Reviewerly/exHarmony huggingface dataset:
Analysis of the Influence of Diffeomorphic Normalization in the Prediction of Stable VS Progressive MCI Conversion with Convolutional Neural Networks

We study the effect of the selection of diffeomorphic normalization in the performance of Spasov's deep-learning system for the problem of progressive MCI vs stable MCI discrimination.We considered different degrees of normalization (no, affine and non-rigid normalization) and two diffeomorphic registration methods (ANTS and BL PDE-LDDMM) with different image similarity metrics (SSD, NCC, and lNCC) yielding qualitatively different deformation models and quantitatively different degrees of registration accuracy.BL PDE-LDDMM NCC achieved the best performing accuracy with median values of 89%.Surprisingly, the accuracy of no and affine normalization was also among the highest, indicating that the deep-learning system is powerful enough to learn accurate models for pMCI vs sMCI discrimination without the need for normalization.However, the best sensitivity values were obtained by BL PDE-LDDMM SSD and NCC with median values of 97% and 94% while the sensitivity of the remaining methods stayed under 88%.
A Low-Cost Wearable Rehabilitation Device

Stroke or accidental disabilities has increased in number due to various reasons. Physiotherapeutic rehabilitation of any kind is a lengthy and expensive process. Moreover, due to the scarcity of physiotherapist and rehabilitation centers in developing countries like Bangladesh, makes it even more difficult for most patients to get due time support. In this paper, a cheaper alternative to conventional rehabilitation program is proposed with the use of a motion capture device that utilizes an IMU (Inertial Measurement Unit) sensor. This device is capable of recording movements and store the data for further analysis. This enables a patient to perform certain rehabilitation exercises at home without the need for an instructor to be physically present during the exercise to guide or asses the patient. The motion capture sensor used in this project, is an IMU with 6 D.O.F (Degrees of Freedom) to obtain patients' body part (upper limb in this prototype) rotations in 3 dimensions. The sensor is composed of a 3-axis accelerometer that measures the acceleration and force producing it. Also, a 3-axis gyroscope that helps to determine orientation by using Earth's gravity. The sensor data is coupled with an Arduino for data processing and storage. This device is very small, and the sensor accuracy is almost the same as expensive mobile phones. It shows 0.855 correlation with the similar data from Samsung A50 mobile phone. The cost of our first prototype is under 30 USD.
DiBA: n-Dimensional Bitslice Architecture for LSTM Implementation

A hardware architecture for the implementation of LSTM neural networks that can be sized to the specific size of the problem is proposed here. Implementation of an LSTM application requires iteration of multiplications, additions, and the activation functions that operate on the stream of data inputs. To handle the iterations, the concept of bitslicing is done to cascade enough slices for an optimum performance depending on the problem size. In order to avoid a large linear array of MAC slices, which would require large adders, these slices are arranged into an n-dimensional structure. Such a structure forces the adder units to become slices of their own, which also operate concurrent with the rest of the hardware in a pipeline fashion. This paper presents this bitslice architecture that can become a fabric for a programmable general-purpose LSTM implementation. The paper also shows an FPGA implementation that uses an on-chip FPGA RAM for the LSTM required memory. The work is compared with other works not considering multidimensional structures, as well as one that considers multi-dimensional cascading. In both cases we show that our structure is faster and uses smaller adder structures.
Small Object Detection Base on YOLOv3 For Pedestrian Recognition

Object detection is the most important algorithm in pattern recognition. However, there is plenty of challenging issue as the gap for algorithm improvement. In the case of the small object and partial occlusion detection in pattern recognition, it can be considered as the main interference for detector improvement. Our method is basing on the one-stage method, we are improving the detector performance by reconstructing the network with the deconvolution method. Our work showed robustness results of small object detection challenges. The experiment is conducting by the one-stage and two-stage detector algorithms and datasets benchmark, the application utilized in the real scene for method validation and improvement.
Factor analysis of mixed data for anomaly detection

Abstract Anomaly detection aims to identify observations that deviate from the typical pattern of data. Anomalous observations may correspond to financial fraud, health risks, or incorrectly measured data in practice. We focus on unsupervised detection and the continuous and categorical (mixed) variable case. We show that detecting anomalies in mixed data is enhanced through first embedding the data then assessing an anomaly scoring scheme. We propose a kurtosis‐weighted Factor Analysis of Mixed Data for anomaly detection to obtain a continuous embedding for anomaly scoring. We illustrate that anomalies are highly separable in the first and last few ordered dimensions of this space, and test various anomaly scoring experiments within this subspace. Results are illustrated for both simulated and real datasets, and the proposed approach is highly accurate for mixed data throughout these diverse scenarios.
Face recognition algorithm based on Prewitt and convolutional neural network

In order to improve the recognition performance of face recognition algorithm, a convolutional neural network face recognition method based on Prewitt operator is proposed. Firstly, the face image is preprocessed by histogram equalization and Prewitt operator. Then, the preprocessed images are input into the convolutional neural network for training, and exponential decay method is adopted to set the learning rate to accelerate the convergence rate, and L2 regulariz ation and Dropout are used to prevent overfitting. The recognition time of this method on ORL face database is 0.2s, and the recognition rate reaches 98.1%. The experimental results show that Prewitt operator and improved convolutional neural network can shorten the recognition time and improve the recognition rate.
BlessMark: a blind diagnostically-lossless watermarking framework for medical applications based on deep neural networks

Nowadays, with the development of public network usage, medical information is transmitted throughout the hospitals. A watermarking system can help for the confidentiality of medical information distributed over the internet. In medical images, regions-of-interest (ROI) contain diagnostic information. The watermark should be embedded only into non-regions-of-interest (NROI) regions to keep diagnostically important details without distortion. Recently, ROI based watermarking has attracted the attention of the medical research community. The ROI map can be used as an embedding key for improving confidentiality protection purposes. However, in most existing works, the ROI map that is used for the embedding process must be sent as side-information along with the watermarked image. This side information is a disadvantage and makes the extraction process non-blind. Also, most existing algorithms do not recover NROI of the original cover image after the extraction of the watermark. In this paper, we propose a framework for blind diagnostically-lossless watermarking, which iteratively embeds only into NROI. The significance of the proposed framework is in satisfying the confidentiality of the patient information through a blind watermarking system, while it preserves diagnostic/medical information of the image throughout the watermarking process. A deep neural network is used to recognize the ROI map in the embedding, extraction, and recovery processes. In the extraction process, the same ROI map of the embedding process is recognized without requiring any additional information. Hence, the watermark is blindly extracted from the NROI. Furthermore, a three-layer fully connected neural network is used for the detection of distorted NROI blocks in the recovery process to recover the distorted NROI blocks to their original form. The proposed framework is compared with one lossless watermarking algorithm. Experimental results demonstrate the superiority of the proposed framework in terms of side information.
Water Security and Sustainability in Mexico

This chapter provides an analysis of the water risk in Mexico based upon its geographic location, the current situation of its water resources, its population growth, and the impact of economic globalization and of current and future technological changes, as well as the effects of global climate change, all of which place several regions of the country at high water risk and lead to the conclusion that Mexico needs a sound water-security program.It is acknowledged that, since 1976, upon developing the National Water Plan and setting it as a guiding instrument for water-policy actions that must be carrifed out by the federal administration every 6 years—through cross-cutting, sectorial, regional, special, and institutional programs—Mexico has had the elements for establishing a water-security program based on three guidelines: efficiency, sustainability, and a cross-cutting approach, with a multidimensional application of at least six guiding principles: governance, social, economic, environmental, human development, and global.Finally, the point is made that scientific and technological knowledge is crucial for bolstering a water-security program.
Coronary Wall Segmentation in CCTA Scans Via a Hybrid Net with Contours Regularization

Providing closed and well-connected boundaries of coronary artery is essential to assist cardiologists in the diagnosis of coronary artery disease (CAD). Recently, several deep learning-based methods have been proposed for boundary detection and segmentation in a medical image. However, when applied to coronary wall detection, they tend to produce disconnected and inaccurate boundaries. In this paper, we propose a novel boundary detection method for coronary arteries that focuses on the continuity and connectivity of the boundaries. In order to model the spatial continuity of consecutive images, our hybrid architecture takes a volume (i.e., a segment of the coronary artery) as input and detects the boundary of the target slice (i.e., the central slice of the segment). Then, to ensure closed boundaries, we propose a contour-constrained weighted Hausdorff distance loss. We evaluate our method on a dataset of 34 patients of coronary CT angiography scans with curved planar reconstruction (CCTA-CPR) of the arteries (i.e., cross-sections). Experiment results show that our method can produce smooth closed boundaries outperforming the state-of-the-art accuracy.
Reducing Geographic Performance Differentials for Face Recognition

As face recognition algorithms become more accurate and get deployed more widely, it becomes increasingly important to ensure that the algorithms work equally well for everyone. We study the geographic performance differentials-differences in false acceptance and false rejection rates across different countries-when comparing selfies against photos from ID documents. We show how to mitigate geographic performance differentials using sampling strategies despite large imbalances in the dataset. Using vanilla domain adaptation strategies to fine-tune a face recognition CNN on domain-specific doc-selfie data improves the performance of the model on such data, but, in the presence of imbalanced training data, also significantly increases the demographic bias. We then show how to mitigate this effect by employing sampling strategies to balance the training procedure.
Multi-modal U-Nets with Boundary Loss and Pre-training for Brain Tumor Segmentation

Gliomas are the most common primary brain tumors, and their manual segmentation is a time-consuming and user-dependent process. We present a two-step multi-modal U-Net-based architecture with unsupervised pre-training and surface loss component for brain tumor segmentation which allows us to seamlessly benefit from all magnetic resonance modalities during the delineation. The results of the experimental study, performed over the newest release of the BraTS test set, revealed that our method delivers accurate brain tumor segmentation, with the average DICE score of 0.72, 0.86, and 0.77 for the enhancing tumor, whole tumor, and tumor core, respectively. The total time required to process one study using our approach amounts to around 20 s.
Flink-ER: An Elastic Resource-Scheduling Strategy for Processing Fluctuating Mobile Stream Data on Flink

As real-time and immediate feedback becomes increasingly important in tasks related to mobile information, big data stream processing systems are increasingly applied to process massive amounts of mobile data. However, when processing a drastically fluctuating mobile data stream, the lack of an elastic resource-scheduling strategy limits the elasticity and scalability of data stream processing systems. To address this problem, this paper builds a flow-network model, a resource allocation model, and a data redistribution model as the foundation for proposing Flink with an elastic resource-scheduling strategy (Flink-ER), which consists of a capacity detection algorithm, an elastic resource reallocation algorithm, and a data redistribution algorithm. The strategy improves the performance of the platform by dynamically rescaling the cluster and increasing the parallelism of operators based on the processing load. The experimental results show that the throughput of a cluster was promoted under the premise of meeting latency constraints, which verifies the efficiency of the strategy.
Interacting Convolution with Pyramid Structure Network for Automated Segmentation of Cervical Nuclei in Pap Smear Images

Pap smear method which is based on the morphological properties of cell nuclei is used to detect pre-cancerous cells in the uterine cervix. An automated and accurate segmentation of nuclei is essential in detection. In this paper, we propose an Interacting Convolution with Pyramid Structure Network (ICPN), which consists of a sufficient aggregating path that focus on more nucleus contexts and a selecting path that enable nucleus localization. The two paths are built on Interacting Convolutional Modules (ICM) and Internal Pyramid Resolution Complementing Modules (IPRCM) respectively. ICM reciprocally aggregates different details of contexts from two sizes of kernels for capturing distinguishing features of diverse sizes and shapes of nuclei. Meanwhile, IPRCM hierachically complements kinds of resolution features to prevent information loss in encoding precedure. The proposed method shows a Zijdenbos similarity index (ZSI) of 0.972±0.04 on Herlev dataset compared to the state-of-the-art approach.
Double attention recurrent convolution neural network for answer selection

Answer selection is one of the key steps in many question answering (QA) applications. In this paper, a new deep model with two kinds of attention is proposed for answer selection: the double attention recurrent convolution neural network (DARCNN). Double attention means self-attention and cross-attention. The design inspiration of this model came from the transformer in the domain of machine translation. Self-attention can directly calculate dependencies between words regardless of the distance. However, self-attention ignores the distinction between its surrounding words and other words. Thus, we design a decay self-attention that prioritizes local words in a sentence. In addition, cross-attention is established to achieve interaction between question and candidate answer. With the outputs of self-attention and decay self-attention, we can get two kinds of interactive information via cross-attention. Finally, using the feature vectors of the question and answer, elementwise multiplication is used to combine with them and multilayer perceptron is used to predict the matching score. Experimental results on four QA datasets containing Chinese and English show that DARCNN performs better than other answer selection models, thereby demonstrating the effectiveness of self-attention, decay self-attention and cross-attention in answer selection tasks.
Mobile-Based Indian Currency Detection Model for the Visually Impaired

According to surveys held in 2019, India holds the largest population standing just after China, but when it comes to visually impaired people, India ranks number one. There are approximately 37 million people across India who are suffering from visual impairment. Special care and measures are taken to help these people live a peaceful life as any other citizen of India, but with the demonetization that happened in the recent years, the Indian economy was replaced with newer currency notes as an attempt to stop black money and fight corruption. Even though the objectives were clear and attainable, with the newer currency notes, the visually impaired people are facing various problems, as there is no provision for them to actually check the currency as the notes are not equipped with Braille system and the sizes of each and every currency is also the same in many cases. To counteract this problem, a mobile-based Indian currency detection model would be a better solution as it enables a visually impaired person to identify the value of specific currency he is holding. The mobile-based Indian currency detection model is the proposed model which will be using image processing for feature extraction and a basic CNN (convolutional neural network) for identification of currency with the given feature inputs. This model is being made into a mobile-based application so as to enable a visually impaired person to check for any possible frauds as fast as possible.
Intermittent Connectivity for Exploration in Communication-Constrained Multi-Agent Systems

Motivated by exploration of communication-constrained underground environments using robot teams, we study the problem of planning for intermittent connectivity in multi-agent systems. We propose a novel concept of information-consistency to handle situations where the plan is not initially known by all agents, and suggest an integer linear program for synthesizing information-consistent plans that also achieve auxiliary goals. Furthermore, inspired by network flow problems we propose a novel way to pose connectivity constraints that scales much better than previous methods. In the second part of the paper we apply these results in an exploration setting, and propose a clustering method that separates a large exploration problem into smaller problems that can be solved independently. We demonstrate how the resulting exploration algorithm is able to coordinate a team of ten agents to explore a large environment.
Advanced Encryption Standard New Instructions (AES-NI) Analysis: Security, Performance, and Power Consumption

Advanced Encryption Standard New Instructions (AES-NI), which is a hardware accelerated version of the Advanced Encryption Standard (AES), are a set of instructions that enable fast and secure data encryption and decryption. AES-NI consists of seven instructions and supports all usage and modes of operations of AES.
Attention Mechanism for Human Motion Prediction

Human motion prediction aims to forecast the most likely future frames of motion conditioned on a given sequence of frames. Because of its importance to many applications especially robotics, it has received a lot of interest and has become an active area of research. Since humans are very flexible in nature, human motion prediction is very challenging Recently, deep learning methods have been dominant in many tasks due to their successful results. Particularly, Recurrent Neural Networks (RNNs) have shown excellent performance on human motion prediction task and other tasks that depend on sequential data, where preserving the order of the sequence items is crucial. The well-known Sequence-to-Sequence (Seq2Seq) architectures have been used for sequence learning where two RNNs namely the encoder and the decoder work cooperatively to transform one sequence to another. In the context of neural machine translation, the use of attention decoders yields state-of-the-art results. This work employs a simple but efficient Seq2Seq model with attention decoder. Both encoder and decoder are trained jointly to predict 15 different categories of human motion. Our experiments have shown that the attention decoder clearly outperforms earlier methods and achieves state-of-the-art results in the short-term (<; 500ms) motion prediction task. Contrary to earlier methods that show progressive deterioration as the time of prediction increases, our model shows high quality long-term (> 500ms) motion prediction which stays as high even after 1000ms of prediction.
Cloud calculations within the optional course Optimization Problems for 10th-11th graders

The article deals with the problem of introducing cloud calculations into 10th–11th graders’ training to solve optimization problems in the context of the STEM-education concept. After analyzing existing programmes of optional courses on optimization problems, the programme of the optional course Optimization Problems has been developed and substantiated implying solution of problems by the cloud environment CoCalc. It is a routine calculating operation and not a mathematical model that is accentuated in the programme. It allows considering more problems which are close to reality without adapting the material while training 10th–11th graders. Besides, the mathematical apparatus of the course which is partially known to students as the knowledge acquired from such mathematics sections as the theory of probability, mathematical statistics, mathematical analysis and linear algebra is enough to master the suggested course. The developed course deals with a whole class of problems of conventional optimization which vary greatly. They can be associated with designing devices and technological processes, distributing limited resources and planning business functioning as well as with everyday problems of people. Devices, processes and situations to which a model of optimization problem is applied are called optimization problems. Optimization methods enable optimal solutions for mathematical models. The developed course is noted for building mathematical models and defining a method to be applied to finding an efficient solution.