Reviewerly/exHarmony huggingface dataset:
Efficient Attribute-Based Data Sharing Scheme with Hidden Access Structures

Abstract Online data sharing has become a research hotspot while cloud computing is getting more and more popular. As a promising encryption technique to guarantee the security shared data and to realize flexible fine-grained access control, ciphertext-policy attribute-based encryption (CP-ABE) has drawn wide attentions. However, there is a drawback preventing CP-ABE from being applied to cloud applications. In CP-ABE, the access structure is included in the ciphertext, and it may disclose user’s privacy. In this paper, we find a more efficient method to connect ABE with inner product encryption and adopt several techniques to ensure the expressiveness of access structure, the efficiency and security of our scheme. We are the first to present a secure, efficient fine-grained access control scheme with hidden access structure, the access structure can be expressed as AND-gates on multi-valued attributes with wildcard. We conceal the entire attribute instead of only its values in the access structure. Besides, our scheme has obvious advantages in efficiency compared with related schemes. Our scheme can make data sharing secure and efficient, which can be verified from the analysis of security and performance.
Engaging with and reflecting on the materiality of digital media technologies: Repair and fair production

How do people think about and engage with the materiality of digital media technologies and thereby try to transform the devices and society? The article discusses this question by presenting the results of two qualitative studies in which people reflect on and engage with the materiality of media technologies. In the first case, the repairing of media devices in Repair Cafés was analyzed, in the second, the focus was on the production and appropriation of the Fairphone, a smartphone which should be produced under fair working conditions. In both initiatives people reflect on the materiality of media technologies, criticize the production and disposal of the devices, and engage with the materiality of the apparatuses in order to change not only the technologies but also society as a whole by contributing to sustainability and the “good life.” The article adds to the field dealing with materiality and digital media technologies.
Continuous Modeling of Affordances in a Symbolic Knowledge Base

As robots start to execute complex manipulation tasks, they are expected to improve their skill set over time as humans do. A prominent approach to accomplish this is having robots to keep models of their actions based on their experiences in order to improve their action executions in the future. In this paper, we present such a methodology where robots start to execute some actions with random parameters and record their generic execution logs with semantic annotations in a symbolic knowledge base for robots. Using the data inside logs, multivariate Gaussian mixture models are fitted to the high-level action parameters for later executions. These affordance models are being updated whenever a new execution is carried on. In essence, robots can use these continuously-updated probabilistic model for improving their actions To prove the applicability we demonstrate opening-a-fridge-door experiments with a PR2 robot.
Rating-based Preference Elicitation for Recommendation of Stress Intervention

In recent years, recommender systems have emerged as a key component for personalization in health applications. Central in the development of recommender systems is rating-based preference elicitation, based both on single-criterion and multi-criteria rating. Though its use has already been studied in various domains of recommender systems, far too little attention has been paid to preference elicitation in health recommender systems~(HRS). The purpose of this paper is to develop a better understanding of this preference elicitation by studying the criteria that users consider when they rate a health promotion recommendation from HRS, and accordingly, to offer a design solution as a functional feedback model for mobile health applications. This paper investigates the user-perceived importance of various criteria, as well as latent factors for eliciting user feedback on the recommendations. It also reports the relationship of explanation and trust to the overall rating. By aggregating a list of all possible criteria, we further discover that not all criteria are equally important to users, and that the effectiveness of a recommendation plays a dominant role.
Deriving Impact-driven Security Requirements and Monitoring Measures for Industrial IoT

The emerging Industrial Internet of Things (IIoT) is characterized by heterogeneous systems, loose topologies, cross-company data flows, changing entities, and high cybersecurity requirements. This development makes a sound security architecture an even more pressing matter than before. The design of a valid security architecture should always reflect the protection needs, enable the derivation of security requirements, and ways to validate their effectiveness. While access management at the application layer is well established, securing the underlying network layers of an increasing number of communication links remains an open question. Currently, adapting to the dynamics of rapid technology changes requires reiterating time- and resource-intensive threat and risk analyses. Therefore, we introduce and apply a lightweight, graph-based process to create easy-to-build and machine-readable models of the reviewed IIoT systems, highlighting the assets they contain. Such a model allows us to derive abstract security requirements in a semi-automatic way. We use these requirements to propose appropriate protection and advanced monitoring measures as well as methods to validate their effective implementation. The catalog provided in this paper represents a security toolbox for these two security layers, tailored for the IIoT domain. Finally, it allows for deriving rules for current anomaly detection solutions. Thus, we support the often-laborious definition and prioritization of monitoring rules by an impact-based automated approach. By connecting the catalog with the lightweight impact analysis, we provide a framework that dynamically derives recommendations and requirements from a variety of monitoring measures and techniques. Thereby we provide a general methodology that helps operators to strengthen the overall security of their IIoT systems.
Using NTFS Cluster Allocation Behavior to Find the Location of User Data

Digital forensics is heavily affected by the large and increasing amount of data to be processed. To solve the problem there is ongoing research to find more efficient carving algorithms, use parallel processing in the cloud, and reduce the amount of data by filtering uninteresting files. Our approach builds on the principle of searching where it is more probable to find what you are looking for. We therefore have empirically studied the behavior of the cluster allocation algorithm(s) in the New Technology File System (NTFS) to see where new data is actually placed on disk. The experiment consisted of randomly writing, increasing, reducing and deleting files in 32 newly installed Windows 7, 8, 8.1 and 10 virtual computers using VirtualBox. The result show that data are (as expected) more frequently allocated closer to the middle of the disk. Hence that area should be getting higher attention during a digital forensic investigation of a NTFS formatted hard disk. Knowledge of the probable position of user data can be used by a forensic investigator to prioritize relevant areas in storage media, without the need for a working file system. It can also be used to increase the efficiency of hash-based carving by dynamically changing the sampling frequency. Our findings also contributes to the digital forensics processes in general, which can now be focused on the interesting regions on storage devices, increasing the probability of getting relevant results faster.
Designing a musical robot for Chinese bamboo flute performance

Robotic art is one of the important methods to study the relationship between humans and robots. Among different art forms, music is like a barrier-free language that all the people from the world can understand. Therefore, exploring the capability of musical robots is quite interesting that music could probably build a bridge between humans and robots for their emotional connections.
MinFinder: A New Approach in Sorting Algorithm

Sorting a set of unsorted items is a task that happens in computer programming while a computer program has to follow a sequence of precise directions to accomplish that task. In order to find things quickly by making extreme values easy to see, sorting algorithm refers to specifying a technique to arrange the data in a particular order or format where maximum of communal orders is in arithmetic or lexicographical order. A lot of sorting algorithms has already been developed and these algorithms have enhanced the performance in the factors including time and space complexity, stability, correctness, definiteness, finiteness, effectiveness, etc. A new approach has been proposed in this paper in sorting algorithm called MinFinder to overcome some of the downsides and performs better compared to some conventional algorithms in terms of stability, computational time, complexity analysis.
A robust deep style transfer for headshot portraits

While the recently reported deep photo style transfer [1] has shown improved results in photographic style transfer, it is found sensitive to spatial differences in the semantic segmentation of the inputs when applied to head portraits. The stylized image could incur ghost shadows when the segmented regions between the input image and the style reference image are spatially different. To minimize such a risk and reduce the influence of spatial differences between the input image and the style image, we introduce a spatial transformation strategy in this paper before style transfer. By maximizing the normalized cross-correlation of the feature maps, we propose to apply a series of affine transformations to the reference image and use those spatially-transformed images as the style reference to achieve a robust style transfer, when the semantically segmented regions of both the input image and the reference image are used as inputs to the pre-trained convolutional neural network. Consequently, the incurred ghost shadows can be minimized or eliminated. Experiments show that the proposed can perform well even when the semantic segmentation of the two images have large spatial differences, achieving significant level of robustness compared with the existing benchmark [1].
Alerting Users About Phishing Attacks

Cyber attacks are emerging as problems caused not only by technological aspects but also by human factors neglected when designing interactive systems. In this paper, we show how one of the most popular attacks on the Web, phishing, is very much related to UI aspects and how a wrong UI design determines a greater vulnerability of users. We performed a heuristic evaluation to assess the most recent applications such as browsers and mail clients that adopt warning messages as prevention of phishing attacks. The results highlighted that different aspects of UI should be better designed to limit phishing attacks. In addition, as a prevention of cyber attacks, we described an ongoing work of a questionnaire that aims to make users aware of the risks of cyber attacks.
Mutual attraction between high-frequency verbs and clause types with finite verbs in early positions: corpus evidence from spoken English, Dutch, and German

(2019). Mutual attraction between high-frequency verbs and clause types with finite verbs in early positions: corpus evidence from spoken English, Dutch, and German. Language, Cognition and Neuroscience: Vol. 34, Thirty years of 'Speaking'. Guest Editors: Antje S. Meyer, Ardi Roelofs & Laurel Brehm, pp. 1140-1151.
T-depth Optimization for Fault-Tolerant Quantum Circuits

The Clifford+T gate library consisting of Hadamard, T, and CNOT gates has attracted much interest in quantum circuit synthesis, particularly due to its applicability to fault tolerant realizations. Since fault tolerant implementations of the T gate have very high latency, recent work in this area is aiming at minimizing the number of T stages, referred to as the T-depth. In this paper, we present an approach to exploit additional ancilla qubits in the mapping of reversible circuits consistina of multiple controlled Toffoli gates (MCT gates) into califford+T quantum circuits, with the primary optimization objective to minimize the T-depth. Our proposed approach takes advantage of and generalizes earlier work on corresponding mapping algorithms. An experimental evaluation shows that our approach leads to a significant T-depth reduction compared to earlier approaches.
The DevOps Phenomenon

Stressful emergency releases are a thing of the past for companies that subscribe to the DevOps method of software development and delivery. New releases are frequent. Bugs are fixed rapidly. New business opportunities are sought with gusto and confidence. New features are released, revised, and improved with rapid iterations. DevOps presents a strategic advantage for organizations when compared with traditional software-development methods. Leadership plays an important role during that transformation. DevOps is about providing guidelines for faster time to market of new software features and achieving a higher level of stability. Implementing cross-functional, product-oriented teams helps bridge the gaps between software development and operations. By ensuring their transformations include all of the principles outlined in CALMS, teams can achieve superior performance and deliver value to their organizations. DevOps is often challenging, but stories from across the industry show that many organizations have already overcome the early hurdles and plan to continue their progress, citing the value to their organizations and the benefits to their engineers.
On the design of S-box constructions with genetic programming

In this paper, we try to combine the best from the world of heuristics and algebraic constructions for the design of S-boxes: we evolve algebraic constructions that produce S-boxes with as low as possible differential uniformity. Our approach is novel yet very simple and is allowing us to obtain constructions valid for any S-box size of practical interest.
Regularized ensemble neural networks models in the Extreme Learning Machine framework

Extreme Learning Machine (ELM) has proven to be an efficient and speedy algorithm for classification. In order to generalize the results of standard ELM, several ensemble meta-algorithms have been implemented. On this manuscript, we propose a hierarchical ensemble methodology that promotes diversity among the elements of an ensemble, explicitly through the loss function in the single-hidden-layer feedforward network version of ELM. The diversity term in the loss function is justified using the concept of regularization from the Negative Correlation Learning framework. Statistical tests show that our proposal is competitive in both performance and diversity measures against bagging and boosting ensemble methodologies.
The logic of qualitative probability

In this paper we present a theory of qualitative probability. The usual approach of earlier work was to specify a binary operator ⪯ on formulas with ϕ ⪯ ψ having the intended interpretation that the event expressed by ϕ is no more probable than that expressed by ψ . We generalise these approaches by extending the domain of the operator ⪯ from the set of events to the set of finite sequences of events. If Φ and Ψ are finite sequences of events, Φ ⪯ Ψ has the intended interpretation that the combined probabilities of the elements of Φ are no greater than those of Ψ. A sound and complete axiomatisation for this operator over finite outcome sets is given. We argue that our approach is more perspicuous and intuitive than previous accounts. As well, we show that the approach is sufficiently expressive to capture the results of axiomatic probability theory and to encode rational linear inequalities. We also prove that our approach generalises the two major accounts for finite outcome sets.
Research on Coverless Text Steganography Based on Single Bit Rules

Abstract Text steganography has become a hotspot in the field of information security in recent years. As an important steganographic carrier, text does not have the advantage that other multimedia could easily use more redundancy to hide information. Traditional methods were realized by changing the appearance attributes of text or by equivalent replacement based on grammar and semantics, but there were some shortcomings more or less. Based on Markov chain model, this paper proposed a coverless text steganography method. In order to enhance the effect of steganography, the method focused on the most important concepts in Markov model, the transition probability, and guided the generation of steganographic text based on it. The relevant experiments were designed to evaluate the model. The experimental results showed that the model had higher concealment and hiding capacity.
A Novel Convolutional Neural Network Based Localization System for Monocular Images

The authors present a robust and extendable localization system for monocular images. To have both robustness toward noise factors and extendibility to unfamiliar scenes simultaneously, our system combines traditional content-based image retrieval structure with CNN feature extraction model to localize monocular images. The core model of the system is a deep CNN feature extraction model. The feature extraction model can map an image to a d-dimension space where image pairs in the real word have smaller Euclidean distances. The feature extraction model is achieved using a deep Convnet modified from GoogLeNet. A special way to train the feature extraction model is proposed in the article using localization results from Cambridge Landmarks dataset. Through experiments, it is shown that the system is robust to noise factors supported by high level CNN features. Furthermore, the authors show that the system has a powerful extendibility to other unfamiliar scenes supported by a feature extract model's generic property and structure.
Hierarchical Group-level Emotion Recognition in the Wild

We propose a method for group-level emotion recognition in the wild. The main novelty of our method lies with the recognition of group-level emotions using a hierarchical classification approach. We consider that using the facial expressions of people will only be effective in differentiating images labeled as "Positive" because those labeled as "Neutral" or "Negative" are likely to include similar facial expressions (i.e., less discriminative). Therefore, we first perform binary classification based on facial expression recognition to distinguish "Positive" labels that include discriminative facial expressions (e.g., smile) from the others. We evaluate outcomes that are not classified as "Positive" at the first classification by exploiting scene features that describe what type of events (e.g., demonstration or funeral) are taking place in the image. Classification using scene features will not only be effective in differentiating "Negative" and "Neutral" labels but also in recognizing "Positive" labels, where facial expression features show less discriminative characteristics. The other novelty of the proposed method is to the exploitation of visual attention. Using visual attention allows us to estimate which faces are the main subjects in the target image, thereby suppressing the influences of faces in the background that contribute less to group-level emotion. We demonstrate the effectiveness of our proposed method through experiments using a public dataset.
Semantic learning machine improves the CNN-Based detection of prostate cancer in non-contrast-enhanced MRI

Considering that Prostate Cancer (PCa) is the most frequently diagnosed tumor in Western men, considerable attention has been devoted in computer-assisted PCa detection approaches. However, this task still represents an open research question. In the clinical practice, multiparametric Magnetic Resonance Imaging (MRI) is becoming the most used modality, aiming at defining biomarkers for PCa. In the latest years, deep learning techniques have boosted the performance in prostate MR image analysis and classification. This work explores the use of the Semantic Learning Machine (SLM) neuroevolution algorithm to replace the backpropagation algorithm commonly used in the last fully-connected layers of Convolutional Neural Networks (CNNs). We analyzed the non-contrast-enhanced multispectral MRI sequences included in the PROSTATEx dataset, namely: T2-weighted, Proton Density weighted, Diffusion Weighted Imaging. The experimental results show that the SLM significantly outperforms XmasNet, a state-of-the-art CNN. In particular, with respect to XmasNet, the SLM achieves higher classification accuracy (without neither pre-training the underlying CNN nor relying on backprogation) as well as a speed-up of one order of magnitude.
A Novel Hybrid Image Encryption Algorithm Based on Data Compression and Chaotic Key Planning Algorithms

In this study, a hybrid image encryption algorithm which provides data compression and encryption requirements is proposed. Both JPEG and fractal compression algorithms can be used in the data compression process of the proposed new algorithm. In the encryption process, a chaotic key planning algorithm has been used. Chaotic systems are used as an entropy source. This approach, based on the one time pad principle, is unconditionally secure because it uses different secret keys in each encryption process. Successful analysis results show that the proposed new method can be used in many practical applications in the field of information security.
Multitemporal SAR Image Despeckling Based on a Scattering Covariance Matrix of Image Patch

This paper presents a despeckling method for multitemporal images acquired by synthetic aperture radar (SAR) sensors. The proposed method uses a scattering covariance matrix of each image patch as the basic processing unit, which can exploit both the amplitude information of each pixel and the phase difference between any two pixels in a patch. The proposed filtering framework consists of four main steps: (1) a prefiltering result of each image is obtained by a nonlocal weighted average using only the information of the corresponding time phase; (2) an adaptively temporal linear filter is employed to further suppress the speckle; (3) the final output of each patch is obtained by a guided filter using both the original speckled data and the filtering result of step 3; and (4) an aggregation step is used to tackle the multiple estimations problem for each pixel. The despeckling experiments conducted on both simulated and real multitemporal SAR datasets reveal the pleasing performance of the proposed method in both suppressing speckle and retaining details, when compared with both advanced single-temporal and multitemporal SAR despeckling techniques.