Reviewerly/exHarmony huggingface dataset:
Classifying Breast Density from Mammogram with Pretrained CNNs and Weighted Average Ensembles

We are currently experiencing a revolution in data production and artificial intelligence (AI) applications. Data are produced much faster than they can be consumed. Thus, there is an urgent need to develop AI algorithms for all aspects of modern life. Furthermore, the medical field is a fertile field in which to apply AI techniques. Breast cancer is one of the most common cancers and a leading cause of death around the world. Early detection is critical to treating the disease effectively. Breast density plays a significant role in determining the likelihood and risk of breast cancer. Breast density describes the amount of fibrous and glandular tissue compared with the amount of fatty tissue in the breast. Breast density is categorized using a system called the ACR BI-RADS. The ACR assigns breast density to one of four classes. In class A, breasts are almost entirely fatty. In class B, scattered areas of fibroglandular density appear in the breasts. In class C, the breasts are heterogeneously dense. In class D, the breasts are extremely dense. This paper applies pre-trained Convolutional Neural Network (CNN) on a local mammogram dataset to classify breast density. Several transfer learning models were tested on a dataset consisting of more than 800 mammogram screenings from King Abdulaziz Medical City (KAMC). Inception V3, EfficientNet 2B0, and Xception gave the highest accuracy for both four- and two-class classification. To enhance the accuracy of density classification, we applied weighted average ensembles, and performance was visibly improved. The overall accuracy of ACR classification with weighted average ensembles was 78.11%.
Distance Education at Universities during the COVID-19 Pandemic (on the Example of the Republic of Kazakhstan)

Today, there is hardly an area of social life that has not been affected by the consequences of the spread of COVID-19. The impact of the coronavirus pandemic on the higher education system varies from country to country and varies from region to region. Nevertheless, we can trace the general vector of these changes: the pandemic has put universities in difficult conditions, forcing them to adapt to current events as quickly as possible, spend significant funds on accelerated digitalization and often make decisions without taking into account the possible consequences. The pandemic negatively affected international cooperation in the field of education and science: international travel was canceled, exchange and academic mobility programs for students and academic staff were suspended, and many research cooperation programs were suspended.
Segment Routing with Digital Annealing

The growing demand in broadband and data networks is a tremendous driver for innovation and investment. Efficient usage of resources and reliable operations are key objectives for network management. Optimization plays a pivotal role to make the best decisions in limited time. To tackle such difficult and elaborate (typically NP-hard) computational problems the latest developments in Quantum Computing are coming into focus. Successes with models for road traffic, logistics or financial industry have already demonstrated the applicability of these new methods to real-world problems. In this paper we formulate a model for resource assignment for transporting data over networks. For applicability in existing network management environments the model was designed on top of Segment Routing (SR) concepts. The validity of the model is proven for different cost and reliability targets. Based on a Digital Annealer (DA) as quantum-inspired hardware and an appropriate decomposition approach realistic data sets of a tier-1 provider can be processed. The quality of the results is comparable to classical optimization methods while the new approaches outperform those in computation time and have potential for a higher number of demands. An end-to-end comparison of quadratic model creation and solving on large data sets versus a processing as linear integer problem is presented. This shows that quantum approaches and algorithms are not only a preparation for a more distant future of fully functional quantum computers but can generate business advantages even today.
Nerfels: Renderable Neural Codes for Improved Camera Pose Estimation

This paper presents a framework that combines traditional keypoint-based camera pose optimization with an invertible neural rendering mechanism. Our proposed 3D scene representation, Nerfels, is locally dense yet globally sparse. As opposed to existing invertible neural rendering systems which overfit a model to the entire scene, we adopt a feature-driven approach for representing scene-agnostic, local 3D patches with renderable codes. By modelling a scene only where local features are detected, our framework effectively generalizes to unseen local regions in the scene via an optimizable code conditioning mechanism in the neural renderer, all while maintaining the low memory footprint of a sparse 3D map representation. Our model can be incorporated to existing state-of-the-art hand-crafted and learned local feature pose estimators, yielding improved performance when evaluating on ScanNet for wide camera baseline scenarios.
Visualization of Football Tactics with Deep Learning Models

In the last several years, computer vision tasks involving visual identification and tracking have seen a rise in the usage of deep learning technologies in recent years. An extremely difficult but rewarding endeavor is identifying and following football players’ targets. This may be used to study football tactical visualization. Due to the similar appearance and frequent occlusion of targets in football video, traditional methods often can only segment targets such as players and balls in the image but cannot track them or can only track them for a short time. Based on the related research of computer vision and deep learning, using several cameras, this study develops a system that can properly monitor many targets in a football stadium for a lengthy period of time. The main research contents of this paper are as follows: (1) a CNN for target displacement prediction is proposed, which no longer relies on the previous linear motion model or quadratic motion model, so that the multitarget tracking algorithm can be applied to more scenes. (2) For the first time in a multitarget tracking algorithm, a continuous conditional random field is used to model the asymmetric nature of the target relationship. At the same time, the CNN for target displacement prediction can be cascaded with the continuous conditional random field for end-to-end training, which greatly reduces the training difficulty. The parameters of the experiment in this paper are simple, and comprehensive and systematic experiments verify the validity and correctness of this work from different aspects.
Forward-backward Transliteration of Punjabi Gurmukhi Script Using N-gram Language Model

Transliterating the text of a language to a foreign script is called forward transliteration and transliterating the text back to the original script is called backward transliteration. In this work, we perform both forward as well as backward transliteration on Punjabi. We transliterate Punjabi person names from Gurmukhi script to English Roman script and from English Roman script back to Gurmukhi script using n-gram language model. We used more than one million parallel entities of person names in Gurmukhi and Roman script as the training corpus. We generated English to Punjabi and Punjabi to English n-grams databases from the corpus. To get better results, we tried to create as long n-grams as possible ranging from bi-gram to 30-gram. Our n-grams database contains more than 10 million n-grams, with each n-gram having multiple mappings of the other script. The most challenging part is to find the mapping for the given n-gram from the parallel name entity while creating n-grams databases. As per the orthography rules, the same combination of letters may have different pronunciation, depending upon its location in the word. Therefore, we categorized n-grams into starting, middle, and ending n-grams and used them accordingly in the transliteration process. The transliteration process works like the merge sort. We start searching the longest possible n-gram in the database and split the string recursively until the match is found. The transliterated strings are merged back to form the final output. In English to Punjabi transliteration, we achieved 96% accuracy using gold standard and 99.14% accuracy using minimum edit distance. In Punjabi to English transliteration, the result showed 96.85% and 99.35% accuracy for the gold standard and minimum edit distance, respectively.
Exploring an Augmented Reality Serious Game for Motorized Wheelchair Control

This work describes an Augmented Reality Serious Game (ARSG) focused on facilitating the process of acquiring new skills in individuals with motor disabilities. In particular, this game aims to help them control a robotic wheelchair. A racing track was used as a game narrative, including restriction areas, static and dynamic objects, obstacles and various signs. A user study with 20 participants was conducted to compare different methods used to place virtual content on the real-world environment while the user interacts with the game and control the wheelchair in the physical space: C1 - motion tracking using cloud anchors; C2 - offline motion tracking. Results suggest condition C1 is more precise and robust, while condition C2 seems to be easier to configure.
Fall Detection with the Spatial-Temporal Correlation Encoded by a Sequence-to-Sequence Denoised GAN

Falling is a major cause of personal injury and accidental death worldwide, in particular for the elderly. For aged care, a falling alarm system is highly demanded so that medical aid can be obtained immediately when the fall accidents happen. Previous studies on fall detection lacked practical considerations to deal with real-world situations, including the camera's mounting angle, lighting differences between day and night, and the privacy protection for users. In our experiments, IR-depth images and thermal images were used as the input source for fall detection; as a result, detailed facial information is not captured by the system for privacy reasons, and it is invariant to the lighting conditions. Due to the different occurrence rates between fall accidents and other normal activities, supervised learning approaches may suffer from the problem of data imbalance in the training phase. Accordingly, in this study, anomaly detection is performed using unsupervised learning approaches so that the models were trained only with the normal cases while the fall accident was defined as an anomaly event. The proposed system takes sequential frames as the inputs to predict future frames based on a GAN structure, and it provides (1) multi-subject detection, (2) real-time fall detection triggered by motion, (3) a solution to the situation that subjects were occluded after falling, and (4) a denoising scheme for depth images. The experimental results show that the proposed system achieves the state-of-the-art performance and copes with the real-world cases successfully.
Fault-Injection Based Chosen-Plaintext Attacks on Multicycle AES Implementations

Hardware implementations of cryptographic algorithms offer significantly higher throughput on both encryption and decryption than their software counterparts. Advanced Encryption Standard (AES) is a widely used symmetric block cipher for data encryption. The most commonly used architecture for AES hardware implementations is the multicycle design, where each round uses the same hardware resource multiple times to increase area efficiency. In this paper, we successfully decouple the interdependency of multiple key bytes from the AES encryption. Thus, we solve each key byte separately with an overall attack complexity in O(28). Moreover, we uniquely determine each key byte through a chosen set of three plaintext-ciphertext pairs. We propose two novel chosen-plaintext attacks on multicycle AES implementations. Both attacks can eliminate the key diffusion from the MixColumns and Key Schedule modules. The first attack takes advantage of vulnerable AES implementations where an adversary can observe the output of each round. The second attack is based on fault injection, where a single fault on the completion-indicator register is sufficient to launch the attack. Because no faults are injected in the internal computations of AES, the current fault detection mechanisms are bypassed as no intermediate result has been altered. Lastly, we explore the theoretical aspect for the inherent property of our attacks.
Gamified mobile learning: effects on English learning in technical college students

This study developed a realistic, interactive English blockade-running game that integrates virtual environments and mobile devices to conduct a collaborative and competitive contest to promote English learning among technical college students. In addition, it also explored students’ learning effectiveness, perception of peer collaboration, and the use of grouping strategies for collaborative learning. This study adopted a quasi-experimental design and separated 120 participants into four groups (Baseline, Smartphone, Collaborative Learning, and Smartphone + Collaborative Learning). The procedure included the pretest, English blockade-running contest, peer collaboration perception questionnaire, focus interview, and the posttest at the end of the semester. Our findings in terms of learning effectiveness, the Collaborative Learning Group and the Smartphone + Collaborative Learning Group exhibited significant improvements in their scores. In terms of peer collaboration, we also found that the Smartphone + Collaborative Learning Group reported increased perceptions of collaboration in Take Action and Group Organization dimensions when compared with the Collaborative Learning Group. Regarding grouping strategies for collaborative learning, the Smartphone + Collaborative Learning Group used smartphones to ensure continuous communication between team members during both individual and collaborative blockades. Team members performed their respective duties, jointly participated in the contest, and worked together to overcome the challenges of the different blockades. The implications and research limitations are also discussed in this paper.
Making Crank-Powered Interactions: Methods, Demonstrators, Materials

We here present a series of design explorations focusing on batteryless interactions powered by hand cranking body movements. The explorations concern not only the matter of powering but also the use of human physical engagement for controlling interactive systems, as well as how to support designers in testing, exploring, and making such interactions. This pictorial illustrates contributions concerned with the embodied and electro-physical prototyping of such experiences, in addition to our efforts to facilitate such design work through new technical tools.
Aerial Chasing of a Dynamic Target in Complex Environments

Rapidly generating an optimal chasing motion of a drone to follow a dynamic target among obstacles is challenging due to numerical issues rising from multiple conflicting objectives and non-convex constraints. This study proposes to resolve the difficulties with a fast and reliable pipeline that incorporates 1) a target movement forecaster and 2) a chasing planner. They are based on a sample-and-check approach that consists of the generation of high-quality candidate primitives and the feasibility tests with a light computation load. We forecast the movement of the target by selecting an optimal prediction among a set of candidates built from past observations. Based on the prediction, we construct a set of prospective chasing trajectories which reduce the high-order derivatives, while maintaining the desired relative distance from the predicted target movement. Then, the candidate trajectories are tested on safety of the chaser and visibility toward the target without loose approximation of the constraints. The proposed algorithm is thoroughly evaluated in challenging scenarios involving dynamic obstacles. Also, the overall process from the target recognition to the chasing motion planning is implemented fully onboard on a drone, demonstrating real-world applicability.
The Emerging Role of Implementing Machine Learning in Food Recommendation for Chronic Kidney Diseases Using Correlation Analysis

Kidneys are vital organs in the human body, and their effective functioning determines life quality. Chronic kidney illness is a kind of nephrotic syndrome in which the kidneys’ capacity to cope normally steadily deteriorates and remains asymptomatic for a long period as the disease progresses. An early CKD detection would help the patient recover faster and easier. Using an artificial intelligence system that can effectively aid in CKD detection in time and suggest the required food nutrition for its treatment and recovery would reap immense benefits for healthcare professionals as well as the patient. ML is a part of AI technology that has been used for effective medical development. This technology helps physicians in the accurate diagnosis of kidney disease and helps in effective treatment prediction by recommending required nutrition. The present research relates to the use of ML in proper kidney disease diagnosis and food recommendations for treatment accordingly. A correlation analysis has been done in this research to observe the strength of ML using the effective finding for renal malfunctioning and identifying the best food products that could help in its treatment and recovery. IBM SPSS version 26 has been used for this research. The correlation analysis has been done to observe the impact of eight independent variables that are age, gender, blood sugar, serum albumin, creatinine, potassium, bacteria, and pus secretion on the two dependent variables that are the risk of CKD occurrence and ML accuracy. The results have exposed that the autonomous values consist of a strong positive correlation with the dependent variable ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>p</mi> <mo>&lt;</mo> <mn>0.005</mn> </math> ). The statistical significance values have proved that the dependent values are statistically significant (0.001). The value of ML accuracy at a 95% confidence level has been observed at 88.85%, and the CKD occurrence value is 86.95%. The results have proved that the ML algorithm detects the risk of CKD occurrence accurately in each stage via analyzing blood sugar, creatinine, and potassium levels. The result also shows that the risk of CKD enhances with an increase in age.
AttenSy-SNER: software knowledge entity extraction with syntactic features and semantic augmentation information

Abstract Software knowledge community contains a large scale of software knowledge entity information, complex structure and rich semantic correlations. It is significant to recognize and extract software knowledge entity from software knowledge community, as it has great impact on entity-centric tasks such as software knowledge graph construction, software document generation and expert recommendation. Since the texts of the software knowledge community are unstructured by user-generated texts, it is difficult to apply the traditional entity extraction method in the domain of the software knowledge community due to the problems of entity variation, entity sparsity, entity ambiguity, out-of-vocabulary (OOV) words and the lack of annotated data sets. This paper proposes a novel software knowledge entity extraction model, named AttenSy-SNER, which integrates syntactic features and semantic augmentation information, to extract fine-grained software knowledge entities from unstructured user-generated content. The input representation layer utilizes Bidirectional Encoder Representations from Transformers (BERT) model to extract the feature representation of the input sequence. The contextual coding layer leverages the Bidirectional Long Short-Term Memory (BiLSTM) network and Graph Convolutional Network (GCN) for contextual information and syntactic dependency information, and a semantic augmentation strategy based on attention mechanism is introduced to enrich the semantic feature representation of sequences as well. The tag decoding layer leverages Conditional Random Fields (CRF) to solve the dependency between the output tags and obtain the global optimal label sequence. The results of model comparison experiments show that the proposed model has better performance than the benchmark model in software engineering domain.
Real-Time Object Detection with Tensorflow Model Using Edge Computing Architecture

This paper presents the capturing of objects using Wi-Fi enabled modular esp32 camera and processes the captured stream of data using machine learning and computer vision techniques, then sends the processed data to the cloud, there are major cloud providers in the market who occupied more than 80&#x0025; of the global public market the cloud providers are Google Cloud, Amazon AWS, Microsoft Azure. Google Cloud Platform (GCP) is been our primary choice because of its good documentation availability, The Cloud IoT-Core Gateway, as well as a serverless cloud layer to store all of the data. The cloud functions help to trigger the notifications to the users when the cameras detect what we have trained the model. The Edge computing project uses an ESP32 With cameras as a device listener and a raspberry pi as an edge server which has an image classifier model trained with TensorFlow.
A new meta‐heuristic approach for load aware‐cost effective workflow scheduling

Abstract Workflow scheduling is an important way to manage the execution of a workflow. It introduces the concept of providing suitable resources to workflow tasks in order to finish workflow execution and meet the user's objectives. However, the problem becomes more complex when scheduling must balance two conflicting objectives, such as minimizing execution cost and maximizing load across all computing resources. A workflow has many interdependent tasks, and the cloud datacenter has many computing resources to execute the workflow. There can be an asymptotically infinite number of mappings of tasks‐to‐computing resources. Every mapping produces different execution costs with different workloads on computing resources. The main challenge for the researcher is to develop an intelligent scheduling algorithm to identify an optimal mapping that produces minimal execution cost with fair workload distribution on resources. We developed a novel meta‐heuristic algorithm named Investment‐Based Optimization (IBO) to identify an optimal mapping. The IBO algorithm was first tested on optimization benchmark functions and then simulated in CloudSim to see its performance for scheduling workflows. Finally, IBO was tested over Montage, Epigenomics, Sipht, and a sample workflow, and it was found that IBO reduces execution costs by 33%, 16%, 16.36%, and 20% with a fair workload distribution.