Reviewerly/exHarmony huggingface dataset:
Differentiable Light-Weight Architecture Search

In this paper, we propose a differentiable light-weight architecture search method, called DLWAS. It can search for CNNs with fewer parameters and floating point operations (FLOP-s) while maintaining the SOTA performance. Concretely, we first build a new light-weight search space, which contains the latest and effective light-weight operations, limiting the parameters and FLOPs from the source of neural architecture search (NAS). Secondly, we propose an effective neural architecture optimization method, which results in a more sparse and robust topology for differentiable NAS. Experimental results show that DLWAS achieves an error rate of 2.76% on CIFAR10 comparable to the state-of-the-art methods, using only 2.4M params and 336M FLOPs. The parameters and FLOPs are reduced by 30% and 36% compared with the closest counterpart DARTS, respectively. On ImageNet, our model achieves 3.2% better top-1 accuracy than the SOTA MobileNet, while using fewer parameters and FLOPs.
Defining Features of Behavior Design

Since the early recognition of Persuasive Technologies, the notion of digital design with the intent to influence and change attitudes and behaviors has evolved tremendously. Digital influence and behavior design is now recognize in a variety of theoretical and methodological approaches, and behavior design is continuously influencing more complex application domains. With the development of the field emerges a need for clearer understanding of these different design approaches, their possibilities and limitations, in order for designers to identify and apply the best approach depending on the intent and context. In this paper, we discuss some of the primary overlaps between Nudging and Persuasive Design. Moreover, we point towards research in neuroscience in order to highlight a potential need to reconsider central elements of persuasive design, such as efficient praise and rewards. The aim of the paper is to contribute to the ongoing discussion about the role of behavior design in digital media, and the ethical considerations that emerge from this domain.
Regulating Storage Overhead in Existing PoW-based Blockchains

Proof of Work (PoW) blockchains regulate the frequency and security of extensions to the blockchain in a decentralized manner by adjusting the difficulty in the network. However, analogous decentralized measures to regulate the replication level of the associated transactions and blocks data are completely missing so far. We argue that such measures are required as well. On the one hand, the smaller the number of replicas, the higher the vulnerability of the system against compromises and DoS-attacks. On the other hand, the larger the number of replicas, the higher the storage overhead, and the higher the operational blockchain cost are. In this paper, we propose a novel solution, EWoK (Entangled proofs of WOrk and Knowledge), that regulates in a decentralized manner the minimum number of replicas that should be stored by miners in the blockchain. EWoK achieves this by tying replication to the only directly-incentivized process in PoW-blockchains -- which is PoW itself. EWoK only incurs small modifications to existing PoW protocols and is fully compliant with the specifications of existing mining hardware. Our implementation results confirm that EWoK can be easily integrated within existing mining pool protocols, such as GetBlockTemplate and Stratum mining, and does not impair the mining efficiency.
Comprehensive Unified Indicator of the Smart System's Quality: Application to e-Learning

This paper discusses the issue of algebraic methods in the digitalization of smart systems that allow building complex characteristics of qualitative and quantitative measures of the system, and on this basis, a comprehensive unified indicator of the system’s some properties is introduced. Within the framework of the Zermelo–Fraenkel set theory axiomatic, the existence of such an indicator is proved. In fact, thereby the solution of the generalization of multicriteria Operation Research problem has been obtained within the framework of the Zermelo–Fraenkel set theory axiomatic. Received results can be used in e-learning and in smart learning theory in assessment of control function of smart learning system’s functioning.
Action Prediction Network with Auxiliary Observation Ratio Regression

This paper focuses on predicting the category of an ongoing action with incomplete observations. We propose a novel Action Prediction Network with Auxiliary Observation Ratio regression (AORAP Net), which enhances the discriminative power of partial video features by encoding the prior knowledge of complete actions. The proposed AORAP Net consists of an encoder, an observation ratio regression module, and an action classifier. The encoder transfers global action information from full videos to construct enhanced partial video features. The observation ratio regression module is developed to achieve an auxiliary task of inferring the progress level of an ongoing action. We demonstrate that this module can guide the encoder to learn enhanced features similar to full video features and discriminative for action prediction. The classifier makes the final decision of the action category in terms of the enhanced features. Considering the fact that there is not enough discriminative information at the early stage of actions, a new classification loss is designed to adapt to the action prediction task and alleviate the over-fitting of training videos. Extensive experiments on the BIT-Interaction dataset and the UT-Interaction dataset validate the effectiveness of the proposed method.
Cybersecurity and Privacy Capabilities Model for Data Management Against Cyber-Attacks in the Health Sector

In this paper, we propose a model of capabilities that identify the reliability degree of Cybersecurity and Privacy elements applied to the Health Sector. The increasing interactions between technology and the health sector have brought a new set of risks to be confronted, such as data breaches and cyberattacks. However, in order to improve, a greater understanding of their current situation is needed. The proposal identifies the capability level for the organizations to know their maturity level comprehensively. This was achieved by selecting existing models, frameworks, and regulations, increasing their complexity, integrating their privacy and cybersecurity capabilities, and health data management. In this way, the proposal is supported by a tool prepared for outcome estimation and diagnosis. The model structure is organized into categories and subcategories, and the assessment is made according to the level of compliance with controls, for which five levels of maturity were defined: 1. Basic, 2. In Progress, 3. Defined, 4. Differentiated, 5. Continuous Improvement. The model was validated and proven in a private hospital in Lima, Peru. The preliminary results are related to the model application in the selected process. As a result, we found that the private hospital obtained a level of maturity of 2-In Progress. Based on this, we made some recommendations to improve the capacities of the assessed health provider. A comparison was made between the proposed model's results and the ones obtained through its root components. Said results were similar, thus proving that a coherent and comprehensive integration was achieved.
‘She’s Nice Company and a Good Friend’: Thinking with Haraway to Reconceptualise Children’s Playful Interactions with Alexa in the Family Home

The chapter explores how children think about Alexa—the conversational agent that inhabits Amazon’s The Echo device—and what happens when they playfully engage with it/her in a family context. The chapter examines potentials of children’s play and playfulness with Alexa through Donna Haraway’s concepts of ‘the cyborg’ and ‘making kin’ (Haraway, Staying with the Trouble: Making Kin in the Chthulucene, Duke University Press, Durham, 2016), which trouble the divide between human/non-human. The chapter explores observations and conversations gathered in a single family context in relation to Haraway’s theoretical framing. It argues that the observed interactions with Alexa suggest some active troubling of the human/non-human divide, but that simultaneously parental mediation, difficulties in children’s interactions with the Alexa interface, and the commercialisation of Alexa’s responses, work to inhibit children’s playfulness.
Decoding Neural Signals with a Compact and Interpretable Convolutional Neural Network

In this work, we motivate and present a novel compact CNN. For the architectures that combine the adaptation in both space and time, we describen a theoretically justified approach to interpreting the temporal and spatial weights. We apply the proposed architecture to Berlin BCI IV competition and our own datasets to decode electrocorticogram into finger kinematics. Without feature engineering our architecture delivers similar or better decoding accuracy as compared to the BCI competition winner. After training the network, we interpret the solution (spatial and temporal convolution weights) and extract physiologically meaningful patterns.
QoS for best‐effort batch jobs in container‐based cloud

Summary The resource orchestrators in the cloud provide different QoS classes. Existing studies have mainly focused on resource guarantee for high‐priority class jobs, but yet to consider delicate QoS control of low‐priority jobs in the cloud. In this paper, we propose the differentiated shares that provide weighted CPU scheduling for low‐priority batch jobs in container‐based cloud. To implement the differentiated shares, we extend resource reservation interfaces provided by Kubernetes and suggest an algorithm that maps the resource management attributes of Kubernetes into those of Linux. The proposed differentiated shares can avoid worsening interference with high‐priority containers by exploiting the hierarchical resource sharing of task groups in Linux. In addition, we suggest a node scoring policy to provide efficient inter‐node job scheduling with the consideration of the differentiated shares. The performance measurement results show that the differentiated shares can provide weighted CPU scheduling for best‐effort batch containers without interference with high‐priority containers (less than 3% with respect to application‐level performance).
Deep Neural Message Passing With Hierarchical Layer Aggregation and Neighbor Normalization

As a unified framework for graph neural networks, message passing-based neural network (MPNN) has attracted a lot of research interest and has been shown successfully in a number of domains in recent years. However, because of over-smoothing and vanishing gradients, deep MPNNs are still difficult to train. To alleviate these issues, we first introduce a deep hierarchical layer aggregation (DHLA) strategy, which utilizes a block-based layer aggregation to aggregate representations from different layers and transfers the output of the previous block to the subsequent block, so that deeper MPNNs can be easily trained. Additionally, to stabilize the training process, we also develop a novel normalization strategy, neighbor normalization (NeighborNorm), which normalizes the neighbor of each node to further address the training issue in deep MPNNs. Our analysis reveals that NeighborNorm can smooth the gradient of the loss function, i.e., adding NeighborNorm makes the optimization landscape much easier to navigate. Experimental results on two typical graph pattern-recognition tasks, including node classification and graph classification, demonstrate the necessity and effectiveness of the proposed strategies for graph message-passing neural networks.
Multi-source Transfer Learning for Deep Reinforcement Learning

Deep reinforcement learning has obtained impressive performance in challenging tasks in recent years. Nevertheless, it has important limitations such as long training times and the number instances that are needed to achieve acceptable performance. Transfer learning offers an alternative to alleviate these limitations. In this paper, we propose a novel method for transferring knowledge from more than one source tasks. First, we select the best source tasks using a regressor that predicts the performance of a pre-trained model in the target task. Then, we apply a selection of relevant convolutional kernels for the target task in order to find a target model with similar number of parameters compared to the source ones. According to the results, our approach outperforms the accumulated reward obtained when learning from scratch in 20.62% using lower parameters (about 56% of the total, depending on the specific game).
Learning through gaming in times of COVID-19: The pedagogical value of Edugames

Distance learning in the COVID-19 period has led teachers to use digital skills and to update themselves in order to promote learning. Starting from OECD, DESI and ISTAT data, only a small percentage of teachers are ready for distance teaching; in the literature it is agreed that the didactic action follows technological innovation but has not yet managed it. An implicit factor could be the lack of knowledge of digital content and digital tools by teachers. The guidelines issued by the Ministry during the COVID-19 period highlighted the importance of using digital tools to motivate student learning and foster relationships. Many authors and studies have shown the effectiveness of edugames, video games with educational purposes, as mediators that enhance the cognitive skills of students, which motivate students to learn and encourage the creation of relationships: crucial factors for inclusive teaching. Statistics have shown that during the first wave of the COVID-19 pandemic, the use of videogames significantly increased. Given the lack of digital knowledge of teachers and the high use of video games, the aim of the article is to provide an overview of edugames, giving a crucial role to the teaching action of the teacher who must support students in the organization of thought and in the development of new types of skills that they will need to understand digital materials.
Multiplierless MP-Kernel Machine for Energy-Efficient Edge Devices

We present a novel framework for designing multiplierless kernel machines that can be used on resource-constrained platforms such as intelligent edge devices. The framework uses a piecewise linear (PWL) approximation based on a margin propagation (MP) technique and uses only addition/subtraction, shift, comparison, and register underflow/overflow operations. We propose a hardware-friendly MP-based inference and online training algorithm that has been optimized for a field-programmable gate array (FPGA) platform. Our FPGA implementation eliminates the need for digital signal processor (DSP) units and reduces the number of Look-Up Tables (LUTs). By reusing the same hardware for inference and training, we show that the platform can overcome classification errors and local minima artifacts that result from MP approximation. The implementation of this proposed multiplierless MP-kernel machine on FPGA results in an estimated energy consumption of 13.4 pJ and power consumption of 107 mW with ~9 k LUTs and Flip Flops (FFs) each for a 256 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 32 sized kernel making it superior in terms of power, performance, and area compared with other comparable implementations.
Application of Multi-objective Optimization to Feature Selection for a Difficult Data Classification Task

Many different decision problems require taking a compromise between the various goals we want to achieve into account. A specific group of features often decides the state of a given object. An example of such a task is the feature selection that allows increasing the decision’s quality while minimizing the cost of features or the total budget. The work’s main purpose is to compare feature selection methods such as the classical approach, the one-objective optimization, and the multi-objective optimization. The article proposes a feature selection algorithm using the Genetic Algorithm with various criteria, i.e., the cost and accuracy. In this way, the optimal Pareto points for the nonlinear problem of multi-criteria optimization were obtained. These points constitute a compromise between two conflicting objectives. By carrying out various experiments on various base classifiers, it has been shown that the proposed approach can be used in the task of optimizing difficult data.
Study on Flood Simulation of Tallo Watershed, Makassar City, South Sulawesi Province

Abstract Tallo watershed has a length of 70 km, its area width is 407.00 ¬¬km 2 , the river upstream or downstream is at an altitude of ± 1,100 m above sea level located on Mount Kaliapolompo in which the overall length of the river in Tallo watershed totaling ± 131.50 km2. The research objective was to analyze the effect of five-year flood discharge and tidal changes on the inundation area and flood depth in Tallo watershed. The method of rainfall analysis used the Log Pearson type III method where the flow is considered to be two horizontal dimensions with even velocity at each depth. A five-year period with a discharge value of 127.64 m 3 /s will statistically reoccur every five years with an estimated chance of occurrence of 0.2 or 20% for each year referred to in a 5-year review cycle. The results of the simulation were then known that the five-year flood discharge resulted in flooding with a depth of 0-3 meters from the maximum tide condition (HWSL), and an inundation area of 31.91 km 2 inundated the Tallo District, Tamalanrea District, Panakukang District, West Biringkanaya District and northern Manggala District. These areas are located on the left and right of the Tallo River with an altitude between 0 and 2 meters above sea level.
Convolutional Neural Networks for Breast Tumor Classification using Structured Features

Breast cancer is the second major reason for cancer death among women. Early diagnoses are incredibly crucial to increase the possibility of survival. Computer-aided detection has gained a lot of interest in the literature to help pathologists discriminate between malignant and benign tumors. Following remarkable successes on a wide range of diagnosis tasks, machine learning techniques attract substantial interest from medical researchers. In this paper, we propose a deep learning architecture to support breast tumors detection using structured features. First, we evaluated and compared the performance of multiple state-of-the-art machine learning approaches, including Support Vector Machine, Decision Tree, Logistic Regression, and Convolutional Neural Networks (CNN). Additionally, a combined ensemble model with three base models has been constructed to induce better generalization performance. We evaluated these approaches to automatically classify the tumors using a publicly available breast cancer dataset, the Wisconsin Carcinoma dataset (WBCD). Experimental results indicate that the highest classification accuracy (98 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ) belongs to the proposed CNN deep model classifier.
Purify and Generate: Learning Faithful Item-to-Item Graph from Noisy User-Item Interaction Behaviors

Matching is almost the first and most fundamental step in recommender systems, that is to quickly select hundreds or thousands of related entities from the whole commodity pool. Among all the matching methods, item-to-item (I2I) graph based matching is a handy and highly effective approach and is widely used in most applications, owing to the essential relationships of entities described in a powerful I2I graph. Yet, the I2I graph is not a ready-made product in a data source. To obtain it from users' behaviors, a common practice in the industry is to construct the graph based on the similarity of item embeddings or co-occurrence frequency directly. However, these methods tend to lose the complicated correlations (high-ordered or nonlinear) inside decision-making actions and cannot achieve the global optimal solution. Moreover, the correlations between items are usually contained in users' short-term actions, which are full of noise information (e.g. spurious association, missing connection). It is vitally important to filter out noise while generating the graph. In this paper, we propose a novel framework called Purified Graph Generation (PGG) dedicated to learn faithful I2I graph from sparse and noisy behavior data. We capture the 'confidence value' between user and item to get rid of exception action during decision making, and leverage it to re-sample purified sets that are fed into an unsupervised I2I graph structure learning framework called GPBG. Extensive experimental results from both simulation and real data demonstrate that our method could significantly benefit the performance of I2I graph compared to the typical baselines.
Neurophysiological Assessment of Image Quality from EEG Using Persistent Homology of Brain Network

The neuropsychological characteristics inside the brain are still not sufficiently understood during the conventional image quality assessment procedure. In this paper, we extract the physiologically meaningful features of brain responses to different distortion levels images in conventional image assessment by combining persistent homology analysis with electroencephalogram (EEG). The experimental results show that more brain regions in the frontal lobe are involved when the subject perceives an unclear image compared to a clear one, which indicates that human perception of image quality might be related to the advanced cognitive processes to some extent. Meanwhile, a statistically significantly higher persistent entropy of EEG data evoked by a clear image compared to that of an unclear image is observed in several frequency bands. In general, this paper evaluates and quantifies quality-related neural correlates by persistent homology features of EEG signal, which provides an approach for a utility neurophysiological assessment of image quality.
Matching Ontologies with Word2Vec Model Based on Cosine Similarity

The ontology matching technique aims at solving the ontology heterogeneity problem by finding the semantically identical entities between two ontologies. Modeling ontologies in vector space and using similarity measures based on vector space to calculate two entities’ similarity is an effective method. In this work, a Word2Vec based ontology matching technique (W2V-OM) is proposed to calculate entities’ similarity, and the Wikipedia training data are used to improve the generalizability of the model and the accuracy of alignments. In the experiment, the performance of W2V-OM is tested with the benchmark track provided by Ontology Alignment Evaluation Initiative (OAEI). The experimental results show that W2V-OM can determine better alignments than OAEI’s participants.