Reviewerly/exHarmony huggingface dataset:
A Machine Learning-Driven Analysis of Phantom E911 Calls

Phantom Enhanced 911 (E911) calls are automatically generated 2 second calls, are a serious concern on cellular networks, and consume critical resources. As networks become increasingly complex, detecting and troubleshooting the causes of phantom E911 calls is becoming increasingly difficult. In this paper machine learning (ML) tools are used to analyze anonymized call detail record data collected by a major US telecom network service provider. The data is carefully pre-processed and encoded using an efficient encoding method. Classification algorithms K Nearest Neighbors (KNN) and Decision Trees (DTs) are then implemented to study correlations between device and network level features and a mobile device's ability to initiate phantom calls. Based on the results, this work also suggests certain policy changes for network operators that may decrease the high volume of phantom E911 calls or alleviate the pressure of phantom E911 calls on a cellular network.
New Developments in Chinese Studies of Contemporary Inductive Logic

Contemporary Chinese studies in inductive logic have long revolved around the unfolding of a philosophical investigation into Hume’s problem. Led by research in probabilistic logic, the principal content of contemporary Chinese logic consists of research into Pascalean and non-Pascalean probabilistic logic, precise and imprecise probabilistic logic, pure inductive logic and material inductive logic. A newly arisen trend in the development of Chinese inductive logic is represented by the research on causal inference, which came into vogue within the field of artificial intelligence (AI) in the last few years. Consequently, the future developmental tendency will probably gravitate towards the new paradigms that will emerge from the mutual contest and interactions between probabilistic logic and causal logic.
A Closed-Form Estimate for the Correlation Coefficient of Noise-Type Radars

The target detection performance of radars with noise-like waveforms, including both standard noise radars and quantum two-mode squeezing radars, is dictated by a correlation coefficient which specifies how similar the received signal is to the template used for matched filtering. Previous work has shown that an estimator for this correlation coefficient can be used as a detector for target detection. However, this estimator requires the use of an expensive numerical minimization. Inspired by certain mathematical similarities between noise radar and two-channel synthetic aperture radar, we propose a simplified estimator which leads to the same detection performance in simulations. However, this estimator is significantly less computationally expensive, being a closed-form expression that can easily be calculated from radar data.
Considering ‘technically possible’ alternative meanings for data traces found during a digital forensic examination

As part of a digital forensic examination, a practitioner may identify data traces that they believe to be relevant to their inquiry and seek to interpret their meaning, forming a primary investigative hypothesis. In addition, practitioners should also consider whether any traces could mean something else. This work discusses the need for practitioners to consider ‘technically possible’ alternative meanings (TPAMs) as a standard component of their interpretive process. It is proposed that, when considering whether any TPAMs exist in addition to the practitioner’s primary investigative hypothesis regarding a data trace, the practitioner’s position may be expressed in one of six ways – ‘the six categories of TPAM’, based upon the available objective support related to or present within their case, from which the TPAM is derived. These six categories are proposed in order to help a practitioner effectively communicate their reasoning for offering a TPAM in regards to any data trace found during an investigation and are defined and discussed.
WITHDRAWN: Robust Least Squares Projection Twin Support Vector Machine and its Sparse Solution

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.
AI-Based Cyberbullying Prevention in 5G Networks

Children and teenagers that have been victims of bullying can possibly suffer its psychological effects for a lifetime. With the increase of online social media, cyberbullying incidents have been increased as well. In this paper, the authors discuss how they can detect cyberbullying with AI techniques, using term frequency-inverse document frequency. The authors label messages as benign or bully. The authors want their method of cyberbullying detection to be privacy-preserving, such that the subscribers' benign messages should not be revealed to the operator. Moreover, the operator labels subscribers as normal, bully, and victim. The operator utilizes policy control in 5G networks to protect victims of cyberbullying from harmful traffic.
Structure-Based Analysis of Different Categories of Cyberbullying in Dynamic Social Network

Cyberbullying is the online fight between individuals or groups, and it can be viewed like harassment, rumor, denigration, exclusion, etc. Social networks are the main source of cyberbullying as various types of users interact with each other through text, audio, video, and images. One set of users uses the social media for the benefit of the whole society and the other set of users uses the social media for destructive purpose in the form of spreading rumors, harassment or to threaten others, etc., which is also called anomalous behavior. This article worked to detect the anomalous patterns using an exponential function and then proceeds to find the category of cyberbullying to which user belongs using subtractive clustering and fuzzy c-means clustering. The identification of category helps to find the extent to which these messages are harmful and based on which the culprit is apprehended or entrapped. State-of-the-art studies are focused on the detection of cyberbullying but this article captured different categories of cyberbullying.
Research on Methods of Physical Aided Education Based on Deep Learning

In order to better meet the training needs of sports and improve the standardization of sports training, an openpose-based sports posture estimation method and assisted training system are proposed, combining the basic structure and principle of openpose network. Firstly, the human posture estimation algorithm is constructed by combining with the openpose network; secondly, the overall framework, specific operation process, image acquisition, posture estimation, and other modules of the sports assistance system are designed in detail; finally, the openpose posture estimation method constructed above is validated. The results show that the value of the loss function obtained by the algorithm gradually stabilizes after 250 iterations. By using the COCO dataset as the training base and comparing it with the standard posture, it is found that the algorithm can correctly identify different badminton action postures, and the recognition rate can reach up to 94%. This shows that the algorithm is feasible and can be used for posture estimation and training of badminton sports movements.
Research on the development and application of operation and maintenance management platform based on digital monitoring for highway tunnels

With the improvement of construction technology and the rapid development of society and economy, highway tunnels show the features, such as large dimensions and lengths in structure, rapidly increased traffic volume, large vehicles for transportation of dangerous goods, and increased moving speed, leading to the occasional serious incidents, which poses great threat to the operational safety of highway tunnels. Taking the northern extended tunnel of Yingbin Avenue in Hangzhou area as an example, this study integrates the technologies such as the Internet of Things based sensor monitoring and artificial intelligence to obtain and analyze the characteristics of tunnel traffic flow, aiming to study the characteristics of traffic environment changes when traffic accidents occur in the tunnel. The results show that: 1) the operation and maintenance management platform based on digital monitoring for the tunnel can handle the problems regarding monitoring and characterization in terms of multiple facilities, multiple sources of information, and multiple types of vehicle behaviors in the complex environment of the tunnel, to ensure the interconnection and communication among the various devices in the tunnel; 2) the system can realize one-click emergency response with respect to incident disposal, which can shorten the disposal time and can select, add, start and stop the plan, to form a log storage, therefore, it can provide the necessary technical support for the safe, efficient, and environmentally-friendly operation of traffics in the tunnel.
Auto-Detection of Human Factor Contents on Social Media Posts Using Word2vec and Long Short-Term Memory (LSTM)

The threat posed by cyberbullying to the mental health in our society cannot be overemphasized. Victims of this menace are reported to have suffered poor academic performance, depression, and suicidal thoughts. There is need to find an efficient and effective solution to this problem within the academic environment. In this research, one of the popular deep learning models—long short-term memory (LSTM)—known for its optimized performance in training sequential data was combined with Word2Vec embedding technique to create a model trained for classifying the content of social media post as containing cyberbullying content or otherwise. The result was observed to have shown improvements in its performance with respect to accuracy in the classification task with over 80% of the test dataset correctly classified as against the existing model with about 74.9% accuracy.
Automatic Nuclear Atypia Scoring of Breast Cancer Pathological Images Based on Deep Residual Network and Meta-decision Tree

Breast cancer has been the most commonly occurred cancer worldwide, and the patient survival is closely related to correct diagnosis and early treatment. Nuclear atypia is the only qualitative index for histological grading of invasive breast cancer, with the lowest consistency of manual diagnosis. In this paper, a model based on deep learning and aggregation strategy is proposed to automatically score the nuclear atypia of breast tumors, in order to provide an efficient and accurate auxiliary diagnosis basis for pathologists. We employed the public dataset ICPR2014 ATYPIA. Base classifiers based on deep residual network were trained separately on the images of each patient at three magnifications (×10, ×20, ×40), and then utilized the meta-decision tree method to combine the outputs of the three classifiers to obtain the diagnosis of breast tumor nuclear atypia for each patient. The classification accuracy of our proposed model reaches 0.8800, and the sensitivity reaches 0.8645. Compared with other algorithms, it is highly competitive in terms of accuracy and efficiency, so it has great potential to be applied in clinical work.
A Low-Power Current Readout for 77K Cryo-CMOS Quantum Systems with In-Circuit Model Extraction and Embedded Leakage-Based Temperature Monitoring

A low-power cryo-CMOS interface operating at 77 K, including the pulse generator and the current readout, is implemented for charge-based quantum systems. To compensate the inaccuracy caused by amateur cryo-CMOS models at 77K, in-circuit model extraction is proposed in this work. Key model parameters such as the transconductance and the threshold voltage are successfully extracted at 77 K by configuring the readout circuits in the extraction mode. The pulse generator and the current readout are implemented in TSMC 40 nm CMOS 1P10M process. A leakage-based temperature sensor is also embedded on the same chip. The supply voltage is 0.9V, and the overall power consumption is about 150 µW.
Bridging the Domain Gap for Stance Detection for the Zulu Language

Misinformation has become a major concern in recent last years given its spread across our information sources. In the past years, many NLP tasks have been introduced in this area, with some systems reaching good results on English language datasets. Existing AI based approaches for fighting misinformation in literature suggest automatic stance detection as an integral first step to success. Our paper aims at utilizing this progress made for English to transfers that knowledge into other languages, which is a non-trivial task due to the domain gap between English and the target languages. We propose a black-box non-intrusive method that utilizes techniques from Domain Adaptation to reduce the domain gap, without requiring any human expertise in the target language, by leveraging low-quality data in both a supervised and unsupervised manner. This allows us to rapidly achieve similar results for stance detection for the Zulu language, the target language in this work, as are found for English. We also provide a stance detection dataset in the Zulu language. Our experimental results show that by leveraging English datasets and machine translation we can increase performances on both English data along with other languages.
Performance Optimization for MLP Accelerators using ILP-Based On-Chip Weight Allocation Strategy

It is generally impossible to store all weights into an MLP accelerator because of limited on-chip SRAM capacity. However, the performance can still be improved if a portion of weights are allocated in faster SRAM. In this paper, we first present an analytical method for performance evaluation under different weight allocation approaches. We then propose an ILP-based on-chip weight allocation strategy that can maximize the overall performance. Experiment results show that the proposed strategy constantly outperforms several trivial heuristic methods over a large set of various MLP models, MLP accelerator configurations, and on-chip SRAM capacities.
Hardware-Friendly Progressive Pruning Framework for CNN Model Compression using Universal Pattern Sets

Pattern-based weight pruning on CNNs has been proven an effective model reduction technique. In this paper, we first present how to select hardware-friendly pruning pattern sets that are universal to various models. We then propose a progressive pruning framework, which produces more globally optimized outcomes. Moreover, to the best of our knowledge, this is the first paper dealing with the pruning issue of the first and also the most sensitive layer of a CNN model through a two-staged pruning strategy. Experiment results show that the proposed framework achieves 2.25x/2x computation/model reduction while minimizing the accuracy loss.
Full-Span Log-Linear Model and Fast Learning Algorithm

The full-span log-linear (FSLL) model introduced in this letter is considered an nth order Boltzmann machine, where n is the number of all variables in the target system. Let X=(X0,…,Xn-1) be finite discrete random variables that can take |X|=|X0|…|Xn-1| different values. The FSLL model has |X|-1 parameters and can represent arbitrary positive distributions of X. The FSLL model is a highest-order Boltzmann machine; nevertheless, we can compute the dual parameter of the model distribution, which plays important roles in exponential families in O(|X|log|X|) time. Furthermore, using properties of the dual parameters of the FSLL model, we can construct an efficient learning algorithm. The FSLL model is limited to small probabilistic models up to |X|≈225; however, in this problem domain, the FSLL model flexibly fits various true distributions underlying the training data without any hyperparameter tuning. The experiments showed that the FSLL successfully learned six training data sets such that |X|=220 within 1 minute with a laptop PC.
Knowledge modeling of power grids with SysMD

We give an overview of the Systems modeling language SysMD and demonstrate its use in the modeling of innovative power grid systems. The language SysMD allows us to structure and document the knowledge about system-wide dependencies in a formal, textual way linked with its related documentation in the Markdown (MD) format. SysMD models can be evaluated locally or uploaded to a cloud backend, where they are used as a knowledge-base for continuous verification. We describe syntax and semantics of the language and demonstrate its application for evaluating power grid scenarios of the future.
Analytic velocity obstacle for efficient collision avoidance computation and a comparison study with sampling and optimization based approaches

Abstract Velocity obstacle (VO) is one of the popular reactive navigation algorithms for the path planning of autonomous agents. The collision-free property can be guaranteed if the agent is able to choose a velocity outside the VO region under the assumption that obstacles maintain a constant velocity within the control cycle time of the agent. To date, the selection of the optimal velocity relies on either sampling or optimization approaches. The sampling approach can maintain the same amount of computation cost but may miss feasible solutions under collision risks with an insufficient number of samples. The optimization approach such as the linear programming demands convexity of the constraints in the velocity space which may not be satisfied considering non-holonomic agents. In addition, the algorithm has varying computation demands depending on the navigation situation. This paper proposes an analytic approach for choosing a candidate velocity rather than relying on sampling or optimization approaches. The analytic approach can significantly reduce computation costs without sacrificing performance. Agents with both holonomic and non-holonomic constraints are considered to demonstrate the performance and efficiency of the proposed approach. Extensive comparison studies with static, non-reactive, and reactive moving obstacles demonstrate that the analytical VO is computationally much more efficient than the optimization-based approach and performs better than the sampling-based approach. Major video results of this paper can be accessed online.
Versatile Hardware Framework for Elliptic Curve Cryptography

We propose versatile hardware framework for ECC. The framework supports arithmetic operations over P-256, Ed25519 and Curve25519 curves, enabling easy implementation of various ECC algorithms. Framework finds its application area e.g. in FIDO2 attestation or in nowadays rapidly expanding field of hardware wallets. As the design is intended to be ASIC-ready, we designed it to be area efficient. Hardware units are reused for calculations in several finite fields, and some of them are superior to previously designed circuits in terms of time-area product. The framework implements several attack countermeasures. It enables implementation of certain countermeasures even in later stages of design. The design was validated on SoC FPGA.
<i>Hate Speech in Asia and Europe: Beyond Hate and Fear</i>, Myungkoo Kang, Marie-Orange Rivé-Lasan, Wooja Kim and Philippa Hall (eds) (2020)

Review of: Hate Speech in Asia and Europe: Beyond Hate and Fear , Myungkoo Kang, Marie-Orange Rivé-Lasan, Wooja Kim and Philippa Hall (eds) (2020) Oxford: Routledge, 206 pp., ISBN 978-0-36720-900-1, h/bk, £120.00
Object Segmentation Based on the Integration of Adaptive K-means and GrabCut Algorithm

Image segmentation is a well-known topic in image processing, and it remains as a hotspot and focal point for image processing techniques. In this paper, we propose a hybrid segmentation method, combining an Adaptive K-Means clustering algorithm and a novel automatic GrabCut segmentation algorithm to improve the performance of the object segmentation from the scene image. The proposed method is divided into six steps: Firstly, the RGB image normalization step is introduced to eliminate light variation and remove bright and shaded regions. Secondly, RGB colour space is converted to L&#x20F0;a&#x20F0;b&#x20F0; colour space to maintain accurate colour balance. Thirdly, we propose a novel automatic GrabCut segmentation algorithm to eliminate user interaction and make the segmentation process faster. Fourthly, the Adaptive K-Means clustering algorithm and the proposed automatic GrabCut segmentation algorithm are combined to segment foreground objects from the background. Fifthly, the shape refinement step is used to eliminate occlusion, noise, and smear issues from the segmented image. Finally, morphological operations are carried out to enhance the segmentation performance. The performance of the hybrid segmentation method is assessed using the MSRA benchmark dataset.
Informatization of National Public Service Fitness in Constructing a Smart City Using Big Data

The public service system of national fitness is the condition and guarantee for the public to participate in various fitness activities. In order to effectively integrate the existing national fitness service resources and to improve the efficiency of the public service supply, this paper aims to study the informatization of building national fitness public services in smart city under the background of big data. Firstly, we build a public service information platform of national fitness in the smart city, consisting of different modules, and then describe each module in detail. Secondly, we consider different types of fitness projects and establish the database structure model of network resources. Thirdly, we obtain the multitree cascade system of the national fitness and the heterogeneous data model of the national fitness benefit index by using high-dimensional statistical analysis method. Combined with the national fitness behavior, data mining, and support vector machine (SVM), the objective function of data mining statistical decision-making is established. Finally, the well-known particle swarm optimization (PSO) method is used to optimize the target parameters so as to realize the national fitness, big data mining, and feature analysis. The simulation results show that the proposed model has superior performance, as it can realize heterogeneous data mining of the national fitness benefit index. In addition, we quantitatively analyze the promoting effect of the national fitness on the physical quality and health level of the public.
Forming an investigative opinion in digital forensics

Abstract As we now see digital evidence play a role in many investigative scenarios, it is imperative that those seeking to rely upon it as part of criminal justice processes can do so, absent any concern regarding its validity. Interpreting the meaning of digital data and its potential value to a criminal inquiry as part of a digital forensic examination is a complex and multifaceted process requiring the practitioner to possess the relevant knowledge, experience, and insight needed to determine the case‐significance of a given data trace accurately. Erroneously interpreted data that is communicated to a client and subsequently relied upon can have far‐reaching consequences for all those involved in the investigative process. This work discusses the process of forming investigative opinions in digital forensic science examinations, what this means in practice, and the ways in which it can be achieved. Focus will be given to the process of forming an investigative opinion when underpinned through the reconstruction and testing of a suspect system/setup, with a formal three‐stage methodology for doing this outlined. This article is categorized under: Digital and Multimedia Science &gt; Cybercrime Investigation