Miracle-dz/UniFAR-FTUs huggingface dataset:
Acoustic scene classification using a convolutional neural network ensemble and nearest neighbor filters

This paper proposes Convolutional Neural Network (CNN) ensembles for acoustic scene classification of tasks 1A and 1B of the DCASE 2018 challenge. We introduce a nearest neighbor filter applied on spectrograms, which allows to emphasize and smooth similar patterns of sound events in a scene. We also propose a variety of CNN models for single-input (SI) and multi-input (MI) channels and three different methods for building a network ensemble. The experimental results show that for task 1A the combination of the MI-CNN structures using both of log-mel features and their nearest neighbor filtering is slightly more effective than the single-input channel CNN models using log-mel features only. This statement is opposite for task 1B. In addition, the ensemble methods improve the accuracy of the system significantly, the best ensemble method is ensemble selection, which achieves 69.3% for task 1A and 63.6% for task 1B. This improves the baseline system by 8.9% and 14.4% for task 1A and 1B, respectively.
A 24/77 GHz Dual-Band Receiver for Automotive Radar Applications

A fully-integrated 24/77 GHz dual-band receiver is presented for automotive radar applications. The proposed receiver consists of a dual-band LNA, a dual-band sub-sampling PLL, and a wideband mixer, operating at 24 and 77 GHz alternatively. Implemented in 65 nm CMOS technology, the receiver achieves 760 and 1800 MHz bandwidth with 52 and 60 mW power consumption in 24 and 77 GHz operation modes, respectively. To the best of authors’ knowledge, this is the first fully-integrated CMOS 24/77 GHz dual-band receiver for automotive radar applications.
Application of cortical learning algorithms to movement classification towards automated video forensics

The need for proper and acceptable video forensics process is necessary due to the proliferation and advancement of video technologies in all aspects of our life, hence the legal system has heavily invested in this area. Also, the classification of the movements of objects detected from a video feed is an essential module to automate the forensic video process. Recently, new bio-inspired machine learning techniques have been proposed in the attempt of mimicking the function of the human brain. Hierarchical Temporal Memory (HTM) theory has proposed new computational learning models, Cortical Learning Algorithms (CLA), inspired from the neocortex, which offer a better understanding of how our brains function. This research aims to study the requirements of video forensic investigation and Police procedures to propose a new semi-automated post-incident analysis framework and to investigate the application of the CLA to movement classification towards an automated video forensics process. This research starts by reviewing the research related to police practices for video forensics as well as various proposed video forensic frameworks. Then a Questionnaire targeting CCTV/Video Forensics practitioners has been developed to capture the requirements for automating the video forensics process, this has been followed by proposing a new post-incident analysis framework. Then, a literature review covering state-of-the-art movement classification algorithms has been carried out. Finally, a novel CLA-based movement classification algorithm has been proposed and devised to classify the movements of moving objects in realistic video surveillance scenarios, and the test results have been evaluated. Tests applied on twenty-three videos have been conducted to detect movement anomalies in different scenarios. Additionally, in this study, the proposed algorithm has been evaluated and compared against several state-of-the-art anomaly detection algorithms. The proposed algorithm has achieved 66.29% average F-measure, with an improvement of 15.5%compared to the k-Nearest Neighbour Global Anomaly Score (kNN-GAS) algorithm. The Independent Component Analysis-Local Outlier Probability (ICA-LoOP) scored 42.75%, the Singular Value Decomposition Influence Outlier (SVD-IO) achieved 34.82%, whilst the Connectivity Based Factor algorithm (CBOF) scored 8.72%. The proposed models, which are based on HTM, have empirically portrayed positive potential and had exceeded in performance when compared to state-of-the-art algorithms
KNOWLEDGE MANAGEMENT STRATEGIES AND ORGANIZATIONAL PERFORMANCE IN ABU DHABI: A STRUCTURAL EQUATION MODELING (SEM) APPROACH

The aim of this study is to measure the knowledge management strategies and organizational performance in Abu Dhabi utilizing a structural equation modeling (SEM) approach. The research method of this research is a solely quantitative method where the total sample size is considered 331 employing simple random sampling. For this research, the structural model exhibited the relationships among the variables. The findings reveal that, knowledge management structure is positively related to knowledge management strategy, same to knowledge management practice is positively related to knowledge management strategy. However, knowledge management structure was found not really positively related to organizational performance. Unexpectedly, knowledge management practice has not also found significant either in terms of positively related to organizational performance. On the other hand, knowledge management strategy is positively related to organizational performance as well as reciprocal relationship found between knowledge management structure and knowledge management practice. This research has contributed to the existing knowledge by providing an empirically validated model which could be used to predict the organizational performance as a whole. Article visualizations:
Wideband Circularly Polarized Cross-Dipole Antenna With Parasitic Elements

A wideband circularly polarized (CP) cross-dipole antenna with parasitic elements is proposed. The proposed antenna consists of a pair of driven crossed strip dipoles, two kinds of parasitic elements (inner and outer parasitic elements), and a pair of vacant-quarter printed rings. The driven crossed strip dipoles can generate a CP mode due to the sequential phases given by the vacant-quarter printed rings. Another CP mode can also be stimulated by placing these parasitic elements to the edge of crossed strip dipoles. By combining with two CP modes of the cross-dipoles, the proposed antenna achieves a wideband CP performance. The measured results indicate that the proposed antenna exhibits wideband CP characteristics with an impedance bandwidth (IBW) of 77.6% (1.11 – 2.52 GHz) and axial-ratio bandwidth (ARBW) of 66% (1.25 – 2.48 GHz). The proposed antenna would be suitable for CP applications in wireless communication systems, such as the global positioning system (GPS; 1.575 GHz) and wireless broadband access service (WiBro; 2.3 – 2.39 GHz).
Design and Investigation of 7T2M-NVSRAM With Enhanced Stability and Temperature Impact on Store/Restore Energy

This paper proposes a seven-transistor–two-memristor (7T2M) nonvolatile static random access memory (NVSRAM). The designed 7T2M-NVSRAM is resilient to data loss while powered off and renders substantial read/write margins as compared to the conventional SRAM and the previous NVSRAM structures. One memristor is connected to the sources of driver transistors of 6T SRAM, and the motive of this memristor is to enhance the read and write stability. It is ascertained that the read margin is improved by 16%, and the write margin is improved by 30% in regards to the conventional SRAM, whereas another memristor is connected to the data node of the 6T SRAM along with an additional transistor, and this memristor enables the nonvolatile nature. Moreover, a temperature-dependent memristor model is employed to investigate the temperature effects on power dissipation along with storing and restoring energy during power-down and power-up conditions, respectively. The results reveal that the power dissipation rises ten times for the increase of every 100 K temperature, and both storing and restoring energies decrease at higher temperatures. The designed structure strengthens the applicability of SRAM in advance processor used for temperature sensor design and also enables it to be utilized in the persistent memory design.
A Survey of Delay-Based and Hybrid TCP Congestion Control Algorithms

Congestion control (CC) has a significant influence on the performance of transmission control protocol (TCP) connections. Over the last three decades, many researchers have extensively studied and proposed a multitude of enhancements to standard TCP CC. However, this topic still inspires both academic and industrial research communities due to the change in Internet application requirements and the evolution of Internet technologies. The standard TCP CC infers network congestion based on packet loss events which leads to long queuing delay when bottleneck buffer size is large. A promising solution to this problem is to use the delay signal (RTT or one-way delay measurements) to infer congestion earlier and react to the congestion before the queuing delay reaches a high value. In this survey paper, we describe the delay signal and the algorithms that completely or partially utilize this type of signal. Additionally, we illustrate standard CC and modern active queue management (AQM) principles and discus the interaction between AQM and the delay signal.
THE NEW APPROACH TO MEASURING COMPUTER LITERACY AT THE UWB

Nowadays computer literacy is one of the required conditions demanded by employer, who wants to success in today’s labor market. Unfortunately so called necessary minimum of knowledge is not exactly defined. In the Czech Republic computer literacy is an important part of basic education. Our educators face the difficult issue of assessing a level of computer literacy among our students. This case study compares two tools used for testing of computer literacy. For this study we selected two testing tools, the Original Testing System (OTS) and the Modified Testing System (MTS), and investigated them thoroughly by quantitative comparison. In our case study, this quantitative comparison of those testing tools was done using data collected from a freshman-level Word and Excel course in fall 2010 semester in the University of West Bohemia (UWB). Comparison was based on analysis of the performance of 138 students attending this course. We analyzed correlations between the scores from our two testing instruments. We conducted a paired sample t-test for the sake of comparison of the students’ performance between the examination scores. This study has limitations, which were discussed and based on them, recommendations for future works and directions were included.
Segmentation of Dashboard Screen Images: Preparation of Inputs for Object-based Metrics of UI Quality

Using object-based metrics to analyze design aspects of user interfaces (UI) is a suitable approach for the quantitative evaluation of the visual quality of user interfaces. Balance or Symmetry are examples of such metrics. On the other hand, we need to deal with the problem of a detection of objects within a user interface screen which represent the inputs for the object-based metrics. Today’s user interfaces (e. g., dashboards) are complex. They consist of several color layers, and it is complicated to segment them by well-known page segmentation methods which are usually used for the segmentation of printed documents. We also need to consider the subjective perception of users and principles of objects grouping (as Gestalt laws). Users usually group simple objects (graphical elements and shapes) into coherent visually dominant objects. We analyzed the experience of 251 users manually segmenting dashboard screens and designed a novel method for the automatic segmentation of dashboard screen images. The method initially focuses on the reduction of image colors which represents image layers. Then, it detects the primitives which makes a screen layout. Finally, the method processes the screen layout using the combination of the top-down and bottom-up segmentation strategy and detects visually dominant regions.
Practical Solutions to Save Bitcoins Applied to an Identity System Proposal

In a recent work by Augot et al. (2017), a scheme is proposed to build an identity system on top of the Bitcoin network. However, this proposal incurs very high costs since Bitcoin transactions require heavy fees. The current work introduces modifications to their scheme to make it more cost efficient while preserving its potential. Namely, we build on features of Bitcoin’s scripting language, which allows swapping coins between two compatible blockchains, and also on off-chain transactions.
A Stein-Papangelou Goodness-of-Fit Test for Point Processes

Point processes provide a powerful framework for modeling the distribution and interactions of events in time or space. Their flexibility has given rise to a variety of sophisticated models in statistics and machine learning, yet model diagnostic and criticism techniques remain underdeveloped. In this work, we propose a general Stein operator for point processes based on the Papangelou conditional intensity function. We then establish a kernel goodness-of-fit test by defining a Stein discrepancy measure for general point processes. Notably, our test also applies to non-Poisson point processes whose intensity functions contain intractable normalization constants due to the presence of complex interactions among points. We apply our proposed test to several point process models, and show that it outperforms a two-sample test based on the maximum mean discrepancy.
3-D NAND Flash Value-Aware SSD: Error-Tolerant SSD Without ECCs for Image Recognition

This paper proposes Value-Aware solid-state drive (SSD) with fast access speed and low power consumption by eliminating error-correcting code (ECC). Value-Aware SSD utilizes the error tolerance of image recognition application using a deep neural network (DNN) to enhance reliability. In a previous paper, which proposes Value-Aware SSD, fast ECC decoder is implemented and SSD is evaluated with the 32-bit floating-point data format. On the other hand, in this paper, the proposed Value-Aware SSD is analyzed with 32-bit and 8-bit fixed-point data format and achieves the higher reliability even without ECC by newly proposed two techniques, Critical Bit Error Reduction (CBER) and Middle & Lower Page Error Reduction (M&L-PER). CBER and M&L-PER are proposed for 32-bit and 8-bit data format of application, respectively. These techniques modulate the threshold voltage ( $V_{\mathrm {TH}}$ ) distribution of memory cells by recognizing the importance of each stored bit. By proposed CBER, as much as 15% bit error rate (BER) of NAND flash is allowed while the application provides high image recognition accuracy. Even if bit precision is truncated to 8 bit, 3.9% BER is accepted by M&L-PER. The fast read access and low power consumption are realized because ECC is not required. Finally, this paper analyzes the Value-Aware techniques with 3-D multi-level cell (MLC) NAND flash to compare the effects for 3D-MLC and triple-level cell (TLC) NAND flash.
Network topology with imperfect spectral templates on graphs

Traditionally, when talking about the use of graphs for signal processing, its topology has always been supposed known, from which multiple techniques have been developed over the years. Nowadays, a new mentality has appeared in which the knowledge of the topology of graphs is considered unknown. This change of focus has caused a whole wave of research on techniques to be able to estimate the connections between nodes from data collected from the graph itself. This thesis follows this new current of thought and creates a solid base of knowledge that later is applied in the design of filters. To begin with, the problem is formulated: identify the structure of a graph based on the information attached to its nodes. Following, a basic technique for identifying an optimal network structure minimizing its number of connections is developed. Analyzing the results obtained, it is decided to improve the technique to obtain a more robust algorithm in front of the different scenarios that can be found. Once a solution is found that works well for any case, procedures are investigated to be able to work with this new technique when working with few data. At the end of this thesis, we investigate the design of filters with graphs. Specifically, the design of an average filter is developed in order to investigate the load of a net. The techniques studied in this thesis are applied to investigate how the method to design filters would work when the topology of the graph has been estimated.
Real-time 3D Pose Estimation from Single Depth Images

To allow for safe Human-Robot-Interaction in industrial scenarios like manufacturing plants, it is essential to always be aware of the location and pose of humans in the shared workspace. We introduce a real-time 3D pose estimation system using single depth images that is aimed to run on limited hardware, such as a mobile robot. For this, we optimized a CNN-based 2D pose estimation architecture to achieve high frame rates while simultaneously requiring fewer resources. Building upon this architecture, we extended the system for 3D estimation to directly predict Cartesian body joint coordinates. We evaluated our system on a newly created dataset by applying it to a specific industrial workbench scenario. The results show that our system’s performance is competitive to the state of the art at more than five times the speed for single person pose
Exploring how experienced and unexperienced designers use and evaluate a usability-oriented interaction and navigation model

Representing quality aspects in models used in the design of interactive systems can support to design solutions with higher quality of use. However, the quality of the designed solutions can be influenced by the designers’ experience and by the models’ expressiveness for representing the quality aspects. Recently, we proposed USINN (USability-oriented INteraction and Navigation model) to express usability mechanisms in interaction and navigation modeling solutions. In this paper, we present an experimental study conducted with students, characterized as experienced and unexperienced designers, in order to investigate how is USINN adopted and evaluated by designers with different levels of experience in the software industry. The results indicated that the quality of artifacts produced by experienced and unexperienced designers was similar. However, the unexperienced designers indicated higher intention to adopt USINN in the software industry.
Can Sit-to-walk Assessment Maximize Instrumented Timed Up & Go Test Output?

Daily human activities commonly include standing from a seated position. In research this transition is investigated, among others, as a part of a functional Timed Up & Go test. Spatio-temporal parameters are widely used to assess the sit-to-walk transition. Usually, the parameters calculated for the sit-to-walk signal is in its entirety. Another approach primarily splits the transition into phases and then calculates parameters for individual phases separately. The objective of this work is to examine whether splitting the Timed Up & Go test into subphases provides additional value for transition assessment. In order to compare both approaches, we utilized angular rate parameters (duration, peak value, mean, variance) and analyzed their reliability. The reliability proved to be dependent on the subject group and transition phase. In addition, we compared transition parameters from the entire transition and individual phases between the two subject groups. The mean only differentiated between the subject groups in individual phases, but not is entire transition. To summarize, splitting the transition into phases turned out to be beneficial for sit-to-walk transition assessment.
Stochastic shelter site location under multi-hazard scenarios

STOCHASTIC SHELTER SITE LOCATION UNDER MULTI-HAZARD SCENARIOS Eren Özbay M.S. in Industrial Engineering Advisor: Bahar Yetiş Co-Advisor: Özlem Çavuş İyigün June 2018 In some cases, natural disasters happen successively (e.g. a tsunami following an earthquake) in close proximity of each other, even if they are not correlated. This study locates shelter sites and allocates the affected population to the established set of shelters by considering the aftershock(s) following the initial earthquake, via a three-stage stochastic mixed-integer programming model. In each stage, before the uncertainty, which is the number of affected people, in the corresponding stage is resolved, shelters are established, and after the uncertainty is resolved, affected population is allocated to the established set of shelters. To manage the inherent risk related to the uncertainty, conditional value-atrisk is utilized as a risk measure in allocation of victims to the established set of shelters. Computational results on the Istanbul dataset are presented to emphasize the necessity of considering secondary disaster(s), along with a heuristic method to improve the solution times and qualities. During these computational analyses, it is observed that the original single-objective model poses some obstacles in parameter selection. As in humanitarian operations, choosing parameters may cause conflict of interests and hence may be criticized, a multi-objective framework is developed with various formulations. Some generalizations regarding the performance and applicability of the developed formulations are discussed and finally, another heuristic for the multi-objective formulation is presented to tackle the curse of dimensionality and improve the solution times.
Study on Pavement Defect Detection Based on Image Processing Utilizing UAV

With the continuous expansion of traffic volume in China, pavement deterioration recognition is a key technique to reflect the real state of the roads and to provide the basis for decision of the maintenance plan. In this paper, a new pavement defect detection system based on Image Processing was developed which utilized Unmanned Aerial Vehicle (UAV) to avoid blocking the traffic flow and to enlarge monitoring range. It was divided into two subsystems: UAV intelligent control modulus and defect detection modulus. For the former, special programs were designed to achieve the functions such as automatically taking off and landing, automatically trajectory control, and camera and gimbal control. For the latter, based on the comparative analysis of the basic principles and multiple image groups experiments of normal edge detection algorithms, a combined method was proposed. This proposed method can not only automatically provide the high threshold that Canny algorithm needs to input manually, but also solve the lack of edge detection ability in OTSU segmentation algorithm. The efficiency and accuracy of the image processing is verified through the experiments. It is also showed that the weak edges in the image can be better retained by taking 1/3 to 1/2 of the high threshold as a low threshold in this combined method. According to both qualitative and quantitative analysis, it can be concluded that, among the normal algorithms taken into study, the proposed combination algorithm performs best in terms of recognition accuracy of crack types and crack geometry features extraction.