Reviewerly/exHarmony huggingface dataset:
Research on Indoor Scene Classification Mechanism Based on Multiple Descriptors Fusion

This study aims at the great limitations caused by the non-ROI (region of interest) information interference in traditional scene classification algorithms, including the changes of multiscale or various visual angles and the high similarity between classes and other factors. An effective indoor scene classification mechanism based on multiple descriptors fusion is proposed, which introduces the depth images to improve descriptor efficiency. The greedy descriptor filter algorithm (GDFA) is proposed to obtain valuable descriptors, and the multiple descriptor combination method is also given to further improve descriptor performance. Performance analysis and simulation results show that multiple descriptors fusion not only can achieve higher classification accuracy than principal components analysis (PCA) in the condition with medium and large size of descriptors but also can improve the classification accuracy than the other existing algorithms effectively.
Teleportation of a multiphoton qubit using hybrid entanglement with a loss-tolerant carrier qubit

It was shown that using multiphoton qubits, a nearly deterministic Bell-state measurement can be performed with linear optics and on-off photodetectors [Phys. Rev. Lett. 114, 113603 (2015)]. However, multiphoton qubits are generally more fragile than single-photon qubits under a lossy environment. In this paper, we propose and analyze a scheme to teleport multiphoton-qubit information using hybrid entanglement with a loss-tolerant carrier qubit. We consider three candidates for the carrier qubit: a coherent-state qubit, a single-photon polarization qubit, and a vacuum-and-single-photon qubit. We find that teleportation with the vacuum-and-single-photon qubit tolerates about 10 times greater photon losses than with the multiphoton qubit of the photon number $N\ensuremath{\ge}4$ in the high fidelity regime ($F\ensuremath{\ge}90%$). The coherent-state qubit encoding may be even better than the vacuum-and-single-photon qubit as the carrier when its amplitude is as small as $\ensuremath{\alpha}&lt;0.78$. We further point out that the fidelity of the teleported state by our scheme is determined by loss in the carrier qubit while the success probability depends on loss only in the multiphoton qubit to be teleported. Our study implies that the hybrid architecture may complement the weaknesses of each qubit encoding.
INFORMATION AND COMMUNICATION TECHNOLOGY, ENERGY CONSUMPTION AND FINANCIAL DEVELOPMENT IN AFRICA

The study investigated the impact of information and communication technology (ICT) and energy consumption on financial development in Africa using dynamic Generalised Methods of Moments (GMM) with secondary annual data spanning from 2001 to 2015. Literature is unanimous that ICT and energy consumption separately contributes towards financial development although there are so far scarce case studies which focused on the African continent. When domestic credit to private sector (% of GDP) was used as a measure of financial development, ICT and energy consumption were found to had a non-significant negative influence on financial development, a finding that contradicts majority of literature on the subject matter. When broad money (% of GDP) was used as a proxy of financial development, both ICT and energy consumption had a significant positive effect on financial development. The finding generally resonates with Kirmani et al (2015) whose study argued that ICT increases efficiency, reliability, effectiveness, performance and other characteristics of modern-day commercial operations through the way transactions are catered for in any financial system. African nations are therefore urged to increase their use of modern ICT technology and increase energy consumption in order to boost financial development. Future studies can also focus whether ICT and energy consumption influence financial development through other channels such as economic growth, among others.Keywords: Information and Communication Technology; Energy Consumption; Financial Development; AfricaJEL Classifications: N7, Q4, E44, O55DOI: https://doi.org/10.32479/ijeep.8721
Audio Watermarking Algorithm Implementation using Patchwork Technique

In a previous couple of years, the web innovation has been doing the advancement of extensive network facilities and digital data communication. Because of this information is exchanged from one device to another device in the form of audio, text, video, images and so forth. The essential documentations like property papers, military records, restorative reports, multimedia data or any personal documents can be sent from one device to another over the web. With the expanding use of computerised data and audio, the security of licensed innovation rights creation has turned into an important issue. Advanced watermarking is currently drawing consideration as a technique for shielding interactive media content from unapproved duplicating. This paper proposed audio watermarking algorithm implemented on Raspberry pi 3 using patchwork, least significant bit and base64 encoding techniques. Experimental results show that the proposed algorithm is superior regarding perceptual quality, security, and robustness.
Image Processing to Quantitate Hemoglobin Level for Diagnostic Support

Digital Image processing techniques can be used to explore primitive diagnostics methods fordisease detection at early stages with limited resources and skilled labor. These techniques canalso assist doctors during clinical examination without any need for invasive pathological test andthis facilitates patient comfort and avoids infection during blood test. Various blood componentssuch as hemoglobin and billirubin whose approximate measure can directly be identified by justviewing the color of patient skin, nails, eye or any other target area can be measured and classifiedin terms of the color content of the image of the targeted area. Analysis of image processingtechniques in conjunction with specialized supervision can provide significant exploration in thefield of biomedicine and clinical applications. This research work proposes an image processingbased non-invasive method of measuring hemoglobin (Hb) concentration present in patient'sblood by analyzing the color and texture of digital photographs of patient's palpebral conjunctiva.The images of patient's palpebral conjunctiva were processed and eight relevant features wereextracted .Artificial neural network classifier was used to correlate the output quantity to bemeasured with the values of the quantity measured by the standard method as per the guidelinesgiven by WHO. Further, based on the testing results obtained by the classifier the patients whoseHb concentration was less than 11g/dL were screened as anaemic patients. A confusion matrixwas then plotted to evaluate and compare the predicted classification results with the actual valueof Hb obtained from invasive test. It was found that the proposed algorithm was able to diagnoseanemia with 71.42% sensitivity and 89.47% specificity. The proposed method is helpful fordetection of not only severe anemia but works well in detection of moderate anemia too thuspredicting the hemoglobin value to an accuracy of 81.81%.The proposed work is useful for givingassistance to medical practitioners for reliable diagnosis of anaemia in the clinic itself and in lowresource settings.
Coarse and Fine Granularity Graph Reasoning for Interpretable Multi-Hop Question Answering

Interpretable multi-hop question answering requires step-by-step reasoning over multiple documents and finding scattered supporting facts to answer the question. Prior works have proposed the entity graph method to aggregate the entity information to improve the ability of reasoning. However, the entity graph loses some non-entity information that is also important to understand the semantics. Moreover, the entities distributed in the noisy sentences may mislead the reasoning process. In this paper, we propose the Coarse and Fine Granularity Graph Network (CFGGN), a novel interpretable model that combines both sentence information and entity information to answer the multi-hop questions. The CFGGN consists of a coarse-grain module to perform sentence-level reasoning and a fine-grain module to make entity-level reference. In sentence-level reasoning, the sentence graph is constructed to filter out the noisy sentences and capture the sentence features. In entity-level reference, a dynamic entity graph is used for the entity-level reasoning. We design a fusion module to integrate information of different granularity. To enhance the interpretability of the overall process, we calculate the reasoning score for each step and present the reasoning path from the multiple documents to the final answer. Evaluation on the HotpotQA dataset in the distractor setting shows that our method outperforms the published SOTA entity-based method in five out of six metrics.
Acoustic Data-Driven Subword Units Obtained through Segment Embedding and Clustering for Spontaneous Speech Recognition

We propose a method to extend a phoneme set by using a large amount of broadcast data to improve the performance of Korean spontaneous speech recognition. In the proposed method, we first extract variable-length phoneme-level segments from broadcast data and then convert them into fixed-length embedding vectors based on a long short-term memory architecture. We use decision tree-based clustering to find acoustically similar embedding vectors and then build new acoustic subword units by gathering the clustered vectors. To update the lexicon of a speech recognizer, we build a lookup table between the tri-phone units and the units derived from the decision tree. Finally, the proposed lexicon is obtained by updating the original phoneme-based lexicon by referencing the lookup table. To verify the performance of the proposed unit, we compare the proposed unit with the previous units obtained by using the segment-based k-means clustering method or the frame-based decision-tree clustering method. As a result, the proposed unit is shown to produce better performance than the previous units in both spontaneous, and read Korean speech recognition tasks.
Convex Hull Monte-Carlo Tree-Search

This work investigates Monte-Carlo planning for agents in stochastic environments, with multiple objectives. We propose the Convex Hull Monte-Carlo Tree-Search (CHMCTS) framework, which builds upon Trial Based Heuristic Tree Search and Convex Hull Value Iteration (CHVI), as a solution to multi-objective planning in large environments. Moreover, we consider how to pose the problem of approximating multi-objective planning solutions as a contextual multi-armed bandits problem, giving a principled motivation for how to select actions from the view of contextual regret. This leads us to the use of Contextual Zooming for action selection, yielding Zooming CHMCTS. We evaluate our algorithm using the Generalised Deep Sea Treasure environment, demonstrating that Zooming CHMCTS can achieve a sublinear contextual regret and scales better than CHVI on a given computational budget.
Smart Home Security: How Safe is Your Data? [Opinion]

Reports on the emergence of smart homes, the technologies and services that support these structures, and issues of security and privacy that impact people that live in these homes.
3D-generalization of impulse noise removal method for video data processing

The paper proposes a generalized method of adaptive median impulse noise filtering for video data processing. The method is based on the combined use of iterative processing and transformation of the result of median filtering based on the Lorentz distribution. Four different combinations of algorithmic blocks of the method are proposed. The experimental part of the paper presents the results of comparing the quality of the proposed method with known analogues. Video distorted by impulse noise with pixel distortion probabilities from 1% to 99% inclusive was used for the simulation. Numerical assessment of the quality of cleaning video data from noise based on the mean square error (MSE) and structural similarity (SSIM) showed that the proposed method shows the best result of processing in all the considered cases, compared with the known approaches. The results obtained in the paper can be used in practical applications of digital video processing, for example, in systems of video surveillance, identification systems and control of industrial processes.
Analysis of the value orientations of students – future state and municipal employees in terms of modern challenges

Е. Ю. Сизганова, А. К. Кайдашова
Self-service Data Science for Healthcare Professionals: A Data Preparation Approach

Knowledge Discovery and Data Mining are two well-known and still growing fields that, with the advancements of data collection and storage technologies, emerged and expanded with great strength by the many possibilities and benefits that exploring and analyzing data can bring.However, it is a task that requires great domain expertise to really achieve its full potential.Furthermore, it is an activity which is done mainly by data experts who know little about specific domains, like the healthcare sector, for example.Thus, in this research, we propose means for allowing domain experts from the medical domain (e.g.doctors and nurses) to also be actively part of the Knowledge Discovery process, focusing in the Data Preparation phase, and use the specific domain knowledge that they have in order to start unveiling useful information from the data.Hence, a guideline based on the CRISP-DM framework, in the format of methods fragments is proposed to guide these professionals through the KD process.
An Efficient Privacy-Preserving Bidirectional Friends Matching Scheme in Mobile Social Networks

In recent years, mobile social networks, which combine mobile communication and Internet technology, have developed rapidly and gradually become an indispensable part of people's daily life. In addition to sharing their own dynamics, users in mobile social networks also hope to find some new friends with the same interests through some attribute matching strategies. In view of this requirement and the implicit problem of protecting user privacy, this paper combines the idea of Matchmaking Encryption which is a new cryptographic primitive and of attribute-base encryption with outsourcing, and proposes an efficient and secure bidirectional friends matching scheme. Under the condition of protecting the privacy of users' secret attributes and matching strategies, the scheme enables both parties to make their own matching policies, thus achieving the goal that both parties can match to their expected new friends. In addition, the security and efficiency of the scheme are analyzed, and its feasibility is proved.
Security of finite-key-length measurement-device-independent quantum key distribution using an arbitrary number of decoys

In quantum key distribution, measurement-device-independent and decoy-state techniques enable the two cooperative agents to establish a shared secret key using imperfect measurement devices and weak Poissonian sources, respectively. Investigations so far are not comprehensive as they restrict to less than or equal to four decoy states. Moreover, many of them involves pure numerical studies. Here I report a general security proof that works for any fixed number of decoy states and any fixed raw key length. The two key ideas involved here. The first one is the repeated application of the inversion formula for Vandermonde matrix to obtain various bounds on certain yields and error rates. The second one is the use of a recently proven generalization of the McDiarmid inequality. These techniques rise the best provably secure key rate of the measurement-device-independent version of the BB84 scheme by at least 1.25 times and increase the workable distance between the two cooperative agents from slightly less than 60 km to slightly greater than 130 km in case there are $10^{10}$ photon pulse pair sent without a quantum repeater.
Pengembangan Media Pembelajaran Berbasis Game Edukasi Pada MatPel IPA Tematik Kebersihan Lingkungan

Game is one of the interactive multimedia products which is developing rapidly in the world, including Indonesia. Educational games can be used as an alternative interactive learning media that has the goal of supporting the learning process with the concept of "playing while learning". The educational game aims to stimulate students to understand the material provided by the teacher and help teachers provide alternative teaching methods that are fun for students. Environmental cleanliness is a learning theme for grade 1 elementary school presented on Natural Sciences (IPA) subjects. First grade elementary school students are early students who want fun learning so the purpose of this research is to create an educational mobile game application that can be used for learning media for elementary school class I science that can motivate students to learn, understand and apply environmental cleanliness starting from home, school and can distinguish waste that can be recycled or not. In the construction of this game using the version 2 construct application with system development with the waterfall system development method
An Effective Verifiable Symmetric Searchable Encryption Scheme in Cloud Computing

With the booming Internet industry, users' demand for resources is also increasing. In order to meet the needs of users, cloud computing came into being. In today's era, cloud storage is already the most popular application in cloud computing, and users are increasingly inclined to store important and private information in the cloud. One of the most common methods is to encrypt the information before uploading it to the cloud, but searching the information it needs in the cloud server becomes an obstacle, and symmetric searchable encryption can help users achieve this. In this paper, we design a verifiable method based on an efficient symmetric searchable encryption scheme to verify the correctness of search results returned by the cloud server. Through safety analysis and performance evaluation tests, we demonstrate that our solutions are safe and effective.
A Rao-Blackwellized Particle Filter With Variational Inference for State Estimation With Measurement Model Uncertainties

This paper develops a Rao-Blackwellized particle filter with variational inference for jointly estimating state and time-varying parameters in non-linear state-space models (SSM) with non-Gaussian measurement noise. Depending on the availability of the conjugate prior for the unknown parameters, the joint posterior distribution of the state and unknown parameters is approximated by using an auxiliary particle filter with a probabilistic changepoint model. The distribution of the SSM parameters conditionally on each particle is then updated by using variational Bayesian inference. Experiments are first conducted on a modified nonlinear benchmark model to compare the performance of the proposed approach with other state-of-the-art approaches. Finally, in the context of GNSS multipath mitigation, the proposed approach is evaluated based on data obtained from a measurement campaign conducted in a street urban canyon.
Foreground Information Guidance for Siamese Visual Tracking

Existing Siamese network based trackers are easily disturbed by large deformation, occlusion and distractor objects in the background. By comparing these trackers, we observe that the monotonous positive pairs usually have limited challenging factors (Occlusion, Deformation, etc.), which may make the learned features less robust. In addition, the foreground information of the substantial training data is utilized directly without deeper exploration. Thus, the trackers cannot effectively discriminate the foreground from the semantic backgrounds. In this paper, we focus on modifying the Siamese tracker by enriching the positive pairs and taking further advantage of the foreground information. During the offline training phase, a simple sampling strategy is adopted to enrich the challenging factors in positive pairs, which can effectively enhance the robustness of the tracker. At the same time, we highlight the foreground information by padding the background, and the information is utilized to generate a novel padding loss, which guides the tracker to pay less attention to the distractors in the background. Moreover, an improved feature information fusion is adopted to update the template, so that the tracker can adapt to the drastic appearance changes. Comprehensive experiments on the OTB and the VOT benchmarks demonstrate that our proposed tracker can achieve outstanding performance in both accuracy and robustness.
Discriminating bipartite mixed states by local operations

Unambiguous state discrimination of two mixed bipartite states via local operations and classical communications (LOCC) is studied and compared with the result of a scheme realized via global measurement. We show that the success probability of a global scheme for mixed-state discrimination can be achieved perfectly by the local scheme. In addition, we simulate this discrimination via a pair of pure entangled bipartite states. This simulation is perfect for local rather than global schemes due to the existence of entanglement and global coherence in the pure states. We also prove that LOCC protocol and the sequential state discrimination (SSD) can be interpreted in a unified view. We then hybridize the LOCC protocol with three protocols (SSD, reproducing, and broadcasting) relying on classical communications. Such hybridizations extend the gaps between the optimal success probability of global and local schemes, which can be eliminated only for the SSD rather than the other two protocols.
Investigating the Contributing Factors Affecting High School Students’ Self-confidence and the Solutions for Enhancement: A Case Study of Arabu Qala High School, Kandahar, Afghanistan

This research paper examines the contributing factors affecting high school students’ self-confidence and the solutions to enhance students’ self-confidence. The study used quantitative approach, the researcher conducted a survey study by developing a questionnaire comprising 20 items based on five Likerts scales. The data was collected from 100 high school students through random sampling which 75 were male and 25 females. The data was analysed and interpreted in terms of mean and standard deviation through SPSS software (24) version. It was concluded that different factors like, students’ feeling depressed, fear of making a mistake and others’ much focus toward students affected their self-confidence. On the other hand, factors like, teachers’ humor, students’ feeling a sense of pride and students’ encouragement and support from teachers’ side could enhance their self-confidence.
A Heuristic Approach for Distributing Pilgrims over Mina Tents

t. Every year, more than two million Muslims come to the holy city, Makkah, to perform Hajj (the Islamic pilgrimage). One of Hajj rituals is to spend two to three nights in Mina, one of the holy sites. Distributing Muslim pilgrims in Mina, respecting different kinds of constraints, over limited number of tents is a real-world optimization problem. In this paper, a heuristic based algorithm is proposed, called Mina Tent Distribution Algorithm (MTDA), attempting to better utilize the available capacity of Mina’s area in the best possible way with an efficient use of the available resources. MTDA employs seven functions during the search process to find the best fit accommodation for pilgrims on the available tents of Mina. Experimental results revealed that MTDA achieves better performance compared with eight algorithm schemes in all experimental cases. The best result of MTDA was obtained through allocating 80% of the total number of pilgrims over 76.2% of the total available accommodation space of Mina area.