Miracle-dz/UniFAR-FTUs huggingface dataset:
Modified Carrier-Overlapped PWM With Balanced Capacitors and Eliminated Dead-Time Spikes for Four-Level NNPC Converters Under Low Frequency

Due to the lack of sufficient redundant states, four-level nested neutral-point clamped (4L-NNPC) converters suffer from severe flying capacitor (FC) voltage fluctuations under low fundamental frequencies, which restricts their applications in practice. To deal with this issue, a modified carrier-overlapped pulsewidth modulation (PWM) (COPWM) is proposed in this article, which could achieve zero average current flow through the two FCs over each carrier period. As a result, their voltages can be balanced naturally under steady states theoretically, and the low-frequency FC voltage fluctuation issue would no longer exist. In order to compensate for any nonideal factors in real industrial applications, an active control by dynamically adjusting the correspondence between carriers and switches is further applied for better control performance. A dead-time compensation scheme is proposed accompanied by this modified COPWM to eliminate the unexpected dead-time-induced voltage spikes of 4L-NNPC converters. The effectiveness of the proposed modified COPWM in regulating the FCs under low frequency and eliminating the dead-time-induced voltage spikes is verified by both simulation and experimental results.
Deep learning approaches for object co-segmentation and one-shot segmentation

Image co-segmentation is an active computer vision task that aims to discover and segment the shared objects given multiple images. Recently, researchers design various learning-based algorithms to handle the co-segmentation task. The main di culty in this task is how to e↵ectively transfer information between images to infer the common object regions. In this thesis, we present CycleSegNet, an e↵ective and novel approach for the co-segmentation task. Our network design has two key components: a region correspondence module which is the basic operation for exchanging information between local image regions, and a cycle refinement module which utilizes ConvLSTMs to progressively update image embeddings and exchange information in a cycle manner. Experiment results on four popular benchmark datasets — PASCAL VOC dataset, MSRC dataset, Internet dataset, and iCoseg dataset indicate that our proposed approach greatly outperforms the existing networks and achieves new state-of-the-art performance. In addition to image co-segmentation, we also explore a method to solve one-shot segmentation with only weak supervision (bounding box). One-shot semantic segmentation has recently gained attention for its strong generalization ability to segment unseen-class images given only limited annotated image. However, existing methods in one-shot object segmentation have mainly relied on manually pixel-wise labeled segmentation masks. The main challenge in this task is limited data and weak supervision. In this thesis, we present an e↵ective approach, which utilizes the recent weakly-supervised semantic segmentation method to generate pseudo mask labels in the bounding box regions and then integrates the detailed information and correlation between support image and query image for solving one-shot image segmentation. Extensive experiments on the PASCAL5 dataset show that our weakly-supervised method narrows down the performance gap between bounding box supervision and pixel-wise annotations, and performs comparably with the state-of-the-art fully-supervised one-shot methods.
Assessment of torrential flood and landslide susceptibility of terrain: Case study - Mlava River Basin (Serbia)

Torrential floods and landslides are frequent natural disasters in Serbia, but also in the Mlava River Basin. Due to the large number of settlements, the main goal of this research is to determine the locations that are most susceptible to torrential floods and landslides in the Mlava River Basin. Using geographic information systems (GIS), the first step is the analysis the susceptibility of the terrain to torrential floods using the Flash Flood Potential Index (FFPI) method. According to the obtained data, it was determined that 31.53% of the Mlava River Basin is susceptible, and 10.46% is very susceptible to torrential floods. The second step is the analysis of the susceptibility of the terrain to landslides, for which the statistical Probability method (PM) and the Landslide Susceptibility Index (LSI) were used. According to the results of the LSI index and PM method, 8.09% and 14.04% of the basin area is in the category of high and very high susceptibility to landslides. This paper represents a significant step towards a better understanding of unfavorable natural conditions in the Mlava River Basin, and the obtained results are applicable to numerous human activities in the research area (environmental protection, sustainable management of agricultural plots, protection of water and forest resources and ecosystems, etc.).
Deep Learning Framework for Secure Communication With an Energy Harvesting Receiver

In this paper, we consider wireless-powered secure communication with an energy harvesting receiver, which is allowed to harvest energy from the transmitted signals but not to decode information, and there is thus a requirement to keep the information secret from this potential eavesdropper. Considering co-channel interference among signal links, we find the optimal transmit power to maximize the sum rate of the signal links, while ensuring the requirements of information secrecy and energy harvesting. Due to the non-convexity of the optimization problem formulated here, we first derive suboptimal solutions using an iterative algorithm based on a dual method. In order to address the limitations caused by the use of the iterative algorithm, i.e., long computation time and suboptimality, we design an efficient deep neural network (DNN) framework and a novel training strategy as a means of combining supervised and unsupervised training. Specifically, the DNN is first pre-trained using labeled training data with the suboptimal solutions obtained from the iterative algorithm in a supervised manner; further training is then applied to the DNN using a well designed loss function in an unsupervised manner to enhance the training performance. Simulation results reveal that the proposed scheme achieves a near-optimal performance with a lower computation time than existing schemes. We also verify that the pre-training and the new loss function are effective in improving the speed of training of the DNN.
Cognitive Memory-Guided AutoEncoder for Effective Intrusion Detection in Internet of Things

With the development of the Internet of Things (IoT) technology, intrusion detection has become a key technology that provides solid protection for IoT devices from network intrusion. At present, artificial intelligence technologies have been widely used in the intrusion detection task in previous methods. However, unknown attacks may also occur with the development of the network and the attack samples are difficult to collect, resulting in unbalanced sample categories. In this case, the previous intrusion detection methods have the problem of high false positive rates and low detection accuracy, which restricts the application of these methods in a real situation. In this article, we propose a novel method based on deep neural networks to tackle the intrusion detection task, which is termed Cognitive Memory-guided AutoEncoder (CMAE). The CMAE method leverages a memory module to enhance the ability to store normal feature patterns while inheriting the advantages of autoencoder. Therefore, it is robust to the imbalanced samples. Besides, using the reconstruction error as an evaluation criterion to detect attacks effectively detects unknown attacks. To obtain superior intrusion detection performance, we propose feature reconstruction loss and feature sparsity loss to constrain the proposed memory module, promoting the discriminative of memory items and the ability of representation for normal data. Compared to previous state-of-the-art methods, sufficient experimental results reveal that the proposed CMAE method achieves excellent performance and effectiveness for intrusion detection.
SSSL: Secure Search Space Locking of Behavioral IPs

Raising the level of VLSI design abstraction from the register transfer level (RTL) to the behavioral level has multiple advantages: 1) It reduces the turn-around-time; 2) allows faster verification; and 3) extends the reusability of the design as high-level synthesis (HLS) automatically reoptimizes the synthesized circuit when new process technologies are available by simply selecting a different technology library. Moreover, HLS makes extensive use of synthesis directives that control how to synthesize mainly loops (unroll or pipeline), arrays (register or RAM), and functions (inline or not). This further increases the reusability of the behavioral code as it enables the generation of micro-architectures with different area versus performance tradeoffs. These advantages open the door to third-party IP (3PIP) vendors providing behavioral IPs (BIPs). Unfortunately, the market of third-party BIPs is still very small and mostly limited to the HLS vendors themselves. Being so flexible is also their main weakness as it makes them only economically viable if the BIP provider can charge a large premium as it is highly unlikely that the BIP consumer will require their service again. Traditional IP vendors discriminate the price of the IP based on the amount of flexibility of the IP, e.g., RTL description versus providing a synthesized gate netlist. We envision a similar price discrimination strategy for BIPs by limiting the reusability of the BIP by partially encrypting the BIP source code. The main idea is to limit the search space, and hence, the reusability of the BIP such that it only allows the BIP consumer to generate micro-architectures within a predefined search space range. This is accomplished by selectively fixing some of the synthesis directives in the form of pragmas at the source code while leaving others explorable. By encrypting the portion of the BIP that contains the fix pragmas we can guarantee that no designs outside of the predefined search space are generated. We believe that this work could serve as catalyst to grow the BIP market.
Patients Contributing to Visit Notes: Mixed Methods Evaluation of OurNotes

Background Secure patient portals are widely available, and patients use them to view their electronic health records, including their clinical notes. We conducted experiments asking them to cogenerate notes with their clinicians, an intervention called OurNotes. Objective This study aims to assess patient and provider experiences and attitudes after 12 months of a pilot intervention. Methods Before scheduled primary care visits, patients were asked to submit a word-constrained, unstructured interval history and an agenda for what they would like to discuss at the visit. Using site-specific methods, their providers were invited to incorporate the submissions into notes documenting the visits. Sites served urban, suburban, and rural patients in primary care practices in 4 academic health centers in Boston (Massachusetts), Lebanon (New Hampshire), Denver (Colorado), and Seattle (Washington). Each practice offered electronic access to visit notes (open notes) to its patients for several years. A mixed methods evaluation used tracking data and electronic survey responses from patients and clinicians. Participants were 174 providers and 1962 patients who submitted at least 1 previsit form. We asked providers about the usefulness of the submissions, effects on workflow, and ideas for the future. We asked patients about difficulties and benefits of providing the requested information and ideas for future improvements. Results Forms were submitted before 9.15% (5365/58,652) eligible visits, and 43.7% (76/174) providers and 26.76% (525/1962) patients responded to the postintervention evaluation surveys; 74 providers and 321 patients remembered receiving and completing the forms and answered the survey questions. Most clinicians thought interim patient histories (69/74, 93%) and patient agendas (72/74, 97%) as good ideas, 70% (52/74) usually or always incorporated them into visit notes, 54% (40/74) reported no change in visit length, and 35% (26/74) thought they saved time. Their most common suggestions related to improving notifications when patient forms were received, making it easier to find the form and insert it into the note, and educating patients about how best to prepare their submissions. Patient respondents were generally well educated, most found the history (259/321, 80.7%) and agenda (286/321, 89.1%) questions not difficult to answer; more than 92.2% (296/321) thought sending answers before the visit a good idea; 68.8% (221/321) thought the questions helped them prepare for the visit. Common suggestions by patients included learning to write better answers and wanting to know that their submissions were read by their clinicians. At the end of the pilot, all participating providers chose to continue the OurNotes previsit form, and sites considered expanding the intervention to more clinicians and adapting it for telemedicine visits. Conclusions OurNotes interests patients, and providers experience it as a positive intervention. Participation by patients, care partners, clinicians, and electronic health record experts will facilitate further development.
Effects of microalgal biomass as biofertilizer on the growth of cucumber and microbial communities in the cucumber rhizosphere

Resource, Shanxi Academy of Agricultural Sciences, Taiyuan, China Abstract: In the present study, microalgal biomass ( Anabeana circinalis and Scenedesmus quadricauda ) was used as biofertilizer in cucumber cultivation. The response of cucumber growth characters to microalgal biofertilizer was evaluated. Moreover, rhizosphere microbial diversity and community composition of cucumber was analyzed through 16S rRNA high-throughput Illumina sequencing data. The results showed that microalgal biomass as biofertilizer significantly promoted height, number of leaves and flower buds, and stem diameter of cucumber. The application of a high concentration of S. quadricauda to soil raised the diversity of rhizosphere fungi of cucumber. The rhizosphere microbial community of cucumber also responded to microalgal biofertilizer. The most abundant bacterial phylum in all samples was Proteobacteria . Acidobacteria , Actinobacteria , and Gemmatimonadetes were also highly abundant in all samples. Ascomycota was the most predominant fungal phylum in all samples. Zygomycota was the dominant phylum in the control, whereas Basidiomycota was dominant in all treatments. At the genus level, both growth-promoting bacteria and fungi ( Azotobacter , Bacillus , Pseudomonas , Cryptococcus , Fusarium , Penicillium , and Trichoderma ) from the rhizosphere of cucumber were enhanced in all treatments after the application of microalgae fertilizer. All results indicate a significant impact of microalgal biofertilizer on the growth of
Towards understanding the temporal accuracy of OpenStreetMap: A quantitative experiment

This abstract was accepted to the Academic Track of the State of the Map 2021 Conference after peer-review. The ability to provide timely information compared to traditional collection methods of geographic information is generally considered as one of the main advantages of volunteered geographic information (VGI) since its emergence in the 2000s [1]. In addition to several anecdotal examples illustrating how VGI data can provide more up-to-date information than authoritative sources, the literature provides ample evidence on the usefulness of VGI in applications that require timely geodata, such as disaster management [2, 3]. For example, the Haiti earthquake relief effort in 2010 laid the foundations for how remote contributors of OpenStreetMap (OSM) and other platforms can make a difference and aid responding humanitarian agencies after a crisis [4]. The Humanitarian OpenStreetMap Team has made numerous contributions and helped save lives at numerous instances ever since [5]. However, apart from these examples, the temporal dimension of VGI has not received much research attention outside the application of disaster management, and there is a huge gap between assessing temporal accuracy and other factors of data quality, such as spatial accuracy [6, 7]. Abreicht et al. highlighted the lack of formal acknowledgment of temporal aspects in the concept of VGI and proposed a framework called ‘Volunteered Geo-Dynamic Information’ to fully integrate spatial and temporal aspects of VGI [8]. Other works utilizing the temporal component in VGI often focus on the behavior of contributors rather than the currency and temporal validity of map features they contributed [9–11], or studied the evolution of data over time [12, 13]. While these approaches are useful, by nature they cannot provide a quantitative measure of how current OSM (or VGI in general) is. [14] noted during their investigations that the temporal accuracy of OSM could not be measured using their traditional extrinsic method, because OSM data was compared to
Scalable p-i-n Diode Modeling and Parameter Extraction for Use in the Design of W-Band GaAs Switch

Scalable modeling and parameter extraction of the millimeter-wave GaAs-based p-i-n diode for W-band switch design are presented in this article. A direct-extraction method based on the S-parameters on-wafer measurement is utilized to determine the extrinsic and intrinsic model parameters without any de-embedding test structures. Under turn-on and turn-off bias conditions, the modeled input reflection coefficients agree well with the measured data in the entire frequency ranges for GaAs-based p-i-n diode. The scalable normalization rules have been used to design W-band p-i-n diode switch successfully; good agreements are obtained between the simulated and measured data to verify the accuracy of the proposed model.
Penegakkan Etika Profesional Pemeriksa BPK Untuk Peningkatan Kualitas Pengelolaan Keuangan Negara

Berdasarkan Undang-Undang Republik Indonesia Nomor 15 Tahun 2006, BPK merupakan lembaga negara yang diamanatkan oleh UUD 1945 untuk melakukan pemeriksaan pengelolaan serta tanggung jawab atas keuangan negara. Agar tercipta kesuksesan pemeriksaan tersebut, pemeriksa BPK harus menegakkan etika profesional di setiap pemeriksaannya. Tujuan penelitian ini adalah menganalisis strategi untuk menegakkan etika profesional pemeriksa BPK. Penelitian ini adalah penelitian kualitatif dengan metode deskriptif dimana peneliti mendeskripsikan temuan dan melakukan analisa atas temuan tersebut. Berdasarkan documentary research diketahui bahwa kesadaran ketuhanan yang dibangun dari religiusitas membentuk moral dan kesadaran pemeriksa BPK untuk menegakkan kode etik sehingga menuntun pribadi pemeriksa BPK agar selalu memutuskan bersikap etis ketika dihadapakan pada dilema etis. Pada akhirnya, penelitian ini memberikan tujuh strategi atau rekomendasi sebagai upaya menegakkan etika profesional pemeriksa BPK yaitu, kemampuan manajemen moral, menjadi pemeriksa BPK yang amanah, memiliki pengetahuan hukum yang cukup, memiliki pemahaman kode etik profesi yang komprehensif, memiliki kecintaan pada alam, mengenali setiap praktik tidak etis dan tidak berusaha mencari-cari kesalahan.
Priority-Based Dedicated Slot Allocation With Dynamic Superframe Structure in IEEE 802.15.6-Based Wireless Body Area Networks

Wireless body area networks (WBANs) support various types of medical applications with heterogeneous requirements. Therefore, we need to use an efficient medium access control (MAC) protocol to ensure reliable data transmission. In this article, we propose a dynamic superframe structure-based MAC protocol extending the principles of the IEEE 802.15. 6 standard. In this work, to allocate dedicated slots for each sensor device, a prioritized dedicated slot allocation mechanism using the criteria importance through intercriteria correlation (CRITIC) is proposed. With the help of this method, the priority value of sensor devices is calculated based on different sensors’ parameters. We compared the performance of our proposed work with standard IEEE 802.15. 6 MAC and a few other MAC protocols. The simulation result shows that our proposed MAC protocol performed better in terms of energy efficiency and reliability, as well as reducing the packet drop probability. Results show that the reliability of data transmission increases over the IEEE 802.15. 6 MAC protocol by more than 50%.
Multi-Frequency Electric Field Measurement Method for Optical Under-Sampling System

This paper introduces an under-sampling E-field measurement system involving three femtosecond pulse lasers, which can be used to measure a multi-frequency electric field (E-field) in a wide frequency range. Optical under-sampling technology utilizes femtosecond laser pulses to sample the electrical signals and convert the high-frequency signal down to the first pulse repetition frequency (RPF) intervals for measurement, i.e., tens to hundreds of MHz. However, previous work rarely studied the measurement of multi-frequency signals. Therefore, in this work, we use three RPFs and propose the Remainder Matching (RM) method of three RPFs to eliminate false-alarm-triggered frequencies. Moreover, to solve the challenging problem of grouping the down-conversion components of each measured frequency, we suggest the Eigenvalue Matching of Remainder’s Difference (EMRD) method to obtain the correct group accurately. Hence, this work combines the RM and EMRD methods to improve the system’s measurement accuracy. To evaluate the suggested measurement technique, detailed theoretical analysis and simulation are performed to demonstrate the universality of our method that can effectively guide the design of E-field measuring systems using optical under-sampling technology. The proposed scheme manages an error rate less than 0.1% without any measurement error of the down-converted components when six frequencies exist simultaneously and is less than 5% when the measured frequency deviation is ±25kHz. Finally, the proposed system is challenged on noise perturbations, with the results demonstrating that the suggested combined RM and RDEM scheme attains an appealing performance.
Minimally invasive pectus excavatum surgery: nuss procedure

Background: We reviewed pre-, intra-, and post-operative clinical data, including morbidity and mortality rates, for patients who underwent Nuss surgery to repair pectus excavatum. Materials and Methods: Nuss procedure was performed in 140 patients at our clinic between 2012 and 2019 (males: 108; females: 32 females; mean age: 15.7 ± 7.8 years; range: 3-40 years). The Haller index was mild (2.5-3.2) in 72, moderate (3.2-3.5) in 40, and severe (3.6-6.0) in 28 patients. Results: None of the patients died. Morbidity or bad blood loss was not observed. The mean duration of surgery was 59.5 ± 21 min (range: 30-120 min). The mean duration of postoperative hospitalization was 4.6 ± 2.9 days (range: 3-19 days). The procedure was performed using one (n = 107; 76.4%), two (n = 31; 22.2%), or three (n = 2; 1.4%) bars. There were five cases of early pneumothorax, four pleural effusions, five bar displacements, two wound infections, one hematoma, seven bar exposures due to allergies, and three costal fractures. Conclusions: The Nuss operation is an effective, minimally-invasive treatment for pectus excavatum. It has the advantages of a short duration of surgery and low morbidity and mortality rates. Therefore, the Nuss operation may be considered the treatment of choice in cases of pectus excavatum.