Miracle-dz/UniFAR-FTUs huggingface dataset:
DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations

Drug combination therapies are superior to monotherapy for cancer treatment in many ways when addressing tumor heterogeneity issue. For wet-lab experiment, screening out novel synergistic drug pairs is challenging due to the enormous searching space of possible drug pairs. Thus, computational methods have been developed to predict drug pairs with potential synergistic function. Notwithstanding the success of current models, the power of generalization to other datasets as wells as understanding of mechanism for chemical-chemical interaction or chemical-sample interaction are lack of study, hindering current algorithms from real application. In this paper, we proposed a deep neural model termed DTSyn (Dual Transformer model for drug pair Synergy prediction) based on multi-head attention mechanism to identify novel drug combinations. We designed a fine-granularity transformer for capturing chemical substructure-gene and gene-gene associations and a coarse-granularity transformer for extracting chemical-chemical and chemical-cell line interactions. DTSyn achieves highest Receiver operating characteristic area under curve (ROC AUC) of 0.73, 0.78. 0.82 and 0.81 on four different cross validation tasks, outperforming all competing methods. Further, DTSyn achieved best True Positive Rate (TPR) over five independent datasets. The ablation study showed that both transformer blocks contributed to the performance of DTSyn. In addition, DTSyn can extract interactions among chemicals and cell lines, which may represent the mechanisms of drug action. Thus, we envision our model a valuable tool to prioritize synergistic drug pairs by utilizing chemicals and transcriptome data.
Warmer temperatures result in maladaptive learning of sexual preferences

The impact of learning ability and sexual selection on the climate and biodiversity crisis are currently unclear. Using the African butterfly Bicyclus anynana, which shows strong phenotypic plasticity (i.e., polyphenism) in response to temperature, we tested whether learning affects mate preferences under climate warming. We first modelled climate warming scenarios and then showed experimentally that as temperature becomes an unreliable cue to the onset of the dry season, adult butterflies displayed the wet season rather than the dry season form. Experienced females that were exposed to different male seasonal phenotypes during sexual maturation changed sexual preferences. Female fertilization success was reversed for naive compared to experienced females, likely reducing female fitness following climate warming. Our results emphasize the importance of sexual selection, learning, and their fitness consequences for understanding (mal)adaptation of natural populations to climate warming.
Case Study: Predicting Future Forex Prices Using MLP and LSTM Models

This study compiles 10 years of past daily closing prices for numerous trading assets, and economic reports. The data will be used to train, test, and validate a variety of ANN and LSTM models. This paper introduces a learning window that trains the network using a predefined number of days in the past to predict several days' closing prices in the future. A search grid algorithm is used to select the optimal hyperparameters for the networks. The MLP network managed to achieve an accuracy of above 80% when 30 days of the previous closing price were used as input to the network.
Augmentation Method of Test Data for Path Coverage based on K-means Clustering

Regression testing is the most costly testing method. The purpose of test data augmentation method is to make full use of the original test cases to augment the test data with higher path coverage and reduce the cost of regression testing. K-means algorithm is used to cluster the test data, and the proximity was measured according to Hamming distance, so that the test data assigned to the same cluster have higher path similarity. Evaluate the intra-cluster data according to the statement coverage information, select the better data to form the initial population, use the correlation between the test data crossing path and the target path to design the fitness function, and evolve to generate the augmented data. Experimental results show that compared with similar methods, the proposed method improves the path coverage by 2.19 ~ 6.62%, and the fault detection rate by 2.84 ~ 6.58%.
The Wasserstein Distance Using QAOA: A Quantum Augmented Approach to Topological Data Analysis

This paper examines the implementation of Topological Data Analysis methods based on Persistent Homology to meet the requirements of the telecommunication industry. Persistent Homology based methods are especially useful in detecting anomalies in time series data and show good prospects of being useful in network alarm systems. Of crucial importance to this method is a metric called the Wasserstein Distance, which measures how much two Persistence Diagrams differ from one another. This metric can be formulated as a minimum weight maximum matching problem on a bipartite graph. We here solve the combinatorial optimization problem of finding the Wasserstein Distance by applying the Quantum Approximate Optimization Algorithm (QAOA) using gate-based quantum computing methods. This technique can then be applied to detect anomalies in time series datasets involving network traffic/throughput data in telecommunication systems. The methodology stands to provide a significant technological advantage to service providers who adopt this, once practical gate-based quantum computers become ubiquitous.
Scorpio: an Automated Penetration Testing Tool and Its Integration with a Cyber Range

Executing cyber attacks attribution experiments for training defenders is an important usage of cyber ranges. During the experiment, attack simulation process needs manual operation of highly skilled personnel, but the procedure is labor intensive and human could cause errors, an attack simulation tool for cyber ranges is needed. We propose Scorpio, an automated penetration testing tool to simulate an attacker's behavior with seamless scanning and automated exploit process. And for integrate with cyber ranges, we find a method for virtual machines in a cyber range to communicate with the manage server without breaking the network isolation, with this method, visualization mechanism is also supported. We conducted an experiment with Scorpio in a range for showing the whole automated attack simulation and visualization process. Result shows that, with Scorpio equipped, the manual operation of professional penetration testers for attack simulation could be replaced, that is cost-efficient and time-saving.
A Novel Cluster Prediction Approach Based on Locality-Sensitive Hashing for Fuzzy Clustering of Categorical Data

This paper addresses the problem of fuzzy clustering for categorical data. During the last two decades, many attempts have been made to extend the k-means algorithm, making it applicable to clustering for categorical data, due to its simplicity and efficiency. However, as k-means-like algorithms are local optimization methods, their clustering results are highly sensitive to initialization. In this paper, we propose to use Locality-Sensitive Hashing (LSH) to reduce the categorical data dimensions and predict the initial fuzzy clusters in low-dimensional space. Different from the existing cluster initialization optimization methods that aim to create only crisp initial clusters, the proposed method aims at predicting ‘high quality’ fuzzy clusters at the initialization step before proceeding in the k-means-like fashion. The numerical results show that the proposed method yields relatively accurate results on 16 UCI datasets and outperforms all other related approaches in terms of both crisp and fuzzy clustering effectiveness.
SWELLING VOLTAGE ORGANIZER ACROSS CRITICAL LOADS WITH STIPULATE RESPONSES

Within this paper, the main focus would be to compare the potency of anchorman current control using STATCOM against distributed current control using several ESs. It includes an electrical source serving as the primary power company along with a separate controllable source of energy to emulate a good intermittent renewable. The novel idea of electric spring (ES) continues to be suggested as a good way of distributed current control. The concept would be to regulate the current over the critical loads while allowing the noncritical (NC) impedance-type loads to alter their ability consumption and therefore lead to demand-side response too. Alternatively, the ES could be permitted to inject a current with any phase position with regards to the current requiring exchange of both active and reactive power using the system that is possible through incorporation of one's storage in to the ES. At occasions of generation shortfall or network constraint, the current from the NC loads is reduced while controlling the voltages over the C loads. This addresses the generation shortfall or network constraint as well as facilitates better current regulating the C loads through manipulation from the supply impedance current drop. Generally, it's simpler to manage the current at locations that are electrically farther from a stiff current source which within this situation will be the upstream MV/HV network.
Smart Web Dictionary using OCR

Language and linguistics play an important part in our daily lives more than we realize. Language is a system of communication which we use to express our thoughts and ideas. As the times have progressed language and communication has played a vital role in shaping our societies allowing us to work together and coexist with each otherDue to diverse nature of the geography and climate of ur planet it has resulted in varying language systems each vastly different from one another making it harder for a person knowing one language to understand and another person knowing another language this leads to miscommunication. Miscommunication can lead to misunderstandings and complicate problems which could be solved simply. Hence understanding each other and communicating are extremely important especially in this modern day and age. Our pipeline approach which uses a combination of CNN, NLP and iNLTK and yielding a global accuracy of 94% Hence its essential people understand each others language and linguistic for smooth communication and dialogue which can lead to solving of disputes and exchange of ideas
Service Level Agreement with Differentiated Reliability Requirements : Efficiency of Application in Communication Networks and On-Board Systems

The reliability of the operation of fiber-optic networks and communication systems, i.e. the ability of a communication network to perform specified functions for transmitting information under specified conditions and for a specified time is the main requirement of customers for a telecommunications operator. The customer’s needs and expectations regarding the quality and reliability of the network and communication systems determine the requirements. They may differ not only qualitatively and quantitatively, but also depend on time. Customers of telecommunications companies may need certain periods of time during which additional stability of communication is required. The rest of the time, the standard level of communication stability is sufficient for them. The overall framework of the SLA usually provides increased reliability for the entire duration of the contract concluded between the customer and the operator, which is sometimes redundant and expensive for both the customer and the operator. The SLA framework is considered, which allows customers to independently determine the critical time slots in which the operator undertakes to fulfill their requirements for communication stability. This structure is called the SLA with differentiated requirements (SLAd). Critical Time Slots (CTS) correspond to periods when it is especially important for the customer to have reliable and uninterrupted communication. Therefore, connections during these periods are reserved using cross-duplication of customer data streams. The article evaluates the effectiveness of the implementation of a service level agreement (SLA) with time-differentiated reliability requirements. Net present value (NPV) is considered as a measure of efficiency; to take into account the probabilistic characteristics, the availability factor is applied. The results of calculations are presented and their brief analysis is given.
Analysis on the Prediction of Sales using Various Machine Learning Testing Algorithms

The amount of data created is so large that we are unable to process it all on our own. For this goal, a variety of machine learning approaches have been developed. The objective is to perform prediction of sales for super markets using different regression and boosting approaches, and figuring out which algorithm is suitable for the job. Traditional sales and marketing goals do not assist organizations keep up with the pace of a competitive market because they are carried out without any knowledge about customers' purchasing behaviors. The advancements in machine learning have had a tremendous impact on the world of sales and marketing. Item weight, item fat content, item visibility, item kind, item MRP, outlet establishment year, outlet size, and outlet location type are all included in the prediction.
Trajectory optimization of interceptor missile based on hybrid genetic algorithm

Sequence quadratic programming and hybrid genetic algorithm are used to optimize the interceptor trajectory. The plane motion model of interceptor projectile considering curvature and rotation of the earth is established. According to the requirements of subsistent trajectory restraint, the trajectory problem was established with maximum terminal velocity, minimum miss distance and minimum whole heat quantity. The results show that the hybrid genetic algorithm is better than the genetic algorithm. The hybrid genetic algorithm is used to optimize the trajectory of aircraft interceptor projectiles. There is no dependence on initial value of traditional optimization methods, and the optimal value can be searched globally. By using hybrid genetic algorithm and genetic algorithm, the normal overload of interceptor missile channel is much more stable, and it is easy to maneuver interceptor missile control.
Lorentz System Based Pseudo-Random Sequences in PAPR Reduction and Information Security Enhancement of the OFDM Communication

The article presents the results of the influence of the statistical characteristics of the pseudorandom sequence generator on improving the energy efficiency and information security of the communication system with OFDM. The influence of the modulation depth of a discrete-nonlinear Lorentz system subject to quasi-resonant influences on the autocorrelation function and on the probability of passing the universal set of computer security tests FIPS-140-2 is considered. The paper describes the results of the influence of the modulation depth on the peak factor of the signals of the considered communication system with OFDM and the entropy of the transmitted information. The results presented in the work can be used in the design of the modern communication systems with OFDM in order to increase the energy efficiency and information security of the transmitted information.
On the Estimation of the ICI in OFDM in Conditions of Multipath Doppler Spreading and Carrier Frequencies Offset

In this paper, we consider the negative simultaneous effect of fast Relay fading in the channel and mutual "frequency shifts of subcarrier" subchannels between the transmitting and receiving sides of the channel, causing a violation of orthogonality between the signals of individual subchannels of the OFDM system. Violation of orthogonality is manifested by the appearance of a mutual interference signal on the receiving side of the channel in each OFDM subchannel. The paper evaluates the joint effect of fast fading and frequency shifts on the magnitude of transient interference between subchannels (ICI).
Chromatic Dispersion Monitoring for Long-haul 28 GBaud PDM-QPSK Optical Signal based on CNN

A CNN-based neural network for chromatic dispersion monitoring method is proposed in this paper, named OptInception. It adopts the parallel structure for convolutional layers with different sizes. Experimentally, the mean absolute error and root-mean-squared error of estimation on 28 GBaud PDM-QPSK optical signals are below 0.1% with less than 32,000 ps/nm of residual chromatic dispersion. The effects of OSNR and signal power on estimation performance are investigated in experiment as well. In general, the proposed method presents high accuracy, robustness and adaptive characteristic in practice.
Nonlinear Distortions Compensation of Amplitude Phase Shift Keying Signals with Different Orders in Communication Channels

This paper is dedicated to solving the task of linearization an amplified tract in order to reduce the nonlinear distortions level of amplitude phase shift keying signals with different orders. Wiener model was used as power amplifier (PA). Digital predistortion method based on a memory polynomial model was used as a linearization method. The studies were carried out in MATLAB/Simulink visual programming environment. Evaluation of the efficiency of the realized compensation algorithm was based on the following parameters: Adjacent Channel Power Ratio (ACPR), Error Vector Magnitude (EVM), Amplitude Modulation/Amplitude Modulation (AM/AM) and Amplitude Modulation/Phase Modulation (AM/PM) characteristics, Bit Error Rate (BER) dependence on bit signal-to-noise ratio (SNR). Thanks to the inclusion of Digital Predistorter (DPD) in the channel, we were able to achieve significant improvements in all parameters for each modulation case.
Case Recommendation Algorithm of Discipline Inspection and Supervision based on Knowledge Graph

In view of work problems in the field of disciplinary and supervision such as the large number of cases, the complexity of the content of the documents, the large workload of data compilation, and the low efficiency of conviction and sentencing, intelligent recommendation of similar cases is an important measure to alleviate this situation. In order to improve the accuracy and interpretability of similar case recommendation, based on the constructed laws and regulations knowledge graph of disciplinary inspection and supervision and the disciplinary case dataset, an intelligent recommendation algorithm for similar cases that integrates the characteristics of the legal knowledge graph of disciplinary inspection and supervision is proposed. The method constructs a keyword dictionary of legal elements that map the keywords in the case text with the entities in the knowledge graph, and merges the knowledge features of the matched legal entities with the feature vectors of the original case text so that enhances the representation of legal knowledge characteristics corresponding to disciplinary. Experiments with several machine learning methods and deep learning methods show that the proposed method has a certain improvement in F-score.
Development of a Microstrip Coupler with Reduced Dimensions

The article is aimed at solving the problem of miniaturization of power dividers that perform their work at low frequencies. The design of a coupler with reduced dimensions due to the installation of artificial transmission lines instead of conventional segments is described. Such artificial lines have a similar set of frequency characteristics, which allows them to be installed in devices and at the same time receive minimal performance losses. The results of not only modeling, but also the experiment confirmed that the proposed design provides power division in equal proportions at a given center frequency, but in a smaller frequency band, which is a disadvantage of miniaturization. The proposed design occupies 4 times less area on the substrate.
Improving Performance of Log Anomaly Detection With Semantic and Time Features Based on BiLSTM-Attention

In recent years, with the increase in the scale and complexity of system software, how to effectively capture, analyze and locate the abnormal behavior generated during system operation has increasingly become a recognized problem in the software testing field. Traditional white-box-based anomaly detection methods need to provide system source code and cannot effectively detect abnormal behaviors that occur when the program is running. Black-box-based anomaly detection methods rely on test cases and have low code coverage. Anomaly detection based on the logs generated during system operation can alleviate the abovementioned problems. However, the existing system log-based abnormality detection method mainly extracts log template characteristics, and cannot effectively determine the logical and temporal abnormalities related to the log. Therefore, in order to detect anomalies in the log sequence more comprehensively, this paper proposes an anomaly detection method based on BiLSTM. This method combines the semantic and temporal characteristics of the log for modeling. In terms of semantics, the Bert natural language processing model is used to extract the contextual semantic information of the logs; in terms of time, the log time interval characteristics are extracted based on the three potential relationships of the logs. In addition, the proposed method also adds an attention mechanism to balance feature weights. Finally, we evaluate our method in experiments on two real datasets, HDFS and OpenStack. The experimental results show that our method has a great improvement in accuracy and recall.
Research on two-dimensional Sum-difference Network of phased array antenna

In order to meet the requirements of microwave radiation antenna array for its feeding system, a new high-performance two-dimensional Sum-difference Network is designed. The two-dimensional Sum-difference Network consists of a sum and difference device, a Wilkinson power divider and a vertical transition of strip-line to SSMP. The sum port has the characteristics of equal amplitude and phase with port 24 outputs; The pitch difference output port has the characteristics that the amplitude of the upper part and the lower part is equal and the phase difference is 180 degrees; The azimuth difference output port has the characteristics that the amplitude of the left half and the right half is equal and the phase difference is 180 degrees. The simulation optimization design and processing test of the network in K-band are carried out. The simulation and test results agree well. The test results show that the network has good two-dimensional Sum-difference Network characteristics, the reflection coefficient is better than 13dB, and the insertion loss is better than 19.4db.
Artificial Intelligence based Architecture and Implementation of Wireless Network

The explosion of 5G network data continues to advance the evolution of air interface technology and network architecture, which enables the network to support more frequency bands, antennas, more diversified frame structures and physical layer control procedures. Traditional operation and maintenance patterns of human intervention or threshold setting control are no longer reliable, thus the automated and intelligent management mechanisms are proposed to improve the network resource utilization and user experience. This article summarizes the research of implementations, algorithms, and optimizations of AI firstly. Then through the research of various international standards, different AI networked architecture design of the core network, RAN and O-RAN are introduced. The paper highlights UE trajectory prediction and energy-saving use cases of the wireless network, the details mainly focus on the comparative study of algorithms and signaling realization. Last but not the least, the intelligent network platform that has been applied in the industry is introduced, which enriches the ideas for future development of providing more reliable networks to vertical industries.