Reviewerly/exHarmony huggingface dataset:
Component-specific temporal decomposition: application to enhanced speech coding and co-articulation analysis

Atal in 1983 proposed a speech coding algorithm called temporal decomposition (TD), which decomposes a time sequence of LPC derived log-area parameters, into a sequence of overlapping event/interpolation functions corresponding to their associated event vectors. The method provides a “global” event function corresponding to all the area parameters. In this work, we extend Atal's methodology to obtain the component level event functions corresponding to each area parameter and empirically show that the signal reconstruction error reduces using the component level target function rather than a global target function. This work can further be used to study the coarticulation behavior corresponding to individual articulators by inferring the component level event functions when the proposed method is used for decomposing the articulatory representations.
Leukocyte recognition algorithm in leucorrhea microscopic images based on ResNet-34 neural network

Automatic leucocyte recognition for leukorrhea microscopic images is a digital image processing technology in the field of machine learning. The existence and quantity of leukocytes in leucorrhea microscopic image is an important sign and basis to judge the inflammation of vagina or cervix. Therefore, the recognition and count of leucocyte is an effective means to evaluate the condition of the disease. To solve the problem of low efficiency of leucocyte recognition in traditional artificial microscopy, this paper proposes an automatic recognition algorithm based on ResNet-34 neural network. Firstly, Canny edge detection algorithm based on genetic algorithm is used to extract the foreground target in the leucorrhea microscopic image. Secondly, the leucocyte target is selected according to the connected region and boundary rectangle parameters of the foreground target. Finally, ResNet-34 neural network is applied for the classification of leukocytes. Experiments show that the recognition accuracy of leukocytes in leucorrhea microscopic image is 92.8%, and the recall is 97.1%, which is higher and better than other methods.
Shared-Mode Resource Allocation for Cloud-Based Load Testing

Resource allocation is essential for cloud-based load testing. The existing techniques use coarse-grained resource allocation methods with an entire virtual machine occupied by a single test task for cloud-based load testing. The idle resources in a virtual machine are unable to be used by other load testing tasks. This may result in uneconomical use of test resources and increase test costs. To optimize the use of test resources, this paper presents a shared-mode resource allocation method for cloud-based load testing. The method shares client-side virtual machine resources among load testing tasks. It takes minimizing resource redundancy, test execution cost, and network communication cost as optimization objectives of resource allocation, with the assurance of enough test resources as a basic constraint. We introduce a multi-objective optimization algorithm to create an optimized resource allocation plan for load testing tasks within a time window. The experiments show that the proposed method can reduce resource demands for load testing and thereby save the test costs.
Secure Ciphertext Policy Using Blowfish Hybridized Weighted Attribute-Based Encryption

There are lots of application which are been used by the people where a user’s data has to be protected from the attackers who steal the data by using fake identities which makes difficult to find the real culprit. The main issue is to give strong security to the data and to minimize computational cost with an efficient key management system. We propose a new encryption method in this paper, based on user attributes and access control policy using blowfish which is hybridized and weighted encryption which is a combination of features from two different algorithms to provide a high security to user data and to generate key efficiently. The two algorithms ciphertext policy attribute encryption with access control policy and the hierarchical identity-based encryption with weighted attributes are included. Here, based on the priority, the weights are assigned to the attributes and then the message is combined with access control policy given by the user and encrypted. This algorithm is used to find the culprit who is misusing the application. Also we can add that person to the blacklist, and all the accounts associated with that identity can also be blacklisted. The proposed system provides multi-authority security, efficiency, collusion resistant, secure against chosen plain text attack and reliable.
Automatic Summarization Method for First-Person-View Video Based on Object Gaze Time

Several first-person lifelog videos are lengthy in duration, and often include scenes that are not useful. This can be problematic for users as it requires a considerable amount of time to watch such a video. Therefore, in this study, we propose an automatic video summarization system for first-person-view videos by employing gaze tracking and object detection, based on human gaze time on an object. Because gaze is useful for capturing a user’s intention and interest, our approach summarily captures their interest and conscious focal points while watching videos. As a result of the experiment, the evaluation value of the summary video generated by the proposed system exceeded that of the summary video in which important scenes are randomly extracted. From these results, it can be said that our system is useful for rapidly watching videos, and summarizing them to reflect user interest. Our system is applicable in many fields, including behavior recognition, visual diary creation, and support for patients having memory impairment.
The Numerical Results of Binary Coherent-State Signal’s Quantum Detection in the Presence of Noise

In the optical frequency level communication, quantum effect becomes the source of significant communication errors. One alternative to classical detection is orthogonal projection measurement. In this paper, based on the coherent state’s quantum measurement and discrimination, the quantum optimal detection of binary coherent state signal in the presence of thermal noise was studied. Our numerical results showed the error probabilities of quantum detection under the OOK and BPSK modulation, and verified the performance of these two modulation methods in the thermal noise field background.
Non Chaotic Crypt: An Implementation based on Modified Pseudo Hadamard Transformation

In this paper, transformation and non-chaotic substitution based image cryptosystem has been proposed. The transformation phase involves 2-D Pseudo Hadamard Transform with constant co-efficient (PHTC) and substitution is performed using S-box with 128 bits of secrete key. Due to two stage encryption process, the strong correlation between adjacent pixels is effectually reduced and high entropy is observed in the cipher image. The cipher image is imperiled for several security analysis and the result proves strong resistivity against differential attacks. Even though the algorithm encrypts image in two stages, utilizes very less execution time. The results attained are relatively better compared to other prevailing algorithms.
Challenges to Multimedia Privacy and Security Over Social Media

With the rise in usage of social media, large quantities of data like pictures, videos, audio, and computer graphics are created. As the data is shared over the network, security and privacy become important concerns. Social networks are most popular and have become a medium for instant information sharing. Facebook, Instagram, WhatsApp are the most popular social networks. There has been extreme increase in the usage of audiovisual data over the social network. When multimedia data are stored in a networked environment, billions of users get connected to it for different purposes. The privacy challenges are personification, harassment, password sharing, location-oriented services, and privacy vs. marketing. The other challenges are data semantics, content management rights, and false information. There has been significant contribution by the researchers in development of security systems against these threats; still, the problems of clickjacking, short URL, phishing remain unresolved for some network architectures.
Study of an Improvement Plan for the Efficient Operation of Korean CAIND

This study explored an improvement plan for the overall preventive activity of Korean Citizen Corps Active in Disaster (CAIND). To this end, it utilized the statistical yearbooks of the Korea Ministry of Public Administration, Security, and Fire Service, and the National Statistical Office, for a period of four years from 2015 to 2018, and drew out the practical problems through statistical analysis. Through the results, the study provided four major suggestions for improvement. First, the education and training system of Korean CAIND should be established to satisfy regional conditions. Second, the quota management system of Korean CAIND that considers the characteristics of rural areas should be supplemented. Third, the reward system for Korean CAIND activities should be improved. Fourth, management members' readership competency of operating organizations, including that of the Korean CAIND captain in charge, should be strengthened. The results of this study are expected to be utilized as basic data to develop Korean CAIND in the future.
Mathematical Analysis of Multiple Spike Time Series Synchronized in Phase with Collective Theta Wave Using Neural Cell Model

Information processing in the brain is performed by the interactions of numerous number of neurons. However, much remains unknown about complex behaviors of a network composed of numerous number of neurons. Especially, in what case cross frequency coupling emerges and how it alters timings of individual firing are open questions. Therefore, in order to quantitatively evaluate the dynamics of the nervous group, we reproduced phenomena such as cross frequency coupling observed in the brain on a mathematical model and analyzed. The analyses of Fokker-Planck equation provides the region of gamma/theta oscillations as well as their cross frequency couplings. In addition, we demonstrated that the populational oscillation synchronizes sequences of spike trains, which is supposed to be a possible mechanism of memory coding in hippocampus by theta-gamma neural code.
Fault diagnosis and analysis of water temperature sensor

Water temperature sensor was researched in the paper. Type of water temperature sensor was first introduced. Then, the working principle of water temperature sensor was explained comprehensively and the waveforms of two typical water temperature sensors are analyzed. At last, a logical flow chart of maintenance was put forward. It concluded that the logical flow chart in this paper can be also applied to the other sensors of engine.
Constructions and Some Search Results of Ternary LRCs with &lt;i&gt;d&lt;/i&gt; = 6

Locally repairable codes (LRCs) are a type of new erasure codes designed for modern distributed storage systems (DSSs). In order to obtain ternary LRCs of distance 6, firstly, we propose constructions with disjoint repair groups and construct several families of LRCs with 1 ≤ r ≤ 6, where codes with 3 ≤ r ≤ 6 are obtained through a search algorithm. Then, we propose a new method to extend the length of codes without changing the distance. By employing the methods such as expansion and deletion, we obtain more LRCs from a known LRC. The resulting LRCs are optimal or near optimal in terms of the Cadambe-Mazumdar (C-M) bound.
Combining PENCIL with AMDIM for image classification with noisy and sparsely labeled data

Recent years have seen an increase in data availability and computational power, which has led to superior performance in training deep learning models for image classification. In many real-world use-cases, however, the training datasets come with noisy labels that have been automatically generated for a small subset of the available data. In this paper we explore strategies for maintaining classification performance when the labels become noisy and sparse. In particular, we evaluate the effectiveness of combining PENCIL, a framework for correcting noisy labels during training and AMDIM, a self-supervised technique for learning good data representations from unlabeled data. We find that this combination is significantly more effective when dealing with sparse and noisy labels, compared to using either of these approaches alone.
Image Segmentation of Manganese Nodules Based on Background Gray Value Computation

Aiming at two problems from the process of target and background segmentation in the field of manganese nodule image processing, the uneven illumination and the morphological defects of manganese nodules caused by white sand cover, this paper proposes an improved method. After the image is processed by this method, the above two problems are solved and segmentable manganese nodule images are obtained. Finally, the processed image is segmented to verify the feasibility and stability of the method. The original method is only used in document images processing. It is not suitable for the processing of manganese nodule images because it can’t repair the morphology of manganese nodules and reduce the contrast between the target and the background. The image of manganese nodules has the following characteristics: the pixels with higher gray value of white sand are scattered around the pixels with lower gray value of manganese nodules, and the area covered by white sand is smaller. In view of this feature, controllable artifacts are used to repair the morphology of manganese nodules. First, the image is preprocessed, and the background gray value is calculated and subtracted from the original image. Then the gray value of the image is adjusted by the block diagram. Finally, by performing the above operation again, the problem of uneven illumination in the image can be solved, and the morphology of manganese nodules in the image can be repaired without affecting the gap between them The simulation results show that, compared with the original method and other methods, several images processed by this method have significant effect in the process of segmentation, which proves that the method is effective and stable.
New Product Development Architectural Framework for Sustainability and Innovation within Telecommunication Industry

New product development is a ready-to-use innovation method that elaborate the relevant factors such as production and development cost, time to market and product value to own a higher level of technical, logical and organizational uncertainty rather than simply redesigning the product to attract the customer. Given the increasing uncertainty in the new product development process, standard general rules cannot be applied to improving an existing product and it is necessary to conduct a more open stage of brainstorming. This study explores the investigation phase and design approach to developing a new product development framework that includes resources, activity and interference by examining three telecommunication companies as case study. To allow the identification reveal comprehensive, concrete and consistent component, the study focused on several companies within the telecommunication industry.
Dual Residual Global Context Attention Network for Super-Resolution

Recently, deep convolutional neural networks have enabled SISR to achieve amazing performance and visual experience. However, we note that most current methods fail to model effective long-distance dependence, either considering only channel-wise attention modeling or considering a self-attention mechanism to model long-range dependencies resulting in a number of parameters and memory costs have increased dramatically, hindering CNNs representation capabilities and deployment on edge devices. To solve this problem, we proposed a novel network named Dual Residual Global Context Attention Network (DRGCAN), which is lightweight and can effectively model global context information. Specifically, we proposed a dual residual in residual structure in which introduce dual residual learning in the residual block of each residual group of the traditional residual in residual (RIR) to fully capture the rich feature information. Furthermore, we further proposed a global context attention mechanism to insert it at the end of each residual group to effectively model long-distance dependence to improve the representation ability of the model. Extensive experiments shown that the proposed model is superior to state-of-the-art SR methods in both visual quality and memory footprint.
Image Content Location Privacy Preserving in Social Network Travel Image Sharing

It is a well-known truth that online image sharing can lead to privacy leakage. Although the privacy information may vary in forms, image contents only with private property will be concerned, while other contents that seems public but could reveal personal information are always ignored. In online image sharing, some images may contain labels or landmarks which could reveal the geographic positions where these photos were taken, in which the public content could disclose private location information. To handle such problem, we proposed a travel image location privacy protection system that aims at protecting travel images location. The key issue in this problem is which part of image content is related to the scene and to what extent. To determine such relevance, in our proposed system, several image process methods are utilized to find out potential objects and their belonging classes, and two proposed privacy strategies that respectively focus on quantity and relative position are further implemented to define initial privacy level of each classes. Finally, a privacy predict model is trained in online learning way so that it can be updated with future user feedbacks. We conducted experiment on travel image dataset that related to specific keyword, and the results have demonstrated the effectiveness of our proposed system.
Design and Implementation of an Anonymous and Secure Online Evaluation Protocol

Course evaluations have become a common practice in most academic environments. To enhance participation, evaluations should be private and ensure a fair result. Related privacy-preserving method and technologies (e.g., anonymous credentials, Privacy Attribute-Based Credentials, and domain signatures) fail to address, at least in an obvious way, the minimal security and practicality requirements. In this paper, we propose, evaluate, and implement an efficient, anonymous evaluation protocol for academic environments. The protocol borrows ideas from well-known and efficient cryptographic approaches for anonymously submitting ballots in Internet elections for issuing one-time credentials and for anonymously broadcasting information. The proposed protocol extends the above approaches in order to provably satisfy properties such as the eligibility, privacy, fairness and verifiability of the evaluation system. Compared to the state of the art, our approach is less complex and more effective, while security properties of the proposed protocol are verified using the ProVerif cryptographic protocol verifier. A web-based implementation of the protocol has been developed and compared to other approaches and systems.
An Efficient System for Predicting Hand-Object Contact Probability Based on RGB Image Sequences

Predicting the probability of contact between objects is a very important research direction in computer vision. We propose an efficient system to predict the motion path of the hand in a sequence of RGB images, and get the probability of contact between the hand and objects. For this problem, first, we use YOLOv3 as target detector and Social LSTM as the predictor to accurately predict the movement path of hand. Second, in terms of contact probability, we adopt the strong feature extraction capability of the convolutional neural networks to learn the potential relation between the position of objects. The experimental results show that our system can effectively predict the path and the probability of contact.
Towards The Use of Smart Home Sensor Networks to Generate Predictive Activity Models

There are many use cases in the areas of cognition studies, physical therapy, and other medical related fields that stand to benefit from the ability to study the activities of individuals at home instead of a clinical environment. By monitoring their daily movements, various behavioral models can be generated that can aid in the early detection, diagnostic, and recovery processes relating to certain ailments. Many approaches to monitoring a person's behavior in the home focus on instrumenting the individual in some way, such as using a smart watch or band, and trying to determine the types of activities in which the user is engaged, such as eating, sleeping, etc. This can be burdensome to the user as it requires vigilance to ensure the device is able to perform its task. We propose a method to unobtrusively monitor a persons movements within the home to generate an activity model through the use of a smart home sensor network. Using this model, we explore various methods to measure model differences that can be used to determine when an individual's activities deviate from an established routine. Our platform, the Automatic eXtensible Inferential Occupancy Monitor, or AXIOM, allows seamless data collection from multiple sensors as well as multi-vector predictive analysis using the generated activity model.
OCEAN: A Non-Conventional Parameter Free Clustering Algorithm Using Relative Densities of Categories

In this paper, we propose a fully autonomous density-based clustering algorithm named ‘Ocean’, which is inspired by the oceanic landscape and phenomena that occur in it. Ocean is an improvement over conventional algorithms regarding both distance metric and the clustering mechanism. Ocean defines the distance between two categories as the difference in the relative densities of categories. Unlike existing approaches, Ocean neither assigns the same distance to all pairs of categories, nor assigns arbitrary weights to matches and mismatches between categories that can lead to clustering errors. Ocean uses density ratios of adjacent regions in multidimensional space to detect the edges of the clusters. Ocean is robust against clusters of identical patterns. Unlike conventional approaches, Ocean neither makes any assumption regarding the data distribution within clusters, nor requires tuning of free parameters. Empirical evaluations demonstrate improved performance of Ocean over existing approaches.
IMPACT OF USING MOBILE LEARNING APPLICATIONS IN THE LEARNING PROCESS

Development of the world education and the learning process triggers a teaching trend that places teachers and students in an equally active position through the use of technology and media in the learning process. The use of technology in learning such as mobile technology application can be used as one of the factors driving the development of the learning process in formal and non-formal educational institutions. For that, we need a mobile technology facility that can be accessed by anyone anywhere and anytime. Of course, the role of mobile devices such as mobile phones or Android smartphones is the best solution for learning. So in this study, it will be seen the impact of applying Mobile Learning applications in the learning process. The object of mobile learning research in this study is the English course. Will measure the extent of the development of students in learning English with m-learning compared to conventional learning.