Reviewerly/exHarmony huggingface dataset:
Detecting Pedestrian Crossing Events in Large Video Data from Traffic Monitoring Cameras

Pedestrian safety on the road is a priority for transportation system managers and operators. While there are a number of treatments and technologies to effectively improve pedestrian safety, identifying the location where these are most needed remains a challenge. Mid-block locations, where safety countermeasures are often needed the most, are typically harder to monitor. Current practice often requires manual observation of candidate locations for limited time periods, leading to an identification process that is often time consuming, lags behind traffic pattern changes over time, and lacks scalability. As a result, target locations are often selected reactively, after serious traffic incidents reveal an underlying safety issue. We propose an approach to use data collected by existing traffic monitoring cameras to automatically identify pedestrian activities on the road. We propose an algorithm to detect pedestrian crossing events based on the detection of individuals on individual video frames using a deep neural network model. Resulting pedestrian locations and movement trajectories can be visualized on a background image, which is automatically extracted at the analyzed location from the video. We demonstrate and evaluate our approach with a real-world use case. The case study considered in this work uses cameras owned by the City of Austin, Texas to study pedestrian road use before and after the deployment of a pedestrian-hybrid beacon. We explore qualitative and quantitative metrics to describe pedestrian activity and corresponding changes, which may be used to prioritize the deployment of pedestrian safety solutions, or evaluate their performance. We compared the number of crossing events detected per hour with manually reviewed results from a selected day. The result shows 67 percent overall accuracy, although we observe significant variability across times-of-day. Despite observed limitations, our work illustrates how the value of existing traffic camera networks can be augmented beyond everyday traffic monitoring, and used to collect valuable information on road usage by pedestrians.
Automated Ham-Spam Lexicon Generation Based on Semantic Relations Extraction

One of the current essential methods for communication is electronic email (e-mail). It is currently considered the official method for different business activities such as conducting agreements, the setup of official meetings, and team collaboration. This continuous interest in e-mails as a communication channel has drawn the attention to the need for eliminating spam which have a vital effect on both network resources and business activities. This research focuses on generating a ham-spam lexicon based on text analysis which is aimed to be one of the main resources for detecting personal spam e-mails. The lexicon generation is a key step to efficiently and economically successful spam elimination. The proposed framework has proven its applicability on a dataset of six groups and the classification algorithms have been examined to prove the efficient classification. The research is a step in a wider view for general intelligent business communication and collaboration framework.
Real-World Anomaly Detection Using Deep Learning

In this paper, we have carried out a comparative study on two deep learning models for detecting real-world anomalies in surveillance videos. Anomalous event is the one which deviates from the normal behavior. The anomalies considered are related to thefts such as robbery, burglary, stealing, and shoplifting. A framework is set up using supervised learning approach to train the models using the weakly labeled videos. The deep learning models considered are VGG-16 and inception model which are trained with both anomalous and normal videos to detect any anomalous activity in the video frame. UCF-Crime dataset is used which comprises long, untrimmed surveillance videos. The deep learning models are evaluated using various metrics. The experimental results show that the Inception V3 model performs significantly better in detecting the anomalies as compared to the VGG-16 model with an accuracy of 94.54%.
Eye Movement Based Cursor Control and Home Automation for Disabled People

Access to computer technology is of vital importance to people with disabilities. Eyes can be regarded as one of the organs that can help a paralyzed person to communicate suitably. This paper provides a novel idea to control home appliances for disabled people who cannot move anything except their eyes by controlling the computer mouse cursor through their eye movement. The proposed method here uses an eye tracking method for eye movement acquisition, further following a simple circuitry. This system is very helpful for solving the HMI problems of the disabled.
Image-based visual servoing improvement through utilization of adaptive control gain and pseudo-inverse of the weighted mean of the Jacobians

The use of information acquired by stationary or onboard vision sensors for feedback control of pose and motion of a robot is known as visual servoing. In this paper, an image-based visual servoing controller with improved rate and domain of convergence is presented using adaptive control gain and pseudo-inverse of the weighted mean of the Jacobians. Furthermore, the stability of the proposed controller is proved by means of Lyapunov criteria, and simulations results are provided to demonstrate the validity and advantages of this control scheme. In comparison with the pseudo-inverse of the mean of the Jacobians scheme, the proposed controller increased the convergence rate of the servoing system up to seven times when used in complex motion tasks, and widened the convergence domain of the system up to 179° initial camera yaw angle when used in pure rotation tasks.
Speaker and Language Aware Training for End-to-End ASR

The end-to-end (E2E) approach to automatic speech recognition (ASR) is a simplified and an elegant approach where a single deep neural network model directly converts the acoustic feature sequence to the text sequence. The current approach to end-to-end ASR uses the neural network model (trained with sequence loss) along with an external character/word based language model (LM) in a decoding pass to output the text sequence. In this work, we propose a new objective function for end-to-end ASR training where the LM score is explicitly introduced in the attention model loss function without any additional training parameters. In this manner, the neural network is made LM aware and this simplifies the model training process. We also propose to incorporate an attention based sequence summary feature in the ASR model which allows the system to be speaker aware. With several E2E ASR experiments on TED-LIUM, WSJ and Librispeech datasets, we show that the proposed speaker and LM aware training improves the ASR performance significantly over the state-of-art E2E approaches. We achieve the best published results reported for WSJ dataset.
Assessment of Word-Level Neural Language Models for Sentence Completion

The task of sentence completion, which aims to infer the missing text of a given sentence, was carried out to assess the reading comprehension level of machines as well as humans. In this work, we conducted a comprehensive study of various approaches for the sentence completion based on neural language models, which have been advanced in recent years. First, we revisited the recurrent neural network language model (RNN LM), achieving highly competitive results with an appropriate network structure and hyper-parameters. This paper presents a bidirectional version of RNN LM, which surpassed the previous best results on Microsoft Research (MSR) Sentence Completion Challenge and the Scholastic Aptitude Test (SAT) sentence completion questions. In parallel with directly applying RNN LM to sentence completion, we also employed a supervised learning framework that fine-tunes a large pre-trained transformer-based LM with a few sentence-completion examples. By fine-tuning a pre-trained BERT model, this work established state-of-the-art results on the MSR and SAT sets. Furthermore, we performed similar experimentation on newly collected cloze-style questions in the Korean language. The experimental results reveal that simply applying the multilingual BERT models for the Korean dataset was not satisfactory, which leaves room for further research.
Inclusion of Functional and Non-Functional Parameters for the Prediction of Overall Efforts of Mobile Applications.

Size is a major and main parameter for the estimation of efforts and cost of software applications in general and mobile applications in particular and estimating effort, cost and time has been a key step in the life cycle of the software project. In order to create a sound schedule for the project, it is therefore important to have these estimates as soon as possible in the software development life cycle. In past years, many methods have been employed to estimate size and efforts of mobile applications but till now these methods do not meet the expected needs from customer. In this paper, we present a new size measurement method i.e., Mobile COSMIC Function Points (MCFP) based on the COSMIC approach, which is a primary factor for estimation of efforts in mobile application development. This paper analyzes the possibility of using a combination of Functional and Non-functional parameters including both Mobile Technical Complexity Factors (MTCF) and Mobile Environmental Complexity Factors (MECF) for the purpose of mobile application sizing prediction and hence effort estimation. For the purpose of this study, thirty six mobile applications were analyzed and their size and efforts were compared by applying the new effort estimation approach. In this context of a mobile application, few investigations have been performed to compare the effectiveness of COSMIC, FP's and the proposed approach “COSMIC Plus Effort Estimation Model (CPEEM)”. The main goal of this paper is to investigate if the inclusion of Non functional parameters imposes an effect on the functional size of mobile application development. Upon estimating efforts using the proposed approach, the results were promising for mobile applications when compared the results of our approach with the results of the other two approaches
Group Password Strength Meter Based on Attention Mechanism

User authentication is an important means to ensure the security of users' cyber accounts. Although there are various authentication means such as irises and fingerprints, passwords are still the main authentication method for the foreseeable future due to their low cost and easy implementation. Password strength evaluation is to measure the security strength of passwords, which has been widely studied. However, we found that the current password strength evaluation methods ignore the characteristics from password creators and do not consider the impact of regional groups on password generation. In this paper, we propose the concept of group passwords to analyze the password characteristics of different groups. Based on this notion, a group-based password strength evaluation method is proposed. In addition, we analyze the vulnerabilities of largescale real-world password groups leaked in previous security incidents. The analysis results show that different password groups have different characteristics. Then, we use the attention mechanism (AM) in the neural network model to learn the dependence between group characteristics and password context features. A long short-term memory (LSTM) model with natural advantages in processing timing features is used to process the password to achieve a more accurate password strength evaluation. We demonstrate the effectiveness of groupbased password evaluation using the real-world password data sets.
Simple Gated Convnet for Small Footprint Acoustic Modeling

Acoustic modeling with recurrent neural networks has shown very good performance, especially for end-to-end speech recognition. However, most recurrent neural networks require sequential computation of the output, which results in large memory access overhead when implemented in embedded devices. Convolution-based sequential modeling does not suffer from this problem; however, the model usually requires a large number of parameters. We propose simple gated convolutional neural networks (Simple Gated ConvNet) for acoustic modeling and show that the network performs very well even when the number of parameters is fairly small, less than 3 million. The Simple Gated ConvNet (SGCN) is constructed by combining the simplest form of Gated ConvNet and one-dimensional (1-D) depthwise convolution. The model has been evaluated using the Wall Street Journal (WSJ) Corpus and has shown a performance competitive to RNN-based ones. The performance of the SGCN has also been evaluated using the LibriSpeech Corpus. The developed model was implemented in ARM CPU based systems and showed the real time factor (RTF) of around 0.05.
Laplacian-Weighted Random Forest for High-Dimensional Data Classification

Random forest has been an important technique in ensemble classification, due to its effectiveness and robustness in handling complex data. But many of the previous random forest models tend to treat all features equally and often lack the ability to well reflect the potentially different importance of different features, especially in high-dimensional feature space. To tackle this issue, this paper proposes a novel approach termed Laplacian-weighted random forest (LWRF). Particularly, we first build a k-nearest neighbor graph to depict the underlying structure of the data. Then, we take advantage of the Laplacian score to estimate the importance of different features by considering their locality preserving power, based on which an efficient feature weighting scheme can be devised. With the feature weights via Laplacian scores, we proceed to generate a set of diverse subspaces by weighted random sampling, and thereafter present the LWRF approach with the decision trees trained on these diverse subspaces. Experiments have been conducted on twelve high-dimensional gene expression datasets to demonstrate the effectiveness and efficiency of our approach.
Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation Problem

Segmentation of tumors in brain MRI images is a challenging task, where most recent methods demand large volumes of data with pixel-level annotations, which are generally costly to obtain. In contrast, image-level annotations, where only the presence of lesion is marked, are generally cheap, generated in far larger volumes compared to pixel-level labels, and contain less labeling noise. In the context of brain tumor segmentation, both pixel-level and image-level annotations are commonly available; thus, a natural question arises whether a segmentation procedure could take advantage of both. In the present work we: 1) propose a learning-based framework that allows simultaneous usage of both pixel-and image-level annotations in MRI images to learn a segmentation model for brain tumor; 2) study the influence of comparative amounts of pixel-and image-level annotations on the quality of brain tumor segmentation; 3) compare our approach to the traditional fully-supervised approach and show that the performance of our method in terms of segmentation quality may be competitive.
Multi-homotopy class optimal path planning for manipulation with one degree of redundancy

Redundant manipulators have an infinitely large set of joint paths that yield a desired end-effector path in the task space. A unique joint path can be obtained by minimizing a global cost function. Prior optimal control methods minimize a global cost function to find a local minimum within a homotopy class. Many possible locally optimal joint paths are in different homotopy classes. This paper presents an algorithm that effectively searches the solution space and finds many locally optimal paths in all relevant homotopy classes. The path with the lowest cost is very likely the globally optimal path. The algorithm is demonstrated in a case study for which the globally optimal path would be impossible to find using traditional methods.
Construction of the similarity matrix for the spectral clustering method: Numerical experiments

Spectral clustering is a powerful method for finding structure in a dataset through the eigenvectors of a similarity matrix. It often outperforms traditional clustering algorithms such as k-means when the structure of the individual clusters is highly non-convex. Its accuracy depends on how the similarity between pairs of data points is defined. Two important items contribute to the construction of the similarity matrix: the sparsity of the underlying weighted graph, which depends mainly on the distances among data points, and the similarity function. When a Gaussian similarity function is used, the choice of the scale parameter σ can be critical. In this paper we examine both items, the sparsity and the selection of suitable σ's, based either directly on the graph associated to the dataset or on the minimal spanning tree (MST) of the graph. An extensive numerical experimentation on artificial and real-world datasets has been carried out to compare the performances of the methods.
Sentiment Classification on Movie Reviews and Twitter: An Experimental Study of Supervised Learning Models

Sentiment classification refers to the act of putting in for natural language processing and text mining strategies to distinguish subjective textual data. Due to the huge availability of online data that coincide with the growth of social media, there has been a big interest from researchers in sentiment analysis and its applications. In this paper, we review the state of the art to determine how the previous researches have addressed this task. we also introduce an empirical study on two annotated datasets; 25,000 IMDB movie reviews and 25,000 tweets, where we used nine supervised learning models, the next step was to implement a voting ensemble classifier using the top four models we get from the previous steps. In the end, we outline a benchmark evaluation, the results show that the ensemble classifier outperforms all the machine learning models.
Automated interpretability of linked data ontologies: : an evaluation within the cultural heritage domain

Publication and usage of linked data has been highly pursued by cultural heritage institutions and service providers in this domain. Much research and cooperation are taking place in adapting and improving cultural heritage data models for linked data and in defining ontologies and vocabularies, as well as the setting up of services based on linked data. This article presents an evaluation of ontologies and vocabularies published as liked data, which originate from the cultural heritage domain, or are frequently used and linked to in this domain. Our study aims to evaluate their usability by crawlers operating on the web of data, according to specifications and practices of linked data, the Semantic Web and ontology reasoning. We evaluate having in mind the use case of general data consumption applications based on RDF, RDF Schema, OWL, SKOS and linked data's guidelines. We have evaluated twelve ontologies and vocabularies and identified that four were not fully compliant, and that alignments between ontologies are not included in the definitions of the ontologies. This study contributes to the research of novel services consuming linked data. It also allows to better assess the automation that can be achieved to handle the variety and large volume of linked data, when assessing the viability of new services based on linked data in cultural heritage.
A GA-based Framework for Mining High Fuzzy Utility Itemsets

Comparing to frequent itemset mining (FIM), utility-pattern mining receives increasing attention in the field of data mining recently. With the flourishing development of utility-pattern mining, most studies focused on the efficiency problem by considering the efficient data structure to compress the original data and pruning strategies to reduce the search space for knowledge discovery. However, those approaches can only handle the binary situation, thus the discovered knowledge cannot be represented as the linguistic variables. Previous works have addressed this problem by introducing the generic approaches to find the high fuzzy utility itemsets in a small database. In real-world situations, the dataset may be very large, and it is costly to mine all the required information from a very large database. In this paper, we first present a HFUI-GA framework to discover the high fuzzy utility itemsets in a limited time. Several improvement strategies are also proposed to speed up the evolutionary progress. Experiments are then conducted to show the performance of the variants of the designed HFUI-GA framework in terms of number of the discovered high fuzzy utility itemsets (HFUIs) and the results are convincing to show that the designed GA-based HFUI-GA framework is a promising solution to mine for HFUIs.
Massively Simulating Adiabatic Bifurcations with FPGA to Solve Combinatorial Optimization

Combinatorial optimizations are widely adopted in scientific and engineering applications, such as VLSI design, automated machine learning (AutoML), and compiler design. Combinatorial optimization problems are notoriously challenging to exactly solve due to the NP-hardness. Scientists have long discovered that numerically simulating classical nonlinear Hamiltonian systems can effectively solve many well-known combinatorial optimization problems. However, such physical simulation typically requires a massive amount of computation, which even outstrips the logic capability of modern reconfigurable digital fabrics. In this work, we proposed an FPGA-based general combinatorial optimization problem solver which achieved ultra-high performance and scalability. Specifically, we first reformulated a broad range of combinatorial optimization problems with a general graph-based data structure called the Ising model. Second, instead of utilizing classical simulated annealing to find an approximate solution, we utilized a new heuristic algorithm, simulated bifurcation, to search for solutions. Third, we designed an efficient hardware architecture to fully exploit FPGAs' potentials to accelerate the algorithm, and proposed three hardware-software co-optimizations to further improve the performance. By experimenting on benchmarks, our proposal outperformed the state-of-the-art simulated annealing optimization solver by up to 10.91 times.
In Defense of P Values

Recently, a controversy has erupted regarding the use of statistical significance tests and the associated P values. Prominent academic statisticians have recommended that the use of statistical tests be discouraged or not used at all. This has naturally led to a lot of confusion among research investigators about the support in the academic statistical community for statistical methods in general. In fact, the controversy surrounding the use of P values has a long history. Critics of P values argue that their use encourages bad scientific practice, leading to the publication of far more false-positive and false-negative findings than the methodology would imply. The thesis of this commentary is that the problem is really human nature, the natural proclivity of scientists to believe their own theories and present data in the most favorable light. This is strongly encouraged by a celebrity culture that is fueled by academic institutions, the scientific journals, and the media. The importance of the truth-seeking tradition of the scientific method needs to be reinforced, and this is being helped by current initiatives to improve transparency in science and to encourage reproducible and replicable research. Statistical testing, used correctly, has an important and valuable place in the scientific tradition.