Reviewerly/exHarmony huggingface dataset:
SwipeCut: Interactive Segmentation via Seed Grouping

Interactive image segmentation algorithms rely on the user to provide annotations as the guidance. When the task of interactive segmentation is performed on a small touchscreen device, the requirement of providing precise annotations could be cumbersome to the user. We design a new interaction mechanism that actively queries seeds for guiding the user to label. Our method enforces sparsity and diversity criteria on the selection of query seeds, and at each round of interaction, the user is only presented with a small number of informative query seeds that are far apart from each other. Therefore, the user merely has to swipe through on the ROI-relevant query seeds for checking which ones of the query seeds are inside the region of interest (ROI). This kind of interaction should be easy since those gestures are commonly used on a touchscreen. As a result, we are able to derive a user-friendly interaction mechanism for annotation on small touchscreen devices. The performance of our algorithm is evaluated on six publicly available datasets. The evaluation results show that our algorithm achieves high segmentation accuracy, with short computational time and less user feedback.
Verification of Architectural Views Model 1+5 Applicability

The paper presents architectural views model 1+5 which has been proposed for designing integration solutions of collaborating software systems. The author has introduced modeling extensions of Unified Modeling Language (UML) in form of UML profiles. The author has proposed an Integration flow diagram which is special form of UML activity diagram. The diagram arranges mediation mechanisms from UML Profile for Integration Flows into an integration flow. The paper presents transformations of model-to-model and model-to-code types which automate design of integration platform. The 1+5 was successfully applied in Service-Oriented Architecture. The approach reveals its potential in Domain-Driven Design, Micro-services and blockchain solutions.
Enhancing Playfair Cipher using Seed Based Color Substitution

Encryption is one of the most powerful methods to achieve data security and privacy in terms of viewing or modifying the data. Different techniques such as substitution, shifting, and application of mathematical operations have been used to perform encryption through the generation of different forms of the original data for security and privacy purposes. Data encryption techniques such as Playfair cipher being the first of its kind polyalphabetic encryption technique, which uses digraph/bigram or a pair of letters to encrypt a pair of plaintext, has exhibited strong security over the years. With the advent of computer advancements, many vulnerabilities of the cipher have been made, therefore, to further improve Playfair cipher against attacks such as cryptanalytic, brute force attack, and shoulder surfing, a modified Playfair cipher is presented. The proposed Playfair cipher highlights the integration of randomly generated numbers and color substitution techniques that further strengthens the encryption process, which leads to improved data privacy.
Subsequence-Level Entity Attention LSTM for Relation Extraction

Relation extraction is a basic component in many knowledge base construction systems aiming at discovering structured facts from raw sentences. Traditional neural network models directly encode raw sentences to obtain semantic features based on which the relations are extracted. However, few efforts have been made to fully exploit the text between the entities that often contains important relation information. Therefore, our paper propose a subsequence-level entity attention LSTM network (EA-LSTM) for relation extraction. EA-LSTM first splits the input sentence into two subsequences which have duplicate content of the text between entities. An entity attention mechanism is then introduced in feature fusion stage to guide the model to selectively attend to the potential relation information of entities. The experimental results on the SemEval-2010 Task 8 dataset show that EA-LSTM is comparable to state-of-the-art systems even without using external features.
Directed Acyclic Graph as Hashgraph: an Alternative DLT to Blockchains and Tangles

Distributed Ledger Technologies are one of the pillars of future technologies, prognozing to have a great impact to many aspects of our lives, including social, economic, juristic, security and many others. Bitcoin is still the most popular blockchain currency, but the opportunities to use Distribute Ledger Technologies are much more wide, outperforming financial applications as most known and popular. Besides blockchains, there are also other architectures of Distributed Ledger Technologies. This paper observes and analyses one technology as a very strong alternative to blockchains: hashgraphs, which are promising to outperform blockchains, but also tangles. Basis of their architecture and functionality will be explained and directions and prognosis of the further development will be given. The main paper contribution is a comparison of a hashgraph technology to its concurrent architectures, i.e. blockchains and tangles, considering different segments and different properties that define a quality of Distributed Ledgers.
Does FDI Aﬀect Productivity and Growth in Sub-Saharan Africa?

This study investigates the direct and indirect effects of foreign direct investment (FDI) on economic growth and total factor productivity (TFP) using data for 25 countries in sub-Saharan Africa during the period 1980–2014. The main findings reveal that the direct effect of FDI on both economic growth and TFP is largely negative. Furthermore, the results suggest that the influence of financial development and human capital accumulation on the effect of FDI is negligible. The paper proposes policy considerations to facilitate the potential role of financial access and human capital in exerting a positive impact of FDI on growth.
Designing Virtual Agents for Simulation-Based Learning in Virtual Reality

In this chapter, the authors review and explore propositions, approaches, and core elements of designing virtual agents that contextualize and scaffold simulation-based learning. They start the chapter by reviewing the literature and prior research on the nature, role, design claims, and evidence of virtual agents in digital and multimedia learning environments. They then analyze the educational affordances of virtual reality (VR) for agent-supported, simulation-based learning as well as the design challenges for creating interactive virtual agents. Through an empirical design case, they describe a conceptual and design framework of creating and using virtual agents for VR simulation-based teaching training. Specifically, they provide an analytical and contextualized synthesis of core design elements, including specific design problems associated with virtual agents, the design solutions, and the patterns of transferring or scaling these design solutions to other cases of virtual agent development.
Markov Model for Password Attack Prevention

With the rapid increase in multi-user systems, the strength of passwords plays a crucial role in password authentication methods. Password strength meters help the users for the selection of secured passwords. But existing password strength meters are not enough to provide high level of security that makes the selection of strong password by users. Rule-based methods that measure the strength of passwords fall short in terms of accuracy and password frequencies differ among platforms. Use of Markov model-based strength meters improves the strength of password in more accurate way than the existing state-of-the-art methods. This paper describes how to proactively evaluate passwords with a strength meter by using Markov models. A mathematical proof of the prevention of guessable password attacks is presented. The proposed method improves the accuracy of current password protection methods significantly with a simpler, faster, and more secure implementation.
Classifying WiFi "physical fingerprints" using complex deep learning

Wireless communication is susceptible to security breaches by adversarial actors mimicking Media Access Controller (MAC) addresses of currently-connected devices. Classifying devices by their “physical fingerprint” can help to prevent this problem since the fingerprint is unique for each device and independent of the MAC address. Previous techniques have mapped the WiFi signal to real values and used classification methods that support solely real-valued inputs. In this paper, we put forth four new deep neural networks (NNs) for classifying WiFi physical fingerprints: a real-valued deep NN, a corresponding complex-valued deep NN, a real-valued deep CNN, and the corresponding complex-valued deep convolutional NN (CNN). Results show state-of-the-art performance against a dataset of nine WiFi devices.
Fast Visibility Restoration Using a Single Degradation Image in Scattering Media

Visibility restoration of an image degraded by scattering media (such as haze, fog, smog) is a challenging task which requires careful design of computational methods. The fundamental problem which limits clear image reconstruction is the random diffusion of light in the scattering medium. Recently, the dark channel prior method has emerged as a powerful approach for haze removal under adverse weather conditions. However, this method requires high computational time, and fails to achieve haze-free image in the sky region. To overcome these problems, we propose a novel imaging method based on quadtree theory combined with transmission optimization (QTCTO) to restore the visibility of images degraded by scattering medium. The proposed method uses the quadtree theory (for region segmentation) to estimate the global atmospheric light, while the transmission in the media is estimated by coefficient modification for the dark channel. The experimental results show that the proposed method is robust against scattering (under smoke and turbid water conditions) and has the advantage of low computational time. The proposed method can be used for security surveillance, remote sensing, and various military applications.
A New Construction of Codebooks Meeting the Levenshtein Bound

Codebooks with low coherence have extensive applications in many fileds such as code division multiple access (CDMA) communication systems, MIMO communications, compressed sensing and so on. In this paper, based on additive characters over finite fields, we propose a construction of optimal codebook with respect to the Levenshtein bound and verify that it is a new construction. By shortening the length of the optimal codebooks, we present a construction of codebooks asymptotically meeting the Levenshtein bound. To the best of our knowledge, the parameters of the asymptotically optimal codebooks are new.
Read in or check out: A four-year analysis of circulation and in-house use of print books

As libraries offer more multi-dimensional study areas and online resources, it is important to understand how print books are being used. The reported decline in print book circulation has largely been based on books checked out of the library without recording in-house use (books used in the library but not checked out). Including in-house use gives a more accurate representation of book circulation, helping to demonstrate the value of the physical library and print collections, and informing collection development. To better understand how our print collections are being used, we analyze holdings, checkout data, and in-house use data by subject, as well as circulation in regard to patron group, library gate count and student enrollment. Our findings show declining use of all print collections with the steepest decline in reference books. The majority of books used in house were not checked out, affirming the need to include in-house use data to provide an accurate picture of print book use and inform collection development. We use our findings to inform the redesign of our reference collection and suggest ways to integrate print and digital formats and promote the value of books and reading.
Adaptive E-Learning Authentication and Monitoring

E-learning enables the transfer of skills, knowledge, and education to a large number of recipients. The E-Learning platform has the tendency to provide face-to-face learning through a learning management system (LMS) and facilitated an improvement in traditional educational methods. The LMS saves organization time, money and easy administration. LMS also saves user time to move across the learning place by providing a web-based environment. However, a few students could be willing to exploit such a system's weakness in a bid to cheat if the conventional authentication methods are employed. In this scenario user authentication and surveillance of end user is more challenging. A system with the simultaneous authentication is put forth through multifactor adaptive authentication methods. The proposed system provides an efficient, low cost and human intervention adaptive for e-learning environment authentication and monitoring system.
Source Code Summarization Using Attention-Based Keyword Memory Networks

Recently, deep learning techniques have been developed for source code summarization. Most existing studies have simply adopted natural language processing techniques, because source code summarization can be considered as machine translation tasks from source code into descriptions. However, source code and its description are very different, not only in the languages of writing, but also in the purpose of writing. There is a large semantic gap between source codes in programming languages and their descriptions in natural languages. To respond to the semantic gap, we propose a two-phase model that consists of a keyword predictor and a description generator. The keyword predictor captures the natural language keywords semantically associated with the source code, and the generator generates a description by referring to the natural language keywords provided by the predictor. Using such keywords as scaffolding, we can effectively reduce the semantic gap and generate more accurate descriptions of source codes. To evaluate the proposed method, we use datasets collected from GitHub and StackOverflow. We perform various experiments with these datasets. Our methods show outstanding performance compared with baselines that include state-of-the-art methods, which concludes that keyword prediction is very helpful to the generation of accurate descriptions.
Asymptotic properties of linear filter for deterministic processes

It is known that Kalman–Bucy filter is stable with respect to initial conditions under the assumptions of uniform complete controllability and uniform complete observability. In this paper, we prove the stability of Kalman-Bucy filter for the case of noise free dynamical system, i.e., for deterministic processes. The earlier stability results cannot be applied for this case, as the system is not controllable. We further show that the optimal linear filter for a general class of non-Gaussian initial conditions is asymptotically proximal to Kalman–Bucy filter. It is also shown that the filter corresponding to non-zero system noise in the limit of small system noise approaches the filter corresponding to zero system noise in the case of Gaussian initial conditions.
Multi-scale single image rain removal using a squeeze-and-excitation residual network

Rain adversely affects the performance of collaborative robots in outdoor applications. In machine vision, single image rain removal is an extremely difficult problem due to the disordered and irregular rain streaks in the image. Existing methods either fail to achieve satisfactory rain removal results or destroy image details. In this paper, we propose a novel multi-scale rain removal model to address these problems by decomposing images into base layers and detail layers. The proposed method adapts a two-branch squeeze-and-excitation residual network architecture that learns the basic structure and texture details of the corresponding clean image. By decomposing the image into multiple layers and merging these layers, the network can effectively remove rain streaks from an image to restore its structural information and details. Extensive experiments on synthetic and real datasets demonstrate that the proposed method significantly outperforms recent state-of-the-art algorithms in terms of both qualitative and quantitative measures.
Secure Outsourcing Algorithms for Composite Modular Exponentiation Based on Single Untrusted Cloud

Abstract Modular exponentiation, as a fundamental operation used in many public-key cryptosystems, has always be considered to be very time-consuming. It is difficult for some devices with limited computation capability, such as mobile devices and low-cost radio frequency identification (RFID) tags, to perform large-scale modular exponentiations. In cryptosystems, one typical case of modular exponentiation is that the modulus is a composite number. For instance, in RSA algorithm, the modulus is the product of two distinct prime numbers. In this paper, we investigate how to securely and efficiently outsource composite modular exponentiations and put forward two secure outsourcing algorithms for composite modular exponentiations based on single untrusted cloud. The first algorithm, named MCExp, is designed for outsourcing single composite modular exponentiation, i.e. $u^a$ mod $N$. The second algorithm, named SMCExp, is designed for outsourcing simultaneous composite modular exponentiation, i.e. $\prod ^{n}_{i=1}u^{a_i}_{i}$ mod $N$. Different from algorithms based on two untrusted servers, the proposed algorithms are very practical because they avoid the strong assumption that there must exist two servers without collusion. The proposed algorithms not only protect the privacy of the exponent and the base simultaneously, but also enable users to verify the correctness of the result returned by the cloud with high probability. Compared with using the square-and-multiply algorithm, the user can achieve higher efficiency by using the proposed algorithms. Besides, we prove the security of our algorithms and conduct several experiments to demonstrate the efficiency of the proposed algorithms. Finally, we show that the proposed algorithms can be used to construct the secure outsourcing algorithms for Shamir’s identity-based signature and identity-based multi-signature.
Development of Visible/Near-Infrared Hyperspectral Imaging for the Prediction of Total Arsenic Concentration in Soil

Soil total arsenic (TAs) contamination caused by human activities—such as mining, smelting, and agriculture—is a problem of global concern. Visible/near-infrared (VNIR), X-ray fluorescence spectroscopy (XRF), and laser-induced breakdown spectroscopy (LIBS) do not need too much sample preparation and utilization of chemicals to evaluate total arsenic (TAs) concentration in soil. VNIR with hyperspectral imaging has the potential to predict TAs concentration in soil. In this study, 59 soil samples were collected from the Daye City mining area of China, and hyperspectral imaging of the soil samples was undertaken using a visible/near-infrared hyperspectral imaging system (wavelength range 470–900 nm). Spectral preprocessing included standard normal variate (SNV) transformation, multivariate scatter correction (MSC), first derivative (FD) preprocessing, and second derivative (SD) preprocessing. Characteristic bands were then identified based on Spearman’s rank correlation coefficients. Four regression models were used for the modeling prediction: partial least squares regression (PLSR) (R2 = 0.71, RMSE = 0.48), support vector machine regression (SVMR) (R2 = 0.78, RMSE = 0.42), random forest (RF) (R2 = 0.78, RMSE = 0.42), and extremely randomized trees regression (ETR) (R2 = 0.81, RMSE = 0.38). The prediction results were compared with the results of atomic fluorescence spectrometry methods. In the prediction results of the models, the accuracy of ETR using FD preprocessing was the highest. The results confirmed that hyperspectral imaging combined with Spearman’s rank correlation with machine learning models can be used to estimate soil TAs content.
Opinion: Does the field of digital forensics have a consistency problem?

Consistency is a term which should be used cautiously in the context of digital forensics (DF). Its meaning indicates regular, repeatable performance, where with regards to the undertaking of forensic analysis procedures, such behaviour is synonymous with the traits of reliability and trustworthiness, which in turn provides a foundation for deriving dependable results. In the context of investigatory processes, consistency is generally considered a mark of quality; a gold standard to be achieved where conversely, any evidential interpretations gleaned from inconsistently functioning forensic processes are inherently unsafe (Lander, 2016). Great value should be placed upon attaining consistency in all aspects of the forensic process in all investigations, yet the field of DF in its current form is arguably far from achieving it. This is partly due in England and Wales to its regionalised infrastructure, and arguably impacted by a lack of robust regulation and oversight.
Availability analysis of design configurations to compose virtual performance‐optimized data center systems in next‐generation cloud data centers

Summary Next‐generation cloud data centers are based on software‐defined data center infrastructures that promote flexibility, automation, optimization, and scalability. The Redfish standard and the Intel Rack Scale Design technology enable software‐defined infrastructure and disaggregate bare‐metal compute, storage, and networking resources into virtual pools to dynamically compose resources and create virtual performance‐optimized data centers (vPODs) tailored to workload‐specific demands. This article proposes four chassis design configurations based on Distributed Management Task Force's Redfish industry standard applied to compose vPOD systems, namely, a fully shared design, partially shared homogeneous design, partially shared heterogeneous design, and not shared design; their main difference is based on the used hardware disaggregation level. Furthermore, we propose models that combine reliability block diagram and stochastic Petri net modeling approaches to represent the complexity of the relationship between the pool of disaggregated hardware resources and their power and cooling sources in a vPOD. These four proposed design configurations were analyzed and compared in terms of availability and component's sensitivity indexes by scaling their configurations considering different data center infrastructure. From the obtained results, we can state that, in general, when one increases the hardware disaggregation, availability is improved. However, after a given point, the availability level of the fully shared, partially shared homogeneous, and partially shared heterogeneous configurations remain almost equal, while the not shared configuration is still able to improve its availability.
Quantum walks on embeddings

We introduce a new type of discrete quantum walks, called vertex-face walks, based on orientable embeddings. We first establish a spectral correspondence between the transition matrix U and the vertex-face incidence structure. Using the incidence graph, we derive a formula for the principal logarithm of $$U^2$$ , and find conditions for its underlying digraph to be an oriented graph. In particular, we show this happens if the vertex-face incidence structure forms a partial geometric design. We also explore properties of vertex-face walks on the covers of a graph. Finally, we study a non-classical behavior of vertex-face walks.