Reviewerly/exHarmony huggingface dataset:
GPSRL: Learning Semi-Parametric Bayesian Survival Rule Lists from Heterogeneous Patient Data

Survival data is often collected in medical applications from a heterogeneous population of patients. While in the past, popular survival models focused on modeling the average effect of the covariates on survival outcomes, rapidly advancing sensing and information technologies have provided opportunities to further model the heterogeneity of the population as well as the non-linearity of the survival risk. With this motivation, we propose a new semi-parametric Bayesian Survival Rule List model in this paper. Our model derives a rule-based decision-making approach, while within the regime defined by each rule, survival risk is modelled via a Gaussian process latent variable model. Markov Chain Monte Carlo with a nested Laplace approximation on the Gaussian process posterior is used to search over the posterior of the rule lists efficiently. The use of ordered rule lists enables us to model heterogeneity while keeping the model complexity in check. Performance evaluations on a synthetic heterogeneous survival dataset and a real world sepsis survival dataset demonstrate the effectiveness of our model.
A DNN Optimization Framework with Unlabeled Data for Efficient and Accurate Reconfigurable Hardware Inference

Open-source deep-learning frameworks are prevalent in designing, training, and deploying deep neural networks (DNNs) on general-purpose computing devices, such as CPU, GPU, and DSP. However, for custom-designed reconfigurable hardware accelerators, there is no existing universal framework, capable of optimizing DNN deployment configuration and guiding the hardware design with specific accuracy and efficiency requirements. In the paper, we proposed a cross- platform framework, which can convert deep-learning models from popular open-source frameworks to intermediate representation and optimize weight/activation dynamic ranges and quantization strategy to achieve better efficiency and accuracy based on a baseline reference design of hardware accelerator. With a few unlabeled data, the proposed framework can analyze the statistical inference information, compare different bit-width impacts, and optimize network structure. We further illustrate the detailed experiment results using the framework, showing mAP and top-1 accuracy loss is less than 1.5% and 1.2% with 12-bit and 8-bit activation-constrained quantization schemes respectively for object detection and image classification.
Incremental Real-time Learning Framework for Sentiment Classification: Indian General Election 2019, A Case Study

Indian General Election 2019 was one of the major global political events. The prominence of social media in contemporary life, and the ubiquity of political messaging on it has necessitated a systematic study of sentiments and make inferences about future moods and trends. In absence of standard tools or software we propose a machine learning-based generic system framework and REST (Representation State Transfer) plugin component, that extracts and filters authentic tweets from Twitter, captures the prevalent mood, and predicts the sentiment of any live incoming tweet in a resource-constrained setup. The system can predict user sentiments by discovering completely new features from the web, along with the process of continuous incremental learning and improvement of model accuracy.
Model of Adaptive System of Neuro-Fuzzy Inference Based on PID- and PID-Fuzzy-Controllers

The goal of this work is to develop a model of adaptive system of neuro-fuzzy inference based on PID and PID-FUZZY-controllers, which would allow connecting formalized and informal knowledge in the design of modern automated and automatic control systems for technical processes. To achieve this, we developed an approach to the control of a technical object using an adaptive system of neuro-fuzzy inference. The main control elements of the developed adaptive system of neuro-fuzzy inference are the PID and PID-FUZZY-controllers, as well as the classical and fuzzy control models designed on their basis. The interaction of the two models is provided by the developed hybrid control system. Resulting from the interaction of the two models, the rules base of the fuzzy controller is automatically formed, based on the knowledge about the object obtained when it is controlled by the classical controller. This completely excludes the expert from setting and tuning the parameters of the fuzzy controller. In the developed adaptive system of neuro-fuzzy output, the deviation signals and the deviation and control differential in the classical model are used as data for building a hybrid network. Deviation and control signals in the fuzzy model with automatically generated fuzzy inference rules are used as data to test the hybrid network in order to detect the fact of its overtraining. Thus, the application of the adaptive neuro-fuzzy inference model based on PID and PID-FUZZY controllers allows the effective control of a technical object in the conditions of uncertainty #CSOC1120.
Integrating Ethically Align Design into Agile and CRISP-DM

The growth of Autonomous/Intelligent systems has come with notable failures to address ethical concerns. The IEEE and European Union are progressing guides defining ethical systems' behaviors; however, organizations must consider integrating ethical processes into their development methodology. The paper outlines two current ethical guides and two development frameworks to give context for a proposed integration of steps to address ethical concerns into the Agile framework. By making ethical questions part of the standard processes, the organization can convert a one-time check for ethical issues into a long-term move to an ethical organization.
Leveraging phone-based mobile technology to improve data quality at health facilities in rural Malawi: a best practice project

To further reduce malaria burden, identification of areas with highest burden for targeted interventions needs to occur. Routine health information has the potential to indicate where and when clinical malaria occurs the most. Developing countries mostly use paper-based data systems however they are error-prone as they require manual aggregation, tallying and transferring of data. Piloting was done using electronic data capture (EDC) with a cheap and user friendly software in rural Malawian primary healthcare setting to improve the quality of health records.Audit and feedback tools from the Joanna Briggs Institute (Practical Application of Clinical Evidence System and Getting Research into Practice) were used in four primary healthcare facilities. Using this approach, the best available evidence for a malaria information system (MIS) was identified. Baseline audit of the existing MIS was conducted in the facilities based on available best practice for MIS; this included ensuring data consistency and completeness in MIS by sampling 25 random records of malaria positive cases. Implementation of an adapted evidence-based EDC system using tablets on an OpenDataKit platform was done. An end line audit following implementation was then conducted. Users had interviews on experiences and challenges concerning EDC at the beginning and end of the survey.The existing MIS was paper-based, occupied huge storage space, had some data losses due to torn out papers and were illegible in some facilities. The existing MIS did not have documentation of necessary parameters, such as malaria deaths and treatment within 14 days. Training manuals and modules were absent. One health centre solely had data completeness and consistency at 100% of the malaria-positive sampled records. Data completeness and consistency rose to 100% with readily available records containing information on recent malaria treatment. Interview findings at the end of the survey showed that EDC was acceptable among users and they agreed that the tablets and the OpenDataKit were easy to use, improved productivity and quality of care.Improvement of data quality and use in the Malawian rural facilities was achieved through the introduction of EDC using OpenDataKit. Health workers in the facilities showed satisfaction with the use of EDC.
Further Results on the Relative Generalized Hamming Weight

Based on the applications to the wire-tap channel and bounds on the relative generalized Hamming weight, we will introduce optimal codes of type <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</i> and type <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">II</i> . We will give some necessary conditions and the explicit construction for a linear code <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> to be optimal of type <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</i> and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">II</i> with respect to a subcode <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> . We will also present a new bound on two different parameters of the relative generalized Hamming weight.
SFC: A Lightweight Blockchain Model for Smart Food Industry

Introducing Blockchain (BC) within the Internet of Things (IoT) is a great combination for ensuring privacy and security to any kind of data. Blockchain ensures the integrity of the data and the nodes connected to it are authentic. In the automation industry, IoT devices are getting highly popular. The food industry is such an example. But BC requires high resources and computational capabilities. Whereas, most of the IoT devices do not have the capabilities to provide such resources and power. This paper proposes a simplified blockchain model, called Smart Food Chain (SFC), for food industry automation by using IoT network. This model introduces the Sub-Blockchain concept in focusing to reduce the resources and the computational complexity compared to the conventional blockchain network.
Regular echo state networks

Reservoir computing is a computational paradigm derived from recurrent neural network models. One of its most representative technique is the Echo State Network (ESN), which is usually composed by two salient components: reservoir and readout. The former is responsible by mapping temporal (or sequential) inputs into a high-dimensional space and the latter aims at learning the patterns in such a new space. Despite ESN has attracted a lot of attention nowadays, most works in the literature are focused on the development of additional features to the model, while there is a considerable lack of investigations related to understand and evaluate some of its inherent concepts as well as their relationships. In this paper we address such a limitation by investigating ESN components related to the reservoir structure and readout layer. To be specific, we evaluate regular and small-world network models besides the extensively adopted random one, and also analyze a total of eight classification techniques instead of considering just the few techniques largely adopted in the literature (mostly linear ones). In order to consistently evaluate the alternative ESN methods, we analyzed a wide range of parameters in both reservoir and readout layers through of several experiments with five real-world data sets. The results revealed that some problems can be considerably benefited from some level of organization in the reservoir, such as those provided by regular or small-world network models; and that the non-linear support vector machine classifier achieved the best predictive performance, although it was statistically comparable with the k-nearest neighbors one, which has much smaller time complexity. Interestingly, such findings may make the adoption of ESN methods more efficient from the point of view of embedded systems and large scale problems.
Design of a Line Following Wheelchair for Visually Impaired Paralyzed Patient

Wheelchair is an integral part of life for movement of disabled people especially if they are also visually impaired. These patients need assistance to move the wheelchair, even if they need to move on from one room to another room in an indoor environment or inside house. A manual wheelchair is not very useful for this category of patient without any caregiver. Therefore, this paper aims the design and implementation of a low-cost Arduino based line following and mobile control prototype of automated wheelchair for assisting the move of these people in an indoor environment. The pathways were so designed that the wheelchair can follow two lines - a white line and a black line. The white line will guide the wheelchair to the drawing room and the black line will guide the wheelchair to the washroom without the support of any caregiver. A Bluetooth based remote control system has been designed for people who are paralyzed but not visually impaired. The same remote control can be used by a caregiver who needs to control the wheelchair remotely. A pulse oximeter and temperature sensor were added for monitoring the health of the user. A GSM module along with a GPS module were used to send SMS of health monitoring data and wheelchair location to the concerned person. An ultrasonic obstacle detection sensor was added to detect and avoid obstacles. Options for both solar and conventional ways of charging the batteries have been kept. Arduino Uno, Arduino Nano, GPS, GSM, Ultrasonic Sensor, Bridge Rectifier, Bluetooth Module, DC to DC Step Down Buck converter, Motor Driver and Solar Panel were used to design the wheelchair prototype. The project is hoped to have impact on the society by providing opportunity to the blind and paralyzed people by helping them move independently inside their house and thereby improve their quality of life.
Towards accurate estimation for visual object tracking with multi-hierarchy feature aggregation

Many methods achieve the visual object tracking task with deep learning technologies. As the deep features of different levels contain various semantic information and functions, this paper presents a multi-hierarchy feature aggregation approach to tackle the specific issues in the tracking task, which consists of two aspects. On one hand, this paper integrates the features captured by the offline and online classifiers at the score level, which constructs complementary roles of these classifiers to enhance the stability of classification. Besides, the proposed offline classifier is continuously optimized with different levels of features to reinforce classification constraints. On the other hand, we design a butterfly attention module to promote the capacity of multi-hierarchy feature aggregation in the regression network, which aims to fuse and strengthen the multi-scale features by attending to their spatial information. It can capture more spatial contexts by utilizing the self-attention mechanism during the fusion procedure, and preserve the hierarchy of the features during the strengthening process. Extensive experiments on four public datasets, i.e., VOT2018, OTB100, NFS and LaSOT datasets, demonstrate the effectiveness of the proposed methods.
Best Practices for Designing and Training Neural Networks Subjected to Random Variations

When a neural network is trained and used for inference in a digital hardware architecture, it is normally expected that the performance will translate exactly to new hardware. When implemented in low-power analog systems, there is no such luxury. This paper explores methods for reducing the impact of inter-device variations on the final performance of a neural network. An analysis of the effect of the network shape, including the number of layers and neurons in each layer, reveals a general pattern for minimizing the variability in accuracy. A number of means to reduce overfitting in neural networks are discussed and compared, and a more thorough demonstration is given to training methods that mitigate device overfitting.
Colocation for SLAM-Tracked VR Headsets with Hand Tracking

In colocated multi-user Virtual Reality applications, relative user positions in the virtual environment need to match their relative positions in the physical tracking space. A mismatch between virtual and real relative user positions might lead to harmful events such as physical user collisions. This paper examines three calibration methods that enable colocated Virtual Reality scenarios for SLAM-tracked head-mounted displays without the need for an external tracking system. Two of these methods—fixed-point calibration and marked-based calibration—have been described in previous research; the third method that uses hand tracking capabilities of head-mounted displays is novel. We evaluated the accuracy of these three methods in an experimental procedure with two colocated Oculus Quest devices. The results of the evaluation show that our novel hand tracking-based calibration method provides better accuracy and consistency while at the same time being easy to execute. The paper further discusses the potential of all evaluated calibration methods.
Ground-truthing Large Human Behavior Monitoring Datasets

We present a groundtruthing approach which is applicable to large video datasets collected for studying people's behavior, and which are recorded at a low frame per second (fps) rate. Groundtruthing a large dataset manually is a time consuming task and is prone to errors. The proposed approach is semi-automated (using a combination of deepnet and traditional image analysis) to minimize human labeler's interaction with the video frames. The framework employs mask-rcnn as a people counter followed by human assisted semi-automated tests to correct the wrong labels. Subsequently, a bounding box extraction algorithm is used which is fully automated for frames with a single person and semi-automated for frames with two or more people. We also propose a methodology for anomaly detection i.e., collapse on table or floor. Behavior recognition is performed by using a fine-tuned alexnet convolutional neural network. The people detection and behavior analysis components of the framework are primarily designed to help reduce human labor in ground-truthing so that minimal human involvement is required. They are not meant to be employed as fully automated state-of-the-art systems. The proposed approach is validated on a new dataset presented in this paper, containing human activity in an indoor office environment and recorded at 1 fps as well as an indoor video sequence recorded at 15 fps. Experimental results show a significant reduction in human labor involved in the process of ground-truthing i.e., the number of potential clicks for office dataset was reduced by 99.2% and for the additional test video by 99.7%.
Generating Predicate Logic Expressions from Natural Language

Formal logic expressions are commonly written in standardized mathematical notation. Learning this notation typically requires many years of experience and is not an explicit part of undergraduate academic curricula. Constructing and comprehending logical predicates can feel difficult and unintuitive. We hypothesized that this process can be automated using neural machine translation. Most machine translation techniques involve word-based segmentation as a preprocessing step. Given the nature of our custom dataset, hosts first-order-logic (FOL) semantics primarily in unigram tokens, the word-based approach does not seem applicable. The proposed solution was to automate the translation of short English sentences into FOL expressions using character-level prediction in a recurrent neural network model. We trained four encoder-decoder models (LSTM, Bidirectional GRU with Attention, and two variants of Bi-directional LSTM with Attention). Our experimental results showed that several established neural translation techniques can be implemented to produce highly accurate machine translators of English sentences to FOL formalisms, given only characters as markers of semantics. We also demonstrated that attention-based enhancement to the encoder-decoder architecture can vastly improve translation accuracy. Most machine translation techniques involve word-based segmentation as a preprocessing step. Given the nature of our custom dataset, hosts first-order-logic (FOL) semantics primarily in unigram tokens, the word-based approach does not seem applicable. The proposed solution was to automate the translation of short English sentences into FOL expressions using character-level prediction in a recurrent neural network model. We trained four encoder-decoder models (LSTM, Bidirectional GRU with Attention, and two variants of Bi-directional LSTM with Attention). Our experimental results showed that several established neural translation techniques can be implemented to produce highly accurate machine translators of English sentences to FOL formalisms, given only characters as markers of semantics. We also demonstrated that attention-based enhancement to the encoder-decoder architecture can vastly improve translation accuracy. We trained four encoder-decoder models (LSTM, Bidirectional GRU with Attention, and two variants of Bi-directional LSTM with Attention). Our experimental results showed that several established neural translation techniques can be implemented to produce highly accurate machine translators of English sentences to FOL formalisms, given only characters as markers of semantics. We also demonstrated that attention-based enhancement to the encoder-decoder architecture can vastly improve translation accuracy.
Creative Software Development with Team Boundary Management

The practice of software development continues to evolve in the composition of development teams and the cross-functional nature of software. Managing the boundaries of the team is difficult under cross-functional requirements while at the same time retaining effective processes to arrive at creative systems. We employ boundary spanning theory to develop a model of activity for communications internal and external to the development team that effectively delivers a creative software product. Strengthening the boundary can improve both creativity and effectiveness, although with different activities. Spanning the team boundary promotes creative outcomes but impairs the effective performance of the project. The results show the importance of developing a comprehensive strategy for boundary management as activities can be either complementary or contradictory.
Real Time Expression Detection of Multiple Faces Using Deep Learning

A facial expression is like a gesture which is executed with the facial muscles, and it conveys the emotional state of the subject to the observer. As technology is progressing it is important for the robots to understand human emotions for better communication. We investigate the field of facial expression recognition with deep learning by using Convolutional Neural Network (CNN) algorithm. The proposed framework is trained with FER2013 dataset and tested in real time. Several pooling layers are used in the training part for extracting the features out of the images and haarcascade classifier is used to identify the presence of face in the frame. Our system can recognize five universal emotions of the Facial Action Coding System (FACS) i.e., happy, sad, neutral, surprise and angry which can be identified by the face of a person. It can detect the expressions of multiple faces at the same time. The training accuracy obtained by the system is 73.12%. The real time experiment showed an excellent result.