Reviewerly/exHarmony huggingface dataset:
From Latin to Modern Italian: Some Notes on Negation

This article aims at investigating some diachronic aspects of the Italian negative system, considering a time span ranging from Old Latin to Modern Italian. Most of the negative polarity phenomena populating the Modern Italian system are consequences of a crucial change that occurred in Old Latin: The Latin negative morpheme nōn (“not”), which initially displayed a maximal projection status, and became a syntactic (negative) head. This change caused the shift from a double negation system to a negative concord one, which affects many Romance languages (and their dialects). It also determines the availability of the expletive reading of negation in Italian, as well as in other Romance languages (ex. French), calling for a new generalization: only languages (and structures) displaying a negative head allow the expletive interpretation of negation, languages displaying a maximal projection status do not.
Privacy-Preserving Bin-Packing With Differential Privacy

With the emerging of e-commerce, package theft is at a high level: It is reported that 1.7 million packages are stolen or lost every day in the U.S. in 2020, which costs $25 million every day for the lost packages and the service. Information leakage during transportation is an important reason for theft since thieves can identify which truck is the target that contains the valuable products. In this paper, we address the privacy and security issues in bin-packing, which is an algorithm used in delivery centers to determine which packages should be loaded together to a certain truck. Data such as the weight of the packages is needed when assigning items into trucks, which can be called bins. However, the information is sensitive and can be used to identify the contents in the package. To provide security and privacy during bin-packing, we propose two different privacy-preserving data publishing methods. Both approaches use differential privacy (DP) to hide the existence of any specific package to prevent it from being identified by malicious users. The first approach combines differential privacy with k-anonymity, and the other one applies clustering before differential privacy. Our extensive analyses and experimental results clearly show that our proposed approaches have better privacy guarantees, better efficiency, and better performance than the existing works that use either differential privacy or k-anonymity.
Dense Optical Flow using RAFT

RAFT is a deep network architecture for the detection of optical flow in the images. The RAFT model relates the per pixel motion between images even for minor changes in the position of the objects. It also updates the flow of field through recurrent units that perform lookups on the performance of the model. RAFT also works well with different datatypes and also it has better efficiency, training speed and count of parameters. Experiments were performed by using different parameters and also by changing certain values in the model itself. One cycle learning was also used to find the best parameters for the model. We also found that the RAFT model performs better than most of the other existing models for optical flow calculation in to images.
Abnormal Behaviour Detection in Smart Home Environments

The study of user behaviour patterns in activities of daily living is significantly challenging due to the ambiguity in identifying a usual and unusual activities. Capturing the user activities using ambient sensors in a smart home environment serves as the safest and most effective way. Detecting the unusual activities of the residents helps in regularizing their daily tasks. The paper focuses on unsupervised methods for anomaly detection based on clustering. The comparison study is done between different unsupervised clustering models: self-organizing map, density-based clustering, and Gaussian mixture models. The unsupervised clustering recognizes the normal user behaviour of each activity and looks for any deviations or abnormalities. The experimental study is done on real-time public datasets, and the silhouette score were used to identify the best abnormality rate for each activity.
Hyperchaos and MD5 Based Efficient Color Image Cipher

While designing and developing encryption algorithms for text and images, the main focus has remained on security. This has led to insufficient attention on the improvement of encryption efficiency, enhancement of hyperchaotic sequence randomness, and dynamic DNA-based S-box. In this regard, a new symmetric block cipher scheme has been proposed. It uses dynamic DNA-based S-box connected with MD5 and a hyperchaotic system to produce confusion and diffusion for encrypting color images. Our proposed scheme supports various size color images. It generates three DNA based S-boxes for substitution namely DNA_1_s-box, DNA_2_s-box and DNA_3_s-box, each of size . Next, the 4D hyperchaotic system followed by MD5 is employed in a novel way to enhance security. The three DNA-based S-boxes are generated from real DNA sequences taken from National Center for Biotechnology Information (NCBI) databases and are dependent on the mean intensity value of an input image, thus effectively introducing content-based confusion. Finally, Conservative Site-Specific Recombination (CSSR) is applied on the output DNA received from DNA based S-boxes. The experimental results indicate that the proposed encryption scheme is more secure, robust, and computationally efficient than some of the recently published similar works. Being computational efficient, our proposed scheme is feasible on many emergent resource-constrained platforms.
SEM: Safe exploration mask for q-learning

Most reinforcement learning algorithms focus on discovering the optimal policy to maximize reward while neglecting the safety issue during the exploration stage, which is not acceptable in industrial applications. This paper concerns the efficient method to improve the safety of the agent during the exploration stage in q-learning without any prior knowledge. We propose a novel approach named safe exploration mask to reduce the number of safety violations in q-learning by modifying the transition possibility of the environment. To this end, a safety indicator function consisting of distance metric and controllability metric is designed. The safety indicator function can be learned by the agent through bootstrapping without additional optimization solver. We prove that the safety indicator function will converge in tabular q-learning and introduce two tricks to mitigate the divergence in approximation-based q-learning. Based on the safety indicator function, the safe exploration mask is generated to modify the original exploration policy by reducing the transition possibility of unsafe actions. Finally, the simulations in both discrete and continuous environments demonstrate the advantages, feasibility, and safety of our method in both discrete and continuous q-learning algorithms.
“It Was Really All About Books:” Speech-like Techno-Masculinity in the Rhetoric of Dot-Com Era Web Design Books

The future of Human-computer interaction (HCI) communication requires researchers to develop a strong understanding of the factors that influence design practitioners. As a step towards building that understanding, based on interviews conducted with veteran web designers, we analyze a corpus of popular web design books published during and shortly after the dot-com boom. Using a combination of ethnographic methods and discourse analysis, we identify the rhetorical strategies in these books and why they were successful in shaping our participants’ ideas about web design. We find that the books exhibit a particular style of technical writing defined by a speech-like techno-masculinity . Despite their short shelf-lives, the books and their writing style contributed to the disciplinary identity of web design which exists today. Studying the history of best practice books is an important opportunity to reflect on the genre of best practices in design, and how we should frame them in the future.
Athlete Gait Feature Recognition Method Based on Multisource Sensing Information

Aiming at the problem of low accuracy of two-dimensional gait recognition at present, a gait feature recognition method based on multisource sensing information is proposed. The multisource sensing information is combined to collect the athlete’s gait characteristics, collect the single frame gait image sequence of the human lower limbs during the movement, and extract the human body’s three-dimensional feature data during human walking by using the body structure and multisource sensing information, so as to realize the separation of the athlete’s gait image background. Finally, it is confirmed by experiments that the recognition rate of athlete gait feature recognition method based on multisource sensing information is significantly improved.
Getting Insight into Noise, Vibration, and Harshness Simulation Data

Due to ever stricter noise regulations and increasing passenger comfort requirements, noise, vibration, and harshness (NVH) simulation remains very important in the automotive industry. Noise simulation data analysis is challenging due to the data size and complexity. The Campbell diagram is a standard tool for noise analysis that summarizes noise for all frequencies and all operational points. We propose an interactive approach to a detailed analysis of the noise simulation data by means of interactive visualization. This approach, as an addition to the conventional analysis, provides additional insight into the data and facilitates the understanding of the simulated phenomenon. We deploy a coordinated multiple views configuration consisting of a parallel coordinates view, a 3D view, and three multi-view projections. The approach is illustrated using NVH simulation data for a four cylinder internal combustion engine.
Decision Framework for Cross-Platform Mobile Development Frameworks Using an Integrated Multi-Criteria Decision-Making Methodology

Because of the growing popularity of smartphones and tablets, the use of mobile applications has exploded recently. However, the variety of mobile platforms compels developers to create an app for each, making the process more complex and expensive. Thus, open-source cross-platform mobile frameworks have been developed to address this problem, allowing the same code to be imported across various operating systems. This paper comes to propose a new framework for the selection of the appropriate platform for the implementation of a cross-platform mobile application. This framework is based on the most used multi-criteria decision-making (MCDM) methods, namely analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) methods are used. A demonstrative example is proposed to illustrate the suggested methodology.
Understanding Machine Learning Through Data-Oriented and Human Learning Approaches

As machine learning, a subset of artificial intelligence, has inspired numerous breakthroughs, a recent surge in interest in this field is observed across a wide variety of disciplines. This chapter aims to point out some of the main concepts and models of this attractive area of research while considering data-oriented and human learning approaches. We try to provide an understandable overview of common models, including supervised and unsupervised models, while also delving into the algorithms that make these models function, such as the k-nearest-neighbor, decision trees, and clustering. Furthermore, the importance of bias and fairness in the ML models, overfitting and overgeneralization, validation strategies, and big data are presented. Finally, we transition to concepts and examples in more complex models, such as deep learning, computer vision, transfer learning, autoencoders, and natural language processing, to highlight the applicability of these exciting machine learning models.
Enhancement of Biomass Material Characterization Images Using an Improved U-Net

For scanning electron microscopes with high resolution and a strong electric field, biomass materials under observation are prone to radiation damage from the electron beam. This results in blurred or non-viable images, which affect further observation of material microscopic morphology and characterization. Restoring blurred images to their original sharpness is still a challenging problem in image processing. Traditional methods can't effectively separate image context dependency and texture information, affect the effect of image enhancement and deblurring, and are prone to gradient disappearance during model training, resulting in great difficulty in model training. In this paper, we propose the use of an improved U-Net (U-shaped Convolutional Neural Network) to achieve image enhancement for biomass material characterization and restore blurred images to their original sharpness. The main work is as follows: use of depthwise separable convolution instead of standard convolution in U-Net to reduce model computation effort and parameters; embedding wavelet transform into the U-Net structure to separate image context and texture information, thereby improving image reconstruction quality; using dense multi-receptive field channel modules to extract image detail information, thereby better transmitting the image features and network gradients, and reduce the difficulty of training. The experiments show that the improved U-Net model proposed in this paper is suitable and effective for enhanced deblurring of biomass material characterization images. The PSNR (Peak Signal-to-noise Ratio) and SSIM (Structural Similarity) are enhanced as well.
Self-Paced Knowledge Distillation for Real-Time Image Guided Depth Completion

Image guided depth completion aims to generate a dense depth map from a sparse one with the guidance of a color image. Previous high-accuracy methods often rely on complex networks that are large in size and expensive in computational cost, making them inapplicable to real-time platforms. In this letter, we propose a self-paced knowledge distillation method, which obtains a lightweight but accurate depth completion model via distilling knowledge from a complex teacher network. Specifically, by taking advantage of the easy-to-hard learning curriculum in deep networks, we first design a groundtruth-free hard-pixel mining module to tell hard and noisy pixels in the teacher&#x2019;s output. Then, we design two self-paced distillation losses, which gradually introduce hard pixels to distill the depth and structure knowledge from the teacher to the compact student network. Experiments on the KITTI benchmark show that the proposed method can improve the original student model by a considerable margin. The distilled compact and real-time student model outperforms all previous lightweight networks, mitigating the performance gap with state-of-the-art high-accuracy but complex models.
An Improved Method for Online Teacher Training Courses for Bilingual Education Based on Cyber-Physical Systems

In recent years, bilingual education has become a critical index, and with its growth, internationalization has been significantly improved in the Republic of China (Taiwan). Therefore, countries worldwide are promoting all-English teaching, taking English as a medium of instruction and indicator of university education while moving toward an internationalized curriculum and teaching excellence. Finding the required number of bilingual teachers is also one of the keys to increasing the index. If information technology can be adopted to reduce the time and cost of bilingual teacher training, it will significantly improve the effectiveness of bilingual education promotion. However, the current traditional online training system only focuses on the delivery of one-way training content, and it cannot assist in judging the training effectiveness of the trainees. In this paper, we integrate the pose estimation technology into the online teacher training system to analyze the interactive content of the trainees during the training. We can assist in recording and interpreting the teaching demonstration process in classroom observation. We also verify the result by comparing our method’s efficiency and judgment accuracy with the traditional way. The results show that our approach is more convenient and cost-efficient.
Weighted-adaptive Inertia Strategy for Multi-objective Scheduling in Multi-clouds

One of the fundamental problems associated with scheduling workflows on virtual machines in a multi-cloud environment is how to find a near-optimum permutation. The workflow scheduling involves assigning independent computational jobs with conflicting objectives to a set of virtual machines. Most optimization methods for solving non-deterministic polynomial-time hardness (NP-hard) problems deploy multi-objective algorithms. As such, Pareto dominance is one of the most efficient criteria for determining the best solutions within the Pareto front. However, the main drawback of this method is that it requires a reasonably long time to provide an optimum solution. In this paper, a new multi-objective minimum weight algorithm is used to derive the Pareto front. The conflicting objectives considered are reliability, cost, resource utilization, risk probability and makespan. Because multi-objective algorithms select a number of permutations with an optimal trade-off between conflicting objectives, we propose a new decision-making approach named the minimum weight optimization (MWO). MWO produces alternative weight to determine the inertia weight by using an adaptive strategy to provide an appropriate alternative for all optimal solutions. This way, consumers’ needs and service providers’ interests are taken into account. Using standard scientific workflows with conflicting objectives, we compare our proposed multi-objective scheduling algorithm using minimum weigh optimization (MOS-MWO) with multi-objective scheduling algorithm (MOS). Results show that MOS-MWO outperforms MOS in term of QoS satisfaction rate.
Verification of Mixed Production Rules Correctness in Intelligent Systems for Diagnosing Industrial Equipment

In this paper, we propose an approach to verifying the correctness of mixed production rules (MPR) in intelligent systems for diagnosing industrial equipment (IE) using the rule evaluation that is necessary to determine the degree of similarity between two rules. Complexity, relevance, and significance of the rules serve as the rule evaluation. Using rule evaluations, it is easy to detect incorrect and redundant rules in the knowledge base (KB) of IE diagnostic systems under conditions of heterogeneous information. The examples of main structural errors that occur in MPR systems (redundancy, cyclicity, incompleteness) are considered in details. The approach proposed is firstly aimed at simplifying the KB containing MPR that are added to the intelligent system of IE diagnostics which results in usefulness and accessibility for the knowledge interpretation by an expert; secondly, it will allow structuring the fuzzy knowledge and facilitating the organization of output control.
Wor(l)d-GAN: Toward Natural-Language-Based PCG in <i>Minecraft</i>

This article presents Wor(l)d-GAN, a method to perform data-driven procedural content generation via machine learning in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Minecraft</i> from a single example. Based on a 3-D generative adversarial network (GAN) architecture, we are able to create arbitrarily sized world snippets from a given sample. Our method applies dense representations used in natural language processing in two ways. First, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">block2vec</i> representations based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">word2vec</i> . Second, we use the pretrained large language model bidirectional encoder representations from transformers (BERT) to generate representations directly from the token names. These representations make Wor(l)d-GAN independent of the number of different blocks, which can vary a lot in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Minecraft</i> , and enable the generation of larger levels. We evaluate our approach on creations from the community as well as structures generated with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Minecraft</i> World Generator under several metrics. Wor(l)d-GAN enables its users to generate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Minecraft</i> worlds based on parts of their creations.
Graph Neural Network Based Behavior Prediction to Support Multi-Agent Reinforcement Learning in Military Training Simulations

We introduce a computational behavioral model for non-player characters (NPCs) that endows them with the ability to adapt to their experiences &#x2014; including interactions with human trainees. Most existing NPC behavioral models for military training simulations are either rule-based or reactive with minimal built-in intelligence. Such models are unable to adapt to the characters&#x0027; experiences, be they with other NPCs, the environment, or human trainees. Multi-agent Reinforcement Learning (MARL) presents opportunities to train adaptive models for both friendly and opposing forces to improve the quality of NPCs. Still, military environments present significant challenges since they can be stochastic, partially observable, and non-stationary. We discuss our MARL framework to devise NPCs exhibiting dynamic, authentic behavior and introduce a novel Graph Neural Network based behavior prediction model to strengthen their cooperation. We demonstrate the efficacy of our behavior prediction model in a proof-of-concept multi-agent military scenario.
Transfer Learning: Leveraging Trained Models on Novel Tasks

This chapter provides a brief introduction to transfer learning with history and its importance. As data collection and labeling in a new domain are challenging, transfer learning can play a vital role to build a reusable model. After explaining the fundamentals of transfer learning, the strategies are presented followed by different pre-trained models in the fields of computer vision and natural language processing. We explored prominent models like VGG-16, Inception, ULMFiT, and BERT. After mentioning the successful models, applications and limitations of transfer learning have been discussed.