Reviewerly/exHarmony huggingface dataset:
A pragmatic signal processing approach for nurse care activity recognition using classical machine learning

Nursing activity recognition adds a new dimension to the healthcare automation system. But nursing activity recognition is very challenging than identifying simple human activities like walking, cycling, swimming, etc. due to intra-class variability between activities. Besides, the lack of proper dataset does not allow researchers to develop a generalized method for nursing activity or comparing baseline methods on different datasets. Nurse Care Activity Recognition Challenge 2020 provides a dataset of twelve nursing activities. In this paper, we have described our (Team Hex Code) approach where we have emphasized on developing method, which can cope up with real-world data with noise and uncertainty. In our method, we have resampled our data to deal with a variable sample frequency of dataset and we have also applied feature selection method on the extracted feature to have the best combination of feature set for classification. We have used random forest classifier which is a classical machine learning algorithm. Applying our methodology, we have got 78% validation accuracy on the dataset. We have trained our model on the lab dataset and validate them on the field dataset.
Foreign Ownership in Sub-Saharan Africa: Do Governance Structures Matter?

It has been widely argued that governance structures have roles in the predominance of foreign ownership in Sub-Saharan African countries. Our paper sought to challenge this conventional wisdom by investigating the ways in which country-level governance structures influenced the predominance of foreign holdings in Sub-Saharan African countries for the period 2010–2015. The study used panel sampling annual data from thirty countries in Sub-Saharan Africa, with Ordinary Least Squares (OLS) and Feasible Generalized Least Squares (FGLS) as our discussion estimators. Our statistical results reveal that there is a significant positive relationship between government effectiveness and the predominance of foreign ownership in Sub-Saharan African countries. Furthermore, foreign ownership predominates in Sub-Saharan African economies that have sound political stability and embrace effective and efficient regulations. Moreover, the relationship between corruption and the prevalence of foreign ownership is negative but significant. However, the rule of law, and voice and accountability, are insignificant to the predominance of foreign ownership in Sub-Saharan Africa. Our results suggest that governments in Sub-Saharan Africa should adopt robust and efficacious measures, strengthen their policies and institutions to promote the control of corruption, provide quality regulations, and minimize political violence.
A Decision Support System for Pattern-Driven Software Architecture

The selection process of architectural patterns is challenging for software architects, as knowledge about patterns is scattered among a wide range of literature. Knowledge about architectural patterns must be collected, organized, stored, and quickly retrieved when it needs to be employed. In this tool paper, we introduce a decision support system that uses a decision model for supporting software architects with the pattern selection problem according to their requirements, including functional and quality requirements. The decision model is built based on a technology selection framework for modeling multi-criteria decision-making problems in software production. Twenty-four software architects in the Netherlands have evaluated the tool. They confirm that the tool supports them with their daily decision-making process.
Assessment of optimum refactoring sequence to improve the software quality of object-oriented software

Software Maintenance is the most expensive phase of Software Development Lifecycle (SDLC) and therefore requires maximum effort and time. Any modification in the source code also introduces some bad smell which may be harmful for the design of the source code. These bad smells get accumulated with every phase of the SDLC if not corrected timely, therefore, they need to be removed as early as possible with the help of refactoring which improves the internal attributes of the software without affecting the external attributes. In order to complete the software under time and budget constraints, authors have attempted to determine the optimum sequence of refactoring technique (RT) in advance so that maintenance team can directly apply them in order to remove the bad smell which further enhances the quality of the software. Authors have applied different types of Hill- Climbing algorithms. Results shows that that the Steepest-Ascent Hill-Climbing algorithm is the most promising algorithm in finding the most optimum refactoring sequence with 20% improvement in the maintainability value.
New dynamic and verifiable multi‐secret sharing schemes based on LFSR public key cryptosystem

A verifiable multi-secret sharing (VMSS) scheme allows distributors to share multiple secrets simultaneously and can detect fraud by both distributors and participants. After analysing the security of the VMSS schemes proposed by Dehkordi and Mashhadi in 2015, the authors point out that they could not detect the fraudulent behaviour of the dealer. By using the non-homogeneous linear recursion and linear feedback shift rigister (LFSR) public key cryptosystem, they introduce two new VMSS schemes. The proposed schemes can not only overcome the defects mentioned above, but also have shorter private and public key lengths at the same level of security. Besides, the proposed schemes are dynamic.
Are You Sure You Want to View This Community? Exploring the Ethics of Reddit’s Quarantine Practice

In the United States, social media organizations are not legally liable for what users do or say on their platforms and are free to regulate expression in any way they see fit. As a result, dark corners of the Internet have emerged to foster communities whose sole purpose is to create and share content that subjugates members of traditionally marginalized groups. The subreddit,/r/TheRedPill, is one such community. This article explores whether hiding this offensive content through digital "quarantine" or removing the community altogether is more ethically justifiable. We draw on theorizing about the ethics of social media content moderation to develop a framework for ethical decision-making based on transparency, corporate social responsibility, and human dignity to guide decisions about content removal. Using/r/TheRedPill as a case study, we argue that the most ethically justified course of action is for Reddit to remove the site entirely from its platform.
RDFFrames

Knowledge graphs represented in RDF are becoming increasingly popular and are essential to many machine learning applications. A rich ecosystem of RDF data management systems and tools has evolved over the years, most notably RDF database management systems that support the SPARQL query language. Surprisingly, machine learning tools for knowledge graphs typically do not use SPARQL despite the obvious advantages of using a database system. This is due to the mismatch between SPARQL and machine learning tools in terms of expected data model and interface style. Machine learning tools work on data in tabular format and process it using imperative relational API calls, while SPARQL matches graph patterns to RDF triples. To access knowledge graphs for machine learning, we observe that it is more natural to use a navigational paradigm based on graph traversal rather than the SPARQL paradigm based on triple patterns. We demonstrate RDFFrames, a framework that bridges the gap between machine learning tools and RDF database systems by offering the usability and flexibility of machine learning tools together with the performance of a database system. RDFFrames enables the user to make a sequence of Python calls to define the data to be extracted from a knowledge graph stored in an RDF database system, and it translates these calls into a compact SPARQL query, executes it on the database system, and returns the results in a standard tabular format.
Deep Neural Network Watermarking Based on Texture Analysis

In recent years, deep neural network is active in the field of computer image vision. The existing digital watermarking technology based on deep neural network can resist image attacks, but image quality is not satisfied. In order to improve the quality of the watermarked images, a neural network watermarking method based on image texture analysis is proposed. Firstly, image texture features are analyzed by gray co-occurrence matrix, and the image is divided into texture complex region and flat region. Secondly, in order to reduce the degree of image modification for better quality, StegaStamp network is adopted to embed the watermark into the flat texture area. Finally, from the perspective of traditional multiplicative watermarking embedding, the watermark embedding process of deep neural network is improved to enhance the watermarked image quality. Experimental results show that the proposed method can effectively improve the quality of the watermarked images without degrading the robustness.
Implementing, and Keeping in Check, a DSL Used in E-Learning

We discuss a DSL intended for use in an education setting when teaching the writing of interactive Haskell programs to students. The DSL was previously presented as a small formal language of specifications capturing the behavior of simple console I/O programs, along with a trace-based semantics. A prototypical implementation also exists. When going for productive application in an actual course setting, some robustness and usability questions arise. For example, if programs written by students are mechanically checked and graded by the implementation, what guarantees are there for the educator that the assessment is correct? Does the implementation really agree with the on-paper semantics? What else can inform the educator's writing of a DSL expression when developing a new exercise task? Which activities beyond testing of student submissions can be mechanized based on the specification language? Can we, for example, generate additional material to hand to students in support of task understanding, before, and feedback or trusted sample solutions, after their own solution attempts? Also, how to keep the framework maintainable, preserving its guarantees when the expressiveness of the underlying DSL is to be extended? Our aim here is to address these and related questions, by reporting on connections we have made and concrete steps we have taken, as well as the bigger picture.
Big Data Computing and Mining in a Smart World

A smart world can be considered as a convergence of the physical world, cyber world, social world, and thinking world. In these four worlds, huge amounts of valuable data are generated and gathered at a rapid rate from a broad range of data sources. Although the quality of these big data depend on their degrees of uncertainty, rich sets of valuable information and useful knowledge can be mined from the big data. This paper focuses on big data computing and mining, which aims to (a) analyze these rich sets of big data, and (b) discover implicit, previously unknown, and potentially useful information and knowledge from the big data. In particular, we present data science solutions for discover frequent patterns. Through our presentation, we discuss how these solutions interconnecting (a) big data generated and collected from the physical world, (b) frequent pattern mining algorithms in the cyber world, (c) social interactions among social individuals in the social world, and (d) user preference and interest reflecting the user cognitive thinking in the thinking world. We show these interactions through our discussion on mining coronavirus disease 2019 (COVID-19) data in a smart world environment. The interconnections link the physical, cyber, social and thinking worlds together to establish a better environment towards big data computing and mining in a smart world.
DAFNE: A dataset of fresco fragments for digital anastlylosis

Restoring artworks seriously damaged or completely destroyed is a challenging task. In particular, the reconstruction of frescoes has to deal with problems such as very small fragments, irregular shapes and missing pieces. Several attempts have been done to develop new techniques for helping restorers in the matching process, starting from traditional image processing methods to the more recent deep learning approaches. However, as often happens in the Cultural Heritage field, the availability of labeled data to test new strategies is limited, and publicly available datasets contain only few samples. For this reason, in this paper we introduce DAFNE, a large dataset that includes hundreds of thousands of images of fresco fragments artificially generated to guarantee a high variability in terms of shapes and dimensions. Fragments have been obtained starting from 62 images of famous frescoes of various artists and historical periods, in order to consider different artistic styles, subjects and colors.
Negative results for approximation using single layer and multilayer feedforward neural networks

We prove a negative result for the approximation of functions defined on compact subsets of Rd (where d≥2) using feedforward neural networks with one hidden layer and arbitrary continuous activation function. In a nutshell, this result claims the existence of target functions that are as difficult to approximate using these neural networks as one may want. We also demonstrate an analogous result (for general d∈N) for neural networks with an arbitrary number of hidden layers, for activation functions that are either rational functions or continuous splines with finitely many pieces.
Neural Rendering for Game Character Auto-Creation

Many role-playing games feature character creation systems where players are allowed to edit the facial appearance of their in-game characters. This paper proposes a novel method to automatically create game characters based on a single face photo. We frame this "artistic creation" process under a self-supervised learning paradigm by leveraging the differentiable neural rendering. Considering the rendering process of a typical game engine is not differentiable, an "imitator" network is introduced to imitate the behavior of the engine so that the in-game characters can be smoothly optimized by gradient descent in an end-to-end fashion. Different from previous monocular 3D face reconstruction which focuses on generating 3D mesh-grid and ignores user interaction, our method produces fine-grained facial parameters with a clear physical significance where users can optionally fine-tune their auto-created characters by manually adjusting those parameters. Experiments on multiple large-scale face datasets show that our method can generate highly robust and vivid game characters. Our method has been applied to two games and has now provided over 10 million times of online services.
Efficient construction of an approximate similarity graph for minimum spanning tree based clustering

Minimum spanning tree (MST) based unsupervised learning techniques are popular due to their ability to identify intrinsic clusters of heterogeneous structures. One of the important factors which affects their effectiveness is how to construct a sparse similarity graph which can effectively capture the local neighborhood information in sub quadratic time. In this paper, we propose a technique which efficiently uses the local nearest neighbors of data points to construct a similarity graph. The proposed approach consists of two steps. In the first step, the dataset is divided into groups using the dispersion level of data points and then all pair intra-partition edges are computed. In the second step, the boundary data points across the neighboring partitions are considered to produce inter-partition edges for increasing the accuracy. The resulting graph is generated by considering all intra- and inter-partition edges. Approximate MST of the similarity graph is constructed to show its efficacy. Experimental analyses demonstrate that the similarity graph captures shorter edges and discards the longest edges, based on graph diameter, all pair shortest path and weight error of MST. Moreover, the quality of the approximate MST is also validated by applying clustering technique on various synthetic and real data sets of different characteristics and cluster quality analyses demonstrate that it has a satisfying performance over other competing approximate MST construction techniques.
The Dark (and Bright) Side of IoT: Attacks and Countermeasures for Identifying Smart Home Devices and Services

We present a new machine learning-based attack that exploits network patterns to detect the presence of smart IoT devices and running services in the WiFi radio spectrum. We perform an extensive measurement campaign of data collection, and we build up a model describing the traffic patterns characterizing three popular IoT smart home devices, i.e., Google Nest Mini, Amazon Echo, and Amazon Echo Dot. We prove that it is possible to detect and identify with overwhelming probability their presence and the services running by the aforementioned devices in a crowded WiFi scenario. This work proves that standard encryption techniques alone are not sufficient to protect the privacy of the end-user, since the network traffic itself exposes the presence of both the device and the associated service. While more work is required to prevent non-trusted third parties to detect and identify the user's devices, we introduce Eclipse, a technique to mitigate these types of attacks, which reshapes the traffic making the identification of the devices and the associated services similar to the random classification baseline.
Digital Competences of Polish and Slovak Students—Comparative Analysis in the Light of Empirical Research

Skills in using information and communication technology solutions in the modern world determine the quality of professional and social life for everyone. The last decade has shown that digital competences are an important group of key competences for every employee and every organization. The purpose of this study is to present results of surveys on digital competences of Polish and Slovak students. Generally, results show that assessment of the level of digital competence of Polish students is not only higher but also less diverse. The rationale for addressing the subject of digital competences is its significance, constant changes resulting from development of ICT and the need to improve quality of life of individuals and organizations in the modern digital world. In this questionnaire, the students were asked to evaluate their level of understanding both existing and non-existing terms from the ICT area. The results have shown that there are significant differences between the questioned groups of students especially in case of IT competences.
Intelligent querying for target tracking in camera networks using deep Q-learning with n-step bootstrapping

Surveillance camera networks are a useful infrastructure for various visual analytics applications, where high-level inferences and predictions could be made based on target tracking across the network. Most multi-camera tracking works focus on target re-identification and trajectory association problems to track the target. However, since camera networks can generate enormous amount of video data, inefficient schemes for making re-identification or trajectory association queries can incur prohibitively large computational requirements. In this paper, we address the problem of intelligent scheduling of re-identification queries in a multi-camera tracking setting. To this end, we formulate the target tracking problem in a camera network as an MDP and learn a reinforcement learning based policy that selects a camera for making a re-identification query. The proposed approach to camera selection does not assume the knowledge of the camera network topology but the resulting policy implicitly learns it. We have also shown that such a policy can be learnt directly from data. Using the NLPR MCT and the Duke MTMC multi-camera multi-target tracking benchmarks, we empirically show that the proposed approach substantially reduces the number of frames queried.
Generative comics: a character evolution approach for creating fictional comics

Comics can be a suitable form of representation for generative narrative. This paper provides an argument for this based on an analysis of properties of the comics medium, and describes a tool for character design and comic strip creation that applies interactive evolution methods to characters in a virtual environment. The system is used to interactively create artificial characters with extreme personality traits inspired by well-known comics characters.
Provable Data Possession using Additive Homomorphic Encryption

Efficiency, public verification, and robustness are essential characteristics of data possession schemes. The efficiency of such kinds of Provable Data Possession (PDP) schemes is possible due to homomorphic encryption, which is used to verify the integrity of data. Most of the existing schemes use multiplicative homomorphic encryption. The multiplicative homomorphic encryption is unsuitable for the low configured devices, which are quite constrained in terms of computation and storage. In this work, we have provided the solution using additive homomorphic encryption techniques like Paillier, ElGamal and Elliptic Curve Cryptosystem (ECC). The proposed solution is based on ECC, which requires less time to execute setup, tag generation, proof generation and proof verification phases to provide public verifiability. The proposed solution is secured under a random Oracle model. Correctness analysis of the proposed algorithm is given at the end of the algorithm.
Zero-shot sketch-based image retrieval with structure-aware asymmetric disentanglement

The goal of Sketch-Based Image Retrieval (SBIR) is using free-hand sketches to retrieve images of the same category from a natural image gallery. However, SBIR requires all test categories to be seen during training, which cannot be guaranteed in real-world applications. So we investigate more challenging Zero-Shot SBIR (ZS-SBIR), in which test categories do not appear in the training stage. After realizing that sketches mainly contain structure information while images contain additional appearance information, we attempt to achieve structure-aware retrieval via asymmetric disentanglement. For this purpose, we propose our STRucture-aware Asymmetric Disentanglement (STRAD) method, in which image features are disentangled into structure features and appearance features while sketch features are only projected to structure space. Through disentangling structure and appearance space, bi-directional domain translation is performed between the sketch domain and the image domain. Extensive experiments demonstrate that our STRAD method remarkably outperforms state-of-the-art methods on three large-scale benchmark datasets.