Reviewerly/exHarmony huggingface dataset:
The Adapter-Bot: All-In-One Controllable Conversational Model.

Considerable progress has been made towards conversational models that generate coherent and fluent responses by training large language models on large dialogue datasets. These models have little or no control of the generated responses and miss two important features: continuous dialogue skills integration and seamlessly leveraging diverse knowledge sources. In this paper, we propose the Adapter-Bot, a dialogue model that uses a fixed backbone conversational model such as DialGPT (Zhang et al., 2019) and triggers on-demand dialogue skills (e.g., emphatic response, weather information, movie recommendation) via different adapters (Houlsby et al., 2019). Each adapter can be trained independently, thus allowing a continual integration of skills without retraining the entire model. Depending on the skills, the model is able to process multiple knowledge types, such as text, tables, and graphs, in a seamless manner. The dialogue skills can be triggered automatically via a dialogue manager, or manually, thus allowing high-level control of the generated responses. At the current stage, we have implemented 12 response styles (e.g., positive, negative etc.), 8 goal-oriented skills (e.g. weather information, movie recommendation, etc.), and personalized and emphatic responses. We evaluate our model using automatic evaluation by comparing it with existing state-of-the-art conversational models, and we have released an interactive system at this http URL.
Exploring Thai EFL Learners' Attitudes toward the Use of Mobile Applications for Language Learning.

EFL learners have been using their smartphones to support and develop their language learning due to the great availability of numerous mobile applications. Therefore, this study aims to explore Thai EFL learners’ attitudes toward their use of mobile applications for language learning. The participants were 175 first-year university students, and they were asked to complete a questionnaire to examine their attitudes regarding Aim-Mobile Technologies Fit (A-MTF), Appropriateness of Branch (AB), and Forms of M-learning Application and Tools’ Sufficient Adequacy of Communication (FMA & TSAC). The data obtained from the questionnaire was analyzed using quantitative methods. The results show that they have overall positive attitudes towards mobile applications in terms of language support and development. This study also found that the majority of participants want to develop listening skill with the use of mobile applications. Suggestions for the pedagogical benefits are also discussed following the findings of this study.
A Brief Survey of Token Curated Registries

A list is a simple and efficient way for organizing data. Traditional lists have higher chances of unfairness, manipulation, and completely give misinformation because these are managed by the individuals or central authorities. Unlike a traditional list, Token Curated Registry or list also known as TCR is completely decentralized blockchain-based platform which uses intrinsic token along with an incentive system to produce an accurate list curation. This paper gives a brief survey about this decentralized list along with their different types, applications, and various open challenges.
Information-Theoretic Generalization Bounds for Stochastic Gradient Descent

We study the generalization properties of the popular stochastic optimization method known as stochastic gradient descent (SGD) for optimizing general non-convex loss functions. Our main contribution is providing upper bounds on the generalization error that depend on local statistics of the stochastic gradients evaluated along the path of iterates calculated by SGD. The key factors our bounds depend on are the variance of the gradients (with respect to the data istribution) and the local smoothness of the objective function along the SGD path, and the sensitivity of the loss function to perturbations to the final output. Our key technical tool is combining the information-theoretic generalization bounds previously used for analyzing randomized variants of SGD with a perturbation analysis of the iterates.
When Proof-of-Work (PoW) based blockchain meets VANET environments

Smart vehicles, through dedicated sensors, can gather and announce information about road traffic events. This information can be exploited to improve the transportation system. However, with the ubiquity of cyber-attacks, it is challenging to safely store and share event messages collected through Vehicular Ad hoc NETworks (VANETs). Therefore, in this work, we leverage blockchain technology capability to establish a decentralized, transparent and robust database to build, in turn, a robust and secure traffic event management protocol. We adapt the Proof of Work (PoW) consensus mechanism to this context while testing network configurations via NS-3. The performance of the blockchain is evaluated and its reliability in the presence of attackers (malicious vehicles) is examined and discussed. The results highlight a trade-off between the events' trustworthiness and the blockchain's reliability and security.
Comparison of Object Detection and Classification Methods For Mobile Robots

As one of today's popular research field, mobile robots, are widely used in entertainment, search and rescue, health, military, agriculture and many other fields with the advantages of technological developments. Object detection is one of the methods used for mobile robots to gather and report information about its environment during those tasks. With the ability to detect and classify objects, a robot can determine the type and number of objects around it and use this knowledge in its movement and path planning or reporting the objects with the desired features. Considering the dimensions of mobile robots and weight constraints of flying robots, the use of these algorithms is more limited. While the size and weight of mobile devices should be kept relatively small, successful object classification algorithms require processors with high computational power. In this study, to be able to use object detection information for mapping and path planning object detection and classification methods were examined, and for the usage in low weight and low energy consuming platforms through developer boards, detection algorithms were compared to each other.
How Cognition came into being

Jacques Mehler and I started Cognition, to break the grip of empiricist associationist behaviorism and stultifying style requirements on scientific discourse, and to liberate Cognitive Psychology's contributions to social issues. The journal provided opportunities for upcoming generations to expand Cognitive Psychology, publishing new concepts unhindered by established topics and standard formats. As the journal matured, Jacques kept. it fresh for 4 decades, seeking young scientists, novel ideas, and elegant writing, as it midwifed the emergence from Reductionist Behaviorism through Cognitive Psychology to rationalist Cognitive Science. The journal now has opportunities to nurture further progress in the future of Cognitive Science. I speculate that the field will keep associationist processes but integrate them with a new kind of non-reductionist theory that eschews detailed predictions, and which which interprets the brain as an enactor of thought, but not its structural cause. Ideally it will provide a set of constraints on the action of brain and mind that subsume and explain behavioral regularities, the role of frequency, how the brain externalizes those constraints and how the externalization processes emerge developmentally as a function of innate factors, structures unique to the mind and brain, experience and natural law.
Construction of legal incentive evaluation model based on BP neural network with multiple hidden layers

Abstract BP neural network, the basic algorithm of deep learning, is a multilayer feedforward algorithm trained according to the error back propagation algorithm. BP neural network has the function of realizing any complex nonlinear mapping, which makes it especially suitable for solving problems with complex internal mechanism and has strong fitting ability. In this paper, the number, type, propagation rate and usage rate of the objects from 2013 to 2019 are collected as input and output neurons to establish a 6-layer BP neural network model. After experimental verification, the average relative error between the statistical value and the predicted value is 0.23%, which proves that the model has high prediction accuracy and can be applied in the evaluation of professional development.
Size and Depth Separation in Approximating Natural Functions with Neural Networks

When studying the expressive power of neural networks, a main challenge is to understand how the size and depth of the network affect its ability to approximate real functions. However, not all functions are interesting from a practical viewpoint: functions of interest usually have a polynomially-bounded Lipschitz constant, and can be computed efficiently. We call functions that satisfy these conditions ``benign, and explore the benefits of size and depth for approximation of benign functions with ReLU networks.As we show, this problem is more challenging than the corresponding problem for non-benign functions.We give complexity-theoretic barriers to showing depth-lower-bounds: Proving existence of a benign function that cannot be approximated by polynomial-sized networks of depth $4$ would settle longstanding open problems in computational complexity. It implies that beyond depth $4$ there is a barrier to showing depth-separation for benign functions, even between networks of constant depth and networks of nonconstant depth. We also study size-separation, namely, whether there are benign functions that can be approximated with networks of size $O(s(d))$, but not with networks of size $O(s'(d))$. We show a complexity-theoretic barrier to proving such results beyond size $O(d\log^2(d))$, but also show an explicit benign function, that can be approximated with networks of size $O(d)$ and not with networks of size $o(d/\log d)$. For approximation in the $L_\infty$ sense we achieve such separation already between size $O(d)$ and size $o(d)$.Moreover, we show superpolynomial size lower bounds and barriers to such lower bounds, depending on the assumptions on the function.Our size-separation results rely on an analysis of size lower bounds for Boolean functions, which is of independent interest: We show linear size lower bounds for computing explicit Boolean functions (such as set disjointness) with neural networks and threshold circuits.
Did My Hand Move in a Mirror? Body Ownership Induced by the Mirror Hand Illusion

Although the illusion that the mirror image of a hand or limb could be recognized as a part of one’s body behind the mirror, the effect of adding tactile stimulation to this illusion remains unknown. We, therefore, examined how the timing of tactile stimulation affects the induction of body ownership on the mirror image. Twenty-one healthy, right-handed participants (mean age = 23.0 ± 1.0 years, no medical history of neurological and/or psychiatric disorders) were enrolled and a crossover design was adopted in this study. Participants’ right and left hands were placed on the front and back sides of the mirror, respectively, then they were asked to keep looking at their right hand in the mirror. All participants experienced two experiments; one was with tactile stimulation that was synchronized with the movement of a mirror image (synchronous condition), and the other one was with tactile stimulation that was not synchronized (asynchronous condition). The qualitative degree of body ownership for the mirrored hand was evaluated by a questionnaire. Proprioceptive drift (PD), an illusory shift of the felt position of the real hand toward the mirrored hand was used for quantitative evaluation of body ownership and measured at “baseline,” “immediately after stimulation,” “2 min after stimulation,” and “4 min after stimulation.” The results of the questionnaire revealed that some items of body ownership rating were higher in the synchronous condition than in the asynchronous condition ( p &amp;lt; 0.05). We found that PD occurred from immediately after to 4 min after stimulation in both conditions ( p &amp;lt; 0.01) and there was no difference in the results between the conditions. From the dissociation of these results, we interpreted that body ownership could be elicited by different mechanisms depending on the task demand. Our results may contribute to the understanding of the multisensory integration mechanism of visual and tactile stimulation during mirror illusion induction.
Generalization and Robustness Implications in Object-Centric Learning.

The idea behind object-centric representation learning is that natural scenes can better be modeled as compositions of objects and their relations as opposed to distributed representations. This inductive bias can be injected into neural networks to potentially improve systematic generalization and learning efficiency of downstream tasks in scenes with multiple objects. In this paper, we train state-of-the-art unsupervised models on five common multi-object datasets and evaluate segmentation accuracy and downstream object property prediction. In addition, we study systematic generalization and robustness by investigating the settings where either single objects are out-of-distribution -- e.g., having unseen colors, textures, and shapes -- or global properties of the scene are altered -- e.g., by occlusions, cropping, or increasing the number of objects. From our experimental study, we find object-centric representations to be generally useful for downstream tasks and robust to shifts in the data distribution, especially if shifts affect single objects.
Histological Image Segmentation and Classification Using Entropy-Based Convolutional Module

As the powerful performance of deep learning has been proven, many computer vision researchers have applied deep learning methods to their works as a breakthrough that could not be achieved with conventional computer vision algorithms. Particularly in pathological image analysis, deep learning plays an important role because some diagnosis requires a considerable cost or much time. In a recent, convolutional neural network (CNN)-based deep learning models have shown meaningful results in pathological image analysis, reducing time and cost. However, existing CNN-based segmentation models perform the same convolution operation for all channels of a feature map. It could be an inefficient operation according to information theory. We propose (Shannon) entropy-based convolutional module (ECM) for efficient convolutional operation in terms of a communication system. The fundamental coding manner of a communication system based on information theory is to allocate fewer bits for data showing the high probability of occurrence, and vice versa. Following up this coding manner, a feature is divided into dominant and recessive features according to the channel importance calculated from the channel attention module, and a heavy operation is conducted on the recessive feature and a light operation is conducted on the dominant feature. This operating manner can make a network perform efficient calculations and improve its performance. Furthermore, our proposed module is a portable unit, thus it can be a replacement of any convolution without modification of the whole architecture. To the best of our knowledge, our proposed module is the first trial to mimic the coding manner of information theory. The models equipped with our proposed module outperform the original models achieving 0.855 of F1 score and 0.832 of Jaccard score on colorectal cancer (CRC) image data-set.
Asking the Right Questions: Learning Interpretable Action Models Through Query Answering.

This paper develops a new approach for estimating an interpretable, relational model of a black-box autonomous agent that can plan and act. Our main contributions are a new paradigm for estimating such models using a rudimentary query interface with the agent and a hierarchical querying algorithm that generates an interrogation policy for estimating the agent's internal model in a user-interpretable vocabulary. Empirical evaluation of our approach shows that despite the intractable search space of possible agent models, our approach allows correct and scalable estimation of interpretable agent models for a wide class of black-box autonomous agents. Our results also show that this approach can use predicate classifiers to learn interpretable models of planning agents that represent states as images.
Research on Data Security Protection System of Monitoring and Acquisition System Based on Block Chain Technology

The monitoring collection system is a system that uses computer, control and other technologies to realize the data storage, collection and monitoring of the field environment. It is widely used in data collection and monitoring in various fields. It often collects data generated by various types of equipment during operation through various sensors and actuators, and these collected data are often the key to analyzing system failures and discovering that equipment is under network attack. In the current environment, attacks occur frequently, and how to protect and utilize these data is very important. Therefore, data security protection system of monitoring and acquisition system Based on blockchain is proposed. Its purpose is to improve data security and better use of data. By combining the blockchain technology, the collected data is stored on the blockchain, and the integrity check mechanism is used to ensure the integrity of the collected data and prevent the data from being tampered with. The association rule base is formed by establishing association rules, and the rule base is used to analyze data, detect data anomalies and feed back the analysis results to the administrator. The data security and system security of the monitoring acquisition system is improved, and the burden of manpower analysis of data is reduced.
Active Learning for Concept Prerequisite Learning in Wikipedia

The prerequisite relationship of the concept plays an important role in education. Previously, the prerequisites were given by experts, which is very costly. With the development of the Internet, many new concepts have emerged. And there are a growing number of electronic materials available. In this case, it's important to produce an efficient and accessible prerequisite annotator that is beneficial to make an efficient learning plan. This paper proposes a method to mine prerequisite relationships of concepts from Wikipedia by using active learning, which can use fewer artificial labels to obtain an accurate model. The proposed method extracts features from Wikipedia articles, and designs a new active learning algorithm based on the characteristics of concept prerequisites. Experimental results show that the proposed model outperforms existing active learning methods for concept prerequisite learning.
Survey of Transfer Learning and a Case Study of Emotion Recognition Using Inductive Approach

In the era of rapid processing, there is a need for application developments that work with different datasets. The novel learning algorithms designed to handle different training and testing sets. Transfer learning allows domains, tasks, and distributions used in training and testing to be different. The concept of Transfer Learning [TL] is motivated by the detail that we can smear knowledge learned formerly to resolve new complications with ease, reusing the knowledge domain. In this paper, we would like to present inductive TL mechanisms to predict emotions from two different languages English and German, where knowledge of emotion identification exists in English language and is extended to learn emotions in German speech. We have attempted to uncover latent features of one language in another language.
Web Service Composition Security

The continuous adoption of service-oriented architecture (SOA) and web services across vertical industries, geographies, and organization sizes generates new major security concerns both for individuals and organizations. Many of the features that make web services attractive, including greater accessibility of data, dynamic application-to-application connections, and relative autonomy are at odds with traditional security models and controls. Current web service composition technologies still present serious risks to individual and enterprise security and privacy. In the presence of multiple providers that coordinate to provide a composite web service, these issues are amplified. Web services security, privacy, and forensics investigations are therefore becoming a serious concern, especially with the tremendous increase in cybernetic crimes. The aim of this work is to draw a comprehensive overview about security aspects related to web services composition by considering different branches related to security, privacy, and forensics investigations. For each security aspect, the problem statement and related works are discussed. Then, a proposed approach to deal with the aspect under consideration is presented.
Incentives in Blockchain Design and Applications

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Quantum Pseudorandomness and Classical Complexity

We construct a quantum oracle relative to which BQP = QMA but cryptographic pseudorandom quantum states and pseudorandom unitary transformations exist, a counterintuitive result in light of the fact that pseudorandom states can be broken by quantum Merlin-Arthur adversaries. We explain how this nuance arises as the result of a distinction between algorithms that operate on quantum and classical inputs. On the other hand, we show that some computational complexity assumption is needed to construct pseudorandom states, by proving that pseudorandom states do not exist if BQP = PP. We discuss implications of these results for cryptography, complexity theory, and quantum tomography.
Survey and Comparison of Video Summarization Techniques

Video summarization is a keenly intellective video compression technique to select a subset of keyframes or keyshot which are combined to represent shorter and compendious summary of the original input video without losing the contextual semantics of the same. In context of summary generated, it can be divided into static (static storyboard) and dynamic (video skimming) video summarization. Supervised ,unsupervised and reinforcement learning techniques have been introduced in the literature of video summarization. Earlier development in this field marked the introduction of various unsupervised techniques that used handcrafted heuristics to select mutually independent keyframes. In recent years, supervised techniques and deep reinforcement learning techniques have been introduced that models the structural semantics of the original video to generate summaries using frame - level ground truth generated by humans, so that we get the result as close as possible to the human understanding of the video. This paper aims to introduce different architectures proposed for video summarization and provides a qualitative and quantitative comparison of these methods.