Reviewerly/exHarmony huggingface dataset:
Verifiable E-Voting with Resistance against Physical Forced Abstention Attack

E-voting schemes give more efficiency and convenience than the traditional voting systems. However, in a large scale voting scenario, faith in the traditional system remain unaltered because of the several potential security issues in e-voting methodology. Two important concerns are - receipt-freeness and coercion resistance of e-voting schemes. It is observed that there are schemes which are receipt-free but do not achieve coercion-resistance. We investigate the possibility of transforming receipt-free schemes into a scheme which can resist physical forced abstention attack. Our proposal is suitable for large scale voting schemes where polling stations are used during the process.
A privacy-preserving protocol for efficient nighttime haze removal using cloud based automatic reference image selection and color transfer as a service

The advanced Internet technologies have migrated the people to rejoice a virtual environment known as cloud computing. The user can avail the desired services on a pay-as-you-go model, without worrying about the burden of infrastructure maintenance. However, privacy is one of the major issues in cloud computing. This issue is further widened for highly confidential multimedia data like surveillance images and videos. In the context of cloud based smart multimedia systems, it has been found that due to inconsistent weather conditions, there is a usual requirement of post-processing the captured multimedia for better appearance. However, privacy related concerns are resisting users to move their data to the cloud. One such problem is addressed in this paper, specializing the task of efficient nighttime haze removal using privacy-preserving cloud based automatic reference image selection and color transfer as a service. Different from daytime conditions, nighttime haze image consists of multiple light sources, which makes an ambiguous situation for haze removal. We address this problem by first selecting an appropriate gray image as the reference and then transferring its colors to nighttime haze image. This makes the transformed image a suitable candidate for radiance recovery. The proposed protocol is designed to securely outsource this considerable burden from user end. We accomplish this by first proposing an automatic reference gray image selection method, followed by efficient handling mechanisms for technical challenges arising due to performing color transfer operations securely over cloud. Experimental results and validation demonstrates superiority of the proposed method over state-of-the-art schemes. Security analysis of the proposed protocol is established through a challenge-response game model.
Hie-Transformer: A Hierarchical Hybrid Transformer for Abstractive Article Summarization

Abstractive summarization methods based on neural network models can generate more human-written and higher qualities summaries than extractive methods. However, there are three main problems for these abstractive models: inability to deal with long article inputs, out-of-vocabulary (OOV) words and repetition words in generated summaries. To tackle these problems, we proposes a hierarchical hybrid Transformer model for abstractive article summarization in this work. First, the proposed model is based on a hierarchical Transformer with selective mechanism. The Transformer has outperformed traditional sequence-to-sequence models in many natural language processing (NLP) tasks and the hierarchical structure can handle the very long article inputs. Second, the pointer-generator mechanism is applied to combine generating novel words with copying words from article inputs, which can reduce the probability of the OOV words. Additionally, we use the coverage mechanism to reduce the repetitions in summaries. The proposed model is applied to CNN-Daily Mail summarization task. The evaluation results and analyses can demonstrate that our proposed model has a competitively performance compared with the baselines.
Analysis of the water availability in Dante Spring to supply the water needs of the people of Bontongan Village, Enrekang District

Abstract Water absorption on the land surface is influenced by several factors, including coverage or area, soil conditions, and vegetation. Drinking water Supply System has not a national policy that aims to increase public access to water with quality ready to drink. By respect to the necessary to supervise the quality of water distribution to the public by the provider of drinking water service. The tightly of surface conditions, the infiltration rate will be smaller and the greater the runoff this happens because the solid soil is difficult to penetrate by water. The soil surface nature determines it is factors; density and the presence of plants. To increase the power of capacity or soil absorption, carried out using tree roots as an alternative to minimize the occur of inundation, so that research was conducted on samples of inundation land grass and tree roots vegetation in the laboratory. The results land natural was runoff obtained 25456.79 ml/minute and while the land with tree cover runoff was 19665.10 ml/minute. Through testing the Rainfall Simulator, tree root material in soil samples can reduce 22.75% of the inundation and while samples with grass cover can reduce as much as 19.35%. From this research, it was concluded that the roots of pucuk merah plant used in testing could be an alternative to the vegetation of land cover to minimize surface runoff.
Distribution is not enough: going Firther

Much work in contemporary computational semantics follows the distributional hypothesis (DH), which is understood as an approach to semantics according to which the meaning of a word is a function of its distribution over contexts which is represented as vectors (word embeddings) within a multi-dimensional semantic space.In practice, use is identified with occurrence in text corpora, though there are some efforts to use corpora containing multi-modal information.In this paper we argue that the distributional hypothesis is intrinsically misguided as a selfsupporting basis for semantics, as Firth was entirely aware.We mention philosophical arguments concerning the lack of normativity within DH data.Furthermore, we point out the shortcomings of DH as a model of learning, by discussing a variety of linguistic classes that cannot be learnt on a distributional basis, including indexicals, proper names, and wh-phrases.Instead of pursuing DH, we sketch an account of the problematic learning cases by integrating a rich, Firthian notion of dialogue context with interactive learning in signalling games backed by in probabilistic Type Theory with Records.We conclude that the success of the DH in computational semantics rests on a post hoc effect: DS presupposes a referential semantics on the basis of which utterances can be produced, comprehended and analysed in the first place.
Gamelan as a Learning Media Speaking Skills to Indonesian Language Students For Foreign Speakers (BIPA)

Abstract This research was written with the aim of describing the use of gamelan media for speaking skills learning in the form of descriptions for A3 level BIPA students at the Indonesian Institute of Arts, Yogyakarta. The method used in conducting research is a qualitative descriptive research method, with observation data collection techniques, questionnaires, interviews, and documentation. Based on the results of the study, it was found some errors in mentioning the type of gamelan and found the benefits of using learning media in the form of gamelan in speaking competency learning in the form of descriptions for BIPA students at the Indonesian Art Institute, Yogyakarta. This gamelan learning media can also be used as a means of promoting culture in sociocultural aspects. The results of this study are (1) students feel motivated in participating in learning, (2) increased curiosity about Indonesian culture; (3) facilitate students in describing something because they see directly the object to be written; (4) strengthen the memory of BIPA learners on a vocabulary and concept because the media is iconic; (5) learners are more active in exploring the media; (7) through gamelan media, Indonesian culture can be recognized by foreigners; (8) increase vocabulary understanding and mastery, and (9) learning becomes more meaningful.
A Blockchain-based Communication Non-repudiation System for Conversational Service

Communication non-repudiation not only can reflect the users' recognition of the communication process, but also is an important way to trace the trust relationship and resolve trust disputes. However, in the case of conversational service, existing traditional solutions can no longer work. Conversation oriented non-repudiation system should consider the characteristics of real-time scenario. In this context, a system of communication non-repudiation for conversational service based on blockchain technology (B-CNr) is proposed. With the advantage of centerless feature in blockchain, the distribution of communication evidence can easily and quickly trace the communication users and verify the communication facts. The model and strategy of B-CNr are illustrated, the design of communication evidence and corresponding process and policy are demonstrated as well. The proposed model is realized on Ubuntu platform and has satisfactory performance.
PHD: A Deep Learning Based Human Detection Framework for Panoramic Videos

Panoramic video has attracted substantial research attention as the coming video format. It is capable of providing 360 degree immersive experience of omnidirectional visual information. State-of-the-art detection networks may fail to detect humans on spherical images, which are normally represented in deformed rectangular shapes. In this paper, we propose a socalled Panoramic Human Detection (PHD) scheme to address the task of human detection in panoramic videos. Moreover, the PHD method is designed to detect humans by extracting multiple overlapping sub-images from each integral spherical image, where three-dimensional rotation of spherical images is employed to ensure consistency of sub-images. Two detection box filters are designed for removing redundant boxes. Our PHD method is capable of accomplishing the task of human detection in various panoramic video types. Experiments prove that our PHD method outperforms the baseline by 35% and 48.6% in terms of precision and recall, respectively.
Transparent, Provenance-assured, and Secure Software-as-a-Service

Orchestration is one of the cornerstone facilities in Cloud computing, and it has become critical with the advent of the Software-as-a-Service (SaaS) paradigm. It allows service providers to automatize the deployment of their software in Cloud computing infrastructure, thus making the process swift and scalable. However, trust remains a paramount concern still. In particular, transparency, provenance, and security present significant challenges for SaaS. Blockchain possesses the needed qualities to address these challenges. We propose a system capable of creating transparent, provenance-assured, and secure SaaS. The proposed solution addresses these concerns by dividing the orchestration process, i.e., the SaaS deployment, into self-contained steps, each of which is related to a specific domain, e.g., networking, access rules. The input for these steps, and the output of their execution, are both recorded into the blockchain, creating a trail of trust. An external user requiring to validate the orchestration process can query the blockchain. In this way, if the (infrastructure) Cloud provider is fully trusted, end users do not need to trust the service provider: the SaaS deployment becomes fully transparent, and the provenance of the deployed software stack can be ensured. This capability also guarantees security because it allows, e.g., source code auditing for any security threats. The proposed solution presents a generic ledger interface to interact with several blockchain solutions. The approach gives to the Cloud provider the freedom to select the blockchain technology to be used. We present an early evaluation of the overhead of our system against a standard orchestration framework, also presenting a discussion about the limitations of the current approach and possible solutions.
Secure Information Flow Analysis Using the PRISM Model Checker

Secure information flow checks whether sensitive information leak to public outputs of a program or not. It has been widely used to analyze the security of various programs and protocols and guarantee their confidentiality and robustness. In this paper, the problem of verifying secure information flow of concurrent probabilistic programs is discussed. Programs are modeled by Markovian processes and secure information flow is specified by observational determinism. Then, two algorithms are proposed to verify observational determinism in the Markovian model. The algorithms employ a trace-based approach to traverse the model and check for satisfiability of observational determinism. The proposed algorithms have been implemented into a tool called PRISM-Leak, which is constructed on the PRISM model checker. An anonymity protocol, the dining cryptographers, is discussed as a case study to show how PRISM-Leak can be used to evaluate the security of programs. The scalability of the tool is demonstrated by comparing it to the state-of-the-art information flow tools.
The Phase Sensitivities for Different Phase-Shift Configurations in an SU(1,1) Interferometer*

Abstract We theoretically study the phase sensitivities of two different phase-shift configurations in an SU(1,1) interferometer with coherent ⊗ squeezed vacuum states. According to quantum Cramér-Rao theorem, we analytically obtain the ultimate phase sensitivities for two types of phase shift accumulating in one- and two-arm. Compared with the case of one-arm phase shift, the model with phase shift encoding in both arms may provide a better sensitivity when the strength of squeezed vacuum state is large enough. Furthermore, we discuss the achievable sensitivities with the homodyne measurement by invoking of error-propagation formula. In addition, we study the effect of internal and outernal photon losses on the phase sensitivity of the SU(1,1) interferometer and find that the unbalanced interferometer is helpful to improve precision even with high external losses .
Towards a Cross-Company Data and Model Platform for SMEs

The global volume of data is increasing. As a result, companies are increasingly concerned with using the available data and generating added value from it. The development of data products is necessary to obtain information from data and to integrate it into decision making processes. One possibility is the application of artificial intelligence. However, large companies such as Google or Facebook benefit most from this technology. SMEs in particular are falling by the wayside and are confronted with many challenges. The cross-company platform presented in this article represents an approach to enable even smaller companies to access artificial intelligence and to support data management in machine learning projects.
Item-Based Privacy-Preserving Recommender System with Offline Users and Reduced Trust Requirements

Safeguarding privacy of ratings assigned by users is an important issue for recommender systems. There are several existing protocols that allow a server to generate recommendations from homomorphically encrypted ratings, thereby ensuring privacy of rating data. After collecting the encrypted ratings, the server may require further interaction with each user, which is problematic in case some users were to go offline. To solve the offline user problem previous solutions use additional semi-honest third parties. In this paper, we propose a privacy-preserving recommender system that does not suffer from the offline user problem. Unlike previous works, our proposal does not require any additional third party. We demonstrate with the help of experiments that the time required to generate recommendations is efficient for practical applications.
Automatic feature engineering from very high dimensional event logs using deep neural networks

As communication networks have grown, event logs have increased in both size and complexity at a very fast rate. Thus, mining event logs has become very challenging due to their high variety and volume. The traditional solution to model raw event logs is to transform the raw logs into features with fewer dimensions through manual feature engineering. However, feature engineering is very time-consuming, and its quality is highly dependent on data scientists' domain knowledge. Furthermore, repeatedly preprocessing event logs significantly delays the scoring process, which must scan all items in the logs.
A Deep Learning-Based Hybrid Data Fusion Method for Object Recognition

Multi-source heterogeneous data are playing increasingly important roles. It becomes important to harness multi-source heterogeneous data for effectively managing and mining data intelligence. This paper presents a hybrid data fusion model called FD-DFM (Feature and Decision-Data Fusion Model) based on deep learning. The FD-DFM integrates feature fusion and decision fusion into neural networks with the D-S evidence theory. The experiment shows that the FD-DFM model has higher accuracy than other existing methods with fruit recognition.
Degradation of Performance in Reinforcement Learning with State Measurement Uncertainty

We detail the use of open source training environments to investigate the applicability of standard reinforcement learning techniques to inherently error prone tasks expected in real world application of artificial intelligence. Numerical experiments were conducted in which the performance of both Q Learning and Policy Gradient agents' ability to obtain high reward was compared as the observation state measurement uncertainty was increased. The purpose of the research was to assess the applicability of reinforcement learning to real world applications of self-protection of military platforms, where it is expected that the observed state space is uncertain at best. We found in our experiments that Q Learning is more stable in the presence of state uncertainty than policy gradient learning.
Formation of General Professional Competences of Future Teachers as a Pedagogical Problem

The process of formation of the general professional competencies of future teachers is the subject of research by many domestic and foreign scientists, which come down to the updating of general professional competencies, the study of their essence and content, become the subject of planning in documents of state and strategic importance, are reflected in the development strategies of domestic and foreign education systems. The analysis of regulatory documents in the field of higher professional education of the Kyrgyz Republic and the analysis of the results of the final state certification of university graduates led to the conclusion that to date, a mechanism for assessing the level of competency formation is not sufficiently developed. A survey was conducted with employers, a conversation with schoolteachers, on the basis of which some facts were revealed that indicate a low level and even lack of formality of some competencies, from among both universal and professional competencies. Over the past five years, the task of effective innovative functioning of the domestic system of higher professional education has been positioned with particular acuteness, which is associated with a number of different reasons that inevitably occur at the educational level. And the requirements of employers to improve the quality of higher professional education and to carry out high-quality training of graduates from year to year become a leading guideline not only for employers, but also for consumers in the format of state and social orders. Sources are analyzed that made it possible to focus on the problems of teacher education that exist both in the domestic system and in the countries of the post-Soviet space. For the formation of the general professional competencies of future history teachers, it is important to develop modern mechanisms for implementing this process that would meet the requirements of the basic skills of the 21st century in the context of person-centered education throughout life.
Image Denoising Networks with Residual Blocks and RReLUs

Discriminative learning methods have been widely studied in image denoising due to their swift inference and favorable performance. Nonetheless, their application range is greatly restricted by the specialized task (i.e., a specific model is required for each considered noise level), which prompts us to train a single network to tackle the blind image denoising problem. To this end, we take the advantages of state-of-the-art progress in deep learning to construct our denoising networks. Particularly, residual learning is utilized in our deep CNNs (convolutional neural networks) with pre-activation strategy to accelerate the training process. Furthermore, we employ RReLU (randomized leaky rectified linear unit) as the activation rather than the conventional use of ReLU (rectified linear unit). Extensive experiments are conducted to demonstrate that our model enjoys two desirable properties, including: (1) the ability to yield competitive denoising quality in comparison to specifically trained denoisers in several predetermined noise level and (2) the ability to handle a wide scope of noise levels effectively with a single network. The experimental results reveal its efficiency and effectiveness for image denoising tasks.
Analysis Of Impact Android-Based Literacy Learning Media (Mpba) Application To The Development Of Skills In Interest And Literacy Of Kindergarten Students

Abstract Early childhood education is very important and is a very important period for children’s growth. The education process is a process for generating activities in education. Activities can produce changes in cognitive, psychomotor and affective students. This applies to every learning in the teaching and learning process. Student learning success is a learning process that is innovative and innovative. Android games are alternative software that is chosen, with the reason Android tends to be used on mobile drivers such as smartphones and tablet PCs. In the current technological development, children tend to be more familiar with various gadgets, children under five are now events exposed to various gadgets. With the existence of Android games and supported by attractive features, it can be seen from the t-test for the purpose of the game that is obtained p value in the t test that is p = 0.000 which means that there is an influence of android learning games on child development.