Reviewerly/exHarmony huggingface dataset:
Low-parameter hybrid attention model based image classification

The hybrid domain attention model was introduced into deep learning network and has good performance in computer vision recognition and classification tasks for considering both spatial and channel domain information, which leads to higher network complicity, more parameters and much training time. In this paper, an Efficient Hybrid Attention(EHA) model was proposed. The spatial and channel domain information was extracted and fused into the EHA-block, that can be embedded in deep network Resnet50 and lightweight mobile network MobileNetV2 to improve their capabilities with low parameter- increment. The experimental results on CIFAR10&100 and miniImageNet show that the number of parameters in EHA model decreases by 15.4% compared with CA hybrid model, and only increases by 0.49% compared with ECA channel model. Moreover, the accuracy of EHA classification is also improved.
Research on key technologies and development of blockchain

Blockchain is considered as a revolutionary technology that has recently fueled extensive attention across many industries. Except for Bitcoin, it has been applied into diverse areas ranging from finance, logistics to food traceability and medical care. It will not take long for blockchain to change the whole society. Although there have been a large number of papers concentrating on blockchain application in a variety of areas, there is lack of a comprehensive survey about blockchain from the perspective of technology and application. To this end, we present a comprehensive overview on the core technologies in blockchain, including cryptography, smart contract and consensus mechanism. In addition, this paper reviews blockchain application and discusses existing problems and bottlenecks in the development of blockchain technology.
An intelligent model for news and publishing industry

News and publishing industry had encountered dramatically changes in the last two decades. The traditional companies in the industry are facing challenges in the so-called new media era from two aspects. On the one hand, technology companies consistently occupy their territories, on the other hand, the traditional publication lifecycle suffers a long supply chain management and can hardly cope with the fast-changing internet realities. In order to deal with these problems, this paper proposes an intelligent publishing model. The model integrates modern information technologies into the publishing workflow in which artificial intelligence plays an important role. By machine learning technologies, the vast amount of intelligent properties accumulated by publishing houses can be converted to machine understandable knowledge and further to enhance users' experiences. The proposed model refines the publishing process and helps the industry entities facing the process of incoming media convergence.
Research on artificial intelligence search algorithm for optimization problem

With the continuous innovation of information technology, the field of artificial intelligence has seen unprecedented development. At present, artificial intelligence is involved in all aspects of our life, and solving the problems in our life is essentially to optimize the problem structure. For artificial intelligence algorithm, the simplest search mode is to search the problem directly. The artificial intelligence search algorithm does not need the derivative information with the constraint function and the objective function, but only needs the function information. The algorithm is an effective way to optimize the solution of undifferentiable or relatively high cost of differentiable. In this paper, the optimization problem is described, and artificial intelligence search algorithm is studied, and the special use of artificial intelligence technology in the field of dynamic programming algorithm is analyzed. This paper studies the applicable fields and characteristics of breadth search algorithm and depth search algorithm, and finally tests the results of the algorithm research.
An efficient image encryption algorithm based on chaos systems and DNA operations

The chaotic systems and DNA operations are used to encrypt images jointly. The fractional Chen Chaotic System is used to generate key DNA images, and the logical map is almost applied to all encryption steps. The values of all the image pixels are first converted from decimal to quaternary, and then represented by DNA sequences. And the DNA computing methods are applied to the diffusion step between the key DNA and the DNA image converted from the plain image. Experiments can prove that the image encryption algorithm proposed by us can meet the requirements of image encryption, and can resist various attack methods, including noise attack, differential attack, etc.
ISD-SSD: image splicing detection by using modified single shot MultiBox detector

Detecting image splicing has become essential to fight against malicious forgery. To solve the problems of low accuracy of authenticity classification and low detection efficiency in some image splicing detection methods proposed in recent years, we propose an image splicing forgery detection method based on modified SSD network (ISD-SSD). In this method, the residual network model ResNet-50 is used to replace the feature extraction backbone network VGG-16 in SSD, which solves the problem of degradation caused by the deepening of the network and enhances the feature extraction ability of the model. In the multi-scale detection part, a multi-scale feature fusion module is introduced based on the feature pyramid idea, which organically combines the low-level visual features and high-level semantic features in the network structure. Finally, Focal loss is selected as the loss function in the loss calculation part to solve the problem of positive and negative samples and the imbalance of difficult and easy samples. Experiments on standard image manipulation datasets demonstrate that our ISD-SSD algorithm is superior to the existing image tamper detection algorithms (such as MFCN, Faster R-CNN algorithm, etc.), the evaluation metrics AP and F1 reach 77.86% and 75.18% respectively. In addition, it shows robustness in terms of detection speed.
Research on the route of ground search

According to the specific situation of the designated area, the appropriate search route is formulated to complete the comprehensive search of the predetermined area in the shortest time. In this model, the search problem is transformed into an approximate Hamiltonian problem, that is, each grid center is regarded as a point in the graph, and a search route with the shortest total distance and as balanced as possible for everyone is designed. The optimization principle is used to solve the ground search problem intuitively, and the optimal search time is obtained.
Human pose estimation: current trend and related improvements

Human pose estimation is a technique for estimating human pose from input information obtained from a sensor. It has been widely learned in the field of computer vision. And the human body information provided by human pose estimation has been widely used in many applications and it helps a lot with artificial intelligence. With the development of deep learning, human pose estimation has made significant progress and achieved significant performance using deep learning techniques. But there still are some problems such as occlusion, insufficient training data, etc. are still challenges that need to be handeled. Because of the rapid development of human pose estimation and the gradually emerging problems, this article tries to refine three types of problems in human pose estimation and summarize some teams’ latest methods to deal with these problems.
The Sleepiness of International Students in Online Chinese Classroom: An Experimental Study Based on Hybrid Technology of Facial Feature Point Distribution Model

The study presented in this article is a ground-breaking investigation into the quick advancement of intelligent online language instruction based on wireless communication networks, learner physiological and psychological alterations;to predict and prompt learners’ sleepiness, a hybrid technology of facial feature point distribution model is applied to the process of online learning of Chinese for international students. This technology aids teachers in timely monitoring and discovering learners’ levels of sleepiness in the process of online learning and adjusting it, ensuring effective teacher-student interaction and the accomplishment of learning objectives in the classroom. The following specific designs are carried out in order to increase the contribution of teaching and training samples to the classification effect: (1) To train the neural network classifier and ensure more stable and accurate recognition results, the system employs the particle swarm optimization approach with adaptive parameter adjustment; (2) A simulated annealing technique is suggested to address the premature convergence flaw of the conventional particle swarm optimization algorithm, and an adjustable inertia weight factor is created to enhance the optimization capability of particle swarm optimization; (3) A PSO-SVM based facial expression recognition system based on multi-scale wavelet transform theory and support vector regression machine is created in order to further enhance the network’s generalization capability. The simulation findings demonstrate some improvement in the convergence of the two widely used face recognition techniques. Additionally, this study provides a principal component analysis approach based on DCT coefficient singular decomposition and a texture feature selection method based on LDA model in an effort to apply wavelet analysis theory to face recognition.HighlightsA system for predicting and prompting learners’ sleepiness in online foreign language classroom teaching is constructedIn the case of being unable to actively integrate into online classroom teaching, learners are prone to lack of concentration, sleepiness, even anxiety, irritability and facial muscle twitchingEye fatigue identified at a distance of 0.3–1.5 m from ordinary cameras is relatively easy to identifyThe appearance of a large proportion of tired students in the online classroom suggests that teachers should adjust the teaching design schemeThe reasons leading to the burnout of online learners are very complex, and it still needs interdisciplinary in-depth research
Virtual Tabletops (VTT) for Role-Playing Games (RPG) and Do-It-Yourself (DYI) Interactive Surfaces as Examples of Vernacular Design

Pop culture-driven nostalgia for the times of the second half of the 20th century - with their music, aesthetics and entertainment, in combination with the increased need for casual interpersonal contacts observed during the COVID-19 pandemic led to a kind of renaissance of classic tabletop board and Role-Playing Games. At the same time, the pandemic necessity of isolation has led to the emergence of new solutions in the field of social but remote entertainment - specially through software called Virtual Tabletops (VTT). In classic RPGs such as "Dungeons and Dragons" players often use boards, mockups, models, dioramas and miniatures for tactical orientation in situations where it is essential from the mechanics point of view (e.g. during combat sequences). Virtual game tables allowed for this types of props to be digitized including automation of some mechanical activities related to the game (statistics of objects, markers for area effects, fields of view, fogs of war etc.). This turned out to be so attractive that some players began to mix the classic pen and paper RPG’s gameplay (requiring the physical presence of players at the table and face to face communication), with virtual tools which then allow for projection onto table’s surface, walls or to the TVs, tablets and smartphones. This niche but extremely interesting example of vernacular practices in computer based media seem worth further investigation.
Fuzzy Logic Interferences in Medicine ”Medical Verbal Diagnosis and Treatment Methods are Fuzzy”

This chapter presents the principles of uncertainty, imprecision, vagueness, incomplete and random data types, treatments and diagnostics principles in medicine. For example, a disease can manifest quite differently from a patient, doctor and social perspective. It is stated that knowledge-based medical treatment decision support systems will develop further with fuzzy logic evaluation due to uncertainty involvement. This chapter offers a series of recommendations on aspects of uncertainty in the medical sciences. The comparison of fuzzy and bivalent (crisp) logic and the fuzzy logic preference in medicine are explained with evidence. Fuzzy logic inference modeling with medical vague words is presented on a sample with four inputs (temperature, blood pressure, urine quality and heart rate), for diagnosing disease as an output estimate. Finally, the use of fuzzy logic in medicine is explained by a set of diseases classifications.
Exploring learning rate scaling rules for distributed ML training on transient resources

Training Machine Learning (ML) models to convergence is a long-running and expensive procedure, as it requires large clusters of high-end accelerators such as GPUs and TPUs. Many ML frameworks have proposed elastic distributed training, which enables using transient resources such as spot VMs in the cloud, reducing the overall cost. However, the availability of transient resources varies over time, creating an inherently dynamic environment that requires special handling of training hyperparameters. Techniques such as gradient accumulation enable using the same hyperparameters upon resource preemptions, however sequentially accumulating gradients stalls synchronous distributed training. On the other hand, scaling the batch size according to the available resources requires tuning of other hyperparameters, such as the learning rate. In this work, we study how learning rate scaling rules perform under dynamic environments when the batch size changes frequently and drastically, as we observed in real cloud clusters. We build a PyTorch-based system to evaluate Stochastic Gradient Descent on Image Recognition and Object Detection tasks under various learning rate scaling rules and resource availability traces. We observe minor or no degradation in model convergence when choosing the correct learning rate scaling rule. Identifying the appropriate scaling rule for a given model is non-trivial. Automating this decision remains an open question.
SmartNICs at edge for transient compute elasticity

This paper proposes a new architecture that strategically harvests the untapped compute capacity of the SmartNICs to offload transient microservices workload spikes, thereby reducing the SLA violations while providing better performance/energy consumption. This is particularly important for ML workloads at Edge deployments with stringent SLA requirements. Usage of the untapped compute capacity is more favorable than deploying extra servers, as SmartNICs are economically and operationally more desirable. We propose Spike-Offload, a low-cost and scalable platform that leverages machine learning to predict the spikes and orchestrates seamless offloading of generic microservices workloads to the SmartNICs, eliminating the need for pre-deploying expensive host servers and their under-utilization. Our SpikeOffload evaluation shows that SLA violations can be reduced by up to 20% for specific workloads. Furthermore, we demonstrate that for specific workloads our approach can potentially reduce capital expenditure (CAPEX) by more than 40%. Also, performance per unit energy consumption can be improved by upto 2X.
Towards predicting client benefit and contribution in federated learning from data imbalance

Federated learning (FL) is a distributed learning paradigm that allows a cohort of clients to collaborate in jointly training a machine learning model. By design, FL assures data-privacy for clients involved, making it the perfect fit for a wide range of real-world applications requiring data privacy. Despite its great potential and conceptual guarantees, FL has been found to suffer from unbalanced data, causing the overall performance of the final model to decrease and the contribution of individual clients to the federated model to vary greatly. Assuming that imbalance does not only affect contribution but also the extent to which individual clients benefit from participating in FL, we investigate the predictive potential of data imbalance metrics on benefit and contribution. In particular, our approach comprises three phases: (1) we measure data imbalance of clients while maintaining data privacy using secure aggregation, (2) we measure how individual clients benefit from FL participation and how valuable they are for the cohort, and (3) we train classifiers to pairwisely rank clients regarding benefit and contribution. The resulting classifiers rank pairs of clients with an accuracy of 0.71 and 0.65 for benefit and contribution, respectively. Thus, our approach contributes towards providing an indication for the expected value for individual clients and the cohort prior to their participation.
Activation sparsity and dynamic pruning for split computing in edge AI

Deep neural networks are getting larger and, therefore, harder to deploy on constrained IoT devices. Split computing provides a solution by splitting a network and placing the first few layers on the IoT device. The output of these layers is transmitted to the cloud where inference continues. Earlier works indicate a degree of high sparsity in intermediate activation outputs, this paper analyzes and exploits activation sparsity to reduce the network communication overhead when transmitting intermediate data to the cloud. Specifically, we analyze the intermediate activations of two early layers in ResNet-50 on CIFAR-10 and ImageNet, focusing on sparsity to guide the process of choosing a splitting point. We employ dynamic pruning of activations and feature maps and find that sparsity is very dependent on the size of a layer, and weights do not correlate with activation sparsity in convolutional layers. Additionally, we show that sparse intermediate outputs can be compressed by a factor of 3.3X at an accuracy loss of 1.1% without any fine-tuning. When adding fine-tuning, the compression factor increases up to 14X at a total accuracy loss of 1%.
Creating Dynamic Prototypes from Web Page Sketches

While web designers draw user interface sketches as a first step toward creating a Web application, transforming those sketches into a prototypical coded interface is still a manual and time-consuming task. Recently, researchers focused on easing this part of the design process by applying machine learning techniques to generate code from sketches automatically. These methods effectively convert a sketch into a skeleton structure of the web page but are not designed to deal with dynamic behaviors of the page, such as links, buttons, or dropdowns menu. Indeed, to our knowledge, they only allow the creation of static prototypes. In this paper, we move the first steps to support the creation of dynamic prototypes from sketches. We introduce both a set of symbols that a designer can use on their sketches to model dynamic behaviors and the related implementation to generate dynamic prototypes. Finally, we test our method on a few sketched components to assess the suitability of the approach.
Causal computational complexity of distributed processes

This article studies the complexity of π-calculus processes with respect to the quantity of transitions caused by an incoming message. First, we propose a typing system for integrating Bellantoni and Cook's characterisation of polytime computable functions into Deng and Sangiorgi's typing system for termination. We then define computational complexity of distributed messages based on Degano and Priami's causal semantics, which identifies the dependency between interleaved transitions. Next, we apply a necessary syntactic flow analysis to typable processes to ensure a computational bound on the number of distributed messages. We prove that our analysis is decidable; sound in the sense that it guarantees that the total number of messages causally dependent of an input request received from the outside is bounded by a polynomial of the content of this request; and complete, meaning that each polynomial recursive function can be computed by a typable process.
Toward a VR-Native Live Programming Environment

Fast feedback loops between performing code changes and seeing their outcome help developers to be productive. For development of virtual reality (VR) applications, developers use a separate device, forcing them to switch devices whenever they want to test their application, thus significantly increasing the length of the feedback loop. In this paper, we describe a prototypical development environment that allows writing VR applications while inside VR. Unlike previous work in this area that projected traditional 2D editors into the 3D world, we explore the use of direct manipulation in a structured editor for the general-purpose programming language Smalltalk. We present and discuss insights from a preliminary user study with four participants. Our findings demonstrate that the concept does work if users are given prior instructions, especially for smaller features where direct feedback is valuable, but ergonomics of both the hardware and our prototype have to be improved before extended programming sessions are viable.
Composing Linear Types and Separation Logic Proofs of Memory Safety

Separation logic and linear types are two formal techniques for reasoning about the memory safety of programs with manual memory management. To obtain the advantages of both methods when proving that a program is memory-safe, we investigate how to unify the notion of the frame from lin- ear types and the notion of the frame from a memory safety characterisation inspired by separation logic. That allows us to investigate the necessary conditions under which linear types and separation logic proofs of memory safety may be successfully composed. The benefit of that composition would be to design a framework that supports verifying that a program is memory-safe by proving the memory safety of parts of it using the more appropriate technique and then composing the proofs to certify that the entire program is safe.