Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Legal Judgment Prediction Based on Bert and LSTM-CNN Fusion Model

In recent years, artificial intelligence has been advancing and intelligence in the judicial field has been developing, and legal Judgment prediction based on legal documents has become a hot research direction. The task of legal verdict prediction is to analyze the factual descriptions of real cases and mine the textual features in the factual descriptions. Most of the present-day legal verdict prediction uses deep learning methods, but its accuracy rate still needs to be improved. In this paper, we propose a fusion model based on Bert and LSTM-CNN for prediction, and use part of the datasets from the "China Law Research Cup" Judicial Artificial Intelligence Challenge to filter and conduct experiments. The experimental results show that compared with other common models, the proposed model has more accurate prediction results and achieves better standards in model evaluation criteria.
Design of a Single Chip, Single Core, ZVS Buck Converter in MHz Domain

Venturing into MHz switching frequency promotes the integration of inductive DC-DC converter into mobile devices. Combining GaN power switch with low-loss ferrite core materials opens up new possibilities for high-frequency operations. In this work, we investigate the design of a single core inductor and single chip half-bridge switch for ZVS buck converter. Design guidelines are accompanied by preliminary performance assessment via simulations. To demonstrate the performance of our device, we fabricated and tested a 20 W prototype converting 6 − 12 V to 4 V at 2 MHz switching frequency. The prototype achieves η > 90 % at Pout > 15 W and power density of 9.26 W/m2 with < 5 mm thickness.
A High-Efficient Wireless Power Receiver for Hybrid Energy-Harvesting Sources

This article presents a power-efficient hybrid energy-harvesting system that scavenges energy from solar, vibration, and radio frequency (RF) energy sources and converts into regulated output dc voltage courtesy buck–boost dc–dc converter. The proposed architecture incorporates tetra-paths for maintaining high power conversion efficiency (PCE) over extended input power range (−10 to 30 dBm). A time-domain maximum power point tracking technique is proposed for solar energy harvester. A high-efficiency full wave rectifier is designed for triboelectric rectifier. A 5.8-GHz RF-dc converter with adaptive matching is proposed to increase dynamic range of the input power. The chip is implemented in 0.18-µm bipolar-CMOS-DMOS process. The die area of the chip is 2.8 mm × 5.0 mm, including the pads. The solar and triboelectric energy harvesters achieve a measured peak efficiencies of 75.4% and 92.3%, respectively. The high-power 5.8-GHz RF-dc converter achieves measured PCE of 76% at 30 dBm input power. The low-power dual-band RF-dc converter operating at 900 MHz and 2.4 GHz obtains measured peak efficiencies of 73% and 71.9% at 0 dBm input, respectively. The buck–boost dc–dc converter employed in the proposed hybrid energy harvesting system achieves a measured peak PCE of 94.5%.
Hybrid PSO-GWO Optimization Based Parameter Extraction Method Applied to GaN Devices

In this research, four different equivalent circuit models that characterize the substrate/buffer loading effect for GaN HEMT on Si substrate have been investigated. Z-parameter measurements of an open de-embedded structure have been used to characterize this effect in 16×200 μm GaN HEMT on Si. An optimization technique based on hybridization between two meta-heuristic techniques which are Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) has been developed to extract the models' elements. Each of those techniques has been tested separately to evaluate the performance of the proposed technique. The validation process of the extracted elements is done by comparing the simulated and the measured data of the model. The accuracy and the rate of convergence of the extraction results are considered as evaluation criteria. The proposed PSO-GWO technique shows a very good agreement with previous research results, which accordingly validates the applicability of this technique for small- and large-signal modeling of transistors.
Research on the Spatial Pattern of Living Service Industry in Kunming City Based on POI Data

Based on the POI data of Kunming urban area obtained by Python, the spatial pattern characteristics of life service industry in Kunming urban area are studied by using kernel density analysis, standard deviation ellipse direction analysis and spatial autocorrelation. The results show that : (1) Overall, the spatial distribution of the residential service industry in the main urban area of Kunming is uneven, and the overall spatial distribution pattern of the "old urban area" as the center, contiguous clusters and multi-center development is presented. (2) There is also an uneven distribution of the life service industry in the "old city". The distribution density in the northeast corner of the "old city", Panlong District, is significantly higher than that in other parts, and the distribution density in the north is higher than that in the south, and the Guandu district in the south is obviously the lowest. (3) Kunming's life service industries all show a "northwest-southeast" trend distribution, which is mainly due to the fact that Kunming's administrative district is basically northwest-southeast distribution, and the degree of dispersion of various industries varies significantly. (4) Transportation, population and urban planning are the main factors affecting the spatial distribution of life service industry in Kunming.
Augmenting Search-based Techniques with Static Synthesis-based Input Generation

Automated test generation helps programmers to test their software with minimal intervention. Automated test generation tools produce a set of program inputs that maximize the possible execution paths, presented as a test coverage metric. Proposed approaches fall within three main approaches. Search-based methods work on any program by randomly searching for inputs that maximize coverage. Heuristic-based methods can be used to have better performance than pure random-search. Constraint-based methods use symbolic execution to restrict the random inputs to those guaranteed to explore different paths. Despite making the execution slower and supporting very few programs, these methods are more efficient because the search space is vastly reduced. The third approach combines the previous two to support any program and takes advantage of the space search reduction when able, at the cost of slower execution. We propose a fourth approach that also refines search-based with constraints. However, instead of requiring a slower symbolic execution when measuring coverage, constraints are statically extracted from the source code before the search procedure occurs. Our approach supports all programs (as in Search-Based) and reduces the search-space (as in Constraint-based methods). The innovation is that static analysis occurs only once and, despite being less exact that symbolic execution, it can significantly reduce the execution cost in every coverage measurement. This paper introduces this approach, describes how it can be implemented and discusses its advantages and drawbacks.
Emotion Cause Extraction - A Review of Various Methods and Corpora

Emotion mining and analysis are increasingly becoming crucial with the amount of text generated. Emotion cause is contemplated as one of the most vital emotion elements for extensive emotion analysis. An event usually triggers a corresponding feeling and thus, extracting it, for emotion analysis is pivotal to mine opinion and information retrieval. Emotion cause extraction aims to identify the reason or stimuli of an observed emotion. Recent years have seen a rise in research around emotion cause extraction. They are however still limited because of less availability of annotated data. This paper aims to present some significant research and methods of Emotion Cause extraction and the most used corpora of the past decade, along with a detailed review of their corpora, approach, and evaluation.
Stability of Neutral Stochastic Dynamic Systems driven by fBm with Markovian Switching

The stability of neutral stochastic dynamic systems driven by fractional Brownian motion (fBm) have received broad attention, because the problems are not only have played an important role in many ways such as communication equipment, engineering control, military technology, finance, etc, but also great theoretical significance. In this paper, we discuss the stability of neutral stochastic dynamic systems driven by fBm with Markovian Switching. A new set of sufficient conditions proving the mean square stability of neutral stochastic dynamic systems with Markovian Switching is derived by means of the Banach fixed point technique. Some well-known results are generalized and improved.
Resource Allocation for Low-Latency NOMA-V2X Networks Using Reinforcement Learning

With the development of the Internet of things (IoT), vehicle-to-everything (V2X) plays an essential role in wireless communication networks. Vehicular communications meet tremendous challenges in guaranteeing low-latency transmission for safety-critical information due to dynamic channels caused by high mobility. To handle the challenges, non-orthogonal multiple access (NOMA) has been considered as a promising candidate for future V2X networks. However, it is still an open issue on how to organize multiple transmission links with suitable resource allocation. In this paper, we investigate the problem of the resource allocation for the low-latency NOMA-integrated V2X (NOMA-V2X) communication networks. First, a cross-layer optimization problem is formulated to consider user scheduling and power allocation jointly while satisfying the quality-of-service (QoS) requirements, including the delay requirements, rate demands, and power constraints. To cope with the limited time-varying channel information, a machine learning based resource allocation algorithm is proposed to find solutions. Specifically, reinforcement learning is applied to learn the dynamic channel information for reducing the transmission delay. The numerical results indicate that our proposed algorithm can significantly reduce the system delay compared with other methods while satisfying the QoS requirements, so as to tackle the congestion issues for V2X communications.
Discovery of Hybrid Ensemble Models Resilient to Input Resolution Deterioration

Recent advances in deep learning (DL) and ensemble learning frameworks significantly improved performance of machine learning (ML) models in different scopes. However, severe data limitations and absence of relevant problems for transfer and meta learning can drastically reduce capabilities of these techniques. Previously we have shown that hybrid approaches leveraging domain-expert knowledge, boosting and DL could discover robust solutions in cases with very limited data availability. Similar challenges are encountered in applications with reduced data sampling rate and resolution due to technological and operational restrictions such as longterm data collection by wearable biomedical devices and fully autonomous robotic systems. Multi-scale features discovered by DL or other empirical ML techniques from high-resolution data could become much less effective or unstable when used with lower resolution inputs. Direct training on low-resolution data may result in the selection of oversimplified and biased model with suboptimal performance. Here we argue that models resilient to input resolution deterioration can be discovered using our previously proposed hybrid framework where boosting-like algorithms are used for effective utilization and enhancement of existing domain-expert models with further non-linear combination of boosted ensemble components via DL or other ML algorithms. Our approach is illustrated in the biomedical context using cardio data from www.physionet. org.
Cash for the Register? Capturing Rationales of Early COVID-19 Domain Registrations at Internet-scale

The COVID-19 pandemic introduced novel incentives for adversaries to exploit the state of turmoil. As we have witnessed with the increase in for instance phishing attacks and domain name registrations piggybacking the COVID-19 brand name. In this paper, we perform an analysis at Internet-scale of COVID-19 domain name registrations during the early stages of the virus’ spread, and investigate the rationales behind them. We leverage the DomainTools COVID-19 Threat List and additional measurements to analyze over 150,000 domains registered between January 1st 2020 and May 1st 2020. We identify two key rationales for covid-related domain registrations. Online marketing, by either redirecting traffic or hosting a commercial service on the domain, and domain parking, by registering domains containing popular COVID-19 keywords, presumably anticipating a profit when reselling the domain later on. We also highlight three public policy take-aways that can counteract this domain registration behavior.
GAN-LSTM Predictor for Failure Prognostics of Rolling Element Bearings

Failure prognostics is the process of predicting the remaining useful life (RUL) of machine components, which is vital for the predictive maintenance of industrial machinery. This paper presents a new deep learning approach for failure prognostics of rolling element bearings based on a Long Short-Term Memory (LSTM) predictor trained simultaneously within a Generative Adversarial Network (GAN) architecture. The LSTM predictor takes the current and past observations of a well-defined health index as an input, uses those to forecast the future degradation trajectory, and then derives the RUL. Our proposed approach has three unique features: (1) Defining the bearing failure threshold by adopting an International Organization of Standardization (ISO) standard, making the approach industry-relevant; (2) Employing a GAN-based data augmentation technique to improve the accuracy and robustness of RUL prediction in cases where the deep learning model has access to only a small amount of training data; (3) Integrating the training process of the LSTM predictor within the GAN architecture. A joint training approach is utilized to ensure that the LSTM predictor model learns both the original and artificially generated data to capture the degradation trajectories. We utilize a publicly available accelerated run-to-failure dataset of rolling element bearings to assess the performance of the proposed approach. Results of a five-fold cross-validation study show that the integration of the LSTM predictor with GAN helps to decrease the average RUL prediction error by 29% over a simple LSTM model without GAN implementation.
Computational Approach for Heart Disease Prediction using Machine Learning

Machine Learning is refers to the field of study that offers computers the capacity to find out the results while not being explicitly programmed. i.e., Machine learning includes algorithms that improve through occurrence. It is the subset of Artificial Intelligence. Finding out physiological information, environmental influences, and genetic factors permit practitioners to diagnose diseases early and effectively. Machine learning permits to make models that associate a broad range of variables with an illness. The analysis of clinical information allows to grasp the biological mechanisms, diseases and the way risk factors influence their development. However, there’s a scarcity of effective analysis tools to get hidden relationships and patterns in information. An automatic system in diagnosing would enhance medical potency and cut back prices. The appliance of machine learning within the field of diagnosing is increasing bit by bit. This may be accorded primarily to the development within the classification and recognition systems present in unwellness designation that is ready to supply information that aids doctors in early detection of deadly sickness and so, decreases the death rate of patients considerably.
Using an Instant Visual and Text Based Feedback Tool to Teach Path Finding Algorithms: A Concept

Methods of teaching path finding algorithms, based purely on programming, provide an additional challenge to students. Indeed many courses use graphs and other visualisations to aid students in grasping concepts quickly. Globally we are rapidly altering our teaching tools to suit the current blended or remote learning style due to the global COVID-19 pandemic. We propose a method that provides instant feedback showing how their programmed path finding algorithm works based upon games. The tool will provide feedback to the student about their code quality. Along with an element of gamification we aim to improve both initial understanding and further exploration into the algorithms taught. This tool aims to provide useful feedback to students in the absence of immediate laboratory support and gives students the flexibility to conduct laboratory worksheets outside of scheduled laboratory slots. Position: Software tools and teaching assistants heavily assist undergraduate students in learning how to program. In developing enhanced software tools, we can provide immediate feedback to learners. Thus, allowing them to gain an initial understanding of the algorithm before facilitated sessions. This further enriches their experience and learning during contact hours with teaching assistants.
Double microphone blind source separation based on improved joint diagonalization

Two improved methods are proposed for the problem of the underdetermined double microphone blind source separation of speech signals. Firstly, for the blind separation problem, the objective function maximization problem is equivalent to a joint diagonalization problem of a feature matrix that are simultaneously satisfied by a second-order delay correlation matrix and a fourth-order cumulant matrix. The joint approximate diagonalization algorithm of eigen matrix (JADE) has been improved to improve the separation accuracy. Further, for the underdetermined problem, that is, the number of microphones is less than the number of source signals, the improved joint diagonalization algorithm and the time-frequency (T-F) mask are combined to realize the underdetermined blind source separation based on the double microphones, and a variety of algorithms are compared with it for performance evaluation. The simulation results verify that the algorithm can accurately separate three sound sources from different orientations in a semi-anechoic chamber environment, and the extracted target speech has high clarity and intelligibility.
Exploiting Wi-Fi Signals for Human Activity Recognition

Human activity recognition is gaining much attention due to its role in medical alert systems, interactive video games, smart home systems, and many more. One of the main objectives of any human activity recognition system is recognizing the different human activities without affecting them. In this work, we utilize the information embedded in the overflowing Wi-Fi signal to determine which activity is being performed. A dataset obtained by observing 20 subjects performing activities in two different environments adds to this study’s credibility. The performed experiments show that an average activity recognition accuracy of 94% is possible.
CPW Fed Substrate Integrated Waveguide Packaged Slot Array Antenna for K band Automotive Radar Applications

This paper presents a high gain planar Substrate Integrated Waveguide slot antenna array which is fed through an optimized Coplanar Waveguide (CPW)-to-SIW transition. The design offers promising response for applications in K band automotive radars at 24 GHz. A back-to-back CPW to SIW transition has been analyzed followed by integration of the transition with the slot array antenna. The effect of aluminum packaging has also been simulated and it has been observed that the gain is slightly increased from 9.4 dBi to 10.9 dBi by adding aluminum walls.
A High Resolution Image Based Approach for Estimating the Canopy Cover of a Semi-Deciduous Brazilian Atlantic Forest Fragment

The Atlantic Forest in Brazil has been suffering natural and anthropic perturbations over the years, which has been impacting, qualitatively and quantitatively, with different degrees and intensities, its canopy cover. These perturbations can be caused by the selective cut of species, burning, natural death of trees, among several other factors, which act directly on the composition and the floristic diversity of fragments of the forest. The main motivation for conducting this work was to alert about the relevance of the Atlantic forest conservation and the need for a constant monitoring system to preserve what has been left of it. This can be partially achieved by permanently estimating its canopy cover. This paper describes the implementation and evaluation of the descriptors: energy, entropy, homogeneity, contrast and the sum of the high-frequency Discrete Fourier Transform (DFT), for estimating canopy cover based on High Resolution Camera (HRC) satellite images. The analyses carried out based on the results of the experiments showed that the energy descriptor was the best descriptor among those used and, comparatively to the others, obtained the higher correlation (r), determination (R) and significance (p). Based on the experiments, the energy descriptor presented an expressive potential to be used in processes for estimating the canopy cover based on satellite images, in large areas of the semi-deciduous forest.
Impact of Good Corporate Practices for Security of Digital Products on Global Cyber Stability

The exploitation of vulnerabilities in digital products and services is an essential component of sophisticated cyberattacks. Well-resourced adversaries increasingly exploit vulnerabilities for economic, political, or military gain, causing effects that destabilise cyberspace. Several multilateral and multi-stakeholder fora develop norms and principles to reduce such vulnerabilities. The main challenge lies in implementation. Under the Geneva Dialogue on Responsible Behaviour in Cyberspace11The Geneva Dialogue (https://genevadialogue.ch) is an initiative of the Swiss government and DiploFoundation. Partners of the Geneva Dialogue include Bi. Zone, Cisco, EnSign, FireEye Mandiant, Kaspersky, Huawei, Microsoft, UBS, PNG ICT Cluster, SICPA, Siemens, SwissRe, Tata Consultancy Services, VU, and Wisekey. Good corporate practices regarding the security of digital products and services, discussed in detail in this paper, have been developed through 15 group online meetings and continuous collaboration in the shared document, conducted over 7 months in 2020. 25 (Geneva Dialogue), a dozen leading global companies jointly developed a set of good corporate practices that translate high-level principles into day-to-day operations. This paper argues that these practices make cyberspace less vulnerable, and thus contribute to the implementation of global norms and principles. It further analyses key global norms and principles related to the security of digital products and services and the role of industry. It then presents the most relevant results of the ongoing work of the Geneva Dialogue, particularly good corporate practices related to security by design: threat modelling, supply chain security, development and deployment, and vulnerability processes. It discusses how these measures may reduce vulnerabilities, especially for smaller producers whose importance in the supply chain was elevated by COVID-19. It reflects on the need to turn good practices into baseline requirements to support market newcomers and regulators worldwide.