Reviewerly/exHarmony huggingface dataset:
A Collaborative Enforcement Mechanism for Spectrum Sharing Using Blockchain and Smart Contracts: An Application for the 1695-1710MHz Band

Traditionally, spectrum allocation has been governed by centralized schemes (e.g., command-and-control). Nonetheless, other mechanisms, such as collaborative enforcement, have proven to be successful in a variety of scenarios. In Collaborative enforcement (i.e., collective action), the stakeholders agree on decision-making arrangements (i.e., access, allocation and control of the resources) while being involved in monitoring the adherence to the rules as a shared effort.Blockchain is a distributed ledger of records/transactions (i.e., database) that brings many benefits such as decentralization, transparency, immutability, etc. One of the most notable characteristics of blockchain-based platforms is their definition as trustless environments, as there is no central entity in charge of controlling the network interactions. Instead, trust is a group effort, achieved through repeated interactions, consensus algorithms, and cryptographic tools; therefore, converting blockchain systems into prominent examples of collaborative governance regimes. In this paper, our goal is to analyze a particular application of blockchain and smart contracts for the 1695-1710MHz sharing scenario. In this way, we provide a theoretical analysis of the feasibility and the required characteristics to implement such a system. In addition, through the implementation of a Proof of Concept, we evaluate how the implementation of a blockchain-based organization can be the motor to build a collaborative governance scheme in the spectrum sharing arrangement of the 1695-1710MHz band.
Diagnosis of breast lumps based on breast imaging reporting and data system score and histopathological examination: a comparative study

Background: Breast cancer incidence in India is increasing and has now become the most common cancer among women. Preoperative pathology diagnosis and mammography (using breast imaging reporting and data system (BI-RADS) scoring system) constitute an essential part of the workup of breast lesions. The present study was aimed to compare the diagnostic accuracy of BI-RADS score with histopathological finding in diagnosis of benign and malignant lesions of breast.Methods: This is a cross-sectional study. The present study was conducted on 100 randomly selected newly diagnosed cases of breast lump attending the General Surgery Department (OPD).Results: Considering histopathological examination as gold standard, the sensitivity and specificity of BI-RADS score is 93.9% and 82.3% respectively. The positive predictive value, negative predictive value and diagnostic accuracy of BI-RADS score is 91.1%, 87.5% and 90.0% respectively.Conclusions: Author conclude from the present study that BI-RADS score being non-invasive, it may become a very useful test for evaluating Breast lump lesions. However, BI-RADS score cannot be considered as gold standard and thus cannot be used as an alternative to histopathology in diagnosis of breast lumps.
A Rights Expression Model and a License Structure for ChinaDRM

As an organization that develops DRM (digital right management) standards in China, ChinaDRM proposed a set of solutions for the DRM application. This paper mainly focuses on the rights expression and the content authorization mechanism of ChinaDRM. We built a basic model for ChinaDRM's rights expression, introduced the format of ChinaDRM license and gave a license instance for analysis. At the end of this paper, we introduced the content authorization mechanism of ChinaDRM.
Information System Design Reminder Inventory Control At PT Nuansa Timur Lestari

The current information needs in the business world to be very important in determining the progress of a company. A good activity on a single company or technology is also determined by the available information. PT Lestari Eastern Nuances is a company engaged in the restaurant. The logging system supplies the raw materials of food are running currently still using Microsoft Excel to record incoming and outgoingraw food. The current running system still has many flaws and weaknesses as it takes quite a long time to figure out the amount of supplies of goods raw materials because an admin shed have to calculate the physical stock quantities directly, the occurrence of the difference in the number of requests the raw material with raw material expenses due to miscalculations by admin warehouse, takes a long time to make a request and expense report raw materials causing the company's performance be hampered, the absence of a reminder to remind Admins about the number of warehouse stock of raw materials so that when the raw materials needed by the chef stock no. Based on existing problems then needed a system to help the admin staff stock in managing demand and pengerluaran raw material so that it doesn't happen the difference in stocks of raw materials. The system will be created using the PHP programming language and the Mysql database, the method of analysis PIECES and using a systems development method in System Development Life Cycle (SDLC).&#x0D;
Need Analysis on Development of Teaching Writing Materials ‘Genre’ Based on Gayo Local Culture

&lt;p class="abstrak"&gt;&lt;em&gt;The research aims to analyze the needs of development teaching writing material ‘genre’ based on students’ local culture in Central Aceh. Mix methodologies are conducted in collecting the data, interviewing the teacher and giving the questionnaires to the students. The participants of this research are two English teachers and thirty students of tenth grade from two different Islamic High Schools in Central Aceh. The samples are taken by conducting purposive sampling. The result of the study showed three things; first, writing is the most difficult skill for students to be mastered, second, teaching material based on local culture has not been available yet, the last, teaching writing material ‘genre’ based on local culture is needed in order to improve students’ writing ability.&lt;/em&gt;&lt;/p&gt;
[Kidney tumor segmentation in ultrasound images using adaptive sub-regional evolution level set models].

Kidney tumor is one of the diseases threatening human health. Ultrasound is widely applied in kidney tumor diagnosis due to its high popularization, low price and no radiation. Accurate segmentation of kidney tumor is the basis of precise treatment. Kidney tumors often grow in the middle of cortex, so that segmentation is easy disturbed by nearby organs. Besides, ultrasound images own low contrast and large speckle, leading to difficult segmentation. This paper proposed a novel kidney tumor segmentation method in ultrasound images using adaptive sub-regional evolution level set models (ASLSM). Regions of interest are firstly divided into subareas. Secondly, object function is designed by integrating inside and outside energy and gradient, in which the ratio of these two parts are adjusted adaptively. Thirdly, ASLSM adapts convolution radius and curvature according to centroid principle and similarity inside and outside zero level set. Hausdorff distance (HD) of (8.75 ± 4.21) mm, mean absolute distance (MAD) of (3.26 ± 1.69) mm, dice-coefficient (DICE) of 0.93 ± 0.03 were obtained in the experiment. Compared with traditional ultrasound segmentation method, ASLSM is more accurate in kidney tumor segmentation. ASLSM may offer convenience for doctor to locate and diagnose kidney tumor in the future.肾脏肿瘤已经成为威胁人类健康的重要疾病之一。超声检查具有普及率高、价格低廉、无辐射等诸多优点，已广泛应用于肾脏肿瘤的诊断中。超声图像中肾脏肿瘤的准确分割是制定治疗方案的基础。肾脏肿瘤往往生长在肾皮质中间，分割容易受到周围脏器干扰，而且超声图像对比度低、斑点噪声严重，使得肿瘤分割困难。本文根据肾脏超声图像的特点，提出基于自适应分区演化水平集（ASLSM）的肿瘤分割算法。首先，将感兴趣区域图像分区；然后，融合内外能量项和梯度设计目标函数，并自适应调整二者比例；最后，根据质心原理和零水平集内外相似度自适应卷积半径及曲率，进行曲线演化。将本算法用于肾脏超声图像，实验结果中豪斯多夫距离（HD）为（8.75 ± 4.21）mm，平均绝对距离（MAD）为（3.26 ± 1.69）mm，戴斯系数（DICE）为 0.93 ± 0.03。与传统的方法进行比较，实验结果证明本算法可以获得更加准确的肿瘤分割结果，今后本算法或可为辅助医生定位和诊断肾脏肿瘤提供便利。.
Differential Abnormality-Based Tampering Detection in Digital Document Images

Copy-move and splicing are familiar operations for tempering digital document images. In this paper, we propose a simple yet effective method for detecting such document image tampering blindly. The spatial-domain differential abnormality is often discovered along image splicing boundaries under the assumption of being without or with weak post-processing. In order to capture such a forensic clue, the row- and column-wise first-order image differences are computed and then binarized via thresholding. Such binarized difference maps are median-filtered and fused to resist background noise. The quadrangle structure consisting of four latent boundary lines is searched particularly via adaptive sliding windows within the fused map. Preliminary test results on an elaborately collected image dataset verify the effectiveness and efficiency of our proposed document image tampering detection method.
Impact of Economic and Non-Economic Factors on Income Inequality in ASEAN Countries

This study examined the impact of economic and non-economic factors on income inequality in several ASEAN countries: Indonesia, Malaysia, Thailand, Vietnam, and the Philippines. The macroeconomic determinants of income inequality in certain countries were empirically modeled and panel data analysis undertaken for the 2012–2016 period. In light of previous studies, non-economic variables, such as the democratic and corruption perceptions indices, that can increase income inequality were examined. The results revealed that income inequality in ASEAN countries was influenced by the contribution of the agricultural sector to GDP, number of automatic teller machines per 100,000 adults, depth of credit information index, dependency ratio, corruption perceptions index, democracy index, and unemployment rate. Based on these results, it is recommended that governments of ASEAN countries reduce income inequality by encouraging: investment to create a formal labor market and subsequent informal employment; and the provision of more bank services in rural areas and design innovative products for the lower middle class, improving access to banks for not only savings but also credit.
Advancing the measurement rigour of organizational and programmatic knowledge management in health and development

Knowledge management (KM) plays an important role in global health and development where resources for programme implementation are limited and needs to collaborate and learn across organization and sector boundaries are great. Health and development professionals depend on KM approaches to access, share, and use critical health knowledge. However, measuring the contribution of KM to achieving programme goals is complex. The Guide to Monitoring and Evaluating Knowledge Management for Global Health addresses this challenge by offering a standard practice and comprehensive guidance to measure the process, reach, engagement, and usefulness of KM as well as the learning and application of knowledge. In this paper, we first outline a collaborative effort among KM practitioners to develop the guide and describe the theoretical basis of the logic model and common indicators that have continued to evolve over the last decade. Using a case study approach, we demonstrate how the guide has been used in organizational (internal) and programmatic (external) KM initiatives. We also discuss recent efforts to address emerging KM themes that resulted in the development of additional indicators on adaptive practice, organizational partnership, and social interaction. These efforts have advanced the measurement rigour of KM in health and development.
Intra-School Support for Professional Self-Development of Teachers

The paper shows that independent work of students in current conditions has acquired the status of one of the critical forms of organization of students' activities aimed at the development of graduates' readiness for permanent self-development as a necessary condition for successful adaptation in a continuously changing educational, professional, and social environment.The structural components of the independent work of students as a type of educational activity carried out by students as indirect management from teachers are also revealed.The pedagogical conditions for increasing efficiency of independent work of students also include a clear definition of places, goals, and objectives of independent work in the structure of educational process; use of methods for motivation of students to perform independent work; design and application of a system of successive complicating level of problems of competence-oriented educational tasks; as well as creation of information and educational environment.
A COMPARISON STUDY FOR IMAGE DENOISING

Image denoising is the detection and removal of outliers in a image. A measured analog signal is affected by both the device from which the measurement is performed and the noise from the environment. Various types of noise are available. With the developed noise reduction methods, it is tried to eliminate the existing noise. In this study, Bandelet Transform and Bilateral Filter denoising methods are compared. Both methods have been used to eliminate noise of different types and different rates added to the benchmark and retina images. Bandelet transform is performed for both hard and soft threshold. Peak Signal-to-Noise Ratio, Mean Squared Error, Mean Structural Similarity and Feature Similarity Index are used as a comparison method.
Infinity Learning: Learning Markov Chains from Aggregate Steady-State Observations

We consider the task of learning a parametric Continuous Time Markov Chain (CTMC) sequence model without examples of sequences, where the training data consists entirely of aggregate steady-state statistics. Making the problem harder, we assume that the states we wish to predict are unobserved in the training data. Specifically, given a parametric model over the transition rates of a CTMC and some known transition rates, we wish to extrapolate its steady state distribution to states that are unobserved. A technical roadblock to learn a CTMC from its steady state has been that the chain rule to compute gradients will not work over the arbitrarily long sequences necessary to reach steady state —from where the aggregate statistics are sampled. To overcome this optimization challenge, we propose ∞-SGD, a principled stochastic gradient descent method that uses randomly-stopped estimators to avoid infinite sums required by the steady state computation, while learning even when only a subset of the CTMC states can be observed. We apply ∞-SGD to a real-world testbed and synthetic experiments showcasing its accuracy, ability to extrapolate the steady state distribution to unobserved states under unobserved conditions (heavy loads, when training under light loads), and succeeding in difficult scenarios where even a tailor-made extension of existing methods fails.
On the intuitionistic fuzzy implication →191

A new intutionistic fuzzy implication, → 191 , is constructed.Some of its basic properties are studied.
An Evaluation of a State-Predictive Controller and a Jitter Buffer for Remote Controlled Autonomous Vehicles via the Internet

In the age of Internet of Things, when various devices connect to communication networks, Unmanned Vehicle'.;;s (UV's) remote autonomous driving applications are attracting attention. Autonomous driving technology is expected to enable not only automobiles but also various interesting applications. Examples include patrol monitoring inside and outside a building by a small traveling vehicle loaded with surveillance cameras, delivery of goods on office floor, and traveling of wheelchairs. In the case of a traveling vehicle controlled remotely via the Internet, there is a problem that control stability is degraded due to packet transmission delay and its jitter. This belongs to the more general problem of stabilizing feedback control with “dead time” in the control theory. Many studies have shown that state-predictive controllers are effective against delay. Also, it has been shown that a jitter buffer is effective for delay jitter. Our research goal is to realize a system that controls multiple small vehicles via the Internet and a Wi-Fi. In the system, remote control can be disturbed by transmission delay and its jitter. And the faster the driving speed, the lower the stability of the traveling route. In this study, we carried out experiments with a UV simulator. We show experimental evaluation results of a state-predictive controller and a jitter buffer for improving the stability of the traveling route. We also clarified the maximum traveling speed of a UV, that can be achieved under conditions of transmission delay and its jitter.
Parameterized Indexed Value Function for Efficient Exploration in Reinforcement Learning

It is well known that quantifying uncertainty in the action-value estimates is crucial for efficient exploration in reinforcement learning. Ensemble sampling offers a relatively computationally tractable way of doing this using randomized value functions. However, it still requires a huge amount of computational resources for complex problems. In this paper, we present an alternative, computationally efficient way to induce exploration using index sampling. We use an indexed value function to represent uncertainty in our action-value estimates. We first present an algorithm to learn parameterized indexed value function through a distributional version of temporal difference in a tabular setting and prove its regret bound. Then, in a computational point of view, we propose a dual-network architecture, Parameterized Indexed Networks (PINs), comprising one mean network and one uncertainty network to learn the indexed value function. Finally, we show the efficacy of PINs through computational experiments.
An Exploratory Study on the Domain and Level of the Adult’s Digital Literacy for Survey Development

정보기술의 발달과 함께, 정보문해능력이 삶을 살아가기 위한 기초 소양으로서 요구됨에 따라, 성인 누구나 기본적인 정보문해능력을 갖출 수 있도록 실증 데이터에 기반한 국가정책적 관심과 지원이 필요한 상황이다. 본 연구는 우리나라 성인의 정보문해능력 측정도구 개발에 시사점을 얻고자 정보문해의 영역과 수준 탐색을 목적으로 실시되었다. 구체적으로 선행연구를 분석하여 정보문해능력의 개념적 특성을 파악하고, 국내&#8228;외 정보문해능력 측정도구의 내용 및 방법을 분석하여 정보문해능력 측정 관련 특성을 파악하였다. 그리고 관련 분야 전문가를 대상으로 델파이조사를 실시하여 정보문해 개념, 측정 영역 및 수준, 방법을 폭넓게 탐색하였다. 이에 기반하여 향후 성인의 정보문해능력 측정도구를 개발할 때, 정보문해능력 개념의 다양성, 기술&#8228;도구의존성 및 다층위성을 종합적으로 고려하여 수행 기반 도구로서 개발할 필요가 있음을 제안하였다.With the development of information technology, digital literacy is required as a basic literacy for living in daily life, so national policy interest and support based on empirical data is needed to ensure that all adults have basic digital literacy skills. The purpose of this study is to explore the domain and level of the digital literacy in order to obtain implications for the development of the adult’s digital literacy measuring tools. To this end, we analyze the preceding researches to identify the conceptual characteristics of digital literacy. And we also analyze the contents of digital literacy measurement tools to identify the characteristics related to digital literacy measurement. In addition, Delphi research is conducted on experts in related fields to explore concepts, domains, levels and methods of digital literacy. Based on this, it is necessary to consider the diversity of digital literacy concepts, technology and tool dependence, and multi-layered features, when developing adult’s digital literacy measuring tools in the future. We also suggest that digital literacy is characterized as a competency, and therefore it is necessary to examine the level of digital literacy through the use of performance-based tools.
Subword Segmentation for Machine Translation Based on Grouping Words by Potential Roots

This paper proposes a new subword segmentation method for machine translation.The algorithm, which we call GenSeg, is generic in the sense that it can be applied to any language, but is designed with an emphasis on inflectional splitting, i.e. it attempts to split words on boundaries corresponding to inflectional suffixes.The main principle of the method is grouping together words that share a common middle substring, and then separating the best such substring from the rest of the word.GenSeg is a cross-language method extended with some language-specific morphological analysis rules (currently for the Latvian language).To verify its effectiveness, we performed machine translation experiments in two directions: Latvian-English and English-Latvian, obtaining minor improvements in translation quality when using our pre-processing method.
Analysis of the Lemma Mateřství (Motherhood)

Abstract The paper presents results of analysis of the lemma mateřství ‘motherhood’. The authors applied methods of corpus linguistics and discourse analysis – the corpus assisted discourse studies approach – in order to survey representations of the lemma in Czech journalistic texts published from 2010 to 2014, sorted the results into discourse categories on the basis of collocation and concordance analysis, and found out that chief referential discourse-of-motherhood categories were surrogate motherhood, relationship of motherhood and career, delight from motherhood, family relationships, financial and time aspects of motherhood, changes brought by motherhood, and active motherhood. Surrogate motherhood was presented as a solution to women who cannot have a baby themselves, nevertheless also as a complicated issue, in which case emphasis was put on relevant legislation. Motherhood was presented as a danger for a woman’s career, however also as a source of joy, an essential relationship within a family, a right for financial support from the state, a life change, an activity, and an entity closely connected to time factors.
Multi-Question Learning for Visual Question Answering

Visual Question Answering (VQA) raises a great challenge for computer vision and natural language processing communities. Most of the existing approaches consider video-question pairs individually during training. However, we observe that there are usually multiple (either sequentially generated or not) questions for the target video in a VQA task, and the questions themselves have abundant semantic relations. To explore these relations, we propose a new paradigm for VQA termed Multi-Question Learning (MQL). Inspired by the multi-task learning, MQL learns from multiple questions jointly together with their corresponding answers for a target video sequence. The learned representations of video-question pairs are then more general to be transferred for new questions. We further propose an effective VQA framework and design a training procedure for MQL, where the specifically designed attention network models the relation between input video and corresponding questions, enabling multiple video-question pairs to be co-trained. Experimental results on public datasets show the favorable performance of the proposed MQL-VQA framework compared to state-of-the-arts.