Reviewerly/exHarmony huggingface dataset:
Formalizing Cost Fairness for Two-Party Exchange Protocols using Game Theory and Applications to Blockchain

Existing fair exchange protocols usually neglect consideration of cost when assessing their fairness. However, in an environment with non-negligible transaction cost, e.g., public blockchains, high or unexpected transaction cost might be an obstacle for wide-spread adoption of fair exchange protocols in business applications. For example, as of 2021-12-17, the initialization of the FairSwap protocol on the Ethereum blockchain requires the selling party to pay a fee of approx. 349.20 USD per exchange. We address this issue by defining cost fairness, which can be used to assess two-party exchange protocols including implied transaction cost. We show that in an environment with non-negligible transaction cost where one party has to initialize the exchange protocol and the other party can leave the exchange at any time cost fairness cannot be achieved.
A Short Text Topic Model Based on Semantics and Word Expansion

In recent years, with the increasing amount of short text information, there are more and more researches on short text information, and the topic information analysis of short texts is one of the key researches. In order to overcome the sparsity problem of short text datasets, this paper conducts research on the basis of the short text topic model Biterm Topic Model (BTM). Aiming at the problem of lack of semantic association in BTM model, this paper proposes a biterm acquisition method based on semantic dependencies. The method firstly apply semantic analysis on the text, and then combines words with strong correlation into biterm. The semantic relevance between words in biterm is enhanced. In order to further solve the text sparse problem, this paper proposes to expand the number of biterms based on similarity calculation of words and calculation of relationship between words. This method not only solves the sparsity problem, but also enhances the topic tendency of text.
Reinforcement Learning for assistance of Alzheimer's disease patients

Alzheimer's disease is a chronic brain disease with multi-factorial causes that begins in the middle of life. It affects the patient in many ways, like the ability to perform daily life activities. In this paper, we proposed an assistance system for Alzheimer's patients to assist them in performing their activities of daily living autonomously. The developed system is based on a human activity recognition system and a prompt system to provide alerts to the patient in case of need. To detect the anomalies in the patient's behavior and provide assistance, we used a reinforcement learning (RL) module as a decision-making system. This module may be responsible for identifying and prompting the patient's wanted assistance based on his or her behavior. The efficiency of the proposed system was proven after testing using the Dem Care dataset.
Detrimental Starfish Detection on Embedded System: A Case Study of YOLOv5 Deep Learning Algorithm and TensorFlow Lite framework

There is a great range of spectacular coral reefs in the ocean world. Unfortunately, they are in jeopardy, due to an overabundance of one specific starfish called the coral-eating crown-of-thorns starfish (or COTS). This article provides research to deliver innovation in COTS control. Using a deep learning model based on the You Only Look Once version 5 (YOLOv5) deep learning algorithm on an embedded device for COTS detection. It aids professionals in optimizing their time, resources and enhancing efficiency for the preservation of coral reefs all around the world. As a result, the performance over the algorithm was outstanding with Precision: 0.93 - Recall: 0.77 - F1-score: 0.84.
BLCR: Towards Real-time DNN Execution with Block-based Reweighted Pruning

Accelerating DNN execution on resource-limited computing platforms has been a long-standing problem. Prior works utilize ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -based group lasso or dynamic regularization such as ADMM to perform structured pruning on DNN models to leverage the parallel computing architectures. However, both of the pruning schemes and pruning methods lack universality, which leads to degraded performance and limited applicability. Considering mobile devices are becoming an important carrier for deep learning tasks, current approaches are not ideal for fully exploiting mobile parallelism while achieving high inference accuracy. To solve the problem, we propose BLCR, a novel block-based pruning framework that comprises a general and flexible structured pruning scheme that enjoys higher flexibility while exploiting full on-device parallelism, as well as a powerful and efficient reweighted regularization method to achieve the proposed sparsity scheme. Our framework is universal, which can be applied to both CNNs and RNNs, implying complete support for the two major kinds of computation-intensive layers (i.e., CONV and FC layers). To complete all aspects of the pruning-for-acceleration task, we also integrate compiler-based code optimization into our framework that can perform DNN inference on mobile devices in real-time. To the best of our knowledge, it is the first time that the weight pruning framework achieves universal coverage for both CNNs and RNNs with real-time mobile acceleration and no accuracy compromise.
Hybrid Machine Learning for Anomaly Detection in Industrial Time-Series Measurement Data

This paper presents a parallel hybrid machine learning system for the identification of anomalies in large sets of multivariate time-series (MVTS) measurement data. The goal is to achieve a more reliable detection of anomalies in safety relevant applications. Key performance indicators (KPI) are used as a measure for predicted possible sources of error. Whereas, a long short-term memory (LSTM-VAE) variational autoencoder is used to model the system behavior; the variational portion ensures the statistical uncertainty of the data is taken into account during training of the network. Combined in a parallel hybrid manner this provides a more reliable anomaly detection. The proposed structure is validated with a case study relating to a ground improvement process for building foundations. The data consists of large sets of real-time multi-variate time-series sensor data, emanating from the instrumented drilling rig. The performance of the LSTM-VAE is optimized using a genetic algorithm to select the optimal values for the hyperparameters. The implemented framework will also support future research into hybrid learning systems applied to real-time machine data analysis.
AI Education Course using AI Needs Creating Method based on User Experience Design and its Evaluation for Sales Representatives

AI, Artificial Intelligence, is being used in many products and services while still being developed. Each company needs to give customers more chances to buy its products by constantly evaluating their AI needs. To accomplish this, sales representatives in companies need to determine significant AI needs after they have learned AI technologies and AI case studies. Therefore, they have to improve comprehension of AI. We're studying AI education course and the understanding for people who haven't majored in AI. In this paper, we propose the course using UX (User Experience) design after sales representatives and other business persons who are not familiar with IT have learned AI. Also, we describe the practical evaluation and result of their understanding.
A VECM analysis of Bitcoin price using time-varying cointegration approach

This study proposed an optimal model to examine the relationship between the Bitcoin price and six macroeconomic variables – the Bitcoin price, Standard and Poor's 500 volatility index, US treasury 10-year yield, US consumer price index, gold price and dollar index. It also examined the effectiveness of the vector error correction model (VECM) in analyzing the interrelationship among these variables. The authors employed the following approach: first, the authors sampled the period August 2010–February 2022. This is because Bitcoin achieved a market capitalization of more than US$1 tn over this period, gaining market attention and acceptance from retail, corporate and institutional investors. Second, the authors employed a VECM with the six macroeconomic variables. Finally, the authors expanded the long-run equilibrium relationship (time-invariant cointegration)-based VECM to develop a time-varying cointegration (TVC) VECM. The authors estimated the TVC VECM using the Chebyshev polynomial specification based on various information criteria. The results showed that the Bitcoin price can be modeled with the VECM ( p = 1, r = 1). The TVC approach generated more explanatory power for Bitcoin pricing, indicating the effectiveness of the approach for modeling the long-run relationship between Bitcoin price and macroeconomic variables.
LASII: Lightweight Authentication Scheme using IOTA in IoT Platforms

Internet of Things (IoT) has emerged as a key technology with a large spectrum of applications in various fields. However, it still faces several security challenges, in particular authentication-related challenges. Efficient “traditional” authentication solutions are often centralized, which cannot meet all the IoT requirements, especially in term of scalability. This paper proposes a new authentication scheme using the promising distributed ledger technology “IOTA” and the Masked Authenticated Messaging module. The proposed scheme is implemented, simulated and compared to other existing schemes. Its effectiveness is evaluated according to the IoT requirements. The implementation was done In IOTA v1 where IRI played an important role as a coordinator to verify transactions and interact with client library, in the next publication IOTA v2 will be implemented benefiting from Coordicide with the elimination of the coordinator in order to provide a benchmark showing the differences between IOTA v1 and IOTA v2.
Phase Compensation for Continuous Variable Quantum Key Distribution Based on Convolutional Neural Network

Phase drift extremely limits the secure key rate and secure transmission distance, which is non-negligible in local oscillation continuous variable quantum key distribution (LLO CV-QKD). In order to eliminate the impact caused by phase drift, we analyze the phase noise of the system and propose a phase compensation method based on convolutional neural network (CNN). Moreover, the compensation is performed on the signal according to the estimated value of phase drift before coherent detection. In numerical simulation, we compare the performance of phase compensation methods based on CNN and Kalman filter (KF), and the results show that CNN-based phase compensation has higher accuracy and stability.
Hierarchical hybrid trajectory planning for autonomous vehicle considering multiple road types

This paper combines A* algorithm and quintic polynomial to propose a novel hybrid trajectory planning method that can be used on both structured and unstructured roads for autonomous vehicle. The architecture of our approach can be generally divided into two layers. In the upper level, an improved A* algorithm is adopted to search for a feasible path as a reference based on the indication of global path, static and dynamic obstacles, road conditions and traffic rules. Particularly, relating to the road type, the evaluation functions are appropriately selected. According to this reference path, the relative position of the vehicle to the obstacles is evaluated. Hence, the target vehicle velocity and related position can be determined, and the desired velocity curve is then generated by a cubic polynomial. In the lower level, a segmented quintic polynomial is utilized for smooth the trajectory. Finally, the proposed method has been validated via Hardware-in-the-loop (HIL) and real-vehicle tests. The experimental results show that it can not only ensure a safe, curvature-continuous and kinodynamically feasible trajectory planning, but also own good adaptability to complex scenarios.
Reinforcement Learning-Based Algorithm to Avoid Obstacles by the Anthropomorphic Robotic Arm

In this paper, the application of the policy gradient Reinforcement Learning-based (RL) method for obstacle avoidance is proposed. This method was successfully used to control the movements of a robot using trial-and-error interactions with its environment. In this paper, an approach based on a Deep Deterministic Policy Gradient (DDPG) algorithm combined with a Hindsight Experience Replay (HER) algorithm for avoiding obstacles has been investigated. In order to ensure that the robot avoids obstacles and reaches the desired position as quickly and as accurately as possible, a special approach to the training and architecture of two RL agents working simultaneously was proposed. The implementation of this RL-based approach was first implemented in a simulation environment, which was used to control the 6-axis robot simulation model. Then, the same algorithm was used to control a real 6-DOF (degrees of freedom) robot. The results obtained in the simulation were compared with results obtained in laboratory conditions.
Efficient Lifting of Symmetry Breaking Constraints for Complex Combinatorial Problems

Abstract Many industrial applications require finding solutions to challenging combinatorial problems. Efficient elimination of symmetric solution candidates is one of the key enablers for high-performance solving. However, existing model-based approaches for symmetry breaking are limited to problems for which a set of representative and easily solvable instances is available, which is often not the case in practical applications. This work extends the learning framework and implementation of a model-based approach for Answer Set Programming to overcome these limitations and address challenging problems, such as the Partner Units Problem. In particular, we incorporate a new conflict analysis algorithm in the Inductive Logic Programming system ILASP , redefine the learning task, and suggest a new example generation method to scale up the approach. The experiments conducted for different kinds of Partner Units Problem instances demonstrate the applicability of our approach and the computational benefits due to the first-order constraints learned.
Analysis of Athletes’ Training Characteristics Based on Action Statistics of Image Processing

Sports and physical exercise are so closely related that they cannot be described separately. The standardization of physical movements requires athletes to constantly improve their planning. Only when they approach the standard, the improvement of sports level is just around the corner. However, the traditional training standard method is limited by human factors and backward technology, and there are many drawbacks, and athletes’ training cannot reach the best state. In this paper, image deblurring is processed to get the reconstruction model of high-resolution image. Through the experiment on the statistics of the competition, the movement of transfer, out of bounds and arm holding in the standing stage, and the movement of rolling bridge and lifting and holding in the kneeling stage are predicted and prevented. For 55 kg, the key to win is the rolling bridge and lifting action; 60 kg attaches importance to the importance of tactical defense. These two lightweight competitions focus on comprehensive training, while the 120 kg heavyweight competition is more in pursuit of strength. Finally, the experiment runs well, and the data and effect can meet the design goal of this paper. The specific details of the optimization and improvement need to be improved in the next step.
Topologically Protected All‐Optical Memory

Abstract The in‐memory processor has played an essential role in overcoming the von Neumann bottleneck, which arises from the partition of memory and a processing unit. Although photonic technologies have recently attracted attention for ultrafast and power‐efficient in‐memory computing, the realization of an all‐optical in‐memory processor remains a challenge. This difficulty originates from the contradiction between robustness and sensitivity in wave dynamics, requiring both noise‐immune memory states and modulation‐sensitive transitions between these states. Here, a building block that provides an all‐optical transition between topologically protected memory states is proposed. A nonlinear photonic molecule that satisfies parity‐time (PT) symmetry, revealing multiple oscillation quenching states with different degeneracies determined by PT‐symmetric phases is investigated. In terms of topology for dynamical systems, these quenching states support topologically protected dynamical trajectories suitable for stable memory states. An all‐optical bidirectional transition between these states, which allows incoherent memory switching is demonstrated. The result provides design criteria for all‐optical in‐memory processors with multilevel operations, enabling the classical‐wave counterpart of electronic memristors.
Exploring the Design Space for Body Transformation Wearables to Support Physical Activity through Sensitizing and Bodystorming

Negative or disturbed body perceptions are often interwoven with people's physical inactivity. While wearables can support body perception changes (body transformation), the design space of body transformation wearables supporting physical activity remains narrow. To expand this design space, we conducted an embodied co-design workshop with users. Using conceptual and tangible sensitizing tools, we explored/reflected on bodily sensations at three moments of movement execution (before/during/after). Conceptual tools were used to evoke/reflect/capture past lived experiences, while tangible tools were used as ideation probes for sensory bodystorming. Two design concepts emerged, reflecting diverging approaches to body transformation wearables: one focused on reminders and movement correction; the other on sensory augmentation and facilitation. We reflect on how each facilitates useful representations of body sensations during movement, and present methodological recommendations for designing technology for sensory augmentation in this area. Finally, we propose a preliminary prototype based on our design concepts and discuss future steps.
DevelNet: Earthquake Detection on Develocorder Films with Deep Learning: Application to the Rangely Earthquake Control Experiment

Abstract There exists over a century of instrumental seismic data; however, most seismograms recorded before the 1980s are only available in analog form. Although analog seismograms are of great value, they are underutilized due to the difficulties of making quantitative measurements on the original media and in converting them to digital time series. In this study, we present an alternative workflow, based on deep learning, to reconstruct an earthquake catalog from images of analog data without conversion to vector time series. We trained a convolutional neural network—DevelNet, using synthetic analog data to detect earthquakes on scanned multichannel Develocorder film images. We then developed an image-based processing workflow to measure arrival times, locate, and determine the magnitudes of earthquakes in the data. We demonstrate the performance of this approach on two years of continuous Develocorder film recordings from the Rangely earthquake control experiment in the mid-1970s. Our approach detects twice the number of events reported in the original catalog (Raleigh et al., 1976). This demonstrates that DevelNet efficiently detects earthquakes from Develocorder film scans, performs consistently over time, and is robust to changes in network geometry. Our locations generally agree with the original study, although the automatically measured arrival times are less precise than manual reading, leading to increased location scatter. Our automatic workflow of Develocorder films rivals the performance of skilled analysts in earthquake detection, but with minimal human intervention. This image-based processing offers a new approach for effectively and efficiently extracting earthquake information from analog seismic data.
Fracture pattern projection on 3D bone models as support for bone fracture simulations

Background and objective: Obtaining bone models that represent certain types of fractures is limited by the need for such fractures to occur in real life and to be processed from medical images. This work aims to propose a method that starts from the design of specific fracture patterns in order to be projected on 3D geometric bone models, being prepared for their subsequent geometric fracturing. Methods: The process of projecting expert-generated fracture patterns has been approached in such a way that they contain geometrical and topological information for the subsequent fracture of the triangle mesh representing the bone model, giving information about the validity of the fracture pattern due to the design process, the validation performed, and the relationships between the fracture lines. Results: Different 3D models of long bones have been used (femur, humerus, ulna and fibula). Also, different types of fracture patterns have been created. These patterns have been used to obtain their projection on three-dimensional bones. In this study, an expert validation of the fracture patterns projected on the bone models is performed. A forensic validation of the fracture patterns used as starting point for the projection is also performed for cases in which this fracture is produced by impact, for which there is scientific evidence based on forensic analysis. This validation also supports the experts, giving them the necessary feedback to complete or modify their fracture patterns according to criteria analyzed from a forensic point of view. Conclusions: The patterns fit the bone models correctly, despite the irregularities of the bone models, and correspond to the expected projection. In addition, it provides us with a clear line of work, by using the topological information of the fracture pattern and the bone model, which allows us to establish a consistent basis for future guided fractures.
Deep Active Visual Attention for Real-Time Robot Motion Generation: Emergence of Tool-Body Assimilation and Adaptive Tool-Use

Sufficiently perceiving the environment is a critical factor in robot motion generation. Although the introduction of deep visual processing models have contributed in extending this ability, existing methods lack in the ability to actively modify what to perceive; humans perform internally during visual cognitive processes. This letter addresses the issue by proposing a novel robot motion generation model, inspired by a human cognitive structure. The model incorporates a state-driven active top-down visual attention module, which acquires attentions that can actively change targets based on task states. We term such attentions as role-based attentions, since the acquired attention directed to targets that shared a coherent role throughout the motion. The model was trained on a robot tool-use task, in which the role-based attentions perceived the robot grippers and tool as identical end-effectors, during object picking and object dragging motions respectively. This is analogous to a biological phenomenon called tool-body assimilation, in which one regards a handled tool as an extension of one’s body. The results suggested an improvement of flexibility in model’s visual perception, which sustained stable attention and motion even if it was provided with untrained tools or exposed to experimenter’s distractions.