Reviewerly/exHarmony huggingface dataset:
An Adaptive, Affordable, Humanlike Arm Hand System for Deaf and DeafBlind Communication with the American Sign Language

To communicate, the ~ 1.5 million Americans living with deafblindess use tactile American Sign Language (t-ASL). To provide Deafßilind (DB) individuals with a means of using their primary communication language without the use of an interpreter, we developed an assistive technology that promotes their autonomy. The TATUM (Tactile ASL Translational User Mechanism) anthropomorphic arm hand system leverages previous developments of a fingerspelling hand to sign more complex ASL words and phrases. The TATUM hand-wrist system is attached onto a 4 DOF robot arm and a human motion recognition and human to robot gesture transfer framework is used for signing recognition and replication. In particular, signing trajectories based on vision-based motion capture data from a sign demonstrator were used to control the robot's actuators. The performance of the system was evaluated through tactile based sign recognition performed by a blinded user and for its accuracy with novice, sighted users.
Research Partner Recommender System for Academia in Higher Education using Non-Linear Approach

Academic collaboration can be grouped by different faculty members. It involves searching for relevant research topics to collect and analyze information and establishing a new research partner collaboration. A research partner recommender system for academia in higher education can help reduce the time and effort required to establish a new collaboration. Naive Bayes classification makes recommendations with dynamic text search as input values that may extract not only relevant to research topics but also object locations and case studies into consideration. However, classification as output representation is not satisfactory for academia. Therefore, this paper proposes a non-linear approach to provide a score value instead of classes for more suitable relevant recommendations that can leverage a powerful Sigmoid activation function. We demonstrate our approach using actual data from faculty members at the Faculty of Information Technology and Business in a private university in Indonesia. The proposed web-based system helps increase recommendation accuracy for new users.
Blockchain-based Electronic Health Record and Open Research Challenges

Blockchain technology has recently paved a significant role for a broad range of uses where data distribution and sharing are the primary issues. This ledger technology enables the safe interchange of medical data in the healthcare industry. The primary goal of this study is to examining the research on blockchain technology utilization in the healthcare industry. The literature was reviewed on popular databases like IEEE Xplore, Science Direct and Google Scholar. The major findings of this study are that majority of the scientists are using the Ethereum platform for blockchain simulations that suffers from scalability issues. This study also found that still there is a need to explore IoT and blockchain based healthcare solutions on Hyperledger fabric.
Improvement method for cervical cancer detection: A comparative analysis

Cervical cancer is a prevalent and deadly cancer that affects women all over the world. It affects about 0.5 million women anually and results in over 0.3 million fatalities. Diagnosis of this cancer was previously done manually, which could result in false positives or negatives. The researchers are still contemplating how to detect cervical cancer automatically and how to evaluate Pap smear images. Hence, this paper has reviewed several detection methods from the previous researches that has been done before. This paper reviews pre-processing, detection method framework for nucleus detection, and analysis performance of the method selected. There are four methods based on a reviewed technique from previous studies that have been running through the experimental procedure using Matlab, and the dataset used is established Herlev Dataset. The results show that the highest performance assessment metric values obtain from Method 1: Thresholding and Trace region boundaries in a binary image with the values of precision 1.0, sensitivity 98.77%, specificity 98.76%, accuracy 98.77% and PSNR 25.74% for a single type of cell. Meanwhile, the average values of precision were 0.99, sensitivity 90.71%, specificity 96.55%, accuracy 92.91% and PSNR 16.22%. The experimental results are then compared to the existing methods from previous studies. They show that the improvement method is able to detect the nucleus of the cell with higher performance assessment values. On the other hand, the majority of current approaches can be used with either a single or a large number of cervical cancer smear images. This study might persuade other researchers to recognize the value of some of the existing detection techniques and offer a strong approach for developing and implementing new solutions.
HierRL: Hierarchical Reinforcement Learning for Task Scheduling in Distributed Systems

The distributed system Ray has attracted much attention for many decision-making applications. It provides a flexible and powerful distributed running mechanism for the training of the learning algorithms, which could map the computation tasks to the resources automatically. Task scheduling is a critical component in Ray, adopting a two-layer structure. It uses a simple general scheduling principle, which leaves much space to optimize. In this paper, we will study the two-layer scheduling problem in Ray, setting it as an optimization problem. We firstly present a comprehensive formulation for the problem and point out that it is a NP-hard problem. Then we design a hierarchical reinforcement learning method, named HierRL, which consists of a high-level agent and a low-level agent. Sophisticated state space, action space, and reward function are designed for this method. In the high level, we devise a value-based reinforcement learning method, which allocates a task to an appropriate node of the low level. With tasks allocated from the high level and generated from applications, a low-level reinforcement learning method is constructed to select tasks from the queue to be executed. A hierarchical policy learning method is introduced for the training of the two-layer agents. Finally, we simulate the two-layer scheduling procedure in a public platform, Cloudsim, with tasks from a real Dataset generated by the Alibaba Cluster Trace Program. The results show that the proposed method performs much better than the original scheduling method of Ray.
NavDreams: Towards Camera-Only RL Navigation Among Humans

Autonomously navigating a robot in everyday crowded spaces requires solving complex perception and planning challenges. When using only monocular image sensor data as input, classical two-dimensional planning approaches cannot be used. While images present a significant challenge when it comes to perception and planning, they also allow capturing potentially important details, such as complex geometry, body movement, and other visual cues. In order to successfully solve the navigation task from only images, algorithms must be able to model the scene and its dynamics using only this channel of information. We investigate whether the world model concept, which has shown state-of-the-art results for modeling and learning policies in Atari games as well as promising results in 2D LiDAR-based crowd navigation, can also be applied to the camera-based navigation problem. To this end, we create simulated environments where a robot must navigate past static and moving humans without colliding in order to reach its goal. We find that state-of-the-art methods are able to achieve success in solving the navigation problem, and can generate dream-like predictions of future image-sequences which show consistent geometry and moving persons. We are also able to show that policy performance in our high-fidelity sim2real simulation scenario transfers to the real world by testing the policy on a real robot. We make our simulator, models and experiments available at https://github.com/danieldugas/NavDreams.
Attentional Neural Factorization Machine for Web Services Classification via Exploring Content and Structural Semantics

Due to the rapid development of Web 2.0, a lot of Web services emerge over the Internet. How to efficiently manage Web services through classification is very important for Web service discovery. Although there have been a lot of works on Web services classification, they still have drawbacks. On one side, the feature extraction from the service repository is insufficient, which will influence the classification accuracy no matter what classification model is used. On the other side, the extracted features are used with the same weights and they lack depth fusion when training the classification model. In real-world application scenarios, different feature interactions often have different predictive capabilities, and not all feature interactions contain useful or positive information for estimating the target. In addition, both low-and high-order feature interactions are usually underlain real-world data. To solve the problems above, this paper proposes a novel Web services classification approach via fully exploring and integrating the content and structural semantics of Web services. In the proposed approach, the content representation is explored with the BERT-based document embedding model, and the structural representation is explored with the Node2vec network embedding model. Finally, attentional neural factorization machine is used for both the deep fusion of features and Web services classification. A set of experiments are done on real-world datasets crawled from Programmable Web. And solid experimental results show that the proposed approach outperforms the state-of-the-art approach and the other baselines.
Dynamic Sign Language Recognition and Emotion Detection using MediaPipe and Deep Learning

Sign languages facilitate natural interaction by lowering the communication barrier between the hearing-impaired and society. Recognition of words in sign language has always been a difficult task if the gestures of two more words turn out to be similar. Moreover, due to the rapid switching of gestures while communication, creating sentences with the assistance of recognized words is a mammoth task. Further, recognition of alphabets and numbers will not help the user to convey sentences and emotions effectively. Therefore to address the outset issues, this paper proposes a dynamic sign language recognition and emotion detection (DSLRED) model that uses MediaPipe’s holistic pipeline along with an LSTM network which also classifies the emotion on a person’s face into one of seven categories, using deep convolutional neural networks. The results shows that the proposed model achieved an accuracy of 98.95% and can effectively convey emotions and sentences in sign language, independent of the signer who performed the actions in the training data.
Membership Inference Countermeasure With A Partially Synthetic Data Approach

When publishing a machine learning model trained with personal data, the privacy threats, such as membership inference attacks and model inversion, of information leakage on training data from the model should be considered. This paper explores the relations between the resistance against membership inference attack and the mixing ratio of a partially synthetic dataset, which is a mixture of a fully synthetic dataset and the original dataset. In our experiments, we considered the membership inference attack and adopted perturbation using the post-randomization method to generate synthetic data. We confirmed that models trained with partially perturbed datasets could achieve an attack resistance comparable to that of fully perturbed datasets. Conversely, the prediction accuracy of the models could be retained more effectively as compared to the models with the fully perturbed whose accuracy degraded to random guess level. The experiments also suggest that a high mixing ratio, i.e., only a small portion of the original dataset is retained, can potentially contribute to the sustaining prediction accuracy. This suggestion can provide a potential guideline about the tuning of partially synthetic data to the field of membership inference countermeasure using synthetic data.
Uncertainty Quantification in Depth Estimation via Constrained Ordinal Regression

Monocular Depth Estimation (MDE) is a task to predict a dense depth map from a single image. Despite the recent progress brought by deep learning, existing methods are still prone to errors due to the ill-posed nature of MDE. Hence depth estimation systems must be self-aware of possible mistakes to avoid disastrous consequences. This paper provides an uncertainty quantification method for supervised MDE models. From a frequentist view, we capture the uncertainty by predictive variance that consists of two terms: error variance and estimation variance. The former represents the noise of a depth value, and the latter measures the randomness in the depth regression model due to training on finite data. To estimate error variance, we perform constrained ordinal regression (ConOR) on discretized depth to estimate the conditional distribution of depth given image, and then compute the corresponding conditional mean and variance as the predicted depth and error variance estimator, respectively. Our work also leverages bootstrapping methods to infer estimation variance from re-sampled data. We perform experiments on both simulated and real data to validate the effectiveness of the proposed method. The results show that our approach produces accurate uncertainty estimates while maintaining high depth prediction accuracy. The code is available at https://github.com/timmy11hu/ConOR
Model Compression via Position-Based Scaled Gradient

We propose the position-based scaled gradient (PSG) that scales the gradient depending on the position of a weight vector to make it more compression-friendly. First, we theoretically show that applying PSG to the standard gradient descent (GD), which is called PSGD, is equivalent to the GD in the warped weight space, a space made by warping the original weight space via an appropriately designed invertible function. Second, we empirically show that PSG acting as a regularizer to the weight vectors is favorable for model compression domains such as quantization, pruning, and knowledge distillation. PSG reduces the gap between the weight distributions of a full-precision model and its compressed counterpart. This enables the versatile deployment of a model either as an uncompressed mode or as a compressed mode depending on the availability of resources. The experimental results on CIFAR-10/100 and ImageNet datasets show the effectiveness of the proposed PSG in model compression including an iterative pruning method and the knowledge distillation.
Self-Configuring Genetic Programming Feature Generation in Affect Recognition Tasks

Feature extraction is one of the main parts of Machine Learning. Regardless of the nature of solving tasks, developers either need to use standard sets of features for a certain problem or try to generate their own features from raw data. In this paper, we present the genetic programming (GP) algorithm for feature generation issues in affect recognition tasks. We tested this approach in human affect recognition tasks on two corpora the WESAD and the RECOLA. We also used classical methods for feature space reduction Principal Component Analysis (PCA) and Independent Component Analysis (ICA). The results show the effectiveness of the GP approach in comparison with PCA and ICA and its capability to significantly reduce the feature space saving a high performance of classifiers in affect recognition tasks.
Design and Fabrication of Alarming System Based on Laser and LDR

Illegal crimes such as robbery, theft, and intrusion are common in the community nowadays. The use of locks and alarms on doors and windows is a common deterrent to burglaries of this type. In this research, a new security system is proposed, the proposed system is constructed based on a multi-laser alarm system and Light Dependent Resistors (LDRs). Multiple lasers are used to find movement in the location, just like the laser systems in action movies. This is based on the voltage divider circuit theory. When a laser beam is continually focused on an LDR, the voltage drop across it is extremely low as the LDR’s resistance decreases. When a laser beam is interrupted by an item or a barrier, the voltage drop across it increases due to a change in the resistance of the LDRs. This activates the circuit’s alarm or siren. The proposed system is described in detail. The results prove the proposed system works in detecting different objects.
The Impact of Formal and Informal Institutions on Stock Market Reactions to Divestment Announcements: A Meta-Analysis

This study examines the impact of institutions and culture on stock market reactions to divestment announcements. We posit that divestment announcements in countries with stronger shareholder protection generate higher positive returns than divestments in countries with weaker shareholder protection. Furthermore, we contend that stock market reactions to divestment announcements are negative in countries with a long-term orientation culture or high corruption. A meta-analysis of 144 primary studies with 202 effect sizes and 90,449 firm-level observations is used to test our hypothesis. The results indicate that, in general, divestments result in a strong positive stock market reaction. Our findings support the hypothesis that a stable regulatory environment promotes shareholder rights, and thus stock market reactions to divestments are positive. While the stock market reactions to divestments are negative in countries with a long-term orientation culture, they are positive in the presence of external corruption.
Introducing the Hampton Roads Seismic Network: Minimizing the Risk of Injection-Induced Seismicity in Virginia

Groundwater over-extraction from the Potomac Aquifer in southeast Virginia has led to a number of deleterious effects, including saltwater intrusion, falling groundwater levels, and land subsidence. To mitigate these effects, Hampton Roads Sanitation District (HRSD) is developing the Sustainable Water Initiative for Tomorrow (SWIFT) program. SWIFT is an innovative aquifer recharge program designed to enhance long-term groundwater sustainability while mitigating land subsidence and salt water intrusion within the Virginia Coastal Plain. SWIFT will replenish the Virginia Potomac Aquifer by recharging highly treated wastewater at a rate of up to 379,000 cubic meters per day (100 million gallons per day) by year 2032. To achieve this target, the SWIFT program comprises a network of up to five advanced water treatment and aquifer recharge sites throughout southeast Virginia. While there are numerous economic and societal benefits of the SWIFT program, there may also be the potential for fluid injections to cause unintended seismic activity. To begin understanding the potential for injection-induced seismicity, the Hampton Roads Seismic Network (HRSN) was installed in December 2021. The HRSN comprises five autonomous seismic monitoring stations. The primary objectives for HRSN are to (i) record the baseline rate of natural seismicity (if any) prior to full-scale aquifer recharge operations and (ii) detect seismicity that may be induced by fluid injections when full-scale aquifer recharge operations commence in 2026. This paper presents the rationale for HRSN, along with its spatial configuration, hardware specifications, autonomous capabilities, results from preliminary testing, and plans for long-term operations as the SWIFT program comes online.
Universal Semantic Markup and Top-Level Ontology Representation

The article is dedicated to Natural Language Processing in the Theory for Automatic Generation of Knowledge Architecture (TAPAZ-2) paradigm and the immersion of the obtained semantic formalisms into the software environment through the Open Semantic Technology for Intelligent Systems (OSTIS). A specific feature of the approach is the formalization of natural language semantics based on the World Model and the combination of Semantic Coding with the ontology and taxonomy of semantic networks.
How Do Data Science Workers Communicate Intermediate Results?

Data science workers increasingly collaborate on large-scale projects before communicating insights to a broader audience in the form of visualization. While prior work has modeled how data science teams, oftentimes with distinct roles and work processes, communicate knowledge to outside stakeholders, we have little knowledge of how data science workers communicate intermediately before delivering the final products. In this work, we contribute a nuanced description of the intermediate communication process within data science teams. By analyzing interview data with 8 self-identified data science workers, we characterized the data science intermediate communication process with four factors, including the types of audience, communication goals, shared artifacts, and mode of communication. We also identified three overarching challenges in the current communication process. We also discussed design implications that might inform better tools that facilitate intermediate communication within data science teams.
Query-Efficient Generation of Adversarial Examples for Defensive DNNs via Multiobjective Optimization

Due to the inherent vulnerability of deep neural networks (DNNs), the adversarial example (AE) attack has become a serious threat to intelligent systems, e.g., the failure cause of an image classification system. Different to existing works, in this article we are interested in the generation of AEs for DNNs with defensive mechanisms. To make the attack more practical, we exploit a query-based method to generate image AEs in a black-box attack setting. Considering that the generation of AEs is inherently a constrained optimization problem, this article first formulates three objectives regarding defensive DNNs, i.e., attack effectiveness, attack evasiveness and attack coverage. Then, this article proposes a query-efficient AE attack based on the genetic algorithm (GA) and particle swarm optimization (PSO) to address the perturbation optimization problem. To improve the efficiency of search and query, AE-specific operators including block-level and pixel-level crossovers, discrete perturbation mutation and direction-driven reproduction are designed within the GA-based search framework. In addition, predication-based adaptation of reproduction-related parameters is implemented to speed up the search convergence. PSO-based jumping process is further devised to avoid stuck in local optimum. Benchmark-based experiments evaluated the efficiency of our method, which can achieve an attack success rate of 100% with averagely 52.95% reduced queries in contrast to existing black-box attacks on nondefensive models. For defensive DNN models, our method can obtain top attack performance with the query reduction up to 70.92% comparing with the candidates.