Reviewerly/exHarmony huggingface dataset:
Toward Building Specialized Information Systems as Software Platforms

A specialized information system (SIS) is a type of information system that is designed to support a specific business or organization. SISs are typically used to manage large amounts of data. A software platform is a kind of software that creates value through a synergy, by surrounding itself with an ecosystem of external connections, data, and processes. The goal of platform thinking is to build a software platform that is a foundation for a new ecosystem. In this paper, we distill the best practice of building a successful software platform by learning from the history of Salesforce, SAP, and Amazon. We also offer a set of guidelines to help SISs transform into software platforms. However, we describe that a software platform is not a superior software architecture, but rather a new business model.
Historical Training Game Model with Mathematical and Information Aspects

This paper explores some of the challenges of enhancing artificial intelligence tools in computer games. In particular, the possibility of enhancing the realism of modeling due to the dynamic model of the impressive ability of the armed forces is considered. It is proposed to build a model with consideration of the interaction of the units of the parties similar to the Mono model. An effective method of identification of the classical Mono model is also proposed.
EMG Data Collection for Multimodal Keystroke Analysis

User authentication based on muscle tension manifested during password typing seems to be an interesting additional layer of security. It represents another way of verifying a person’s identity, for example in the context of continuous verification. In order to explore the possibilities of such authentication method, it was necessary to create a capturing software that records and stores data from EMG (electromyography) sensors, enabling a subsequent analysis of the recorded data to verify the relevance of the method. The work presented here is devoted to the design, implementation and evaluation of such a solution. The solution consists of a protocol and a software application for collecting multimodal data when typing on a keyboard. Myo armbands on both forearms are used to capture EMG and inertial data while additional modalities are collected from a keyboard and a camera. The user experience evaluation of the solution is presented, too.
Lecturers’ Perspectives on Mobile Learning in Higher Education: Experiences and Implementation Barriers

This paper reports the experiences of university lecturers towards mobile learning from two points of view: (i) lecturers’ needs and experiences in relation to mobile learning design and implementation, and (ii) possible barriers to their adoption in higher education classrooms. The findings from a qualitative case study that conducted semi-structured interviews with 12 lecturers from a developed and developing country contexts revealed; the need for strong commitment from institutional leadership, presence of an institutional and national mobile learning policy, a culture of innovation and willingness to revise the traditional curriculum to incorporate the new mobile pedagogies.
The Record of Interdisciplinary Dialogue: Literature and Language Education in College, 1996-2016

This study analyses articles addressing literature and language education in The Journal of Teaching English Literature of The Korean Society for Teaching English Literature, this year celebrating its 30th anniversary. By employing literature to promote students’ communicative abilities, English literature scholars have translated literary texts’ values through the language of English education. Thus, they have produced valuable research studies, specifically on how to use literary texts in language education and how to lead classes based on students’ interests. So, during this era of literary crisis, this paper demonstrates research trends in The Journal of Teaching English Literature centered around various scholarly thinking, for instance, purpose and content, text selection, and teaching approaches in literature classes. Therefore, this study aims to anticipate the direction that The Journal of Teaching English Literature will pursue by tracing the journal’s footsteps from 1996 to 2016.
LSEGNN: Encode Local Topology Structure in Graph Neural Networks

Learning robust representations for nodes in graphs is crucial for graph learning tasks. Graph Neural Networks(GNNs) attract much attention recently as the frameworks achieve great success in node representation learning. Existing state-of-the-art GNN methods (like GCN) aggregate messages from neighbor nodes through message passing neural network to update representations for nodes. However, the message passing strategy fails to capture the structural similarity between nodes. Besides, it assumes that neighbor nodes are independent and ignores abundant local neighbor structures around nodes in real networks. This weakness may hurt the performance of GNNs in some classification tasks. To capture the overlooked information, in the experimental investigation, we found that co-occurrence probabilities based on random walks can preserve local neighbor structures among nodes well. Furthermore, we propose a novel but effective method to encode local structure information into node features by co-occurrence probabilities. We call this method Local Structure Enhanced Graph Neural Network, short as LSEGNN. Extensive experiments are conducted in benchmark datasets and the results show the effectiveness of our method.
zk-PCN: A Privacy-Preserving Payment Channel Network Using zk-SNARKs

Payment channel network (PCN) is a layer-two scaling solution that enables fast off-chain transactions but does not involve on-chain transaction settlement. PCNs raise new privacy issues including balance secrecy, relationship anonymity and payment privacy. Moreover, protecting privacy causes low transaction success rates. To address this dilemma, we propose zk-PCN, a privacy-preserving payment channel network using zk-SNARKs. We prevent from exposing true balances by setting up public balances instead. Using public balances, zk-PCN can guarantee high transaction success rates and protect PCN privacy with zero-knowledge proofs. Additionally, zk-PCN is compatible with the existing routing algorithms of PCNs. To support such compatibility, we propose zk-IPCN to improve zk-PCN with a novel proof generation (RPG) algorithm. zk-IPCN reduces the overheads of storing channel information and lowers the frequency of generating zero-knowledge proofs. Finally, extensive simulations demonstrate the effectiveness and efficiency of zk-PCN in various settings.
Cross-domain Resemblance Detection based on Meta-learning for Cloud Storage

Recently, cloud storage has been widely used in our daily life. And there are lots of redundancy among these outsourced data. Conventional deduplication technology efficiently splits these data at the chunk level and removes the duplicate chunks to save the network bandwidth and improve the cloud storage utility. But it ignores the redundancy among similar chunks. Resemblance detection has recently become a hot issue with detecting these redundant parts among similar data. CARD, the state-of-the-art work, can efficiently and effectively remove these redundancies by introducing the neural network with resemblance detection. However, the source domain of the CARD model may have an explicitly different input distribution. The cloud cannot deal with the possible future domain data based on CARD design. This cross-domain setting may serials degrades the performance of CARD. To overcome this problem, we propose a cross-domain resemblance detection scheme called MetaContext. Integrating the chunk-context aware model and the learn-to-learn idea can produce a more robust chunk feature than CARD. As a byproduct, it also outperforms the CARD in speed. Finally, we implement the MetaContext and conduct serial experiments on real workloads. The results show that our method can efficiently and effectively detect and remove the redundancy among similar data.
Performance Evaluation of Resource Management Schemes for Cloud Native Platforms with Computing Containers

Businesses have made increasing adoption and incorporation of cloud technology into internal processes in the last decade. The cloud-based deployment provides on-demand availability without active management. More recently, the concept of cloud-native application has been proposed and represents an invaluable step toward helping organizations develop software faster and update it more frequently to achieve dramatic business outcomes. Cloud-native is an approach to build and run applications that exploit the cloud computing delivery model’s advantages. It is more about how applications are created and deployed than where. The container-based virtualization technology, e.g., Docker and Kubernetes, serves as the foundation for cloud-native applications. This paper evaluates the performance of deep learning applications in a cloud-native environment.
MFIP: Multi-Factor Interlinked Point-of-Interest Recommendation in Location-Based Social Network

The paper proposes a multi-factor interlinked POI recommendation model called MFIP. Extracting user similarity for user-sensitive implicit modeling to enrich user representation. Using contextual information such as sequential, geographical and social to construct a POI recommendation model with collaborative influence of multi-factor, alleviating data sparsity. A novel multi-factor interlinked strategy(FIS) is proposed that can dynamically adjust the user preferences of different factors to obtain the comprehensive impact of user personalization. In addition, we propose a active area selection algorithm based on segmentation to model the geographical information more effectively. Finally, we conduct a comprehensive performance evaluation for MFIP on two large-scale real world check-in datasets collected from Gowalla and Yelp. Experimental results show that MFIP achieves significantly superior recommendation performance compared to other state-of-the-art POI recommendation models.
HRCache: Edge-End Collaboration for Mobile Deep Vision Based on H.264 and Approximated Reuse

To accomplish computationally intensive visual tasks on mobile devices with limited memory and computation capability, a common solution is to offload the tasks to the edge with more powerful computing resources. Nevertheless, due to the extra communication, even edge computing is challenged by the growing demands for real-time interactive tasks (e.g., VR and AR), which are generally performed by deep CNNs with heavy computation. In this paper, we design a novel system called HRCache to reduce the end-to-end latency by effectively recompressing video data and reusing cached inference results on the edge. As similar image regions have been indicated in the offloading video coding, the edge can approximately infer these regions with results stored on the edge to save the time for calculating these regions. Moreover, since the data in these regions do not require calculation, the transmitted data can be further simplified to reduce transmission latency. Furthermore, HRCache can quickly and continuously adjust the coding parameters to adapt the accuracy loss and overall latency with application requirements. Compared with the original offloading schemes, HRCache significantly reduces the average latency, about 13.60% to 18.83%, at little accuracy loss of 1.25% in Top-1 accuracy for classification and 0.135 in IoU for object detection.
Optimal Incentive Mechanisms for Fair and Equitable Rewards in PoS Blockchains

Blockchain technology that came with the introduction of Bitcoin offers many powerful use-cases while promising the establishment of distributed autonomous organizations (DAOs) that may transform our current understanding of client-server interactions on the cyberspace. They employ distributed consensus mechanisms that were subject to a lot of research in recent years. While most of such research focused on security and performance of consensus protocols, less attention was given to their incentive mechanisms which relate to a critical feature of blockchains. Unfortunately, while blockchains are advocating decentralized operations, they are not egalitarian due to existing incentive mechanisms. Many current consensus protocols inadvertently incentivize centralization of mining power and inequitable participation. This paper explores and evaluates alternative incentive mechanisms for a more decentralized and equitable participation. We first evaluate inequality in existing Proof of Stake (PoS) based incentive mechanisms, then we examine three alternatives in which rewards scheme is more partial to low-stakeholders. Through simulation, we show that two of our alternative mechanisms can reduce inequality and offer an attractive solution for sustainability of blockchain-based applications and DAOs.
PickyMan: A Preemptive Scheduler for Deep Learning Jobs on GPU Clusters

Deep learning (DL) jobs normally run on GPU clusters. Some DL jobs need to be scheduled preemptively to avoid long waiting times. However, preempting a DL job is time-consuming, which consists of suspending and resuming. Suspending needs to complete the training process of the current epoch, and resuming needs to reload the model and the training data. The existing schedulers almost do not consider the overhead of preempting jobs; thus, they may preempt jobs with large time loss, increasing the waiting time and the makespan.In this paper, we present PickyMan, a preemptive scheduler to minimize the overhead of preempting jobs to reduce the average waiting time and the makespan. PickyMan has some innovations. (1) Predict execution time using network traffic and database. It predicts the execution time of a DL job by profiling the network traffic from storage nodes to computation nodes and using a database, without the requirement of allocating extra resources from the cluster. It can use profiled information of only four jobs to predict the execution times for other same-model jobs, and most of the predicted errors are less than 10%. (2) Modeling the overhead of preemption. It builds a model to predict the time loss of job suspensions and resumptions with an average error of less than 5%. (3)We abstract the problem of choosing the appropriate jobs for preemption as one of finding an ordered division of the set of running jobs and solve it quickly with a greedy algorithm. By conducting experiments on the small-scale actual cluster and making large-scale simulations, PickyMan reduces the average waiting time by 10%–92% and further reduces the makespan by up to 14%, compared to existing methods.
SMPI: Scalable Serverless MPI Computing

Running HPC in the cloud has gained more and more practice. As a new cloud paradigm, serverless is highly attractive for HPC service providers due to its distinctive benefits such as scalability. However, it is difficult for serverless to meet the demands of MPI programming and running, resulting in that MPI programs cannot scale with serverless functions. We introduce a serverless parallel function model to solve the problems. It divides parallelism at function and worker levels to bridge gaps in programming and running between serverless and MPI. Then we present the SMPI framework atop the model. For programming, SMPI redefines the function generation pipeline for parallel functions to prepare metadata for MPI parallel functions. For running, SMPI employs the parallel function gateway and scheduler to realize parallel function invocation and instantiation for MPI parallel functions. It is implemented and evaluated with OpenFaaS. Experiments show that SMPI supports MPI programming and running in a complete serverless manner. Compared to server-centric methods, it reduces efforts on cluster maintenance, provides scalable serverless MPI computing with competitive performance (0.559–1.048s slower of start-up time, and 0.145s–0.945s slower of computing time than best-behaved baseline), and is potential to scale on multiple clusters for higher scalability.
RCM: Residue-aware Consolidation for Heterogeneous MLaaS Cluster

With the rapid development of Machine Learning (ML), Machine-Learning-as-a-Service (MLaaS) clusters appear in large numbers to support cloud platforms services, which adopt virtual machine (VM) to improve the availability, resilience and security. However, low energy efficiency is a major problem in such clusters. Previous work focused on reducing the number of physical machines by centralizing resources migration. Nevertheless, for ML tasks with frequent memory switching, blind migration is not worth the cost because the remaining time is less than the migration time, since the migration time can not be ignore due to the memory intensive of ML tasks. Therefore, this paper explores how the remaining time and memory replacement states in ML tasks, which we summarize as residue, affect migration, and proposes an online residue-aware migration algorithm based on Lyapunov optimization. Through rigorous proof, the gap between the algorithm and the optimal solution is ensured. Extensive simulations show that the proposed algorithm is better than the previous migration.
Max-Min Fairness based Scheduling Optimization Mechanism on Switches

Multiple types of flows with contradictory service requirements, namely mixed flows, coexist in the data center network. Similar flows will be aggregated into the same queue after flow classification and are scheduled in switches by using fair queueing and its extension schemes which are capable of flow isolation. These schemes allocate different bandwidths by adjusting weights to realize differentiated services. However, existing solutions only focus on the requirements of some flows, which leads to the failure to satisfy the requirements of other flows. Therefore, it is necessary to make a trade-off between the service requirements of different flows when allocating bandwidth. In this paper, a max-min fairness based scheduling optimization (MMFSO) mechanism is proposed to schedule mixed flows. First, the bandwidth requirements of each queue are calculated by statistics of the switch. To reduce the influence of sampled statistics while forecasting bandwidth requirements, we introduce the exponentially weighted moving average for bandwidth requirements computation. Second, the bandwidth is allocated to each queue according to the max-mix fairness. The queue weight is determined by the allocated bandwidth of the queue. Finally, the performance of the proposed mechanism is evaluated on the hardware testbed in which workloads include coarse-grained flows and fine-grained flows. The results show that MMFSO can effectively schedule mixed flows.
Reinforced Contrastive Graph Neural Networks (RCGNN) for Anomaly Detection

Despite the recent state-of-the-art performance of Deep Learning (DL), imbalanced graph-structured data remains an open challenge in social science, traffic networks, and biomedical informatics. Recently, a surge in research on Representation Learning has significantly improved the performance of DL algorithms on imbalanced non-graph-structured data. In addition, Graph Neural Networks (GNNs) already in widespread use for representing graph-structured data in DL models with more advanced techniques in neural message-passing and deep graph embedding. However, most existing works are based on assumptions that oversimplify the complexity of real-world problems. In this paper, we propose Reinforced Contrastive GNNs (RCGNN), a novel graph representation learning model for anomaly detection with multi-relational graph-structured data. The proposed model produces a neighbor selection with Reinforcement Learning (RL) based on the similarity of neighborhoods in multi-relational structured graphs. In addition, the graph representation is learned by an adaptive AutoEncoder (AE) with Triplet Loss (TL) in Contrastive Learning. By aggregating the nodes with the highest similarities in their features and the importance of each node, our model is able to construct the multi-relational graphs by keeping the complexity of the graph structure as well as the relation-dependency representations. Experiments on multiple benchmark data sets demonstrate the advantage of RCGNN in learning better representations for multi-relational graphs. Furthermore, compared to other GNN models, our model shows better performance in accuracy, F1, and PR AUC scores.
Privacy-Preserving Closest Point Determination Based on Ciphertext Comparison

Closest point determination is a fundamental problem that arises in location-based services. In this paper, we study privacy-preserving closest point determination problem involving at least three parties, which focuses on determining the closest point without revealing location information. First, we present a three-party integer comparison protocol using homomorphic encryption systems in order to achieve the ciphertext comparison. Second, we propose a three-party and a multi-party protocol based on the integer comparison protocol to determine the closest point and prove that they are secure against semi-honest adversaries in the standard model. The proposed protocols are built on partially homomorphic encryption systems and can keep location and distance confidential. Finally, we confirm that our protocols reach a better balance between complexity and security. And experimental results demonstrate that our protocol is feasible and effective.
Iterative Qubits Management for Quantum Index Searching in a Hybrid System

Recent advances in quantum computing systems attract tremendous attention. Commercial companies, such as IBM, Amazon, and IonQ, have started to provide access to noisy intermediate-scale quantum computers. Researchers and entrepreneurs attempt to deploy their applications that aim to achieve a quantum speedup. Grover’s algorithm and quantum phase estimation are the foundations of many applications with the potential for such a speedup. While these algorithms, in theory, obtain marvelous performance, deploying them on existing quantum devices is a challenging task. For example, quantum phase estimation requires extra qubits and a large number of controlled operations, which are impractical due to low-qubit and noisy hardware. To fully utilize the limited onboard qubits, we propose IQuCS, which aims at index searching and counting in a quantum-classical hybrid system. IQuCS is based on Grover’s algorithm. From the problem size perspective, it analyzes results and tries to filter out unlikely data points iteratively. A reduced data set is fed to the quantum computer in the next iteration. With a reduction in the problem size, IQuCS requires fewer qubits iteratively, which provides the potential for a shared computing environment. We implement IQuCS with Qiskit and conduct intensive experiments. The results demonstrate that it reduces qubits consumption by up to 66.2%.