Reviewerly/exHarmony huggingface dataset:
ESPRESSO-GPU: Blazingly Fast Two-Level Logic Minimization

Two-level logic minimization has found applications in new problems such as efficient realization of deep neural network inference. Important characteristics of these new applications are that they tend to produce very large Boolean functions (in terms of the supporting variables and/or initial sum of product representation) and have don't-care-sets that are much larger in size than the on-set and off-set sizes. Applying conventional single-threaded logic minimization heuristics to these problems becomes unwieldy. This work introduces ESPRESSO-GPU, a parallel version of ESPRESSO-II, which takes advantage of the computing capabilities of general-purpose graphics processors to achieve a huge speedup compared to existing serial implementations. Simulation results show that ESPRESSO-GPU achieves an average speedup of 97x compared to ESPRESSO-II.
CAPEN: Cryptographic Accumulator based Privacy Preserving Exposure Notification

Contact tracing has a significant contribution in preventing the rapid spread of infectious diseases. During the COVID-19 pandemic, local authorities, researchers and the tech industry focused on developing digital contact tracing applications to automate tracing efforts and improve its efficiency. While digital contact tracing was proven to be an efficient method to mitigate transmission, the security and privacy of the user data remains a major concern. In this paper we focused on digital tracing application data privacy issues and proposed Cryptographic Accumulator based Privacy Preserving Exposure Notification module (CAPEN). In the CAPEN module we used an asymmetric cryptographic accumulator and greatest common divisor (gcd) function to provide privacy for the user data while providing accurate verification. Additionally, the CAPEN module has a significantly lower communication complexity while sharing larger datasets. The CAPEN module is architecture independent and can be easily incorporated with any digital contact tracing system in order to prevent enumeration and social graph attacks without compromising contact verification accuracy.
Parameter Estimation of Lorenz Attractor: A Combined Deep Neural Network and K-Means Clustering Approach

This research is mainly aimed at introducing a deep learning approach to solve chaotic system parameter estimates like the Lorenz system. The reason for the study is that because of its dynamic instability, the parameter of the chaotic system cannot be easily estimated. Moreover, due to the complexity of chaotic systems based on existing approaches, some parameters may be difficult to determine in advance. Therefore, it is crucial to assess the parameter of chaotic systems. To solve the issue of parameter estimation for a chaotic system, deep learning is utilized. After that, it has been suggested to improve the efficiencies in the Deep Neural Network (DNN) model by combining the DNN with an unsupervised machine learning algorithm, the K-Means clustering algorithm. This study constructs the flow of DNN based method with the K-Means algorithm. DNN techniques is suitable in solving nonlinear and complex problem. The most popular method to solve parameter estimation problem is using optimization algorithm that easily trap to local minima and poor in exploitation to find the good solutions. Due to the flow, 80% of training and 20% test sets for each class are divided between the Lorenz datasets. Accuracy by using 80:20 ratio of training and test data gives result 98% of accurate training data, and 73% of test data are predicted with the proposed algorithm while 91 and 40% of the DNN models are predicted in training and test data.
Revisiting Tibetan Word Segmentation with Neural Networks

Tibetan Word Segmentation is a basic and essential task in Tibetan Natural Language Processing workflow. Performance of TWS can directly affect many other downstream Tibetan NLP tasks since errors propagate in a multi-stage NLP pipeline. Traditionally the majority of researchers leverage linear statistical approaches to tackle Tibetan Word Segmentation, which often requires hand-crafted linguistic feature engineering with great care. In this work, we propose a neural network architecture for Tibetan Word Segmentation, which is a stacked combination of CNN, Bi-LSTM and CRF. By using tagged data for supervised learning and unlabeled data for representation learning, with no involvement in feature engineering, our model can produce promising performance on the test set, surpassing our baseline models by a large margin, and indicating the effectiveness of the proposed neural model.
Preserving Gender and Identity in Face Age Progression of Infants and Toddlers

Realistic age-progressed photos provide invaluable biometric information in a wide range of applications. In recent years, deep learning-based approaches have made remarkable progress in modeling the aging process of the human face. Nevertheless, it remains a challenging task to generate accurate age-progressed faces from infant or toddler photos. In particular, the lack of visually detectable gender characteristics and the drastic appearance changes in early life contribute to the difficulty of the task. We address this challenge by extending the CAAE (2017) architecture to 1) incorporate gender information and 2) augment the model’s overall architecture with an identity-preserving component based on facial features. We trained our model using the publicly available UTKFace dataset and evaluated our model by simulating up to 100 years of age progression on 1,156 male and 1,207 female infant and toddler face photos. Compared to the CAAE approach, our new model demonstrates noticeable visual improvements. Quantitatively, our model exhibits an overall gain of 77.0% (male) and 13.8% (female) in gender fidelity measured by a gender classifier for the simulated photos across the age spectrum. Our model also demonstrates a 22.4% gain in identity preservation measured by a facial recognition neural network.
A Norm Emergence Framework for Normative MAS – Position Paper

Norm emergence is typically studied in the context of multiagent systems (MAS) where norms are implicit, and participating agents use simplistic decision-making mechanisms. These implicit norms are usually unconsciously shared and adopted through agent interaction. A norm is deemed to have emerged when a threshold or predetermined percentage of agents follow the “norm”. Conversely, in normative MAS, norms are typically explicit and agents deliberately share norms through communication or are informed about norms by an authority, following which an agent decides whether to adopt the norm or not. The decision to adopt a norm by the agent can happen immediately after recognition or when an applicable situation arises. In this paper, we make the case that, similarly, a norm has emerged in a normative MAS when a percentage of agents adopt the norm. Furthermore, we posit that agents themselves can and should be involved in norm synthesis, and hence influence the norms governing the MAS, in line with Ostrom’s eight principles. Consequently, we put forward a framework for the emergence of norms within a normative MAS, that allows participating agents to propose/request changes to the normative system, while special-purpose synthesizer agents formulate new norms or revisions in response to these requests. Synthesizers must collectively agree that the new norm or norm revision should proceed, and then finally be approved by an “Oracle”. The normative system is then modified to incorporate the norm.
The Cognitive Study of Immersive Experience in Science and Art Exhibition

In terms of the history of design development, human engineering emerged after World War II in the 1950s, and continued to the rise of personal computers in the 1980s. Human-oriented interface design and human-oriented interactive interfaces are more important. In the 21st century, the age of sensibility is approaching. The so-called pleasure or experience, when it comes to cultural creativity, its interaction with “people” becomes more important. Since the 1980s, science and technology art exhibitions have begun to focus on audience participation. Facing the challenges of the new century, why do you need to apply interactive technology in the art exhibition scene? What are the factors that affect the audience’s experience of the immersive space? How to integrate resources to provide service innovation? It is a topic of contemporary art exhibition. This research is based on literature discussion and case studies. It first sorts out the application of technology in Taiwan’s art exhibitions in the past three years, and then focuses on the case of all-tech immersive performances. Through observation of the audience experience and focus group interviews at the exhibition site, we can understand the audience’s perception of immersive immersion. The cognitive model of space is used as a reference for the modification of exhibition design. In the future cultural performance stage, it is necessary to combine interactive technology with cultural connotation through the audience’s experience, with educational concepts and interpretation creation as the purpose, and practical application in the new creative planning project of the exhibition space to complete cultural added value. And further develop service innovations to achieve the goals of “Co-produce”, “Co-design” and “Co-deliver” for exhibitors and participants.
The Efficient Mining of Skyline Patterns from a Volunteer Computing Network

In the ever-growing world, the concepts of High-utility Itemset Mining (HUIM) as well as Frequent Itemset Mining (FIM) are fundamental works in knowledge discovery. Several algorithms have been designed successfully. However, these algorithms only used one factor to estimate an itemset. In the past, skyline pattern mining by considering both aspects of frequency and utility has been extensively discussed. In most cases, however, people tend to focus on purchase quantities of itemsets rather than frequencies. In this article, we propose a new knowledge called skyline quantity-utility pattern (SQUP) to provide better estimations in the decision-making process by considering quantity and utility together. Two algorithms, respectively, called SQU-Miner and SKYQUP are presented to efficiently mine the set of SQUPs. Moreover, the usage of volunteer computing is proposed to show the potential in real supermarket applications. Two new efficient utility-max structures are also mentioned for the reduction of the candidate itemsets, respectively, utilized in SQU-Miner and SKYQUP. These two new utility-max structures are used to store the upper-bound of utility for itemsets under the quantity constraint instead of frequency constraint, and the second proposed utility-max structure moreover applies a recursive updated process to further obtain strict upper-bound of utility. Our in-depth experimental results prove that SKYQUP has stronger performance when a comparison is made to SQU-Miner in terms of memory usage, runtime, and the number of candidates.
Moving from employee compliance to employee success in the cyber security domain

Organisations frequently use fear as a tool to bring employees in line with cyber security policies, assuming that it’s an effective way to assure compliance. But many people respond negatively and this approach can have the opposite effect to the one intended. Karen Renaud, Stephen Flowerday and Marc Dupuis look at how turning cyber security education into a game makes it more readily embraced by staff and can have deeper, longerlasting results.
Discourse Analysis in Voice User Interface Research

This study examines how two approaches within discourse analysis, Conversation Analysis (CA) and Interactional Sociolinguistics (IS), have been and can be applied to voice user interface (VUI) research. I review how CA has been adapted as a key tool for emulating the structure of human conversation and introduce IS as another potentially useful analytical approach. Applying IS concepts to a short transcript excerpt, I show how IS, with its ethnographic, interpretive focus, can help researchers better examine the role that sociocultural context and speaker knowledge play in shaping speakers’ engagement with VUI.
RL-based Path Planning for Autonomous Aerial Vehicles in Unknown Environments

Unmanned Aerial Systems (UASs) have become a relevant sector in the aerospace industry. In the last decade, the increase in the capabilities of Unmanned Aerial Vehicles (UAVs), paired with a drop in their price, has led them to be used in many different applications where they are employed for their versatility and efficiency. A challenge that is being addressed in this field is that of autonomous UAVs fleets, i.e., the coordinated use of multiple UAVs to perform a common task. A particularly interesting application of UAV fleets is their use in the exploration and mapping of unknown or critical environments. This topic brings with it a significant number of challenges, from the design of the policy used to coordinate the fleet to the path planning algorithm that each UAV uses to move in the environment while exploring it. In this paper, a Reinforcement Learning (RL)-based approach for the cooperative exploration of unknown environments by a fleet of UAVs is presented. Two RL agents are trained to address this problem: the first has the task of coordinating the exploration task, optimizing the way the UAVs spread in the unknown area by assigning some waypoints to them. The waypoints are placed in order to optimize the distribution of the fleet and to maximize the exploration process efficiency. The second RL agent is a path planning algorithm and is used by each UAV to move in the environment to reach the region pointed by the first agent. The combined use of the two agents allows the fleet to coordinate in the execution of the exploration task.
Overwhelming Uncertainty in Self-adaptation: An Empirical Study on PLA and CobRA

Self-adaptation is a promising approach to enable software systems to address the challenge of uncertainty. Different from traditional reactive adaptation mechanisms that focus on the system’s current environment state only, proactive adaptation mechanisms predict the potential environmental changes and make better adaptation plan accordingly. Proactive Latency-aware Adaptation (PLA for shot) and Control-based Requirements-oriented Adaptation (CobRA for short) are two representative approaches to build proactive self-adaptation mechanisms. Despite their different design and implementation details, PLA and CobRA are reported to have a very similar performance in supporting self-adaptation. In this paper, we conduct an in-depth comparison between these two approaches, trying to explain their effectiveness. We separate a proactive self-adaptation mechanism into three modules, namely system modelling, environment predicting, and uncertainty filtering. We identify the design choices of PLA and CobRA approaches, in terms of these three modules. We performed an ablation study on the three modules of PLA and compared their performance with CobRA. Our study reveals the very important role of uncertainty filtering in supporting self-adaptation, as well as the huge impact of a fluctuant environment on a self-adaptation mechanism. Based on this observation, we briefly discuss a conceptual self-adaptation mechanism, MAPE-U (monitoring, analyzing, planning, executing with uncertainty).
Intrinsic Dimension Adaptive Partitioning for Kernel Methods

We prove minimax optimal learning rates for kernel ridge regression, respectively, support vector machines, based on a data dependent partition of the input space, where the dependence of the dimension of the input space is replaced by the fractal dimension of the support of the data generating distribution. We further show that these optimal rates can be achieved by a training validation procedure without any prior knowledge on this intrinsic dimension of the data. Finally, we conduct extensive experiments which demonstrate that our considered learning methods are actually able to generalize from a dataset that is nontrivially embedded in a much higher dimensional space just as well as from the original dataset.
MicroWorld: An Augmented-Reality Arabian App to Learn Atomic Space

The visualization of objects of an abstract nature has always been a challenge for chemistry learners. Thus, augmented reality (AR) and virtual reality (VR) have been heavily invested in as immersive learning methods for these concepts. This study targets the segment of the chemistry curriculum involving the chemical elements of the periodic table. For this purpose, we developed the AR educational tool called MicroWorld. This Arabic educational AR app was developed in unity with Vuforia SDK. Using MicroWorld, students can visualize chemical elements microstructures in 3D, see 3D models of the elements in their substantial forms, and combine two chemical elements to see how certain chemical compounds can be formed. In this work, MicroWorld’s usability was evaluated by junior high school students and chemistry teachers using the Arabic system usability scale (A-SUS). The A-SUS average score was 71.5 for junior high school students, while the scale for teachers reached 76. This research aims to design, develop, and evaluate the AR app, MicroWorld. This app was built and evaluated through the lens of the design science research paradigm.
Predicting Signed Distance Functions for Visual Instance Segmentation

Visual instance segmentation is a challenging problem and becomes even more difficult if objects of interest varies unconstrained in shape. Some objects are well described by a rectangle, however, this is hardly always the case. Consider for instance long, slender objects such as ropes. Anchor-based approaches classify predefined bounding boxes as either negative or positive and thus provide a limited set of shapes that can be handled. Defining anchor-boxes that fit well to all possible shapes leads to an infeasible number of prior boxes. We explore a different approach and propose to train a neural network to compute distance maps along different directions. The network is trained at each pixel to predict the distance to the closest object contour in a given direction. By pooling the distance maps we obtain an approximation to the signed distance function (SDF). The SDF may then be thresholded in order to obtain a foreground-background segmentation. We compare this segmentation to foreground segmentations obtained from the state-of-the-art instance segmentation method YOLACT. On the COCO dataset, our segmentation yields a higher performance in terms of foreground intersection over union (IoU). However, while the distance maps contain information on the individual instances, it is not straightforward to map them to the full instance segmentation. We still believe that this idea is a promising research direction for instance segmentation, as it better captures the different shapes found in the real world.
A Deep Learning Approach for Text Generation

One of the most challenging language modeling problems is text generation. The importance of language modeling comes from its involvement in many language processing tasks such as conversational system, speech to text, and text summarization. The language models are typically trained to learn the occurrence of next word in a sequence based on previous words in the text. However, when it comes to testing, it is highly expected that the entire sequence will be generated from the scratch which is computationally not suitable to many applications. In this paper, one of the popular deep learning architectures called bidirectional recurrent neural network (BRNN) to develop a text generation approach. The recurrent neural network used in the developed approach uses long short-term memory (LSTM). By using LMST recurrent neural network, the proposed approach is well suited to making predictions based on time series data. Two representations are considered in this paper; word to vector (word2vec) and one-hot representations. The word2vec model is used with two datasets called Twilight and Alice in Wonderland stories while the one-hot model is used only with Alice in Wonderland story only. The experimental results show that the word2vec model outperforms the one-hot model when using to large data sets.
Daily Forecasting of Demand Orders With Optimal Architecture Artificial Neural Network Learning Models

In recent years, with the increase in volume of buying orders, demand forecast based on the order data is important for improvement of production, distribution and selling services. For this reason, the predictability of orders will increase efficiency in many areas by timely delivering orders, increasing earnings, and customer satisfaction in trading. This article aims to estimate total amount of daily orders by using an optimal structured artificial neural network learning model. To optimize rectangular architecture of artificial neural network model, a metaheuristic optimization, which determines the number of hidden layers and number of neurons, is used. In the study, training of neural networks was carried out with the Levenberg-Marquardt backpropagation algorithm for daily orders collected for 60 days. During this training, the network's layer and neuron number were optimized with a gray wolf optimization algorithm. Results indicate that optimal architecture neural network can better estimate total daily demand orders.
Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation

Normalizing flow models have risen as a popular solution to the problem of density estimation, enabling high-quality synthetic data generation as well as exact probability density evaluation. However, in contexts where individuals are directly associated with the training data, releasing such a model raises privacy concerns. In this work, we propose the use of normalizing flow models that provide explicit differential privacy guarantees as a novel approach to the problem of privacy-preserving density estimation. We evaluate the efficacy of our approach empirically using benchmark datasets, and we demonstrate that our method substantially outperforms previous state-of-the-art approaches. We additionally show how our algorithm can be applied to the task of differentially private anomaly detection.
Verifiable Multi‐Party Universal Blind Quantum Computing in Distributed Networks

Blind quantum computing (BQC) ensures that a classical client could delegate complex computing tasks to a remote quantum server safely. In order to detect the dishonest behavior of the participants, we present a verifiable multi-party universal BQC protocol in distributed networks. By using the stabilizer formalism, we propose an honesty check method to test the correctness of the graph states generated by the servers. The honesty of both the clients and the servers can be judged fairly with the help of the arbitrator. Moreover, a load balancer is introduced to control the possible breakdown of servers in the network. No-signaling principle ensures the unconditional security of the protocol. Through the use of universal resource states, our protocol can be applied in more multi-party verifiable universal BQC protocols. The failure management and workload balancing of the protocol also provide a promising prospect for the future BQC network.