Reviewerly/exHarmony huggingface dataset:
ρ

Applications in the cloud are vulnerable to several attack scenarios. In one possibility, an untrusted cloud operator can examine addresses on the memory bus and use this information leak to violate privacy guarantees, even if data is encrypted. The Oblivious RAM (ORAM) construct was introduced to eliminate such information leak and these frameworks have seen many innovations in recent years. In spite of these innovations, the overhead associated with ORAM is very significant. This paper takes a step forward in reducing ORAM memory bandwidth overheads. We make the case that, similar to a cache hierarchy, a lightweight ORAM that fronts the full-fledged ORAM provides a boost in efficiency. The lightweight ORAM has a smaller capacity and smaller depth, and it can relax some of the many constraints imposed on the full-fledged ORAM. This yields a 2-level hierarchy with a relaxed ORAM and a full ORAM. The relaxed ORAM adopts design parameters that are optimized for efficiency and not capacity. We introduce a novel metadata management technique to further reduce the bandwidth for relaxed ORAM access. Relaxed ORAM accesses preserve the indistinguishability property and are equipped with an integrity verification system. Finally, to eliminate information leakage through LLC and relaxed ORAM hit rates, we introduce a deterministic memory scheduling policy. On a suite of memory-intensive applications, we show that the best Relaxed Hierarchical ORAM (ρ) model yields a performance improvement of 50%, relative to a Freecursive ORAM baseline.
Traffic Analysis Attack for Identifying Users’ Online Activities

Traffic analysis is a serious threat over the network. An attacker can analyze network traffic patterns to infer packet's content, even though it is encrypted. This article demonstrates a traffic analysis attack that exploits vulnerabilities in encrypted smartphone communications to infer the web pages being visited by a user.
The application of Gammatone frequency cepstral coefficients for forensic voice comparison under noisy conditions

Compared with humans, who have more powerful auditory ability in discriminating and identifying speakers in noisy environments, traditional forensic automatic speaker recognizers do not perform well when dealing with noisy recordings. This paper proposes a GMM-UBM Forensic Automatic Speaker Recognition (FASR) System to reduce the effect of noise on performance. The system uses Gammatone Frequency Cepstral Coefficients (GFCC) based on an auditory periphery model and also incorporates a Principal Component Analysis (PCA) algorithm. The system was tested and validated using Mandarin voice databases compromised with different levels of white noise and office noise. The performance of the system was compared with a baseline system using Mel Frequency Cepstral Coefficients (MFCC) and also PCA under the same conditions. The results show that the performance of the combined GFCC system achieved a substantial improvement when compared with the baseline MFCC system under conditions of a high level of office noise.
Magic-Wall: Visualizing Room Decoration by Enhanced Wall Segmentation

This paper presents an intelligent system named Magic-wall, which enables visualization of the effect of room decoration automatically. Concretely, given an image of the indoor scene and a preferred color, the Magic-wall can automatically locate the wall regions in the image and smoothly replace the existing wall with the required one. The key idea of the proposed Magic-wall is to leverage visual semantics to guide the entire process of color substitution, including wall segmentation and replacement. To strengthen the reality of visualization, we make the following contributions. First, we propose an edge-aware fully convolutional neural network (Edge-aware-FCN) for indoor semantic scene parsing, in which a novel edge-prior branch is introduced to identify the boundary of different semantic regions better. To further polish the details between the wall and other semantic regions, we leverage the output of Edge-aware-FCN as the prior knowledge, concatenating with the image to form a new input for the Enhanced-Net. In such a case, the Enhanced-Net is able to capture more semantic-aware information from the input and polish some ambiguous regions. Finally, to naturally replace the color of the original walls, a simple yet effective color space conversion method is proposed for replacement with brightness reserved. We build a new indoor scene dataset upon ADE20K for training and testing, which includes six semantic labels. Extensive experimental evaluations and visualizations well demonstrate that the proposed Magic-wall is effective and can automatically generate a set of visually pleasing results.
Tangent-V: Math Formula Image Search Using Line-of-Sight Graphs

We present a visual search engine for graphics such as math, chemical diagrams, and figures. Graphics are represented using Line-of-Sight (LOS) graphs, with symbols connected only when they can 'see' each other along an unobstructed line. Symbol identities may be provided (e.g., in PDF) or taken from Optical Character Recognition applied to images. Graphics are indexed by pairs of symbols that 'see' each other using their labels, spatial displacement, and size ratio. Retrieval has two layers: the first matches query symbol pairs in an inverted index, while the second aligns candidates with the query and scores the resulting matches using the identity and relative position of symbols. For PDFs, we also introduce a new tool that quickly extracts characters and their locations. We have applied our model to the NTCIR-12 Wikipedia Formula Browsing Task, and found that the method can locate relevant matches without unification of symbols or using a math expression grammar. In the future, one might index LOS graphs for entire pages and search for text and graphics. Our source code has been made publicly available.
ProbeGuard

Many modern defenses against code reuse rely on hiding sensitive data such as shadow stacks in a huge memory address space. While much more efficient than traditional integrity-based defenses, these solutions are vulnerable to probing attacks which quickly locate the hidden data and compromise security. This has led researchers to question the value of information hiding in real-world software security. Instead, we argue that such a limitation is not fundamental and that information hiding and integrity-based defenses are two extremes of a continuous spectrum of solutions. We propose a solution, ProbeGuard, that automatically balances performance and security by deploying an existing information hiding based baseline defense and then incrementally moving to more powerful integrity-based defenses by hotpatching when probing attacks occur. ProbeGuard is efficient, provides strong security, and gracefully trades off performance upon encountering more probing primitives.
Seedless Fruit Is the Sweetest: Random Number Generation, Revisited

The need for high-quality randomness in cryptography makes random-number generation one of its most fundamental tasks. A recent important line of work (initiated by Dodis et al., CCS '13) focuses on the notion of robustness for pseudorandom number generators (PRNGs) with inputs. These are primitives that use various sources to accumulate sufficient entropy into a state, from which pseudorandom bits are extracted. Robustness ensures that PRNGs remain secure even under state compromise and adversarial control of entropy sources. However, the achievability of robustness inherently depends on a seed, or, alternatively, on an ideal primitive (e.g., a random oracle), independent of the source of entropy. Both assumptions are problematic: seed generation requires randomness to start with, and it is arguable whether the seed or the ideal primitive can be kept independent of the source. This paper resolves this dilemma by putting forward new notions of robustness which enable both (1) seedless PRNGs and (2) primitive-dependent adversarial sources of entropy. To bypass obvious impossibility results, we make a realistic compromise by requiring that the source produce sufficient entropy even given its evaluations of the underlying primitive. We also provide natural, practical, and provably secure constructions based on hash-function designs from compression functions, block ciphers, and permutations. Our constructions can be instantiated with minimal changes to industry-standard hash functions SHA-2 and SHA-3, or key derivation function HKDF, and can be downgraded to (online) seedless randomness extractors, which are of independent interest. On the way we consider both a computational variant of robustness, where attackers only make a bounded number of queries to the ideal primitive, as well as a new information-theoretic variant, which dispenses with this assumption to a certain extent, at the price of requiring a high rate of injected weak randomness (as it is, e.g., plausible on Intel's on-chip RNG). The latter notion enables applications such as everlasting security. Finally, we show that the CBC extractor, used by Intel's on-chip RNG, is provably insecure in our model.
Automatic geometry calibration for multi‐projector display systems with arbitrary continuous curved surfaces

A large-scale multi-projector display system offers high-resolution, high-brightness and immersive visualisation for realistic experience to end users. It has been demonstrated to be effective tackling the conflict between the increasing demands of super-resolution display and the resolution limitation of a single display system. However, there is still no standardisation method for curved-surface projection screen. In this study, we propose a novel approach for calibrating multi-projector display systems, which have curved surfaces. First, based on a detailed analysis on arbitrarily curved surfaces, we present a three-dimensional reconstruction algorithm based on Bezier surface models. Then, for fully utilising the projection area of each projector, we propose a novel curved-surface stitching algorithm to achieve geometry seamlessness of multi-projector display systems. Experimental results show that by constructing local Bessel models for the curved screen, the proposed method performs better than traditional approaches, i.e. the new method achieves geometric calibration with higher accuracy. The proposed method of modelling projection screen and the corresponding automatic geometric correction scheme effectively increase the utilisation ratio of the original projection area of each projector and improve the calibration accuracy of multi-projector system with continuous curved surface.
LICD: A Language-Independent Approach for Aspect Category Detection

Aspect-based sentiment analysis (ABSA) deals with processing and summarizing customer reviews and has been a topic of interest in recent years. Given a set of predefined categories, Aspect Category Detection (ACD), as a subtask of ABSA, aims to assign a subset of these categories to a given review sentence. Thanks to the existence of websites such as Yelp and TripAdvisor, there exist a huge amount of reviews in several languages, and therefore the need for language-independent methods in this task seems necessary. In this paper, we propose Language-Independent Category Detector (LICD), a supervised method based on text matching without the need for any language-specific tools and hand-crafted features for identifying aspect categories. For a given sentence, our proposed method performs ACD based on two hypotheses: First, a category should be assigned to a sentence if there is a high semantic similarity between the sentence and a set of representative words of that category. Second, a category should be assigned to a sentence if sentences with high semantic and structural similarity to that sentence belong to that category. To apply the former hypothesis, we used soft cosine measure, and for the latter, word mover's distance measure is utilized. Using these two measures, for a given sentence we calculate a set of similarity scores as features for a one-vs-all logistic regression classifier per category. Experimental results on the multilingual SemEval-2016 datasets in the restaurant domain demonstrate that our approach outperforms baseline methods in English, Russian, and Dutch languages, and obtains competitive results with the strong deep neural network-based baselines in French, Turkish, and Spanish languages.
The Structure of Deep Neural Network for Interpretable Transfer Learning

Training a deep neural network requires a large amount of high-quality data and time. However, most of the real tasks don't have enough labeled data to train each complex model. To solve this problem, transfer learning reuses the pretrained model on a new task. However, one weakness of transfer learning is that it applies a pretrained model to a new task without understanding the output of an existing model. This may cause a lack of interpretability in training deep neural network. In this paper, we propose a technique to improve the interpretability in transfer learning tasks. We define the interpretable features and use it to train model to a new task. Thus, we will be able to explain the relationship between the source and target domain in a transfer learning task. Feature Network (FN) consists of Feature Extraction Layer and a single mapping layer that connects the features extracted from the source domain to the target domain. We examined the interpretability of the transfer learning by applying pretrained model with defined features to Korean characters classification.
Measuring and Analyzing Search Engine Poisoning of Linguistic Collisions

Misspelled keywords have become an appealing target in search poisoning, since they are less competitive to promote than the correct queries and account for a considerable amount of search traffic. Search engines have adopted several countermeasure strategies, e.g., Google applies automated corrections on queried keywords and returns search results of the corrected versions directly. However, a sophisticated class of attack, which we term as linguistic-collision misspelling, can evade auto-correction and poison search results. Cybercriminals target special queries where the misspelled terms are existent words, even in other languages (e.g., "idobe", a misspelling of the English word "adobe", is a legitimate word in the Nigerian language). In this paper, we perform the first large-scale analysis on linguistic-collision search poisoning attacks. In particular, we check 1.77 million misspelled search terms on Google and Baidu and analyze both English and Chinese languages, which are the top two languages used by Internet users. We leverage edit distance operations and linguistic properties to generate misspelling candidates. To more efficiently identify linguistic-collision search terms, we design a deep learning model that can improve collection rate by 2.84x compared to random sampling. Our results show that the abuse is prevalent: around 1.19% of linguistic-collision search terms on Google and Baidu have results on the first page directing to malicious websites. We also find that cybercriminals mainly target categories of gambling, drugs, and adult content. Mobile-device users disproportionately search for misspelled keywords, presumably due to small screen for input. Our work highlights this new class of search engine poisoning and provides insights to help mitigate the threat.
Structural and Nominal Cross-Language Clone Detection

In this paper we address the challenge of cross-language clone detection. Due to the rise of cross-language libraries and applications (e.g., apps written for both Android and iPhone), it has become common for code fragments in one language to be ported over into another language in an extension of the usual "copy and paste" coding methodology. As with single-language clones, it is important to be able to detect these cross-language clones. However there are many real-world cross-language clones that existing techniques cannot detect. We describe the first general, cross-language algorithm that combines both structural and nominal similarity to find syntactic clones, thereby enabling more complete clone detection than any existing technique. This algorithm also performs comparably to the state of the art in single-language clone detection when applied to single-language source code; thus it generalizes the state of the art in clone detection to detect both single- and cross-language clones using one technique.
Prepare for Takeoff: Improving Audit Efficiency and Effectiveness with Drone-enabled Inventory Audit Procedures

Auditors increasingly employ technologies to improve audit quality. Using a design science approach, we examine whether using drones and automated counting software can improve audit quality and thereby improve financial reporting. We assess three dimensions of audit quality—efficiency, effectiveness, and quality of audit documentation. We provide evidence that auditors can perform inventory counts with these technologies much more efficiently than they can with manual techniques, decreasing count time in our study from 681 hours to 19 hours. At the same time, auditors can maintain or improve audit effectiveness with these technologies, decreasing error rates in our study from 0.15% to 0.03%, while providing higher-quality audit documentation. Interviews with national-level partners and audit standard setters highlight impediments to adopting these technologies, including firm concerns about being first movers combined with inability of standard setters to provide guidance at a pace that matches the pace of technological development. Collectively, our results suggest that technology-enabled inventory audits can improve audit quality, and further regulatory guidance on using such technologies would enhance adoption.
A<i>k</i>-mean characteristic function for optimizing short- and long-term-average-ratio-based detection of microseismic events

Event detection is an essential component of microseismic data analysis. This process is typically carried out using a short- and long-term-average-ratio (STA/LTA) method, which is simple and computationally efficient but often yields inconsistent results for noisy data sets. We have aimed to optimize the performance of the STA/LTA method by testing different input forms of 3C waveform data and different characteristic functions (CFs), including a proposed [Formula: see text]-mean CF. These tests are evaluated using receiver operating characteristic (ROC) analysis and are compared based on synthetic and field data examples. Our analysis indicates that the STA/LTA method using a [Formula: see text]-mean CF improves the detection sensitivity and yields more robust event detection on noisy data sets than some previous approaches. In addition, microseismic events are detected efficiently on field data examples using the same detection threshold obtained from the ROC analysis on synthetic data examples. We recommend the use of the Youden index based on ROC analysis using a training subset, extracted from the continuous data, to further improve the detection threshold for field microseismic data.
Machine Learning Based Analysis of Finnish World War II Photographers

In this paper, we demonstrate the benefits of using state-of-the-art machine learning methods in the analysis of historical photo archives. Specifically, we analyze prominent Finnish World War II photographers, who have captured high numbers of photographs in the publicly available Finnish Wartime Photograph Archive, which contains 160,000 photographs from Finnish Winter, Continuation, and Lapland Wars captures in 1939-1945. We were able to find some special characteristics for different photographers in terms of their typical photo content and framing (e.g., close-ups vs. overall shots, number of people). Furthermore, we managed to train a neural network that can successfully recognize the photographer from some of the photos, which shows that such photos are indeed characteristic for certain photographers. We further analyzed the similarities and differences between the photographers using the features extracted from the photographer classifier network. We make our annotations and analysis pipeline publicly available, in an effort to introduce this new research problem to the machine learning and computer vision communities and facilitate future research in historical and societal studies over the photo archives.
A Further Optimized Mix Column Architecture Design for the Advanced Encryption Standard

With the evolution of The Internet, there has been a huge spurt in online transactions and also an increase in sharing of private, confidential and sensitive information over the web. This in turn has increased the requirement of highly secure and swift methodologies to protect such data using modern cryptographic techniques such as the Advanced Encryption Standard (AES). In order to achieve the same, this paper discusses significant and novel modifications to the existing hardware architecture of the mix column step of the AES algorithm. By adopting these techniques, a speed efficiency of over 1.41 times was achieved as compared to previous algorithms. Moreover, in a VLSI perspective, an average area optimization of 3 times was also achieved. All experiments were conducted using the Xilinx Artix-7 series of FPGA.
Long Text Analysis Using Sliced Recurrent Neural Networks with Breaking Point Information Enrichment

Sliced recurrent neural networks (SRNNs) are the state-of-the-art efficient solution for long text analysis tasks; however, their slicing operations inevitably result in long-term dependency loss in lower-level networks and thus limit their accuracy. Therefore, we propose a breaking point information enrichment mechanism to strengthen dependencies between sliced subsequences without hindering parallelization. Then, the resulting BPIE-SRNN model is further extended to a bidirectional model, BPIE-BiSRNN, to utilize the dependency information in not only the previous but also the following contexts. Experiments on four large public real-world datasets demonstrate that the BPIE-SRNN and BPIE-BiSRNN models always achieve a much better accuracy than SRNNs and BiSRNNs, while maintaining a superior training efficiency.
Multi-scale Spatial-temporal Network for Person Re-identification

Video-based person re-identification (ReID) is an important task, which has received much attention in recent years due to its efficiency in the field of surveillance. Researchers have employed many effective approaches for video-based person ReID, but there are still two problems. Firstly, the same pedestrian in the video sequences differs in size. Secondly, traditional RNNs can only process one-dimension features, which are not suitable for dealing with video sequences. To solve above problems, we propose a new network called Multi-scale Spatial-Temporal Network (MSTN), which combines multi-scale feature extractor and CLSTM together to tackle the discrepant sizes of pedestrians and extract more representative temporal information for the video sequences. We conduct the experiments on the iLIDS-VID, PRID-2011 and MARS datasets, and our approach outperforms state-of-the-art methods by a large margin.