Reviewerly/exHarmony huggingface dataset:
A novel interpretable machine learning algorithm to identify optimal parameter space for cancer growth

Recent years have seen an increase in the application of machine learning to the analysis of physical and biological systems, including cancer progression. A fundamental downside to these tools is that their complexity and nonlinearity makes it almost impossible to establish a deterministic, a priori relationship between their input and output, and thus their predictions are not wholly accountable. We begin with a series of proofs establishing that this holds even for the simplest possible model of a neural network; the effects of specific loss functions are explored more fully in Appendices. We return to first principles and consider how to construct a physics-inspired model of tumor growth without resorting to stochastic gradient descent or artificial nonlinearities. We derive an algorithm which explores the space of possible parameters in a model of tumor growth and identifies candidate equations much faster than a simulated annealing approach. We test this algorithm on synthetic tumor-growth trajectories and show that it can efficiently and reliably narrow down the area of parameter space where the correct values are located. This approach has the potential to greatly improve the speed and reliability with which patient-specific models of cancer growth can be identified in a clinical setting.
Social e-commerce consumer behaviour prediction model based on hierarchical polarisation characteristics

In this paper, a social e-commerce consumer behaviour prediction model based on layered polarisation characteristics is constructed. Consumer characteristics, product characteristics and interaction features are the three aspects used to construct the social electricity consumer behaviour prediction index system; with the improved locally linear embedding method for social electricity consumer behaviour predictors of high-dimensional data dimension used to reduce processing. According to the hierarchical polarisation characteristics, social electricity consumer behaviour prediction index weight calculation was based on the weight coefficient to construct the complete prediction model of social e-commerce consumer behaviour based on the characteristics of stratified polarisation. The simulation results show that the established model can predict the consumer behaviour of social e-commerce with high accuracy and short prediction time.
Effect of intensity-dependent couplings and the atomic dissipation on the interaction of two three-level atoms and two modes of radiation field

The effect of intensity-dependent coupling and the atomic dissipation on the interaction of a pair of three-level atoms in the $$\Xi$$ configuration with a bi-mode cavity field are studied. The solution of the proposed model is obtained in the resonance case. Some statistical quantities are discussed to discover the characteristics of the model. With different nonlinearity functions, the population inversion and the photon number dynamics, which display the phenomenon of collapses and revivals are discussed. The non-classical effects by examining the Mandel Q-parameter are considered. The amount of correlation between the two atoms and the field is estimated by analyzing the results of the fidelity. The entanglement periods between the parts of the quantum system, the collapse and revival periods were affected by the choice of coupling intensity, where the collapse periods improved with increasing the effect of the Rabi frequency and decreased when the effect of the Rabi frequency decreased.
Machine vs. deep learning comparision for developing an international sign language translator

This study aims to enable deaf and hard-of-hearing people to communicate with other individuals who know and do not know sign language. The mobile application was developed for video classification by using MediaPipe Library in the study. While doing this, considering the problems that deaf and hearing loss individuals face in Turkey and abroad modelling and training stages were carried out with the English language option. With the real-time translation feature added to the study individuals were provided with instant communication. In this way, communication problems experienced by hearing-impaired individuals will be greatly reduced. Machine learning and Deep learning concepts were investigated in the study. Model creation and training stages were carried out using VGG16, OpenCV, Pandas, Keras, and Os libraries. Due to the low success rate in the model created using VGG16, the MediaPipe library was used in the formation and training stages of the model. The reason for this is that, thanks to the solutions available in the MediaPipe library, it can normalise the coordinates in 3D by marking the regions to be detected in the human body. Being able to extract the coordinates independently of the background and body type in the videos in the dataset increases the success rate of the model in the formation and training stages. As a result of an experiment, the accuracy rate of the deep learning model is 85% and the application can be easily integrated with different languages. It is concluded that deep learning model is more accure than machine learning one and the communication problem faced by hearing-impaired individuals in many countries can be reduced easily.
Implementing Reinforcement Learning to Design a Game Bot

Artificial intelligence is the technology of the future, it has accomplished tasks that were assumed to be impossible earlier. It has very promising future aspects. It has proven to be useful in almost every field. Training an artificial intelligence model requires a lot of data. 90% of world’s total data has been generated over last two years only. Sometimes it is not feasible to have such huge amounts of data to train models. Here, comes in the situation “reinforcement learning” where the agent learns from the environment. Reinforcement learning has been applied to various fields like manufacturing, inventory management, delivery management, power systems, finance sectors and self-driving cars. Using the same procedure an artificial agent can be trained to perform desirable tasks. In this paper we discuss the implementation of reinforcement learning to develop a bot that plays a game just like humans.
Multilayer Perceptron Optimization Approaches for Detecting Spam on Social Media Based on Recursive Feature Elimination

Social Media Platforms are becoming increasingly popular around the world. Various social media platforms allow people to discuss their personal activities, opinions, and beliefs. Simultaneously, social spam is becoming increasingly prevalent and in a variety of styles across prominent social media platforms. As a result, spam identification seems to have become a significant and well-known issue. Users can use social media sites to stay virtually connected with their friends. Because of the growing popularity of social networking sites, users can now collect a vast amount of sensitive data about their connections. Twitter is the fastest-growing platform among a slew of others. Because of its popularity, several spammers have taken advantage of it by sending massive amounts of spam to legitimate users’ accounts. In this work, a model is proposed to detect the Spam users’ profiles on Twitter network. This work is built upon the user behavior-based and content-based features like Retweets, Mentions, Replies, Hashtags, and URLs. In this work, the three Multilayer Perceptron optimization approaches are used along with the Recursive Feature Elimination namely Stochastic Gradient Descent (SGD), Limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS), and Adam. Weka has been used for data pre-processing, and implemented the Recursive Feature Elimination with the three optimization approaches mentioned using sklearn in Python. For the proposed model performance evaluation, the performance metrics such as Accuracy, Precision, TP Rate, FP Rate and F-Measure are used.
Movie Recommendation Based on Fully Connected Neural Network with Matrix Factorization

Recommendation System (RS) is a subclass of information filtering system which tracks “rating” given by users to in-use items. Movie RS act as a tool to provide services to users. With the massive increase of data on internet, users face challenge in searching information and sometimes it is time-consuming process. Hence there is a need to enhance RS in a way to resolve basic problems faced by the users during the search. In this research a hybrid technique is proposed to build a movie recommendation system. In this system, matrix factorization is used to know, low dimensional embeddings of movies and users. Thus, movie and user embeddings are combined to examine ratings on unseen movies. Experiments are done on the MovieLens dataset to examine the performance of the proposed movie RS. The hybrid technique in the paper achieves a better performance than other non-hybrid recommendation methods.
Object Detection and Foreground Extraction in Thermal Images

The primary task of any machine learning algorithm is feature Extraction. Feature varies from one machine learning task to another Machine learning task. The feature may be texture information, shape, edges etc. Sometimes the selection of feature also influences the accuracy result of machine learning task. For example, detecting Horse and Zebra based on the shape feature is extremely difficult. One such important feature used in detecting objects is Histogram of Oriented Gradients (HOG) which contains both shape and texture information. Here we considered two different approaches like HOG+SVM (Support Vector Machines) which is a single stage detection and RCNN, F-RCNN and Mask RCNN which are two stage detectors for object detection and the performance of the algorithms are estimated on Mean Average Precision values and extracting Foreground information using Graphcut for HOG+SVM and segmentation in Mask-RCNN on CSIR-CSIO moving object Thermal dataset.
A Multilingual iChatbot for Voice Based Conversation

In the present pandemic crisis, chatbots are assisting consumers in resolving difficulties and consulting experts without the need for human intervention. Chatbot can serve them in analyzing their problem and delivering intelligent responses. In this paper, authors propose a multilingual iChatbot which allows users to communicate in six national languages and an international language. Along with text, it also supports questions through voice in their preferred language and receives responses in the same language, removing language as a barrier to communication. iChatbot can be utilized to answer relative questions of inquiring individuals in an interactive manner through voice command. A multilingual database utilizing bag of words algorithm and natural language processing techniques as a data processor is created. To make the chatbot intelligent feed forward deep neural network is used which enhances the overall performance of iChatbot for multilingual input. The proposed iChatbot outperforms the traditional method of chatbot and achieve accuracy more than 95%.
Parametric chamfer alignment based on mesh deformation

Alignment for natural images in unconstrained environment is a challenging task. Despite the success for complex deformation, existing feature-based methods may be confused in low-textured regions where features are insufficient, while pixel-based approaches may fail when color changes. In this paper, a parametric chamfer alignment method based on mesh warping model is proposed. Warped positions of mesh vertices are considered as parameters and estimated by optimizing an object function, which measures the chamfer distance of edges and the smoothness of warping. To distinguish the sharpness of pixels, edges are detected through K-means cluster and weights are attached to different levels of edge points. In addition, after the warping model is initialized by feature-based alignment, a growing technique for registering the vertices is presented. Experiment shows that the proposed method outperforms some state-of-the-arts on real data and can be applied in stitching ceramic sanitary ware images.
Teach me to play, gamer! Imitative learning in computer games via linguistic description of complex phenomena and decision trees

In this article, we present a new machine learning model by imitation based on the linguistic description of complex phenomena. The idea consists of, first, capturing the behaviour of human players by creating a computational perception network based on the execution traces of the games and, second, representing it using fuzzy logic (linguistic variables and if-then rules). From this knowledge, a set of data (dataset) is automatically created to generate a learning model based on decision trees. This model will be used later to automatically control the movements of a bot. The result is an artificial agent that mimics the human player. We have implemented, tested and evaluated this technology from two different points of view: performance by using classical metrics (accuracy, ROC area and PRC area) and believability by using a Turing test for trained bots. The results obtained are interesting and promising, showing that this method can be a good alternative to design and implement the behaviour of intelligent agents in video game development.
Segmentation of mammogram images using level set with Cuckoo search optimisation

Breast cancer is alone accounted for 30% of all new cancer diagnoses for women. Early identification of breast cancer is a key stage in the process of diagnosis and treatment. Segmenting of low contrast mammograms is not an easy task due to irregular mass form, speculated margins and variation in intensity levels. Most of the region-based technique works well for homogenous images but fails for intensity inhomogeneity. Multiphase level set segmentation is optimised by the cuckoo search approach for energy minimisation. The proposed multiphase level set with the cuckoo optimisation method performs well in segmenting homogeneous as well as inhomogeneous regions. The Chan Vese (CV) and the proposed method are applied to the Mammographic Image Analysis Society (MIAS) database to obtain the accurate contour of the masses. To validate the proposed approach using quantitative performance measures such as Jaccard coefficient (JC), dice coefficient (DC), Hausdorff distance (Hd), sensitivity, specificity and accuracy were used for the MIAS data set. The results indicate that the suggested method can successfully segment the breast mass in mammograms and more robust than the classical CV method.
More optimal relativistic quantum key distribution

Abstract A great challenge in the field of quantum cryptography is the design and implementation of optimal quantum key distribution (QKD) scheme. An optimal scheme in terms of security is the so-called relativistic quantum key distribution; it ensures the security of the system by using both quantum phenomena and relativity. However, the existing relativistic schemes have not demonstrated optimality in terms of efficiency and rate (including secret key rate). Here we report two point-to-point relativistic quantum key distribution schemes implemented with weak coherent pulses. Both schemes rely on high-dimensional quantum systems (phase and polarization encodings are utilized for establishing key bits). One of the proposed schemes is a system comprised of two sequentially connected interferometers, as the first (interferometer) controls the behavior of the second one. The other proposed scheme represents a setup of a classic relativistic QKD, but with slight modification. Both of the proposed schemes are characterized with high secret key rate. The latter scheme has the highest secret key rate of all the relativistic QKD protocols. However, the values for the secret key rate are relevant for distances of up to 150 km. The former scheme has lower secret key rate, but longer operating distances (the work could operate at distances of up to 320 km). Those values of rate are obtained without disturbing the security. Secret-key-rate comparison between distinct models is reported. The proposed relativistic models are compared to twin-field QKD protocols. Furthermore, the work proposes a metric for evaluating the optimality of a QKD. It is defined as a ratio between the secret key rate (at a given distance) and the amount of quantum resources (qubits) used in the QKD of concern. It is shown that one of the proposed schemes in this article is the most optimal relativistic key distribution and more optimal than the original twin-field. It is also verified that the proposed schemes excels the original twin-field in terms of secret key rate, but for short distances.
A practical radio frequency fingerprinting scheme for mobile phones identification

Radio frequency fingerprint (RFF) has attracted a remarkable surge of attention to wireless transmitter identification due to the inimitable hardware imperfection. However, most RFF-based schemes devised in controlled environments are unable to reach the claimed performance in real-world scenarios. This paper proposes a practical RFF identification method for mobile phones in Global System for Mobile (GSM) system. Specifically, the instantaneous amplitude of near-transient part in normal burst (NB) is regarded as RFF that extracted from up-link communications. In addition, an anomaly filtering and stacking (AFS) method is introduced to obtain stable RFF in Time Division Multiple Access (TDMA) system. Lastly, the impacts of frequency point and sending power variations on RFF are fully dissected in comparison experiments. Experiments on 10 mobile phones show that the proposed RFF scheme yields 99.17% True Acceptance Rate (TAR) in real wireless environments. Experiments also show that the varying transmission power decreases the accuracy of RFF identification.
Performance analysis for parallel grouping‐based labeled multi‐Bernoulli filter

The Labeled Multi-Bernoulli (LMB) filter exhibits “size effect”, namely, the number of hypotheses constituting the multi-object posterior increases super-exponentially with the size of the entire current label space. To alleviate this “size effect”, a promising solution, Parallel Grouping-based implementation of the LMB (PG-LMB) filter has been proposed in [1]. While it can achieve great improvement on computational efficiency through parallel LMB filtering within smaller size of groups, several important theoretical issues remain to be further addressed. On the basis of [1], this paper further exploits the rationale behind the PG-LMB filter and provides a comprehensive performance analysis. At the core of the PG-LMB filter is an Inter-Group Independence Approximation (IG-IA) of the exact posterior. One major contribution of this paper is to propose a principled grouping criterion based on which the upper bound of the approximation error between the IG-IA and the exact posterior can be established explicitly. Another contribution is to exploit the “low dimensional” compression property of the IG-IA, taking advantage of which, the PG-LMB filter has the potential to achieve performance gain under limited computational resources, benefiting its application in low-cost and miniaturized processing platforms.
Textual Resources of Chinese Nominal Groups

In this chapter, I will describe the Chinese nominal group in terms of another metafunction, namely textual metafunction. Textually speaking, a text can be regarded as a flow of information. From a textual point of view, nominal groups are important in that they present the identity information of the Thing in context. Through nominal groups, the identity information of the Thing can be presented either as new, which appears for the first time in a text, or as given, which is identifiable to the listeners.
The Use of Conductive Lycra Fabric in the Prototype Design of a Wearable Device to Monitor Physiological Signals

Wearable technology has become commonplace for the measurement of heart rate, steps taken, and monitoring exercise regimes. However, wearables can also be used to enable or enhance the lives of persons living with disabilities. This paper discusses the design of a wearable device that aims to facilitate the assessment of physiological signals using conductive Lycra fabric. The device will be applicable for daily use within diverse contexts including the evaluation of emotional experiences of children with Severe Motor and Communication Impairment and the detection of Obstructive Sleep Apnea in children with Down Syndrome. The Lycra fabric sensors are used to acquire electrocardiographic signals, galvanic skin response, and respiratory signals. Articulated design requirements include constraints related to the ability to fit children of all sizes, and meeting medical device standards and biocompatibility, and criteria related to low costs, comfortability, and maintainability. Upon prototyping and preliminary testing, this device was found to offer an affordable, comfortable, and accessible solution to the monitoring of physiological signals. Clinical Relevance- This research provides initial knowledge and momentum towards an affordable wearable device using conductive Lycra to effectively monitor and assess physiological signals in children with disabilities.
Automatic detection of falling of the elderly subject among his daily activities

Most elderly patients after falling, being not able to rise up or call for help, are particularly at risk of complication. This urges for the development of autonomous devices for earliest detection of falls. This paper is an overview of the current design approaches to autonomous fall detectors - sensors and algorithms- and a methodology to assess their efficiency. We then propose our fall sensor, which features high sensitivity (95%) and specificity (99%) on simulated falls in lab settings, and lower sensitivity (62.5%) in real settings in a group of 10 patients, with 8 falls detected over a period of 28 days.
Blockchained Federated Learning for Privacy and Security Preservation: Practical Example of Diagnosing Cerebellar Ataxia

Cerebellar ataxia (CA) refers to the incoordination of movements of the eyes, speech, trunk, and limbs caused by cerebellar dysfunction. Conventional machine learning (ML) utilizes centralised databases to train a model of diagnosing CA. Despite the high accuracy, these approaches raise privacy concern as participants' data revealed in the data centre. Federated learning is an effective distributed solution to exchange only the ML model weight rather than the raw data. However, FL is also vulnerable to network attacks from malicious devices. In this study, we depict the concept of blockchained FL with individual's validators. We simulate the proposed approach with real-world dataset collected from kinematic sensors of CA individuals with four geographically separated clinics. Experimental results show the blockchained FL maintains competitive accuracy of 89.30%, while preserving both privacy and security.