Reviewerly/exHarmony huggingface dataset:
Characterization of Handwritten Signature Images in Dissimilarity Representation Space

The offline Handwritten Signature Verification (HSV) problem can be considered as having difficult data since it presents imbalanced class distributions, high number of classes, high-dimensional feature space and small number of learning samples. One of the ways to deal with this problem is the writer-independent (WI) approach, which is based on the dichotomy transformation (DT). In this work, an analysis of the difficulty of the data in the space triggered by this transformation is performed based on the instance hardness (IH) measure. Also, the paper reports on how this better understanding can lead to better use of the data through a prototype selection technique.
ДОБІР ХМАРО ОРІЄНТОВАНИХ ЗАСОБІВ НАВЧАННЯ БАЗ ДАНИХ МАЙБУТНІХ ФАХІВЦІВ З ІНФОРМАЦІЙНИХ ТЕХНОЛОГІЙ

The article describes the selection of cloud-oriented learning tools for educational process of the students of higher education establishments by the expert appraisal method. The criteria and appropriate indicators for learning tool selection have been defined. Three learning tools to study databases were selected out of the list of offered tools, such as: Amazon RDS, Google Cloud SQL, Heroku PostgreSQL, Microsoft SQL Azure, Oracle Database Cloud Service, Rackspace Cloud Databases, SQLite Viewer. The expert appraisal method has been used with involvement of teachers, lecturers, departments’ heads and deans that have experience in teaching databases at the Ukrainian higher education establishments. The comparing analysis of the cloud-oriented database learning tools, such as Google Cloud SQL, Microsoft SQL Azure, SQLite Viewer with Google Drive, has been conducted. The functionally-didactical and organizational criteria were defined. The possibility to use such cloud-oriented learning tools in the higher education establishments were examined. Appropriate indicators for each criterion were defined. The functionally-didactical criterion has the next indicators: possibility to create, to edit and drop tables in the databases; finding the primary and external keys for the tables; creation the connections for the tables in the databases; data modification in the tables of databases; functionality to analyze the results and errors of the queries execution in the databases. The organizational criterion has the following indicators: affordability (presence of the free of charge version; validity term for the free of charge version); need in the financial ability of the customer prove; customer interface usability. As the result of the expert appraisal selection method, has been revealed that SQLite Viewer with Google Drive is the most reasonable learning tool, by the manifestation of all the criteria, for the students of higher education establishments.
Detection of copy-move image forgery using normalized cross correlation and fast fourier transform

Digital images are being used as a rich source of information in the present digital era but the advancement of digital cameras & mobile phones and a rapid growth of image tempering software nowadays made digital images’ integrity critical. Hence, to determine the trustworthiness of an image, the need for image forensics has become a necessity now.Digital image forensics is playing a major role in this direction to identify authentic digital images and has classified forgery detection techniques into two type broadly-Active and Passive. The active process needs to authenticate pictures by taking out the watermark or digital signature inserted in it. On the other hand, passive technique either required using special devices not the original content presented to demonstrate forgery of the picture. This paper proposed a search area optimization algorithm in which we used Normalized Cross-Correlation for feature matching or to detect the highly correlated areas of the forged image and Fast Fourier Transform to optimize the search area by converting the image into the frequency domain. The experimental outcomes portray that copy-paste image forgery can be effectively perceived by using the proposed approach and the computed value of precision rate and recall rate is also higher than the earlier methods
A Balanced Routing Algorithm for Blockchain Offline Channels Using Flocking

Offline channels have the potential to mitigate the scalability problem of blockchains. A Path-Based fund Transfer (PBT) uses a path in the channel network. PBTs can make the channel network imbalanced, i.e., funds in a few channels become very low and funds in other channels become very high. Imbalanced channel network may make PBT infeasible. Hence we need a routing algorithm for PBT that keeps the channel network balanced. In existing solutions for this problem have privacy problem as the channels have to reveal their balances in order to find suitable routes for PBTs. In this paper, we mitigate this problem as we propose a flocking based algorithm for PBTs that keeps the channel network balanced.
Network Packet Processing Mode-Aware Power Management for Data Center Servers

In data center servers, power management (PM) exploiting Dynamic Voltage and Frequency Scaling (DVFS) for processors can play a crucial role to improve energy efficiency. However, we observe that current PM policies (i.e., governors) not only considerably increase tail response time (i.e., violate a given Service Level Objective (SLO)) but also hurt energy efficiency. Tackling limitations of current PM governors, we propose NMAP, Network packet processing Mode-Aware Power management. NMAP improves energy efficiency while satisfying given SLOs, considering packet processing status on a core for PM by monitoring transitions between network packet processing modes - interrupt and polling. Tracking the transitions, NMAP detects moments that a core cannot process packets fast enough and forces the core to immediately raise the voltage and frequency (V/F) state. As a result, NMAP can provide not only low response time but also low energy consumption. Our experiment shows that NMAP improves tail response time by up to 4.1× compared with the ondemand governor, reducing energy by up to 44.6 percent compared with the performance governor.
A fingertips-based approach to select maintenance tool automatically in virtual environment

Virtual maintenance plays an important role in improving product maintainability at the early stage of design and its simulation provides an economic and efficient method for product design and analysis. However, while carrying on simulations, it is cumbersome for operators to adjust the hand joints one-by-one to match the virtual tools to the virtual hand. Furthermore, it’s repetitive to complete the action frames with the aid of kinematic tools. This paper presents a methodology for the automatic selection of virtual maintenance tools. It’s based on the mapping relationship between hand gesture and maintenance tool. Inverse kinematics theory verifies that hand gesture can be determined by its poses of five fingertips. So, by making certain hand gesture, its corresponding tool can be selected and set to virtual hand in a well-defined pose, which reduces the time consuming for maintenance tool selection and hand gesture adjustment. A test case is presented to demonstrate the application of the suggested methodology on a real simulation.
HandyTool: Object Manipulation Through Metaphorical Hand/Fingers-to-Tool Mapping

In this paper, we introduce “HandyTool” a method (and an interface) for virtual object manipulation based on a metaphorical/structural mapping of various everyday tools to our hands and fingers. The basic idea is to virtually transform the hand/fingers into a proper tool (e.g. a fist becoming a hammer head) and gesturally apply it (e.g. hammer in to insert) to and manipulate the target object (e.g. a nail) directly. The main intended objective of HandyTool is to enhance the tool usage experience by one (or one’s body part) becoming the tool itself and thereby also possibly improving the task performance. A usability experiment was carried out to assess the projected merits, comparing HandyTool to the case of the as-is emulation of the tool usage (i.e. the tracked hand/finger controlling the tool to apply it to the target object) and to the case of using the controller. Our experiment was not able to show the clear and full potential of HandyTool because of the current performance limitation of the hand/fingers tracking sensor and due to the simplicity in the structural mapping between the tool and hand/fingers. The structural metaphor itself was still shown to be helpful when the controller was used (i.e. stable sensing).
Investigating modern cars

Car forensic is one of the branches of digital forensics that is recently becoming more and more popular and important. Because of the accelerated growth of technology and its implementation in various industries, modern cars have additional features to raise the level of security, improve the driving experience, and now add the advanced option to connect the car to the internet. The possibilities today are much more advanced, which means that today's cars can drive autonomously, read the email, respond to messages, automatically receive software updates over the internet that provide new features. Today “smart” cars can even be controlled over the key fob or mobile app. This paper will show common information that can be found in the car; who drove it, how he drove, where and in what state did he drive, information that is related to the car and its mechanics, all the synchronized information etc. It will also explain the extraction process, software used for the extraction, and which difficulties the forensic examiner can encounter during the extraction. Furthermore, the information extracted may prove to be useful in resolving different types of crime, and it can show the importance of digital forensics of cars.
REI: An integrated measure for software reusability

Abstract To capitalize upon the benefits of software reuse, an efficient selection among candidate reusable assets should be performed in terms of functional fitness and adaptability. The reusability of assets is usually measured through reusability indices. However, these do not capture all facets of reusability, such as structural characteristics, external quality attributes, and documentation. In this paper, we propose a reusability index (REI) as a synthesis of various software metrics and evaluate its ability to quantify reuse, based on IEEE Standard on Software Metrics Validity. The proposed index is compared with existing ones through a case study on 80 reusable open‐source assets. To illustrate the applicability of the proposed index, we performed a pilot study, where real‐world reuse decisions have been compared with decisions imposed by the use of metrics (including REI). The results of the study suggest that the proposed index presents the highest predictive and discriminative power; it is the most consistent in ranking reusable assets and the most strongly correlated to their levels of reuse. The findings of the paper are discussed to understand the most important aspects in reusability assessment (interpretation of results), and interesting implications for research and practice are provided.
Referring to Objects in Videos Using Spatio-Temporal Identifying Descriptions

This paper presents a new task, the grounding of spatio-temporal identifying descriptions in videos. Previous work suggests potential bias in existing datasets and emphasizes the need for a new data creation schema to better model linguistic structure. We introduce a new data collection scheme based on grammatical constraints for surface realization to enable us to investigate the problem of grounding spatio-temporal identifying descriptions in videos. We then propose a two-stream modular attention network that learns and grounds spatio-temporal identifying descriptions based on appearance and motion. We show that motion modules help to ground motion-related words and also help to learn in appearance modules because modular neural networks resolve task interference between modules. Finally, we propose a future challenge and a need for a robust system arising from replacing ground truth visual annotations with automatic video object detector and temporal event localization.
Alexa, Can You Help Me Shop?

In a previous keynote address [1], we have described how voice-enabled intelligent assistants are revolutionizing the way humans interact with machines and how their ubiquitous presence in homes, cars, offices are taking us closer to the old dream of ambient computing. We also explained how Alexa, Amazon's intelligent assistant, is pioneering the domain of voice shopping, considering the customer's shopping journey in a holistic manner. In this talk, we will discuss in more details the key stages required in addressing customer's needs, namely (1) understanding customers, (2) satisfying their needs, and (3) predicting them. We will also provide a finer analysis of customers' needs, be they informational, transactional or navigational needs [2]. We will conclude by presenting some associated research challenges and encouraging the community to explore the unchartered area of voice-enabled product discovery.
Eliciting specialized frames from corpora using argument-structure extraction techniques

Abstract Frame Semantics provides a powerful cross-lingual model to describe the conceptual structure underlying specialized language. Building specialized frames is challenging because of the complex nature of predicate-argument structures, and because of the domain-specific uses of general-language predicates. Our semi-automatic method elicits semantic frames from specialized corpora. It aims to discover lexical patterns that reveal the structure of specialized frames and to populate them with corpus-based data. Firstly, we automatically extracted verb-noun triples from corpora using bootstrapping to identify noun-verb-noun phraseological patterns. Secondly, we annotated each noun-verb-noun triple with the lexical domain of the verbs and the semantic class and role of the noun filling each argument slot. We then used these annotations and patterns to classify similar triples. Thus, the structure and the types of lexical units that belong to each specialized frames were inferred. Specialized corpora analysis of environmental science texts in English and in Spanish illustrate our methodology.
On plane curves given by separated polynomials and their automorphisms

Abstract Let 𝓒 be a plane curve defined over the algebraic closure K of a finite prime field 𝔽 p by a separated polynomial, that is 𝓒 : A ( Y ) = B ( X ), where A ( Y ) is an additive polynomial of degree p n and the degree m of B ( X ) is coprime with p . Plane curves given by separated polynomials are widely studied; however, their automorphism groups are not completely determined. In this paper we compute the full automorphism group of 𝓒 when m ≢ 1 mod p n and B ( X ) = X m . Moreover, some sufficient conditions for the automorphism group of 𝓒 to imply that B ( X ) = X m are provided. Also, the full automorphism group of the norm-trace curve 𝓒 : X ( q r – 1)/( q –1) = Y q r –1 + Y q r –2 + … + Y is computed. Finally, these results are used to show that certain one-point AG codes have many automorphisms.
Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging

Character-level models of tokens have been shown to be effective at dealing with within-token noise and out-of-vocabulary words. However, they often still rely on correct token boundaries. In this paper, we propose to eliminate the need for tokenizers with an end-to-end character-level semi-Markov conditional random field. It uses neural networks for its character and segment representations. We demonstrate its effectiveness in multilingual settings and when token boundaries are noisy: It matches state-of-the-art part-of-speech taggers for various languages and significantly outperforms them on a noisy English version of a benchmark dataset. Our code and the noisy dataset are publicly available at http://cistern.cis.lmu.de/semiCRF.
Locally Weighted Adjustable Parameter-Based LPVG in the Identification of Functional Regions

Mobile users' online traffic records are a type of data that are closely related to human activities. To explore the differences in lifestyles of Internet users, online records of base stations expressed as time series need to be classified. However, due to the circadian rhythms, such time series have the same trend and small-scale local differences. Traditional similarity measurement using original time series is difficult to adequately capture local features of time series. By mapping the time series into visibility graph, the similarity measurement in the graph domain is obtained preserving the dynamic structure and local features to the greatest extent. In this paper, a locally weighted adjustable parameter-based limited penetrable visibility graph (LWAPLPVG) is proposed to further improve the noise resistance of visibility graph and local identification of limited penetrable visibility graph. We get APLPVG by modifying the visibility criteria, which has noise resistance while preserving local features as well. Simple measurement, such as degree sequence in graph domain, is extracted and proved, which is able to amplify small-scale differences. By considering the circadian rhythms of cyclical and trend time series, APLPVG is weighted locally. We use a real dataset of usage detail records (UDRs) to verify that LWAPLPVG can better improve the identification of time series of functional regions with noise resistance.
Cooperative hierarchical Dirichlet processes: Superposition vs. maximization

The cooperative hierarchical structure is a common and significant data structure observed in, or adopted by, many research areas, such as: text mining (author–paper–word) and multi-label classification (label–instance–feature). Renowned Bayesian approaches for cooperative hierarchical structure modeling are mostly based on hierarchical Bayesian models. However, these approaches suffer from a serious issue in that the number of hidden topics/factors needs to be fixed in advance and an inappropriate number may lead to overfitting or underfitting. One elegant way to resolve this issue is Bayesian nonparametric learning, but existing work in this area still cannot be applied to cooperative hierarchical structure modeling. In this paper, we propose a cooperative hierarchical Dirichlet process (CHDP) to fill this gap. Each node in a cooperative hierarchical structure is assigned a Dirichlet process to model its weights on the infinite hidden factors/topics. Together with measure inheritance from hierarchical Dirichlet process, two kinds of measure cooperation, i.e., superposition and maximization, are defined to capture the many-to-many relationships in the cooperative hierarchical structure. Furthermore, two constructive representations for CHDP, i.e., stick-breaking and international restaurant process, are designed to facilitate the model inference. Experiments on synthetic and real-world data with cooperative hierarchical structures demonstrate the properties and the ability of CHDP for cooperative hierarchical structure modeling and its potential for practical application scenarios.
The Sense of Presence through the Humanization Created by Virtual Environments

This work focus on the study of solutions that using video 360 and virtual reality that allow children&rsquo;s and older people that are away of their family environments for various reasons to be able to feel they are participating at family or school events. The solutions proposed should deliver a strong sense of presence to the users and the interface must be friendly. The validation will be made by user observation and inquiries.
One Additional Qubit is Enough: Encoded Embeddings for Boolean Components in Quantum Circuits

Research on quantum computing has recently gained significant momentum since first physical devices became available. Many quantum algorithms make use of so-called oracles that implement Boolean functions and are queried with highly superposed input states in order to evaluate the implemented Boolean function for many different input patterns in parallel. To simplify or enable a realization of these oracles in quantum logic in the first place, the Boolean reversible functions to be realized usually need to be broken down into several non-reversible sub-functions. However, since quantum logic is inherently reversible, these sub-functions have to be realized in a reversible fashion by adding further qubits in order to make the output patterns distinguishable (a process that is also known as embedding). This usually results in a significant increase of the qubits required in total. In this work, we show how this overhead can be significantly reduced by utilizing coding. More precisely, we prove that one additional qubit is always enough to embed any non-reversible function into a reversible one by using a variable-length encoding of the output patterns. Moreover, we characterize those functions that do not require an additional qubit at all. The made observations show that coding often allows one to undercut the usually considered minimum of additional qubits in sub-functions of oracles by far.
Unstructured Multi-view Depth Estimation Using Mask-Based Multiplane Representation

This paper presents a novel method, MaskMVS, to solve depth estimation for unstructured multi-view image-pose pairs. In the plane-sweep procedure, the depth planes are sampled by histogram matching that ensures covering the depth range of interest. Unlike other plane-sweep methods, we do not rely on a cost metric to explicitly build the cost volume, but instead infer a multiplane mask representation which regularizes the learning. Compared to many previous approaches, we show that our method is lightweight and generalizes well without requiring excessive training. We outperform the current state-of-the-art and show results on the sun3d, scenes11, MVS, and RGBD test data sets.
ANALISIS PERFORMA ALGORITMA NAIVE BAYES PADA DETEKSI OTOMATIS CITRA MRI

The brain in humans becomes part of the central nervous system of the human body. The use of imaging with MRI is one that can be used as a first step to detect parts of the human brain. The imaging step is the first step in diagnosing brain tumor. By performing feature extraction, which aims to process the classification of brain tumors, between normal and abnormal brain images using the naive Bayes method. Obtained 41 images which then became 39 datasets. Feature extraction results with 2 classes, normal as many as 20 data and abnormal data 19. The calculation results obtained the value of the normal class of 0.513 and the abnormal class of 0.487 the value of the calculation accuracy of 84.17%.
A Modified McEliece Public-Key Cryptosystem Based On Irregular Codes Of QC-LDPC and QC-MDPC

In this paper, a novel structure was proposed for the McEliece cryptosystem. The McEliece public key cryptosystem utilizes Goppa's codes properties to provide security. Due to its robust security and fast speed of executing the encryption and decryption algorithm, McEliece cryptosystem was suggested as an option for post-quantum systems. However, this cryptosystem is not commonly used nowadays due to several major drawbacks. The most important defect in the McEliece cryptosystem is its lengthy key. In this research, Goppa code was replaced by irregular codes of QC-LDPC and QC-MDPC that are utilized simultaneously in order to resolve the prior bottlenecks of this system. The obtained results further verified that the key length was reduced reasonably. Another advantage of this release compared to the traditional version of McEliece cryptosystem is that it has been more secure against message-resend attacks.