Reviewerly/exHarmony huggingface dataset:
Implementing Choreography Extraction

Choreographies are global descriptions of interactions among concurrent components, most notably used in the settings of verification and synthesis of correct-by-construction software. They require a top-down approach: programmers first write choreographies, and then use them to verify or synthesize their programs. However, most software does not come with choreographies yet, which prevents their application. To attack this problem, previous work investigated choreography extraction, which automatically constructs a choreography that describes the behaviour of a given set of programs or protocol specifications. We propose a new extraction methodology that improves on the state of the art: we can deal with programs that are equipped with state and internal computation and time complexity is dramatically better. We also implement this theory and show that, in spite of its theoretical exponential complexity, it is usable in practice. We discuss the data structures needed for an efficient implementation, introduce some optimisations, and perform a systematic practical evaluation.
Improvement of a continuous-variable measurement-device-independent quantum key distribution system via quantum scissors

We propose a new scheme to enhance the performance of the Gussian-modulated coherent-state continuous-variable measurement-device-independent quantum key distribution (CV-MDI-QKD) system via quantum scissors (QS) operation at Bob’s side. As an non-deterministic amplifying setup, we firstly introduce the QS-enhanced CV-MDI-QKD protocol and then investigate the success probability of the QS operation in accordance with the equivalent one-way scheme. Afterwards, we investigate the effect of the QS operation on the proposed scheme and analyze the performance of the QS-enhanced CV-MDI-QKD system under the extreme asymmetric circumstance. Simulation results show that the QS operation can indeed improve the performance of the CV-MDI-QKD system considerably. QS-enhanced CV-MDI-QKD protocol outperforms the original CV-MDI-QKD protocol in both the maximum transmission distance and the secret key rate. Moreover, the better the performance of QS operation, the more significant the improvement of performance of the system.
Design and Implementation of Interactive Platform for Operation and Maintenance of Multimedia Information System Based on Artificial Intelligence and Big Data

In order to cope with the challenges that operators face after the impact of diversified social channels in the field of interactive services, we actively build a new generation of intelligent interactive systems based on artificial intelligence technology, a semantic understanding, and intention recognition, covering rich media content, omnichannel coverage, high-frequency knowledge updates, consistent service response, high-quality, and low-cost intelligent interactive solutions is proposed. The solution provides overall business modeling for scenario design and a scenario-based knowledge expression system, with the function of fragmented knowledge processing. With complete text and voice information, combined with pictures, text, audio, video, and other multimedia, we intelligently interact with users, allowing users to obtain required information and solve problems in a pleasant and relaxing interaction. Therefore, the research and exploration of the intelligent interactive system architecture based on artificial intelligence is a useful practice and strong support for operators to redefine the connotation and elements of "smart service" in the process of building "smart operation." Through repeated tests, it can be seen that the language similarity has reached 0.75549, which is very close to 1.0000. It can be seen that the design of this platform has been successful.
On the Utility of Prediction Sets in Human-AI Teams

Research on human-AI teams usually provides experts with a single label, which ignores the uncertainty in a model's recommendation. Conformal prediction (CP) is a well established line of research that focuses on building a theoretically grounded, calibrated prediction set, which may contain multiple labels. We explore how such prediction sets impact expert decision-making in human-AI teams. Our evaluation on human subjects finds that set valued predictions positively impact experts. However, we notice that the predictive sets provided by CP can be very large, which leads to unhelpful AI assistants. To mitigate this, we introduce D-CP, a method to perform CP on some examples and defer to experts. We prove that D-CP can reduce the prediction set size of non-deferred examples. We show how D-CP performs in quantitative and in human subject experiments ($n=120$). Our results suggest that CP prediction sets improve human-AI team performance over showing the top-1 prediction alone, and that experts find D-CP prediction sets are more useful than CP prediction sets.
Robust Conversational Agents against Imperceptible Toxicity Triggers

Warning: this paper contains content that maybe offensive or upsetting. Recent research in Natural Language Processing (NLP) has advanced the development of various toxicity detection models with the intention of identifying and mitigating toxic language from existing systems. Despite the abundance of research in this area, less attention has been given to adversarial attacks that force the system to generate toxic language and the defense against them. Existing work to generate such attacks is either based on human-generated attacks which is costly and not scalable or, in case of automatic attacks, the attack vector does not conform to human-like language, which can be detected using a language model loss. In this work, we propose attacks against conversational agents that are imperceptible, i.e., they fit the conversation in terms of coherency, relevancy, and fluency, while they are effective and scalable, i.e., they can automatically trigger the system into generating toxic language. We then propose a defense mechanism against such attacks which not only mitigates the attack but also attempts to maintain the conversational flow. Through automatic and human evaluations, we show that our defense is effective at avoiding toxic language generation even against imperceptible toxicity triggers while the generated language fits the conversation in terms of coherency and relevancy. Lastly, we establish the generalizability of such a defense mechanism on language generation models beyond conversational agents.
Distributed Symmetric Key Exchange: A scalable, quantum-proof key distribution system

We propose and implement a protocol for a scalable, cost-effective, information-theoretically secure key distribution and management system. The system, called Distributed Symmetric Key Exchange (DSKE), relies on pre-shared random numbers between DSKE clients and a group of Security Hubs. Any group of DSKE clients can use the DSKE protocol to distill from the pre-shared numbers a secret key. The clients are protected from Security Hub compromise via a secret sharing scheme that allows the creation of the final key without the need to trust individual Security Hubs. Precisely, if the number of compromised Security Hubs does not exceed a certain threshold, confidentiality is guaranteed to DSKE clients and, at the same time, robustness against denial-of-service (DoS) attacks. The DSKE system can be used for quantum-secure communication, can be easily integrated into existing network infrastructures, and can support arbitrary groups of communication parties that have access to a key. We discuss the high-level protocol, analyze its security, including its robustness against disruption. A proof-ofprinciple demonstration of secure communication between two distant clients with a DSKE-based VPN using Security Hubs on Amazon Web Server (AWS) nodes thousands of kilometres away from them was performed, demonstrating the feasibility of DSKEenabled secret sharing one-time-pad encryption with a data rate above 50 Mbit/s and a latency below 70 ms.
University Students’ Perceptions of the Use of Technologies in Educational Activities and Mental Effort Invested

Different generations (digital natives, resident students, generation Y, X, or Z) show a preference towards technologies and the digital world. This "ex-post-facto" study is presented to learn the preferences that university students have regarding the use of different resources for various educational activities, as well as the investment of mental effort and perceived ease of learning concerning different means and technological resources. The sampling used is non-probabilistic, conventional, and intentional. 2,148 university students from different scientific fields of twelve Spanish public universities participate in it. To do this, an "ad hoc" instrument is designed whose data show high reliability and validity indices. The results indicate that students tend to have different perceptions regarding the various means and resources presented, in terms of the level of mental effort needed, along with the degree of easiness to learn through them. In this sense, the casuistry of the results is discussed and compared with the theory of mental effort proposed by Salomon (1981). The findings can be explained by the result of the interaction of three elements: the symbolic systems mobilized to elaborate messages, the message, and the technology that packages, formalizes, and transmits them. In the same way, it concludes by discussing the applicability of the results for the improvement of the digital competence of the students.
LINKING MODAL AND AMODAL REPRESENTATIONS THROUGH LANGUAGE COMPUTATIONAL MODELS

"Language computational models such as Latent Semantic Analysis (LSA) has been criticized for not having direct contact with the real world. However, recent findings have shown the ability of the LSA to capture embodied features such as words’ emotional content. In the present study we tested whether LSA can predict the emotions contained in short written texts such as tweets. It was found that a multiple logistic regression model receiving as input LSA information classified correctly 73,9% of the tweets analyzed according to the emotional content. These results provide additional evidence underlying the representative power of abstract symbols and showing the link between modal representations (emotional) and amodal representations (abstract symbols) through the LSA."
Federated Self-Supervised Learning in Heterogeneous Settings: Limits of a Baseline Approach on HAR

Federated Learning is a new machine learning paradigm dealing with distributed model learning on independent devices. One of the many advantages of federated learning is that training data stay on devices (such as smartphones), and only learned models are shared with a centralized server. In the case of supervised learning, labeling is entrusted to the clients. However, acquiring such labels can be prohibitively expensive and error-prone for many tasks, such as human activity recognition. Hence, a wealth of data remains unlabelled and unexploited. Most existing federated learning approaches that focus mainly on supervised learning have mostly ignored this mass of unlabelled data. Furthermore, it is unclear whether standard federated Learning approaches are suited to self-supervised learning. The few studies that have dealt with the problem have limited themselves to the favorable situation of homogeneous datasets. This work lays the groundwork for a reference evaluation of federated Learning with Semi-Supervised Learning in a realistic setting. We show that standard lightweight autoencoder and standard Federated Averaging fail to learn a robust representation for Human Activity Recognition with several realistic heterogeneous datasets. These findings advocate for a more intensive research effort in Federated Self Supervised Learning to exploit the mass of heterogeneous unlabelled data present on mobile devices.
Triangular Dropout: Variable Network Width without Retraining

One of the most fundamental design choices in neural networks is layer width: it affects the capacity of what a network can learn and determines the complexity of the solution. This latter property is often exploited when introducing information bottlenecks, forcing a network to learn compressed representations. However, such an architecture decision is typically immutable once training begins; switching to a more compressed architecture requires retraining. In this paper we present a new layer design, called Triangular Dropout, which does not have this limitation. After training, the layer can be arbitrarily reduced in width to exchange performance for narrowness. We demonstrate the construction and potential use cases of such a mechanism in three areas. Firstly, we describe the formulation of Triangular Dropout in autoencoders, creating models with selectable compression after training. Secondly, we add Triangular Dropout to VGG19 on ImageNet, creating a powerful network which, without retraining, can be significantly reduced in parameters. Lastly, we explore the application of Triangular Dropout to reinforcement learning (RL) policies on selected control problems.
Discrete Isothermic Nets Based on Checkerboard Patterns

This paper studies the discrete differential geometry of the checkerboard pattern inscribed in a quadrilateral net by connecting edge midpoints. It turns out to be a versatile tool which allows us to consistently define principal nets, Koenigs nets and eventually isothermic nets as a combination of both. Principal nets are based on the notions of orthogonality and conjugacy and can be identified with sphere congruences that are entities of Moebius geometry. Discrete Koenigs nets are defined via the existence of the so called conic of Koenigs. We find several interesting properties of Koenigs nets, including their being dualizable and having equal Laplace invariants. Koenigs nets that are also principal are defined as isothermic nets. We prove that the class of isothermic nets is invariant under both dualization and Moebius transformations. Among other things, this allows a natural construction of discrete minimal surfaces and their Goursat transformations.
Recommended System Optimization in Social Networks based on Cooperative Filter with Deep MVR Algorithm

Abstract Today, social networks have become very popular due to their high usage in communicating with each other. But this popularity requires the development of backend to communicate with each other. Hence, a topic called identifying users is created by making recommendations or propositional systems, and so on, link prediction. The most important issue is the new users' social networks, so that they can offer suggestions. In this research, we tried to provide a system of recommendations for introducing new users to previous users and vice versa based on the principles of machine learning. The proposed method is that the data is entered into the program and then the keywords are extracted from them. Then a sampling between the data is performed based on the Pearson and Cronbach method. In the process of extraction operations along with diminishing dimensions, selection and finally extraction of the best features is done using the cooperative filter which named here based on deep learning- Modified Vector Rotational (MVR) algorithm and its operators. In the following, due to the lack of probabilistic and statistical training of the Deep and Reinforcement Learning with random structure that is used to select users and also to offer users with respect to the tastes of the extracted, there is an optimization algorithm for MVR consider the best features with training. In the following, a series of evaluation criteria have been used to ensure the proposed approach, indicating the appropriate results of the proposed method.
Functioning Fuzzy Logic in Optimizing the Solar Systems Work

Fuzzy logic has been used in many fields, either to control a specific movement, improve the productivity of a machine, or monitor the work of an electrical or mechanical system or the like. In this chapter, we will discuss what are the basic factors that must be taken to use the fuzzy logic in the aforementioned matters in general, and then focus on its employment in the field of renewable energy. Three main axes for renewable energy are solar panels, a wind turbine and finally, solar collectors. The key to working and the basis of the static system is the mechanism for selecting the inputs that directly affect the output in addition to the methods and activation functions of the fuzzy logic.
A Multi-Path Based Embedding Scheme at Perfect Maze

Steganography is a process of hiding data in a media, e.g., image, audio, video, etc. There are lots of areas where steganography can be used. Many researchers are devoting their valuable efforts to hide messages before sending them to a destination through maze-based steganography. However, maze-based steganography has the drawback of reducing embedding capacity. Another problem of maze application at embedding is the imperfection of its solution paths. Imperfect paths are not used for embedding by the maze. That is why while selecting multiple paths, the process of Niwayama et al. in 2010 prunes many paths considering their overlapping or crossing affairs with other ones. In this paper, a solution to those stated problems of Niwayama et al. is outlined and successful results found are demonstrated here too. First, an imperfect maze is tried to be changed virtually to a perfect maze. Then solution paths from start to end are generated. While generating solution paths, the longest path among them is considered to cross or join a point along their pathways. Therefore, one path among those imperfect paths is considered embedded at and extraction was not employed by any earlier proposal, so far we know. Contribution to the stated research area will increase the embedding capacity. For measuring the longest path, the tie is broken by comparing x coordinates and then, if required, y coordinates. The illustrated result in the result analysis section details the justification of our claim.
The Self-Study of University Students under the COVID-19 Pandemic

The article discusses the problem of self-study as an effective form of online education during the pandemic. There has been conducted a conceptual analysis of the essence and content of self-study. Based on the content analysis, the self-study is considered as a personally and professionally significant self-educational activity, which determines the development of self organized, self-managed and self-created professional personality. The main emphasis is focused on creating the university online learning environment for effective and successful implementation of students’ self-educational activity. The findings from the survey clarify the advantages of e-learning tools in organizing self-study. It is proved that using e-learning tools in self-study is the best way to improve higher education during the COVID-19 pandemic. There have been highlighted the results of the survey of using distance learning and e-learning tools in university students’ self-study. The most popular and effective e-learning tools for self-study under the COVID-19 pandemic are described, and the criteria for the development of students’ self-learning skills are determined. The main idea of research is improving the university students’ self education process by using effective e-learning tools and preparing students to be self-organized, self-directed, self-managed, autonomous and independent in their learning to achieve high academic outcomes and professional skills.
Detection of Change in Body Motion With Background Construction and Silhouette Orientation

Background subtraction techniques have been widely implemented and improvised to obtain a stable background model. The novelty of the proposed work is to generate a stable background model under dynamic changes in the environmental conditions where a) an improved background subtraction algorithm is proposed based on GMM with EM algorithm for computing granulometry and run faster for the generation of a stable background model; b) Detecting the foreground by curvelet based denoising process with improvised semisoft thresholding techniques with morphological operations; c) background maintenance is done by an adaptive algorithm in which the intensity values are mapped to remove the connected components with less than P pixels. The proposed scheme works for the Spatio-temporal motion of the object in both spatial and temporal modes. The experimental outcome for the proposed model results in the accurate shape analysis of the object in motion thereby dipping the complexity
Outdoor Monocular Depth Estimation: A Research Review

Depth estimation is an important task, applied in various methods and applications of computer vision. While the traditional methods of estimating depth are based on depth cues and require specific equipment such as stereo cameras and configuring input according to the approach being used, the focus at the current time is on a single source, or monocular, depth estimation. The recent developments in Convolution Neural Networks along with the integration of classical methods in these deep learning approaches have led to a lot of advancements in the depth estimation problem. The problem of outdoor depth estimation, or depth estimation in wild, is a very scarcely researched field of study. In this paper, we give an overview of the available datasets, depth estimation methods, research work, trends, challenges, and opportunities that exist for open research. To our knowledge, no openly available survey work provides a comprehensive collection of outdoor depth estimation techniques and research scope, making our work an essential contribution for people looking to enter this field of study.
Open Science Roadmap for Swedish Higher Education Institutions

In the spring of 2021, a National Open Science Roadmap for Swedish Higher Education Institutions (HEI) was adopted by The Association of Swedish HEIs. The roadmap’s eight principles aim to guide the HEIs’ development of local structures and processes, speed up their concrete actions and encourage their collaboration in the shift to Open Science. The recommendations are concentrated on specific measures for open access to research data and research publications at HEIs. The primary target group for the roadmap is university management at Swedish HEIs. In the spring of 2022 the roadmap is to be supplemented by an action plan for Open Science.