Reviewerly/exHarmony huggingface dataset:
REPD: Source code defect prediction as anomaly detection

In this paper, we present a novel approach for within-project source code defect prediction. Since defect prediction datasets are typically imbalanced, and there are few defective examples, we treat defect prediction as anomaly detection. We present our Reconstruction Error Probability Distribution (REPD) model which can handle point and collective anomalies. We compare it on five different traditional code feature datasets against five models: Gaussian Naive Bayes, logistic regression, k-nearest-neighbors, decision tree, and Hybrid SMOTE-Ensemble. In addition, REPD is compared on 24 semantic features datasets against previously mentioned models. In order to compare the performance of competing models, we utilize F1-score measure. By using statistical means, we show that our model produces significantly better results, improving F1-score up to 7.12%. Additionally, REPD’s robustness to dataset imbalance is analyzed by creating defect undersampled and non-defect oversampled datasets.
Stealthy Information Leakage Through Peripheral Exploitation in Modern Embedded Systems

Embedded systems are being aggressively integrated in every aspect of modern life, with uses ranging from personal devices to devices deployed in critical systems, such as autonomous vehicles, aircrafts, and industrial control systems. Embedded systems handle sensitive information, which can be potentially exposed leveraging their poor security posture. In this paper, we present a novel attack vector that automates stealthy information leakage from modern embedded systems. Specifically, we leverage the Device Tree, a data structure that describes the hardware profile of a system, to extract detailed information about the target system. Utilizing this information, we introduce a stealthy attack that attempts to bridge the air-gap by transferring data from memory directly to analog peripherals. The attack resides solely in the peripherals, completely transparent to the main CPU, by judiciously short-circuiting specific components. We implement this attack on a commercial Programmable Logic Controller (PLC), leaking information over the available LEDs. We evaluate the presented attack vector in terms of stealthiness, and we demonstrate no observable overhead on both CPU performance and DMA transfer speed. Furthermore, we propose a generalized defense scheme for peripheral exploitation attacks by establishing a hardware root of trust through JTAG debugging. Our methodology keeps track of peripheral traffic through JTAG-enabled monitoring, alerts the system for possible malicious behavior and handles the threat removal. We test our defense in terms of imposed performance overhead and overall potency, achieving solid detection of the underlying attack at a low performance cost.
Deployment of Deep Learning Models on Resource-Deficient Devices for Object Detection

Detection of images or objects in motion have been highly worked upon, and the detection has been incorporated, required and utilized in applications. A few of the limitations include the lack of computational resources, lack of strategic and methodological data analysis of the observed trained data. The accuracy of detection is dependent on movement, velocity of the objects and Illuminacy. Therefore, it is required that new techniques and strategies of detection are drafted, applied and recognized. In this work, we have worked upon a model based on scalable object detection, using Deep Neural Networks to localize and track people, cars, potted plants and other categories in the camera preview in real-time. The trained model from google inception model has been implemented in an android application. This integrated application works real-time and can be used handy in a mobile phone or any other smart device with minimal computational resources.
Going Beyond the Regression Paradigm with Accurate Dot Prediction for Dense Crowds

We present an alternative to the paradigm of density regression widely being employed for tackling crowd counting. In the prevalent regression approach, a model is trained for mapping images to its crowd density rather than counting by detecting every person. This framework is motivated from the difficulty to discriminate humans in highly dense crowds where unfavorable perspective, occlusion and clutter are prevalent. Though regression methods estimate overall crowd counts pretty well, localization of individual persons suffers and varies considerably across the entire density spectrum. Moreover, individual detection of people aids more explainable practical systems than predicting blind crowd count or density map. Hence, we move away from density regression and reformulate the task as localized dot prediction in dense crowds. Our dot detection model, DD-CNN, is trained for pixel-wise binary classification to detect people instead of regressing local crowd density. In order to handle severe scale variation and detect people of all scales with accurate dots, we use a novel multi-scale architecture which does not require any ground truth scale information. This training regime, which incorporates top-down feedback, helps our model to localize people in sparse as well as dense crowds. Our model delivers superior counting performance on major crowd datasets. We also evaluate on some additional metrics and evidence superior localization of the dot detection formulation.
An Effective Imbalanced JPEG Steganalysis Scheme Based on Adaptive Cost-Sensitive Feature Learning

Steganalysis in real-world application often exhibit skewed sample distribution which poses a massive challenge for steganography detection. Conventional steganalysis algorithms are not effective when the training data distribution is imbalanced, and may fail in the scenario of imbalanced data distribution. To address imbalanced data distribution issue in steganalysis, a novel framework termed adaptive cost-sensitive feature learning via F-measure maximization is proposed, which is inspired by the fact that F-measure is a more suitable performance metric compared to accuracy for imbalanced data. We investigate the adaptive cost-sensitive strategy by generating and assigning different weight to each instance with misclassification occurrence. This scheme adaptively determines the weights according to the intra-class and inter-class costs from the imbalanced distribution. Features corresponding to the largest F-measure can be obtained by solving a series of adaptive cost-sensitive feature learning problems with optimization theory. In this way, the learned features are the most representative features between the cover and stego images so that imbalanced steganalysis can significantly alleviate. Extensive experiments on various imbalanced steganalysis tasks show the superiority of the proposed method over the state-of-the-art methods, and it can recognize more minority samples and has excellent classification performance.
Multi-factor of path planning based on an ant colony optimization algorithm

We propose an improved ant colony algorithm for avoiding obstacles in complex static environments that addresses the problems of a single evaluation factor and low path quality of the traditional ant colony algorithm in path planning. The improvements are: 1) a fuzzy planner is constructed according to the comprehensive evaluation method of fuzzy mathematics and the analytic hierarchy process to comprehensively evaluate and determine the impact of environmental factors, 2) the probability selection formula of the ant colony algorithm is optimized, 3) the pheromone update formula is optimized, and 4) the corner system mechanism is introduced as a post-processing method of path optimization to further smooth the path. Results from simulation experiments of the traditional ant colony algorithm were analysed and compared with those of the improved ant colony algorithm, showing that the latter has a stronger path planning ability and higher algorithm efficiency, resulting in a smoother path with a lower negative impact by environmental factors. Thus, the proposed algorithm is expected to provide a computational basis for effective multi-factor path planning in realistic environments, thereby saving human and material resources.
Restricted Boltzmann Machine-driven Interactive Estimation of Distribution Algorithm for personalized search

Effective and efficient personalized search is one of the most pursued objectives in the era of big data. The challenge of this problem lies in its complex quantifying evaluations and dynamic user preferences. A user-involved interactive evolutionary algorithm is a good choice if it has reliable preference surrogate and powerful evolutionary strategies. A Restricted Boltzmann Machine (RBM) assisted Interactive Estimation of Distribution Algorithm (IEDA) is presented to enhance the IEDA in solving the personalized search. Specifically, a dual-RBM module is developed to simultaneously provide a preference surrogate and a probability model for conducting the individual selection and generation of the IEDA. Firstly, the positive and negative preferences of the currently involved user in IEDA are distinguished and combined to achieve a dual-RBM, and then the weighted energy functions of the RBM model together with social group information from users with similar preferences are designed as the preference surrogate. The probability of the trained positive RBM on the visible units is fetched as the reproduction model of EDA since it reflects the attribute distributions of more preferred items. Some benchmarks from the Movielens and Amazon datasets are applied to experimentally demonstrate the superiority of the proposed algorithm in improving the efficiency and effectiveness of the interactive evolutionary computations served personalized search.
Ground Plane Context Aggregation Network for Day-and-Night on Vehicular Pedestrian Detection

Ground plane context is an essential semantic information in on-road pedestrian detection task. Due to viewpoint geometry constraints, pedestrians only appear in certain regions of the image, which is close to the horizon area of the ground plane. As a result, the lacking to ground plane context information may cause pedestrian detection system suffering from severe false alarm (i.e. high false positive (FP) rate). For Advanced Driver Assistance System (ADAS), high FP rate not only distracts the driver, but also causes frequent unexpected braking to damage the vehicle’s hardware. In this paper, a novel pedestrian detection method called ground plane context aggregation network (GPCAnet) is proposed, which integrates ground plane context information into deep learning based detector to drastically reduce the FP rate. The proposed GPCAnet consists of two modules: i) a ground area predication (GAP) branch is appended on top of convolutional feature map of the backbone network, in parallel with existing branches, for region proposal, classification and bounding box regression; ii) based on GAP, a ground-region proposal network (GRPN) is designed to filter FP cases in order to reduce computations. To evaluate the effectiveness of proposed GPCAnet, experiments on day and night on-road pedestrian detection are performed on both visible and far infrared pedestrian detection datasets, e.g. Caltech and SCUT. Experimental results show that GPCAnet achieves better performance than state-of-the-art methods, while drastically reducing FP rate in pedestrian detection.
Hindi Multi-document Word Cloud based Summarization through Unsupervised Learning

Managing documents is a critical and significant task and supports many applications ranging from information retrieval to clustering search engine results. The multilinguistic facility provided by websites makes Hindi as a major language in the digital domain of information technology today. This work focuses on document management and summarization of Hindi corpus. The objective is to manage the documents and summarize Hindi corpus by applying extracting tokens and document clustering. The work is better in terms of scalability and supports consistent quality of cluster for incremental data set. Most of the past and contemporary research works have targeted English corpus document management. Hindi corpus has been mostly exploited by the researchers for exploring stemming, single- document summarization and classifier design on Hindi corpus. Implementing unsupervised learning on Hindi corpus for summarization of multiple documents through Word Cloud is still an untouched area. Technically speaking, the current work is an application of TF-IDF, cosine-based document similarity measures and cluster dendrograms, in addition to various other Natural Language Processing (NLP) activities. Entropy and precision are used to evaluate the experiments carried on different live and available/tested datasets and results.
Few-Shot Scene Adaptive Crowd Counting Using Meta-Learning

We consider the problem of few-shot scene adaptive crowd counting. Given a target camera scene, our goal is to adapt a model to this specific scene with only a few labeled images of that scene. The solution to this problem has potential applications in numerous real-world scenarios, where we ideally like to deploy a crowd counting model specially adapted to a target camera. We accomplish this challenge by taking inspiration from the recently introduced learning-to-learn paradigm in the context of few-shot regime. In training, our method learns the model parameters in a way that facilitates the fast adaptation to the target scene. At test time, given a target scene with a small number of labeled data, our method quickly adapts to that scene with a few gradient updates to the learned parameters. Our extensive experimental results show that the proposed approach outperforms other alternatives in few-shot scene adaptive crowd counting.
Deep residual networks with a fully connected reconstruction layer for single image super-resolution

Recently, deep neural networks have achieved impressive performance in terms of both reconstruction accuracy and efficiency for single image super-resolution (SISR). However, the network model of these methods is a fully convolutional neural network, which is limited to exploit the differentiated contextual information over the global region in the input image because of the weight sharing in convolution height and width extent. In this paper, we discuss a new SISR method where features are extracted in the low-resolution (LR) space, and then we use a fully connected layer which learns an array of upsampling weights to reconstruct the desired high-resolution (HR) image from the final obtained LR features. By doing so, we effectively exploit the differentiated contextual information over the input image, whilst maintaining the low computational complexity for the overall SR operations. In addition, we introduce an edge difference constraint into our loss function to preserve edges and restore textures. Extensive experiments validate that our method outperforms the existing state-of-the-art SISR methods.
TailorGAN: Making User-Defined Fashion Designs

Attribute editing has become an important and emerging topic of computer vision. In this paper, we consider a task: given a reference garment image A and another image B with target attribute (collar/sleeve), generate a photorealistic image which combines the texture from reference A and the new attribute from reference B. The highly convoluted attributes and the lack of paired data are the main challenges to the task. To overcome those limitations, we propose a novel self-supervised model to synthesize garment images with disentangled attributes (e.g., collar and sleeves) without paired data. Our method consists of a reconstruction learning step and an adversarial learning step. The model learns texture and location information through reconstruction learning. And, the model's capability is generalized to achieve single-attribute manipulation by adversarial learning. Meanwhile, we compose a new dataset, named GarmentSet, with annotation of landmarks of collars and sleeves on clean garment images. Extensive experiments on this dataset and real-world samples demonstrate that our method can synthesize much better results than the state-of-the-art methods in both quantitative and qualitative comparisons.
Cross-domain retrieving sketch and shape using cycle CNNs

In this paper, we present a deep learning approach for cross-domain retrieval of 3D shape and 2D sketch image. Cross-domain retrieval has received significant attention to flexibly find information across different modalities of data. Effective measuring the similarity between different modalities of data is the key of cross-domain retrieval. Different modalities such as shape and sketch have imbalanced and complementary relationships, which contain unequal amount of information when describing the same semantics. Existing methods based on deep learning networks mostly construct one common space for different modalities, and these nets usually loss exclusive modality-specific characteristics. To address this problem, we propose a novel Cycle CNNs to estimate the cross-domain mapping between the space of 3D shape descriptors and the one of 2D sketch features. First, we employ the existing networks to construct independent feature spaces for each modality. For each feature space, modality-specific properties within one modality are fully exploited. Next, we use the designed Cycle CNNs to learn the mapping function between different feature spaces. This network can capture the mapping relationship between 3D shape feature space and 2D sketch feature domain. Finally, we use the explored mapping between the feature spaces of different modalities to perform cross-domain retrieval. We demonstrate a variety of promising results, where our method achieves better retrieval accuracy than existing state-of-the-art approaches.
Drone-based Augmented Reality Platform for Bridge Inspection: Effect of AR Cue Design on Visual Search Tasks

The use of augmented reality (AR) with semi-autonomous aerial systems in civil infrastructure inspection offers an extension of human capabilities by enhancing their ability to access hard-to-reach areas, decreasing the physical requirements needed to complete the task, and augmenting their visual field of view with useful information. Still unknown though is how helpful AR visual aids may be when they are imperfect and provide the user with erroneous data. A total of 28 participants flew as an autonomous drone around a simulated bridge in a virtual reality environment and participated in a target detection task. In this study, we analyze the effect of AR cue type across discrete levels of target saliency by measuring performance in a signal detection task. Results showed significant differences in false alarm rates in the different target salience conditions but no significant differences across AR cue types (none, bounding box, corner-bound box, and outline) in terms of hits and misses.
Improving the security of multi-party quantum key agreement with five-qubit Brown states

Secure quantum key agreement schemes should satisfy four security features, namely correctness, security, privacy, and fairness. Recently, Cai et al. (2018) used five-qubit Brown states to propose an efficient and flexible multi-party quantum key agreement scheme. We review and analyze the security of their protocol and we prove its inability to secure the private information of participants against a proposed strategy of collusion attacks. Also, we suggest a simple solution to close the loophole in Cai et al.'s protocol.
Reactive Alignment of Virtual and Physical Environments Using Redirected Walking

Virtual reality applications may accomplish richer and more immersive experiences by incorporating physical interactions such as passive haptic feedback. These interactions require that the coordinate mapping between the physical and virtual environment remains fixed. However, a static relationship is not maintained by many common locomotion techniques, including redirected walking, resulting in a state of misalignment. In this work, we address this limitation by proposing a novel reactive algorithm that uses redirected walking techniques to transition the system from a misaligned state to an aligned state, thereby enabling the user to interact with the physical environment. Traditionally, redirected walking algorithms primarily optimize for avoiding collisions with the boundaries of the physical space, whereas the proposed method leverages redirection techniques to achieve a desired system configuration. Simulation-based experiments demonstrate an effective use of this strategy when combined with redirected walking using artificial potential functions. In the future, reactive environment alignment can enhance the interactivity of virtual reality applications and inform new research vectors that combine redirected walking and passive haptics.
Logarithm-approximate floating-point multiplier is applicable to power-efficient neural network training

Recently, emerging "edge computing" moves data and services from the cloud to nearby edge servers to achieve short latency and wide bandwidth, and solve privacy concerns. However, edge servers, often embedded with GPU processors, highly demand a solution for power-efficient neural network (NN) training due to the limitation of power and size. Besides, according to the nature of the broad dynamic range of gradient values computed in NN training, floating-point representation is more suitable. This paper proposes to adopt a logarithm-approximate multiplier (LAM) for multiply-accumulate (MAC) computation in neural network (NN) training engines, where LAM approximates a floating-point multiplication as a fixed-point addition, resulting in smaller delay, fewer gates, and lower power consumption. We demonstrate the efficiency of LAM in two platforms, which are dedicated NN training hardware, and open-source GPU design. Compared to the NN training applying the exact multiplier, our implementation of the NN training engine for a 2-D classification dataset achieves 10% speed-up and 2.3X efficiency improvement in power and area, respectively. LAM is also highly compatible with conventional bit-width scaling (BWS). When BWS is applied with LAM in five test datasets, the implemented training engines achieve more than 4.9X power efficiency improvement, with at most 1% accuracy degradation, where 2.2X improvement originates from LAM. Also, the advantage of LAM can be exploited in processors. A GPU design embedded with LAM executing an NN-training workload, which is implemented in an FPGA, presents 1.32X power efficiency improvement, and the improvement reaches 1.54X with LAM + BWS. Finally, LAM-based training in deeper NN is evaluated. Up to 4-hidden layer NN, LAM-based training achieves highly comparable accuracy as that of the accurate multiplier, even with aggressive BWS.