Reviewerly/exHarmony huggingface dataset:
Decentralised and Collaborative Auditing of Workflows

Workflows involve actions and decision making at the level of each participant. Trusted generation, collection and storage of evidence is fundamental for these systems to assert accountability in case of disputes. Ensuring the security of audit systems requires reliable protection of evidence in order to cope with its confidentiality, its integrity at generation and storage phases, as well as its availability. Collusion with an audit authority is a threat that can affect all these security aspects, and there is room for improvement in existent approaches that target this problem. This work presents an approach for workflow auditing which targets security challenges of collusion-related threats, covers different trust and confidentiality requirements, and offers flexible levels of scrutiny for reported events. It relies on participants verifying each other’s reported audit data, and introduces a secure mechanism to share encrypted audit trails with participants while protecting their confidentiality. We discuss the adequacy of our audit approach to produce reliable evidence despite possible collusion to destroy, tamper with, or hide evidence.
Bayesian Clustering for HIV1 Protease Inhibitor Contact Maps

We present a probabilistic model for clustering which enables the modeling of overlapping clusters where objects are only available as pairwise distances. Examples of such distance data are genomic string alignments, or protein contact maps. In our clustering model, an object has the freedom to belong to one or more clusters at the same time. By using an IBP process prior, there is no need to explicitly fix the number of clusters, as well as the number of overlapping clusters, in advance. In this paper, we demonstrate the utility of our model using distance data obtained from HIV1 protease inhibitor contact maps.
Paying Attention to Style: Recognizing Photo Styles with Convolutional Attentional Units

The notion of style in photographs is one that is highly subjective, and often difficult to characterize computationally. Recent advances in learning techniques for visual recognition have encouraged new possibilities for computing aesthetics and other related concepts in images. In this paper, we design an approach for recognizing styles in photographs by introducing adapted deep convolutional neural networks that are attentive towards strong neural activations. The proposed convolutional attentional units act as a filtering mechanism that conserves activations in convolutional blocks in order to contribute more meaningfully towards the visual style classes. State-of-the-art results were achieved on two large image style datasets, demonstrating the effectiveness of our method.
3D shape measurement in the presence of interreflections by light stripe triangulation with additional geometric constraints

Traditional optical 3D shape measurement methods, such as light stripe triangulation, binary coding, and fringe projection, cannot acquire complete and correct 3D measurement results in the presence of interreflections. In this research, a 3D shape measurement method in the presence of interreflections based on light stripe triangulation is presented. The wrong measurement results caused by interreflections are excluded by the geometric constraints introduced by an additional camera. Each 3D point reconstructed by light stripe triangulation is projected onto the image plane of the additional camera to determine whether the 3D point is correct measurement result. Experimental results demonstrate that the proposed method can measure 3D shape in the presence of interreflections.
Trends and Challenges of Multimodal User Interfaces

The development of information technologies brings new possibilities for its users. The development is noticeable in user interface design as well - new data input and output methods and facilities are proposed, increasing resource capabilities allow more realistic user interfaces. However together with the growth of user interface possibilities and technologies new challenges rise too. Therefore it is important to understand current challenges in order to understand future trends. In this paper we review the peculiarities of multimodal user interfaces and propose multimodal user interface taxonomy. This taxonomy defines possible types of data input and output and is used for input/output combination matrix generation. We summarize existing solutions and challenges based on the matrix and evaluate each combination. The evaluated combinations in the matrix highlight the main tendencies in natural user interface design area.
A neurodynamic approach to compute the generalized eigenvalues of symmetric positive matrix pair

This paper shows that the generalized eigenvalues of a symmetric positive matrix pair can be computed efficiently under more general hypothesises by the proposed recurrent neural network (RNN) in Liu et al. (2008). More precisely, it is proved that based on more general hypothesises, the state solution of the proposed RNN converges to the generalized eigenvector of symmetric positive pair, and its related generalized eigenvalue depends on the initial point of the state solution. Furthermore, the related largest and smallest generalized eigenvalues can also be obtained by the proposed RNN. Some related numerical experiments are also presented to illustrate our results.
Research on Concept-Drifting Data Stream Based on Fuzzy Integral Ensemble Classifier System

With the arrival of the era of big data, a large amount of data stream generates in the real world. However, the existence of concept drift has brought great challenges to data stream classification. Therefore, this paper proposed an ensemble classifier system based on fuzzy integral to solve the above problem. And after the experimental evaluation, we can approve the proposed algorithm outperforms other algorithms in terms of classification performance and the ability to adapt to new concepts efficiently.
Bouncy Buttons

Buttons are one of the most extended input methods that we use to interact with machines. Videogames, medical devices or coffee makers are controlled with the binary input of buttons. However, it has been shown that pressure information can be used to enhance interaction, improve user authentication or speed up text entry. Here, we show a method to detect the strength of the keystroke on regular buttons by analyzing the bounce signal. When a button is pressed, two metallic parts make contact and bounce several times. This signal has always been "debounced" to get a stable input but for the first time we use it to detect how strongly the button was pressed. Now, existing binary buttons could detect different levels of pressure for extended input capabilities.
Towards the intelligent analysis of ferrograph images

Ferrography determines the wear condition and failure mechanisms of the equipments through quantitative and qualitative analysis on the quantity, size, shape, and color of wear particles. Computer aided intelligent analysis of ferrograph images is an essential way to improve the efficiency and accuracy of ferrography. The historical progress of ferrograph image processing and analysis is reviewed, and a new strategy for ferrograph image analysis based on convolutional neural network is proposed and studied in this paper.
Two-Way Encryption and Decryption Technique Using Chaotic Maps

A cryptosystem incorporating multiple chaotic maps is proposed. The use of chaotic maps for image encryption is a common procedure but this does not ensure absolute security. This paper presents encryption of novel images, a technique that incorporates three different chaotic maps—Tent map, Logistic Map and Gauss Iterative Map for encryption. Image encryption will be a two-step process involving one chaotic map to randomly rearrange the pixels of individual RGB components and other maps to reorganize another image. These two will then be XORed to provide dual security. The technique’s strengths have been analysed and presented.
Information, Communication Technologies and Regulations

Regulations constitute the “corpus” of rules defining the protocols emerging from the interactions between people, organizations, environment in all their aspects and implications. Information and Communication Technologies (ICT) are modifying these rules and call for the improvement and the update of interaction codes in order to manage and organize the emergence of a new reality that was unknown until now. There will be always a delay between the manifestation of a new interaction, encouraged and powered by communication and information technologies, and the setting of appropriate regulations. As a consequence, a situation of constant mismatching between “living technology” and “regulations development” seems a problem of difficult solution. A possibility to face this situation may come out from a choice of civilization.
Key Enumeration from the Adversarial Viewpoint

In this work, we formulate and investigate a pragmatic question related to practical side-channel attacks complemented with key enumeration. In a real attack scenario, after an attacker has extracted side-channel information, it is possible that despite the entropy of the key has been significantly reduced, she cannot yet achieve a direct key recovery. If the correct key lies within a sufficiently small set of most probable keys, it can then be recovered with a plaintext and the corresponding ciphertext, by performing enumeration. Our proposal relates to the following question: how does an attacker know when to stop acquiring side-channel observations and when to start enumerating with a given computational effort? Since key enumeration is an expensive (i.e. time-consuming) task, this is an important question from an adversarial viewpoint. To answer this question, we present an efficient (heuristic) way to perform key-less rank estimation, based on simple entropy estimations using histograms.
Disgruntled yet Deft with IT

Malicious insiders are employees who intentionally harm organizational information systems and technology. It has been shown that these types of insiders tend to be disgruntled with work, often times as a result of termination. We identify an additional source of disgruntlement that is also associated with computer abuse: dissatisfaction with information technology resources and support, or IT dissatisfaction. Using a sample of 271 working adults, we demonstrate that employees with higher IT dissatisfaction are more likely to engage in computer abuse. Moreover, the relationship is significantly stronger among employees who consider themselves as technologically competent. Our findings are robust across models with and without control variables, and when using a residual measure of IT dissatisfaction derived from negative affect. We conclude that IT dissatisfaction is a promising construct for researchers and companies to explore further in relation to information security outcomes.
Bouncer

When discussing safety and security for embedded systems, we typically divide the world into software checks (which are either static or dynamic) or hardware checks (which are dynamic). As others have pointed out, hardware checks offer more than just efficiency. They are intrinsic to the device's functionality and thus are live from power-up; they require little to no dependency on other software functioning correctly, and due to the fact they are wired directly into the operation of the system, are difficult or impossible to bypass. We explore an experimental new embedded system that uses special-purpose hardware for static analysis that prevents all program binaries with memory errors, invalid control flow, and several other undesirable properties from ever being loaded onto the device. Static analysis often requires whole-binary-level, rather than instruction-level, examination. We show that a carefully constructed hardware state machine, using available scratch-pad memory, is capable of efficiently checking functional binaries in a streaming and verifiably non-bypassable way directly in hardware as they are loaded into the embedded program store. The resulting system is surprisingly small (taking no more than .0079 mm2), efficient (capable of checking binaries at an average throughput of around 60 cycles per instruction), and yet guarantees execution free from many of the fragile behaviors that result in security and safety concerns. We believe this is the first time any static analysis has been implemented at the hardware level and opens the door to more complex hardware-checked properties.
Secure Memory for Embedded Tamper-proof Systems

Data leakage and disclosure to attackers is a significant problem in embedded systems, considering the ability of attackers to get physical access to the systems. We present methods to protect memory data leakage in tamper-proof embedded systems. We present methods that exploit memory supply voltage manipulation to change the memory contents, leading to an operational and reusable memory or to destroy memory cell circuitry. For the case of memory data change, we present scenaria for data change to a known state and to a random state. The data change scenaria are effective against attackers who cannot detect the existence of the protection circuitry; furthermore, original data can be calculated in the case of data change to a known state, if the attacker identifies the protection circuitry and its operation. The methods that change memory contents to a random state or destroy memory cell circuitry lead to irreversible loss of the original data. However, since the known state can be used to calculate the original data.
Displaying a taxidermy rhinoceros in a museum: the Lisbon conservation approach

The present problems around the public display of a full taxidermy mounted rhinoceros in Lisbon and the motifs for the replacement of its original horns are presented in this paper. The twentieth century specimen concerned belongs to the National Museum of Natural History and Science of the University of Lisbon (MUHNA C). The several warnings of rhino horn thefts occurring in museums across Europe in the last two decades and the frequency of requests for loans of this specimen for exhibitions, raised an internal debate at the MUHNA C. In this paper, firstly, we present a short overview of the problems in terms of the international context and the vulnerability of these specimens at museums, followed by a brief overview of the history of the Lisbon specimen. Finally, we detail and discuss our conservation, methodology and ethical approach to the removal and replacement of the specimen’s original horns with replicas, based on Norfolk Museums Service’s (UK) protocol and experience.
3D shape from thermal patterns: investigation of projection parameters in simulation and experiment

Structured light projection techniques based on diffuse reflection are widely used for accurate, fast, contactless, and nondestructive optical 3D shape measurements. It cannot be utilized to measure uncooperative materials, i.e., materials with optical properties such as being glossy, transparent, absorbent, or translucent. Recently, it was shown that 3D reconstruction of an uncooperative object can be performed by a two-step process for each camera image pair. In the first step, the object absorbs a projected thermal pattern, e.g., in the long-wave infrared range. In the second step, after energy conversion, the object surface reemits light according to Planck’s law. Whereas the irradiation can be performed by a CO<sub>2</sub> laser at 10.6 &mu;m, the detection of the reemitted light can be carried out by mid-wave infrared (MWIR) cameras sensitive in the wavelength range between 3 and 5 &mu;m. In order to achieve accurate 3D results in a short measurement time, the projection parameters like radiation intensity and illumination time as well as the projection patterns have to be optimized depending on optical and thermal material properties (e.g., complex spectral refractive index, thermal conductivity, specific heat capacity, emissivity). Therefore, we have developed a simulation tool based on the Beer-Lambert law (for absorption of the irradiation) and on the heat diffusion equation (for the illumination-induced thermal pattern on the object surface). In this contribution, we present our simulation tool and several simulation results. We apply our tool to investigate the projection parameters and projection patterns for a given material and a specific total measurement time. Finally, we experimentally verify the theoretical results with our MWIR 3D sensor.
Parkinson’s disease detection using modulation components in speech signals

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder after Alzheimer's. This disorder affects around 2% of elderly population. In Colombia, the prevalence of Parkinson's disease is around 172 cases per 100.000 inhabitants. Furthermore, around 89% of people diagnosed with PD also suffer from speech disorders. This has motivated many advances in speech signal processing for PD patients which allows to perform assisted diagnosis and also monitor the progression of the disease. In this paper, we propose to use slow varying information from speech signals, also known as modulation components, and combine it with an approach to effectively reduce the number of features to be used in a classification system. The proposed approach achieves around 90% accuracy, outperforming the classical mel-frequency cepstral coefficients (MFCC) approach. Results show that information in slow varying components is highly discriminative to support assisted diagnosis for PD.
Parameter optimization of a multiscale descriptor for shape analysis on healthcare image datasets

Shape analysis is a key task in computer vision, and multiscale descriptors can significantly enhance shape characterization. However, these descriptors often rely on parameter adjustments to configure a meaningful set of scales that can enable shape analysis. Parameter adjustment in large image datasets is often done on a trial-and-error basis, and an alternative solution to mitigate such a limitation is the use of metaheuristic optimization. The main contribution of this paper is to provide a strategy that supports the automatic parameter adjustment of a multiscale descriptor within a metaheuristic optimization algorithm, where the choice of the cost function strongly influences and boosts the performance of the shape description, which is closely related to the problem domain, i.e. the image dataset. Our research considers synthetic data in a prior evaluation of the cost functions that optimize the scale parameters of the Normalized Multiscale Bending Energy (NMBE) descriptor through the Simulated Annealing (SA) metaheuristic. The cost functions that drive this metaheuristic are: Silhouette (SI), the Davies–Bouldin index (DB) and the Calinski-Harabasz index (CH). We conduct content-based image retrieval and classification experiments to assess the optimized descriptor using three healthcare image datasets: Amphetamine Type Stimulants (ATS) pills (Illicit Pills), pills from the National Library of Medicine (NLM Pills) and hand alphabet gestures (Hands). We also provide segmentation masks for Illicit Pills to guarantee reproducibility. We report the results of tests using a state-of-art method based on a deep neural network, Inception-ResNet-v2. The optimized NMBE with SI and DB achieved competitive and accurate values of above 94%, in terms of both the Mean Average Precision measure (MAP) and Accuracy (ACC) for Illicit Pills and NLM Pills. The precision recall curves demonstrate that it outperforms the Inception-ResNet-v2 for both of these datasets.
The rise of the remote mix engineer: technology, expertise, star

Emerging technology is facilitating collaboration and peer-production across a wide variety of industries. In the music industry, one example of this is in the use of a remote mix engineer – contracted to work on a recording made by another engineer in a different location and time domain because of the value in their specialised expertise. This article examines the rise of the remote mix engineer in the contemporary music industry. It does this by examining the technology, methods of working, expertise and the concept of stardom using two contrasting examples – one a world-renowned mix engineer with several decades of experience and a high profile client list, the other being a younger and less experienced mix engineer with a different range of clients.
Teaching Reform of Vocational Computer Basic Courses Based on Specialty Needs

How the vocational computer basic courses better serve the specialty is an important measure to deepen the teaching reform and improve the educational informationization. Combining with the teaching reform of computer basic courses for non-computer majors in our college, this paper explores the teaching reform measures about how the computer basic courses take the needs of professional posts as the target and serve the specialties.
A Preliminary Ontology for Human-Agent Collectives

Human-Agent Collectives (HAC) is an approach involving coalitions of humans and agents working together to exploit their intelligence capabilities to optimize tasks in systems with inherent uncertainty. This paper presents a review of the main features of the HAC approach as well as the possible interactions between the entities that take place in the coalitions of a HAC system. An ontology called ONT4HAC is defined to facilitate the management of actions in HAC scenarios.
Evaluating Word Embeddings based on Hypernymy Relations

Word vector representation is the basis of natural language processing tasks. There are many excellent articles that propose different methods of word vector representation. However, there is still no unified and accepted method for evaluating word vector representation. We present an evaluating embedding method-HEWE (hypernymy evaluates word embeddings) based on a combined hypernymy relation detection that integrates both path-based and distributional features. And prove the method by the experiments, comparing with the sentiment classification result.
Aberration analysis and measurement of micro-offset free-form mirrors

Get PDF Email Share Share with Facebook Tweet This Post on reddit Share with LinkedIn Add to CiteULike Add to Mendeley Add to BibSonomy Get Citation Copy Citation Text L. Wu, P. Wang, and Y. Cheng, "Aberration analysis and measurement of micro-offset free-form mirrors," in Imaging and Applied Optics 2019 (COSI, IS, MATH, pcAOP), OSA Technical Digest (Optica Publishing Group, 2019), paper JW2A.42. Export Citation BibTex Endnote (RIS) HTML Plain Text Citation alert Save article
A nonlocal filter for local denoising using the Wigner transform

Nonlocal means denoising (NLM) and block-matching and 3D filtering (BM3D) have become the most popular algorithms for image processing. The disadvantage of these methods is that they cannot be realized by hardware implementation. The Wigner distribution function (WDF) has advantages such as explicit physical meaning, spatial frequency distribution of each pixel, and suitability for optical implementation. In recent years, the wide-field imaging system has been used extensively in multiple situations such as aviation, medicine, and traffic. Large-field images often have multiple point spread functions (PSF) and local random noise. However, the results have shown that most nonlocal filters have inadequate effects on local noise filtering. This paper analytically examines the Wigner transform of a multi-Gaussian function and investigates the effects of cross-term and noise on filter reconstruction in image processing. Based on the results, a new space-variant filter with a simple mathematical formula is proposed and applied to local high-frequency noise filtering. Since the filter is closely related to the intensity distribution in the spatial and frequency domains, it provides a simple way to process local noise while simultaneously preserving the high-frequency information of the image.