions, and fewer inventions progressing through the earlier stages of development. However, greater decentralization is associated with more inventions progressing through the later stages of development and greater sales of new products as a proportion of total sales. Further, I find that firms with decentralized Research & Development units are associated with a higher proportion of externally sourced inventions primarily driven by licensing. This dissertation contributes to the organization design and innovation literatures by highlighting where (in the organization) and when (in the innovation process) design choices can impact both how firms innovate as well as their innovation outcomes. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Management First Advisor Rahul Kapoor This dissertation is available at ScholarlyCommons: https://repository.upenn. edu/edissertations/3369
Magnetic Shielding and Packaging of STT MRAM

A magnetic shield was developed for the standard BGA MRAM DDRS chip to increase the magnetic interference tolerance from 100}}oe to more than 500 oetextbf{{. This enabled the low power and fast speed MRAM devices to operate in more rugged conditions. The shield used a soft ferromagnetic material to provide the passive shielding. In addition, the magnetic shield was a substrate level designed andit did not increase the package size significantly. This was expected to have a lower cost than the device level shielding which required to develop ferromagnetic material physical vapour deposition (spin coating, sputtering and plating) in the foundry. Index Terms}--- Magnetic shielding, MRAM packaging, STT-MRAM
A Coarse to Fine Indoor Visual Localization Method Using Environmental Semantic Information

In this paper, we focus on the camera localization problem using visual semantic information. In contrast to the state of the artworks which often use visual features to do localization, we here propose a coarse to a fine mechanism to localize the camera position. First, a semantic database including object information around the target environment is constructed using a deep learning method. Second, for the coarse step of the visual localization, we match class attributes of objects in the current frame to the object database and find candidate frames that have similar objects. Third, the most similar candidate frame to the current frame is selected by CNN features. For the fine step of localization, the final pose of the camera can be estimated using feature matching with semantic information. Compared to the state of the art visual localization methods, the proposed localization method based on semantic information has higher localization accuracy. Furthermore, the proposed framework is not only useful for visual localization, but also useful for other advanced tasks of robot, e.g., loop closing detection, object searching, and task reasoning.
Sensorimotor Cortex EEG signal classification using Hidden Markov Models and Wavelet Decomposition

In this paper, we present an offline dynamic classification technique based on wavelet decomposition and Hidden Markov Models (HMMs). We leverage the time-frequency analysis capability of wavelets as well as HMMs ability of modeling dynamic signals to classify the movement of hands based on EEG signals captured from the brain. We report our results with dataset IIb from BCI Competition 2008 provided by Graz University, and show an error reduction of 23% i.e. reduction from 34% to 26% error when compared with the best previously reported technique that used HMMs for brain signal classification. A comparison of performance with various types of wavelets combined with HMMs is also presented in this paper. Since wavelet decomposition increases feature dimensions, we anticipate that further improvement can be achieved by reducing the feature size by exploiting spatial correlation of data from EEG sensors.
Network Failure Detection and Autonomous Return Algorithms for a Crawler Mobile Robot Navigation

In this paper we present an algorithm for a mobile robot autonomous return. The algorithm involves a network failure detection module, which is based on analysis of incoming UDP packets. Simultaneous Localization and Mapping (SLAM) and path planning algorithms were used as an integral part of the autonomous return algorithm. The algorithms were integrated into Russian mobile robot Servosila Engineer, and experiments were conducted in order to determine the best configuration of the algorithm parameters.
A Lattice-Based Incremental Signature Scheme

A digital signature is a fundamental cryptographic primitive that is used heavily in information society for guaranteeing the authenticity of digital information. Incremental signature, introduced by Bellare, Goldreich, and Goldwasser (CRYPTO’94), is a digital signature that enables a signer to sign messages quickly when these messages are similar. It is extremely useful for authenticating, e.g., big data since there are huge messages in big data and many of them only have small differences. In this paper, we propose an incremental signature scheme from lattices. Our scheme is proven secure against adaptive chosen-message attacks in the standard model, assuming the $k$ -small integer solutions problem on lattices is intractable. We also conduct experiments to evaluate its efficiency. The experimental results demonstrate that our incremental signature scheme is effective for signing a series of messages with many overlaps.
Detecting NDP Distributed Denial of Service Attacks Using Machine Learning Algorithm Based on Flow-Based Representation

the rapid growth of the Internet usage has caused problem on Internet protocol address space. To solve the space issue of Internet Protocol version 4 addresses, Internet Protocol version 6 was created to expand the availability of address spaces. Internet Protocol version 6 is designed to overcome the main limitations of Internet Protocol version 4 including the lack of security and the exhaustion of Internet Protocol address space. Internet Protocol version 6 protocols are not well supported by Network Intrusion Detection System, as is the case with Internet Protocol version 4 protocols. Several data mining techniques have been introduced to improve the classification mechanism of Intrusion detection system. In addition, extensive researches indicated that there is no Intrusion Detection systems for Internet Protocol version 6 using advanced machine-learning techniques to ward distributed denial of service attacks. With the increasing adoption of Internet Protocol version 6, Internet Protocol version 6-unique security issues become more urgent to address. Unlike Internet Protocol version 4, Internet Protocol version 6 relies on Internet Control Message Protocol version 6 in neighbor discovery. This means that blocking Internet Control Message Protocol version 6 traffic to reduce the possibility of using it as an attack tool, is not a viable option in most scenarios. One of the security threats posed by Internet Control Message Protocol version 6 is its possible use in Denial of Service attacks. This paper introduces a machine-learning based system to detect Distributed Denial of Service attacks that employ Neighbor Discovery protocol by using Machine learning techniques, due to the severity of the attacks and the importance of Neighbor Discovery protocol in Internet Protocol version 6. Decision tree algorithm and Random Forest Algorithm have given the highest accuracy result in comparison to the other algorithms.
Pitch Range Estimation with Multi features and MTL-DNN Model

In speech communication, listeners can normalize the speaker’s pitch by estimating the speaker’s pitch range, even with a very short speech signal. Previous studies about pitch range estimation usually detected the maximum and minimum pitch values with large-scale speech samples through F0 distribution fitting. Based on the finding that spectral information played a role in the perception process of pitch range, this study used the multitask learning deep neural network (MTL-DNN) model to estimate pitch range from the perspective of spectral structure of a very short speech input. The results showed that, with the increase of the input speech length, the prediction performance was constantly improved. And the performance tended to be stable when the input speech length was about 300ms. The pitch range estimation performance of Fbank features outperformed that of MFCC features. With Fbank features, the mean absolute percentage errors (MAPE) and the mean absolute errors (MAE) of the ceiling was 10.16% and 24.57Hz respectively. And the MAPE and MAE of the floor was 11.39% and 11.77Hz respectively. When the pitch information was added to the Fbank and MFCC features, the pitch range prediction performance was not significantly improved if the acoustic input length was more than 300ms. The results showed the feasibility of pitch range estimation model and confirmed the importance of spectral information in pitch range estimation.
A Novel Ramp-based Pulse Shaping Filter for Reducing Out of Band Emission in 5G GFDM System

Due to ever increasing demand of services in the field of mobile communication, the fourth generation based systems has reached a saturation stage. The world today is looking towards fifth generation (5G), which would open doors for a variety of new services. Generalized Frequency Division Multiplexing (GFDM) is a potential candidate for forming the physical layer of the 5G due to its appealing properties such as low out of band (OOB) radiation and optimal use of time-frequency resources. Various filters such as root raised cosine (RRC), Xia pulse, etc. are proposed in the literature for pulse shaping of GFDM waveform. These filters affect the amount of OOB radiations and symbol error rate (SER) performance of the system in different ways. There is always a trade-off between the two performance indices, especially in low latency scenarios. Thus, it is important to design a filter impulse response, which can provide an optimal solution so as to get the best out of the GFDM system. This paper proposes a novel ramp-based finite impulse response filter for pulse shaping the GFDM waveform in the frame structure of tactile internet. The proposed filter makes an effort to address the limitation of the RRC and Xia filters. For the three filters, the power spectral density is calculated with respect to different sub-carriers. The SER v/s signal to noise ratio performance is tested in additive white Gaussian noise (AWGN) channel. Simulation results show that a system with proposed ramp-based filter offers significantly low OOB radiation as compared to RRC filter and Xia pulse. Furthermore, the SER performance of the proposed ramp-based filter is superior to Xia pulse in AWGN channel model.
Automated consistency checking of UML/MARTE based software systems

Development of UML/MARTE based modelling technique is gaining popularity day by day. UML/MARTE model driven approach can be used to describe non real time software systems as well as timing constraints for real time software systems. However it is essential to ensure consistency among the different UML diagrams as it affects the quality of software system. The novelty of this paper lies in proposing comprehensive framework for automatic verification of inter diagram consistency based on UML/MARTE models. A simulator has been developed based on the formal specification to automatically detect inconsistencies at the early stage of design. The proposed methodology is applied and explained on a case study for substantiation of the approach.
Multi-Domain Neural Network Recommender

Multi-domain recommendation is adopted to alleviate data sparsity problem that hurts performance by utilizing information from other domains in recommendation system recently. Considering the powerful ability of knowledge extraction of neural network, we design a novel multi-branch network to discover sharing-pattern features and domain-specific features for multi-domain recommendation. Sharing-pattern features are general preference of user but values are distinct in every domain in our model which is quite different from present other practices. As others use same features as sharing factors among domains while we use same transformation as sharing pattern. Besides that, we conduct a non-linear procedure with probability to form final user latent factor rather than direct adding or multiplying in some works. Experiments on real-world dataset outperforming baseline methods shows effectiveness. Observing results, another finding is sparser domains have more room for improvement as they have more absorption from others.
High-Quality and Fast Dehazing Method Based on Modified Median Dark Channel

This paper presents an efficient dehazing method that can achieve a high quality as well as a fast execution. The original dehazing method based on median dark channel prior (MDCP) achieves a fast execution but has a critical disadvantage regarding the dehazing quality. By finding how the dehazing quality is degraded in the original MDCP-based method, the proposed method performs the median filtering in a modified way in order to mitigate the quality degradation. Experimental results show that the dehazing quality of the proposed method is much superior to that of the original MDCP-based method. The execution time of the proposed method is also very short.
Food Safety Traceability System Based on Blockchain and EPCIS

In recent years, food safety issues have drawn growing concerns from society. In order to efficiently detect and prevent food safety problems and trace the accountability, building a reliable traceability system is indispensable. It is especially essential to accurately record, share, and trace the specific data within the whole food supply chain, including the process of production, processing, warehousing, transportation, and retail. The traditional traceability systems have issues, such as data invisibility, tampering, and sensitive information disclosure. The blockchain is a promising technology for the food safety traceability system because of the characteristics, such as the irreversible time vector, smart contract, and consensus algorithm. This paper proposes a food safety traceability system based on the blockchain and the EPC Information Services and develops a prototype system. The management architecture of on-chain & off-chain data is proposed as well, through which the traceability system can alleviate the data explosion issue of the blockchain for the Internet of Things. Furthermore, the enterprise-level smart contract is designed to prevent data tampering and sensitive information disclosure during information interaction among participants. The prototype system was implemented based on the Ethereum. According to the test results, the average time of information query response is around 2 ms, while the amount of on-chain data and query counts are 1 GB and 1000 times/s, respectively.
Measurement Matrix Design Based on Compressed Sensing for DOA Estimation

It has been well known that Massive multiple-input-multiple-output (MIMO) radar can provide a large aperture and improve the direction of arrival (DOA) estimation performance. However, a significant increased data size will seriously reduce the computational efficiency of DOA estimation. In this paper, we propose an optimization algorithm to design the dimensionality reduction measurement matrix based on compressed sensing (CS), which can extremely reduce the computational complexity. Unlike adopted random projections, we choose the measurement matrix for DOA estimation by minimizing the overall mutual coherence between measurement matrix and spatial sparse dictionary. The optimization problem of designing the dimensionality reduction measurement matrix is non-convex. To solve this problem, an alternating iterative algorithm based on singular value protection is proposed. Moreover, we analyzed the computational complexity of DOA estimators with this dimensionality reduction scheme. Numerous results demonstrate that the proposed scheme has better DOA performance than random projection method and arbitrary selection method.
Coverage of Radio Wave Propagation at UI Campus Surrounding Using Ray Tracing and Physical Optics Near to Far Field Method

In the last few years the demands of communication and data transfer are growing rapidly. To achieve good signal quality and to increase data transfer speed, the site specific model in wireless communication is used to analyze the effect of geographical condition. In this paper we discussed the propagation model based on ray tracing and physical optics with near-to-far field method, in urban area modeling by considering the influence on transmitter height at 800 MHz, 2.3 GHz, and 38 GHz. The simulation was carried out for our campus area to model urban area. The result obtained from the simulation shows the path loss value increases as the transmitter’s height and the frequency increases. Path loss exponents obtained from urban areas model in line of sight condition are range from 1.8 – 2.2.
Improving the Capacity of a Mesh LoRa Network by Spreading-Factor-Based Network Clustering

LoRa is a low-power long-range IoT standard that uses the chirp spread spectrum technique, and we have strived to further extend its coverage by utilizing the direct device-to-device (D2D) links to construct a multi-hop relay network. In LoRa, the spreading factor (SF) is an important parameter, which not only provides great flexibility between the data rate and sensitivity but also presents a new dimension for multiple accesses. Our approach to improving the capacity of a multihop LoRa network is to attempt to off-load the data traffic into several subnets by utilizing this multiple-access dimension. Each subnet rooted at a sink node is allocated a specific SF on the basis of network clustering. This enables packet transmission in parallel with multiple SFs to become feasible. To allow such parallel transmissions, our considerations are: 1) ensuring the connectivity of all subnets; 2) off-loading the traffic according to the number of nodes, data rates, and network topologies of each subnet; and 3) shortening the airtime of each subnet by reducing the hop count. Toward these objectives, we present a tree-based SF clustering algorithm (TSCA) to conduct SF allocation in a multihop LoRa network. The TSCA focuses on balancing the airtime between the subnets while ensuring connectivity. Furthermore, we use simulations to show that our approach can significantly increase the network performance compared with other approaches. We additionally deploy a real-chip experiment to evaluate the feasibility of parallel transmission in practice.
A Case Study of EV Control on Slip Surface based on Driving Force Distribution and Hierarchical LQR

In this paper, we study a motion control of the electric vehicle when accelerate toward the slip surface. To attain the driving comfort, the control scheme have to be able to prevent the electric vehicle from yawing as same as utilize the in-wheel-motor. First study is driving force control distribution based on driving force control. Its use the idea of minimizing the driving force control input to prevent the system from the reduction of total driving force and the generation of yaw-moment. Another control scheme that use for compare is the global motion control which introduces with the hierarchical LQR, this control scheme proposed a new ways to analyze the motion in systematic ways that the system can be control in a hierarchical structure. This paper aims to show the difference between using both control scheme on the test scenario.