Reviewerly/exHarmony huggingface dataset:
Performance Evaluation of Edge Orientation Histograms Based System for Real-time Object Detection in Two Separate Platforms

Background: Real-time object detection has been attracting much attention recently due the increasing market need of such systems. Therefore, different detection algorithms and techniques have been evaluated to create a reliable detection system. The main challenge to implement a realtime reliable detection system relies on the algorithm training phase. During this phase, a large number of object image database needs to be prepared for each object to be detected. Objective: In this work, we implement a simultaneous object detection system based on local Edge Orientation Histograms (EOH) as feature extraction method with a smaller objects image database. Then, we evaluate the performance of this detection system in two separate platforms. Methods: We evaluated the performance of the detection of Ede Orientation Histograms against HAAR and Local Binary Patterns (LBP) algorithms using two different objects. After that, we discussed the evaluation of the detection systems on the standard platform in addition to the porting process into the embedded platform. Results: We achieved excellent results on both face and hands objects using less than 300 samples. This number is really negligible compared to the size of the image database used by state-of-the-art solutions. In terms of quality of detection, we have achieved more than 93% detection accuracy for the standard platform and 91.8% in the embedded platform for both face and hand objects. Conclusion: In this work, we demonstrated how Edge Orientation Histograms-based detection system gives better performance results than the algorithms evaluated against with less than 300 images database in two separate platforms.
Text Representation, Retrieval, and Understanding with Knowledge Graphs

This dissertation aims to improve text representation, retrieval, and understanding with knowledge graphs. Previous information retrieval systems were mostly built upon bag-ofwords representations and frequency-based retrieval models. Effective as they are, wordbased representations and frequency signals only provide shallow text understanding and have various intrinsic challenges. Utilizing entities and their structured semantics from knowledge graphs, this dissertation goes beyond bag-of-words and improves search with richer text representations, customized semantic structures, sophisticated ranking models and neural networks.
Offline Handwritten Hindi Numerals Recognition using Zernike Moments

Feature selection mechanism plays a vital role in attaining higher recognition rate in machine based character recognition systems. Zernike moments and Zernike complex moments are found to be very beneficial as a basic tool to generate shape descriptors of a digital image, as they are capable of representing image features efficiently. These moments have been successfully utilized to solve some of the image processing problems and possess the property of rotation invariance which is a desirable aspect in many applications requiring a proficient offline character recognition system. This paper is devoted for inspecting the efficacy of Zernike moments and Zernike complex moments for offline Hindi numerals recognition. Feature vectors consisting of Zernike moments and Zernike complex moments are supplied to the resilient backpropagation neural network for training and different strategies are considered for the performance evaluation of these moments in the study. Character recognition rate of approx. 80.8% and 94.8% is attained through Zernike moments and Zernike complex moments, respectively, which establishes the supremacy of Zernike complex moments over Zernike moments for offline Hindi numerals character recognition.
TOY: a Total ordering Optimistic sYstem for Permissioned Blockchains.

Blockchains are distributed secure ledgers to which transactions are issuedcontinuously and each block is tightly coupled to its predecessors.Permissioned blockchains place special emphasis on transactions throughput. Inthis paper we present TOY, which leverages the iterative nature of blockchainsin order to improve their throughput in optimistic execution scenarios. TOYtrades latency for throughput in the sense that in TOY the last f+1 blocks ofeach node's blockchain are considered tentative, i.e., they may be rescinded incase one of the last f+1 blocks proposers was Byzantine. Yet, when optimisticassumptions are met, a new block is decided in each communication step, whichconsists of a proposer that sends only its proposal and all other participantsare sending a single bit each. Our performance study demonstrates that in asingle Amazon data-center, TOY running on 10 mid-range Amazon nodes obtains athroughput of up to 160K transactions per second for (typical Bitcoin size) 512bytes transactions. In a 10 nodes Amazon geo-distributed setting with 512 bytestransactions, TOY obtains a throughput of 30K transactions per second.
Building Economic Models of Human Computer Interaction

Economics provides an intuitive and natural way to formally represent the cost and benefits of interacting with applications, interfaces and devices. By using economics models it is possible to reason about interaction and make predictions about how changes to the system will affect performance and behavior. In this course, we provided an overview of relevant economic concepts and then showed how economics can be used to model human computer interaction to generated hypotheses about interaction which can be used to inform design and guide experimentation. As a case study, we demonstrate how various interactions with search and recommender applications can be modeled, before concluding the day with a hands-on modeling session using example and participant problems.
Analysis of Denoising Filters for Source Identification Using PRNU Features

In Digital Image Forensics, one of the important techniques is Source Camera Identification (SCI) that attempts to identify the source camera of captured images. The sensor patterns of captured images are used for identification. The most regular pattern noise is photo response non-uniformity (PRNU) that can be generated by sensor defects during manufacturing process. These noises are distinguishable due to different sensor vendors of devices. In this work, identification is based on mobile camera that is from which mobile model a given image is captured. Here the analysis of three different denoising filters (Wiener, Total Variation and Gaussian) are done, to get the best result in our dataset. For classification, support vector machine (SVM) classifier is used and validation is done using 10-fold cross-validation technique.
An Application Research of Kalman Filter Based Algorithms in ECEF Coordinate System for Motion Models of Sensors

Kalman filter based algorithms such as unscented Kalman filter (UKF), and the unbiased conversion measurement Kalman filter (UCMKF) are the most popular nonlinear filters which used in tracking, navigation, estimation and information fusion. Applying those filters directly in East-North-Up (ENU) coordinates with motion models of sensors causes the degradation or even divergence of filter performance. To address this issue, we first analyzed and discussed the motion model consistency of moving sensor with a constant velocity (CV). Next, we proposed to extend the application of common filter algorithms to Earth Centered Earth Fixed (ECEF) coordinates to filter random errors. We verified the validity of our proposed method by filtering random errors in a constant velocity motion model of radar. The theoretical analysis and simulation results show that the extended algorithms provide better efficiency and compatibility in moving sensors.
The Role of Social Influence and e-Service Quality in Impacting Loyalty for Online Life Insurance: A SEM-based Study

The aim of this research is to investigate the role of e-service quality, social influence and e-customer satisfaction on e-loyalty relationship. Data was collected from online life insurance policyholders in Delhi/NCR using structured questionnaires and in depth interviews and was then analysed and relationships were examined through structural equation modelling. Service quality and social influence were found to have substantial relationship with customer loyalty and satisfaction in online life insurance. E-customer satisfaction was found to have a mediating role in the relationship of customer loyalty with both service quality and social influence in online medium. The results of this study are estimated to offer significant contributions to academia, policy makers and facilitate a foundation for marketers to further examine various ways of interacting with customers that will aid in enhancing e-customer satisfaction leading to customer retention thereby improving overall business profits of the insurers.
Deep Convolutional Sum-Product Networks for Probabilistic Image Representations.

Sum-Product Networks (SPNs) are hierarchical, graphical models that combine benefits of deep learning and probabilistic modeling. SPNs offer unique advantages to applications demanding exact probabilistic inference over high-dimensional, noisy inputs. Yet, compared to convolutional neural nets, they struggle with capturing complex spatial relationships in image data. To alleviate this issue, we introduce Deep Generalized Convolutional Sum-Product Networks (DGC-SPNs), which encode spatial features in a way similar to CNNs, while preserving the validity of the probabilistic SPN model. As opposed to existing SPN-based image representations, DGC-SPNs allow for overlapping convolution patches through a novel parameterization of dilations and strides, resulting in significantly improved feature coverage and feature resolution. DGC-SPNs substantially outperform other SPN architectures across several visual datasets and for both generative and discriminative tasks, including image inpainting and classification. These contributions are reinforced by the first simple, scalable, and GPU-optimized implementation of SPNs, integrated with the widely used Keras/TensorFlow framework. The resulting model is fully probabilistic and versatile, yet efficient and straightforward to apply in practical applications in place of traditional deep nets.
DMEK: Improving Profile Matching in Opportunistic Collaborations

As the number of mobile devices grow, also grows the amount of data exchanged. This ever growing amount of data may overload Internet Service Providers. A possible solution to this problem is to use the mobile devices wireless network capabilities to exchange data by creating mobile P2P networks. These networks should opportunistically collaborate to exchange information to other devices in their proximity, only requiring users to specify their interests. This paper presents DMEK, (Decision Mobile Exchange of Knowledge) a solution where mobile devices disseminate knowledge among their users, opportunistically, using a decision mechanism based on profile matching. Experiments show DMEK feasibility and performance.
Emergence of Hierarchy via Reinforcement Learning Using a Multiple Timescale Stochastic RNN.

Recurrent neural networks (RNNs) for reinforcement learning (RL) have shown distinct advantages, e.g., solving memory-dependent tasks and meta-learning. However, little effort has been spent on improving RNN architectures and on understanding the underlying neural mechanisms for performance gain. In this paper, we propose a novel, multiple-timescale, stochastic RNN for RL. Empirical results show that the network can autonomously learn to abstract sub-goals and can self-develop an action hierarchy using internal dynamics in a challenging continuous control task. Furthermore, we show that the self-developed compositionality of the network enhances faster re-learning when adapting to a new task that is a re-composition of previously learned sub-goals, than when starting from scratch. We also found that improved performance can be achieved when neural activities are subject to stochastic rather than deterministic dynamics.
Automatic Classification of Transformed Protocols Using Deep Learning

Protocol reverse-engineering technique can be used to extract the specification of an unknown protocol. However, there is no standardized method and in most cases, the extracting process is done manually or semi-automatically. Since only frequently seen values are extracted as fields from the messages of a protocol, it is difficult to understand complete specification of the protocol. Therefore, if the information about the structure of the unknown protocol could be acquired in advance, it would be easy to conduct reverse engineering. This paper suggests a method of recognizing 8 commercial protocols and transformed protocols of their own using deep learning techniques. When the proposed method is conducted prior to APRE (Automatic Protocol Reverse Engineering) process, it is possible to obtain useful information beforehand when similarities exist between unknown protocols and learned protocols.
Application of the logistic model in ICM

American college student’s mathematical contest in modeling in 2018 is over. I would like to express my views on task 2c of question D. For task 2c, I use the improved model similar to the exponential growth of population through changes in the numbers of cars. Considering the natural factors, I use the improved model, retarding growth model of population growth (logistic model). And then I analyze the stability of the model later. The curve is on the basis of data fitting.
Entrepreneurship: Getting Your Research Off the Bench and Out Into the Real World [Young Professionals]

Taking an idea off the bench and into the market, often called technology transfer, encompasses a variety of activities by which inventions flow from basic research to commercial entities and then to public use. This article describes two useful tools to help improve the translation of ideas to market. The first is the Business Model Canvas, or Lean Canvas for Business (LCB), an existing tool that helps entrepreneurs identify a minimum viable product (MVP) and channels and methods to take it to market. The second is a new tool (in the early pilot test stages), the Lean Canvas for Invention (LCI), that can help academic researchers identify the most valuable research questions (MVRqs) that may yield inventions that could later move through a Business Model Canvas to become a viable product.
Real-Time Implementation of Image Based PLC Control for a Robotic Platform

In this study, image based real-time control of a linear robotic platform was performed. This robotic platform is used to determine the location of the mushroom and to direct the linear platform to the detected position in real time with PLC control. Haar-Cascade classifier was used to detect mushroom position and Visual Studio C # .NET platform was used to test the Cascade classifier and write other evaluation codes. One of the most important outputs of this work is to determine the actual position in the global coordinate from the pixel-based location of the object in the image using an ordinary USB camera or built-in camera. Calibration technique was used for this determination.
Pratik Açıdan SQLite ve Firebase Veritabanlarının Bir Performans Karşılaştırması

Android is currently the most used mobile operating system all over the world. The two database management systems that Android officially supports are SQLite and Firebase. Android SDK provides built-in packages to let developers implement applications which store its data on these databases. At this point, it is necessary to reveal the performance comparison of these databases. For this reason, an Android application that evaluates several experiments which cover the most used data operations on these databases is implemented within this study. The experimental result indicates that SQLite provides better performance compared to Firebase except deleting data. The performance differences between SQLite and Firebase vary through (1) the type of data operation, and (2) the size of data that is managed.
A General Selection Method for Mutation Strategy in Differential Evolution

How to balance exploration and exploitation is a key issue for evolution algorithm including differential algorithm (DE). Many researchers propose various improved mutation strategies to solve this issue for DE. Most of them can be classified as deterministic rules. That is to say, they select individuals according to predetermined methods and so the balance is static. However, different evolution stages require different balance between exploration and exploitation. In order to solve this problem, a general selection method named adaptive stochastic ranking based mutation strategies in DE(ASR-DE). In ASR-DE, it uses stochastic ranking method to rank all individuals according to their contribution in exploration and exploitation. The parameter P $$_f$$ in stochastic ranking is adaptive controlled by a transform version of success rate. The individuals with the smaller ranking are more likely to be selected. 28 functions of CEC2013 is used here to verify the validity of testing method. The test results show that ASR-DE improves the standard DE and improved DE comparing with other methods.
Pengenalan Pola Emosi Manusia Berdasarkan Ucapan Menggunakan Ekstraksi Fitur Mel-Frequency Cepstral Coefficients (MFCC)

Human emotion recognition subject becomes important due to it's usability in daily lifestyle which requires human and computer interraction. Human emotion recognition is a complex problem due to the difference within custom tradition and specific dialect which exists on different ethnic, region and community. This problem also exacerbated due to objectivity assessment for the emotion is difficult since emotion happens unconsciously. This research conducts an experiment to discover pattern of emotion based on feature extracted from speech. Method used for feature extraction on this experiment is Mel-Frequency Cepstral Coefficient (MFCC) which is a method that similar to the human hearing system. Dataset used on this experiment is Berlin Database of Emotional Speech (Emo-DB). Emotions that are used for this experiments are happiness, boredom, neutral, sad and anger. For each of these emotion, 3 samples from Emo-DB are taken as experimental subject. The emotion patterns are successfully visible using specific values for MFCC parameters such as 25 for frame duration, 10 for frame shift, 0.97 for preemphasis coefficient, 20 for filterbank channel and 12 for ceptral coefficients. MFCC features are then extracted and calculated to find mean values from these parameters. These mean values are then plotted based on timeframe graph to be investigated to find the specific pattern which appears from each emotion. Keywords— Emotion, Speech, Mel-Frequency Cepstral Coefficients (MFCC).
Efficiently extracting frequent patterns from continuous uncertain data

Uncertain frequent pattern mining has been much discussed in recent decades. It is widely used in various fields and helps analysts to comprehend the deep meaning of collected data from the frequencies of items. In past studies, researchers have focused on discrete models. However, a discrete model only explains the presence of combinations of items without giving specific data intervals. To compensate for the drawbacks of discrete models, we focus on continuous uncertain data and improve a continuous uncertain frequent tree for the extraction of frequent patterns, notably time costs. Attribute overlapping usually causes the high time cost in the extraction phase. To avoid long branches in the tree, two approaches are proposed. The first approach is to name each attribute at given level with an uncertain frequent pattern. By using links and reshaping the uncertain frequency tree, the number of combinations decreases. The second approach is called uncertain frequent pattern map transforming. It uses a discrete transformation to decrease the time cost. In experiments, our two approaches were compared with different mainstream approaches. According to the results, our approaches not only cost less time to explore frequent patterns but also exhibited high accuracy for continuous uncertain data.
Privacy Preserving Classification Based on Perturbation for Network Traffic

Network traffic classification is important to many network applications. Machine learning is regarded as one of the most effective technique to classify network traffic. In this paper, we adopt the fast correlation-based filter algorithm to filter redundant attributes contained in network traffic. The attributes selected by this algorithm help to reduce the classification complexity and achieve high classification accuracy. Since the traffic attributes contain a large amount of users’ behavior information, the privacy of user may be revealed and illegally used by malicious users. So it’s demanding to classify traffic with certain segment of frames which encloses privacy-related information being protected. After classification, the results do not disclose privacy information, while may still be used for data analysis. Therefore, we propose a random perturbation algorithm based on relationship among different data attributes’ orders, which protects their privacy, thus ensures data security during classification. The experiment results demonstrate that data perturbed by our algorithm is classified with high accuracy rate and data utility.
A Left-to-Right Algorithm for Likelihood Estimation in Gamma-Poisson Factor Analysis

Computing the probability of unseen documents is a natural evaluation task in topic modeling. Previous work has addressed this problem for the well-known Latent Dirichlet Allocation (LDA) model. However, the same problem for a more general class of topic models, referred here to as Gamma-Poisson Factor Analysis (GaP-FA), remains unexplored, which hampers a fair comparison between models. Recent findings on the exact marginal likelihood of GaP-FA enable the derivation of a closed-form expression. In this paper, we show that its exact computation grows exponentially with the number of topics and non-zero words in a document, thus being only solvable for relatively small models and short documents. Experimentation in various corpus also indicates that existing methods in the literature are unlikely to accurately estimate this probability. With that in mind, we propose L2R, a left-to-right sequential sampler that decomposes the document probability into a product of conditionals and estimates them separately. We then proceed by confirming that our estimator converges and is unbiased for both small and large collections. Code related to this paper is available at: https://github.com/jcapde/L2R , https://doi.org/10.7910/DVN/GDTAAC .
Change Detection and Blob Tracking of Fish in Underwater Scenarios

In this paper, the difficult task of detecting fishes in underwater scenarios is analyzed with a special focus on crowded scenes where the differentiation between separate fishes is even more challenging. An extension for the Gaussian Switch Model is developed for the detection which applies an intelligent update scheme to create more accurate background models even for difficult scenes. To deal with very crowded areas in the scene we use the Flux Tensor to create a first coarse segmentation and only update areas that are with high certainty background. The spatial coherency is increased by the N $$^2$$ Cut, which is a Ncut adaption to change detection. More relevant information are gathered with a novel blob tracker that uses a specially developed energy function and handling of errors during the change detection. This method keeps the generality of the whole approach so that it can be used for any moving object. The proposed algorithm enabled us to get very accurate underwater segmentations as well as precise results in tracking scenarios.