Reviewerly/exHarmony huggingface dataset:
A new stealth address scheme for blockchain

Stealth address has been an effective way to protect privacy of users in blockchain. However, most of the existing schemes either require their users to bear extra storage costs to manage keys, or need one more private communication before each transaction. To overcome these drawbacks, we proposed a new kind of stealth addresses scheme in which a user only needs to keep a single key pair for initial certificate, the user only stores his or her initial key pair for all transactions. In our scheme, when a sender initiates a transaction, he or she creates a one-time transaction address, then the sender attaches it to the transaction. The receiver uses his or her private key to check the transaction from the blockchain, no private channel is needed for each transaction. Considering regulatory issues, transactions can be wholly or partially regulated, depending on security requirements. Finally, we provide provable security analysis to ensure the security of our scheme, and our simulation results show that our scheme is also efficient and practical in computation.
Deep Learning Computer Vision for Sorting and Size Determination of Municipal Waste

This paper presents a mobile robotic system for picking and collecting waste and trash from the ground. The objects that the trash is made up are detected using a camera mounted on the robotic system and are processed for identification using computer vision and deep neural networks. In order to successfully pick waste and trash, the size and distance to the object are important. The proposed method takes into account that the focal length of the camera is constant, and consists of two phases: (a) from the first captured image one can measure the object dimensions l1 and w1 in pixels, (b) the camera moves towards the object by a distance of Δd = 100 mm capturing the second image, where l2 and w2 are the measured object dimensions in pixels. For better results, both captured images should be taken statically (the robot is not moving). The algorithm computes the distance to object as a function of Δd, l1 and l2, i.e., the dimensions in pixels measured on the two different/consecutive captured images (with d2 = d1 − Δd [mm]). Then, the object dimensions are easily determined as a function of l1 and l2 (or w1 and w2), of d1 and of the camera focal length fpx (in pixels). This determination of object size and distance to object is a simple but reliable method, showing good performance in practice. The image processing uses pre-trained deep convolutional networks with Single Shot Detectors (SSD) and MobileNetsV1. MobileNetsV1 architecture consists of 28 convolutional layers, one aggregation layer, and one fully connected layer. All 28 convolutional layers are followed by a non-linear function (RELU). The transition from linear convolutional to non-linear layers is done using a normalization function. After the convolutions are performed, the aggregation takes place, followed by the classification through a fully connected layer.
A SURVEY ON COMPREHENSIVE TRENDS IN RECOMMENDATION SYSTEMS & APPLICATIONS

Recommendation System (RS) gains considerable popularity from the past decade in E-Commerce and other allied fields. This paper investigates the various traditional Recommendation System like Content-based (CB), Collaboration Filtering-based (CF), Demographic-based, Knowledge-based and discussed current trends in recommendation system like location-aware, context-aware, and Deep Learning techniques. Various improvements and limitations in Recommendation systems have been listed out with evolution metrics for analyzing the accuracy of the algorithms. This paper well-elaborated for the past, present and future scope of the Recommendation System which would be useful for researchers to get familiarity with this domain. To cite this document: Ssvr Kumar Addagarla and Anthoniraj Amalanathan, " A SURVEY ON COMPREHENSIVE TRENDS IN RECOMMENDATION SYSTEMS &amp; APPLICATIONS ", International Journal of Electronic Commerce Studies, Vol.10, No.1, pp.45-88, 2019. Permanent link to this document: http://dx.doi.org/10.7903/ijecs.1705
A Generative Model for Punctuation in Dependency Trees

Treebanks traditionally treat punctuation marks as ordinary words, but linguists have suggested that a tree’s “true” punctuation marks are not observed (Nunberg, 1990). These latent “underlying” marks serve to delimit or separate constituents in the syntax tree. When the tree’s yield is rendered as a written sentence, a string rewriting mechanism transduces the underlying marks into “surface” marks, which are part of the observed (surface) string but should not be regarded as part of the tree. We formalize this idea in a generative model of punctuation that admits efficient dynamic programming. We train it without observing the underlying marks, by locally maximizing the incomplete data likelihood (similarly to the EM algorithm). When we use the trained model to reconstruct the tree’s underlying punctuation, the results appear plausible across 5 languages, and in particular are consistent with Nunberg’s analysis of English. We show that our generative model can be used to beat baselines on punctuation restoration. Also, our reconstruction of a sentence’s underlying punctuation lets us appropriately render the surface punctuation (via our trained underlying-to-surface mechanism) when we syntactically transform the sentence.
QoS-Aware Proactive Data Replication for Big Data Analytics in Edge Clouds

We are in the era of big data and cloud computing, large quantity of computing resource is desperately needed to detect invaluable information hidden in the coarse big data through query evaluation. Users demand big data analytic services with various Quality of Service (QoS) requirements. However, cloud computing is facing new challenges in meeting stringent QoS requirements of users due to the remoteness from its users. Edge computing has emerged as a new paradigm to address such shortcomings by bringing cloud services to the edge of the operation network in proximity of users for performance improvement. To satisfy the QoS requirements of users for big data analytics in edge computing, the data replication and placement problem must be properly dealt with such that user requests can be efficiently and promptly responded. In this paper, we consider data replication and placement for big data analytic query evaluation. We first cast a novel proactive data replication and placement problem of big data analytics in a two-tier edge cloud environment, we then devise an approximation algorithm with an approximation ratio for it, we finally evaluate the proposed algorithm against existing benchmarks, using both simulation and experiment in a testbed based on real datasets, the evaluation results show that the proposed algorithm is promising.
Implementasi Rest Api Web Service dalam Membangun Aplikasi Multiplatform untuk Usaha Jasa

The development of tourism activities on the island of Lombok has caused many types of professions to be occupied or utilized by the community, especially those who are services. With the technological advancements, people need various kinds of facilities in carrying out their businesses and activities. One of them is in looking for artisan services including price or service fees, material prices and so on. Multiplatform applications (more than one platform) can make it easy for users to choose the platform to be used. With web service architecture as a computational entity that can be accessed through the internet and intranet networks and certain protocol standards in the platform and interface of an independent programming language. In conducting research, the system development method used by the author is the waterfall method. Waterfall method is a systematic and sequential method of software development that starts at the level and progress of the system up to the analysis, design, code (implementation), test (testing), and maintenance. The application that was built has been tested to several builders. As well as from the results of testing the artisan respondents have a very agreeable answer of 51.4%, because 51.4% is the highest result in the response to the response of the answer, it can be concluded that the application has met the needs of the mason
A Discourse Signal Annotation System for

This paper presents a new system for open-ended discourse relation signal annotation in the framework of Rhetorical Structure Theory (RST), implemented on top of an online tool for RST annotation. We discuss existing projects annotating textual signals of discourse relations, which have so far not allowed simultaneously structuring and annotating words signaling hierarchical discourse trees, and demonstrate the design and applications of our interface by extending existing RST annotations in the freely available GUM corpus.
Understanding Learned Models by Identifying Important Features at the Right Resolution

In many application domains, it is important to characterize how complex learned models make their decisions across the distribution of instances. One way to do this is to identify the features and interactions among them that contribute to a model’s predictive accuracy. We present a model-agnostic approach to this task that makes the following specific contributions. Our approach (i) tests feature groups, in addition to base features, and tries to determine the level of resolution at which important features can be determined, (ii) uses hypothesis testing to rigorously assess the effect of each feature on the model’s loss, (iii) employs a hierarchical approach to control the false discovery rate when testing feature groups and individual base features for importance, and (iv) uses hypothesis testing to identify important interactions among features and feature groups. We evaluate our approach by analyzing random forest and LSTM neural network models learned in two challenging biomedical applications.
Implementasi QRCode Untuk Absensi Perkuliahan Mahasiswa Berbasis Paperless Office

Attendance recording or more commonly known as presence. Presence is one of the important factors in the field of education. The implementation of the current and current presence system at STMIK Ichsan Gorontalo still uses a lot of paper so that the possibility of being scattered or lost can occur. The paperless reduction campaign in an agency both public and private is being intensified to reduce the impact of deforestation to produce paper or tissue. Therefore, the use of paper especially in educational institutions should be reduced or eliminated. This research took the idea of utilizing QR-Code and Android OS Smartphone to become a mobile-based presence system that supports the paperless office.
Learning Neural Sequence-to-Sequence Models from Weak Feedback with Bipolar Ramp Loss

In many machine learning scenarios, supervision by gold labels is not available and conse quently neural models cannot be trained directly by maximum likelihood estimation. In a weak supervision scenario, metric-augmented objectives can be employed to assign feedback to model outputs, which can be used to extract a supervision signal for training. We present several objectives for two separate weakly supervised tasks, machine translation and semantic parsing. We show that objectives should actively discourage negative outputs in addition to promoting a surrogate gold structure. This notion of bipolarity is naturally present in ramp loss objectives, which we adapt to neural models. We show that bipolar ramp loss objectives outperform other non-bipolar ramp loss objectives and minimum risk training on both weakly supervised tasks, as well as on a supervised machine translation task. Additionally, we introduce a novel token-level ramp loss objective, which is able to outperform even the best sequence-level ramp loss on both weakly supervised tasks.
Neural Tensor Network for Multi- Label Classification

The difference of multi-label classification from traditional classification is that an instance may associate a set of labels simultaneously. In recent study, some scholars have proposed that the information which derives from the query instance's nearest neighbors, can be useful when predicting the labels of the query instance. On the basis of their research, we propose a new approach to multi-label classification, which employs neural tensor network (NTN) to explore the relations among the labels of neighbors and classify the query instance with these correlations. This method utilizes the correlations and interdependencies between labels and leverages the potential of data. Experiments on real data show that our method can achieve good performance in multi-label classification.
Fusing Audio, Textual, and Visual Features for Sentiment Analysis of News Videos

This paper presents a novel approach to perform sentiment analysis of news videos, based on the fusion of audio, textual and visual clues extracted from their contents. The proposed approach aims at contributing to the semiodiscoursive study regarding the construction of the ethos (identity) of this media universe, which has become a central part of the modern-day lives of millions of people. To achieve this goal, we apply state-of-the-art computational methods for (1) automatic emotion recognition from facial expressions, (2) extraction of modulations in the participants' speeches and (3) sentiment analysis from the closed caption associated to the videos of interest. More specifically, we compute features, such as, visual intensities of recognized emotions, field sizes of participants, voicing probability, sound loudness, speech fundamental frequencies and the sentiment scores (polarities) from text sentences in the closed caption. Experimental results with a dataset containing 520 annotated news videos from three Brazilian and one American popular TV newscasts show that our approach achieves an accuracy of up to 84% in the sentiments (tension levels) classification task, thus demonstrating its high potential to be used by media analysts in several applications, especially, in the journalistic domain.
Social Network De-anonymization

Previous works on social network de-anonymization focus on designing accurate and efficient de-anonymization methods. We attempt to investigate the intrinsic relationship between the attacker’s knowledge and the expected de-anonymization gain. A common intuition is that more knowledge results in more successful de-anonymization. However, our analysis shows this is not necessarily true if the attacker uses the full background knowledge for de-anonymization. Our findings leave intriguing implications for the attacker to make better use of the background knowledge for de-anonymization and for the data owners to better measure the privacy risk when releasing their data to third parties.
Modular Materialisation of Datalog Programs

The seminaïve algorithm can be used to materialise all consequences of a datalog program, and it also forms the basis for algorithms that incrementally update a materialisation as the input facts change. Certain (combinations of) rules, however, can be handled much more efficiently using custom algorithms. To integrate such algorithms into a general reasoning approach that can handle arbitrary rules, we propose a modular framework for computing and maintaining a materialisation. We split a datalog program into modules that can be handled using specialised algorithms, and we handle the remaining rules using the semina¨ıve algorithm. We also present two algorithms for computing the transitive and the symmetric– transitive closure of a relation that can be used within our framework. Finally, we show empirically that our framework can handle arbitrary datalog programs while outperforming existing approaches, often by orders of magnitude.
Scalable, Semi-Supervised Extraction of Structured Information from Scientific Literature

As scientific communities grow and evolve, there is a high demand for improved methods for finding relevant papers, comparing papers on similar topics and studying trends in the research community. All these tasks involve the common problem of extracting structured information from scientific articles. In this paper, we propose a novel, scalable, semi-supervised method for extracting relevant structured information from the vast available raw scientific literature. We extract the fundamental concepts of “aim”, ”method” and “result” from scientific articles and use them to construct a knowledge graph. Our algorithm makes use of domain-based word embedding and the bootstrap framework. Our experiments show that our system achieves precision and recall comparable to the state of the art. We also show the domain independence of our algorithm by analyzing the research trends of two distinct communities - computational linguistics and computer vision.
STATE CAPACITY, AGRICULTURAL PRODUCTIVITY AND POVERTY REDUCTION IN AFRICAN COUNTRIES

We analyze the effect of state capacity on poverty in Africa. We consider two proxy variables for state capacity: the government effectiveness index, capturing administrative capacity, and the political stability index, a proxy for coercive capacity. Using the system-GMM technique with four-year average panel data from 53 African countries over the period 1995-2014, we find that each of the state capacity variable significantly reduces poverty rate. However, administrative capacity appears to be the most important aspect of state capacity for poverty reduction. The results also suggest that cereal yield, a proxy for agricultural productivity, would be a channel through which state capacity affects poverty in Africa.
Semi-Supervised Compatibility Learning Across Categories for Clothing Matching

Learning the compatibility between fashion items across categories is a key task in fashion analysis, which can decode the secret of clothing matching. The main idea of this task is to map items into a latent style space where compatible items stay close. Previous works try to build such a transformation by minimizing the distances between annotated compatible items, which require massive item-level supervision. However, these annotated data are expensive to obtain and hard to cover the numerous items with various styles in real applications. In such cases, these supervised methods fail to achieve satisfactory performances. In this work, we propose a semi-supervised method to learn the compatibility across categories. We observe that the distributions of different categories have intrinsic similar structures. Accordingly, the better distributions align, the closer compatible items across these categories become. To achieve the alignment, we minimize the distances between distributions with unsupervised adversarial learning, and also the distances between some annotated compatible items which play the role of anchor points to help align. Experimental results on two real-world datasets demonstrate the effectiveness of our method.
Finding Statistically Significant Interactions between Continuous Features

The search for higher-order feature interactions that are statistically significantly associated with a class variable is of high relevance in fields such as Genetics or Healthcare, but the combinatorial explosion of the candidate space makes this problem extremely challenging in terms of computational efficiency and proper correction for multiple testing. While recent progress has been made regarding this challenge for binary features, we here present the first solution for continuous features. We propose an algorithm which overcomes the combinatorial explosion of the search space of higher-order interactions by deriving a lower bound on the p-value for each interaction, which enables us to massively prune interactions that can never reach significance and to thereby gain more statistical power. In our experiments, our approach efficiently detects all significant interactions in a variety of synthetic and real-world datasets.
Leveraging Conceptual Data Modelsto Ensure the Integrity of CassandraDatabases

The use of NoSQL databases for cloud environments has been increasing due to their performance advantages when working with big data. One of the most popular NoSQL databases used for cloud services is Cassandra, in which each table is created to satisfy one query. This means that as the same data could be retrieved by several queries, these data may be repeated in several different tables. The integrity of these data must be maintained in the application that works with the database, instead of in the database itself as in relational databases. In this paper, we propose a method to ensure the data integrity when there is a modification of data by using a conceptual model that is directly connected to the logical model that represents the Cassandra tables. This method identifies which tables are affected by the modification of the data and also proposes how the data integrity of the database may be ensured. We detail the process of this method along with two examples where we apply it in two insertions of tuples in a conceptual model. We also apply this method to a case study where we insert several tuples in the conceptual model, and then we discuss the results. We have observed how in most cases several insertions are needed to ensure the data integrity as well as needing to look for values in the database in order to do it.
An Inflation Rate Prediction Based on Backpropagation Neural Network Algorithm

This article aims to predict the inflation rate in Samarinda, East Kalimantan by implementing an intelligent algorithm, Backpropagation Neural Network (BPNN). The inflation rate data was obtained from the Provincial Statistics Bureau of Samarinda https://samarindakota.bps.go.id/ for the period January 2012 to January 2017. The method used to measure accuracy algorithm prediction was the mean square error (MSE). Based on the experiment results, the BPNN method with architectural parameters of 5-5-5-1; the learning function was trainlm; the activation functions were logsig and purelin; the learning rate was 0.1 and able to produce a good level of prediction error with an MSE value of 0.00000424. The results showed that the BPNN algorithm can be used as an alternative method in predicting inflation rates in order to support sustainable economic growth, so that it can improve the welfare of the people in Samarinda, East Kalimantan.
Automated Negotiation with Gaussian Process-based Utility Models

Designing agents that can efficiently learn and integrate user's preferences into decision making processes is a key challenge in automated negotiation. While accurate knowledge of user preferences is highly desirable, eliciting the necessary information might be rather costly, since frequent user interactions may cause inconvenience. Therefore, efficient elicitation strategies (minimizing elicitation costs) for inferring relevant information are critical. We introduce a stochastic, inverse-ranking utility model compatible with the Gaussian Process preference learning framework and integrate it into a (belief) Markov Decision Process paradigm which formalizes automated negotiation processes with incomplete information. Our utility model, which naturally maps ordinal preferences (inferred from the user) into (random) utility values (with the randomness reflecting the underlying uncertainty), provides the basic quantitative modeling ingredient for automated (agent-based) negotiation.
Using Eight Agile Practices in an Online Course to Improve Student Learning and Team Project Quality

As online learning continues to increase, instructors need different teaching methods and technologies for teaching online. This study explores how Agile practices are integrated into an online IT course. We focus on incorporating eight Agile practices (team charters, daily stand-ups, Kanban boards, story cards, MoSCoW, timeboxing, showcases, and retrospectives). Students were surveyed at the end of the course and the results indicate Agile enhanced students' learning and increased the quality of their final project.