Reviewerly/exHarmony huggingface dataset:
Information Visualization for Diabetes Management

Type 1 diabetes is a chronic illness that affects millions of people. People with type 1 diabetes regularly collect multidimensional data which they use to improve their well-being. Such data often includes blood glucose levels, insulin administration, diet, and physical activity. Monitoring and analysis tools for diabetes care often include information visualizations to help people make sense of this complex data. However, we have only an incomplete understanding of the visualization design approaches used or any justifications for the final design. To address this gap, we surveyed 21 diabetes data analysis tools which use visualization. From this, we derived a design space that consists of data, views, and strategies. We also provide design considerations for future researchers, tool designers, and developers.
Cross-Lingual Knowledge Transferring by Structural Correspondence and Space Transfer

The cross-lingual sentiment analysis (CLSA) aims to leverage label-rich resources in the source language to improve the models of a resource-scarce domain in the target language, where monolingual approaches based on machine learning usually suffer from the unavailability of sentiment knowledge. Recently, the transfer learning paradigm that can transfer sentiment knowledge from resource-rich languages, for example, English, to resource-poor languages, for example, Chinese, has gained particular interest. Along this line, in this article, we propose semisupervised learning with SCL and space transfer (ssSCL-ST), a semisupervised transfer learning approach that makes use of structural correspondence learning as well as space transfer for cross-lingual sentiment analysis. The key idea behind ssSCL-ST, at a high level, is to explore the intrinsic sentiment knowledge in the target-lingual domain and to reduce the loss of valuable knowledge due to the knowledge transfer via semisupervised learning. ssSCL-ST also features in pivot set extension and space transfer, which helps to enhance the efficiency of knowledge transfer and improve the classification accuracy in the target language domain. Extensive experimental results demonstrate the superiority of ssSCL-ST to the state-of-the-art approaches without using any parallel corpora.
Challenges from Fast Camera Motion and Image Blur: Dataset and Evaluation

To study the impact of the camera motion speed for image/video based tasks, we propose an extendable synthetic dataset based on real image sequences. In our dataset, image sequences with different camera speeds featuring the same scene and the same camera trajectory. To synthesize a photo-realistic image sequence with fast camera motions, we propose an image blur synthesis method that generates blurry images by their sharp images, camera motions and the reconstructed 3D scene model. Experiments show that our synthetic blurry images are more realistic than the ones synthesized by existing methods. Based on our synthetic dataset, one can study the performance of an algorithm in different camera motions. In this paper, we evaluate several mainstream methods of two relevant tasks: visual SLAM and image deblurring. Through our evaluations, we draw some conclusions about the robustness of these methods in the face of different camera speeds and image motion blur.
Static Analysis Techniques for Efficient Consistency Checking of Real-Time-Aware DSPL Specifications

Dynamic Software Product Lines (DSPL) have recently gained momentum as integrated engineering methodology for (self-)adaptive software. DSPL enhance statically configurable software by enabling run-time reconfiguration to facilitate continuous adaptations to changing environmental contexts. In a previous work, we presented a model-based methodology for specifying and automatically analyzing real-time constraints of reconfiguration decisions in a feature-oriented and compositional way. Internally, we translate real-time-aware DSPL specifications into timed automata serving as input for off-the-shelf model checkers like Uppaal for automatically checking semantic consistency properties. However, due to the very high computational complexity of model checking timed automata, those consistency checks suffer from scalability problems thus obstructing practical applications of the proposed approach. In this paper, we tackle this issue by investigating various kinds of static-analysis techniques that (1) aim to avoid expensive model checker calls by statically detecting certain classes of inconsistencies beforehand and otherwise (2) perform model reduction by detecting and merging equivalence states prior to model checker calls. The results of our experimental evaluation show very promising performance improvements achievable by those techniques, especially by the model-reduction approach.
Local path planning of intelligent vehicle based on improved artificial potential field

The actual environment of vehicles will inevitably encounter moving obstacles such as pedestrians and vehicles, and the vehicles need to get back to the global path in time after avoiding moving obstacles. In order to avoid obstacles safely, artificial potential field is applied to local dynamic path planning. Aiming at solving the problems of traditional artificial potential field, the traditional artificial potential field is improved in this paper, which include discretizing the boundary of obstacles to ensure the safety of obstacle avoidance, adding random escape force to escape the local minimum and considering the speed and acceleration of obstacles to apply traditional artificial potential field to dynamic path planning. The design of obstacle avoidance for three most common collisions of front collision, rear collision and side collision is carried out. The improved artificial potential field is used to acquire the local path. The simulation results show that the proposed algorithm can obtain local dynamic paths with better safety and real-time performance. Combined with the global path, the path planning of intelligent vehicles is completed in this paper.
VLSI Implementaion of Image Encryption and Decryption Using Reversible Logic Gates

Reversible logic synthesis and testing is a fascinating research area as it is an important approach for low power design and quantum computing. Reversible computations have different applications such as quantum computing, nanotechnology, digital signal processing, bio-information etc. All these applications require a cryptography system to restrict the unauthorized access and thus maintain the confidentiality of data. High area and power requirements are some of the major problems of well secured cryptography algorithms. In this work, a Reversible Logic Gates Cryptography Design (RLGCD) is proposed to overcome these problems. RLGCD is used to design both encryption and decryption architectures. Linear Feedback Shift Register is used to generate the key for encryption and decryption processes. To further improve the security of data watermarking is done using Least Significant Bit (LSB) method. The FPGA performance of RLGCD architecture is evaluated. There is a great improvement in the performance of RLGCD architecture when compared to other conventional systems.
Domestic financial liberalization and economic growth nexus: the role of corruption

Purpose This study aims to examine the effect of domestic financial deregulation on economic growth in five selected sub-Saharan African nation (SSA). The paper also explored the interaction effect of domestic financial deregulation and corruption on growth. Design/methodology/approach The paper used Driscoll and Kraay standard errors based on the pooled ordinary least squares, which is robust to heteroskedasticity, cross-sectional dependence and autocorrelation. Findings The outcome indicates that domestic financial liberalization has accelerated growth in SSA economies. Similarly, evidence reveals that foreign direct investment and credit to the private sector by banks accelerate growth. However, evidence indicates that labour and capital negate growth. Also, the interaction term for domestic financial liberalization and corruption shows a negative influence on growth. The study, therefore, recommends that well-tailored policy design and strategy be implemented to provide a smooth and conducive business environment for investors. Originality/value Numerous studies have analysed the influence of financial deregulation on growth; however, none have examined the effects of domestic financial deregulation on growth in the context of SSA. Also, no studies have explored the interaction effect of domestic financial deregulation and corruption on growth.
Roll control of Underwater Vehicle based Reinforcement Learning using Advantage Actor-Critic

In order for the underwater vehicle to perform various tasks, it is important to control the depth, course, and roll of the underwater vehicle.To design such a controller, it is necessary to construct a dynamic model of the underwater vehicle and select the appropriate hydrodynamic coefficients.For the controller design, since the dynamic model is linearized assuming a limited operating range, the control performance in the steady state is well satisfied, but the control performance in the transient state may be unstable.In this paper, in order to overcome the problems of the existing controller design, we propose a A2C(Advantage Actor-Critic) based roll controller for underwater vehicle with stable learning performance in a continuous space among reinforcement learning methods that can be learned through rewards for actions.The performance of the proposed A2C based roll controller is verified through simulation and compared with PID and Dueling DDQN based roll controllers.
A Prototype of Indoor Hazard Detection System using Sensors and IoT

Dangerous conditions can occur anywhere and anytime even in an indoor environment. Some of the hazardous situations could come from harmful gases, fires, earthquakes, abnormal temperatures and humidity. The purpose of this research is to design and implement a simple prototype as an early warning system in terms to raise human awareness about the possibilities of hazardous conditions, especially inside the room. This research is based on the Spiral method which has the iterative and systematic prototyping properties of the waterfall model. The main components of the prototype are temperature and humidity sensor, smoke sensor, flame sensor, Gyroscope, Accelerometer, Wemos D1 board, and Blynk IoT platform. Those components were set up to build a system which connected to a smartphone as a user interface which intended to monitor the stimulus in the indoor environment while performing any detection and alertness function when various hazardous conditions are detected. The Blynk apps which installed and set up on the user smartphone could display the gauge values and graph read from each sensor in real-time and also pop-up message notifications as an alert when a potentially dangerous condition is detected. The system also provided direct notifications through LEDs indicators and voice alerts for each hazardous condition, to give early warnings to the people inside the room or building so that they can avoid or minimize the effects of any potentially dangerous situations. The system was tested and well function according to the research purpose.
Petrography and geochemistry of sulfurous volcanic scoria from mount Cameroon area, Central Africa: Implications for Au-PGE exploration

This study was initiated to discuss the mineralogical and chemical features of volcanic scoria from Mount Cameroon along the Cameroon Volcanic Line (CVL) for gold and platinum-group elements (Au-PGEs) exploration. The mineral composition has been obtained through thin sections observation and XRD instrument. X-ray fluorescence (XRF) and ICP-MS analytical methods have been used to determine major and trace element composition, respectively. NiS fire Assay using ICP-MS was used to determine Au-PGE contents. Sulfurous rocks are characteristic of strombolian dynamism with dark gray color, vacuolar structure and porphyritic microlithic texture. They are mainly made up of pyroxene, olivine, plagioclase, opaque minerals, quartz and hematite/goethite. The main chemical components of the sulfurous scoria are silicon, aluminum, iron and calcium oxides. They show basanitic composition. The major and trace element distribution reveals that the scoria of Mount Cameroon derived from a common origin. Some samples have negative LOI due to iron oxidation during heating to high temperature that produces weight gains. The high contents in Fe2O3, CaO, Cr, V, Zn, Ni, Cu, Co and light REE in addition to low SiO2 concentration confirm the basic nature of the magma. In these rocks, CaO is associated with Zn, Ba, Ga, Zr, Nb and Rb in plagioclase and/or pyroxene. The (La/Yb)N values and LREE/HREE ratios reveal a REE-fractionation. Some element ratios (Zr/Th, Th/U, Nb/Y and Zr/Nb) indicate crustal contamination. Platinum-group elements (PGEs), especially iridium and platinum-group elements (IPGE: Os, Ir, Ru), have low contents. Au-PGE budget was controlled by a single or a combination of petrological processes which affected the rocks in a similar manner at Mount Cameroon. The sulfurous nature that favors Au enrichment is not responsible for the contents in PGEs. The IPGE depletion, relative to palladium and platinum-group elements (PPGE: Pd, Pt, Rh) enrichment as well as negative Ru-anomalies could be attributed to the nature of the mantle. Contrary to REE, PGEs are associated with CaO (positive correlation) but not with K2O and P2O5 (negative correlation).
The Quality of Electronic Local History Products: Experience of Development of Criteria and Expertise

Libraries provide the variety of electronic local history products to preserve local history and transmit it via remote access. Due to the development of information technologies the quality of these products needs to be evaluated. In the course of study, the authors developed the working definitions of the concepts “Electronic local history products” and “Quality of electronic local history products”. To determine the quality of an electronic local history product, the study considers the existing approaches to their assessment in libraries, museums, archives, as well as in the area of higher education. The article determines the quality criteria, which are formed into four groups: formal, ergonomic, informative and local history. In the course of an expert online survey of 44 practitioners of the central and municipal libraries of the Kemerovo region, the authors coordinated the basic criteria for evaluating the quality of electronic local history products. Based on the developed criteria, there was carried out expert examination of electronic local history products of the V.D. Fedorov State Scientific Library of Kuzbass. The article analyses the digests “Environmental problems of the Kemerovo region” and “Calendar of significant and memorable dates in the Kemerovo region” that prevail on the library’s website. The conformance of quality criteria for calendars of significant and memorable dates and digests published by the library is presented in a matrix format. The study showed that the requirements for the quality of electronic information products in professional press are ambiguous. Specific approaches are required to reflect the local history component. To improve the system of electronic local history products, there is required the constant internal control and expertise of the products presented on the websites. The working list of criteria for their quality is open for completion. In the future, it needs to be approbated on the example of local history products from other libraries.
Solution of two-qubit Rabi model with extended generalized rotating-wave approximation

The generalized rotating-wave approximation (GRWA) method is extended to the two-qubit quantum Rabi model. In the first-order approximation (one photon exchange), the Hamiltonian matrix in photon number space is simplified by introducing two variational parameters. However, the Hamiltonian matrix is not a diagonalizable matrix yet. Furthermore, by presenting a constraint condition on coupling strength and atomic transition frequency, the Hamiltonian matrix is simplified and an effective solvable Hamiltonian with block diagonal form is obtained. In the even and odd parity space, the energy spectra and eigenstates of the two-qubit quantum Rabi model are achieved analytically. Most of the energy spectra, especially the lower energy levels, agree well with the numerical exact results in ultra-strong coupling region, and the ground state wave function can gives a fairly accurate result of mean photon number.
LokaLTE: 600 MHz Community LTE Networks for Rural Areas in the Philippines

In this paper, we present the case for opening up the 600 MHz spectrum for use by community-based cellular networks (CCN) offering LTE service to unserved and underserved populations in the Philippines. To this end, spectrum measurements were conducted at different locations in the country to establish a measure of utilization or occupancy of the UHF TV spectrum, specifically in the 600 MHz band. Our results show significant white space in the UHF TV band in rural areas, which opens the possibility for the reallocation or re-purposing of this spectrum for mobile networks to provide Internet connectivity in said regions. The 3rd Generation Partnership Project (3GPP), in particular, supports operation of the 4G wireless communications standard, Long Term Evolution or LTE, in the 600 MHz band through Band 71 (617-698 MHz), which is currently not adopted in the Philippines. With policy reform in mind, we have developed an LTE base station prototype, LokaLTE, that supports the case for opening Band 71 specifically for use by CCNs to offer connectivity in rural areas. To our knowledge, LokaLTE is the first LTE base station implementation using OpenCellular and srsLTE operating at Band 71. The low utilization of the 600 MHz band in rural areas and the accessibility of cost-effective infrastructure through LokaLTE are therefore intended to provide the national regulatory and planning agencies in the Philippines with evidence that underpins a policy of regionally selective allocation of Band 71 for use by CCNs to offer LTE service in underserved and unserved regions, thus helping to bridge the long standing digital divide in the country.
A Secured Electronic Voting System Using Blockchain

The foundation of sustainable democracy and good governance is the transparency and credibility of elections. For last several years, electronic voting systems have gained much more popularity and have been of growing interests. E-voting has been considered as a promising solution to many challenges of traditional paper-ballot voting. Conventional electronic voting systems are vulnerable due to centralization of information system. Blockchain is one of the most secure public ledgers for preserving transaction information and also allows transparent transaction verification. It is a continuously growing list of data blocks which are linked and secured by using cryptography. Blockchain is emerging as a very potential technology for e-voting as it satisfies the essential requirements for conducting a fair, verifiable and authentic election. In this work, we proposed a blockchain based voting mechanism based on predetermined turn for each node to mine new block in the blockchain rather than performing excessive computation to gain the chance to mine block. We analyzed two possible conflicting situation and proposed a resolving mechanism. Our proposed voting system will ensure voter authentication, anonymity of the voter, data integrity and verifiability of the election result.
Jacks of All Trades, Masters of None: Addressing Distributional Shift and Obtrusiveness via Transparent Patch Attacks

We focus on the development of effective adversarial patch attacks and – for the first time – jointly address the antagonistic objectives of attack success and obtrusiveness via the design of novel semi-transparent patches. This work is motivated by our pursuit of a systematic performance analysis of patch attack robustness with regard to geometric transformations. Specifically, we first elucidate a) key factors underpinning patch attack success and b) the impact of distributional shift between training and testing/deployment when cast under the Expectation over Transformation (EoT) formalism. By focusing our analysis on three principal classes of transformations (rotation, scale, and location), our findings provide quantifiable insights into the design of effective patch attacks and demonstrate that scale, among all factors, significantly impacts patch attack success. Working from these findings, we then focus on addressing how to overcome the principal limitations of scale for the deployment of attacks in real physical settings: namely the obtrusiveness of large patches. Our strategy is to turn to the novel design of irregularly-shaped, semi-transparent partial patches which we construct via a new optimization process that jointly addresses the antagonistic goals of mitigating obtrusiveness and maximizing effectiveness. Our study – we hope – will help encourage more focus in the community on the issues of obtrusiveness, scale, and success in patch attacks.
Combined particle swarm optimization and Ant Colony System for energy efficient cloud data centers

Abstract One of the foremost issues deliberated by the cloud infrastructure providers is to minimize the costs by reducing energy consumption. Though many heuristic algorithms such as Genetic Algorithm (GA), Simulated Annealing (SA), and so on, have been existed for task scheduling or server consolidation to reduce energy consumption, more energy savings in data centers is required. In this paper, a combination of two distinctive heuristic algorithms, namely Particle Swarm Optimization (PSO) technique for proficient task scheduling and Ant Colony System (ACS) for effective server consolidation are proposed. In PSO, each particle in the swarm finds a task scheduling solution and updates the best solution. It is faster than many meta‐heuristic algorithms. In ACS, each ant in the ant colony tries to find the best destination PMs for migrating VMs from overloaded or underloaded PMs. The proposed PSO‐ACS algorithm is experimented with PlanetLab real workload traces available in the CloudSim toolkit. The algorithm provides reduction in energy consumption, number of VM migrations, and Energy SLA Violation (ESV) metrics while maintaining QoS. In the best‐case scenario, the proposed method reduces the ESV metric by 28%, 27%, and 18% in comparison with other algorithms like LR, ACS, and PSO‐LR.
Synthetic Speech Detection Using Neural Networks

Computer generated speech has improved drastically due to advancements in voice synthesis using deep learning techniques. The latest speech synthesizers achieve such high level of naturalness that humans have difficulty distinguishing real speech from computer generated speech. These technologies allow any person to train a synthesizer with a target voice, creating a model that is able to reproduce someone’s voice with high fidelity. This technology can be used in several legit commercial applications (e.g. call centres) as well as criminal activities, such as the impersonation of someone’s voice.In this paper, we analyze how synthetic speech is generated and propose deep learning methodologies to detect such synthesized utterances. Using a large dataset containing both synthetic and real speech, we analyzed the performance of the latest deep learning models in the classification of such utterances. Our proposed model achieves up to 92.00% accuracy in detecting unseen synthetic speech, which is a significant improvement from human performance (65.7%).