Reviewerly/exHarmony huggingface dataset:
Magnetic Fluids Deliver Better Speaker Sound Quality

In the 1960s, Glenn Research Center developed a magnetized fluid to draw rocket fuel into spacecraft engines while in space. Sony has incorporated the technology into its line of slim speakers by using the fluid as a liquid stand-in for the speaker's dampers, which prevent the speaker from blowing out while adding stability. The fluid helps to deliver more volume and hi-fidelity sound while reducing distortion.
Dynamic Discovery of Type Classes and Relations in Semantic Web Data

With the rapidly growing resource description framework (RDF) data on the Semantic Web, processing large semantic graph data has become more challenging. Constructing a summary graph structure from the raw RDF can help obtain semantic type relations and reduce the computational complexity for graph processing purposes. In this paper, we addressed the problem of graph summarization in RDF graphs, and we proposed an approach for building summary graph structures automatically from RDF graph data based on instance similarities. To scale our approach, we utilized locality-sensitive hashing technique for identifying instance pairs which are candidates to be in the same type class. Moreover, we introduced a measure to help discover optimum class dissimilarity thresholds and an effective method to discover the type classes automatically. In future work, we plan to investigate further improvement options on the scalability of the proposed method.
POLICY AND INSTITUTIONAL DIMENSIONS OF AFRICA’S POLITICAL ECONOMY IN AN AGE OF GLOBALIZATION

The advent of political independence allowed most Sub-Saharan countries to determine the appropriate mix of policies and institutions that would enable them to achieve rapid socioeconomic development. But experiences across the continent have so far yielded mixed results, and the search for an effective political economy model in the face of a rapidly globalizing world remains an ongoing challenge for most countries. In this study, we ask why some developing countries seem to be growing much faster and have much better socioeconomic performance than others. Indeed, what macroeconomic policies and institutions should Sub-Saharan countries pursue to enable more sustainable, lasting, and inclusive growth while dealing with the challenges that a rapidly changing political and economic world order present? The study’s main intention was to generate knowledge to enhance the efficacy of Africa’s political economy and development pathways by identifying alternative macroeconomic policy and institutional options that can be deployed to enable deeper socioeconomic transformation.
Projective Reconstruction in Algebraic Vision

Abstract We discuss the geometry of rational maps from a projective space of an arbitrary dimension to the product of projective spaces of lower dimensions induced by linear projections. In particular, we give an algebro-geometric variant of the projective reconstruction theorem by Hartley and Schaffalitzky.
Thing and Non-Thing Ontologies

Thing ontologies are ontologies that consider the universe to be made up of a plurality of discrete objects. Non-thing ontologies can take the form of ontologies of discrete objects, too, but not necessarily so: they can also be conceived as ontologies of one continuous object. The paper considers the central versions of ontologies of discrete objects first, starting with thing ontologies, moving from there to non-thing ontologies of discrete objects and finally ontologies of one continuous object.
Truth validation with evidence

In the modern era, abundant information is easily accessible from various sources, however, only a few of these sources are reliable as they mostly contain unverified contents. We develop a system to validate the truthfulness of a given statement together with underlying evidence. The proposed system provides supporting evidence when the statement is tagged as false. Our work relies on an inference method on a knowledge graph (KG) to identify the truthfulness of statements. In order to extract the evidence of falseness, the proposed algorithm takes into account combined knowledge from KG and ontologies. The system shows very good results as it provides valid and concise evidence. The quality of KG plays a role in the performance of the inference method which explicitly affects the performance of our evidence-extracting algorithm.
Generating Pseudorandom S-Boxes – a Method of Improving the Security of Cryptosystems Based on Block Ciphers

The paper presents a general framework for improving the security of the cryptosystem based on the symmetric block cipher. The main idea is based on possibility of chancing substitution boxes (called S-boxes) in encryption/decryption algorithm. In order to make it possible, it is necessary to generate identical boxes by an encryption and decryption party. This is the main reason, why deterministic methods of generating substitution boxes based on the pseudorandom sequences will be presented.
B-DCGAN: Evaluation of Binarized DCGAN for FPGA

In this work, we demonstrate an implementation of Deep Convolution Generative Adversarial Network (DCGAN), into a Field Programmable Gate Array (FPGA). In order to implement the DCGAN, we modified the DCGAN model with binary weights and activations, and with using integer-valued operations in the forwarding path (train-time and run-time). We call the modified one as Binary-DCGAN (B-DCGAN). Using the B-DCGAN, we do a feasibility study of FPGA’s characteristics and performance for Deep Learning. Because the binarization and using integer-valued operation reduce the memory capacity and the number of the circuit gates, it is very effective for FPGA implementation. On the other hand, these reductions in the model might decrease the quality of the generated data. So we investigate the influence of these reductions.
IMPROVING STUDENTS’ WRITING SKILL BY USING SCAFFOLDING TECHNIQUE TO THE SEVENTH GRADERS OF SMP BOPKRI GODEAN

The background of the study was based on the phenomenon at the seventh graders of the SMP BOPRI Godean. Learning English especially writing skill were still low in mastering vocabulary, sentence structure, and paragraph organizing. The research aimed (1) to analyze the students’ writing by using scaffolding technique; (2) to describe the improvement of the students’ writing skill by using scaffolding technique; and (3) to find out the students’ interests in writing skill by using scaffolding technique. To solve the problem, the researcher conducted Classroom Action Research and applied scaffolding technique in teaching English to improve student’s ability in writing skill. The participants of this research were the 14 students of the seventh graders of SMP BOPKRI Godean. In this study, the researcher conducted a scaffolding technique of teaching writing descriptive text in three cycles by using scaffolding model adapted from Silvia Read. The five steps in scaffolding technique by Silvia Read were inquiry, modeling, shared, collaborative, and independent. The researcherexpected that the students could improve their writing skill by using the scaffolding technique and interested in English writing skill. The result of this study was showed by pre-test and post-test. In pre-test, the students’ average score was 49, whereas in the students’ post-test cycle one, the students’ average score was 64. In the second cycle, the students’ average score was 75.9. Finally, the researcher could conclude that the result of this research showed an improvement of the students’ writing skills by using scaffolding technique.
Gaussian Mixture Reduction for Time-Constrained Approximate Inference in Hybrid Bayesian Networks

Hybrid Bayesian Networks (HBNs), which contain both discrete and continuous variables, arise naturally in many application areas (e.g., image understanding, data fusion, medical diagnosis, fraud detection). This paper concerns inference in an important subclass of HBNs, the conditional Gaussian (CG) networks, in which all continuous random variables have Gaussian distributions and all children of continuous random variables must be continuous. Inference in CG networks can be NP-hard even for special-case structures, such as poly-trees, where inference in discrete Bayesian networks can be performed in polynomial time. Therefore, approximate inference is required. In approximate inference, it is often necessary to trade off accuracy against solution time. This paper presents an extension to the Hybrid Message Passing inference algorithm for general CG networks and an algorithm for optimizing its accuracy given a bound on computation time. The extended algorithm uses Gaussian mixture reduction to prevent an exponential increase in the number of Gaussian mixture components. The trade-off algorithm performs pre-processing to find optimal run-time settings for the extended algorithm. Experimental results for four CG networks compare performance of the extended algorithm with existing algorithms and show the optimal settings for these CG networks.
Cooperative and Social Robots: Understanding Human Activities and Intentions

Object tracking is one important problem in computer vision and surveillance system. The existing models mainly exploit the single-view feature (i.e. color, texture, shape) to solve the problem, failing to describe the objects comprehensively. In this paper, we solve the problem from multi-view perspective by leveraging multi-view complementary and latent information, so as to be robust to the partial occlusion and background clutter especially when the objects are similar to the target, meanwhile addressing tracking drift. However, one big problem is that multi-view fusion strategy can inevitably result tracking into non-efficiency. To this end, we propose to marry ELM (Extreme learning machine) to multi-view fusion to train the global hidden output weight, to effectively exploit the local information from each view. Following this principle, we propose a novel method to obtain the optimal sample as the target object, which avoids tracking drift resulting from noisy samples. Our method is evaluated over 12 challenge image sequences with different attributes including illumination, occlusion, deformation, etc., which demonstrates better performance than several state-of-the-art methods in terms of effectiveness and robustness.
Inducing nontrivial qubit coherence through a controlled dispersive environment

We show how the dispersive regime of the Jaynes-Cummings model may serve as a valuable tool to the study of open quantum systems. We employ it in a bottom-up approach to build an environment that preserves qubit energy and induces varied coherence dynamics. We then present the derivation of a compact expression for the qubit coherence, applied here to the case of a finite number of thermally populated modes in the environment. We also discuss how the model parameters can be adjusted to facilitate the production of short-time monotonic decay (STMD) of the qubit coherence. Our results provide a broadly applicable platform for the investigation of energy-conserving open system dynamics which is fully within the grasp of current quantum technologies.
Sparsity in Deep Neural Networks - An Empirical Investigation with TensorQuant

Deep learning is finding its way into the embedded world with applications such as autonomous driving, smart sensors and augmented reality. However, the computation of deep neural networks is demanding in energy, compute power and memory. Various approaches have been investigated to reduce the necessary resources, one of which is to leverage the sparsity occurring in deep neural networks due to the high levels of redundancy in the network parameters. It has been shown that sparsity can be promoted specifically and the achieved sparsity can be very high. But in many cases the methods are evaluated on rather small topologies. It is not clear if the results transfer onto deeper topologies. In this paper, the TensorQuant toolbox has been extended to offer a platform to investigate sparsity, especially in deeper models. Several practical relevant topologies for varying classification problem sizes are investigated to show the differences in sparsity for activations, weights and gradients.
Proposing packet-key based end-to-end security architecture for the post-LTE networks

This paper proposes the end-to-end security scheme with packet key mechanism to perform ciphering of user traffic between two User Equipments (UEs) attached to the different Long Term Evolution (LTE) networks. In LTE system, each UE derives its master key after a mutual authentication is performed between the UE and its local LTE network. In this paper, two end-to-end UEs use their own master keys to generate real-time packet keys where different packet keys are applied to encrypt packet stream data on a packet basis. Because the life cycle of the packet key is same as a packet period, the proposed security scheme significantly improves the security strength for end-to-end LTE users. This paper also analyzes the security strength of the proposed packet key system as well as the degree of latency for the session initialization, real-time packet encryption, and real-time packet decryption.
Equiangular quantum key distribution in more than two dimensions

We extend the spherical code based key distribution protocols to qudits with dimensions 4 and 16 by constructing equiangular frames and their companions. We provide methods for equiangular frames in arbitrary dimensions for Alice to use and the companion frames, that has one antipode to eliminate one of the possibilities, made up of qudits with $N=4,16$ as part of Bob's code. Non-orthogonal bases that form positive operator valued measures can be constructed using the tools of frames (overcomplete bases of a Hilbert space) and here we apply them to key distribution that are robust due to large size of the bases making it hard for eavesdropping. We demonstrate a method to construct a companion frame for an equiangular tight frame for $\complexes^{p-1}$ generated from the discrete Fourier transform, where $p$ is any odd prime. The security analysis is based on the assumption restricting possible attacks to intercept/resend scenario highlighting the advantages of a qudit over qubit-based protocols.
Hybrid Honey Bees Meta-Heuristic for Benchmark Data Classification

Some bio-inspired methods are cuckoo search, fish schooling, artificial bee colony (ABC) algorithms. Sometimes, these algorithms cannot reach to global optima due to randomization and poor exploration and exploitation process. Here, the global artificial bee colony and Levenberq-Marquardt hybrid called GABC-LM algorithm is proposed. The proposed GABC-LM will use neural network for obtaining the accurate parameters, weights, and bias values for benchmark dataset classification. The performance of GABC-LM is benchmarked against NNs training with the typical LM, PSO, ABC, and GABC methods. The experimental result shows that the proposed GABC-LM performs better than that standard BP, ABC, PSO, and GABC for the classification task.
Target observation of complex networks

How to observe the state of a network from a limited number of measurements has become an important issue in complex networks, engineering, communication, epidemiology, etc. Under some scenarios, it is neither unfeasible nor unnecessary to observe the entire network. Therefore, we investigate the target observation of a network in this paper. We propose a target minimal dominating set problem corresponding to target observation, which is a natural generalization of classical minimal dominating set problem. Three algorithms are proposed to approximate the minimum set of occupied nodes sufficient for target observation. Extensive numerical results on computer-generated random networks and real-world networks demonstrate that the proposed algorithms offer superior performance in identification of a target minimal dominating set.
IMSS-P: An intelligent approach to design &amp; development of personalized meta search &amp; page ranking system

The proposed research work aims to discuss and explore various constraints of traditional web page search and ranking systems primarily in the present generation of big data. The primary objective is to facilitate a web user by presenting a most personalized web page ranking as a response to a user’s search query by considering tastes and browsing history of the user while previously searching on the web. This research intends to design and develop a machine learning based next generation of web page ranking algorithm, i.e., Advanced Cluster Vector Page Ranking algorithm (ACVPR). This ACVPR algorithm is implemented in the form of an Intelligent Mata Search System-Personalized tool to evaluate the performance of the algorithm. The ACVPR algorithm arm the user with a powerful meta-search tool to facilitate the user by providing a web page ranking order to quickly satisfy the personalized needs especially when the search query is erroneous or incomplete. An extensive mathematical and experimental evaluation of the developed logistic regression model by calculating and comparing various evaluation metrics such as specificity, sensitivity, precision, recall using R statistical tool shows the improved efficiency as compared to other popular search engines.
Exploiting Pointer Analysis in Memory Models for Deductive Verification

Cooperation between verification methods is crucial to tackle the challenging problem of software verification. The paper focuses on the verification of C programs using pointers and it formalizes a cooperation between static analyzers doing pointer analysis and a deductive verification tool based on first order logic. We propose a framework based on memory models that captures the partitioning of memory inferred by pointer analyses, and complies with the memory models used to generate verification conditions. The framework guided us to propose a pointer analysis that accommodates to various low-level operations on pointers while providing precise information about memory partitioning to the deductive verification. We implemented this cooperation inside the Frama-C platform and we show its effectiveness in reducing the task of deductive verification on a complex case study.
Baselines Extraction from Curved Document Images via Slope Fields Recovery

Baselines estimation is a critical preprocessing step for many tasks of document image processing and analysis. The problem is very challenging due to arbitrarily complicated page layouts and various types of image quality degradations. This paper proposes a method based on slope fields recovery for curved baseline extraction from a distorted document image captured by a hand-held camera. Our method treats the curved baselines as the solution curves of an ordinary differential equation defined on a slope field. By assuming the page shape is a smooth and developable surface, we investigate a type of intrinsic geometric constraints of baselines to estimate the latent slope field. The curved baselines are finally obtained by solving an ordinary differential equation through the Euler method. Unlike the traditional text-lines based methods, our method is free from text-lines detection and segmentation. It can exploit multiple visual cues other than horizontal text-lines available in images for baselines extraction and is quite robust to document scripts, various types of image quality degradation (e.g., image distortion, blur and non-uniform illumination), large areas of non-textual objects and complex page layouts. Extensive experiments on synthetic and real-captured document images are implemented to evaluate the performance of the proposed method.
Single Image Super-Resolution Using Lightweight CNN with Maxout Units

Rectified linear units (ReLU) are well-known to obtain higher performance for deep-learning-based applications. However, networks with ReLU tend to perform poorly when the number of parameters is constrained. To overcome, we propose a novel network utilizing maxout units (MU), and show its effectiveness on super-resolution (SR). In this paper, we first reveal that MU can make the filter sizes halved in restoration problems thus leading to compaction of the network. To the best of our knowledge, we are the first to incorporate MU into SR applications and show promising results. In MU, feature maps from a previous convolutional layer are divided into two parts along channels, which are compared element-wise and only their max values are passed to a next layer. Along with interesting properties of MU to be analyzed, we further investigate other variants of MU. Our MU-based SR method reconstructs images with comparable quality compared to previous SR methods, even with smaller parameters.
Probabilistically Safe Corridors to Guide Sampling-Based Motion Planning

In this paper, we introduce a new probabilistically safe local steering primitive for sampling-based motion planning. Our local steering procedure is based on a new notion of a convex probabilistically safe corridor that is constructed around a configuration using tangent hyperplanes of confidence ellipsoids of Gaussian mixture models learned from prior collision history. Accordingly, we propose to expand a random motion planning graph towards a sample configuration using its projection onto probabilistically safe corridors, which efficiently exploits the local geometry of configuration spaces for selecting proper steering direction and adapting steering stepsize. We observe that the proposed local steering procedure generates effective steering motion around complicated regions of configuration spaces, while minimizing collision likelihood.
Face Recognition: A Novel Multi-Level Taxonomy based Survey

In a world where security issues have been gaining growing importance, face recognition systems have attracted increasing attention in multiple application areas, ranging from forensics and surveillance to commerce and entertainment. To help understanding the landscape and abstraction levels relevant for face recognition systems, face recognition taxonomies allow a deeper dissection and comparison of the existing solutions. This paper proposes a new, more encompassing and richer multi-level face recognition taxonomy, facilitating the organization and categorization of available and emerging face recognition solutions; this taxonomy may also guide researchers in the development of more efficient face recognition solutions. The proposed multi-level taxonomy considers levels related to the face structure, feature support and feature extraction approach. Following the proposed taxonomy, a comprehensive survey of representative face recognition solutions is presented. The paper concludes with a discussion on current algorithmic and application related challenges which may define future research directions for face recognition.
[Papers] Automatic Self-Improvement Scheme in Optical Flow-Based Motion Estimation for Sequential Fisheye Images

This paper aims to present an innovative design of motion estimation for sequential fisheye images. This design is an extended version of the original Lucas and Kanade's (LK) concept that used to design for calculating optical flow from general perspective images. The extended design consists of the LK concept and an additional self-improvement mechanism that automatically finds the maximum performance of the estimated motion. This extended scheme works much better than the original LK's idea or some block-based motion estimations. Moreover, to some extent, this proposed method is working extremely well to overcome some critical characteristics of the sequential fisheye images. These characteristics include distortion error on the fisheye image area, inconsistent brightness level, fluctuating number of object motion, changing the shape of object motion, or poor camera stability.
Foreword to the Special Issue: “Towards the Multilingual Web of Data”

We are pleased to introduce this special issue on the topic of “Towards the Multilingual Web of Data”, which we feel is a timely and valuable topic in our increasingly multilingual and interconnected world.[...]