Reviewerly/exHarmony huggingface dataset:
Drone Disrupted Denial of Service Attack (3DOS): Towards an Incident Response and Forensic Analysis of Remotely Piloted Aerial Systems (RPASs)

According to the Federal Aviation Administration (FAA), the number of Remotely Piloted Air Systems (RPASs), colloquially known as drones, will rapidly increase in the near future. Challenges with drones are focused not only on the security of these devices, but also on the criminal uses for drones which need to be carefully considered. Incident response and forensic analysis of such cases have not been sufficiently addressed by the research community. This paper focuses on incident response of cybercrimes related to drones as well as some possible anti-forensic techniques that could be used to alter digital evidence associated with drones. In addition, this paper also revisits the enacted regulations that purport to restrict the operation of drones in critical infrastructure areas. We evaluate drone incident response by exploring a case study using a hypothetical drone forensic tool to illustrate the acquisition of GPS metadata from both media files and flight logs, with a view towards aiding the incident responders and digital forensic investigators in analyzing illegal flight activities and report such incidents effectively.
Efficiency of Extreme Gradient Boosting for Imbalanced Land Cover Classification Using an Extended Margin and Disagreement Performance

Imbalanced learning is a methodological challenge in remote sensing communities, especially in complex areas where the spectral similarity exists between land covers. Obtaining high-confidence classification results for imbalanced class issues is highly important in practice. In this paper, extreme gradient boosting (XGB), a novel tree-based ensemble system, is employed to classify the land cover types in Very-high resolution (VHR) images with imbalanced training data. We introduce an extended margin criterion and disagreement performance to evaluate the efficiency of XGB in imbalanced learning situations and examine the effect of minority class spectral separability on model performance. The results suggest that the uncertainty of XGB associated with correct classification is stable. The average probability-based margin of correct classification provided by XGB is 0.82, which is about 46.30% higher than that by random forest (RF) method (0.56). Moreover, the performance uncertainty of XGB is insensitive to spectral separability after the sample imbalance reached a certain level (minority:majority &gt; 10:100). The impact of sample imbalance on the minority class is also related to its spectral separability, and XGB performs better than RF in terms of user accuracy for the minority class with imperfect separability. The disagreement components of XGB are better and more stable than RF with imbalanced samples, especially for complex areas with more types. In addition, appropriate sample imbalance helps to improve the trade-off between the recognition accuracy of XGB and the sample cost. According to our analysis, this margin-based uncertainty assessment and disagreement performance can help users identify the confidence level and error component in similar classification performance (overall, producer, and user accuracies).
Citizen Id Card Detection using Image Processing and Optical Character Recognition

Abstract Since its emergence in 2011, Indonesian Electronic Id-card has been widely used as authentication or citizen identity. Several issues like deep difficulty in detecting id-card field and also difficulty in character recognition data in id-card should be concerned. In this research, we propose a technique detect electronic Id-card using combination of Image Processing and Optical Character Recognition (OCR). The result, we can obtain 98% accuracy of Id-card detection using our image processing techniques and OCR. This research was embedded in website interface which used by automotive company.
Connecting Knowledge Compilation Classes Width Parameters

The field of knowledge compilation establishes the tractability of many tasks by studying how to compile them to Boolean circuit classes obeying some requirements such as structuredness, decomposability, and determinism. However, in other settings such as intensional query evaluation on databases, we obtain Boolean circuits that satisfy some width bounds, e.g., they have bounded treewidth or pathwidth. In this work, we give a systematic picture of many circuit classes considered in knowledge compilation and show how they can be systematically connected to width measures, through upper and lower bounds. Our upper bounds show that bounded-treewidth circuits can be constructively converted to d-SDNNFs, in time linear in the circuit size and singly exponential in the treewidth; and that bounded-pathwidth circuits can similarly be converted to uOBDDs. We show matching lower bounds on the compilation of monotone DNF or CNF formulas to structured targets, assuming a constant bound on the arity (size of clauses) and degree (number of occurrences of each variable): any d-SDNNF (resp., SDNNF) for such a DNF (resp., CNF) must be of exponential size in its treewidth, and the same holds for uOBDDs (resp., n-OBDDs) when considering pathwidth. Unlike most previous work, our bounds apply to any formula of this class, not just a well-chosen family. Hence, we show that pathwidth and treewidth respectively characterize the efficiency of compiling monotone DNFs to uOBDDs and d-SDNNFs with compilation being singly exponential in the corresponding width parameter. We also show that our lower bounds on CNFs extend to unstructured compilation targets, with an exponential lower bound in the treewidth (resp., pathwidth) when compiling monotone CNFs of constant arity and degree to DNNFs (resp., nFBDDs).
Reinforcement Learning for Distantly Supervised Relation Extraction

Relation extraction is a task of identifying semantic relations between entity pairs from plain text, which can benefit a lot of AI applications such as knowledge base construction and answer questioning.The distant supervision strategy is introduced to automatically create large-scale training data, which inevitably suffers from noisy label problem.Recent works handle the sentence-level denoising by reinforcement learning, which regards the labels from distant supervision as the ground-truth.However, few works focus on the label-level denoising that corrects noisy labels directly.In this paper, we propose a reinforcement learning-based label denoising method for distantly supervised relation extraction.The model consists of two modules: extraction network (ENet) and policy network (PNet).The core of our label denoising is designing a policy in the PNet to obtain latent labels, where we can select the actions of using the distantly supervised labels or the predicted labels from the ENet.More concretely, the task can be modeled as an iterative process.First, the ENet predicts the relation probability, through which the model generates state representation.Second, the PNet learns the latent labels with taken actions and uses them to update the ENet.Then the optimized ENet gives the rewards to the PNet.The joint learning of two modules can obtain a reliable latent label and effectively improve the classification performance.The experimental results show that reinforcement learning is effective for noisy label correction and the proposed method can outperform the state-of-the-art relation extraction systems.
A 3.89-GOPS/mW Scalable Recurrent Neural Network Processor With Improved Efficiency on Memory and Computation

Recurrent neural networks (RNNs) perform excellently on sequencing tasks but are severely restricted by the complex computations and intensive memory consumption due to their internal fully connected topologies, thereby making it a great challenge to implement RNNs on embedded devices. In this brief, we propose an energy-efficient RNN processor by exploiting the data locality in network compression using an innovative quantified sparse matrix encoding format. Compared with the conventional processors for compressed RNNs, more than 80% of the weight fetching and matrix-vector multiplications can be further reduced in applications, such as natural language and keyword spotting. To handle different scales of RNN models without introducing significant interactive overhead, scalable hardware architecture is presented to organize multiple processor engines in a spatial fashion with the assistance of the network cross-division strategy. Synthesized in the SMIC 40LL CMOS process, the prototype processor has a total area of 0.65 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> with 95.5 kB of static random-access memory capacity. Based on the simulation, this processor achieves a peak performance of 24 GOPS and dissipates 6.16-mW power with 1.1 V supply and 200 MHz. The peak energy efficiency reaches 3.89 GOPS/mW, which is state of the art among existing RNN accelerators.
Temporal‐aware and sparsity‐tolerant hybrid collaborative recommendation method with privacy preservation

Summary With the explosive growth of cloud services, how to design effective recommendation models has become more and more important. Temporal information has been proved to be an important factor affecting recommendation performance. Actually, both of user behaviors and QoS performance of services are time‐sensitive, especially in dynamic cloud environment. However, most existing collaborative recommendation methods seldom consider temporal influence to QoS performance. Furthermore, with the ever‐increasing number of security threats in clouds, privacy preservation becomes an important problem to be addressed in recommender systems. Based on these observations, in this paper, we propose a temporal‐aware and sparsity‐tolerant hybrid collaborative recommendation method with privacy preservation, where tensor factorization‐based CF model is integrated into neighborhood‐based CF model to achieve improved recommendation performance. Specifically, temporal influence is considered into recommendation model by distinguishing short‐term QoS metrics from long‐term QoS metrics. A privacy‐aware time aggregation mechanism is adopted to preserve the sensitive time information of users. Moreover, to deal with the sparsity problem, a sparsity‐oriented data QoS rating prediction mechanism based on tensor factorization technique is applied to predict the missing or unrated QoS rating data. Finally, predictions are made based on both stable and temporal nearest neighbors. Experiments are conducted to demonstrate the prediction performance of our proposal.
An Enhanced Steganographic Code and its Application in Voice-Over-IP Steganography

Voice-over IP (VoIP) technology is a kind of digital transmission technology based on IP network. It is one of the important methods to use voice service in VoIP as steganographic carrier to ensure secure transmission. However, the traditional steganographic code has some problems, such as low embedding efficiency and weak concealment, which cannot meet the requirements of VoIP streaming media information hiding for the security of secret information. Therefore, a steganographic algorithm combining F5 and simplified wet paper code (SWPC) algorithm is proposed. The main idea is to embed secret messages in each row of the carrier matrix using the F5 algorithm, and then the SWPC algorithm is used to embed the columns according to the wet and dry characteristics of the wet paper code without affecting the results before row embedding. We use the VoIP streams encoded by the ITU-T G.729a codec as a carrier to verify the proposed scheme. The experimental results demonstrate that the proposed scheme can achieve relatively better IP speech data steganographic transparency and that it can outperform F5-WPC and SWPC approaches.
Web Browser Forensics in Google Chrome, Mozilla Firefox, and the Tor Browser Bundle

Browsers are widely used on personal computers, laptops and mobile devices. In this chapter, we seek to determine and compare which forensic artifacts can be recovered from Google Chrome, Mozilla Firefox, their respective private modes, and TOR. Our analysis was primarily conducted using FTK in order to replicate the process and abilities of a digital forensics lab with limited resources. After identical data generation across all browsers and modes of browsing in a controlled virtual environment, forensic images were captured then analyzed. This research not only extends the current field of digital forensics for which artifacts can be found in which locations, but also confirms various claims in regards to the privacy of private browsing modes. As expected, all data was recovered from regular browsing modes, very minimal data from private browsing, and almost no artifacts from TOR.
Multi-Scale Based Context-Aware Net for Action Detection

We address the problem of action detection in continuous untrimmed video streams, based on the two-stage framework: one stage for action proposals generation and the other for proposals classification and refinement. The context features inside and outside a candidate region (proposal) are critical for classification in action detection. Therefore, effective integration of these features with different scales has become a fundamental problem. We contend that different action instances and candidate proposals may need different context features. To address this issue, we present a novel multiple scales based context-aware net (MSCA-Net) to effectively classify the action proposals for action detection in this paper. For each candidate action proposal, MSCA-Net takes its multiple regions with different temporal scales as input and then generates suitable context features. Based on the “candidate-control” mechanism of LSTM, the proposed MSCA-Net specially adopts the two-branch structure: Branch1 generates multi-scale context features for each candidate proposal, whereas Branch2 utilizes the context-aware gate function to control the message passing. Extensive experiments on THUMOS’14, Charades daily and ActivityNet action detection datasets, demonstrate the effectiveness of the designed structure and show how these context features influence the detection results.
Neural recovery machine for Chinese dropped pronoun

Dropped pronouns (DPs) are ubiquitous in prodrop languages like Chinese, Japanese etc. Previous work mainly focused on painstakingly exploring the empirical features for DPs recovery. In this paper, we propose a neural recovery machine (NRM) to model and recover DPs in Chinese to avoid the non-trivial feature engineering process. The experimental results show that the proposed NRM significantly outperforms the state-of-the-art approaches on two heterogeneous datasets. Further experimental results of Chinese zero pronoun (ZP) resolution show that the performance of ZP resolution can also be improved by recovering the ZPs to DPs.
A new class of metrics for learning on real-valued and structured data

We propose a new class of metrics on sets, vectors, and functions that can be used in various stages of data mining, including exploratory data analysis, learning, and result interpretation. These new distance functions unify and generalize some of the popular metrics, such as the Jaccard and bag distances on sets, Manhattan distance on vector spaces, and Marczewski-Steinhaus distance on integrable functions. We prove that the new metrics are complete and show useful relationships with f-divergences for probability distributions. To further extend our approach to structured objects such as ontologies, we introduce information-theoretic metrics on directed acyclic graphs drawn according to a fixed probability distribution. We conduct empirical investigation to demonstrate the effectiveness on real-valued, high-dimensional, and structured data. Overall, the new metrics compare favorably to multiple similarity and dissimilarity functions traditionally used in data mining, including the Minkowski ( $$L^p$$ ) family, the fractional $$L^p$$ family, two f-divergences, cosine distance, and two correlation coefficients. We provide evidence that they are particularly appropriate for rapid processing of high-dimensional and structured data in distance-based learning.
Finite monodromy of some families of exponential sums

Given a prime p and an integer d>1, we give a numerical criterion to decide whether the ℓ-adic sheaf associated to the one-parameter exponential sums t↦∑xψ(xd+tx) over Fp has finite monodromy or not, and work out some explicit cases where this is computable.
Stochastic Normalizations as Bayesian Learning

In this work we investigate the reasons why Batch Normalization (BN) improves the generalization performance of deep networks. We argue that one major reason, distinguishing it from data-independent normalization methods, is randomness of batch statistics. This randomness appears in the parameters rather than in activations and admits an interpretation as a practical Bayesian learning. We apply this idea to other (deterministic) normalization techniques that are oblivious to the batch size. We show that their generalization performance can be improved significantly by Bayesian learning of the same form. We obtain test performance comparable to BN and, at the same time, better validation losses suitable for subsequent output uncertainty estimation through approximate Bayesian posterior.
Graph matching based anime colorization with multiple references

We propose a graph matching-based anime-colorization method from line drawings using multiple reference images. A graph structure of each frame in an input line drawing sequence helps to find correspondences of regions to be colorized between frames. However, it is difficult to find precise correspondences of whole frames only from a single reference image, because the graph structure tends to change drastically during the sequence. Therefore, our method first finds an optimal image from multiple reference images according to a cost function that represents shape similarity between nodes and compatibility of node pairs. While it is necessary to prepare several manually colored reference images, our method is still effective in reducing the effort required for colorization in anime production. We demonstrate the effectiveness of our method using actual images from our production.
Relevant Word Order Vectorization for Improved Natural Language Processing in Electronic Health Records

Electronic health records (EHR) represent a rich resource for conducting observational studies, supporting clinical trials, and more. However, much of the data contains unstructured text, presenting an obstacle to automated extraction. Natural language processing (NLP) can structure and learn from text, but NLP algorithms were not designed for the unique characteristics of EHR. Here, we propose Relevant Word Order Vectorization (RWOV) to aid with structuring. RWOV is based on finding the positional relationship between the most relevant words to predicting the class of a text. This facilitates machine learning algorithms to use the interaction of not just keywords but positional dependencies (e.g. a relevant word occurs 5 relevant words before some term of interest). As a proof-of-concept, we attempted to classify the hormone receptor status of breast cancer patients treated at the University of Kansas Medical Center, comparing RWOV to other methods using the F1 score and AUC. RWOV performed as well as, or better than other methods in all but one case. For F1 score, RWOV had a clear edge on most tasks. AUC tended to be closer, but for HER2, RWOV was significantly better for most comparisons. These results suggest RWOV should be further developed for EHR-related NLP.
Real-Time Vehicle Make and Model Recognition Using Unsupervised Feature Learning

Vehicle Make and Model Recognition (VMMR) systems provide a fully automatic framework to recognize and classify different vehicle models. Several approaches have been proposed to address this challenge; however, they can perform in restricted conditions. Here, in this paper, we formulate the VMMR as a fine-grained classification problem and propose a new configurable on-road VMMR framework. We benefit from the unsupervised feature learning methods, and in more details, we employ Locality-constraint Linear Coding (LLC) method as a fast feature encoder for encoding the input SIFT features. The proposed method can perform in real environments of different conditions. This framework can recognize 50 models of vehicles and has the advantage to classify every other vehicle not belonging to one of the specified 50 classes as an unknown vehicle. The proposed VMMR framework can be configured to become faster or more accurate based on the application domain. The proposed approach is examined on two datasets, including Iranian on-road vehicle (IORV) dataset and CompuCar dataset. The IORV dataset contains images of 50 models of vehicles captured in real situations by traffic-cameras in different weather and lighting conditions. The experimental results show the advantage of the real-time configuration of the proposed framework over the state-of-the-art methods on the IORV datatset and comparable results on CompuCar dataset with 97.5% and 98.4% accuracies, respectively and acceptable running time.
Gray Level Face Recognition Using Spatial Features

Face recognition has always been an active research area with several applications, such as security access control, human-machine interface and gender classification. More often, in real world, grayscale images have been used: video surveillance, for instance. Further, difficulties in face recognition could be due to face poses, orientation, lighting, aging etc. Faces, either in color or grayscale and are having any difficulties (as mentioned earlier) can be learned through edge map and texture, where spatial properties could be learned. Inspired from the fact that face can be considered as line-rich pattern/object, we propose novel face recognition framework that helps learn/recognize via spatial arrangements of edges (and textures as complement). To exploit edge map, we use shape context (SC) and pyramid histogram of orientated gradient (PHOG), and similarly GIST as texture features. Experimental tests (on four different publicly available datasets, such as Caltech, ColorFERET, IndianFaces and ORL) conforms that spatial features are crucial in face representation and recognition.
The Phase Noise and Clock Synchronous Carrier Frequency Offset based RF Fingerprinting for the Fake Base Station Detection

This paper presents very promising radio frequency (RF) fingerprinting techniques for the terminal-based detection of fake base station (FBS) in a wireless cellular network. The proposed schemes are based on identifying the analog hardware impairments of the transmitter. In this regard, phase noise analysis and its measured signatures reveal that low to medium end software defined radio (SDR) acting as FBS can certainly be detected if the detecting terminal's own phase noise is at least 10-dB better than the transmitter. However, for medium to high end FBS, a computationally efficient network synchronized carrier frequency offset (CFO) approach has been proposed and the measurement results confirm that the regular base stations (RBS) that are clock synchronized show identical CFO values whereas, the FBS show large and random offset values. Furthermore, the CFO stability (Frequency offset vs. time) signatures illustrate that an FBS running by its own lazy clock show a large instability in the frequency offset values and even in case of a precision clock, fluctuations vanish though the stabilized offset is still large enough for a user terminal to distinguish the FBS from the RBS.