Miracle-dz/UniFAR-FTUs huggingface dataset:
SIF-NPU: A 28nm 3.48 TOPS/W 0.25 TOPS/mm2 CNN Accelerator with Spatially Independent Fusion for Real-Time UHD Super-Resolution

This paper proposes a convolutional neural network (CNN)-based super-resolution accelerator for up-scaling to ultra-HD (UHD) resolution in real-time in edge devices. A novel error-compensated bit quantization is adopted to reduce bit depth in the SR task. Spatially independent layer fusion is exploited to satisfy high throughput requirements at UHD resolution by increasing parallelism. Burst operation with write mask in the dual-port SRAM increases the process element utilization by allowing the concurrent multi-access without exploiting additional memory. The accelerator is implemented in the 28nm technology and shows at least 4.3 times higher $\text{FoM}(\text{TOPS}/\text{mm}^{2}\times \text{TOPS/W)}$ of 0.87 than the state-of-art CNN accelerators. The implemented accelerator supports up-scaling up to 96 frames-per-seconds in UHD resolution.
Wavelet-based Entropy Analysis on EEG Signal for Detecting Seizures

Epilepsy is one of the world's most common neurological diseases when the morbidity of brain activity occurs. People with epilepsy experience repeated seizures more than once in 24 hours. There are various ways to analyze seizures, one of them is by examining the electroencephalogram (EEG) signal recording. Neurologists usually do manual seizure analysis, yet it is time-consuming, tedious, and costive. Thus, an automated system of seizure detection is needed to assist the analysis. In this study, EEG signals of the CHB-MIT dataset are evaluated in digital signal processing using a machine learning approach. The EEG signals are filtered by applying a Butterworth bandpass filter (BPF). The mean channel selection is then applied to simplify the number of channels used in all patients. The channels are selected based on the energy value. Wavelet Packet Decomposition (WPD) is used to decompose the signals into several scales. The features are extracted using two entropy analyses, the Shannon Entropy (ShEN) and Renyi's Entropy (RE). This study used the Support Vector Machine (SVM) to classify the feature set. The channel selection process reduced the processed channels into two to five channels. The proposed method achieves the average accuracy of 85.64% and 72.48% obtained by REN and ShEN, respectively.
6GS/s 8-channel CIC SAR TI-ADC with Neural Network Calibration

In this paper we introduce an area efficient time-interleaved charge-injection-cell SAR ADC. The prototype TI-ADC interleaves 8 CIC SAR channels for a sampling rate of 6Gs/s and a compact area of 0.0060Smm2. A neural network calibration algorithm corrects multiple error sources in the time-interleaved ADC. The neural calibration method effectively improves the average measured ENOB of the TI-ADC from 4.1 bits to 5.49 bits.
Neural Network-Based and Modeling With High Accuracy and Potential Model Speed

In this brief, we demonstrate a neural network (NN)-based device modeling framework. This NN model is built to model advanced field-effect transistors (FETs). Specific transfer functions and loss functions are chosen to achieve high accuracy and smoothness in the output of this NN model. Both <inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula> (current–voltage) and <inline-formula> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula> (capacitance–voltage) characteristics are studied in this work. Speed comparison between the NN-based model and Berkeley short-channel IGFET model (BSIM) has been done to show that NN has a great potential to accelerate circuit simulation speed. We also present that this NN modeling framework is not only useful for more Moore technologies [e.g., gate-all-around FET (GAAFET)] but also beyond Moore transistors [e.g., negative capacitance FET (NCFET)].
Demo: Real-Time Implementation of Block Orthogonal Sparse Superposition Codes

Short-packet communication is a key enabler of various Internet of Things applications that require higher-level security. This proposal briefly reviews block orthogonal sparse superposition (BOSS) codes, which are applicable for secure short-packet transmissions. In addition, following the IEEE 802.11a Wi-Fi standards, we demonstrate the real-time performance of secure short packet transmission using a software-defined radio testbed to verify the feasibility of BOSS codes in a multi-path fading channel environment.
Phish Finders: Crowd-powered RE for anti-phishing training tools

Many organizations use internal phishing campaigns to gauge awareness and coordinate training efforts based on those findings. Ongoing content design is important for phishing training tools due to the influence recency has on phishing susceptibility. Traditional approaches for content development require significant investment and can be prohibitively costly, especially during the requirements engineering phase of software development and for applications that are constantly evolving. While prior research primarily depends upon already known phishing cues curated by experts, our project, Phish Finders, uses crowdsourcing to explore phishing cues through the unique perspectives and thought processes of everyday users in a realistic yet safe online environment, Zooniverse. This paper contributes qualitative analysis of crowdsourced comments that identifies novel cues, such as formatting and typography, which were identified by the crowd as potential phishing indicators. The paper also shows that crowdsourcing may have the potential to scale as a requirements engineering approach to meet the needs of content labeling for improved training tool development.
Accuracy Improvement for the Diagnosis of Breast Cancer using Different Techniques of Machine Learning

One of the most widespread diseases among women is breast cancer. Almost 20% of women across the globe are suffering from this deadly dangerous disease (DDD). The death ratio of women suffering from DDD is around 55%. Nowadays, this disease is prevalent among women. Detection and timing of the detection of this disease are critical during the phase of treatment and rehabilitation. Early-stage detection of this disease results in recovery, whereas if the condition is detected after some time, it causes severe damage. Its detection is not an easy task as sometimes the machines used for the detection do not prove to be as accurate as they could be. Techniques like thermography and computer-aided diagnosis (CAD) are used to detect this DDD. Nowadays, a lot of work is being carried out on ways that could help accurately detect this DDD. To accurately detect this disease, different types of machine learning (ML) techniques and their algorithms are carried out to develop the tools that could help doctors diagnose this DDD efficiently. In this paper, the most popular and common types of ML techniques to diagnose this disease are discussed and their accuracy is improved. K Nearest Neighbours (KNN), Random Forest (RF), Decision Tree (DT), and XG Boost machine learning techniques are used to train and test the data. The accuracy of the disease diagnosis is increased with the training and testing of the dataset.
Quantum-Inspired Differential Evolution Algorithm in Probiotics Marker Genes Selection

Selected microbial strains have long been used as probiotics for their health-promoting benefits. Genome analysis offers a faster and comprehensive approach for the screening of the probiotic potential of a microbial strain. Use of machine learning algorithm increases the applicability and reliability to perform prediction using genomic features. In this work, a metaheuristic algorithm, the quantum-inspired differential evolution (QDE) algorithm, was employed for probiotic marker genes selection. The genome data of 60 Bacillus spp. strains were annotated and represented with a standard identifier, the K numbers. By classifying the strains into probiotics (27 strains) and non-probiotics (33 strains), the elitist QDE algorithm was used to select a subset of features that can classify the strains into the correct class, with accuracy of 0.9055. A set of 45 features (K numbers) were shortlisted to be useful marker genes for probiotics potential.
Prediction of hourly solar radiation in Indonesia using LSTM

The value of solar radiation has many advantages to support optimal utilization of solar energy, from building design, solar power plant and agricultural system. However, solar radiation is an element that will affect to other weather parameters. Forecasting systems that have been carried out by previous researchers are predictions for data per-hour, per-day, per-month and even per-year. All forecasting processes are carried out using different multivariate meteorological parameters. Fluctuations in solar radiation can occur due to data errors or moving fast of clouds. In this study, we are using meteorological parameter: solar radiation, sun duration, relative humidity, dew point, air temperature, and sky cover. All these parameters will be used to predict hourly solar radiation. Long short-term memory (LSTM) is proposed in our works to discover best activation: ReLU, sigmoid, tanh. As a result, RELU is best activation which has the smallest Mean Square Error (MSE) value is 0.1885, compared with other activation value: sigmoid MSE value is 0.2359 and tanh MSE value is 0.3270. By using ReLU, the wall time is 1min 51s and calculation process stops in 18th epoch. Optimum learning rate become 0.00249 and wall time 1min 38s. The output prediction is hourly solar radiation one-day ahead that we can used as a data solar radiation information.
A 28GHz Area-Efficient CMOS Vector-Summing Phase Shifter Utilizing Phase-Inverting Type-I Poly-Phase Filter for 5G New Radio

A 28GHz CMOS passive vector-summing phase shifter is demonstrated in this paper. The proposed design consists of a phase-inverting type-I poly-phase filter and a 45° switch-type phase shifter. By adopting the phase-inverting poly-phase filter for I/Q generation, I/Q weight assignment and summation, the proposed design can realize ± 22.5° phase tuning with fine resolution in each phase quadrant. Together with the 45° phase shifting provided by the switch-type phase shifter, the proposed design can achieve a 360° phase coverage in a compact area of 0.1mm2. Transformers are utilized for the inter-stage and output matching, which shrink the area compared with transmission lines. In measurement, the fabricated passive vector-summing phase shifter realizes a root mean square (RMS) phase error smaller than 1.77° from 26.5GHz to 29.5GHz and reaches 0.06° at 28GHz with the help of the fine-tuning in phase-inverting polyphase filter. The measured RMS gain error of the design is smaller than 0.27dB in the desired frequency range.
SSH Bruteforce Attack Classification using Machine Learning

One of the problems in computer network security is Intrusion Detection. To detect it, we need a NIDS (Network Intrusion Detection). One example of NIDS is a honeypot. In this research, we use a kippo honeypot as a tool as an IDS. In this paper, honeypot logs are used for classifying SSH attacks using machine learning. The research used the original dataset from Kippo's log to classify the ssh attack as the primary source. We implement four algorithm classifications: Decision Tree, Naive Bayes, SVM, and Random Forest. Our research found that Decision Tree (DT) and random forest have the same score and better accuracy than naive Bayes and SVM. The accuracy and F1 score of RF and DT are 0.92 and 0.92, respectively.
Detection Performance Using Range Dependent SINR for Planar-PMIMO Radar

Multi-antenna radars with overlapping subarray transmit methods known as phased-MIMO (PMIMO) provide many advantages over conventional radars such as phased-array and MIMO radars, i.e. having high flexibility to adjust the beamwidth and beampattern of the entire array generated by each subarray as well as at the same time allows the generation of multiple beams through orthogonal waveforms. In addition, with the planar array configuration when compared to the linear array configuration, transmit-receive (Tx-Rx) gain, signal to interference plus noise ratio (SINR), parameter estimation, detection performance, etc. will drastically increase. So far, the detection performance of the radar has been studied in the form of detection probability and false alarm probability based on signal to noise ratio (SNR). In this paper, we have formulated, evaluated, and investigated the detection performance of planar-PMIMO radar based on the range dependent SINR which was then compared with the performance of traditional multi-antenna radars based on the effect of SINR and the number of subarrays in Tx. The evaluation results show that the planar-PMIMO radar is more flexible due to the variation of its subarrays compared to conventional radars that already exist to be able to detect targets in various target environmental conditions.
Improving and testing live migration for bhyve

When managing a cluster or grid infrastructure, a system administrator uses live migration to move virtual machines that provide certain services from one host to another. The migration process should be fast and should not affect the service. For bhyve, FreeBSD’s hypervisor, we have added a live migration support. This paper presents the improvements we have added to the live migration feature such as cleaning the dirty bit after copying a page and extending the live migration support to support guests with more that 3GB of RAM. Moreover, this paper presents the testing procedure and scenarios we used to asset our live migration support in bhyve.
Solitary states in arrays of excitable FitzHugh-Nagumo units

While coherence-incoherence patterns have been exhaustively studied in systems of coupled oscillators, their mechanisms of emergence and their relationship en route from complete coherence to incoherence in coupled excitable systems remain as yet unresolved. Here we disclose two types of solitary states in arrays of non-locally coupled excitable FitzHugh-Nagumo units with dominant repulsive over attractive interactions. While the prevailing type of solitary states is shown to derive its dynamical features from unbalanced two-cluster states in globally coupled networks, the minority type is fundamentally a consequence of non-locality of interactions. Apart from the states whose local structure is based on successive spiking of units, we also find solitary states where local excitability and slow-fast dynamics give rise to leap-frog activity characterized by an alternating order of units’ spiking. The main impact of noise on system’s behavior is shown to be the reduction of its multistability, whereby the solitary states are suppressed in favour of patched patterns.
Reinforcement learning in a spiking neural network with memristive plasticity

The reinforcement learning paradigm is for the first time presented for spiking neural network architecture with memristor-based local dynamic plasticity. The models of two kinds of such plasticity are used in the simulation study of the Cartpole task. Applying the Gaussian receptive field time-encoding scheme and simple reinforcing current pulses determined by the sign of reward change, the successful learning is demonstrated for both types of memristive plasticity.
Analysis of eye-tracking data during the Sternberg working memory task in subjects with asthenic syndrome

In the current study, we analyzed eye-tracking data from subjects with asthenic syndrome during the performance of Sternberg Working Memory Task. To determine the severity of asthenia we used Multidimensional Fatigue Inventory. We showed that people with the asthenic syndrome are characterized by a rapid increase in subjective fatigue during the experiment. We confirmed the relationship between the subjective level of fatigue and pupil size. We believe that eye-tracking is a suitable method for objectively identifying mental fatigue during a cognitive task.
Open Source Network Management System Based on SharpPcap for QoS and Security Policies

A network management system (NMS) is a vital tool to be used in a corporate network. It ensures that Quality of Service (QoS) policies are correctly applied, but can also have a great impact over the network security preventing attacks and blocking the ongoing actions of attackers in enterprise networks. The current work presents an implementation of an out-of-band NMS with the primary purpose of detecting and blocking ongoing Domain Name System (DNS) exfiltration attacks which are triggered by other hosts in the network. The secondary purpose of the NMS is to adjust the QoS policies of the other devices in the network manipulating the value of the DifferentiatedServices (DiffServ) field of the IP packets. The solution is implemented using the SharpPcap framework for automated packet processing with the desire to make this system open source for further development. An experimental setup was created using a wiretap for traffic duplication to an out-of-band monitoring device. The experimental work proves that the solution is effective in detecting dictionary-based DNS exfiltration attacks and is successful in blocking the attacker from the network.
Ferroelectric Schottky Barrier MOSFET as Analog Synapses for Neuromorphic Computing

In this paper, artificial synapses based on ferroelectric Schottky barrier MOSFETs (FE-SBFETs) are presented. The FE-SBFETs are fabricated with Si doped Hf02 ferroelectric layers scaling down to a gate length of 40 nm and using single crystalline NiSi2 contacts on siliconon-insulator (SOI) substrates. The ferroelectric polarization switching dynamics gradually modulate the Schottky barriers, thus programming the device conductance by applying stimulus on the gate to imitate the short- and long-term plasticity of biological synapse, including excitatory/inhibitory postsynaptic current (EPSC/IPSC), paired-pulse facilitation (PPF) and long-term potentiation (LTP) and long-term depression (LTD) behaviors. Based on a multilayer perceptron artificial neural networks, a high recognition accuracy (83.6%) is achieved for handwritten digits. These findings demonstrate FE-SBFET has high potential as an ideal synaptic component for the future intelligent neuromorphic network.
Electricity Trading in Local Sector-coupled Energy Communities

Local energy communities are an essential part of the future decarbonized energy systems as they promise to increase participation of end-users and provide incentives for an increased utilization of local variable renewable energy and flexibility potentials. Decarbonizing the building sector will increase the use of sector-coupling technologies and introduce additional demands and flexibility potentials. Energy communities are often linked to local trading concepts or markets, but so far have excluded the effects and potentials of sector-coupled heat generation. In this work, electricity trading in communities with various technologies is formulated as a mixed complementarity problem to study their interaction and local prices. The investigation of a case study—a community with diverse end-users and a high share of renewable energy—reveals the benefits of energy communities, namely the reduction of total cost and CO2 emissions, as well as the increase of communal self-consumption. With the access to local electricity markets, the economic feasibility of combined heat and power units and heat pumps also increases. The community as a whole greatly benefits from heterogeneity of energy demand and generation characteristics of peers; and in a perfect market, each peer is also better-off participating. Therefore, the formation of local energy communities including markets should be inclusive and encourage participation of peers with sector-coupling technologies.
Capacitance-Based Voltage Regulation- and Reference-Free Temperature-to-Digital Converter down to 0.3 V and 2.5 nW for Direct Harvesting

A temperature-to-digital converter for direct harvesting is proposed, where no DC-DC conversion is required between the DC harvester and the system. Temperature-induced capacitance differences are read out through ring oscillator frequency. PVT variations are suppressed by the differential nature of the temperature sensor architecture, whereas mismatch is compensated via a self-referenced calibration procedure. No reference, regulator, digital post-processing and digital direct temperature readout is needed to retain true-nW and low-$V_{min}$ operation. A 180-nm testchip tested across corner wafers shows 7bit ENOB, 2.5-4.5nW from solar and thermal direct harvesting at 0.3-0.5 V, as representative of a very wide range of environmental conditions.
Convexity versus average convexity: potential, pmas, the shapley value and simple games

Different propietats of convex and average convex games are developed in terms of unanimity coordinates. A set of necessary and sufficient conditions for the core of balanced games containing the Shapley value are given. Connections between convexity and average convexity, the Shapley value, the potential and population monotonic allocation schemes (PMAS) are shown. Average convex simple games are described. Finally, some differences between convexity and average convexity in relation with other well-known solution concepts of cooperative games are studied.
Surgical Tool Segmentation on the JIGSAWS Dataset for Autonomous Image-based Skill Assessment

In the case of Minimally Invasive Surgery (MIS), the surgeon reaches the internal organs through small skin incisions, and the operating area is visualized by an endoscopic camera. MIS can be traditional (manually performed) or Robot-Assisted (RAMIS). While, the basics of these techniques are similar, the used instruments and endoscopic cameras can be significantly different. Semantic surgical tool segmentation in endoscopic images can be an important step toward pose estimation, task automation and skill assessment in MIS operations. The goal of automated skill assessment solutions is to replace the time-consuming experts’ opinion-based assessment techniques. The most used dataset for skill assessment is JIGSAWS that incorporates video and kinematic data. Tool segmentation in this dataset is challenged by different illumination conditions, low resolution, lack of ground truth labelling and the different background, while the usual training images are made in front of organs. In this work, Deep Neural Network and traditional image processing solutions were examined, aiming to segment the surgical tools to derive information for automated technical skill assessment in the case of RAMIS. We tested four different Deep Neural Network architectures (UNet, TernausNet-11, TernausNet-16, Linknet-34). First, pre-trained models were examined then we trained these models with JIGSAWS dataset as well. The best overall result was achieved with TernausNet-11 trained on JIGSAWS with Intersection over Union (IoU)= 70. 96, Dice Coefficient = 79. 91 and Accuracy = 97. 3S. But Unet and LinkNet34 could also achieve good results on videos of specific surgical tasks. Moreover, an efficient ground truth labelling method was proposed for the JIGSAWS dataset with the help of the Optical Flow algorithm. The ground truth dataset and source codes are publicly available on Github (https://github.conydorapapp96/SurgToo1SegJIGSAWS.git).