Reviewerly/exHarmony huggingface dataset:
An Efficient and Accurate DDPG-Based Recurrent Attention Model for Object Localization

Using image processing algorithm to localize objects, which lack specific patterns and local features, has always been the research focus in industrial production.Compared with the traditional image processing algorithm, RAM (Recurrent Attention Model) in deep learning not only shows advantages in positioning accuracy and stability, but also has good adaptability in situations such as occlusion.However, RAM contains policy gradient (PG) algorithm, which is unstable in training process and has low convergence efficiency.To overcome this shortcoming, in order to improve the learning efficiency and stability of RAM, this paper proposes DDPG-based RAM.In addition, current random sampling algorithm in DDPG (Deep Deterministic Policy Gradient) does not make full use of the information contained in samples.Some samples are repeatedly learned, which slows down the convergence rate of the neural network model, and even causes the model to converge to the local optimal solution.To solve the above problems, a prioritized experience replay algorithm based on Gaussian sampling method is proposed.By constructing the localization and grasping simulation environment in V-rep, it is shown that compared with the traditional image algorithm, the proposed model algorithm in this paper has a greater improvement in localization accuracy, stability and model convergence speed.
A Co-design Approach for the Development and Classroom Integration of Embodied Learning Apps

The development of embodied learning apps has gained traction in the recent years. However, current design approaches present two main limitations: (a) the inadequate involvement of teachers in the design process, and (b) the development of embodied apps, neglecting the context-specific requirements. It is, therefore, no surprising that teachers are often reluctant to integrate such innovations in their classrooms. In this paper we present a co-design approach to address these limitations, thus contributing to the smoother introduction of embodied apps in mainstream education. The suggested design approach was applied for the co-design of the “Young cardiologists” embodied app, including three sequential stages: Teachers as (a) “Users”, (b) “Content experts”, and (c) “Technology integrators”. The co-design approach ensured the active involvement of primary school teachers and guided the development of the app in alignment with the teachers’ educational curriculum. The evaluation of the co-designed app reported learning gains in children’s conceptual understanding.
Segmentation of Larynx Histopathology Images via Convolutional Neural Networks

The larynx is a part of the throat, between the base of the tongue and the trachea. Larynx cancer is a disease in which malign cells form in the tissues of the larynx. It amounts to about 3% of newly diagnosed cancers and has a poor prognosis. The histopathologic analyses of larynx cancer have been performed manually by pathologists. It is time-consuming and correct diagnosis highly depends on pathologists’ experience. However, computerized methods are crucial for early detection and monitoring treatment progress in medicine, also free of human error. In this study, the Convolutional Neural Network-based U-Net model developed for the automatic segmentation of larynx histopathology images. The dataset comprises 55 laryngeal cancer cases. There was a total of 224 P63 stained images of different grades. Among them, 87 were Grade I, 73 Grade II, and 64 Grade III cases. According to the simulation results, the model can quickly and accurately differentiate cell structures on tissue and allows advanced image analysis operations. Moreover, it is suitable for use in a laboratory environment. It also helps pathologists in the decision-making process.
Design for the Decentralized World: Democratization of Blockchain-Based Software Design

The decentralized future is now. The blockchain technology and decentralized applications are changing the way we think about digital software products and the value they bring to our lives. Traditional ways of software design and development are not fully applicable, as there is no central authority to control, decide or direct the future of the product. The blockchain-based software is not controlled by anyone, and at the same time, it belongs to everyone, because the value is distributed among all members in the form of a cryptocurrency. Current product design workflows and methods combined with the blockchain technology need a transformation to become even more inclusive, democratic, autonomous, seek user participation and decision-making. The design solution that is accessible, usable and user-centered can be formed only through the optimization of the product around the users, rather than forcing the users to change their behavior to accommodate the product. Research methods like participatory and non-participatory observations, literature and white paper reviews, analyses, interviews with field experts, are all combined with case studies and explorations of decentralized technology, user-centered design, and democratization. All of which is united in the concept of the decentralized autonomous software, or DAS, which is a software that has decision-making mechanisms embedded in an open distributed peer-to-peer network, that requires vote-based human participation to determine the future direction of its lifecycle, development, and design.
Evaluating a Mouse-Based and a Tangible Interface Used for Operator Intervention on Two Autonomous Robots

This paper presents an experiment where participants used two different interfaces of an application designed for controlling the movements of two vineyard spraying agricultural robots. The goal of the application was to evaluate users’ efficiency and perceived satisfaction while controlling the robots’ movements using a mouse-based and a tangible interface. In a controlled experiment conducted at the Software Quality and Human-Computer Interaction Laboratory of the Computer Engineering and Informatics Department of Patras University 32 participants, experienced in using mouse-based interfaces, used both interfaces. During the experiment, the users interacted with a simulation of the robot movements, without moving and colliding the actual robots, while the robot speed and behavior were based on actual data from our field experiments. The experiment followed a within-group design and for the tangible interface, the Wizard of Oz method was used. For data collection, eye-tracking glasses were used to capture the interaction with the tangible interface combined with recording devices. The hypotheses tested included efficiency measured in time to complete a task, accuracy measured in unsprayed or double-sprayed areas and robot collisions, and perceived user satisfaction measured with a short custom questionnaire. Results indicated that no statistically significant differences were found related to interface efficiency and accuracy, but users liked using the tangible version of the application the most.
Effect of Fear on Behavioral Intention to Comply

Compliance with the information system (IS) security policy is an established theme in IS research for protecting the IS from user actions. The unified model of information security policy compliance (UMISPC) is a recent development within the research of user compliance with IS security policy. The current study evaluated a single path posited by UMISPC to evaluate the effect of the construct of fear on the construct of intention, which is the behavioral intention of IS users to comply with security policy. Our findings, in contrast to the previous research based on UMISPC, did not include a significant relationship between fear and intention. The population of our study consisted of IS users with five years of experience working for large and medium organizations in the United States.
Design of blockchain-based trading mechanism under sharing mode of electric vehicle under smart grid

The P2P energy market represented by electric vehicles will receive more and more attention. Therefore, the P2P energy market platform needs more precise design to ensure that the feedback effect between the transmission and distribution system operation will not reduce the system stability. At the same time, the trading mechanism needs to be redesigned, allowing the platform owner to maximize their own interests by setting prices. Blockchain technology is used to create a shared electric vehicle trading platform, which allows electric vehicles to communicate with the smart grid. The main focus of this paper is to design the combination of blockchain technology and electric vehicles. Blockchain technology has the characteristics of decentralization, distributed peer-to-peer, non-tamperable, consensus trust and economy. From the perspective of feature matching, it can be found that the advantage of the blockchain is actually complementary to the demand of the electric vehicle charging and generating transaction.
Unsupervised Geochemical Analysis of the Eruptive Products of Ischia, Vesuvius and Campi Flegrei

This work aimed to study geochemical data, composed of major and trace elements describing volcanic rocks collected from the Campanian active volcanoes of Vesuvius, Campi Flegrei and Ischia Island. The data were analyzed through the Self-Organizing Map (SOM) unsupervised neural net. SOM is able to group the input data into clusters according to their intrinsic similarities without using any information derived from previous geochemical-petrological considerations. The net was trained on a dataset of 276 geochemical patterns of which 96 belonged to Ischia, 94 to Vesuvius and 86 to Campi Flegrei volcanoes. Two investigations were carried out. The first one aimed to cluster geochemical data mainly characterizing the type of volcanic rocks of the three volcanic areas. The SOM clustering well grouped the oldest volcanic products of Ischia, Vesuvius and Campi Flegrei identifying a similar behaviour for the rocks emplaced in the oldest activity periods (>19 ka), and showing their different evolution over time. In the second test, devoted to inferring information on the magmatic source, the ratios of significant trace elements and K2O/Na2O have been used as input data. The SOM results highlighted a high degree of affinity between the geochemical element ratios of Vesuvius and Campi Flegrei that were separated from the products of Ischia. This result was also evidenced through isotope ratios by using traditional two-dimensional diagrams.
TACTILE – A Novel Mixed Reality System for Training and Social Interaction

Elderly people are frequently affected by a decline of mental and physical abilities, which results in anxiety, frailty and reclusiveness. Often, they live alone, spatially separated from their families or friends, unable to meet them on a regular basis. The TACTILE project addresses these challenges and fosters an active lifestyle and well-being of older adults via an enjoyable, innovative, and user-friendly Mixed Reality (MR) solution. The system enables training both the cognitive and physical state using MR technology and maintaining social contacts by connecting seniors with family and friends. Cognitive trainings are provided by conventional and physically available board games - such as Ludo - that are completed with virtual game pieces of a remote partner. Since the system uses real physical game pieces, the user experiences a tactile feedback that enables a more familiar feeling and thus novel way of digital interaction. Physical trainings are provided by a virtual avatar that explains and shows dedicated exercises, adaptable to the individual needs and physical restrictions of the user. Thus, the avatar accompanies the user during the physical training in a more natural way.
Can an Environmental Feature Influence Interview Anxiety?

This paper presents a study using a virtual reality-based methodology that aims to investigate the effect of nature-like surroundings on interview anxiety. For this, sixty-three volunteers were asked to participate in a study in which a job interview was simulated in a virtual environment. An experimental condition – an office with a nature-like surroundings, and a control condition – without nature-like surroundings, were considered in a between subject design experiment. Self-perceived anxiety and emotional arousal were the dependent variables. Main findings suggests Main results suggest that nature-like surroundings can positively influence IA for men, whereas it negatively influences IA for women. Nature-like surroundings lowered men's concerns regarding their communication and behavioral IA and on the other hand enhanced female's performance IA. These findings are of great interest and can be seen as a step forward towards the understanding of gender differences amongst automatic nonconscious emotion regulation strategies.
<i>k</i>-Vectors: An Alternating Minimization Algorithm for Learning Regression Functions

The k-vectors algorithm for learning regression functions proposed here is akin to the well-known k-means algorithm. Both algorithms partition the feature space, but unlike the k-means algorithm, the k-vectors algorithm aims to reconstruct the response rather than the feature. The partitioning rule of the algorithm is based on maximizing the correlation (inner product) of the feature vector with a set of k vectors, and generates polyhedral cells, similar to the ones generated by the nearest-neighbor rule of the k-means algorithm. Similarly to k-means, the learning algorithm alternates between two types of steps. In the first type of steps, k labels are determined via a centroid-type rule (in the response space), which uses a surrogate hinge-type loss function to the mean squared error loss function. In the second type of steps, the k vectors which determine the partition are updated according to a multiclass classification rule, in the spirit of support vector machines. It is proved that both steps of the algorithm only require solving convex optimization problems, and that the algorithm is empirically consistent - as the length of the training sequence increases to infinity, fixedpoints of the empirical version of the algorithm tend to fixed points of the population version of the algorithm. Learnability of the predictor class posit by the algorithm is also established.
Latent Retrieval for Large-Scale Fact-Checking and Question Answering with NLI training

Passage retrieval is a part of fact-checking and question answering systems that is critical yet often neglected. Most systems usually rely only on traditional sparse retrieval. This can have a significant impact on the recall, especially when the relevant passages have few overlapping words with the query sentence. Recent approaches have attempted to learn dense representations of queries and passages to better capture the latent semantic content of text. While dense retrieval models have been proven effective in question answering, there is no relevant work for improving evidence retrieval in fact-checking. In this work, we show that training a dense retriever is sufficient to outperform traditional sparse representations in both question answering and fact-checking. We constructed a new dataset called Factual-NLI, comprised of factual claims and their supporting evidence, and demonstrate that using it to train a dense retriever can improve evidence retrieval significantly. Experimental results on the MSMARCO dataset indicate that pre-training with Factual-NLI, and other NLI datasets, is also effective for large-scale passage retrieval in question answering. Our model is incorporated in a real world semantic search engine that returns snippets containing evidence related to questions and claims about the COVID-19 pandemic.
Honey Encryption based Hybrid Cryptographic Algorithm:A Fusion Ensuring Enhanced Security

Providing high security to data exchange is one of the most primary concerns with fast evolution in communication technology. So, a Hybrid cryptographic algorithm is proposed where, Rivest Cipher 5 (RC5) is used for encryption and decryption of data and Honey Encryption (HE) is used for secret key exchange, which is a major challenge of symmetric cryptography. In this paper, number of rounds of RC5 is varied and its strength is studied based on randomness, considering avalanche effect. Time complexity of RC5 is also analyzed and increasing strength is chosen over time complexity as RC5 shows very slight increase in time when number of rounds is increased. Additionally, HE involves Distribution Transforming Encoder (DTE) and generation of honey-words. HE acts an additional layer of protection for RC5 cryptographic algorithm. HE is password based and it gives fake messages if any intrusion is detected and have utilized this concept to protect sharing of secret key. Both algorithms are efficiently integrated; obtained results clearly justify that greater number of rounds provide more randomness and hence better security and key exchange is done securely. Thus, proposed Hybrid cryptographic algorithm efficiently provides enhanced security as it involves double encryption.
High-performance differential evolution algorithm guided by information from individuals with potential

Abstract In the differential evolution (DE) algorithm, many adaptive methods have been studied in terms of fitness values. However, few studies exist on the information from individuals with potential, which presents a large difference in fitness values from that of previous individuals and contains much evolution information. This study proposes a high-performance DE (PDE) algorithm guided by information from individuals with potential. In PDE, all individuals are divided into individuals with potential and individuals without potential according to their improvement in fitness values. The experience learned from the generation of individuals with potential is used to guide future individuals. At each generation, the selection probability of each strategy in the strategy pool is determined by the strategy’s contribution to the improvement in fitness values when generating individuals with potential. The parameters are randomly generated with two distributions, and the location parameters of the two distributions are adjusted on the basis of the improvement in fitness values of individuals with potential. Different individuals (with or without potential) may have different characteristics and evolution methods. Therefore, the generation process of individuals with potential is separated into two cases according to whether they are from previous individuals with or without potential. The study results of the two cases are applied to guide the evolution of current individuals with and without potential. The proposed algorithm is evaluated by comparing it with five advanced DE variants on CEC2005 and seven up-to-date evolutionary algorithms on CEC2014. Comparison results demonstrate the competitive performance of the proposed algorithm. The PDE is also applied to estimate the parameters of a kinetic model of p-xylene oxidation process.
Interactive Narrative in Augmented Reality: An Extended Reality of the Holocaust

In this research, the author descripted new narrative media known as Immersive Augmented Reality Environment (IARE) with HoloLens. Aarseth's narrative model [17] and all available input design in IARE were reviewed and summarised. Based on these findings, The AR Journey, a HoloLens app aiming at interactive narrative for moral education purpose, was developed and assessed. Qualitative methods of interview and observation were used and the results were analysed. In general, narrative in IARE were proved to be valid for moral education purpose, and findings including valid narrative structure, input model, design guidelines were revealed.
Traffic Load Balancing based on Probabilistic Routing in Data Center Networks

We investigate a novel load-balancing algorithm exploiting probabilistic dynamic routing for OPSquare DCN. Results show that for 10240-server DCN, the proposed load-balancing decreases the ToR-to-ToR latency by 27.3% and packet loss by 56.45% at a load of 0.6.
The graph of 4-ary simplex codes of dimension 2

We give a complete description of the distance relation on the graph of 4-ary simplex codes of dimension 2. This is a connected graph of diameter 3. For every vertex we determine the sets of all vertices at distance i∈{1,2,3} and describe their symmetries.
Defining a Human-Machine Teaming Model for AI-Powered Human-Centered Machine Translation Agent by Learning from Human-Human Group Discussion: Dialog Categories and Dialog Moves

The vision of human-machine symbiosis is that a human will work closely and harmoniously with the machine. We study interactions among human translators to help define potential Human-Machine Interface (HCI) and Human-Machine Teaming (HMT) models for human-centered machine translation systems. The role of the machine, in this context, is to be an AI-based agent serving as a real-time partner. The questions we ask are that if we have such an agent, what are the main functions of the agent, how does the agent interact with a human translator in a way where they can work symbiotically as partners, what human deficiencies can be augmented by an AI-based agent and how, and what kind of human behaviors should the AI-based agent mimic. We used a data set collected from an online translation study group composed of certified and highly experienced translators (both English-to-Chinese and Chinese-to-English), and altogether we analyzed several hundred dialogs between these translators related to the translation results. Each dialog always started with an initial comment and would focus on one of many possible categories. The first question we asked was which categories were discussed more frequently than the others, and why. At both the word level and above the word level, three dominating categories were found: source misunderstanding, target expression problem, and confirmation on good translation. In addition, we found that the more than half of the dialogs focused only at the word-level. The second question we asked was whether a discussion (represented by a dialog) was effective or not. What we found was that the most common pattern was the one associated with "simple but effective" dialog, while constructive dialogs were conversely very infrequent. Based on these findings, we derive the HCI/HMT design implications for an AI-based translation agent: provide better capability beyond the word/phrase level to complement human deficiencies; focus on building algorithms to support better source understanding and target expression delivery; provide quick information search and retrieval to support real-time interaction; provide confirmation to a human partner's good translation with reasons and explanations; provide help regarding source understanding and target language delivery based on the native language of the human partner; act in the role of a "lead translator" who has better domain and linguistic knowledge, superior cognitive capability, and unique analytic perspectives to complement human deficiency; and perform in-depth constructive dialogs with human partners by stimulating thought.