Reviewerly/exHarmony huggingface dataset:
Using AI for Justice: Principles and Criteria of the “European Ethical Charter on the Use of AI in Judicial Systems”

The combined use of AI and Big Data makes it possible to apply automatic decision-making in the public administration. The debate is currently focused on the possibility of using algorithmic decisions also in the administration of justice: the use of algorithms which are capable of making choices should help overcome the propensity for eventual human-decision errors, in favour of the theoretically more certain efficiency of algorithmic decisions. The creation of a predictive justice system, however, raises doubts, especially in consideration of certain factors that might alter the algorithmic logic, e.g. the quality of data and the possibility of system error, which could lead to situations of indirect discrimination. With reference to the concerns and risks of the use of AI for justice purposes, in December 2018, the European Commission for the Efficiency of Justice adopted the “Ethical Charter on the use of AI in judicial systems and in related areas” that identifies 5 fundamental principles capable of guaranteeing efficient implementation: 1. respect for fundamental rights, 2. non-discrimination, 3. quality and security, 4. transparency—fairness-impartiality in data processing, 5. possibility of control by the user—‘under user control’. The proposed paper, after a brief survey of the critical aspects of the use of AI systems and an analysis of the contents of the EU Ethical Charter, will focus on the possibility of using AI for forecasting and non-decisional purposes, highlighting how the forecasting method can optimize use, especially in civil law systems.
Shorter Linkable Ring Signature Based on Middle-Product Learning with Errors Problem

Abstract DualRing is a novel generic construction introduced by Yuen et al. (CRYPTO’21), which can transform a special kind of (Type-T*) canonical identification scheme to a ring signature scheme. Compared with the classical approaches, this method can get a shorter signature. In this paper, we construct a new middle-product learning with errors (MPLWE)-based ring signature scheme by using this framework. Specifically, we propose a new MPLWE-based identification scheme, which is compatible with the DualRing, then we obtain a ring signature scheme by using DualRing framework. We also show how to achieve linkability from this ring signature by using a collision resistant hash function. In the end, we provide available parameter options for our (linkable) ring signature scheme. Under these parameters, the signature size of our linkable ring signature is $2-40 \times $ shorter (depending on the ring size) than the previous MPLWE-based scheme by Das et al. (Africacrypt’19).
Solving Projected Model Counting by Utilizing Treewidth and its Limits

In this paper, we introduce a novel algorithm to solve projected model counting (PMC). PMC asks to count solutions of a Boolean formula with respect to a given set of projection variables, where multiple solutions that are identical when restricted to the projection variables count as only one solution. Inspired by the observation that the so-called “treewidth” is one of the most prominent structural parameters, our algorithm utilizes small treewidth of the primal graph of the input instance. More precisely, it runs in time O(22k+4n2) where k is the treewidth and n is the input size of the instance. In other words, we obtain that the problem PMC is fixed-parameter tractable when parameterized by treewidth. Further, we take the exponential time hypothesis (ETH) into consideration and establish lower bounds of bounded treewidth algorithms for PMC, yielding asymptotically tight runtime bounds of our algorithm. While the algorithm above serves as a first theoretical upper bound and although it might be quite appealing for small values of k, unsurprisingly a naive implementation adhering to this runtime bound suffers already from instances of relatively small width. Therefore, we turn our attention to several measures in order to resolve this issue towards exploiting treewidth in practice: We present a technique called nested dynamic programming, where different levels of abstractions of the primal graph are used to (recursively) compute and refine tree decompositions of a given instance. Further, we integrate the concept of hybrid solving, where subproblems hidden by the abstraction are solved by classical search-based solvers, which leads to an interleaving of parameterized and classical solving. Finally, we provide a nested dynamic programming algorithm and an implementation that relies on database technology for PMC and a prominent special case of PMC, namely model counting (#Sat). Experiments indicate that the advancements are promising, allowing us to solve instances of treewidth upper bounds beyond 200.
Two efficient selection methods for high‐dimensional <scp>CD‐CAT</scp> utilizing max‐marginals factor from <scp>MAP</scp> query and ensemble learning approach

Computerized adaptive testing for cognitive diagnosis (CD-CAT) needs to be efficient and responsive in real time to meet practical applications' requirements. For high-dimensional data, the number of categories to be recognized in a test grows exponentially as the number of attributes increases, which can easily cause system reaction time to be too long such that it adversely affects the examinees and thus seriously impacts the measurement efficiency. More importantly, the long-time CPU operations and memory usage of item selection in CD-CAT due to intensive computation are impractical and cannot wholly meet practice needs. This paper proposed two new efficient selection strategies (HIA and CEL) for high-dimensional CD-CAT to address this issue by incorporating the max-marginals from the maximum a posteriori query and integrating the ensemble learning approach into the previous efficient selection methods, respectively. The performance of the proposed selection method was compared with the conventional selection method using simulated and real item pools. The results showed that the proposed methods could significantly improve the measurement efficiency with about 1/2-1/200 of the conventional methods' computation time while retaining similar measurement accuracy. With increasing number of attributes and size of the item pool, the computation time advantage of the proposed methods becomes more significant.
Cutting and Adapting Text for the Virtual Performing Landscape

&lt;p&gt;"Cutting is almost inevitable in the process of performing classical texts, for time management; company logistics; structure; or adaptation. Over the last year, as we have written our new book, Cutting Plays for Performance (Routledge, early 2022) we have seen a shift in cutting priorities. When we began, we focused on live physical performance and textual interventions necessary for production concepts and contexts. &lt;/p&gt; &lt;p&gt;&lt;br&gt;&lt;/p&gt; &lt;p&gt;What we--along with the the rest of the world--did not expect was how much changed under the realities of COVID-19. Like so many from our industry, we viewed this shift as an opportunity rather than an impediment, and as companies pivoted to virtual performance, so too did we consider its impact on how plays are cut." -- from p1&lt;/p&gt;
Digital Transformation of Education and Humanization of Relationships in the Educational Environment: Some Aspects of Relationship and Mutual Influence

The modern educational space is permeated with elements of digitalization. Innovation has always been a challenge for the education system, a traditionally conservative cluster of socio-cultural activity. The peculiarity of the digital transformation of modern education is the totality of this process, covering educational and methodological, organizational, and scientific components. Under such conditions, the formation of contradictions in education between scientific and technological and humanities-science dimensions is obvious. Therefore, the purpose of the article is to identify current educational strategies with a clear prediction of trends in the relationship between digitalization and humanization. Two models for the further development of the educational space are proposed - confrontation and interaction at the level: of human-technology. In this context, it is methodologically acceptable to use two scientific-philosophical methodologies: dialectics and synergetic. Consequently, modern education is at a kind of bifurcation point since the coexistence of innovative technologies with traditional humanistic principles is no longer possible in the existing worldview paradigm. It is only a matter of time before a new system of the reciprocal influence of digital technologies on humanistic principles emerges.
Fast Path Planning for Fixed-Wing Unmanned Aerial Vehicle with Multiple Constraints

Aiming at the problem of fixed-wing unmanned aerial vehicle (UAV) path planning, considering the actual flight conditions and flight performance of UAV, a multi-constraint UAV path planning model is constructed with the minimum flight range and correction times as the objective function. The improved A* algorithm is used to solve the problem: in order to adapt to the model, the objective function of the model is used as the evaluation function; in order to speed up the search efficiency, the branch and bound method is used for iterative search. The simulation results show that: the model can achieve bi-objective optimization, and it is reasonable. Compared with the traditional A* algorithm, the improved algorithm can better balance the optimization flight range and correction times, and save the algorithm planning time, and effectively complete the fast path planning of fixed-wing UAV with multiple constraints.
Research on the Top-Level Design of Provincial Smart Service Areas Based on the Enterprise Architecture Approach

To comply with the development trend of service areas from single intelligence to provincial platform construction, this paper uses the enterprise architecture method to sort out the top-level design ideas of the provincial smart service area, and proposes business architecture, application architecture, data architecture and technical architecture of intelligent service area construction from a macro level. The research results can provide theoretical reference for the provincial service area management agencies to carry out intelligent service area planning and construction, which helps promote the construction of integrated, networked, and platform-based provincial intelligent service area system.
Autonomous Vehicle Path Planning Based on Improved Ant Colony Algorithm

Path planning is one of the key technologies for autonomous vehicles. Ant Colony Algorithm can effectively achieve the goal of path planning for autonomous vehicles, but the algorithm has the problems of low search efficiency and local optimal solution in path planning. Therefore, this paper improves the classical ant colony algorithm, using adaptive initial pheromone distribution range build initial pheromone distribution, at the same time improve stimulating factor enhanced heuristic search efficiency, and introduces the rollback strategy self-locking and deadlock problem and adopt preferential set limit to update pheromone strategy, help reduce blind ant search path, and reduce the redundancy of map information. The simulation results show that the improved ant colony algorithm can greatly improve the global search ability and convergence speed, and can help the autonomous vehicle to find the optimal path quickly.
A Lightweight Fine-Grained VRU Detection Model for Roadside Units

Object detection of vulnerable road users (VRU) under low computing resources of roadside units is one of the key technologies to achieve vehicle-infrastructure cooperative perception. In this paper, a lightweight fine-grained VRU detection model is proposed. Analyzing the existing complex traffic environment, the traditional definition of VRU is no longer applicable. Our work includes two parts: One is to redefine the fine-grained VRU and construct a new dataset. This task makes the perceptual information obtained by detection more comprehensive and accurate. Another is to optimize YOLOv4 by using the channel pruning method in model compression. The optimized model is 60% lighter than the original model. Under the limitation of low computing resources at the roadside units, the real-time detection of VRU is realized while ensuring a certain detection accuracy.
D-S Evidence Reasoning Based Transportation Project Investment Decision Model and Its Application

This research demonstrates five approaches of extracting degree of ignorance (DOI) coefficients of index systems based on the DOI factors of investment decision making systems. Based on Dempster -Shafer (D-S) evidence reasoning, A comprehensive decision-making model of transportation project investment (CDM-TPI) is constructed. Lastly, the case comparison studies are carried out to validate the effectiveness and practicability of the model when applying in the investment decision making systems with the DOI factors.
Hybrid Encryption Algorithm Based on Gray Curve and Josephus Permutation

This paper proposes a new image encryption algorithm based on chaos map systems, hash algorithm, and Josephus permutation. The algorithm consists of chaos initialization, pixel position permutation, and pixel information diffusion. The algorithm's initialization is generated by the original image, which has a high sensitivity to the initial value. The permutation step length is composed of Josephus permutation and gray curve permutation, which completely disturbs the pixel distribution. The diffusion process is composed of cross operation and ciphertext feedback, which breaks the strong correlation between pixels. The simulation results of the encryption algorithm are used to analyze its information entropy, the correlation between elements, and other indicators. The ciphertext image is attacked in several ways, and we analyzed its defense ability. Simulation results show that the algorithm can effectively encrypt image information and has a good defense against various attacks.
Deep Echo State Network with Variable Memory Pattern for Solar Irradiance Prediction

Accurate solar irradiance prediction plays an important role in ensuring the security and stability of renewable energy systems. Solar irradiance modeling is usually a time-dependent dynamic model. As a new kind of recurrent neural network, echo state network (ESN) shows excellent performance in the field of time series prediction. However, the memory length of classical ESN is fixed and finite, which makes it hard to map sufficient features of solar irradiance with long-range dependency. Therefore, a novel deep echo state network with variable memory pattern (VMP-DESN) is proposed in this brief. VMP-DESN consists of multiple connected reservoirs in series, and there exist different types of memory modes in VMP-DESN. To remember more input history information in the states, the time delay links are added in each reservoir and between every two reservoirs. The VMP-DESN is more flexible to deal with different input signals due to its variable memory modes in the reservoir states. Additionally, the effect of different memory patterns on the VMP-DESN performance is discussed in detail, including the antidisturbance ability, memory capacity, and prediction accuracy. Finally, the effectiveness of VMP-DESN is evaluated by predicting the real solar irradiance task.
Beyond Purchase Intentions: Mining Behavioral Intentions of Social-Network Users

Advertisers, recommendation-system designers, and public-health campaigners are investing heavily in online targeting, focusing intently on social-network platforms because of their ability to identify unique subpopulations according to the users’ traits. A particularly informative trait is current behavioral intentions, which provide solid information about users’ future behaviors; yet, the ability to infer such intentions constitutes a significant risk to users’ privacy. An important task is therefore understanding to what extent we can infer behavioral intentions of social-network users solely using publicly-available data. In this article, we formulate intention inference as a time-series classification task and design novel Bayesian-network models that can capture the dynamically evolving nature of the human decision-making process by combining data and priors from multiple domains. We then extend our models to the more general case of attribute inference in the presence of scarce labeled data by introducing a new semi-supervised approach to user-modeling in social networks. We evaluate the performance of our models when used for the inference of five behavioral intentions using temporal, real-world social-network data.
Software defined radio implementation of a secure waveform for <scp>real‐time</scp> data transmission using universal software radio peripheral

Abstract In the present work, we propose a new approach to secure waveforms for data transmission in software defined radio (SDR) based communication systems. For more precision, SDR implementation and validation, using Universal software radio peripherals (USRPs) B210 platforms, of a chaos‐based cryptosystem is used for securing transmitted multimedia data such as text, image, and video. For this, new GNU‐radio companion (GRC) blocks are developed and integrated in the GRC software environment to use effectively our solution. In this context, we have integrated GRC block for the Lorenz chaotic generator which generates chaotic signals. These signals are then used to generate the encryption keys by inserting a dedicated GRC block. The latest and largest development GRC block concerns the proposed solution for the chaotic synchronization problem, which is a challenge when applying chaotic systems to secure data transmission. The implemented chaotic cryptosystem is based on one time pad cipher, knowing that it's the most suitable for chaos‐based cryptography. The obtained results of the security analysis of the proposed cryptosystem validate its robustness. In fact, the generated encryption keys deemed robust after passing through two statistical tests batteries namely, NIST and DIEHARD. In addition, a good result is obtained by a large security evaluation of the encrypted data through several metrics, which are compared to some previous works. Finally, we have experimentally validated a secure waveform between two USRPs platforms for different types of data information like text, image, and video. Furthermore, a graphical communication interface is designed to guarantee easy access and broad flexible applicability of the developed solution.
Early Detection of Gestures using Context-and-Gap-aware Predictive Pose Framework

With a growing interest in autonomous vehicles’ operation, there is an equally increasing need for efficient anticipatory gesture recognition systems for human-vehicle interaction. Existing gesture recognition algorithms have been primarily restricted to historical data. In this paper, we propose a novel Context-and-Gap-aware Pose Prediction Framework (CGAP2), which predicts the future pose data for anticipatory recognition of gestures in an online fashion. CGAP2 implements an encoder–decoder architecture paired with a pose prediction module to anticipate future frames followed by a shallow classifier. CGAP2’s pose prediction module uses 3D convolutional layers and is dependent on the number of pose frames supplied, the time difference between subsequent pose frames, and the number of predicted pose frames. The performance of CGAP2 is evaluated on the Human3.6M dataset with the MPJPE metric. For the pose prediction of 15 frames in advance, an error of 79 mm is achieved. The pose prediction module consists of only 26M parameters and can run at 50 FPS on the NVIDIA TITAN RTX. CGAP2 has a 1-s time advantage compared to other gesture recognition systems, which can be crucial for autonomous vehicles. Furthermore, this work formalizes the problem of anticipatory gesture recognition by introducing two hyper-parameters namely context and gap and a relationship is established between the two using carefully designed experiments in ablation testing. As per our knowledge, this work is the first to tackle all the challenges faced by anticipatory gesture recognition and also the first to post a performance benchmark on Human3.6M dataset.
Movie as a media in improving understanding the cooperative principles and the politeness principles

The background of this study is the use of the RRRrrr!!! Movie by Alain Chabat as learning media in pragmatic courses, especially on the cooperative principle and the politeness principle. The use of this movie is based on the existence of 51 violations of the cooperative principle and the politeness principle in RRRrrr!!! movie. This research is a mixed research that applies qualitative research and quantitative research. The results showed that the use of this movie increased student learning outcomes by 40.26%. Student responses are also enthusiastic with the results of the 100% questionnaire using this movie as a learning medium. The results of the questionnaire also explained that the examples in the movie were very helpful in understanding the cooperative and politeness principle. Keywords: the cooperative principle, the politeness principle, movie as learning media
Deep Neural Networks for Text Classification

Text classification is a NLP technique that groups open-ended text into a set of predefined categories. Within a certain amount of text, it is a task we can easily handle using our knowledge and common sense. But as the amount of text waiting to be classified becomes larger, manually labeling the text would cost too many resources. People employ algorithms to automate the classification process to account for the massive input text. In terms of algorithms, there are rule-based algorithms and data-driven algorithms. Because of the limitation inherent in the rule-based procedures, it is only useful in very limited circumstances. On the other hand, data-driven algorithms take advantage of the rich available data we have on the internet and make predictions based on the previously observed data. In general, the latter procedure has a wider application and oftentimes produces more accurate results. In the early years, data-driven procedures in text classification were dominated by machine learning algorithms. But with the development of deep learning, DL methods start to take the place of ML methods in text classification. In recent years, graph neural networks(GNN) gained much traction as researchers explore new DL models. Researchers have examined the use of GNNs in many domains, and a few explored the use of GNNs in TC. This paper serves as an overview of the recent development of TC with a focus on GNNs.
In-Lecture Survey as a Factor of Developing University Students’ Metacognitive Skills

The aim of the study is to determine the impact of a regular in-lecture survey on the development of university students’ metacognitive skills. The article reveals the methodology of a three-stage survey consisting of metacognitive control (self-analysis and self-assessment), registration of results and verification of the actual level of students’ assimilation of educational material. The scientific novelty of the research lies in the fact that the proposed methodology opens opportunities for the development of such students’ metacognitive skills as self-analysis (reflection of the quality of their cognitive process and emotional state) and self-assessment (fixation of the results of this reflection). As a result, it has been found that conducting a regular three-stage in-lecture survey develops students’ self-analysis and self-assessment skills and increases the academic performance.