Reviewerly/exHarmony huggingface dataset:
Kaleido: An Efficient Out-of-core Graph Mining System on A Single Machine

Graph mining is one of the most important categories of graph algorithms. However, exploring the subgraphs of an input graph produces a huge amount of intermediate data. The "think like a vertex" programming paradigm, pioneered by Pregel, cannot readily formulate mining problems, which is designed to produce graph computation problems like PageRank. Existing mining systems like Arabesque and RStream need large amounts of computing and memory resources. In this paper, we present Kaleido, an efficient single machine, out-of-core graph mining system which treats disks as an extension of memory. Kaleido treats intermediate data in graph mining tasks as a tensor and adopts a succinct data structure for the intermediate data. Kaleido implements half-memory-half-disk storage for storing large intermediate data, which treats the disk as an extension of the memory. Kaleido adopts a lightweight isomorphism checking strategy which uses an eigenvalue-based algorithm for small graphs and solves tree isomorphism for the other graphs. Comparing with two state-of-the-art mining systems, Arabesque and RStream, Kaleido outperforms them by a GeoMean 13.2× and 64.8× respectively.
Artificial intelligence driven wireless network remote monitoring based on Diffie–Hellman parameter method

Remote monitoring of wireless networks based on artificial intelligence can build sensitive anomaly recognition mechanisms, automated event analysis engines and accurate global operation and maintenance capabilities for wireless network defense. Firstly, a simple camera is used to realize real-time acquisition and wireless transmission of video signals based on software-based MPEG-4 compression coding method. The self-developed ActiveX control with video decoding function is embedded in the webpage to realize real-time dynamic display of video information in the computer browser of the monitoring terminal. Secondly, in order to realize the intelligent control of wireless network, the research based on the reverse motion degree posture planning is carried out. The DH parameter method is used to establish the linkage coordinate system of wireless network remote monitoring, and the kinematics formula is derived. The geometric analysis method is used to calculate the motion trajectory of remote monitoring, accurately locate the various angles of remote monitoring and obtain the best motion path. Based on the Pm angle control method of fuzzy neural network, the RBF neural network, fuzzy control the combination of control and Pm control, using the self-learning ability of the neural network and the fuzzy reasoning is ability of fuzzy control. The end effector was adjusted to the target position. Finally, the established mathematical model was simulated by MATLAB, and the characteristics of wireless network remote monitoring were verified.
The web is still small after more than a decade

Understanding web co-location is essential for various reasons. For instance, it can help one to assess the collateral damage that denial-of-service attacks or IP-based blocking can cause to the availability of co-located web sites. However, it has been more than a decade since the first study was conducted in 2007. The Internet infrastructure has changed drastically since then, necessitating a renewed study to comprehend the nature of web co-location. In this paper, we conduct an empirical study to revisit web co-location using datasets collected from active DNS measurements. Our results show that the web is still small and centralized to a handful of hosting providers. More specifically, we find that more than 60% of web sites are co-located with at least ten other web sites---a group comprising less popular web sites. In contrast, 17.5% of mostly popular web sites are served from their own servers. Although a high degree of web co-location could make co-hosted sites vulnerable to DoS attacks, our findings show that it is an increasing trend to co-host many web sites and serve them from well-provisioned content delivery networks (CDN) of major providers that provide advanced DoS protection benefits. Regardless of the high degree of web co-location, our analyses of popular block lists indicate that IP-based blocking does not cause severe collateral damage as previously thought.
Towards Participatory Video 2.0

Participatory video (PV) is an established practice for enabling communities to "speak truth to power" and has been widely used by local, national and international Non-Governmental Organizations (NGOs). However, the digital media landscape has changed dramatically since PV became widely accessible with the rise of the camcorder in the 1980s. Current media practices have evolved considerably since, yet PV remains essentially unchanged. We report on an investigation of current PV practices and reflect on these in terms of what the future for PV holds. We conducted interviews with staff at a global humanitarian network who directly and indirectly engage in community story capture; and explore their reflections on the potentials and barriers to PV use. We propose a new vision for PV that draws on current visual media production, consumption and distribution technologies and practices, and propose principles on which PV 2.0, a new generation of Participatory Video can be founded.
Exploring Deep Anomaly Detection Methods Based on Capsule Net

In this paper, we develop and explore deep anomaly detection techniques based on capsule network (named AnoCapsNet) for image data. Being able to encode intrinsic spatial relationship between parts and a whole, CapsNet has been applied as both a classifier and deep autoencoder. This inspires us to design three normality score functions: prediction-probability-based (PP-based), reconstruction-error-based (RE-based), and combination of both (PP+RE-based) for evaluating the "outlierness" of unseen images. Our results on four datasets demonstrate that PP-based and RE-based methods outperform the principled benchmark methods in many cases and the pp-based method performs consistently well, while the RE-based approach is relatively sensitive to the similarity between labeled and unlabeled images. The PP+RE-based approach effectively takes advantages of both methods and achieves state-of-the-art results.
Advanced Recommendation Systems Through Deep Learning

Many companies have realized the importance of deep learning, which justifies their growing interest in the use of recommender systems to boost their sales.
A Wizard-of-Oz Interface and Persona-based Methodology for Collecting Health Counseling Dialog

Health dialog collection is the primary bottleneck for the training and deployment of conversational agents into clinical practice. Current tools for the development of dialog systems are primarily focused on writing intent-slot schemas for natural language understanding and finite-state models of dialog management. However, this is a time-consuming process that can be opaque for clinical teams and the limitations of both approaches are well understood. The health domain presents additional barriers including access to patients and concerns about exposing personal health information. The contribution of this work is two-fold. First, it describes an interface designed to augment the ability of clinicians to efficiently engage in high-quality and empathetic health counseling dialog with their patients. Second, it presents a WOZ protocol for collecting this dialog using standardized patient actors each playing a role from a pool of caregiver personas.
Prompt: Dynamic Data-Partitioning for Distributed Micro-batch Stream Processing Systems

Advances in real-world applications require high-throughput processing over large data streams. Micro-batching has been proposed to support the needs of these applications. In micro-batching, the processing and batching of the data are interleaved, where the incoming data tuples are first buffered as data blocks, and then are processed collectively using parallel function constructs (e.g., Map-Reduce). The size of a micro-batch is set to guarantee a certain response-time latency that is to conform to the application's service-level agreement. In contrast to tuple-at-a-time data stream processing, micro-batching has the potential to sustain higher data rates. However, existing micro-batch stream processing systems use basic data-partitioning techniques that do not account for data skew and variable data rates. Load-awareness is necessary to maintain performance and to enhance resource utilization. A new data partitioning scheme termed Prompt is presented that leverages the characteristics of the micro-batch processing model. In the batching phase, a frequency-aware buffering mechanism is introduced that progressively maintains run-time statistics, and provides online key-based sorting as data tuples arrive. Because achieving optimal data partitioning is NP-Hard in this context, a workload-aware greedy algorithm is introduced that partitions the buffered data tuples efficiently for the Map stage. In the processing phase, a load-aware distribution mechanism is presented that balances the size of the input to the Reduce stage without incurring inter-task communication overhead. Moreover, Prompt elastically adapts resource consumption according to workload changes. Experimental results using real and synthetic data sets demonstrate that Prompt is robust against fluctuations in data distribution and arrival rates. Furthermore, Prompt achieves up to 200% improvement in system throughput over state-of-the-art techniques without degradation in latency.
Open research data in African academic and research libraries: a literature analysis

Purpose The paper presents a literature review on research data management services in African academic and research libraries on the backdrop of the advancing open science and open research data infrastructures. It provides areas of focus for library to support open research data. Design/methodology/approach The literature analysis and future role of African libraries in research data management services were based on three areas as follows:open science, research infrastructures and open data infrastructures. Focussed literature searches were conducted across several electronic databases and discovery platforms, and a qualitative content analysis approach was used to explore the themes based on a coded list. Findings The review reports of an environment where open science in Africa is still at developmental stages. Research infrastructures face funding and technical challenges. Data management services are in formative stages with progress reported in a few countries where open science and research data management policies have emerged, cyber and data infrastructures are being developed and limited data librarianship courses are being taught. Originality/value The role of the academic and research libraries in Africa remains important in higher education and the national systems of research and innovation. Libraries should continue to align with institutional and national trends in response to the provision of data management services and as partners in the development of research infrastructures.
Wearable Reasoner

Human judgments and decisions are prone to errors in reasoning caused by factors such as personal biases and external misinformation. We explore the possibility of enhanced reasoning by implementing a wearable AI system as a human symbiotic counterpart. We present "Wearable Reasoner", a proof-of-concept wearable system capable of analyzing if an argument is stated with supporting evidence or not. We explore the impact of argumentation mining and explainability of the AI feedback on the user through an experimental study of verbal statement evaluation tasks. The results demonstrate that the device with explainable feedback is effective in enhancing rationality by helping users differentiate between statements supported by evidence and without. When assisted by an AI system with explainable feedback, users significantly consider claims supported by evidence more reasonable and agree more with them compared to those without. Qualitative interviews demonstrate users' internal processes of reflection and integration of the new information in their judgment and decision making, emphasizing improved evaluation of presented arguments.
Joint image deblurring and super-resolution with attention dual supervised network

Image deblurring and super-resolution (SR) are computer vision tasks aiming to restore image detail and spatial scale, respectively. Despite significant research effort over the past years, it remains challenging for joint image deblurring and SR via deep networks. Besides, only a few recent literatures contribute to this task, as conventional methods deal with SR or deblurring separately. To rectify the weakness, we propose a novel network that handles both tasks jointly and in this way boosts the SR performance from blurry input greatly. To fully exploit the representation capacity of our model, dual supervised learning is proposed to impose the constraint between low-resolution (LR) and high-resolution (HR) images. Our model consists of three parts: (i) a deblurring module equipped with channel attention residual blocks that removes the fuzziness from input images, (ii) an SR module to super-resolve the image based on the feature maps from the deblurring module serving as input, and (iii) a dual module which exploits the dependencies between LR and HR images. Extensive experiments indicate that the proposed attention dual supervised network (ADSN) not only generates remarkably clear HR images, but also achieves compelling results for joint image deblurring and SR task.
Development and Evaluation of a Smart Contract–Enabled Blockchain System for Home Care Service Innovation: Mixed Methods Study

In the home care industry, the assignment and tracking of care services are controlled by care centers that are centralized in nature and prone to inefficient information transmission. A lack of trust among the involved parties, information opaqueness, and large manual manipulation result in lower process efficiency.This study aimed to explore and demonstrate the application of blockchain and smart contract technologies to innovate/renovate home care services for harvesting the desired blockchain benefits of process transparency, traceability, and interoperability.An object-oriented analysis/design combined with a unified modeling language tool was used to construct the architecture of the proposed home care service system. System feasibility was evaluated via an implementation test, and a questionnaire survey was performed to collect opinions from home care service respondents knowledgeable about blockchain and smart contracts.According to the comparative analysis results, the proposed design outperformed the existing system in terms of traceability, system efficiency, and process automation. Moreover, for the questionnaire survey, the quantitative analysis results showed that the proposed blockchain-based system had significantly (P<.001) higher mean scores (when compared with the existing system) in terms of important factors, including timeliness, workflow efficiency, automatic notifications, insurance functionality, and auditable traceability. In summary, blockchain-based home care service participants will be able to enjoy improved efficiency, better transparency, and higher levels of process automation.Blockchain and smart contracts can provide valuable benefits to the home care service industry via distributed data management and process automation. The proposed system enhances user experiences by mitigating human intervention and improving service interoperability, transparency/traceability, and real-time response to home care service events. Efforts in exploring and integrating blockchain-based home care services with emerging technologies, such as the internet of things and artificial intelligence, are expected to provide further benefits and therefore are subject to future research.
Algorithm Inspection for Chatbot Performance Evaluation

With the usage of Chatbots growing at an unprecedented rate, it is imperative that they are thoroughly tested, verified and validated. This is done to ensure that Chatbots do not fail during operation. Chatbot failures are undesirable and they often occur when the bot is provided with ambiguous or illegible input. The Chatbots have to be thoroughly tested to ensure that they do not fail under any circumstance and have mechanisms to deal with such scenarios. Although various methods are in use to test Chatbots, algorithm testing is a promising solution to the problem. This involves the use of techniques such as cross-validation, grammar and parsing, verification and validation and statistical parsing. This paper aims to explore the prominent types of chatbot testing methods with detailed emphasis on algorithm testing techniques.
Comparative Study of Harmony Search Algorithm and its Applications in China, Japan and Korea

Harmony Search (HS) is a music-inspired optimization algorithm for solving complex optimization problems that imitate the musical improvisational process. This paper reviews the potential of applying the HS algorithm in three countries, China, South Korea, and Japan. The applications represent several disciplines in fields of study such as computer science, mathematics, electrical/electronic, mechanical, chemical, civil, and industrial engineering. We anticipate an increasing number of HS applications from these countries in near future.
AdaDeep: A Usage-Driven, Automated Deep Model Compression Framework for Enabling Ubiquitous Intelligent Mobiles

Recent breakthroughs in deep neural networks (DNNs) have fueled a tremendously growing demand for bringing DNN-powered intelligence into mobile platforms. While the potential of deploying DNNs on resource-constrained platforms has been demonstrated by DNN compression techniques, the current practice suffers from two limitations: 1) merely stand-alone compression schemes are investigated even though each compression technique only suit for certain types of DNN layers; and 2) mostly compression techniques are optimized for DNNs’ inference accuracy, without explicitly considering other application-driven system performance (e.g., latency and energy cost) and the varying resource availability across platforms (e.g., storage and processing capability). To this end, we propose AdaDeep, a usage-driven, automated DNN compression framework for systematically exploring the desired trade-off between performance and resource constraints, from a holistic system level. Specifically, in a layer-wise manner, AdaDeep automatically selects the most suitable combination of compression techniques and the corresponding compression hyperparameters for a given DNN. Thorough evaluations on six datasets and across twelve devices demonstrate that <inline-formula><tex-math notation="LaTeX">${\sf AdaDeep}$</tex-math></inline-formula> can achieve up to <inline-formula><tex-math notation="LaTeX">$18.6\times$</tex-math></inline-formula> latency reduction, <inline-formula><tex-math notation="LaTeX">$9.8\times$</tex-math></inline-formula> energy-efficiency improvement, and <inline-formula><tex-math notation="LaTeX">$37.3\times$</tex-math></inline-formula> storage reduction in DNNs while incurring negligible accuracy loss. Furthermore, <inline-formula><tex-math notation="LaTeX">${\sf AdaDeep}$</tex-math></inline-formula> also uncovers multiple novel combinations of compression techniques.
Deep2s: Improving Aspect Extraction in Opinion Mining With Deep Semantic Representation

Syntactical rule based approaches for aspect extraction, which are free from expensive manual annotation, are promising in practice. These approaches extract aspects mainly through the dependency relations in the surface sentence structures. However, deep and rich semantic information hidden in sentences which can help improve aspect extraction, is difficult for them to capture. In order to address the problem, this paper first proposes to employ Logic Programming to explore the feasibility of deep semantic representation, then proposes Deep2S, a hybrid rule-based method to improve the performance of aspect extraction. Deep2S integrates deep semantic representation such as Abstract Meaning Representation (AMR) with syntactic structure. It can take advantage of the syntactical rules to obtain dependency relations in the surface structure as well as the semantic rules to capture deep semantic information. Our experiments are conducted on eight popular review datasets using two evaluation metrics. Experimental results demonstrate the usefulness of deep semantic representation and the ability of Deep2S to improve the performance of aspect extraction in opinion mining.
Semi-Automatic Definition of Attribute Semantics for the Purpose of Ontology Integration

By ontology, we understand a knowledge structure which well reflects the complexity of a real world.Ontologies are built to store and process knowledge about objects and dependencies between them.Thus, ontologies not only structure raw data, but also contain the meaning of those data.So far, ontology developers have been forced to provide the semantics of modeled objects and relations between them manually.The goal of this paper is to address some still unresolved problems related to providing meanings in ontologies.A typical ontology consists of concepts with attributes, relations between them, and instances.In our research, we focus on concepts level where attributes must be interpretable because they are the primary carriers of the meaning of the entire ontology.In other words, we need to assign semantics for each attribute within a concept.For this purpose, we have proposed a semi-automatic method for defining attribute semantics based on WordNet.The attribute semantics designated by human experts and based on WordNet does not affect the integration result.However, the developed method allows reducing the time for preparing an ontology to the integration process.What is more, it is less sensitive to the subjective evaluations done by experts.
MSGAN: Generative Adversarial Networks for Image Seasonal Style Transfer

Although Generative Adversarial Networks (GANs) have shown remarkable successes in various computer vision tasks, they still face challenges in image season style transfer task. In this paper, we propose a multi-season Generative Adversarial Networks (MSGANs) aimed to transfer input images into other season styles. To improve the quality of the simulated images generated by the proposed MSGAN, we propose a novel loss function to guide the optimization direction of the network. Besides, we adopt the saliency information to guide the seasonal style transformation task, so as to ensure that different image contents can have different optimization weights in MSGAN. The experimental results show that the proposed MSGAN can generate high-quality simulated images from real images, and is superior to other latest methods. Not only that, the synthetic image generated by the proposed method also be used to perform depth estimation task so that prove that the synthetic images can be well applied to other computer vision tasks.