Reviewerly/exHarmony huggingface dataset:
Who knows what? Knowledge misattribution in the division of cognitive labor.

As technology advances, people increasingly outsource cognitive tasks and can more easily access others’ knowledge. While externalized aids often support human abilities, they may also make it more difficult for people to assess their own competence. Indeed, using online search engines leads people to treat searchable information as if they already know it (Fisher et al., Journal of Experimental Psychology: General, 2015, 144, 674). Six primary and two supplemental studies (N = 3,262) extend previous research by exploring how illusions of knowledge result from reliance on other agents. After teaming with knowledgeable partners (artificially intelligent agents or human teammates) on a trivia quiz, people overestimated how well they would perform on future quizzes for which help was not available; this bias was not evident for participants who never received help. Moreover, overconfidence was insensitive to whether assistance was provided on hard versus easy problems, or even whether the assistance was accurate. Receiving outside assistance creates an ambiguity regarding who deserves credit for success or blame for failure. When this ambiguity is removed, people become better calibrated. These results indicate that reliance on technology and outside knowledge may change our view of ourselves—convincing us we are more capable than we really are. (PsycInfo Database Record (c) 2021 APA, all rights reserved)
Multimodal data fusion for sensitive scene localization

The very idea of hiring humans to avoid the indiscriminate spread of inappropriate sensitive content online (e.g., child pornography and violence) is daunting. The inherent data deluge and the tediousness of the task call for more adequate approaches, and set the stage for computer-aided methods. If running in the background, such methods could readily cut the stream flow at the very moment of inadequate content exhibition, being invaluable for protecting unwary spectators. Except for the particular case of violence detection, related work to sensitive video analysis has mostly focused on deciding whether or not a given stream is sensitive, leaving the localization task largely untapped. Identifying when a stream starts and ceases to display inappropriate content is key for live streams and video on demand. In this work, we propose a novel multimodal fusion approach to sensitive scene localization. The solution can be applied to diverse types of sensitive content, without the need for step modifications (general purpose). We leverage the multimodality data nature of videos (e.g., still frames, video space-time, audio stream, etc.) to effectively single out frames of interest. To validate the solution, we perform localization experiments on pornographic and violent video streams, two of the commonest types of sensitive content, and report quantitative and qualitative results. The results show, for instance, that the proposed method only misses about five minutes in every hour of streamed pornographic content. Finally, for the particular task of pornography localization, we also introduce the first frame-level annotated pornographic video dataset to date, which comprises 140 h of video, freely available for downloading.
Iterative Design of Visual Analytics for a Clinician-in-the-Loop Smart Home

In order to meet the health needs of the coming “age wave,” technology needs to be designed that supports remote health monitoring and assessment. In this study we design clinician in the loop (CIL), a clinician-in-the-loop visual interface, that provides clinicians with patient behavior patterns, derived from smart home data. A total of 60 experienced nurses participated in an iterative design of an interactive graphical interface for remote behavior monitoring. Results of the study indicate that usability of the system improves over multiple iterations of participatory design. In addition, the resulting interface is useful for identifying behavior patterns that are indicative of chronic health conditions and unexpected health events. This technology offers the potential to support self-management and chronic conditions, even for individuals living in remote locations.
Efficient Lossless Compression Based Reversible Data Hiding Using Multilayered n-Bit Localization

We proposed an innovative reversible data hiding technique that is formulated on histogram shifting by using multilayer localized n-bit truncation image (LBPTI) , namely, generated form 8-bit plane by means of efficient lossless compression. After selecting the reference point from the block, the neighbor topmost points are used to attain the data embedding without modifying the peak point; in addition, the key information regarding peak point is not mandatory in extraction end to extract the secret information. In order to make the embedded cover-image similar to the histogram of original cover-image, we exploited the localization with efficient lossless compression on lower block level to increase the embedding capacity while controlling extra bit to expand additional embedding capacity on optimum level besides sustaining the quality of cover-image.
Parallel Implementation of Particle Swarm Optimization on FPGA

This brief proposes a parallel implementation, with fixed point, of the particle swarm optimization (PSO) algorithm on field-programmable gate array (FPGA). Results associated with the processing time and area occupancy on FPGA for several numbers of particles and dimensions were analyzed. Studies concerning the accuracy of the PSO response for the optimization problem using the Rastrigin function were also analyzed for the hardware implementation. The project was developed on the Virtex-6 xc6vcx240t 1ff1156 FPGA.
A Comprehensive Review of RFID and Bluetooth Security: Practical Analysis

The Internet of Things (IoT) provides the ability to digitize physical objects into virtual data, thanks to the integration of hardware (e.g., sensors, actuators) and network communications for collecting and exchanging data. In this digitization process, however, security challenges need to be taken into account in order to prevent information availability, integrity, and confidentiality from being compromised. In this paper, security challenges of two broadly used technologies, RFID (Radio Frequency Identification) and Bluetooth, are analyzed. First, a review of the main vulnerabilities, security risk, and threats affecting both technologies are carried out. Then, open hardware and open source tools like: Proxmark3 and Ubertooth as well as BtleJuice and Bleah are used as part of the practical analysis. Lastly, risk mitigation and counter measures are proposed.
Word embeddings and external resources for answer processing in biomedical factoid question answering

Biomedical question answering (QA) is a challenging task that has not been yet successfully solved, according to results on international benchmarks, such as BioASQ. Recent progress on deep neural networks has led to promising results in domain independent QA, but the lack of large datasets with biomedical question-answer pairs hinders their successful application to the domain of biomedicine. We propose a novel machine-learning based answer processing approach that exploits neural networks in an unsupervised way through word embeddings. Our approach first combines biomedical and general purpose tools to identify the candidate answers from a set of passages. Candidates are then represented using a combination of features based on both biomedical external resources and input textual sources, including features based on word embeddings. Candidates are then ranked based on the score given at the output of a binary classification model, trained from candidates extracted from a small number of questions, related passages and correct answer triplets from the BioASQ challenge. Our experimental results show that the use of word embeddings, combined with other features, improves the performance of answer processing in biomedical question answering. In addition, our results show that the use of several annotators improves the identification of answers in passages. Finally, our approach has participated in the last two versions (2017, 2018) of the BioASQ challenge achieving competitive results.
Recognition of Fingerspelling Sequences in Polish Sign Language Using Point Clouds Obtained from Depth Images

The paper presents a method for recognizing sequences of static letters of the Polish finger alphabet using the point cloud descriptors: viewpoint feature histogram, eigenvalues-based descriptors, ensemble of shape functions, and global radius-based surface descriptor. Each sequence is understood as quick highly coarticulated motions, and the classification is performed by networks of hidden Markov models trained by transitions between postures corresponding to particular letters. Three kinds of the left-to-right Markov models of the transitions, two networks of the transition models—independent and dependent on a dictionary—as well as various combinations of point cloud descriptors are examined on a publicly available dataset of 4200 executions (registered as depth map sequences) prepared by the authors. The hand shape representation proposed in our method can also be applied for recognition of hand postures in single frames. We confirmed this using a known, challenging American finger alphabet dataset with about 60,000 depth images.
Denoising applied to spectroscopies – part I: concept and limits

Some spectroscopies are intrinsically poorly sensitive, such as nuclear magnetic resonance (NMR) and Raman spectroscopy. This drawback can be overcome by using singular value decomposition (SVD) and low-rank approximation to denoise spectra and consequently increase sensitivity. However, SVD limits have not been deeply investigated until now. We applied SVD to NMR and Raman spectra and showed that best results were obtained with a square data set in time domain. Automatic thresholding was applied using Malinowski’s indicators. 6 × 7380 noisy spectra with 41 signal-to-noise ratios were compared to their non-noisy counterparts, highlighting that SVD induces a systematic error for Gaussian peaks but faithfully reproduces shape of Lorentzian peaks, thus allowing quantification. Used carefully, SVD can decrease experimental time by a factor of 2.3 for spectroscopies. This study may help scientists to apply SVD to denoise spectra in a more efficient way, without falling into pitfalls.
Magic Carpet: Interaction Fidelity for Flying in VR

Locomotion in virtual environments is currently a difficult and unnatural task to perform. Normally, researchers tend to devise groundor floor-based metaphors to constrain the degrees of freedom (DoFs) during motion. These restrictions enable interactions that accurately emulate the human gait to provide high interaction fidelity. However, flying allows users to reach specific locations in a virtual scene more expeditiously. Our experience suggests that even though flying is not innate to humans, high-interaction-fidelity techniques may also improve the flying experience since flying requires simultaneously controlling additional DoFs. We present the Magic Carpet, an approach to flying that combines a floor proxy with a full-body representation to avoid balance and cybersickness issues. This design space enables DoF separation by treating direction indication and speed control as two separate phases of travel, thereby enabling techniques with higher interaction fidelity. To validate our design space, we conducted two complementary studies, one for each of the travel phases. In this paper, we present the results of both studies and report the best techniques for use within the Magic Carpet design space. To this end, we use both objective and subjective measures to evaluate the efficiency, embodiment effect, and side effects, such as physical fatigue and cybersickness, of the tested techniques in our design space. Our results show that the proposed approach enables high-interaction-fidelity techniques while improving the user experience.
A global digital volume correlation algorithm based on higher-order finite elements: Implementation and evaluation

We propose a DVC technique that is based on higher-order finite-element discretization of the displacement field and a global optimization procedure. We use curvature penalization to suppress non-physical fluctuations of the displacement field and resulting erroneous strain concentrations. The performance of the proposed method is compared to the commercial code Avizo using trabecular bone images and found to perform slightly better in most cases. In addition, we stress that the performance of a DVC method needs to be evaluated using double scans (zero strain), virtual deformation (imposed deformation) and real deformation. Double scans give insight into the presence of noise and artifacts whereas virtual deformation benchmarks allows evaluation of the performance without noise and artifacts. Investigation of the performance for actual deformed heterogeneous materials is needed for evaluation with noise, artifacts and non-zero strains. We show that both decreasing the resolution of the displacement field (increasing subvolume size) as well as (increasing) curvature penalization (regularization) have a similar effect on the performance of evaluated DVC methods: Decreasing the detrimental effect of noise, artifacts and interpolation errors, but also decreasing the sensitivity of a DVC method to displacement peaks, discontinuities and strain concentrations. The needed amount of regularization is a trade-off between accuracy and precision of the estimated strain fields and their resolution. The obtainable accuracy and precision of the estimated displacement fields are influenced by interpolation errors in the DVC procedure and the relative amount of detail, noise and artifacts in the images. Errors in the displacement field are typically magnified during the strain calculation. Based on the tests and subvolume sizes (16–50 voxels) in this study, the expected order of magnitude of the accuracy and precision is 0.1 micro-voxels and 1 milli-voxels for the displacements and 0.1 and 1 milli-strains of the strain fields.
Cloud-Based FPGA Custom Computing Machines for Streaming Applications

A novel platform for launching and using field-programmable gate arrays (FPFA) custom computing machines (CCMs) in clouds and data centers is proposed. Based on a developed FPGA virtualization scheme, it allows users to create independent computing services on network-attached standalone FPGAs. The interface of the virtual FPGA (vFPGA)-based CCM is automatically generated by a virtualization layer and based on the user's specifications. An FPGA hypervisor has been developed that can be easily integrated with any cloud management tool. It allows the users to launch/use/tear down vFPGA-based CCMs in a similar manner to conventional virtual machines (VMs). A complete prototype of the proposed platform has been realized and tested with a streamed image processing application. Its performance was 3-4x and ~1.4-2.4x times better than an SW implementation on a VM and a powerful server, respectively. Compared with other platforms for FPGA attachment to a cloud or datacenter, the proposed platform has relatively low overhead in terms of FPGA resources while providing the highest level of abstraction and virtualization.
Palynological, geochemical, and mineralogical characteristics of the Early Jurassic Liasidium Event in the Cleveland Basin, Yorkshire, UK

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A Needle in a Haystack? Harnessing Onomatopoeia and User-specific Stylometrics for Authorship Attribution of Micro-messages

The world is facing a new era in which social media communication plays a fundamental role in people's lives. Along with irrefutable benefits, several collateral drawbacks have risen, one being the wide spread of false information with malicious intents, what is now commonly called "Fake News". The fight against this problem is not easy, especially when taking into account the nature of text messages involved on social media platforms (a sea of small messages and myriad users). In this work, we cope with the challenging problem of authorship attribution of small text messages posted on social media platforms. Differently from what has been done with longer texts, we rely upon data-driven approaches, exploiting recent advances of deep neural networks in the field of pattern recognition. By viewing small texts usually employed in social media as unidimensional signals, we devise modern deep-learning techniques tailored for this kind of data to find the author of these posts with promising results.
An In-Depth Study on Open-Set Camera Model Identification

Camera model identification refers to the problem of linking a picture to the camera model used to shoot it. In this paper, as this might be an enabling factor in different forensic applications to single out possible suspects (e.g., detecting the author of child abuse or terrorist propaganda material), many accurate camera model attribution methods have been developed. One of their main drawbacks, however, are a typical closed-set assumption of the problem. This means that an investigated photograph is always assigned to one camera model within a set of known ones present during the investigation, i.e., training time. The fact that a picture can come from a completely unrelated camera model during actual testing is usually ignored. Under realistic conditions, it is not possible to assume that every picture under analysis belongs to one of the available camera models. In this paper, to deal with this issue, we present an in-depth study on the possibility of solving the camera model identification problem in open-set scenarios. Given a photograph, we aim at detecting whether it comes from one of the known camera models of interest or from an unknown one. We compare different feature extraction algorithms and classifiers especially targeting open-set recognition. We also evaluate possible open-set training protocols that can be applied along with any open-set classifier, observing that a simple alternative among the selected ones obtains the best results. Thorough testing on independent datasets show that it is possible to leverage a recently proposed convolutional neural network as feature extractor paired with a properly trained open-set classifier aiming at solving the open-set camera model attribution problem even on small-scale image patches, improving over the state-of-the-art available solutions.
Secure IOT System Based on Chaos-Modified Lightweight AES

The increasing The Internet of Things objects and entities in the famous applications causes a needing to provide an unique and secure identifiers with ability to protected data transferring through the network. Secure IoT communication becomes more urgent issue for important IOT entities that content computing-devices and embedded-sensor subsystems used in unofficial industrial machine to machine (M2M) communications, smart energy-grids, home or buildings and other computing devices. In this paper we proposed a secure system by using chaotic system combinate with the modified lightweight AES. The 5-D chaos system (hybrid from logistic and Lorenz chaotic system) was used to generate the sequences chaos keys used in the lightweight AES and SHAKE128. The Lightweight AES was modified to reduce the processing complexity of AES with decreased the processing time (have 145% speed more). The output of modified lightweight AES encryption System has the good statistical tests near to original AES that can avoid many attacks.
Cryptanalysis of Curl-P and Other Attacks on the IOTA Cryptocurrency

We present attacks on the cryptography formerly used in the IOTA blockchain, including under certain conditions the ability to forge signatures. We developed practical attacks on IOTA’s cryptographic hash function Curl-P-27, allowing us to quickly generate short colliding messages. These collisions work even for messages of the same length. Exploiting these weaknesses in Curl-P-27, we broke the EUCMA security of the former IOTA Signature Scheme (ISS). Finally, we show that in a chosen-message setting we could forge signatures and multi-signatures of valid spending transactions (called bundles in IOTA).
Regulation as both enabler of technology use and global competitive tool: The Gibraltar case

Distributed ledger technologies (DLTs) such as Blockchain appear to have disruptive economic potential to traditional finance-sector and non-financial markets as the success of crypto currencies demonstrates. While traditional financial and securities markets are highly regulated, DLT transactions and exchanges have so far widely remained unregulated, which potentially poses major threats to individual investors and, via money laundering, terrorist financing, and tax evasion, to society at large. Governments have been taking different approaches to monitoring and regulating the fledgling DLT provision arena. However, the small jurisdiction of Gibraltar, an affluent self-governed and self-financed British overseas territory with a population of 34,000 at the Southern tip of the Iberian Peninsula has taken a proactive approach. What makes this case stand out is that Gibraltar is not only is the first jurisdiction worldwide to regulate general DLT provision, but it is rather using the regulation as a competitive tool and a means for creating new public value. The study inquires the potential benefits and challenges to this particular approach to DLT provision regulation, and it investigates and presents the various facets of public value generation, which might serve as a poster child for fast and smart regulation in the digital age.
An Improved Automatic Picking Method for Arrival Time of Acoustic Emission Signals

In this paper, an improved automatic method for picking arrival times of acoustic emission (AE) signals, has been proposed to overcome the interference of spike noise and channel crosstalk.First, median filter is applied to eliminate spike noise presented in signals.Second, the preliminary arrival time is obtained by the AIC picker.Then a judgment whether the signal is disturbed by channel crosstalk is conducted.If the signal is triggered normally, the preliminary arrival time is regarded as the final arrival time of the AE signal.If the signal is triggered in advance, the signal series is selected from the preliminary arrival time to the time corresponding to the maximum of absolute values of signal series.At last the AIC picker is used again to pick the arrival time of selected series and the second AIC arrival time is interpreted as the final arrival time.To validate the performance of the improved automatic method, AIC picker, AIC picker applied after the median filter (MF-AIC) are also used simultaneously to pick the arrival times of AE signals and perform event location.The results show that the improved automatic method has better waveform adaptability and location accuracies than other two methods.
Image Forgery Detection Based on Motion Blur Estimated Using Convolutional Neural Network

Currently images are key evidences in many judicial or other identification occasions, and image forgery detection has become a research hotspot. This paper proposes a novel motion blur based image forgery detection method, which includes three steps. First, a convolutional neural network (CNN)-based motion blur kernel reliability estimation method is proposed, which is used to determine whether an image patch should be involved in the image forgery detection process. Second, a shared motion blur kernels-based image tamper detection method is proposed to detect whether a group of motion blur kernels are projected from the same 3D camera trajectory effectively. Third, a consistency propagation method is proposed to localize tampered regions efficiently. Experiments on synthetic images and natural images show the availability of the proposed method.