Reviewerly/exHarmony huggingface dataset:
How are research data governed at Japanese repositories? A knowledge commons perspective

Purpose The purpose of this paper is to survey how research data are governed at repositories in Japan by deductively establishing a governance typology based on the concept of openness in the context of knowledge commons and empirically assessing the conformity of repositories to each type. Design/methodology/approach The fuzzy-set ideal type analysis (FSITA) was adopted. For data collection, a manual assessment was conducted with all Japanese research data repositories registered on re3data.org. Findings The typology constructed in this paper consists of three dimensions: openness to resources (here equal to research data), openness to a community and openness to infrastructure provision. This paper found that there is no case where all dimensions are open, and there are several cases where the resources are closed despite research data repositories being positioned as a basis for open science in Japanese science and technology policy. Originality/value This is likely the first construction of the typology and application of FSITA to the study of research data governance based on knowledge commons. The findings of this paper provide practitioners insight into how to govern research data at repositories. The typology serves as a first step for future research on knowledge commons, for example, as a criterion of case selection in conducting in-depth case studies.
Flexible framework for audio reconstruction

The paper presents a unified, flexible framework for the tasks of audio inpainting, declipping, and dequantization. The concept is further extended to cover analogous degradation models in a transformed domain, e.g. quantization of the signal's time-frequency coefficients. The task of reconstructing an audio signal from degraded observations in two different domains is formulated as an inverse problem, and several algorithmic solutions are developed. The viability of the presented concept is demonstrated on an example where audio reconstruction from partial and quantized observations of both the time-domain signal and its time-frequency coefficients is carried out.
JugglingSwap: Scriptless Atomic Cross-Chain Swaps

The blockchain space is changing constantly. New chains are being implemented frequently with different use cases in mind. As more and more types of crypto assets are getting real world value there is an increasing need for blockchain interoperability. Exchange services today are still dominated by central parties which require custody of funds. This trust imposes costs and security risks as frequent breaches testify. Atomic cross-chain swaps (ACCS) allow mutual distrusting parties to securely exchange crypto assets in a peer-to-peer manner while preserving self-custody. Fundamental ACCS protocols leveraged the scripting capabilities of blockchains to conditionalize the transfer of funds between trading parties. Recent work showed that such protocols can be realized in a scriptless setting. This has many benefits to blockchains throughput, efficiency of swap protocols and also to fungibility and privacy. The proposed protocols are limited to assets transferable by either Schnorr signatures or ECDSA that are assuming the same elliptic curve parameters. In this work we present JugglingSwap, a scriptless atomic cross-chain swap protocol with a higher degree of interoperability. We weaken the assumptions about blockchains that can be included in the ACCS protocol, and only require that (1) a threshold variant exists to the underlying digital signature scheme and (2) it is based on the elliptic curve discrete logarithm problem (ECDLP). The fair exchange is achieved by a gradual release of secrets. To achieve this we use a new building block we call Juggling: a public key verifiable encryption scheme to transfer segments of secret shares between parties, which can also be of separate interest. Juggling is then tailored to a specific private key management system design with threshold signatures security.
“But Wait, There’s More!” a Deeper Look into Temporally Placing Touch Gesture Signifiers

The language used in interaction design is affected by the wide array of academic backgrounds of interaction designers. Therefore one word may have several meanings, which can be confusing when conducting research in this field. In this paper, we define three levels of interaction: macro-, micro- and nanointeractions, the latter of which is the focus of this study. We use Buxton’s three state model to break down common gestures on touch interfaces into nanointeractions, thereby identifying where in the process of a gesture its signifiers can appear. This is useful when overloading controls in small interfaces. We conducted an experiment to determine whether the temporal placement of a signifier before, during, or after a gesture made any difference for the discoverability of a double and long tap affordance. No clear tendencies were found regarding the temporal placement of the signifier. However, the concept of nanointeractions can be a valuable tool for interaction design.
Examination and Extension of Strategies for Improving Personalized Language Modeling via Interpolation

In this paper, we detail novel strategies for interpolating personalized language models and methods to handle out-of-vocabulary (OOV) tokens to improve personalized language models. Using publicly available data from Reddit, we demonstrate improvements in offline metrics at the user level by interpolating a global LSTM-based authoring model with a user-personalized n-gram model. By optimizing this approach with a back-off to uniform OOV penalty and the interpolation coefficient, we observe that over 80% of users receive a lift in perplexity, with an average of 5.2% in perplexity lift per user. In doing this research we extend previous work in building NLIs and improve the robustness of metrics for downstream tasks.
Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking

Existing Multiple-Object Tracking (MOT) methods either follow the tracking-by-detection paradigm to conduct object detection, feature extraction and data association separately, or have two of the three subtasks integrated to form a partially end-to-end solution. Going beyond these sub-optimal frameworks, we propose a simple online model named Chained-Tracker (CTracker), which naturally integrates all the three subtasks into an end-to-end solution (the first as far as we know). It chains paired bounding boxes regression results estimated from overlapping nodes, of which each node covers two adjacent frames. The paired regression is made attentive by object-attention (brought by a detection module) and identity-attention (ensured by an ID verification module). The two major novelties: chained structure and paired attentive regression, make CTracker simple, fast and effective, setting new MOTA records on MOT16 and MOT17 challenge datasets (67.6 and 66.6, respectively), without relying on any extra training data. The source code of CTracker can be found at: github.com/pjl1995/CTracker.
Supply Chain of Relief Materials using Blockchain

Blockchain serves as an immutable ledger which allows transactions to take place in a decentralized manner. The proposed solution incorporates the use of Blockchain for Supply Chain Management of Relief Materials after a Disaster. The proposed solution also provides many additional features when compared to the current system like being tamper-proof, providing anonymity and accountability to the users. Our solution incorporates a box-in-box approach whilst keeping tabs via tamper-proof barcodes. Barcodes will be frequently scanned at designated locations. Users also have the facility to track the donated items online through a dedicated website. For blockchain infrastructure, we have used Hyperledger Fabric.
The quantum version of the shifted power method and its application in quadratic binary optimization

In this paper, we present a direct quantum adaptation of the classical shifted power method. The method is very similar to the iterative phase estimation algorithm; however, it does not require any initial estimate of an eigenvector and as in the classical case its convergence and the required number of iterations are directly related to the eigengap. If the amount of the gap is in the order of $1/poly(n)$, then the algorithm can converge to the dominant eigenvalue in $O(poly(n))$ time. The method can be potentially used for solving any eigenvalue related problem and finding minimum/maximum of a quantum state in lieu of Grover's search algorithm. In addition, if the solution space of an optimization problem with $n$ parameters is encoded as the eigenspace of an $2^n$ dimensional unitary operator in $O(poly(n))$ time and the eigengap is not too small, then the solution for such a problem can be found in $O(poly(n))$. As an example, using the quantum gates, we show how to generate the solution space of the quadratic unconstrained binary optimization as the eigenvectors of a diagonal unitary matrix and find the solution for the problem.
Data Mining Implementation with Clustering Techniques for Drug Inventory Information in Antonius Hospital Pontianak

Competition in the business world, especially in the pharmacy industry, requires developers to find a strategy that can increase sales of special drug sales by maximizing service to consumers. One way is to keep the availability of various types of drugs in the pharmacy warehouse. To find out what medicines are purchased by consumers, it can be done using basket analysis techniques, namely analysis of consumer buying habits. Detection of drugs that are often bought together is called the association rule. Medication is an important factor whose availability must be controlled properly in a hospital. The availability of drugs at the hospital will support hospital performance. One of the data mining methods that can be used from the above analysis is the clustering method, which is grouping data items into small groups so that each group has an essential equation. The evaluation results from this study are grouping drug data with clustering techniques to facilitate the process of grouping drugs based on drugs that are often used in a certain period of time
Determining the Correct Number of Clusters in the CT Image Segmentation

Clustering algorithm plays an essential role in CT image segmentation, and cluster validity index is an essential component in clustering analysis. There are a lot of validity indices used for assessing clustering results, that is, determine the optimal cluster number. But the existing validity indices are often ineffective for the datasets with irregular-shaped clusters and corrupted by noise. This study aims to define a novel validity index which cannot be affected by the shapes of clusters and corrupted by noise of the investigated datasets. Chain-based distance different from original Euclidean distance is defined first, then by a multidimensional scaling (MDS) transformation, all points are mapped into a new data space. After evaluation of compactness and separation twice in datasets, a novel validity index is proposed. A lot of synthetic datasets and several typical CT images were used for validating the proposed validity index. Experimental results validate the proposed index and this index is applicable to the datasets with arbitrary-shaped clusters and corrupted by noise, which is helpful in clustering analysis and computer-aided detection system.
Shifting towards community-building in opening up FabLabs for non-expert users

In the early days of Fablabs and other digital fabrication spaces (e.g. hackerspaces, makerspaces), these community spaces that offer public, shared access to high-end manufacturing equipment (e.g. 3D-printers, laser cutters) were often described as having the democratizing potential to empower everyone as future makers. However, research showed that non-expert users are often impeded to engage in such spaces due to various challenges (among other things, the unfamiliarity of available tools and machines, a lack of skills and experience). By describing a case study in which we elucidate different strategies that were used to engage non-expert users in FabLab [anonymized], this paper focuses on the mediating role of the design researchers who bec
Adoption of Requirements Engineering Methods in Game Development: A Literature and Postmortem Analysis

As the game industry continues to grow in size and revenue, the cost of creating games increases as well, and the successful outcome of game development projects becomes ever more important. In traditional software engineering, it is generally agreed that a successful requirements engineering process has a significant impact on the project. In game development, requirements engineering methods do not seem to be commonly used. As the development of digital games includes specialized aspects of software development, it seems likely that game developers could benefit from adopting these techniques and processes. In this paper, a thorough reading of central and current academic research on the topic is performed to form a holistic picture of the central issues and problems preventing the adoption and widespread use of requirements engineering processes and methods in game development. Additionally, algorithmic analysis of 340 post-mortems written by game developers and published on industry websites is conducted. These post-mortems discuss the factors which contributed to or hindered the successful outcome of these game development projects, and the analysis further supports the identified central issues.
Improving Utility and Security of the Shuffler-based Differential Privacy

When collecting information, local differential privacy (LDP) alleviates privacy concerns of users because their private information is randomized before being sent it to the central aggregator. LDP imposes large amount of noise as each user executes the randomization independently. To address this issue, recent work introduced an intermediate server with the assumption that this intermediate server does not collude with the aggregator. Under this assumption, less noise can be added to achieve the same privacy guarantee as LDP, thus improving utility for the data collection task. This paper investigates this multiple-party setting of LDP. We analyze the system model and identify potential adversaries. We then make two improvements: a new algorithm that achieves a better privacy-utility tradeoff; and a novel protocol that provides better protection against various attacks. Finally, we perform experiments to compare different methods and demonstrate the benefits of using our proposed method.
Decision Support System for Employee Recruitment Using El Chinix Traduisant La Realite (Electre) And Weighted Product (WP)

Management of human resources (HR) is important to achieve company goals. One of the activities in HR management is recruitment, selection, and training. Recruitment and selection are usually done not using a system so that the calculations are still done manually. But by processing data using the system can produce a decision in recommending prospective employees that can have a positive impact on the company. The company selection process is carried out through two stages: administrative selection and final selection in the form of psychological test assessment, interviews, ability tests and communication. The use of the Elimination Et Choix Traduisant La Realite (ELECTRE) method in the administrative selection stage and the Weighted Product (WP) method in the final selection stage is a new discovery made to get the best decision in accordance with the required criteria. By using this method the final results will be obtained namely the recommendation of several prospective employees who are fit to work in the company. The performance results of this system reach one hundred percent, the data from the system is in accordance with the expected calculation.
Active multiview recognition with hidden Markov temporal support

Abstract Our paper deals with active multiview object recognition focusing on the directional support of sequential multiple shots. Since inertial sensors are easily available nowadays, we propose the use of them to estimate the orientation change of the camera and thus to estimate the probability of relative poses. With the help of relative orientation change, we can compute transition probabilities between possible poses and can use a hidden Markov model to evaluate state (pose) sequences and can thus increase the recognition rate. Furthermore, we can plan our next viewing position to minimize the risk of misclassification, resulting in higher overall recognition rates. Besides giving the theoretical details, we use two datasets to illustrate the performance of our model through several tests including occlusion, blur, Gaussian noise, and to compare to a solution with a long short-term memory network.
A first step toward incremental evolution of convolutional neural networks

We introduce a novel algorithm - ConvNEAT - that evolves a convolutional neural network (CNN) from a minimal architecture. Convolutional and dense nodes are evolved without restriction to the number of nodes or connections between nodes. The proposed work advances the field with ConvNEAT's ability to evolve arbitrary minimal architectures with multi-dimensional inputs using GPU processing.
3D FLIGHT PATH PLANNING FOR MULTIROTOR UAV

In recent days, the multirotor UAV urban and semi urban applications predict a need for flight automation both in standard flight missions and in emergency flight tasks performed by UAV automatic flight control systems.The standard flight mission of the multirotor UAV might cover take-off, ascend, leveled flight, descend, and landing phases of the flight.If the flight is organized beyond visual line of sight (VLoS) the UAV onboard flight control system is a necessary condition.If any multirotor UAV flight is planned to be executed between the limits of the safe flight envelope of VLoS, in case of emergency, the multirotor UAV automatic flight control system can select the safe landing zone and it can conduct flight on safe landing paths.Military forward operation bases (FOB) are typically of very limited size and are very busy with military personnel, vehicles, weaponry, ammo, facilities and different equipment.They mostly serve for reconnaissance purposes, and any UAV platform used at FOB can improve their military value and importance.It is easy to see that in such a context the only UAV propulsion system proper to apply, is a rotorcraft propelled aircraft.Their special capabilities of vertical, or very short take-off and landing, and hovering put them into a privileged UAV class accessible to use.This paper deals with 3D flight path planning for multirotor UAVs giving solutions for problems handled in UAV take-off and in UAV landing flight phases.
Extrapolating the profile of a finite population

We study a prototypical problem in empirical Bayes. Namely, consider a population consisting of $k$ individuals each belonging to one of $k$ types (some types can be empty). Without any structural restrictions, it is impossible to learn the composition of the full population having observed only a small (random) subsample of size $m = o(k)$. Nevertheless, we show that in the sublinear regime of $m =ω(k/\log k)$, it is possible to consistently estimate in total variation the \emph{profile} of the population, defined as the empirical distribution of the sizes of each type, which determines many symmetric properties of the population. We also prove that in the linear regime of $m=c k$ for any constant $c$ the optimal rate is $Θ(1/\log k)$. Our estimator is based on Wolfowitz's minimum distance method, which entails solving a linear program (LP) of size $k$. We show that there is a single infinite-dimensional LP whose value simultaneously characterizes the risk of the minimum distance estimator and certifies its minimax optimality. The sharp convergence rate is obtained by evaluating this LP using complex-analytic techniques.
Hybridization of Social Spider Optimization (SSO) Algorithm with Differential Evolution (DE) Using Super-Resolution Reconstruction of Video Images

In video super-resolution reconstruction, the pixel correlation of continuous multi-frame image sequences is used to establish an effective video image super-resolution reconstruction model. Optimization problem is formed by transforming it to pixel sequence of high-resolution image from pixel sequence of low-resolution image. To effectively reconstruct the super-resolution video image, two techniques are proposed by this research. It involves the soft computing technique application as an intelligent hybridization. Differential evolution (DE) is hybridized with social spider optimization (SSO) algorithm to form a SSO-DE algorithm. Effective balance between exploitation and exploration can be achieved by this. The search space of SSO algorithm is controlled as well as adjusted dynamically by introducing a weighting factor which changes with iteration. The trapping on local optimum value is avoided by introducing a mutation operator, after the completion of social spider search. The global search’s ability is strengthened by this. In evolution for super-resolution reconstruction of video images, efficiency of proposed method is demonstrated by the experimentation on set of standard benchmark functions. Effectiveness and feasibility of the proposed algorithm are verified by a set of video super-resolution reconstruction example.
DIGITAL CULTURE, THE ANTHROPOLOGICAL DIMENSION AND THE EDUCATIONAL PROBLEM

This essay explores the current state of communication by focussing in particular on two of its dimensions. They are: 1. The increasingly massive and pervasive spreading of forms of digital communication, and 2. Some of the consequences such situation has on the general mentality and on the way the human being is understood. Of such consequences, it is above all the confusion between online and offline, that is between the two different environments that human beings can inhabit, and the confusion between the analogue and the digital, which are mainly investigated from an ethical point of view. A specific educational project is required to address such problems: a project that the ethics of ICT can inspire and give grounds for.
Implementing Robotic Platforms for Therapies Using Qualitative Factors in Mexico

In recent years, robotic platforms (RP) have been implemented to assist human beings during therapeutic treatments. As a result, successful experimental cases have been reported around the world. However, there is not enough information about them being implemented in developing countries where nobody is familiar with RPs. Moreover, there are economic, cultural, and technological factors that must be satisfied to provide therapy in rehabilitation centers or hospitals in Mexico. On the other hand, there are several RPs that are directly derivative without including those three factors, so occasionally they do not achieve good results in Mexico resulting in the exclusion of RP from the therapy. Nevertheless, in developing countries, it is urgent to implement successful RPs because the birth and death rates are swiftly incrementing; therefore, RPs could be an excellent alternative for increasing the number of therapies that can be provided at hospitals or rehabilitation centers where there are not enough therapists. As a result, implementing an effective RP in a developing country is related to economic, cultural, and technological factors, which cannot always be measured, to make decisions regarding its design. Therefore, a qualitative analysis that allows the presentation of a general guide to implementing RPs in developing countries such as Mexico is required. To assess the proposed qualitative guide, some RPs were implemented in Mexico City. Moreover, this robotic platform could be adapted for several tasks such as an education platform since this robotic platform could deal with end user needs.