Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey of Task Migration Strategies in Mobile Edge Computing

Mobile Edge Computing (MEC) is a new network architecture for the 5G network. It can reduce task execution delay and energy consumption effectively by task migration. Task migration refers to the transfer of all or part of computing tasks carried by mobile devices to MEC for execution. Efficient task migration strategy is the key research point of task migration in MEC. This paper reviews the latest research progress of task migration strategy. First, we introduce the general model of migration strategy, and analyzes the execution delay and energy consumption under different migration strategies. Then, we analyze and compare the existing research on the migration strategies based on execution delay, energy consumption, and tradeoff between them. Finally, we prospect the challenges and future development directions of migration strategy in MEC.
Direct Stress Birefringence Distribution Measurement in Lens Using Refractive Index Matching Liquid

Stress birefringence (SBR) of optical components is significant in polarization-dependent and fine optical applications, such as optical lithography. However, the methods generally used for high-resolution birefringence distribution detection are unsuitable for measuring samples with nonplanar surfaces, such as lenses, owing to refraction and aberration. In this work, we propose and verify a method for directly measuring SBR distributions in a lens. An aqueous glycerol solution is used as the refractive index matching liquid to rectify the measuring light path. The retardation level of the matching liquid is confirmed to be considerably smaller than that of the window, thus simplifying the measurement error. The retardation levels of the optical plate and wave plate obtained using this method are consistent with that measured in air ( $\Delta < 0.025^{\circ }$ and 0.212°, respectively), as well as with the fast axis angle of the wave plate ( $\Delta < 0.15^{\circ }$ ). Additionally, the SBR distribution of the lens sample coincides with its physical state and is repeatable with a coefficient of variation of less than 2.585%. This method provides a convenient tool for SBR distribution measurements in a lens, which could be extended to the measurement of optical components exhibiting any surface shape.
Dimensionality Reduction via the Laplace-Beltrami Operator: Application to EEG-based BCI

Neural oscillations captured by electroencephalography (EEG) can be used by Brain-Computer Interfaces (BCIs) to reveal the underlying mental processes and enable explicitly or implicitly interacting with one's environment. Most feature extraction techniques are based on spatial filters and power analyses in multiple frequency bands. The global geometry feature is seldom investigated. In this paper, the spatial aspects of EEG signals are studied using the Laplace-Beltrami operator. The eigenvectors of the Laplace-Beltrami operator form an orthonormal basis for square-integrable functions over the scalp and capture the geometry of electrodes' position in a hierarchical way. The signals are decomposed into different spatial frequency components by the projection into the eigenspaces of the Laplace-Beltrami operator. Dimensionality reduction could be done by using only the low frequency components. This method is compared with Principal Component Analysis (PCA) filtering on publicly available motor imagery BCI data and achieved comparable results while being unsupervised, data-independent and requiring 33.7% less computation time.
An Efficient Secure System for Fetching Data From the Outsourced Encrypted Databases

Recently, database users have begun to use cloud database services to outsource their databases. The reason for this is the high computation speed and the huge storage capacity that cloud owners provide at low prices. However, despite the attractiveness of the cloud computing environment to database users, privacy issues remain a cause for concern for database owners since data access is out of their control. Encryption is the only way of assuaging users’ fears surrounding data privacy, but executing Structured Query Language (SQL) queries over encrypted data is a challenging task, especially if the data are encrypted by a randomized encryption algorithm. Many researchers have addressed the privacy issues by encrypting the data using deterministic, onion layer, or homomorphic encryption. Nevertheless, even with these systems, the encrypted data can still be subjected to attack. In this research, we first propose an indexing scheme to encode the original table’s tuples into bit vectors (BVs) prior to the encryption. The resulting index is then used to narrow the range of retrieved encrypted records from the cloud to a small set of records that are candidates for the user’s query. Based on the indexing scheme, we then design a system to execute SQL queries over the encrypted data. The data are encrypted by a single randomized encryption algorithm, namely the Advanced Encryption Standard-Cipher-Block Chaining (AES-CBC). In the proposed scheme, we store the index values (BVs) at user’s side, and we extend the system to support most of relational algebra operators, such as select, join, etc. Implementation and evaluation of the proposed system reveals that it is practical and efficient at reducing both the computation and space overhead when compared with state-of-the-art systems like CryptDB.
Channel Estimation based Pilot Signal with BS Identification Code for LTE-TDD Massive MIMO Downlink systems

This paper presents a technique to use an orthogonality property of Hadamard code for reducing the impact of pilot contamination in LTE-TDD Massive MIMO systems. Pilot signal is modulated with Hadamard code to identify base station in cellular network. The low computational complexity suboptimal linear LS criterion is applied to estimate channel state information at base station and mitigates the effect of pilot contamination in case of full pilot reuse in multi-cell scenarios. Numerical results show that SINR performance and achievable downlink rate obtained from the proposed technique are higher than that of the LS conventional algorithm in aspect of time-varying fading channel and high cross gain of pilot power from adjacent cells.
TSV Fault Modeling and A BIST Solution for TSV Pre-bond Test

As semiconductor technology develops, threedimensions integrated circuits (3-D ICs) is thought as a viable solution to further improve the performance of ICs. Throughsilicon-vias (TSVs) are the most important device in 3-D ICs. Therefore, TSVs testing is a very critical flow in 3-D ICs manufacturing. A new pre-bond TSVs test solution is proposed in this paper. The new test structure includes an improved ring oscillator and a voltage-divider structure. Compared with conventional TSV test structure, the output of new test structure is more sensitive to TSV faults. We prove the effectiveness of the new test structure by HSPICE simulation.
Bioelectrodes for high-channel count and small form factor CMOS neural probes

SiNAPS neural probes enable continuous sub-millisecond neural activity recordings from all integrated electrodes (up to 1024) with a very small form factor implantable device. Key for this achievement was the integration of DC-coupled analog frontends within micrometric active electrode-pixels. Rather than filtering the DC electrode signal component as in conventional AC-coupled frontends, the SiNAPS electrode-pixel circuit operates with a periodic calibration that sets a DC offset compensating open circuit potential (OCP) changes of each electrode. This keeps the in-pixel amplifiers within the operational range. However, for optimal performances this circuit requires minimizing the OCP variability among all integrated electrodes. Here, we show on 4-shanks SiNAPS-probes that this variability can be controlled by the electrode material, structuring process and functionalization. Among the tested bioelectrodes, results show that evaporated platinum (Pt) functionalized with a monolayer of (3-aminopropyl) triethoxysilane (APTES) leads to less than 20 mV DC offset variability across 1024 electrodes.
L1 regularization-based selection of EEG spectral power and ECG features for classification of cognitive state

A weighted L1 penalty term may be incorporated into loss functions to regularize some classes of machine learning models. Optimization of system performance in concert with the L1 norm of the model parameter vector drives coefficients for ineffectual features to zero, resulting in sparse models. These properties of L1 regularization can be used for feature selection. In the present study, this method is applied to EEG spectral power and heart rate features extracted from participants undergoing a mental arithmetic task, with the aim of classifying cognitive state. Six classification models were evaluated on full-feature and reduced-feature datasets with six-fold cross-validation; each model was fine-tuned independently to select hyper-parameter values that optimize model performance. These results are contrasted with a more conventional statistical analysis. L1 regularization-based feature selection improved classification accuracy across all of the models investigated. The highest classification accuracy (0.889, 0.092 SD) was achieved with a linear kernel support vector machine classifier. Comparison with conventional analysis highlights a potential limitation of L1 regularization, depending on the desired purpose of the model. By minimizing feature redundancy due to covariance, L1 regularization does not necessarily help us to gain greater understanding of brain dynamics from these EEG signal features. However, if simply improving state classification is the desired outcome, for instance in brain-computer interface applications, L1 regularization may be used effectively for feature selection.
Focus and Concentrate! Exploring the Use of Conversational Robot to Improve Self-Learning Performance during Pandemic Isolation by Closed-Loop Brainwave Neurofeedback

Recently, Human-Robot Interaction (HRI) researchers have paid lots of attentions on the conversational robots that can provide didactic or pedagogy teachings, especially for the children and young adolescents with cognitive disabilities, such as autism and epilepsy. The research and developments of such robots for educational purposes are investigated intensively. Above all the difficult challenges, how to evaluate the effectiveness of a conversational robot, which mimics a teacher communicating with a student, to improve the performance of learning and studying, is the key factor to deploy such robots in our society and be widely adopted. However, we haven't seen much investigation in previous literatures so far. In order to bridge the gap, this preliminary study had explored the use of conversational robot with electroencephalogram (EEG) biosignals as evidence measurements to improve the self-learning performance during COVID-19 pandemic crisis, while the schools are forced to close, and the students are inevitably segregated in social isolations. We had collected 10 student participants' EEG data, which were calculated to find concentration levels, and then the robot had conversations with the students adaptively according to his/her concentration levels. The result showed that the conversations between robot and humans, who are constantly not concentrating on the learning tasks, could effectively increase his/her level of concentrations.
An Efficient Compression Method to Improve Energy Consumption in WBANs

One of the most essential services that allow the patient to remotely monitor and check vital signs of the patient to diagnose and treat the patient without physically attending the treatment center is wireless body area networks (WBANs). In WBANs, tiny sensors that charge limited batteries work together. These sensors can be placed on the human body or inside it. Because of limited energy consumption of these sensors, energy efficiency is one of the most critical challenges in WBANs. One way to improve and reduce energy consumption in these sensors is to compress the data. In this paper, a method is presented that is a modification of Huffman method. In this paper, for the first time in the field of Wireless Body Area Networks, Huffman method has been modified to be more compatible with the limitations of Wireless Body Area Networks. Then Modified Huffman method has been implemented on medical data and finally compared with previous methods presented in this field. The results in the diagrams show the Modified Huffman method performs better than the NIS and saves 1770 units of energy more than the NIS method. As a result, compression data using the Modified Huffman method stores 11.8% more energy than the NIS.
Survey of Energy Harvesting Technologies for Wireless Sensor Networks

Energy harvesting (EH) technologies could lead to self-sustaining wireless sensor networks (WSNs) which are set to be a key technology in Industry 4.0. There are numerous methods for small-scale EH but these methods differ greatly in their environmental applicability, energy conversion characteristics, and physical form which makes choosing a suitable EH method for a particular WSN application challenging due to the specific application-dependency. Furthermore, the choice of EH technology is intrinsically linked to non-trivial decisions on energy storage technologies and combinatorial architectures for a given WSN application. In this paper we survey the current state of EH technology for small-scale WSNs in terms of EH methods, energy storage technologies, and EH system architectures for combining methods and storage including multi-source and multi-storage architectures, as well as highlighting a number of other optimisation considerations. This work is intended to provide an introduction to EH technologies in terms of their general working principle, application potential, and other implementation considerations with the aim of accelerating the development of sustainable WSN applications in industry.
‘Glaucoma - Automating the Cup-to-Disc Ratio Estimation in Fundus Images by Combining Random Walk Algorithm with Otsu Thresholding’

Early determination of the onset of Glaucoma can potentially save the eyesight for millions of people in the developing and developed countries. Cup to Disc ratio in fundus images is one of the most often used parameters to assess the images for the presence of Glaucoma. Several techniques have been proposed from time to time in an effort to automate the determination of CDR in fundus images. These include techniques such as histogram equalization and its adaptive versions, vessel inpainting, etc. In this work we combine Otsu's thresholding technique with random walk algorithm to segment the cup and disc regions and thus determine the cup-to-disc ratio. The technique has been applied on a large number of fundus images, both normal and glaucomatous, obtained from a well-known eye hospital. Results indicate that the disc and cup segmentation determined match closely with those determined by the ophthalmologists.
Efficient Synthesis of Reversible Circuits Using Quantum Dot Cellular Automata

Heat dissipation is one of the major problems in the construction of electronic devices. Reversible computing is one of the emerging computing paradigms to overcome heat dissipation problem. One of the nanoscale devices that has low power consumption and can be used to construct digital logic components is quantum-dot cellular automata (QCA). In this paper, the benefits of reversible computing of QCA will be used to propose QCA designs for two main reversible gates, the CNOT and the TOFFOLI gates. The proposed CNOT gate has been used to design QCA for double CNOT gate to show its reversibility, and QCA design for the SWAP circuit that contains 3 CNOT gates. The suggested TOFFOLI gate is utilized to construct QCA for double TOFFOLI gate and a reversible circuit which consists of several reversible gates. The suggested QCA designs for double CNOT, double TOFFOLI and reversible circuits such as SWAP circuit have been proposed by adding cells with certain clocks to the wires that connects the proposed gates. The proposed QCA designs show better cell count, area used in the construction, number of majority gate, wire crossover, and/or clock cycle delay when compared with relevant designs in literature.
Miniaturized Sequentially Rotated Curved PIFA Circular Array for Portable Handheld RFID Readers

This paper describes the miniaturization process of a sequentially rotated circular array antenna implemented with three curved planar inverted-F antenna (PIFA) elements. The antenna miniaturization obtained by bending curved PIFA elements (resulting in closer antennas) makes the impedance matching more complex and, for this reason, a capacitive coupling mechanism is implemented to reduce the antenna impedance. An antenna with diameter 0. 16λ (calculated at 915 MHz) and height 0. 05λ has been numerically optimized to work within the radio frequency identification (RFID) ultra-high frequency (UHF) bandwidth, showing a joint S11≤-10 dB, axial ratio (AR) ≤3 dB, and right hand circular polarization (RHCP) gain≥2 dBic bandwidth of 908-927 MHz.
Study of Applied Voltage on Ionization Chamber for Radon Detector

Hyunwoong Choi a, Gyohyeok Song a, Jaehyun Park a, Das Sopan a, Hyunduk Kim b, Hojong Chang c, Gyuseong Cho a∗, aDepartment of Nuclear & Quantum Engineering, Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu, Daejeon, Korea, 34141 bIRIS Co., Ltd., 193 Munji-ro, Yuseong-gu, Daejeon, 34051, Republic of Korea cInstitute for Information Technology Convergence, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea. ∗Corresponding author : gscho1@kaist.ac. kr
The “ forgotten ” modified lipoprotein subspecies

Insights from preclinical and clinical studies have attempted to highlight the importance of modified lipoprotein particles in the pathogenesis of cardiovascular diseases (CVD). However, evidence is not conclusive. Since there is a relative dearth of clinical research in collecting useful information from traditional advanced lipoproteins testing, this present editorial introduces the aim of a special issue on modified lipoproteins as potential biomarkers for CVD. This issue aims at gathering a selection of insightful articles that address major challenges related to potential clinical use of modified lipoproteins as new CVD biomarkers. The editors seek to promote better mobilization of lipoproteins measurement for the pursuit of sustainable CVD clinical outcome and development of potential biomarkers. Knowledge and progress in this particular field will certainly help answering questions about clinical relevance of circulating modified lipoprotein subspecies and their potential use for better patient care and disease prevention. We hope that, when taken together, the focus on modified lipoproteins will stimulate new vision and reveal study key aspects for better clinical data outcome and more effective therapeutic strategies.
Community Based Tourism Development as a Community Economic Development Strategies in the District of South Middle East NTT

The research method used is descriptive qualitative, with research and development procedures developed by Borg and Gall with adaptations and modifications in stages. The research is planned for one year. The technique of collecting data is through the stages of observation, interviews and focus group discussions and documentation. Research activities consist of preliminary studies, conceptual modeling, validation and revision, and model testing. Researchers act as active instruments in an effort to collect data in the field. The research subjects were the head and field expert staff of the Culture, Tourism, Youth and Sports Service as key informants. Supporting informants are tourists, community leaders and community actors who live around tourist objects in the South Central East Regency. Data analysis use interactive analysis model from Miles and Huberman. The results of this study indicate: (1) The efforts of the South Central East Regency Government in developing CBT as a strategy for community economic empowerment are through: (a) Tourism awareness socialization development programs and tourist destinations, (b) tourism marketing development programs, and (c) partnership development. (2) Types of tourism that have the potential to be developed into community-based tourism in the South Central East Regency include nature tourism, cultural tourism, educational tourism, handicraft and culinary tourism. (3) The factors inhibiting the development of CBT in the South Middle East District are: (a) infrastructure that is not yet supportive, (b) community participation in developing tourism is still low, and (c) partnerships have not been maximally established. (4) There is no collaboration between Higher Education Institutions, Local Governments and Communities in developing CBT as a strategy for community economic empowerment. Keywords—community based tourisme, economic, development, district TTS
Leveraged Weighted Loss for Partial Label Learning

As an important branch of weakly supervised learning, partial label learning deals with data where each instance is assigned with a set of candidate labels, whereas only one of them is true. Despite many methodology studies on learning from partial labels, there still lacks theoretical understandings of their risk consistent properties under relatively weak assumptions, especially on the link between theoretical results and the empirical choice of parameters. In this paper, we propose a family of loss functions named \textit{Leveraged Weighted} (LW) loss, which for the first time introduces the leverage parameter $\beta$ to consider the trade-off between losses on partial labels and non-partial ones. From the theoretical side, we derive a generalized result of risk consistency for the LW loss in learning from partial labels, based on which we provide guidance to the choice of the leverage parameter $\beta$. In experiments, we verify the theoretical guidance, and show the high effectiveness of our proposed LW loss on both benchmark and real datasets compared with other state-of-the-art partial label learning algorithms.
School meals and classroom effort: Evidence from India

We use the exogenous policy shock of the extension of provision of school meals to upper primary grades in public schools in Delhi to study the effects of school meal intake on the cognitive effort of students within the classroom. Using individual level data on the performance of students in effort games both before and after the extension of the program, we find that the provision of meals significantly improved the classroom effort of students in grade seven. We conclude that school meals have the potential to increase educational attainment in the classroom. JEL Classification: I21, I25, H52