Miracle-dz/UniFAR-FTUs huggingface dataset:
Element-Aware Legal Judgment Prediction for Criminal Cases with Confusing Charges

Legal judgment prediction (LJP) plays an important role in legal assistant systems and aims to provide feasible judgment suggestions, including the charges, applicable law articles, and prison term. In practice, there exist many confusing charges which result in the decline of LJP performance of the existing works. To address this issue, we introduce the legal constitutive elements as the discriminative features to distinguish confusing charges. We propose an element-driven attentive neural network model, EDA-NN, which takes the textual description of a criminal case as the input and learns both element-free and element-aware case representations. Moreover, the element-driven attention mechanism is incorporated with the hierarchical sequence encoders, to generate crucial representations oriented to the legal constitutive elements at both the word and sentence levels. With the concatenation of element-free and element-aware representations, the EDA-NN can jointly predict the legal constitutive elements and judgment results. The experiments are conducted on a real-world dataset of criminal cases in mainland China. The experimental results demonstrate that our approach significantly outperforms all the baseline models on the LJP task for criminal cases with confusing cases.
Energy Management of a PV Energy System and a Plugged-in Electric Vehicle Based Micro-Grid Designed for Residential Applications

In this study, a small-scale AC micro-grid with a photovoltaic (PV) based energy source, a plug-in electric vehicle, and load shifting control is designed for residential applications. Five different operational scenarios have been developed and simulated in MATLAB/Simulink in order to show the effect of energy management on system efficiency. Moreover, monthly electricity bills, profit obtained from savings, cost of investment, and payback periods are calculated for each scenario. The results are compared to that of the standard scenario where the electricity demand of the house is supplied only by the conventional grid with no energy management. Consequently, it has been demonstrated that the electricity consumption from the grid decreases significantly and major savings in electricity bill, ranging from 32.11% to 71.23%, is achieved. In addition, the payback period can be reduced to the durations that change between 6.24 and 13.85 years. The results prove that PV based micro-grid concepts with plug-in electric vehicles require a proper energy management in order to maximize the benefits and minimize the run time of the investment.
Frequency control approach by controlling output power of renewable energy sources considering the priority of power adjustment

In recent years, Renewable Energy Sources (RESs) had developed very fast. However, the RESs cause frequency deviation sharply and decrease the reliability of the power system. It is difficult to suppress the fluctuation only by the thermal generator since the increasing of the RESs is serious. It is necessary to consider the output power of the RESs to adjust the power fluctuation. Then, the maximum power of the RESs has benefit for both economic and environment. Hence, this paper proposes a frequency control approach by controlling the output power of RESs, considering the priority of power adjustment. At the beginning, the power deviation would be compensated by the thermal generator, battery and demand response as usual. However, in case of the output power of the thermal generator decreased to the limit, the proposed control method would be executed. In the proposed way, the output power of the RESs was controlled based on the system frequency information. As a result, the frequency deviation was suppressed by improving the supply-demand balance.
Embedding Learning with Heterogeneous Event Sequence for Insider Threat Detection

The insider threat is one of the most significant cyber security threats that an organisation can be subject to. The recent research on insider threat detection mostly focuses on finding out anomalousness or abnormal changes from a series of behaviors such as logon, file usage and USB connection. Such behaviors can be described as time series set of different types of events, which we call heterogeneous event sequence. Due to the lack of intrinsic temporal relationship measures among events that contain multiple entities with categorical values, most existing work extracts action categorical values within the heterogeneous event to calculate abnormal scores for action sequences. Different from previous work, we synthetically consider multiple entities within the heterogeneous event and propose a principled and probabilistic model IPHE (Insider threat detection via Probabilistic pairwise interaction and Heterogeneous Event’s entity embedding) that models the likelihood of heterogeneous event sequence. The model embeds entities of heterogeneous events into a common latent space to preserve nonlinear relationships between heterogeneous temporal events. Then the likelihood of heterogeneous event sequence can be computed by the pairwise interactions of different entities of heterogeneous event according to entity embeddings. In particular, due to the imbalance of the occurrence rates of different types of events, we propose typewise learning rate for IPHE to adjust step size during model optimization procedure. Experiment results on the CMU-CERT insider threat dataset prove the effectiveness of our proposed approach over competitive baselines.
Analysis of upper limb rehabilitation using muscle mechanics: current and future perspectives using Mechanomyography signals

Muscular-skeletal system is responsible for the movement, force and posture of the human body. It is prone to damage due to frequent engagement in different tasks. Moreover, the loss of body limb limits its proper function. Muscle mechanics provides vital information on the current status of muscular-skeletal system. It plays an extensive role in the screening of the performance rate of muscles in different activities that they undergo. This paper intends to provide an understanding of muscle mechanics in terms of mechanomyography (MMG) signals in muscle function assessment and assistive technology for upper limb. Besides analyzing the application of MMG, signal acquisition, processing, contamination, associated characteristic parameters, as well as their application in functional rehabilitation and prosthesis control are also highlighted. Due to the noise generated from adjacent muscles and body movement, future projection of advanced rendering techniques are presented here. This has also been disclosed by a death of literature that has proven required accuracy on upper limb assistive technology, lost motor functions recovery and clinical practice.
Treatment of 33 cases of hyperuricemic nephropathy of phlegm-blood stasis by "Shizhi Decoction"

Objective To observe the clinical efficacy of "Shizhi Decoction" in treating hyperuricemic nephropathy.Methods Sixty-six subjects were randomly divided into treatment group and control group,and treated respectively with "Shizhi Decoction" and allopurinol.The clinical efficacy,blood uric acid,blood creatinine and blood routine were detected.Results After treatment,the total effective rate was 84.75% in the treatment group and 66.67% in the control group,with a difference between the two groups(P0.05). Blood uric acid was obviously decreased in both groups(P0.05),with no difference between the two groups(P0.05). In treatment group,the levels of 24-hour urinary protein,blood creatinine and hemoglobin were obviously improved,which were improved more obviously than those in the control group(P0.05);in control group,the blood uric acid and hemoglobin were improved(P0.05). Conclusion "Shizhi Decoction" is effective to treat hyperuricemic nephropathy.
Improved Autonomous Navigation Algorithms for Dual Satellite Formations with Online Observability Assessment

Over the next decade, more and more aerospace missions are expected to be implemented using distributed satellite systems. A variety of these missions put forward the requirement of determining the respective absolute orbit and relative orbit of satellite in a formation autonomously. This paper focuses on the method of estimating the absolute and relative positions of a dual satellite formation only based on measurements of the relative position vector from one satellite to the other. A new index which can reflect observability on line is proposed and applied to autonomous navigation filtering process. Improved UKF and CKF with on-line observability evaluation were designed and used as navigation algorithms for synchronous orbit estimation of the two satellites. Several simulations have been carried out, resulting that those improved navigation algorithms can obviously improve the accuracy of the autonomous navigation system for the dual satellite formation.
Within-day Rolling Optimization of Active Distribution Network with Wind and Photovoltaic Distributed Generation

Due to the randomness and uncertainty of wind and solar energy and the change of the active load, the power grid will face great challenges. After large-scale access to the distributed generations, the active distribution network must be optimized. This paper focuses on the within-day rolling optimization of active distribution network. Considering the influence of distribution generation and load characteristics and the randomness of wind and light in the active distribution network, an integrated model of within-day rolling optimization scheduling for active distribution network is established. The output of the distributed generations needs to be controlled. And the three objective functions of the minimum active network loss, the minimum active output reduction rate of controllable power supply and the lowest operating cost are also appropriately optimized. Then, the combination of the improved particle swarm optimization algorithm and the interior point method is beneficial to the calculation of large-scale nonlinear reactive power optimization. Finally, the feasibility of the algorithm and the rapidity of the optimization are verified on the IEEE118 node system. The superiority of the within-day rolling optimization and reactive power optimization algorithm is also confirmed.
Use of Machine Learning algorithms for designing efficient cyber security solutions

With rapidly increment of internet traffic, the importance of cyber security also increases significantly. Several areas like IP traffic classifications, detection of - intrusion, spam and malware becomes important to be considered. To overcomes the cyber security issues traditional techniques are not enough. It is essential to adapt the ongoing changes to keep security always up to date and It can't be denied that the web of Machine Learning keeps increasing in the digital world. The Cyber Security also adopt the Machine Learning to overcome the limitations of algorithms based on traditional rules and to make them more efficient with their integration with Artificial Intelligence. Although complete automation of analysis and detection is enticing goal, but the significant parts of cyber security can be improved. In this paper we will see how several algorithms of Machine Learning can be used to overcome the popular issues faced by cyber security.
The Design of 5kV Precision Digital AC Power Supply Based on DSP

High-voltage AC power supply is widely used in the field of insulation analysis of electrical equipment materials, but the insulation analysis of electrical equipment materials requires the use of high-voltage alternating current in a certain frequency band to better obtain the frequency domain characteristics and insulation characteristics of electrical equipment insulation materials. In this paper, a precision digital AC power supply is designed, whose frequency is adjustable from 50Hz to 400Hz, and the voltage is adjustable from 10V to 5000V. This power system is based on SPWM switching power supply. In hardware design, RCD absorption circuit is used to suppress peak voltage and surge current, multi-level LC filter is designed to achieve wide-band voltage filter output, Buck-Boost PFC circuit is used to obtain high quality DC bus voltage, and double closed-loop voltage and current feedback circuit is used to detect output voltage and current. In software design, repetitive control algorithm and PID control algorithm are used to ensure the accuracy and dynamic characteristics of the output AC voltage.
A Review On Recent Approaches to Recommendation System Model Using Distinct Product Features

Recently, area of recommendation systems have achieved great popularity due to its applicability in the various business domains. Recommendation is an information filtering problem along with some criteria on the basis of which usefulness of an item is decided for a particular user. Each of the criteria defines the separate class of the recommendation problem i.e. collaborative filtering, content-based filtering, and hybrid recommendation systems. In this article, we first discuss the applicability of the recommendation systems in multiple areas. We also discuss the importance of item features in making recommendations more relevant. Next, we discuss the baseline models for content based, collaborative based filtering.
Primary Liver Cancer Early Screening Based on Gradient Boosting Decision Tree and Support Vector Machine

Primary liver cancer has no obvious clinical symptoms and no effective screening method in the early stage, which leads that its first diagnosis is generally late. Early detection is considered to be the main measure to improve this situation. An early screening method utilizing clinical laboratory dataset was proposed. Firstly, gradient boosting decision tree (GBDT) was used for feature selection, and then two classification methods, support vector machine (SVM) and GBDT, were used for training and testing. The results show that the Kappa index reaches the almost perfect level and the accuracy is over 90%. This method can assist doctors to screen primary liver cancer early.
Personalized Vehicle Trajectory Prediction Based on Joint Time-Series Modeling for Connected Vehicles

Motion prediction for the leading vehicle is a critical task for connected autonomous vehicles. It provides a method to model the leading-following vehicle behavior and analysis their interactions. In this study, a joint time-series modeling approach for leading vehicle trajectory prediction considering different driving styles is proposed. The proposed method enables a precise and personalized trajectory prediction for the leading vehicle based on limited inter-vehicle communication signals, such as the vehicle speed and acceleration of the front vehicles. Three different driving styles are first recognized based on an unsupervised clustering algorithm, namely, Gaussian Mixture Model (GMM). The GMM generates a specific driving style for each vehicle based on the speed, acceleration, jerk, time, and space headway features of the leading vehicle. The feature importance of driving style recognition is also evaluated based on the Maximal Information Coefficient (MIC) algorithm. Then, a personalized joint time series modeling (JTSM) method based on the Long Short-Term Memory (LSTM) Recurrent Neural Network model (RNN) is proposed to predict the front vehicle trajectories. The JTSM contains a common LSTM layer and different fully connected regression layers for different driving styles. The proposed method is tested with the Next Generation Simulation (NGSIM) data on the US101, and I-80 highway dataset. The JTSM is tested for making predictions one to five seconds ahead. Results indicate that the proposed personalized JTSM approach shows a significant advantage over the baseline algorithms.
A high gain CMOS LNA for Ka-Band Communication System

This paper designs a low noise amplifier (LNA) for 5G communication. The proposed LNA is designed and simulated based on 65 nm CMOS process. In order to achieve a good input impedance matching, the Gm-boosting technique based on cross-coupled capacitor is employed in the first stage designing. The rest stages are common-source structure with neutralized capacitors to improve gain performance and make LNA stable in differential-mode. The LNA achieves a maximum gain of 30.3 dB at 24.9 GHz with a 3-dB bandwidth from 22.6 to 31.6 GHz and a minimum NF of 2.17 dB at 25.5 GHz.
Motion Control Curve of Snake-like Robot Based on Centroid Stability

To improve the stability of underwater motion of snake-like robot with uneven body mass distribution, based on the principle of centroid stability, by improving the Serpenoid curve, a motion control curve for snake-like robot with uneven body mass distribution is obtained. Firstly, the function of the improved motion control curve is obtained from the centroid equation of the snake-like robot in its motion process. Then, the lateral force of the snake-like robot is analyzed, and the condition equation of zero lateral resultant force is obtained. Finally, the uneven distribution of multi-rigid body and body mass is pointed out. The snake-like robot is simulated and compared with the Serpenoid curve and the improved motion curve. Finally, the underwater motion of the snake-like robot with multi-rigid body structure and uneven mass distribution is simulated, and the simulation results of the Serpenoid curve and the improved motion curve are compared. The results show that in the case of uneven mass distribution of the multi-rigid body snake-like robot, the improved motion control curve can significantly reduce the swing of the centroid perpendicular to the motion direction and improve the stability of the the snakelike robot during the underwater serpenite motion.
The Equivalent Circuit Model of a Four-Winding Inductive Filtering Transformer and its Filtering Characteristics

A four-winding inductive filtering transformer (FW-IFT) that is of a special filtering winding is designed according to the equivalent zero impedance principle of multi-winding transformer. The inductive filtering method with the series connection of the passive filter and the filtering winding of a FW-IFT is proposed to deal with the problem of reactive power and harmonics in a high-voltage distribution network. The equivalent circuit model of the FW-IFT is analyzed in detail from the inductive filtering condition. Then, the voltage equations are established, and harmonic suppression factor is introduced to analyze its filtering characteristics. The simulation and experimental results are provided to verify the feasibility of implementing the inductive filtering for the FW-IFT.
Implementation of a Dependable Smart Device in IoT Era

Internet of Things is built based on smart devices whose reliability is extremely variable. The present work proposes a set of solutions to the challenges of communication, connection management, memory, exceptions, synchronization and data persistence for the development of hardware devices that are part of the distributed systems over the Internet (IoT) and need to communicate with other components of the system. The paper describes the hardware used in the proposal, as well as the algorithms that make the device robust. In the end, the results obtained in real applications such as data persistence during communication outage and EEPROM/FLASH lifespan extension are shown.
Dynamic Multi-objective Evolutionary Algorithm with Fuzzy Weighted Borda Count Method

This paper introduces a novel dynamic multi-objective optimization algorithm. When a change occurs, the optimization algorithm has to give a true response to change. One of the most popular approaches of responding to change is to select some solutions randomly and remove them from the population. Since random selection results in deterioration of the algorithm's performance, the wise selection is crucial. Recently, the Borda count method was applied to find the most appropriate solutions to be eliminated from the population. However, basic Borda suffers from the same weight of information before and after the change. In this paper, we propose a new weighted Borda count so that its parameters are tuned by fuzzy rules. Mamdani fuzzy rules have been employed to tune the weights and distinguish between information before and after the change. Finally, the 'Change Effect' is proposed to calculate the effect of the change on the solutions. The performance of the proposed algorithm is tested on standard functions and is compared with recent algorithms.
Monolithic Heterogeneous Integration of BEOL Power Gating Transistors of Carbon Nanotube Networks with FEOL Si Ring Oscillator Circuits

High performance carbon nanotube (CNT) network transistors with on-resistance (Ron) of <250 Ω are successfully integrated as back-end-of-the-line (BEOL) power gating devices onto Si CMOS wafers manufactured using 28-nm process technology. When the power supply is connected through the BEOL CNT network header array, the front-end-of-the-line (FEOL) Si ring oscillators (ROs) achieve a similar quiescent current (IDDQ) and have the comparable active power (PACTIVE) consumption under the same operation frequency as compared to the operation without the power gating CNT transistors. The fabrication of CNT devices in the BEOL is verified to cause no performance degradation in the underlying FEOL Si CMOS devices. This study has successfully demonstrated heterogeneous integration of advanced Si logic circuits with low-cost and high-mobility CNT transistors in the BEOL fabricated at low, BEOL-compatible temperatures (250 °C).
Miniaturized on-chip lowpass filters with wide stopband and sharp roll-off

In this paper, a compact on chip lowpass filter with wide stopband and sharp roll-off using multi-stage pi-section low-pass cell circuit is presented. In order to obtain the characteristics of wide stopband and sharp roll-off for lowpass filters (LPFs), the pi-section low-pass cell circuit is analyzed and adopted, which is useful and convenient to produce transmission zeros for LPFs. Finally, a third-order LPF and a fourth-order LPF are manufactured and the measurement results show that the filter it has the −3 dB cut-off frequency at 5 GHz, while its insertion loss is less than 1 dB. In addition, the LPFs achieves more than 35 dB sideband suppression format form 6.3 GHz to 40 GHz. The LPFs size are very compact less than $0.41\ \text{mm}\times 1.1\ \text{mm} (0.02\lambda g\times 0.05\lambda g)$ including the pads. ($\lambda g$ is the guided wavelength at 5 GHz).
Bias Field Gradient Effects of Large Superconducting Quantul Inteference Device (SQUID) Arrays (SQAs)

In this investigation we explore the effects of nonuniform magnetic bias fields on the voltage-flux curve of large two-dimensional arrays of superconducting quantum interference devices (SQUIDs). The SQUID arrays (SQAs) contain 1020 SQUIDs modeled with parallel coupling in the x-direction and series coupling in the y-direction. Each array is composed of individual SQUID elements having non-uniform size loop areas, resulting in a non-uniform inductance parameter $\beta_{L}$, and a single “anti-peak” feature at zero magnetic field in the voltage-flux transfer function. We examine the changed induced in the profile of the transfer function by gradients in the magnetic field in both the x- and y-directions. The height, slope, and symmetry of the anti-peak feature is compared with that from the same SQA exposed to a uniform field.