Reviewerly/exHarmony huggingface dataset:
Reading Protocol

In Interactive Information Retrieval (IIR) experiments the user's gaze motion on web pages is often recorded with eye tracking. The data is used to analyze gaze behavior or to identify Areas of Interest (AOI) the user has looked at. So far, tools for analyzing eye tracking data have certain limitations in supporting the analysis of gaze behavior in IIR experiments. Experiments often consist of a huge number of different visited web pages. In existing analysis tools the data can only be analyzed in videos or images and AOIs for every single web page have to be specified by hand, in a very time consuming process. In this work, we propose the reading protocol software which breaks eye tracking data down to the textual level by considering the HTML structure of the web pages. This has a lot of advantages for the analyst. First and foremost, it can easily be identified on a large scale what has actually been viewed and read on the stimuli pages by the subjects. Second, the web page structure can be used to filter to AOIs. Third, gaze data of multiple users can be presented on the same page, and fourth, fixation times on text can be exported and further processed in other tools. We present the software, its validation, and example use cases with data from three existing IIR experiments.
Statistics of natural images as a function of dynamic range

The statistics of real world images have been extensively investigated, but in virtually all cases using only low dynamic range image databases. The few studies that have considered high dynamic range (HDR) images have performed statistical analyses categorizing images as HDR according to their creation technique, and not to the actual dynamic range of the underlying scene. In this study we demonstrate, using a recent HDR dataset of natural images, that the statistics of the image as received at the camera sensor change dramatically with dynamic range, with particularly strong correlations with dynamic range being observed for the median, standard deviation, skewness, and kurtosis, while the one over frequency relationship for the power spectrum breaks down for images with a very high dynamic range, in practice making HDR images not scale invariant. Effects are also noted in the derivative statistics, the single pixel histograms, and the Haar wavelet analysis. However, we also show that after some basic early transforms occurring within the eye (light scatter, nonlinear photoreceptor response, center-surround modulation) the statistics of the resulting images become virtually independent from the dynamic range, which would allow them to be processed more efficiently by the human visual system.
A New Cell-Level Search Based Non-Exhaustive Approximate Nearest Neighbor (ANN) Search Algorithm in the Framework of Product Quantization

Non-exhaustive search is widely used in the approximate nearest neighbor (ANN) search. In this paper, we propose a new cell-level search-based non-exhaustive ANN search algorithm in the framework of product quantization (PQ) called cell-level PQ to speed up the ANN search. The cell-level search is introduced by searching all the PQ cells of the query vector on the cell level, and the length of the candidate list can be significantly reduced with negligible computational costs. Instead of using the high time-consuming coarse quantizers, which are necessary in all of the existing non-exhaustive ANN search algorithms such as inverted files (IVFs) and inverted multi-index (IMI), our proposed cell-level PQ reuses the PQ cells of query vector to reject database vectors so that the ANN search in the framework of PQ can be efficiently speeded up. In addition, because our proposed cell-level PQ does not need to store the auxiliary indexes of coarse quantizers for each database vector, no extra memory consumption is required. The experimental results on different databases demonstrate that our proposed cell-level PQ can significantly speed up the ANN search in the framework of PQ, and meanwhile, the search accuracy is almost the same as that of the standard PQ.
Corporate governance and blockchain: Some preliminary results by a survey

Blockchain technology can influence various aspects of the business even if empirical studies are still lacking to estimate the effective application of technology on corporate governance. Beyond its scientific-practical trendiness, blockchain should not be a matter of “if”, but a matter of “how” organizations are willing and able to integrate blockchain practices into their vision of the future, with ways to manage shareholders issues down to regular operations reaching and managing different levels of activism. It seems almost paradoxical that still not all companies subscribe to the “next best practice”. Therefore, this paper aims to answer the following research question: What is the expected impact of blockchain technology on corporate governance? The purpose of this study is to provide a starting point for research that can be used for further examination of these aspects
Developing a Prototype for Sun Tracker System Based on IoT: Controlled by Mobile App and Online Database Monitoring

IoT (Internet of Things) has become a separate research field since the development of internet technology and other communication media.IoT optimizes several tools such as sensor media radio frequency identification, wireless sensor networks and other smart objects that allow humans to easily interact with all devices connected to the internet network.The purpose of this research is to build an IoT-based solar cell tracker which can be controlled through mobile Apps and the data can be read anywhere as long as there is an internet network.In this research used LDR sensor, DHT11 sensor, Voltage sensor and MPU6050 sensor to optimize the performance of the proposed solar cell tracker system.The control system is built using an Appinventor that allows the system to be controlled via cellular phone from a distance.All sensors will be connected via Arduino and realtime data can be viewed through the Thinger.Io web page using Raspberry and mini LCD.ESP8266 NODE MCU microcontroller is used for a Wi-Fi connection.The results of this study indicate that the voltage value generated by dynamic solar cell tracker is greater than the static solar cell tracker.Dual axis sun tracker devices that are built using four LDRs produce an average voltage of 19.40 Volt when the sunny weather, 18.05 Volt when the cloudy weather and 13.60 Volt when the rainy weather.
An Augmented Reality Approach to Visualize Biomedical Images

This paper describes an innovative methodology which automatically creates a 2D-axisymmetric finite element (FE) model of a quasi-axisymmetric component from its 3D CAD model.The process has been implemented as a script and uses the API to a commercial FE package.The calculation of a volume shape coefficients to be applied in the non-axisymmetric regions of the FE model, to account for the fact the region is not fully axisymmetric, is also described.In the paper, the methodology is demonstrated for a rotating blade of tapered profile analyzing and comparing both stresses and displacement.Excellent results are shown.
A synchronous quantum blind signature scheme with entanglement swapping

We present an arbitrated quantum blind signature scheme by entanglement swapping, which is simplified from the preparation and operation of the quantum states. Compared with the classical blind signature, quantum arbitration has added the role of the third party arbitration, so the structure of the protocol has changed. The traceability depends on the third party arbitration, and the blind operation and the signature operation can be carried out simultaneously.
Incremental Hotel Recommendation with Inter-guest Trust and Similarity Post-filtering

Crowdsourcing has become an essential source of information for tourists and tourism industry. Every day, large volumes of data are exchanged among stakeholders in the form of searches, posts, shares, reviews or ratings. Specifically, this paper explores inter-guest trust and similarity post-filtering, using crowdsourced ratings collected from the Expedia and TripAdvisor platforms, to improve hotel recommendations generated by incremental collaborative filtering. First, the profiles of hotels and guests are created using multi-criteria ratings and inter-guest trust and similarity. Next, incremental model-based collaborative filtering is adopted to predict unknown hotel ratings based on the multi-criteria ratings and, finally, post-recommendation filtering sorts the generated predictions based on the inter-guest trust and similarity. The proposed method was tested both off-line (post-processing) and on-line (real time processing) for performance comparison. The results highlight: (i) the increase of the quality of recommendations with the inter-guest trust and similarity; and (ii) the decrease of the predictive errors with the on-line incremental collaborative filtering. Thus, this work contributes with a novel method, integrating incremental collaborative filtering and inter-guest trust and similarity post-filtering, for on-line hotel recommendation based on multi-criteria crowdsourced rating streams.
Cloud Service Level Agreements and Resource Management

Cloud computing is a technical "as-a-service" usage model utilizing virtualization.Virtual machines are the core of cloud computing that runs as independent machine grouped into different networks within the hypervisor.Practically done by deployment of clouds computing hosts enterprise servers in virtual machines on an array of high-end servers.Managing cloud resources has to do with controlling and limiting access to the pool of available resources.This brings about the conception of an agreement between the resource services cloud providers and the customers otherwise known as Service Level Agreement, in other to restrict access to provisioned resources.Resource provisioning is a flexible on-demand pay-as-you-go package that is negotiated; and signed based on SLAs between customers and cloud providers.SLAs enable cloud providers to evade costly SLA consequences payable when there are violations, optimizing the performance of customers' applications and professionally manage resources to reduce cost.These documents are the agreements called Service Level Agreements (SLAs).SLAs outline the expectations which are: terms, conditions, and services of the clients from their service providers regarding availability, redundancy, uptime, cost, and penalties in cases of violations.These ensure clients' confidence in the services offered.Managing resource is an on-going major issue in cloud computing.Considering the limitation of resources, it poses a challenge for cloud service providers to make provision for all resources as needed.This paper seeks a solution to problems that relates to the present trends and developments of what cloud Service Level Agreements are and it ensures so by reviewing current literature.Thus, this research is a study of cloud Service Level Agreements, cloud resource management and their challenges.This paper has made provision to act as guidance for future research and it is anticipated to be beneficiary to potential cloud end users and cloud service providers.
Rethinking Digital Forensics

In the modern socially-driven, knowledge-based virtual computing environment in which organisations are operating, the current digital forensics tools and practices can no longer meet the need for scientific rigour. There has been an exponential increase in the complexity of the networks with the rise of the Internet of Things, cloud technologies and fog computing altering business operations and models. Adding to the problem are the increased capacity of storage devices and the increased diversity of devices that are attached to networks, operating autonomously. We argue that the laws and standards that have been written, the processes, procedures and tools that are in common use are increasingly not capable of ensuring the requirement for scientific integrity. This paper looks at a number of issues with current practice and discusses measures that can be taken to improve the potential of achieving scientific rigour for digital forensics in the current and developing landscape.
ROLL 1.0: $$\omega $$ -Regular Language Learning Library

We present ROLL 1.0, an $$\omega $$ -regular language learning library with command line tools to learn and complement Büchi automata. This open source Java library implements all existing learning algorithms for the complete class of $$\omega $$ -regular languages. It also provides a learning-based Büchi automata complementation procedure that can be used as a baseline for automata complementation research. The tool supports both the Hanoi Omega Automata format and the BA format used by the tool RABIT. Moreover, it features an interactive Jupyter notebook environment that can be used for educational purpose.
Wireless access to ultimate virtual reality 360-degree video

Virtual reality 360-degree videos will become the first prosperous online VR application. In this paper, we focus on the ultimate VR 360 that satisfies human eye fidelity. The ultimate VR 360 requires downlink 1.5 Gbps for viewing and uplink 6.6 Gbps for live broadcasting, with the round-trip-time of less than 8.3 ms. We explore whether the most advanced wireless communication technologies support the ultimate VR 360. Specifically, we consider 5G in cellular communications, IEEE 802.11ac (operating in 5 GHz) and IEEE 802.11ad (operating in 60 GHz) in WiFi communications. Our results indicate the need for more advanced wireless technologies to fully support the ultimate VR 360. It requires further research to build VR 360 systems within Internet-of-Things platforms.
Towards Optimal Robust Secret Sharing with Security Against a Rushing Adversary

Robust secret sharing enables the reconstruction of a secret-shared message in the presence of up to t (out of n) incorrect shares. The most challenging case is when $$n = 2t+1$$ , which is the largest t for which the task is still possible, up to a small error probability $$2^{-\kappa }$$ and with some overhead in the share size. Recently, Bishop, Pastro, Rajaraman and Wichs [3] proposed a scheme with an (almost) optimal overhead of $$\widetilde{O}(\kappa )$$ . This seems to answer the open question posed by Cevallos et al. [6] who proposed a scheme with overhead of $$\widetilde{O}(n+\kappa )$$ and asked whether the linear dependency on n was necessary or not. However, a subtle issue with Bishop et al.'s solution is that it (implicitly) assumes a non-rushing adversary, and thus it satisfies a weaker notion of security compared to the scheme by Cevallos et al. [6], or to the classical scheme by Rabin and BenOr [13]. In this work, we almost close this gap. We propose a new robust secret sharing scheme that offers full security against a rushing adversary, and that has an overhead of $$O(\kappa n^\varepsilon )$$ , where $$\varepsilon > 0$$ is arbitrary but fixed. This $$n^\varepsilon $$ -factor is obviously worse than the $$\mathrm {polylog}(n)$$ -factor hidden in the $$\widetilde{O}$$ notation of the scheme of Bishop et al. [3], but it greatly improves on the linear dependency on n of the best known scheme that features security against a rushing adversary (when $$\kappa $$ is substantially smaller than n). A small variation of our scheme has the same $$\widetilde{O}(\kappa )$$ overhead as the scheme of Bishop et al. and achieves security against a rushing adversary, but suffers from a (slightly) superpolynomial reconstruction complexity.
Distributive Disjoint Polymorphism for Compositional Programming

Popular programming techniques such as shallow embeddings of Domain Specific Languages (DSLs), finally tagless or object algebras are built on the principle of compositionality. However, existing programming languages only support simple compositional designs well, and have limited support for more sophisticated ones. This paper presents the $$\mathsf {F}_{i}^{+}$$ calculus, which supports highly modular and compositional designs that improve on existing techniques. These improvements are due to the combination of three features: disjoint intersection types with a merge operator; parametric (disjoint) polymorphism; and BCD-style distributive subtyping. The main technical challenge is $$\mathsf {F}_{i}^{+}$$ ’s proof of coherence. A naive adaptation of ideas used in System F’s parametricity to canonicity (the logical relation used by $$\mathsf {F}_{i}^{+}$$ to prove coherence) results in an ill-founded logical relation. To solve the problem our canonicity relation employs a different technique based on immediate substitutions and a restriction to predicative instantiations. Besides coherence, we show several other important meta-theoretical results, such as type-safety, sound and complete algorithmic subtyping, and decidability of the type system. Remarkably, unlike $$\mathsf {F}_{<:}$$ ’s bounded polymorphism, disjoint polymorphism in $$\mathsf {F}_{i}^{+}$$ supports decidable type-checking.
Estimating Solar Irradiance on Tilted Surface with Arbitrary Orientations and Tilt Angles

Photovoltaics modules are usually installed with a tilt angle to improve performance and to avoid water or dust accumulation. However, measured irradiance data on inclined surfaces are rarely available, since installing pyranometers with various tilt angles induces high costs. Estimating inclined irradiance of arbitrary orientations and tilt angles is important because the installation orientations and tilt angles might be different at different sites. The goal of this work is to propose a unified transfer model to obtain inclined solar irradiance of arbitrary tilt angles and orientations. Artificial neural networks (ANN) were utilized to construct the transfer model to estimate the differences between the horizontal irradiance and the inclined irradiance. Compared to ANNs that estimate the inclined irradiance directly, the experimental results have shown that the proposed ANNs with differential outputs can substantially improve the estimation accuracy. Moreover, the trained model can successfully estimate inclined irradiance with tilt angles and orientations not included in the training data.
A High Capacity PDF Text Steganography Technique Based on Hashing Using Quadratic Probing

In this paper, a novel approach of PDF-based text steganography is presented.A covert communication is achieved by embedding secret information at between-characters positions of words within cover PDF text.A stegoencoding technique is being designed to improve the embedding capacity along with sufficiently reduced overheads of stego-cover PDF file.The proposed method constructs multilevel embedding capacities which can be used according to requirements from higher embedding capacity to typical embedding capacity.Encoding and decoding of secret text is being obtained by implementing quadratic probing of hashing technique that builds up a direct encoding table which provides better performance in term of time complexity.Exploratory outcome exhibits that the proposed technique gives productive algorithms in term upgraded security and high payload of concealed data.Experiential verification and examination of the proposed technique with certain prominent text steganographic techniques demonstrate that proposed technique performs better than current methodologies and free of requirements like shading scheme, languages, wordlist, compression and so forth.
Cognitive modeling of factors of influence on the processes of formation and reproduction of fixed assets of agricultural enterprises

The features of financial support for reproduction of fixed assets of agrarian enterprises are considered in the article. These include quantitative and qualitative differences in the basic assets of agrarian enterprises, seasonal production, long payback period of capital investments, the reproduction in agricultural enterprises of a significant part of fixed assets in kind, unsustainable financial condition of enterprises and lack of collateral for obtaining long-term loans, undervalued value of fixed assets, no accrual of depreciation on land. For the agricultural enterprises of Ukraine, a cognitive model of the situation &amp;quot;Fixed Assets&amp;quot; was developed, which allows us to investigate factors that determine the possibilities of expanding their reproduction in the light of unstable conditions of functioning. On the basis of cognitive modeling, studies of a possible scenario for the transformation of this system in accordance with the purpose of management have been carried out and modeling the dynamics of the levers of directing the management influences depending on the chosen behavior. It is established that the most significant factors influencing changes in the system of fixed assets are: net profit received by enterprises of the agrarian sector of Ukraine, state support of agricultural producers, foreign investments in agriculture and financing of fixed assets on the basis of leasing. Based on a correlation-regression analysis based on the estimation of stable cause-and-effect relationships, establishing quantitative relations between random variables of the investigated process, conditional forecasts of changes in the size of fixed assets of agricultural enterprises are constructed.
Confidence random tree-based algorithm for mobile robot path planning considering the path length and safety

This article introduces a novel confidence random tree-based sampling path planning algorithm for mobile service robots operating in real environments. The algorithm is time efficient, can accommodate narrow corridors, enumerates possible solutions, and minimizes the cost of the path. These benefits are realized by incorporating notable approaches from other existing path planning algorithms into the proposed algorithm. During path selection, the algorithm considers the length and safety of each path via a sampling and rejection method. The algorithm operates as follows. First, the confidence of a path is computed based on the clearance required to ensure the safety of the robot, where the clearance is defined as the distance between the path and the closest obstacle. Then, the sampling method generates a tree graph in which the edge lengths are controlled by the confidence. In a low confidence space, such as a narrow corridor, the corresponding graph has denser samples with short edges while in a high confidence space, the samples are widely spaced with longer edges. Finally, a rejection method is employed to ensure a reasonably short computation time by optimizing the sample density by rejecting unnecessary samples. The performance of the proposed algorithm is validated by comparing the experimental results to those of several commonly used algorithms.