Reviewerly/exHarmony huggingface dataset:
Achieve Efficient and Verifiable Conjunctive and Fuzzy Queries over Encrypted Data in Cloud

Due to the high demands of searchability over encrypted data, searchable encryption (SE) has recently received considerable attention and been widely suggested in encrypted cloud storage. Typically, the cloud server is assumed to be honest-but-curious in most SE-based cloud storage systems, i.e., the cloud server should follow the protocol to return valid and complete search results to users. However, this trust assumption is not always true due to some unanticipated situations, such as misconfigurations and malfunctions. Therefore, the function of verifiability of search results becomes crucial for the success of SE-based cloud storage systems. For this reason, many verifiable SE schemes have been proposed; however, they either fail to support query operators “OR”, “AND”, “ <inline-formula><tex-math notation="LaTeX">$\ast$</tex-math></inline-formula> ” and “?” simultaneously, or require many time-consuming operations. Aiming at addressing this problem, in this paper, we propose a new verifiable SE scheme for encrypted cloud storage. The proposed scheme is characterized by integrating various techniques, i.e., bitmap index, radix tree, format preserving encryption, keyed-hash message authentication code and symmetric key encryption, for achieving efficient and verifiable conjunctive and fuzzy queries over encrypted data in the cloud. Detailed security analysis shows that our proposed scheme holds the confidentiality of data and verifiability of search results at the same time. In addition, extensive experiments are conducted, and the results demonstrate our proposed scheme is efficient and suitable for users to retrieve their data from the cloud to their mobile devices.
Knowing-how under uncertainty

Logical systems containing knowledge and know-how modalities have been investigated in several recent works. Independently, epistemic modal logics in which every knowledge modality is labeled with a degree of uncertainty have been proposed. This article combines these two research lines by introducing a bimodal logic containing knowledge and know-how modalities, both labeled with a degree of uncertainty. The main technical results are soundness, completeness, and incompleteness of the proposed logical system with respect to two classes of semantics.
Subjective Evaluation of Media Consumer Vulnerability to Fake Audiovisual Content

Facilitation of fake face generation in recent years, thanks to advancements in computer graphics and artificial intelligence, raises concerns about malicious use of these techniques for personal or political gains. Media consumers are exposed to hours of audiovisual content daily, while their vulnerability to fake audiovisual content is not yet fully studied and understood. In contrast, many recent automated fake content generation techniques are readily accessible to the public. A first step to address this vulnerability is to study the effectiveness of existing methods in passing human judgment. To this end, we examined the performance of 30 participants in the detection of 48 real and fake videos. The fake videos were sourced from six different methods of generation and were collected from a public video sharing website <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , ranging from prosthetic makeup to Deepfakes. Our results show that the participants failed to detect two different types of fake videos. However, participants' detection performance improves when they know of the displayed individual or when a biometric reference video (introducing the individual and its behavior) is available to them during the test.
Differential Privacy-Preserving Density Peaks Clustering Based on Shared Near Neighbors Similarity

Density peaks clustering is a novel and efficient density-based clustering algorithm. However, the problem of the sensitive information leakage and the associated security risk with the applications of clustering methods is rarely considered. To address the problem, we proposed differential privacy-preserving density peaks' clustering based on the shared near neighbors similarity method in this paper. First, the Euclidean distance and the shared near neighbors similarity were combined to define the local density of a sample, and the Laplace noise was added to the local density and the shortest distance to protect privacy. Second, the process of cluster center selection was optimized to select the initial cluster centers based on the neighborhood information. Finally, each sample was assigned to the cluster as its nearest neighbor with higher local density. The experimental results on both the UCI and synthetic datasets show that compared with other algorithms, our method more effectively protects the data privacy and improves the quality of the clustering results.
Single image de-hazing using adaptive dynamic stochastic resonance and wavelet-based fusion

This paper presents enhancement-based single image de-hazing algorithms using dynamic stochastic resonance (DSR) and wavelet fusion. The initial algorithm employs DSR controlled with image perceptual quality metrics to automatically guide the process in an adaptive way via intersecting curves. The extent of de-hazing is determined by a parameter dynamically computed from the relevant regions of the segmented hazy image. The second algorithm combines the strengths of the DSR-based algorithm, partial differential equation (PDE), fractional calculus (FC) and multi-scale filtering-based de-hazing techniques via wavelet fusion. The problems addressed include colour distortion, image darkening, halo effects, edge, noise and sky region over-enhancement. Results indicate that the proposed approaches yield comparable or better results than several de-hazing algorithms from the literature in terms of visual clarity, objective metrics and runtime.
Multi-Site User Behavior Modeling and Its Application in Video Recommendation

As online video service continues to grow in popularity, video content providers compete hard for more eyeball engagement. Some users visit multiple video sites to enjoy videos of their interest while some visit exclusively one site. However, due to the isolation of data, mining and exploiting user behaviors in multiple video websites remain unexplored so far. In this work, we try to model user preferences in six popular video websites with user viewing records obtained from a large ISP in China. The empirical study shows that users exhibit both consistent cross-site interests as well as site-specific interests. To represent this dichotomous pattern of user preferences, we propose a generative model of Multi-site Probabilistic Factorization (MPF) to capture both the cross-site as well as site-specific preferences. Besides, we discuss the design principle of our model by analyzing the sources of the observed site-specific user preferences, namely, site peculiarity and data sparsity. Through conducting extensive recommendation validation, we show that our MPF model achieves the best results compared to several other state-of-the-art factorization models with significant improvements of F-measure by 12.96, 8.24 and 6.88 percent, respectively. Our findings provide insights on the value of integrating user data from multiple sites, which stimulates collaboration between video service providers.
Segmentation Guided Image-to-Image Translation with Adversarial Networks

Recently image-to-image translation has received increasing attention, which aims to map images in one domain to another specific one. Existing methods mainly solve this task via a deep generative model, and focus on exploring the relationship between different domains. However, these methods neglect to utilize higher-level and instance-specific information to guide the training process, leading to a great deal of unrealistic generated images of low quality. Existing methods also lack of spatial controllability during translation. To address these challenge, we propose a novel Segmentation Guided Generative Adversarial Networks (SGGAN), which leverages semantic segmentation to further boost the generation performance and provide spatial mapping. In particular, a segmentor network is designed to impose semantic information on the generated images. Experimental results on multi-domain face image translation task empirically demonstrate our ability of the spatial modification and our superiority in image quality over several state-of-the-art methods.
A Tightly-Secure Lattice-Based Multisignature

Multisignatures enable multiple users to sign a message in an interactive manner. Many instantiations are proposed for multisignatures, however, most of them are quantum-insecure, because these are based on the integer factoring assumption or the discrete logarithm assumption. Although there exist some constructions based on the lattice problems, which are believed to be quantum-secure, their security reductions are loose. In this paper, we construct a first lattice-based multisignature which has a tight security reduction. Our basic strategy is combining the multisignature scheme proposed by El Bansarkhani and Sturm with the lattice-based standard signature scheme by Abdalla, Fouque, Lyubashevsky and Tibouchi which has a tight security reduction from the Ring-LWE (Ring Learning with Error) assumption.
Are quality diversity algorithms better at generating stepping stones than objective-based search?

The route to the solution of complex design problems often lies through intermediate "stepping stones" which bear little resemblance to the final solution. By greedily following the path of greatest fitness improvement, objective-based search overlooks and discards stepping stones which might be critical to solving the problem. Here, we hypothesize that Quality Diversity (QD) algorithms are a better way to generate stepping stones than objective-based search: by maintaining a large set of solutions which are of high-quality, but phenotypically different, these algorithms collect promising stepping stones while protecting them in their own "ecological niche". To demonstrate the capabilities of QD we revisit the challenge of recreating images produced by user-driven evolution, a classic challenge which spurred work in novelty search and illustrated the limits of objective-based search. We show that QD far outperforms objective-based search in matching user-evolved images. Further, our results suggest some intriguing possibilities for leveraging the diversity of solutions created by QD.
Deep-Learning-Based Agile Teaching Framework of Software Development Courses in Computer Science Education

Compared with other courses, especially non-engineering ones, software development courses teaching (SDCT) has many unique characteristics, such as higher requirement to students' logical reasoning, the need for students to have comprehensive problem-solving capabilities, good communication and teamwork skills, etc. The practices of Agile Methodology can be compatible with these characteristics. Therefore, it is a matter of course to integrate Agile Methodology with SDCT. This paper analyses the problems and difficulties in the practices of applying agile teaching in SDCT. In addition, it uses an agile teaching project graph instead of linear teaching schedule to control teaching progress. Moreover, a deep generative model is used to obtain the abstract representation of students' implicit skill status so that the construction and implementation of agile teaching project graph can be carried out. Finally, a deep-learning-based agile teaching framework for SDCT is proposed, which has the potential to positively transform SDCT's future.
Indoor Image Recognition and Classification via Deep Convolutional Neural Network

Indoor navigation (or way finding) still presents a great challenge for autonomous robotic systems and for visually impaired people (VIP). Indeed, the VIP is often enabling to see visual cues such as informational signs, landmarks or geometrical shapes. A Deep Convolution Neural Network (DCNN) has been proven to be highly effective and has achieved an outstanding success comparing to other techniques in object recognition. This paper proposes a robust approach for objects’ classification using a DCNN model. Experimental results in real indoor images with natural illumination (the MCIn-door 20000 dataset) show that the proposed DCNN model achieves the accuracy of 93.7% in objects classification.
Camera Identification from HDR Images

Performance of camera identification methods based on PRNU is very sensitive to geometric operations applied to images during acquisition and processing. Handling images that have been geometrically transformed, such as rotated, downsampled, and/or cropped requires overcoming pixel desynchronization problem. This work expands applicability of camera identification methods based on PRNU to the class of HDR images. Geometric transformations in HDR images revealed in this work are reversed in a series of steps involving block-wise PRNU matching. Efficiency of this method is then tested on HDR images from publicly available UNIFI dataset spanning 26 cameras of mobile devices.
Efficiency-Aware Watermarking using Different Wavelet Families for the Internet of Things

Efficient image transfer in the Internet of Things (IoT) Era, has many requirements that need to be satisfied based on the nature of each underlying application. Internet of Things is a research paradigm that empowers data, data fusion and encompasses embedded images, which need to be transmitted to numerous interconnected devices. The risks of contravention of owner's rights are increasing, while data transfer creates new demands for securing the Internet of Things. In this respect, watermarking schemes can be used to save those rights from illegal usage and copying of digital image data. For IoT applications digital watermarking can be used to guarantee that the data used is protected and the rights of the owner are secured (i.e. both user and machine generated). This work proposes a novel watermarking scheme based on the biorthogonal family, (biorthogonal 2.2, biorthogonal 3.5 and biorthogonal 5.5) wavelet transform, while it uses a convolution for symlets wavelet transform and coiflets wavelet transform. These wavelet family approaches are highly robust against various types of attacks (both passive and active), for the prevention of the piracy and authentication of the data over IoT ecosystems. The proposed framework is thoroughly evaluated showing great robustness against attacks and allowing a higher level of protection compared to other available frameworks and schemes.
Visual Sensor Placement Based on Risk Maps

The research work on camera placement has focused on maximizing the coverage or minimizing the installation cost of video surveillance systems. Typical placement schemes mount surveillance cameras with no emphasis on the coverage demand divergences, which impacts the system's cost and efficiency. This paper addresses the camera placement problem based on an inverse modeling taxonomy. Thus, rather than performing the optimization on uniformly distributed grids, this paper introduces an underlying mechanism to elaborate the security sensitive zones prior to the coverage optimization. The outcome of the prioritization process is termed as Risk Maps. Obtained empirical results show the reliability of the placement using inverse modeling. Finally, the validation of the proposed placement scheme is carried out in a constraint environment.
Sensor-Based Control Using an Image Point and Distance Features for Rivet-in-Hole Insertion

This paper addresses the problem of stabilizing the machine at a pose specified by a reference image and four laser sensor readings. The system configuration combining monocular vision and laser pointers is applied for pose estimation of the end-effector as well as providing the control objective. Specific features are selected to reflect the pose of the end-effector in Cartesian space, and an adaptive controller is designed to implement the alignment between the end-effector and a stationary object for further assembly. The complete control scheme is assessed through simulations and experiments conducted on an automatic drilling and riveting system, the results demonstrate that the assembly task can be accomplished accurately and efficiently even with coarsely calibrated camera and laser pointers. Furthermore, decoupled results can be gained thanks to the selected features.
Sensitive Information Topics-Based Sentiment Analysis Method for Big Data

With the rapid development of the Internet, more and more users expressed their views on the Internet.Therefore, the big data of texts are generated on the Internet.In the era of big data, mining the sentiment tendencies contained in massive texts on the Internet through natural language processing technology has become an important way of public opinion supervision.In this paper, the sensitive information topics-based sentiment analysis method for big data is proposed.This method integrates topic semantic information into text representation through a neural network model.The attention mechanism is introduced into the neural network, and context-aware vector is introduced to calculate the weight of each word.In addition, in order to make the model more adaptable, the method of sentiment dictionary tagging is used to obtain the training data.The experimental results show that the proposed model can effectively improve the accuracy of sentiment analysis results.
Large Scale Sign Language Interpretation

Sign language is the primary way of communication between deaf people, but the majority of hearing people do not know how to sign. The reliance of deaf people on interpreters is both inconvenient and cost inefficient. Many research groups have experimented with using machine learning to develop automatic translators. Largely, these efforts have been constrained to restrictive dictionaries or insufficiently small signers or signed content. We introduce the world's largest sign language dataset to date- a collection of 50,000 video snippets taken from a pool of 10,000 unique utterances signed by 50 signers. We further propose several sequence-to-sequence deep learning approaches to automatically translate from Chinese sign language to both English and Mandarin written text. These methods utilize body joint position, facial expression, as well as finger articulation. While models can overfit on training sets, generalization to unforeseen utterances remains challenging with real-world data. The introduced dataset and methods demonstrate how modern machine learning methods are able to close the communication gap between deaf and hearing people.
Developing speaker independent ASR system using limited data through prosody modification based on fuzzy classification of spectral bins

The primary motive of this study is to develop an automatic speech recognition (ASR) system using limited amount of speech data such that it is least affected by speaker-dependent acoustic variations. The two factors contributing towards inter-speaker variability that are focused upon in this work are pitch and speaking-rate variations. In order to simulate such a limited data scenario, an ASR system is trained on adults' speech and tested using speech data from adult as well as child speakers. Compared to adults' speech test case, the recognition rates are noted to be extremely degraded when the test speech is from child speakers. The observed degradation is due to large differences in pitch and speaking-rate between adults' and children's speech along with other factors leading to inter-speaker acoustic variations. To overcome the mismatch in pitch and speaking-rate, two different approaches are proposed in this paper. In the first approach, the pitch and speaking-rate of children's speech test set are explicitly modified using a recently proposed prosody modification technique that exploits fuzzy classification of spectral bins. In the second approach, pitch and speaking-rate of the training data are modified to create newer versions of the data. In order to capture greater acoustic variability, the original and the modified versions are then pooled together. The ASR system trained on augmented data is noted to be more robust towards pitch and speaking-rate variations. Consequently, relative improvements of 17% and 31% over the baseline are obtained on decoding adults' and children's speech test sets, respectively.
Cloud computing in high tech startups: evidence from a case study

This paper focuses on the implementation of cloud-based systems in startups to answer how and why hi-tech startups use cloud computing, an overlooked question to date. Thus, referring to an extended version of Ross and Blumenstein [2015. "Cloud Computing as a Facilitator of SME Entrepreneurship." Technology Analysis & Strategic Management 27 (1): 87–101] framework, encompassing issues of context and culture following Spanò et al. [2019. "Context, Culture and Control: A Case Study on Accounting Change in an Italian Regional Health Service." Journal of Management and Governance. doi:10.1007/s10997-019-09458-0], we analyse cloud adoption in startup firms and rely upon a case study of an Italian hi-tech startup. The case study allowed us to tap into the main issues relating to cloud implementation that is increased opportunities, reduced costs, scalability, access to global markets, and access to international venture capital. The results offer interesting insights to the literature on cloud computing, and also have some managerial implications in that they better unfold the effects played by context and culture.
Cyber Threat Investigation of SCADA Modbus Activities

The use of inter-connectivity of Supervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS) networks in smart technologies have exposed them to a large variety of security threats. Furthermore, very few investigations are done in this field from the Internet (cyber) perspective. Therefore, this paper investigates unauthorized, malicious and suspicious SCADA activities by leveraging the darknet address space. In particular, this work investigates Modbus service, which is a de facto standard protocol for communication and it is the most available and used to connect electronic devices in critical and industrial infrastructures. This study is based on real Internet data collected throughout a one-month period. Among the 8 various inferred scanning activities, we find that TCP distributed portscan is the only non-typical Modbus scan. Furthermore, our analyses fingerprint a large variety of Modbus scanners and uncover 6 other services that tag along with Modbus 74% of the time. Finally, we list case studies related to synchronized and automated SCADA scanning campaigns originated from unknown sources.