Reviewerly/exHarmony huggingface dataset:
Cyberattack-Resilient Cyberphysical Systems

A great deal of attention is currently being placed on the development of advanced automation for physical systems, including a wide array of applications such as automobiles, unmanned air vehicles (UAVs), the Internet of Things, and 3D printers. These automation opportunities introduce cyberattack risks that can lead to loss of human life or serious injury. The cyberattack risks related to physical systems first received national attention in 2011 due to the Stuxnet cyberattack, which disrupted industrial Iranian centrifuges employed in nuclear reactors.<sup>1</sup>
A Statistical Model for Disocclusions in Depth-based Novel View Synthesis

The occurrence of missing regions in output images is a critical issue when rendering a scene from novel vantage points using depth-based view synthesis. These regions typically have to be filled using inpainting algorithms, which are slow and might yield unconvincing results. Understanding the likelihood of the occurrence of these missing regions can help us design better, application-specific data representations and camera systems by knowing which vantage points should be captured and stored to minimize disocclusion holes in the synthesized novel views. In this paper, we propose a statistical model that predicts the likelihood of missing data in synthesized images as a function of the viewpoint translation. Scene-dependent model parameters are efficiently estimated using simple shift and scaling transformations on the source depth images without needing view synthesis.
Single Episode Policy Transfer in Reinforcement Learning

Transfer and adaptation to new unknown environmental dynamics is a key challenge for reinforcement learning (RL). An even greater challenge is performing near-optimally in a single attempt at test time, possibly without access to dense rewards, which is not addressed by current methods that require multiple experience rollouts for adaptation. To achieve single episode transfer in a family of environments with related dynamics, we propose a general algorithm that optimizes a probe and an inference model to rapidly estimate underlying latent variables of test dynamics, which are then immediately used as input to a universal control policy. This modular approach enables integration of state-of-the-art algorithms for variational inference or RL. Moreover, our approach does not require access to rewards at test time, allowing it to perform in settings where existing adaptive approaches cannot. In diverse experimental domains with a single episode test constraint, our method significantly outperforms existing adaptive approaches and shows favorable performance against baselines for robust transfer.
COMPETITIVENESS INDICATORS IN THE UNIVERSITY DEVELOPMENT PROGRAMS

Most higher education systems set goals for improving competitive positions of national universities as a means to enhance country’s competitiveness. The purpose of the article is to analyze development programs of Russian universities for the availability of competitiveness indicators in them. The content analysis method was used for studying the development programs and strategies of 81 Russian universities by all groups and categories, and further comparison of results with 6 key competitiveness indicators (educational activities, graduates’ employment, research and development, research and innovations, staffing, differentiating characteristics, strategic vision of the university future development) for identification of differences and providing recommendations on strategic planning. As a result, it was revealed that most universities that belonging to the categories of federal, national research and core universities do not pay enough attention to their positioning, target audience identification and strategic vision of the university future development. The main shortcomings in the application of competitiveness indicators were identified and the recommendations that would help universities to analyze past success and to set up the future planning were formulated.
CostEfficient Dynamic Management of Cloud Resources through Supervised Learning

Brief Biography: Muhammad Wajahat is a PhD candi- date in the department of Computer Science at Stony Brook University. He works in the Performance Analysis of Com- puter Systems (PACE) Lab, under the supervision of Dr. Anshul Gandhi. Before joining PACE LAb, he graduated with distinction from Lahore University of Management Sci- ences (LUMS), Pakistan, with an honor thesis that investi- gated performance improvement of cloud gaming using Soft- ware De ned Networking (SDN). His current research fo- cuses on performance modeling and resource management to improving performance and resource utilization in cloud systems. He has worked with leading cloud research groups at IBM Research and AT&amp;T Labs Research on application autoscaling and hotspot mitigation in private clouds.
Wibson Protocol for Secure Data Exchange and Batch Payments.

Wibson is a blockchain-based, decentralized data marketplace that provides individuals a way to securely and anonymously sell information in a trusted environment. The combination of the Wibson token and blockchain-enabled smart contracts hopes to allow Data Sellers and Data Buyers to transact with each other directly while providing individuals the ability to maintain anonymity as desired. The Wibson marketplace will provide infrastructure and financial incentives for individuals to securely sell personal information without sacrificing personal privacy. Data Buyers receive information from willing and actively participating individuals with the benefit of knowing that the personal information should be accurate and current. We present here two different components working together to achieve an efficient decentralized marketplace. The first is a smart contract called Data Exchange, which stores references to Data Orders that different Buyers open in order to show to the market that they are interested in buying certain types of data, and provides secure mechanisms to perform the transactions. The second is used to process payments from Buyers to Sellers and intermediaries, and is called Batch Payments.
Strata, Scarabs and Synchronisms: A Framework for Synchronizing Strata and Artifacts

This paper presents a formal framework for synchronizing strata and datable artefacts in multi-layered sites. We first present a simple set of rules regarding the definition of safe <em>termini post quem</em>, taking into account cases of uncertain dating and/or uncertain stratigraphic attribution of the artifacts. We then propose a definition of chronologically optimal <em>termini post quem</em>, and a procedure to represent these optimal <em>termini</em> graphically by a step function in a two-dimensional graph. We also propose a definition of chronologically critical artifacts, as a minimal set of artifacts that determine all the optimal <em>termini post quem</em> of a stratigraphic sequence. Finally, we define a measure of the robustness of a <em>terminus post quem</em>, expressed in terms of the number of different artifacts supporting this <em>terminus</em>. We illustrate our approach through the case study of Egyptian scarabs from the site of Beth Shean (northern Israel), a well-known Bronze and Iron Age site that hosted an Egyptian garrison during the New Kingdom (ca. 1540–1070 B.C.E.). We also provide a software utility which assists users in applying our methodology.
Location ontologies based on mereotopological pluralism

Location ontologies axiomatize the relationship between physical bodies and the space that they occupy, and there is a rich literature on the philosophical underpinnings of these ontologies. Existing location ontologies have given primacy to the structure of what might be called abstract space. Con sequently, the spatial properties and relations between physical bodies are extracted from the spatial properties and relations of the spatial regions that they occupy. In this paper, we take a different approach by beginning with the philosophical position of mereological pluralism, in which there are different mereologies for different kinds of entities. In particular, we make the fundamental ontological commitment that the mereology for spatial regions is different than the mereology for physical bodies. Different intuitions about location and different classes of physical bodies can be formally specified by imposing different conditions on this mapping, thus leading to different possible location ontologies. We propose an axiomatization of a location ontology and characterize the models of the axiomatization, up to isomorphism, by providing a representation theorem with respect to graph and poset homomorphisms.
EFFICIENCY OF OIL AND GAS GENERATION OF PALAEOZOIC FORMATION OUTCROPS IN NAZDUR AREA, KURDISTAN REGION, NORTHERN IRAQ: INSIGHTS FROM PALYNOLOGICAL STUDY

The studied samples were collected from the Palaeozoic outcrops, Khabour, Pirispiki, Kaista, Ora, Harur, and Chia Zairi formations in Nazdur Anticline, Amadia about 457 km due north of Baghdad, Iraq's capital city. Qualitative assessments of 29 samples were conducted by using textural microscopy in determining the percentage of amorphous organic matter for palynofacies type belonging to different types of kerogen. This organic matter contains acritarchs, spores, and algae deposited in the dysoxic-anoxic environment. The organic matters are from mature to post mature and have a thermal alteration index of -3 to +3 by Pearson's and Staplin's scales, respectively, with palynomorphs of orange to dark brown colors on the palynomorphs. These ranges of colors indicate mature organic matters.
Be-CAPTCHA: Detecting Human Behavior in Smartphone Interaction using Multiple Inbuilt Sensors.

We introduce a novel multimodal mobile database called HuMIdb (Human Mobile Interaction database) that comprises 14 mobile sensors acquired from 600 users. The heterogeneous flow of data generated during the interaction with the smartphones can be used to model human behavior when interacting with the technology. Based on this new dataset, we explore the capacity of smartphone sensors to improve bot detection. We propose a CAPTCHA method based on the analysis of the information obtained during a single drag and drop task. We evaluate the method generating fake samples synthesized with Generative Adversarial Neural Networks and handcrafted methods. Our results suggest the potential of mobile sensors to characterize the human behavior and develop a new generation of CAPTCHAs.
Comparison of two different deep learning architectures on breast cancer

Breast cancer is one of the diseases becoming widespread gradually nowadays. Diagnosis and treatment of breast cancer are performed by some specialist doctors. Timely and accurate detection of this disease is lifesaving. DenseNet-201 and Xception deep learning architectures are used in this study. The performance of these two different deep learning methods are evaluated on the breast cancer dataset. The dataset consists of some benign and malignant cancer images. There are 20748 images for training and 5913 images for testing. According to the results obtained, DenseNet-201 method reaches an F-1 accuracy score of 92.24%, and the Xception method achieves an F-1 accuracy score of 92.41% when trained on the used dataset.
Distributed Subgradient Method for Constrained Convex Optimization with Quantized and Event-Triggered Communication

In this paper, we propose a distributed subgradient-based method over quantized and event-triggered communication networks for constrained convex optimization. In the proposed method, each agent sends the quantized state to the neighbor agents only at its trigger times through the dynamic encoding and decoding scheme. After the quantized and event-triggered information exchanges, each agent locally updates its state by a consensus-based subgradient algorithm. We show a sufficient condition for convergence under summability conditions of a diminishing step-size.
Noise: From nuisance to research subject

The definition has evolved through history from an acoustical term informed by music to a plurality of meanings. Today one person’s noise may be someone else’s signal.
Virtual Region based Multi-robot Path Planning in an Unknown Occluded Environment

This paper introduces an arbitrary-region based shape-control methodology for a swarm-robotics framework to conquer the traditional obstacle-avoidance problem. In this control approach, during the movement of a team of robots through an unknown occluded environment, a virtual boundary for the robotic crowd that could pass through even a very narrow passage, as a swarm, has been created based on the agents' /robots' sensing information. The primary challenge is to force each and every agent to fit into the arbitrary boundary, thus created, so that they can form a swarm and eventually progress towards the goal while avoiding obstacles. Consequently, the process maximizes the inter-agent cohesion. Furthermore, the virtual region is subdivided into so-called cells designated by a non-overlapping circular geometry. The utmost endeavor is to make an agent place itself at the center of a circular cell and a mechanism has been devised, where each agent is duly attracted and eventually is resident at the center of each cell completely blanketing the entire virtual region. To place each agent inside the stipulated contour, a novel circle-packing algorithm has been proposed such that the contour is fitted with a number of identical circles. Finally, the simulation results along with hardware implementation demonstrate the effectiveness of the proposed control technique.
Analysis and Trends on Moving Object Detection Algorithm Techniques

This paper presents a survey on dynamic object detection in video sequences. Many methods have been proposed during several years that intent to solve the problems of dynamic background, jittering, illumination changes, camouflage, among other. The survey is intended to provide a recent analysis of these methods to highlight the main characteristics of each approach. The survey is based on published paper from 2013 up to date. 77 methods were found, and they are described and analyzed in this survey. In addition, the survey proposes a complete classification of the methods based on the type of technique used to achieve the detection of dynamic objects. It also evaluates the limitations of each approach and the tendencies regarding the most used techniques, color space, datasets, hardware, programming language and processing time. One of the most popular approaches correspond to advanced statistical models, and artificial neural networks with 28% and 22% respectively. The analysis reports 11% of use of GPUs, most of them found in the neural network approaches. Regarding datasets, there are authors that report the use of three datasets the most popular is Change Detection. The most used software to implement the methods is MATLAB and the RGB color space, the most employed.
Utilization of Whatsapp Application as Communication Media in Language Teaching and Learning at FBS UWKS

Utilization of ICT as a learning medium is suspected to have been able to create an active learning atmosphere because it provides better and faster learning opportunities for students because it contains the material being taught, displaying assignments to discussions and guidance between students and lecturers, all of which can do outside of formal teaching hours. Currently many applications have been developed that are based on mobile phones and the web. One app that has designed and is in high demand is no exception by college academics today, WhatsApp. This study aims to determine the characteristics of WhatsApp usage and find out the use of WhatsApp Group Language Education social media as a communication medium for the Academic Community of Faculty Language and Science Universitas Wijaya Kusuma Surabaya. The method used in this study is descriptive qualitative by describing, recording, analysing and interpreting the conditions that currently occur or exist in the Language Education Group. The results showed that the use of WhatsApp Group social media in Faculty Language and Science Universitas Wijaya Kusuma Surabaya was extensive. In addition to functioning to convey information, WhatsApp Group also performs as a medium for educating discussion, as an entertainment medium, as well as a medium for influencing policymaking in Faculty Language and Science Universitas Wijaya Kusuma Surabaya.
Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception

In this paper, we introduce and tackle the simultaneous enhancement and super-resolution (SESR) problem for underwater robot vision and provide an efficient solution for near real-time applications. We present Deep SESR, a residual-in-residual network-based generative model that can learn to restore perceptual image qualities at 2x, 3x, or 4x higher spatial resolution. We supervise its training by formulating a multi-modal objective function that addresses the chrominance-specific underwater color degradation, lack of image sharpness, and loss in high-level feature representation. It is also supervised to learn salient foreground regions in the image, which in turn guides the network to learn global contrast enhancement. We design an end-to-end training pipeline to jointly learn the saliency prediction and SESR on a shared hierarchical feature space for fast inference. Moreover, we present UFO-120, the first dataset to facilitate large-scale SESR learning; it contains over 1500 training samples and a benchmark test set of 120 samples. By thorough experimental evaluation on the UFO-120 and other standard datasets, we demonstrate that Deep SESR outperforms the existing solutions for underwater image enhancement and super-resolution. We also validate its generalization performance on several test cases that include underwater images with diverse spectral and spatial degradation levels, and also terrestrial images with unseen natural objects. Lastly, we analyze its computational feasibility for single-board deployments and demonstrate its operational benefits for visually-guided underwater robots. The model and dataset information will be available at: this https URL.
Encrypted Transfer of Confidential Information Using Steganography and Identity Verification Using Face Data

In last few years, information security has become a significant part of data communication. In order to address this crucial point in data communication to secure the confidential data, steganography and cryptography can be combined to give better results. Steganography is a practice to hide any confidential or sensitive information into another information that plays as the cover for the information, adding a layer of security on top of science of secret writing, known as cryptography. In this paper, we discuss an improved LSB steganography technique to optimize the algorithm hiding the bits in different channels selected dynamically using key. We further propose a platform leveraging cryptographic algorithms and verification using face data in pipeline to help keep data more secure.
Unsupervised Learning of Human Action Categories in Still Images with Deep Representations

In this article, we propose a novel method for unsupervised learning of human action categories in still images. In contrast to previous methods, the proposed method explores distinctive information of actions directly from unlabeled image databases, attempting to learn discriminative deep representations in an unsupervised manner to distinguish different actions. In the proposed method, action image collections can be used without manual annotations. Specifically, (i) to deal with the problem that unsupervised discriminative deep representations are difficult to learn, the proposed method builds a training dataset with surrogate labels from the unlabeled dataset, then learns discriminative representations by alternately updating convolutional neural network (CNN) parameters and the surrogate training dataset in an iterative manner; (ii) to explore the discriminatory information among different action categories, training batches for updating the CNN parameters are built with triplet groups and the triplet loss function is introduced to update the CNN parameters; and (iii) to learn more discriminative deep representations, a Random Forest classifier is adopted to update the surrogate training dataset, and more beneficial triplet groups then can be built with the updated surrogate training dataset. Extensive experiments on four benchmark datasets demonstrate the effectiveness of the proposed method.
Internet of Things in Forensics Investigation in Comparison to Digital Forensics

The Internet of Things (IoT)-based forensic investigations have raised new challenges with the increase in the number of objects of legal significance, the applicability of identified and collected devices, indistinct network boundaries, and edgeless networks. IoT releases new opportunities in forensic investigations. Relying on pieces of evidence from the IoT environment, forensic investigators and examiners can face many challenges from the identification, collection, organization and the preservation of shreds of evidence and the clues encountered besides, to the security challenges of IoT devices. Understanding different entities and approaches of IoT, as well as the differences between digital and IoT forensics, is becoming a crucial skill for forensic investigators. In the present manuscript, a plan has been clearly explained to assess different features of IoT forensics. An elucidation has been proposed to foster the connection and support for realistic investigations and challenges in different areas of forensic investigations in forensic science.
AERNs: Attention-Based Entity Region Networks for Multi-Grained Named Entity Recognition

Sequential labeling-based named entity recognition approaches restrict each word belonging to at most one entity region, which will have a problem when recognizing the nested named entities. Various models for nested named entity recognition are designed explicitly and usually do not perform well on non-overlapping named entity recognition compared to sequence labeling models. To tackle the aforementioned problem, we propose a novel model named Attention-Based Entity Region Networks (AERNs) for multi-grained entity recognition where multiple entities in a sentence could be non-overlapping or nested. AERNs consist of an entity region recognizer that examines all possible entity regions and an entity region classifier for classifying the regions. Context information is incorporated to improve the performance of named entity recognition by using an attention mechanism that helps the model focus on entity-related context information. Experiments show that AERNs can effectively recognize both nested and non-overlapping entities, and improves the state-of-the-art result by around 3% on several benchmarks datasets.
On the Steplength Selection in Stochastic Gradient Methods

This paper deals with the steplength selection in stochastic gradient methods for large scale optimization problems arising in machine learning. We introduce an adaptive steplength selection derived by tailoring a limited memory steplength rule, recently developed in the deterministic context, to the stochastic gradient approach. The proposed steplength rule provides values within an interval, whose bounds need to be prefixed by the user. A suitable choice of the interval bounds allows to perform similarly to the standard stochastic gradient method equipped with the best-tuned steplength. Since the setting of the bounds slightly affects the performance, the new rule makes the tuning of the parameters less expensive with respect to the choice of the optimal prefixed steplength in the standard stochastic gradient method. We evaluate the behaviour of the proposed steplength selection in training binary classifiers on well known data sets and by using different loss functions.