Reviewerly/exHarmony huggingface dataset:
An Ultra Low Power AES Architecture for IoT

Internet of Things (IoT) is now becoming a part of our life. Many devices are already connected, and more are expected to be deployed in the next coming years. The main concern for IoT is to provide a practical solution for security, privacy, and trust. The science of cryptography plays an important role for providing security in IoTs. AES algorithm is a well known symmetric key, block cipher that is highly secure. In this paper, we present an ultra-low power architecture for the AES cipher that is need for most IoT applications. The proposed architecture has been implemented on SCL 180 nm technology. We have used 4-bit serializer and deserializer (SerDes) to send and receive 128-bit data. The proposed AES architecture uses 32-bit data path in SubByte transformation, and it requires 44 clock cycles for encryption of 128-bit plaintext with a 128-bit cipher key. To deserialize 128-bit plaintext and cipher key, the architecture requires 32 clock cycles. Similarly, to serialize 128-bit ciphertext, 32 clock cycles are overlapped by 44 clock cycles required by AES module. By this, once, after the first 32 clock cycles, the use of SerDes does not affect on the throughput of the system. At 10 MHz the ASIC implementation of the proposed architecture on SCL 180 nm PDK consumes 52.2 $$\upmu $$ W and 194.7 $$\upmu $$ W power at 1 V and 1.8 V respectively and provides a throughput of 28 Mbps.
Image Restoration Based on Wavelet Semi-soft Threshold Transform and BP Fuzzy Neural Network

Image restoration aimed to recover the original image to from degraded images and degenerate function. Fuzzy logic systems and neural network can complement each other quite well. In this paper, a novel Image Restoration approach is developed. Wavelet Semi-soft Threshold Transform and of our method is utilized to image restoration. Firstly, Wavelet Semi-soft Threshold Transform method is used to image denoising. Then, the image is classified into several regions using fuzzy sets, which are smoothing, texture and edge regions to obtain the input of BP Fuzzy Neural Network. Sliding window is used to extract features and input the training data. Finally, the output of BP Fuzzy Neural Network is the restored image.
An Interactive Multiplayer Mobile Application using Feature Detection and Matching for Tourism Promotion

The idea of developing an interactive multi-player mobile application for tourism promotion using image matching on the Android platform was initiated from the Thai government's policy promoting tourism in order to have a significant impact on the revenues of Thai tourism industry. This application requires a user to be in a real place to complete missions that he or she has selected. The application navigates the user to the selected place. While playing, the user must do missions by taking photos of an object specified in the gallery database in a period of time. The taken photo will be compared with images in the gallery using Oriented FAST and Rotated BRIEF (ORB), the feature detection and matching. Each set of images tested with SSD, SAD, and NCC algorithms with different thresholds in order to find a suitable value that gives good results. In order to make the application more engaging, the application was designed to use/play with multiple users. The experimental results show that the application can detect user's position and lead the user to the target place correctly using GPS technology and can compare images accurately in case of the taken image and the images in the gallery have the same angle or small angle (less than 30 degree) and have little difference in light intensity. The evaluation of overall user satisfaction from thirty testers is at a good level.
FlexiPLE

Annotative approaches are commonly used to specify variation points in a Model-based software product line (MBSPL) implementation. Variant-specific parts are marked using annotated presence conditions in a so-called 150% model. Such approaches lead to a static feature selection (or binding), which is appropriate, i.e., for platform-specific features. However, dynamic binding, i.e. feature selection at run time, is a necessity in many (industrial) contexts. We present our tool FlexiPLE which allows for a feature-wise binding time selection, even after a MBSPL implementation. To this end, our tool a) supports the definition of binding time constraints, b) aids the user in the adaption process based upon variability analysis and c) ensures that only valid feature combinations can be selected statically and dynamically by incorporating a staged configuration approach. Our tool builds upon a robust stack of state-of-the-art technologies and tools in the context of MBSPLs.
A Fast Facial Landmarks Detection and Posture Classification Algorithm

Abstract In the field of face recognition research, the efficiency of facial landmarks detection and posture classification algorithm is a serious problem. This paper proposes a lightweight network based on convolutional neural network to quickly detect the facial landmarks of human faces. Based on this, the posture is predicted using the relative position of the obtained feature points. The model was tested by the face images captured in various scenes in real life. The accuracy and recall rate of the proposed algorithm were 96.3% and 98.2%, respectively. The test time for a single picture is 0.9ms. The proposed algorithm has less running time and its accuracy and recall rate are similar or even better than other models.
Type-Based Resource Analysis on Haskell

We propose an amortized analysis that approximates the resource usage of a Haskell expression. Using the plugin API of GHC, we convert the Haskell code into a simplified representation called GHC Core. We then apply a type-based system which derives linear upper bounds on the resource usage. This setup allows us to analyze actual Haskell code, whereas previous implementations of similar analyses do not support any commonly used lazy functional programming languages.
Speeding-Up the Dynamic Programming Procedure for the Edit Distance of Two Strings

We describe a way to compute the edit distance of two strings without having to fill the whole dynamic programming (DP) matrix, through a sequence of increasing guesses on the edit distance. If the strings share a certain degree of similarity, the edit distance can be quite smaller than the value of non-optimal solutions, and a large fraction (up to 80–90%) of the DP matrix cells do not need to be computed. Including the method's overhead, this translates into a speedup factor from $$3\times $$ up to $$30\times $$ in the time needed to find the optimal solution for strings of length about 20,000.
Experimental Quantum Process Tomography of Controlled-phase Gate for Time-bin Qubits

We demonstrate a controlled-phase gate for time-bin qubits using a 2 × 2 electro-optic switch based on lithium niobate waveguides. We reconstructed the process matrix using quantum process tomography with 16 distinct inputs from which we determined a process fidelity of 82%.
LSTM-Based Kazakh Speech Synthesis

Currently, the level of penetration of speech technology in modern life begins to vary greatly by country and by language environment. This is especially noticeable in the services developed in leading technology companies, where high-resource languages such as English, Russian, etc. have become the main service languages. Whereas, the speech technologies for under-resourced languages lag in their development. The article presents the first speech synthesis system based on the long-term short-term memory (LSTM) neural network architecture for the Kazakh language. The presented text-to-speech (TTS) system includes previously developed methods of prosodic processing for under-resourced languages and an acoustic model based on LSTM. The system receives the linguistic features of the text, including phonetic transcription, and it generates Kazakh speech with an acceptable quality of perception. Briefly summing up, this work describes the method of developing a speech synthesis for the Kazakh language, which has limited resources in terms of natural language processing. This approach can also be applied to other under-resourced languages.
PFed: Recommending Plausible Federated SPARQL Queries

Federated SPARQL queries allow to query multiple interlinked datasets hosted by remote SPARQL endpoints. However, finding federated queries over a growing number of datasets is challenging. In this paper, we propose PFed, an approach to recommend plausible federated queries based on real query logs of different datasets. The problem is not to find similar federated queries, but plausible complementary queries over different datasets. Starting with a real SPARQL query from a given log, PFed stretches the query with real queries from different logs. To prune the research space, PFed proposes semantic summary to prune the query logs. Experimental results with real logs of DBpedia and SWDF demonstrate that PFed is able to prune drastically the logs and recommend plausible federated queries.
Investigating Spherical Fish Tank Virtual Reality Displays for Establishing Realistic Eye-Contact

Eye-contact is a key aspect of non-verbal human communication in everyday tasks [1]. It provides important social and emotional information that can increase the effectiveness of human communication [8]. In a conversation, eye-contact, or the lack thereof, is constantly evaluated by human brains. Conversation partners derive subjective judgment of others' credibility, focus and confidence [4] from it. Seeking another person's eye-contact is a signal for them that focus is put on that person and the main receptive senses are prepared to receive input from the other person. Likewise breaking eye-contact usually indicates distraction, loss of confidence, loss of interest or shifting focus to a different target.
Anomaly Detection Method for Chiller System of Supercomputer

Supercomputer reliability decreases with the increase of its scale. In this situation, the method to reduce the supercomputer MTTR (mean time to repair) plays a critical role in system management. Engineers at present typically use supercomputer metrics to construct anomaly detection methods and reduce the MTTR of supercomputers. However, the infrastructure data, including chilled water data, of supercomputers are neglected. This paper proposes an ensemble learning method for anomaly detection, which includes LSTM (long short-term memory) and linear regression algorithm. On the basis of this method, we construct an anomaly monitor system by using chilled water data. Experimental results show that the method can help engineers precisely detect anomalies.
Study on WeChat Service of Library in University--Based on Readers’ Behavioral Needs

Abstract The paper used Maslow’s hierarchy of needs theory to study library WeChat service based on readers’ needs. Firstly, we designed a questionnaire to find out readers’ WeChat needs. We divided readers’ expectations on library WeChat service into five levels. We carried out a mean analysis for each level of indicators. Then we used PASW statistical analysis methods to analyze relevant questionnaire data. Four main questions on library WeChat services were concluded. Finally, the paper provided four countermeasures which provides inspiration and reference for the library that has already launched and is about to launch WeChat service.
Intelligent Health Nursing System Based on Cloud Platform

Abstract The health of the elderly is mainly disturbed by chronic diseases. As the proportion of older persons living alone is increasing, the health problems of elderly have received extensive attention. Now the rapid development of sensor technology and network technology provide a technical guarantee for the realization of real-time health nursing. The existing intelligent nursing system does not reuse nursing data after judging the user’s health status and choosing alarm processing. This paper proposes a new intelligent health nursing system architecture, which integrates sensor network and wireless communication network, and uses FPGA cloud Platform to complete data storage and processing, which can effectively avoid the loss of function limited by the power consumption and computational performance of the user’s device. In addition, through the deep learning algorithm, the accuracy of abnormal nursing can be effectively improved as well.
Performance Analysis of Image Compression Using LPWCF

Image compression is the most important feature for acheiving an efficient and secure data transfer. One of the main challenges in compression is developing an effective decompression. The input images that is compressed may not be more effectively restored in the decompression process that is based on quantization using Cosine Transformations or Wavelet transformations where the pixel information will be lost. To overcome these challenges, encoding process were employed. In the encoding process the pixel information were well protected but the compression efficiency is not improved. In order to overcome this challenge Lossless Patch Wise Code Formation (LPWCF) is employed. In the patch wise code generation the compression process is based on the pixel grouping and removing the relevant and recurrent pixels. In the proposed method, the images were first reduced in size by combining the current pixel with the previous pixel. The resulting image size is nearly the half of the size of the input image. The resulting image is then divided into small patches. In the patch recurrent pixels and their locations were identified. The identified pixel locations were placed prior to the pixel value and then the process is repeated for the complete image. The result of each patch acts as a code. In the receiver side the same process is reversed inorder to obtain a decompressed image. The process is completely reversible and hence the process can be employed in the process of transmission of the images. The performance of the process is measured in terms of the compression ratio, the image quality analysis of the input and the decompressed image based on PSNR, MSE and SSIM.
From 2D to Next Generation VR/AR Videos: Enabling Efficient Streaming via QoE-aware Mobile Networks

Ranging from traditional video streaming to Virtual Reality (VR) videos, the demand for video applications to mobile devices is booming. In the context of mobile operators a challenging problem is how to handle the increasing video traffic while managing the interplay between infrastructure optimization and QoE. Solving this issue is remarkably difficult, and recent investigations do not consider large-scale networks. In this dissertation paper we explore the solution space of efficient video streaming over mobile networks. First, we propose a model to predict video streaming quality based on the observation of performance indicators of the underlying IP network. Second, we introduce a novel QoE-aware path deployment heuristic for large-scale SDN-based mobile networks. Third, based on the lessons learned with QoE prediction for traditional video streaming, we finally explore the VR video domain by proposing PERCEIVE and VR-EXP. PERCEIVE is a two-stage method for predicting the perceived quality of adaptive VR videos when streamed through mobile networks. In turn, VR-EXP consists of an experimentation platform that allows in-depth evaluation of state-of-the-art VR video optimization techniques. Obtained results show that the combination of the proposed methods for QoE-aware path selection outperformed state-of-the-art approaches.
Will the Phone Number Disappear?

The E.164 format phone number has been around for over 150 years and over this time has provided a ubiquitous means of identification and routing on the PSTN. Experts have been speculating about the demise of the phone number for some time and with the widespread emergence of communication OTTs the general expectation has been that the phone number will soon lose its original functionality as a communication endpoint on the PSTN. Our research suggests, however, that the phone number is not set to go extinct in the medium term but is more likely to evolve into a unique and core element of digital identifying technologies for a wide range of online services, and is thus likely to retain it's significance for at least another decade or so.
Study on environment conscious technologies in a super tall building: Evaluation of PV performance considering aerological climate

In recent years, buildings have tended to be taller, and their energy potential is expected be used effectively . Photovoltaics is considered one of technologies affected by air temperature, outside air velocity, and solar radiation from the aerological climate of supertall buildings with a height of 390 m. The energy potential of the “height” of photovoltaic power generation systems is affected by two factors: aerological climate and shadows cast by surrounding buildings. Taking these effects into account, the predicted annual power generation amount was calculated. At 390 m above ground, it was confirmed that the power generation amount was greater than that on the ground, when considering the effectiveness of photovoltaic systems. Then, we calculated the predicted annual power generation amount on each wall and roof surface of a tall building with a height of 390 m above the ground. By evaluating the energy-saving effect of adopting photovoltaic systems, we evaluated the photovoltaic system using the wall surface from the viewpoint of the primary energy reduction and primary energy consumption of the building.
GPR System Integrated with Bucket for Construction Equipment by Six Degrees of Freedom of Bucket Position and Posture Estimation Algorithm

In recent years, the number of the accident has increased, in which underground lifeline such as gas pipes, optical fiber cables or electric cables is damaged while excavating by a bucket of the underground construction equipment. GPR imaging system integrated with a bucket is a promising tool for detecting buried objects under the bucket. But, the estimation accuracy of antenna position is expected to be within 1cm. In this paper, we propose an estimation algorithm of 6 degrees of freedom of the bucket position and posture with 4 Tx and 4 Rx US transducer array system. We develop a down looking imaging radar system with a bucket integrated with the antennas and US transducer array. We experimentally show that a buried object under the moving bucket is successfully detected by the developed system.
Software Reuse for Mass Customization

This tutorial explores the impact of the socio-economic trends of mass customization on software reuse through software product line development.
Face recognition method with feature fusion and error feedback

Abstract A face recognition method with feature fusion and error feedback is proposed to imitate human cognition process. The intelligent recognition mode with multi-level feedback from global to local and from coarse to fine is implemented. Firstly, based on the idea of feedback control, a multi-level feedback model of face recognition with performance index constraints and supervised learning under finite domain is established. Secondly, based on entropy theory and Stochastic Configuration Networks (SCN), a facial feature space and face image classification network model with multi-feature fusion and sufficient separability under certain feature performance conditions are established from the perspective of information theory. Thirdly, based on entropy theory and error theory, the face recognition error representation and calculation model under certain feature performance conditions is established. Finally, the adjustment index of facial feature performance based on recognition error is constructed, and the face recognition algorithm with feature fusion and error feedback is given out. Test and comparison experiments show that our method is effective and feasible.
Study on Clinical Application of Intelligent Control Technology

The main research direction of intelligent control technology is analyzed and discussed in detail, including fuzzy model and basic control algorithm, fuzzy predictive control, stability analysis of fuzzy control system. The status and function of intelligent control are discussed, and the application and development of intelligent control in medicine are summarized and prospected in this paper.
Genetic Approach to Stable Partitioning in Online Role Based Hedonic Games

In role-based hedonic games, agents are partitioned into teams and matched to suitable roles within their teams. A stable partition and matching is one in which each agent accepts its role and the composition of roles fulfilled on the team. To achieve this, a population must have a suitable distribution of preferences for roles and compositions such that supply and demand can be fulfilled. Many NP-complete problems arise in attempting to match agents into acceptable teams. We propose a genetic local search approach to matching players into stable teams. Our approach adapts to changes in preferences over time, and we validate our approach on real world matchmaking data from an online game, League of Legends. We show improvements on three optimization criteria over greedy local search, and finally, we show how updates to the chromosome vector can be interpreted to discover deficiencies in the supply and demand of particular roles.
An Improved Word Spotting Method for Printed Uyghur Document Image Retrieval

Key word spotting plays an important role in the field of Uyghur printed document retrieval. However, the cursive nature of Uyghur script causes some drawbacks for retrieving a word correctly with word spotting approach based on SIFT (Scale Invariant Feature Transform) feature. To overcome this limitation, this paper proposes a new approach by introducing the concept of considering the retrieval result of EDM (Euclidean distance mapping) matching algorithm as a geometry information by homograph matrix and perspective transformation to further improve the accuracy of word spotting. The comparative experiment of the proposed method is evaluated on a dataset of 190 Uyghur printed document images which contain about 17648 words. Experimental result demonstrates that the proposed approach is an effective method of retrieving the word comparing with the previous word spotting method used in Uyghur printed document image.
Proving Cloud SaaS Layer Security Vulnerabilities

Cloud computing is the new age of the ICT world and it is a powerful computing platform that enables individual and organizations to perform variety level of task with minimum costs. Cloud computing can provide a flexible IT architecture and environment, which are accessible through internet and can be access through a lightweight portable devices. In previous years, the number of people using cloud services had dramatically increased and lots of data had been stored inside the cloud computing environments. Apparently, the data that breaches into the cloud services had also increased every year. It is due to the hackers whom will always try to exploit the security vulnerabilities of the cloud’s architecture. Primarily, this paper describes the vulnerabilities in SaaS layer together with its attack possibilities.
Quantum Based Networks: Analysis of Quantum Teleportation Protocol and Entanglement Swapping (Workshop Paper)

In this paper we consider the quantum teleportation and entanglement swapping protocols used in quantum based networks for passing information between a sender and receiver. For the teleportation protocol we observe and identify relationships that exist between Einstein-Podolsky-Rosen (EPR) Bell states employed as quantum resources, measured sender values and the gates employed at the receiver side. For the entanglement swapping protocol we consider input and output EPR states and the relationship between the two. We include a review of the concepts and our findings from the analysis carried out.