Reviewerly/exHarmony huggingface dataset:
User requirement and usability testing framework for information system development: case study of financial institution

Abstract Due to the fact that financial institution has its limited competency in developing the information system, outsourcing is often used to support the information system development which same as the case study of the issuing documents system in financial institute. The financial institution has received customer’s complaints regarding the mistake of issuing documents caused by the system functionality and human error in operations. Thus, the purpose of this study is to design framework of user requirement and usability testing for information system development to prevent the error in system functionality and set some quality checking process of documents before delivery to customer to prevent mistakes from human error in work operation. System Usability Scale showed that after proposed the framework of user requirement and usability testing to employees who related with the system development, their level of satisfaction toward the framework and non-framework of user requirement and usability testing raise up to 30.93% and 20%, respectively. After six months of implemented quality checking process of documents revealed zero complaints from customers regarding to the mistake of issuing documents.
Handwritten Text Segmentation Approach in Historical Arabic Documents

The sharp increase in the number of historical documents available as images in national libraries only increases their storage capacity. The need for access to content of this cultural heritage is also increasing. The methods of indexing and searching in image content are still very limited. Text segmentation of digital historical documents is an important step in recognizing content as images. In this paper, we present an original method of segmentation of lines and pseudo-words in historical documents based on Gaussian filters. Our method consists in detecting elliptical blobs in scales formed by Gaussian filters. Experimental tests are performed on hundreds of pages of historical documents. The experimental results showed with this method are excellent in front of other methods of text segmentation in manuscript images.
Structured Sparse Attention for end-to-end Automatic Speech Recognition

The Softmax normalization function-based attention mechanism is often employed by End-to-End Automatic Speech Recognition (E2E ASR) models to tell the network where to focus within the input. However, this mechanism leads to the attention distribution becoming increasingly flatter as the input sequence length increases, since the output probability of this function is dense and nonnegative, which makes it unable to highlight the important information in speech. In this paper, we present two sparse attention mechanisms for ASR tasks with long utterances, which try to improve the attention mechanism by introducing the sparse transformation. First, we propose to replace the Softmax with the Sparsemax that normalizes the attention weight by finding the closest point in the probability simplex. Then, considering the structured characteristics, the pronunciation has a relatively stable duration. Therefore, we further present a structured sparse transformation that forces the networks to pay attention to a continuous segment of speech by applying the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> penalty. A noniterative solution algorithm that can be used in the backpropagation is designed here. The experiments show that our methods achieve better ASR results compared to a well-tuned attention-based baseline system on a character ASR task.
Deep learning-based Anomaly Detection on Surveillance Videos: Recent Advances

Surveillance cameras number keep increasing from year to year, in 2017 alone estimated 106 million new CCTV installed. Surveillance cameras are installed to widen surveillance coverage in order to detect anomalies. Conventionally, detecting anomalies through surveillance cameras are using human observers. The increasing number of surveillance cameras installed is increasing the need for an automatic system to replace unreliable human observer. Thus an intelligent computer vision-based system to detect anomalies need to be developed. The goal of this system is to provide a warning to the first responder accurately and as fast as possible while the system is running all the time. The faster the response of the first responder, the better chance of an anomaly to be resolved. Deep learning based method has been proposed to solve anomaly detection on videos. Anomaly detected is composed of a set of violent and non-violent crimes with high impact in society. To increase the accuracy of previous system to detect anomalies in condition that resembles real life situation, multiple processes need to be combined. This paper will review various method used to increase the performance of a deep learning based action recognition on videos. To capture temporal information effectively the model need to be able to use multimodalities to detect motion, incorporating long-range temporal structure, and numerous deep learning architectures with various characteristics.
Thresholded Monitoring in Distributed Data Streams

In this paper, we consider the problem of thresholded monitoring in distributed data streams, that is, given multiple distributed data streams observed by multiple monitors during a certain period, finding the items whose global frequencies over all data streams exceeding a given threshold. We first derive a lower bound of communication overhead for any deterministic algorithm for this problem. Then, we propose two different schemas, i.e., Low-threshold Cascaded Cuckoo Filter (L-CCF) for low-threshold monitoring and High-threshold Cascaded Cuckoo Filter (H-CCF) for high-threshold monitoring. L-CCF and H-CCF can identify items whose frequencies are more than the given threshold while a desired false negative rate (FNR) is achieved and communication overhead is optimized. The key idea is to compress the communication overhead caused by transferring the ID and frequency information at the same time. First, to reduce the communication overhead of transferring IDs, we propose to encode the IDs into separate tiny parts and store these tiny parts in L-CCF or H-CCF. Second, to reduce the communication overhead of transferring frequencies, we adopt a carry-in counter technique in L-CCF and multiple sampling technique in H-CCF. We evaluated L-CCF and H-CCF on two real-world traces and compared their performance with two prior adapted algorithms. Our experimental results show that on average, L-CCF and H-CCF achieve FNRs with 55% and 65% better than that of comparison algorithms while FPRs is maintained at the level of 2%.
Using Structured event to represent complaints of patients: a medical assistant for doctors

Extracting relations between entities from complaints of patients is a significant but challenging problem in intelligent medical diagnose. It can help doctors to record the main information from the complaints of patients. As the development of technologies, Doctors need a more effective and convenient way to capture entire information from patients' complaints and build Electronic Health Records (EHRs). This paper proposes an event generation model which input the complaints of patients directly then output a series of events as complementary to traditional keywords based chief complaint capture. The event generation model adopts an open Chinese Information Extraction (open Chinese IE) and build a part-of-speech tagging to do dependency grammar analysis. Two kinds of evaluations are taken. One is metrics recall-oriented understanding for gusting evaluation (ROUGE). It measures the fitness of the generated events with the standard reference from doctors. The other are accuracy and Matthews Correlation Coefficient (MCC). They test the performance of grammar analysis. The results show our model have an excellent and robust performance.
Multi-Resolution Overlapping Stripes Network for Person Re-Identification

This paper addresses the person re-identification (PReID) problem by combining global and local information at multiple feature resolutions with different loss functions. Many previous studies address this problem using either part-based features or global features. In case of part-based representation, the spatial correlation between these parts is not considered, while global-based representation are not sensitive to spatial variations. This paper presents a part-based model with a multi-resolution network that uses different level of features. The output of the last two conv blocks is then partitioned horizontally and processed in pairs with overlapping stripes to cover the important information that might lie between parts. We use different loss functions to combine local and global information for classification. Experimental results on a benchmark dataset demonstrate that the presented method outperforms the state-of-the-art methods.
Anomaly Detection Based on Locality Sensitive Hashing with Genetic Algorithm

Abstract With the increase of public safety awareness, video anomaly detection has attracted researchers’ attention. In the paper, a novel approach is proposed to detect anomalies in the video. It is based on Locality Sensitive Hashing (LSH), which maps similar data to the same bucket with high probabilities, and non-similar data is mapped to the same bucket with a low probability to detect abnormal videos that are not similar to normal videos. In order to improve the probability of similar data mapping into the same bucket, the Genetic Algorithm (GA) is used to optimize the entire hash function group while maintaining the diversity of the hash function group. The algorithm gets AUC 0.78 on the dataset UCSD ped1 and AUC 0.94 on the dataset UCSD ped2, which confirmed the effectiveness of the algorithm.
Continuous User Authentication on Touchscreen Using Behavioral Biometrics Utilizing Machine Learning Approaches

Nowadays, touchscreen mobile devices make up a larger share in the market, necessitating effective and robust methods to continuously authenticate touch-based device users. A classification framework is proposed that learns the touch behavior of a user and is able afterwards to authenticate users by monitoring their behavior in performing input touch actions. Two models of features are built; the low-level features (stoke-level) model or the high-level abstracted features (session-level) model. In building these models, two different methods for features selection and data classification were weighted features and PCA. Two classification algorithms were used; ANN and SVM. The experimental results indicate the possibility of continuous authentication for touch-input users with higher promises for session-level features than stroke-level features. Authors found out that using weighted features method and artificial neural networks in building the session-level model yields the most efficient and accurate behavioral biometric continuous user authentication.
Analysis of automation in the field of Usability Evaluation

Usability Engineering is one of the domain areas that falls under the concepts of Human-Computer Interaction. The aim is to achieve systems that are efficient with respect to user experience. A combination of various approaches can be implemented at the disparate levels of the design and development procedure in order to obtain the necessary metric of usability efficiency. A structured and systematic approach aids in giving the designer information on the improvement of an interface. Some of the approaches that have been adopted for this purpose have been discussed here.
Deterministic Feature Decoupling by Surfing Invariance Manifolds

We introduce a formalism that justifies and extends a heuristic method for algebraically decoupling deterministic features that recently proved useful for improving feature-based classification. Our new formalism is based on defining transformations inside manifolds, by following trajectories along the features' gradients. Through these transformations we define a normalization that, we demonstrate, allows for decoupling differentiable features. By applying this to sampling moments, we obtain a quasi-analytic solution for the orthokurtosis, a modification of the kurtosis that is not just decoupled from mean and variance, but also from skewness. After theoretically motivating feature decoupling for random data distributions, we illustrate with a regression problem example how decoupled features may perform significantly better than coupled ones.
Follow-me Prefetching for Video Streaming Over Mobile Edge Computing Networks

Mobile video streaming services have increased exponentially in recent years due to the popularity of mobile devices, the advancement of mobile networks and the availability of a variety of video contents over the Internet. Mobile Edge Computing (MEC), in connection with the backend cloud computing, has been used to bring contents close to the end user in order to reduce transmission latency. However, the quality of video streaming services suffers from degradation when an end user moves from the coverage of one node to another or when the condition of a mobile network degrades. In this paper, we propose a novel follow-me Edge Node Prefetching (ENP) scheme to prefetch appropriate video segments in advance in the following-on mobile node to avoid video quality degradation during video streaming. We set up a test bed consisting of a back-end cloud (OpenStack), two edge nodes (LXD Containers) and a mobile device, and implemented the ENP algorithms on the cloud server and client sides. Extensive experiments for Dynamic Adaptive Video Streaming over HTTP (DASH) services were carried out based on dash.js from the DASH Industry Forum. Preliminary results show that the ENP scheme can achieve better video quality (in terms of provisioning of the average video bitrate per segment) and less service migration time between mobile nodes when compared with existing approaches. The scheme might be useful in supporting video streaming services over MEC, and/or in future smart city applications.
Preservation of Anomalous Subgroups On Variational Autoencoder Transformed Data

We investigate the effect of variational autoencoder (VAE) based data anonymization and its ability to preserve anomalous subgroup properties. We present a Utility Guaranteed Deep Privacy (UGDP) system which casts existing anomalous pattern detection methods as a new utility measure for data synthesis. UGDP's approach shows that properties of an anomalous subset of records, identified in the original data set, are preserved through the anonymization of a VAE. This is despite the newly generated records being completely synthetic. More specifically, the Bias-Scan algorithm identifies a subgroup of records that are consistently over- (or under-) risked by a black-box classifier as an area of 'poor fit'. This scanning process is applied on both pre- and post- VAE synthesized data. The areas of poor fit (i.e. anomalous records) persist in both settings. We evaluate our approach using publicly available datasets from the financial industry. Our evaluation confirmed that the approach is able to produce synthetic datasets that preserved a high level of subgroup differentiation as identified initially in the original dataset. Such a distinction was maintained while having distinctly different records between the synthetic and original dataset.
Experiment Based No-Reference Objective Image Quality Metric for Testing Performance of Different Tone Mapped Images

Subjective image quality assessment for a large database is a lengthy and tiring process. The task becomes more complicated when the testing of tone-mapped images is desired as it is not possible to directly display HDR content on standard displays, hence unavailability of reference images for comparison. This limits the quality evaluation methods to comparative or categorical judgement. For this study, we used the comparative judgement method to subjectively test the performance of images tone-mapped by different operators against each other on eight different visual image appearance attributes. Attributes having a higher correlation with overall image preference scores are used to develop the proposed no-reference image quality metric. Results of the psychophysical experiment and performance of the resulting metric are reported here.
The Role of Nonlinear Dynamics in Musicians' Interactions with Digital and Acoustic Musical Instruments

Abstract Nonlinear dynamic processes are fundamental to the behavior of acoustic musical instruments, as is well explored in the case of sound production. Such processes may have profound and under-explored implications for how musicians interact with instruments, however. Although nonlinear dynamic processes are ubiquitous in acoustic instruments, they are present in digital musical tools only if explicitly implemented. Thus, an important resource with potentially major effects on how musicians interact with acoustic instruments is typically absent in the way musicians interact with digital instruments. Twenty-four interviews with free-improvising musicians were conducted to explore the role that nonlinear dynamics play in the participants' musical practices and to understand how such processes can afford distinctive methods of creative exploration. Thematic analysis of the interview data is used to demonstrate the potential for nonlinear dynamic processes to provide repeatable, learnable, controllable, and explorable interactions, and to establish a vocabulary for exploring nonlinear dynamic interactions. Two related approaches to engaging with nonlinear dynamic behaviors are elaborated: edge-like interaction, which involves the creative use of critical thresholds; and deep exploration, which involves exploring the virtually unlimited subtleties of a small control region. The elaboration of these approaches provides an important bridge that connects the concrete descriptions of interaction in musical practices, on the one hand, to the more-abstract mathematical formulation of nonlinear dynamic systems, on the other.
Lightweight Convolution Neural Network Based on Feature Concatenate for Facial Expression Recognition

With outstanding performance of Convolution Neural Networks (CNN)-based recognition model, more researches employed CNN model to solve Facial Expression Recognition (FER) problem. Commonly, the computational complexity of CNN is correlated with the number of its parameters. More parameters result in large amount of calculation which consume more computational resources. Therefore, mobile device and embedded platform, which have limited computational capacity, can hardly provide an environment to cope with actual needs. We proposed a lightweight CNN in this paper, called Concat-Xception with 290,399 parameters which was based on efficient convolutional computing structure and concatenate operation. In addition, we applied Concat-Xception network to the facial expression recognition challenge (fer2013) datasets, and the model showed the best performance with Cohen Kappa of 0.64 and average accuracy of 70.13%.
Optimal Code Pairs Satisfying Relative Chain Condition

The relative generalized Hamming weight (RGHW) of a linear code and a linear subcode is a generalization of generalized Hamming weight (GHW). It characterizes the code performance of the wiretap channel of type II, secure network coding, linear ramp secret sharing scheme and trellis complexity. In this paper, we present a new proof of the Griesmer bound on RGHW by the idea of residue code. Motivated by the proof, we introduce the concept of relative chain condition and show that code pairs meeting the Singleton, weak Plotkin and Griesmer bounds satisfy this condition. This research may be helpful for studying the connection between RGHW and trellis complexity, the weight hierarchy of code pairs, etc.
A Geo-Obfuscation Approach to Protect Worker Location Privacy in Spatial Crowdsourcing Systems

In Spatial Crowdsourcing (SC), workers are required to move to the task location to perform the tasks. To promote cost-effective crowdsourcing work, tasks need to be assigned to workers with low traveling costs. As such, workers are required to disclose their location information to SC servers, which may lead to serious privacy concerns. In this paper, we propose a geo-obfuscation approach to help workers protect their location information while ensuring the accuracy of traveling cost estimation in task assignment. We implemented our approach with a real-world experiment and the experimental results demonstrate the effectiveness of our approach in terms of both privacy and information accuracy.
A Self-adaptive Quantum Genetic Algorithm for Vehicle Routing and Scheduling

Vehicle Routing and Scheduling Problem (VRP) is an NP-hard problem. In this paper, an adaptive quantum genetic algorithm is proposed to solve the problem of vehicle routing and scheduling. The algorithm uses a binary encoding scheme. In the case of quantum variation using quantum rotation gate, the self-adaptive angular acceleration algorithm (SAAA) is proposed according to the difference of fitness value between the current individual and the optimal individual. On this basis, the strategy of probability-based random reverse rotation is adopted when evolution stagnates, and the evolutionary stagnate escape algorithm (ESEA) is proposed. The experimental results show that the proposed algorithm has some advantages in terms of convergence speed and the ability to find the optimal solution in vehicle routing and scheduling. Finally, according to the pre-set requirements for evolutionary efficiency, the evolutionary efficiency assessment algorithm (EEAA) is proposed, which allows evolution to terminate and output the current optimal solution when the threshold is exceeded.
Tool Mark Identification of Skype Traffic

Law enforcement agencies (LEAs) face a challenging problem to detect the tool marks of Skype traffic. Analyzing digital packets is an essential job in digital forensics. This study collects these packets, traces their routings, analyzes the relevant information, and explores the tool mark identification of Skype packets. It uses experimental data on a personal computer, investigates the characteristics of Skype traffic, and provides a tool mark analysis of network traffic. The study can assist the investigator in identifying the suspect using a Skype communication application to commit the crime.