Reviewerly/exHarmony huggingface dataset:
Proximal Policy Optimization for Explainable Recommended Systems

In this paper, in order to intuitively improve the interpretability of the recommendation, we make full use of the knowledge graph and provide an ‘explicit’ recommendation path for the recommended result. Specifically, we embeds the entities and relations in the knowledge graph according to consumers’ behavior. Then a knowledge reasoning method based on reinforcement learning algorithm is proposed, which aims to find a reasonable path after two entities in the knowledge graph are given. The main contributions of this paper are as follows: The current State-of-the-art method in reinforcement learning, PPO is used for improvement. The soft-reward function after adjustment behaves better for the current problem while training. And a Q-value based path exploration method is innovated for testing. With extensive experiments on several real-world datasets provided by Amazon, our method behaves better for the current problem compared with the baseline.
Efficient Named Entity Recognition Based on Broad Learning System and BERT

In this paper, we propose an efficient approach for named entity recognition (NER). This paper applies the broad learning system (BLS) to NER task, which includes the feature learning and incremental learning processes. In the feature learning of BLS, the calculation of each new feature node involves only matrix multiplication, and can effectively reduce the amount of calculation to improve efficiency in obtaining rich features. In the incremental learning of BLS, the feature nodes are calculated by active function and ridge regression in order to obtain incremental nodes. In this paper, we combine the BLS with bidirectional encoder representations from transformers (BERT) and conditional random fields (CRF) algorithms to conduct NER of English corpus. Firstly, we extract features through BERT. Secondly, we use BLS to calculate the extracted features. Finally, we apply CRF to decoding features. In numerical experiments, NER of English corpus based on BIO schema is used as benchmark, And the proposed method shows higher accuracy and faster training than other baseline methods.
An Optimized Transfer Attack Framework Towards Multi-Modal Machine Learning

Deep neural networks (DNNs) have excelled at a wide range of tasks, including computer vision (CV), natural language processing (NLP), and speech recognition. However, past research has demonstrated that DNNs are vulnerable to adversarial examples, which are deliberately meant to trick models into making incorrect predictions by adding subtle perturbations into inputs. Adversarial examples create an exponential threat to multi-modal models that can accept a variety of inputs. By attacking substitute models, we provide a transferable attack framework. The suggested framework optimizes the attack process by modifying the prompt templates and simultaneously raising the attack on multiple inputs. Our experiments demonstrate that the proposed attack framework can significantly improve the success rate of transferable attacks, and adversarial examples are rarely noticed by humans. Meanwhile, experiments show that in transferable attacks, coarse-grained adversarial examples can achieve higher attack success rates than fine-grained ones, and the multi-modal models has some robustness against uni-modal attacks.
A Watermarking Approach to Communicate Patient Data Securely from Distributed Sources

Hospitals or Health organizations collect data from patients and store it in databases. The data are scattered in different locations and significant to share from remote places for serving quality treatment and urgent decisions. Any data alternation while transmitting through the transmission channel may cause security issues like data integrity. Data integrity is essential because any alteration of data can hamper medical diagnoses by losing sensitive data. Watermarking could be a practical approach for ensuring such data security with a lower computational cost. Various strategies of watermarking were used to secure health data, especially images. However, watermarking for patient text data has been difficult due to the availability of the right techniques. Therefore, this paper introduces a new watermarking strategy to share patient text from distributed databases. We use the prescription data for testing the proposed system. The experimental results indicate that the method outperforms the existing method against intended attacks.
Detecting Fake Co-visitation Injection Attack in Graph-based Recommendation Systems

Recommendation systems are vulnerable to injection attacks by malicious users due to their fundamental openness. One of the vulnerabilities is the fake co-visitation injection attack, which significantly impacts recommendation systems since it modifies the system according to the attacker’s wishes. To date, the detection of co-visitation injection attacks is challenging as: (1) the choice of attribute representation of nodes is hard, (2) practical evidence for analyzing and detecting anomalies in real-world data is insufficient, (3) it is challenging to filter between the original and injected co-visitation data in terms of node behaviors. This paper investigates a unified detection framework that combines attribute and network structure information synergistically to detect outlier nodes based on CUR decomposition and residual analysis. At first, co-visitation graphs are constructed using association rules, and attribute representations of their nodes are developed. Then, both attributes and network structure information are blended in order to identify suspicious nodes. Extensive experiments on both synthetic and real-world dataset exhibit the efficacy of the proposed detection approach compared to other state-of-the-art approaches. The experimental results show that the detection performance can improve by up to 50% for co-visitation injection attacks over the baselines in terms of false alarm rate (FAR) while keeping the highest detection rate (DR).
What Do Firebase Developers Discuss About? An Empirical Study on Stack Overflow Posts

Firebase is the leading Platform-as-a-Service (PaaS) solution in the application development industry. It is globally used by millions of mobile applications and websites. Being a very popular and robust development platform, Firebase has introduced and developed many features and components over the years. Various services and modules are being added to the platform. So, the developers who use Firebase in their applications, need to have versatile knowledge of the platform. It is difficult to acquire an overview of the trends and discussions of any vast and dynamic platform such as Firebase. Therefore, learners, enthusiasts, and often developers have to put in a lot of effort to figure out topics they should explore and focus on. As a large number of application developers frequently use it and engage in various discussions about it in popular community discussion forums for developers, it has become a very promising source for identifying the topics and trends related to Firebase. Stack Overflow is the most popular community for developers’ discussions. It has grown to be a vast ocean of knowledge to explore. In this study, a systematic approach is followed to explore, extract, and analyze Stack Overflow discussions on Firebase to present an overview of the platform. Natural language processing and machine learning methods were used for extracting information from Stack Overflow data. Information collected was reviewed, analyzed, and compiled into this paper by the research group who have practical experiences in the Firebase platform and other relevant software engineering fields.
Visual servoing path-planning for approaching cylindrical objects

Eye-in-hand systems have received significant attention for the task of approaching an object identified by its reference image shown in advance. Image based visual servoing (VS) methods demonstrate robustness to image noises, but encounter difficulties especially when camera displacement is large. More challenges exist when the object is cylindrical with less texture. This paper proposes new feature set and pertinent trajectory planning scheme to achieve a convergent path with tolerable violation of camera field of view (FOV) limits, allowing transient loss of part of the observed object. Specifically, new features and interaction matrices are developed to achieve global convergence. Feature trajectories are then planned with tolerable FOV violation through a constrained nonlinear minimization path-planning technique. They are later tracked via an adapted IBVS controller in a switching manner. Simulation with two views of a real drinking vessel validates the proposed method and demonstrates its adaption to partial FOV violation.
A general approach to extension-based semantics in abstract argumentation

After a few decades of development, computational argumentation has become one of the active areas in AI. This paper proposes a general approach to deal with semantic related problems of abstract argumentation frameworks (AAFs, for short) and further develops the graded semantics presented by Grossi and Modgil. We pay attention not only to issues of various extension-based semantics at the object level but also the metatheoretical level. First, an alternative fundamental lemma is given, which generalizes the corresponding result due to Grossi and Modgil by relaxing the constraint on parameters. This lemma provides a new sufficient condition for preserving conflict-freeness and brings a Galois adjunction between admissible sets and complete extensions, which is of vital importance in constructing some special extensions in terms of iterations of the defense function. Applying such a lemma, some flaws in Grossi and Modgil's work are corrected, and the structural property, universal definability of various extension-based semantics and relationships among semantics are considered. Second, the operator ⋂D so-called reduced meet modulo an ultrafilter is presented, which is a simple but powerful tool in exploring infinite AAFs. The neutrality function and the defense function, which play central roles in Dung's abstract argumentation theory, are shown to be distributive over reduced meets modulo any ultrafilter. A variety of fundamental semantics for AAFs (including conflict-free, admissible, complete and stable semantics, etc) and some derived semantics are shown to be closed under this operator. Based on these facts, a number of applications of the operator ⋂D are considered, which show this operator can deal with various aspects of argumentation theory. In particular, we provide a simple and uniform method to prove the universal definability of a family of range related semantics. In addition to these results at the object level, this paper also explores ones at the metatheoretical level. To illustrate the universal applicability of the operator ⋂D as a tool for handling semantic related problems, we introduce the first order language FS(Ω) and establish a connection between ultraproducts of models in model theory and reduced meets of extensions modulo ultrafilters. On the basis of this, we characterize the extension-based semantics that is closed under the operator ⋂D in terms of the FS(Ω)-definability, which provides an example of utilizing model theory to explore the theoretical problems of AAFs and brings a series of metatheorems about extension-based semantics. In a word, our work shows that the operator ⋂D can deal with various problems related to various semantics, which suggests that it provides a general method to study AAFs.
Motion and Meaning: Data-Driven Analyses of The Relationship Between Gesture and Communicative Semantics

Gestures convey critical information within social interactions. As such, the success of virtual agents (VA) in both building social relationships and achieving their goals is heavily dependent on the information conveyed within their gestures. Because of the precision required for effective gesture behavior, it is prudent to retain some designer control over these conversational gestures. However, in order to exercise that control practically we must first understand how gestural motion conveys meaning. One consideration in this relationship between motion and meaning is the notion of Ideational Units, meaning that only parts of a gesture’s motion at a point in time may convey meaning, while other parts may be held from the previous gesture. In this paper, we develop, demonstrate, and release a set of tools that help quantify the relationship between the semantics conveyed in a gesture’s co-speech utterance and the fine-grained motion of that gesture. This allows us to explore insights into the complex relationship between motion and meaning. In particular, we use spectral motion clustering to discern patterns of motion that tend to be associated with semantic concepts, on both an aggregate and individual-speaker level. We then discuss the potential for these tools to serve as a framework for both automated gesture generation and interpretation in virtual agents. These tools can ideally be used within approaches to automating VA gesture performances as well as serve as an analysis framework for fundamental gesture research.
A User-Centered Evaluation of the Data-Driven Sign Language Avatar System: A Pilot Study

Sign Languages (SL) are a form of communication in the visual-gestural modality, and are full-fledged natural languages. Recent years have witnessed the increase in the use of virtual avatars as virtual assistants. Research into sign language recognition has demonstrated promising potential for robust automatic sign language recognition. However, the area of sign language synthesis is still in its infancy. This explains the underdevelopment of virtual intelligent signing systems. Additionally, existing models are often restricted to manually written rules and require expert knowledge, while data-driven approach could provide a better solution. Apart from the development of signing systems, research indicates a gap in the evaluation thereof by sign language users. In this paper, we propose a data-driven sign language interpreting avatar and its subjective evaluation. We present findings from a pilot study with the deaf evaluating two different avatars against a human sign language interpreter using the metrics that are believed to bring out important insights and narratives for the users in terms of their perceptions of the avatars.
VISTURE: A System for Video-Based Gesture and Speech Generation by Robots

This paper proposes VISTURE, a system for generating a robot’s gesture and speech by using video as input. VISTURE assumes a situation in which a robot conveys what it saw with a camera to a person who was absent. The value of this paper is that we have performed a case study to investigate the expressions that Japanese people use to describe video scenes, and used the results to build VISTURE. In particular, we found classification of expressions depicting the video scenes throughout the case study: Foreground information that is the relevant event of the scene and Background one that is not the main point of the description giving the entire scene. Foreground and Background are referred in combination. VISTURE employs the classification to generate human-like expressions. Moreover, we designed the method to determine Foreground and Background, and it can generate multiple combinations of expressions. We investigated the people’s impression of a robot performing the gestures and speech generated by VISTURE to evaluate the quality of those gestures and speech. The results showed that the robot was perceived as more likable and capable when it performed gestures.
A comparison of noun phrase complexity features between native speakers of English and Korean college students using TAASSC program

This study investigates how syntactic features can be used to distinguish essays written by Korean college students from NS college students. Essays from the ICNALE(International Corpus Network of Asian Learners of English) corpus were analyzed using TAASSC(the Tool for the Automatic Analysis of Syntactic Sophistication and Complexity) program. Two statistical analyses were performed. First, a multivariate analysis of variance(MANOVA) was employed to examine whether there are statistically significant differences in noun phrase complexity features between NS and Korean students. Second, a stepwise discriminant function analysis was performed to classify into two groups(i.e., NS and Korean students) in terms of noun phrase complexity features. Results showed that Korean and NS students significantly differed in terms of various noun phrase complexity indices. The best four predictors for distinguishing Korean students’ essays from NS students’ essays were dependents per object of the preposition, relative clause modifier per nominal, prepositions per object of the preposition, and dependents per nominal. It was shown that NS students produced more syntactically complex essays than Korean students by employing phrasal embedding. Implications of this study for English writing instruction are discussed.
Analysing the Needs of Homeless People Using Feature Selection and Mining Association Rules

Homelessness is a social and health problem with great repercussions in Europe. Many non-governmental organisations help homeless people by collecting and analysing large amounts of information about them. However, these tasks are not always easy to perform, and hinder other of the organisations duties. The SINTECH project was created to tackle this issue proposing two different tools: a mobile application to quickly and easily collect data; and a software based on artificial intelligence which obtains interesting information from the collected data. The first one has been distributed to some Spanish organisations which are using it to conduct surveys of homeless people. The second tool implements different feature selection and association rules mining methods. These artificial intelligence techniques have allowed us to identify the most relevant features and some interesting association rules from previously collected homeless data.
Virtual Reality User-Scene Interaction:Head-Rotation versus Joystick Movements

An immersive experience is typically connected to user-scene interaction. The system needs to sense the user's movements and inputs and trigger actions accordingly. When operating on PC and console games, interaction typically occurs through hand controllers, including joysticks. Compared to a mouse and keyboard, joysticks allow for articulated command actions, letting users move objects and viewpoints. When wearing a VR headset, the user's head position is tracked, triggering viewpoint changes. This is a major difference between operating on VR headsets and desktop monitors. Changing observation viewpoint through head-rotation comes naturally, which adds to the sensation already given by a VR headset of isolation from the surrounding world. Both sensations contribute to the increased user immersion in the observed world. However, when overviewing a relatively-complex dynamic scene, changing the viewpoint by head-rotation may not be most suitable. Instead, a change triggered by the controller's joystick may appear more convenient and comfortable. This paper assesses the use of head-rotation and controller joystick to generate differences in observation viewpoint. Our application context is a three-dimensional dynamic-scene viewing within command and control operations, which is typical in military defense. Our assessment includes tasks of identification, discovery and positioning of drone movements. We measure objective user's performance by examining mission success and timing and subjective factors such as ease of use, presence, comfort and depth perception.
Legal Evidence and Metrics

In computer applications to legal evidence, metrics fits in diverse roles. In several forensic science disciplines, measurements and quantitative evaluations are important. In these two decades, there has been a great effort to apply formal methods and mathematical tools for assessing legal evidence from metric findings and testimony in order to make the court’s decision less arbitrary. By now it seemed clear that the typical tools of classical artificial intelligence, i.e. logic and probability, could actually make the judge’s decision fairer. In this article instead we claim that whether a kind of metric is appropriate or inappropriate depends upon what you are going to use it for. Bayesianism as applied to how legal evidence is weighted and evaluated, is the subject of a controversy; it is, indeed, indeed problematic. It would be wrong to embody probabilistic calculations if your software is to propose what the verdict ought to be in a criminal case. A ranking of the plausibility of scenarios other than probabilistically is to be sought and preferred. Probabilities are not as problematic, in such a context that (for example) the prosecution uses a computer tool in order to reach a decision as for whether a suspect is to be prosecuted, or alternatively the prosecution is to rather offer that suspect a plea bargain, or not to proceed, i.e., drop the case. Finally, Metrology and Artificial Intelligence are entering the legal test, and this paper is intended also as an introduction to this wide and varied field, which is rather scattered in publications belonging to scientific areas that are still too much far apart.
Digital content interaction in 3D environments

With the widespread use of sensors able to track human motions and gestures, natural interactions are becoming pervasive in many application contexts. The design of such interactions depends on the precision of the tracking device as well as on the background of the user and his/her level of confidence in such technologies. In this paper, we present a set of 3D interaction techniques where the user does not need to wear any additional device for hand tracking, and they can interact in the virtual environment with his/her bare hands directly. The usability and the naturalness of the proposed interaction have been tested on a case study where users interact with a steam generator for training purposes. (Abstract)
A Study of Synchronization Deviation Between Vision and Haptic in multi-sensorial Extended Reality

Extended Reality(XR) has been fully developed in recent years. Multi-sensorial XR technology augments haptic and olfaction to provide users with a more realistic experience. However, there are few studies on the Quality of Experience (QoE) around the synchronization deviation between vision and haptic in XR. This paper adjusts the scale of the synchronization deviation between vision and haptic to collect the participant’s QoE marks. Through the study of the collected data, this paper establishes a linear regression model for positive and negative deviation. This research finds that participants can not tolerate deviations of more than 2000ms and less than -1200ms. Therefore, maintaining the deviation at [-400ms, 600ms] will lead to a good QoE.