Reviewerly/exHarmony huggingface dataset:
Toward Understanding Students’ Learning Performance in an Object-Oriented Programming Course: The Perspective of Program Quality

This pilot study examines how students' performance has evolved in an Object-oriented (OO) programming course and contributes to the learning analytic framework for similar programming courses in university curriculum. First, we briefly introduce the research background, a novel OO teaching practice with consecutive and iterative assignments consisting of programming and testing assignments. We propose a planned quantitative method for assessing students' gains in terms of programming performance and testing performance. Based on real data collected from students who engaged in our course, we use trend analysis to observe how students' performance has improved over the whole semester. By using correlation analysis, we obtain some interesting findings on how students' programming performance correlates with testing performance, which provides persuasive empirical evidence in integrating software testing practices into an Object-oriented programming curriculum. Then, we conduct an empirical study on how students' design competencies are represented by their program code quality changes over consecutive assignments by analyzing their submitted source code in the course system and the GitLab repository. Three different kinds of profiles are found in the students' program quality in the OO design level. The group analysis results reveal several significant differences in their programming performance and testing performance. Moreover, we conduct systematical explanations on how students' programming skill improvement can be attributed to their object-oriented design competency. By performing principal component analysis on software statistical data, a predictive OO metrics suite for both students' programming performance and their testing performance is proposed. The results show that these quality factors can serve as useful predictors of students' learning performance and can provide effective feedback to the instructors in the teaching practices.
Retina inspired tone mapping method for high dynamic range images

The limited dynamic range of regular screens restricts the display of high dynamic range (HDR) images. Inspired by retinal processing mechanisms, we propose a tone mapping method to address this problem. In the retina, horizontal cells (HCs) adaptively adjust their receptive field (RF) size based on the local stimuli to regulate the visual signals absorbed by photoreceptors. Using this adaptive mechanism, the proposed method compresses the dynamic range locally in different regions, and has the capability of avoiding halo artifacts around the edges of high luminance contrast. Moreover, the proposed method introduces the center-surround antagonistic RF structure of bipolar cells (BCs) to enhance the local contrast and details. Extensive experiments show that the proposed method performs robustly well on a wide variety of images, providing competitive results against the state-of-the-art methods in terms of visual inspection, objective metrics and observer scores.
Separable Reversible Data Hiding in Encrypted Images With High Capacity Based on Median-Edge Detector Prediction

In recent years, reversible data hiding technology in encrypted images has become an important topic for researchers. In addition, separable reversible data hiding technology in encrypted images also has been developed extensively because of its better practicability. In this paper, a new scheme of separable reversible data hiding in encrypted image is proposed based on the median-edge detector prediction. First, the predicted value of each pixel is calculated by using the median-edge detector predictor, and the difference between the original pixel and the predicted pixel is recorded according to the threshold parameter, and a label map is generated. Second, the original image is encrypted. Third, the differences and the secret bits are embedded into the pixels according to certain rules. After being compressed by using Run Length Coding and Huffman Coding, the label map is spliced to the bottom of the encrypted image. Finally, the stego-image is generated. When the receiver possesses the stego-image, which are the encrypted image that contain the hidden data, the desired image can be extracted or decrypted in accordance with a different key. The experimental results showed that our proposed scheme achieved better hiding capacity than the other schemes.
FLDet: A CPU Real-time Joint Face and Landmark Detector

Face detection and alignment are considered as two independent tasks and conducted sequentially in most face applications. However, these two tasks are highly related and they can be integrated into a single model. In this paper, we propose a novel single-shot detector for joint face detection and alignment, namely FLDet, with remarkable performance on both speed and accuracy. Specifically, the FLDet consists of three main modules: Rapidly Digested Backbone (RDB), Lightweight Feature Pyramid Network (LFPN) and Multi-task Detection Module (MDM). The RDB quickly shrinks the spatial size of feature maps to guarantee the CPU real-time speed. The LFPN integrates different detection layers in a top-down fashion to enrich the feature of low-level layers with little extra time overhead. The MDM jointly performs face and landmark detection over different layers to handle faces of various scales. Besides, we introduce a new data augmentation strategy to take full usage of the face alignment dataset. As a result, the proposed FLDet can run at 20 FPS on a single CPU core and 120 FPS using a GPU for VGA-resolution images. Notably, the FLDet can be trained end-to-end and its inference time is invariant to the number of faces. We achieve competitive results on both face detection and face alignment benchmark datasets, including AFW, PASCAL FACE, FDDB and AFLW.
Analyzing the Context of Bug-Fixing Changes in the OpenStack Cloud Computing Platform

Many research areas in software engineering, such as mutation testing, automatic repair, fault localization, and fault injection, rely on empirical knowledge about recurring bug-fixing code changes. Previous studies in this field focus on what has been changed due to bug-fixes, such as in terms of code edit actions. However, such studies did not consider where the bug-fix change was made (i.e., the context of the change), but knowing about the context can potentially narrow the search space for many software engineering techniques (e.g., by focusing mutation only on specific parts of the software). Furthermore, most previous work on bug-fixing changes focused on C and Java projects, but there is little empirical evidence about Python software. Therefore, in this paper we perform a thorough empirical analysis of bug-fixing changes in three OpenStack projects, focusing on both the what and the where of the changes. We observed that all the recurring change patterns are not oblivious with respect to the surrounding code, but tend to occur in specific code contexts.
Software Framework for Data Fault Injection to Test Machine Learning Systems

Data-intensive systems are sensitive to the quality of data. Data often has problems due to faulty sensors or network problems, for instance. In this work, we develop a software framework to emulate faults in data and use it to study how machine learning (ML) systems work when the data has problems. We aim for flexibility: users can use predefined or their own dedicated fault models. Likewise, different kind of data (e.g. text, time series, video) can be used and the system under test can vary from a single ML model to a complicated software system. Our goal is to show how data faults can be emulated and how that can be used in the study and development of ML solutions.
Mixed-mode Magnetic Tunnel Junction-based Deep Belief Network

We present a mixed-mode magneto tunneling junction (m-MTJ)-based Deep Belief Network (DBN). DBNs are unsupervised learning models, suitable for recognition and clustering. m-MTJ is a three-terminal magnetic device with probabilistic free layer switching controlled by the simultaneous actions of voltage-controlled magnetic anisotropy and spin-transfer torque. While DBNs achieve high prediction accuracy even with highly imprecise single-bit weights, the key complexity lies in their activation functions which are stochastic. Using an m-MTJ, we present a novel low area/power DBN neuron with stochastic activation function. We discuss an in-memory computing architecture that allows forward and backward flow of learning dynamics and online learning. Our design achieves ~88.80% accuracy for digit recognition in MNIST even under the worst case variability in nanoscaled m-MTJs.
Machine Learning Pipeline for Fraud Detection and Prevention in E-Commerce Transactions

Fraud has become a major problem in e-commerce and a lot of resources are being invested to recognize and prevent it. Present fraud detection and prevention systems are designed to prevent only a small fraction of fraudulent transactions processed, which still costs billions of dollars in loss. There is an urgent need for better fraud detection and prevention as the online transactions are estimated to increase substantially in the coming year. We propose a data driven model using machine learning algorithms on big data to predict the probability of a transaction being fraudulent or legitimate. The model was trained on historical e-commerce credit card transaction data to predict the probability of any future transaction by the customer being fraudulent. Supervised machine learning algorithms like Random Forest, Support Vector Machine, Gradient Boost and combinations of these are implemented and their performance are compared. While at the same time the problem of class imbalance is taken into consideration and techniques of oversampling and data pre-processing are performed before the model is trained on a classifier.
Learning Lightweight Face Detector with Knowledge Distillation

Despite that face detection has progressed significantly in recent years, it is still a challenging task to get a fast face detector with competitive performance, especially on CPU based devices. In this paper, we propose a novel loss function based on knowledge distillation to boost the performance of lightweight face detectors. More specifically, a student detector learns additional soft label from a teacher detector by mimicking its classification map. To make the knowledge transfer more efficient, a threshold function is designed to assign threshold values adaptively for different objectness scores such that only the informative samples are used for mimicking. Experiments on FDDB and WIDER FACE show that the proposed method improves the performance of face detectors consistently. With the help of the proposed training method, we get a CPU real-time face detector that runs at 20 FPS while being state-of-the-art on performance among CPU based detectors.
Slope disaster monitoring and early warning system based on 3D-MEMS and NB-IoT

Slope disasters are extremely destructive and pose a serious threat to human life and property safety. The conventional communication method (2G/3G/4G) of slope disaster monitoring has large power consumption and high cost. In addition, the warning indexes for slope instability are single and inaccurate. In order to solve the above problems, this paper develops an automatic monitoring and early warning system based on 3D-MEMS acceleration sensing and NB-IoT transmission technology. It includes intelligent sensor acquisition subsystem, data transmission subsystem, photovoltaic self- powered subsystem, data reception and analysis subsystem. Moreover, the successful on-site application of the Hong'en Temple Tunnel entrance slope in Chongqing, China, shows that the system can stably collect and transmit multi-source monitoring data, and the data change trend is consistent with the slope actual state. This system provides multi-source early warning indexes acquisition method and new data transmission scheme for slope disasters.
Making the most of what you have! Profiling biometric authentication on mobile devices

In order to provide the additional security required by modern mobile devices, biometric methods and Continuous Authentication(CA) systems are getting popular. Most existing work on CA are concerned about achieving higher accuracy or fusing multiple modalities. However, in a mobile environment there are more constraints on the resources available. This work is the first to compare between different biometric modalities based on the resources they use. We do this by determining the Resource Profile Curve (RPC) for each modality. This Curve reveals the trade-off between authentication accuracy and resource usage, and is helpful for different usage scenarios in which a CA system needs to operate. In particular, we explain how a CA system can intelligently switch between RPCs to conserve battery power, memory usage, or to maximize authentication accuracy. We argue that RPCs ought to guide the development of practical CA systems.
Cross-Modality Multi-Task Deep Metric Learning for Sketch Face Recognition

Sketch face recognition is to match face sketch images to photo images. The main challenge of it lies in cross-modality differences. To address this challenge, a variety of methods were proposed to bridge cross-modality gap of different modalities. Specially, common subspace-based methods have achieved great performance in this task. These methods enable the data of different modalmes to be comparable by mapping this data into a new and common subspace. However, the problem of non-linear distribution of samples from different modalities has not been well solved by these methods. In this paper, we propose a cross-modality multi-task deep metric learning (CMTDML) approach to address this problem. Firstly, we design a two-channel neural network to extract non-linear features of photo modality and sketch modality, and the parameter sharing characteristics can reduce the differences of features between different modalities. Secondly, we develop the loss function to constrain the features in common space, where intra-class compactness and inter-class separability of features are promoted. In extensive experiments and comparisons with the state-of-the-art methods, the CMTDML approach achieves marked improvements in most cases.
Resource-optimized combinational binary neural network circuits

Designing efficient machine learning algorithms for near-sensor data processing on the edge has been at the research forefront in recent years. To achieve the required edge processing constraints, massively parallel binary neural networks (BNNs) have been developed. BNNs implemented in purely combinational circuits provide resource utilization efficiency and performance. This paper proposes novel resource-optimized architectures of hard-wired combinational BNNs targeting field-programmable gate array (FPGA) edge deployment. The proposed architectures require fewer adders and overall operations for parallel neuron activation computations. A high-level synthesis toolchain is designed, which enables users to produce the hardware description language models of optimized combinational BNN circuits directly from application datasets. Standard and optimized combinational architectures are built for different edge processing applications by using this toolchain. The proposed optimized architecture reaches a 39.9% improvement in terms of FPGA slice usage, a 28.2% improvement in nets used, and a 51.9% reduction in power consumption, which enables deployment possibilities for the state-of-the-art FPGA classification systems.
Security Mandates are Pervasive: An Inter-School Study on Analyzing User Authentication Behavior

Two-factor authentication (2FA) technologies are designed to increase the security and usability of authentication. Adoption of 2FA hardware devices that generate one- time passwords has proven to be effective as a risk mitigating strategy. Despite 2FA addressing user data security concerns, individuals appear either disinterested or unable to adopt 2FA tools. Many institutions are now mandating 2FA to better secure their network and user data. Some have more rigid requirements than others (e.g., offering only one 2FA method vs. offering multiple 2FA options). To better understand the impact of mandatory 2FA policies, we conducted a study of the usability, adoption, and acceptability of 2FA at three different universities. In our study, using the Yubico FIDO U2F security token, we found that mandating the use of 2FA without complementary risk communication is often inadequate. In our interviews, we found that mandatory 2FA did not necessarily increase security, instead leading to less secure user behavior, such as sharing 2FA tokens, storing credentials for a longer time in public devices, and other security avoidance behaviors.
A Deep Learning-based Method for Turkish Text Detection from Videos

The text appearing in videos provides useful information, which can be exploited for developing automatic video indexing and retrieval systems. In this study, we integrated a heuristic and a deep learning-based method using Convolutional Neural Network (CNN) for automatic text extraction from videos. The two independent steps used for text extraction are; candidate text region detection and classification. In first step, rectangular regions were detected that potentially contain text by applying heuristics, which includes morphological processing and geometrical constraints. Then, the obtained candidate text regions were passed through several layers of CNN, that first produced convolutional feature map and then classified the candidate regions into either text or not-text classes. A dataset was prepared by collecting videos from various Turkish channels. 70% of the data was used to train the network while 30% for validation. Experiments showed that our proposed method achieved state-of-the-art performance on our dataset.
Smart city-based e-commerce security technology with improvement of SET network protocol

In this paper, we proposed an improvement of SET protocol for E-commerce transaction. We study the security technology and protocol flow of set protocol, and point out the shortcomings of SET protocol. Due to the lack of security and low scalability of the current protocol, this paper proposes a server e-wallet strategy, which divides the original client into e-wallet client and e-wallet server, and enhances the convenience and security of the transaction by transferring the client to the issuer or the third-party financial institution, while the scalability is stronger. In order to ensure the atomicity of goods and confirm the atomicity of delivery, this paper proposes a secure e-commerce payment protocol based on the fourth party, which includes master protocol, customer return protocol, customer exchange protocol and customer merchant transaction dispute resolution protocol. According to the efficiency of SET protocol, a security hierarchical control model is proposed, which can select different security levels to operate according to the actual needs of customers, and the principle and implementation of the model are analyzed.
A multi-view similarity measure framework for trouble ticket mining

Text similarity measures play a very important role in several text mining applications. Although there is an extensive literature on measuring the similarity between long texts, there is less work related to the measurement of similarity between short texts. And most of these works on short text similarity are based on adaptations of long-text similarity methods. Unfortunately, the description of a trouble ticket is just a kind of short texts. Thus, ticket mining applications such as ticket classification, ticket clustering, and ticket resolution recommendation often suffer from poor performance because of tickets’ particular characteristics of unstructured, short free-text with large vocabulary size, large volume, non-English dictionary words, and so on. Therefore, the ability to accurately measure the similarity between two tickets is critical to the performance of ticket mining. To address this performance issue, this paper proposes a multi-view similarity measure framework that easily integrates several kinds of existing similarity measures including surface matching based measures, semantic similarity measures and syntax based measures. Further, in order to make full use of the strengths of different similarity measures, our framework adopts four different policies to combine them. In particular, we consider a machine learning based policy that can be applied to integrate various similarity measures in a more general way, which makes our framework flexible and extensible. To demonstrate the effectiveness of measures generated from our framework, we empirically validate them on a publicly available short text data set and apply them to a real-world ticket data set from a large enterprise IT infrastructure. Some important findings obtained via the result analysis will be helpful to further improve performance.
Mismatched Guesswork and One-to-One Codes

We study the problem of mismatched guesswork, where we evaluate the number of symbols y ∈ Y which have higher likelihood than X ~ μ according to a mismatched distribution μ. We discuss the role of the tilted/exponential families of the source distribution μ and of the mismatched distribution ν. We show that the value of guesswork can be characterized using the tilted family of the mismatched distribution v, while the probability of guessing is characterized by an exponential family which passes through μ. Using this characterization, we demonstrate that the mismatched guesswork follows a large deviation principle (LDP), where the rate function is described implicitly using information theoretic quantities. We apply these results to one-to-one source coding (without prefix free constraint) to obtain the cost of mismatch in terms of average codeword length. We show that the cost of mismatch in one-to-one codes is no larger than that of the prefix-free codes, i.e., D(μ||ν). Further, the cost of mismatch vanishes if and only if ν lies on the tilted family of the true distribution μ, which is in stark contrast to the prefix-free codes. These results imply that one-to-one codes are inherently more robust to mismatch.
Two polynomials based (<i>t, n</i>) threshold secret sharing scheme with cheating detection

A two-polynomial based (t, n) threshold secret sharing (SS) scheme is proposed by Liu et al., where two functions for detection of share cheating are used. It is found that the scheme is weak as the equations formed by at most (t – 1) dishonest participants in term of unknowns are not equivalent to the cheating detection function, and if one polynomial is kept fixed and other with two cheating detection equations are taken, a set of (t+1) equations with (t+1) unknowns exist, and are found to be easily solvable. In this paper, we also propose a two-polynomial based (t, n) threshold SS scheme; however, the polynomials are taken in such a way that they have an arbitrary common coefficient and as a result, both the polynomials are to be modified simultaneously for cheating of shares and incorporates higher cheating detection capability. Some analytical proofs and comparison with other schemes in support of our claims are provided.